From 54c9e9e1578e5af9a4818dbb8fbdaa10324f5962 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=86=99?= Date: Wed, 18 Oct 2023 14:52:32 +0800 Subject: [PATCH 01/67] add support for keep model --- .../compat/feature_column/feature_column.py | 13 + .../feature_column/feature_column_v2.py | 6 + easy_rec/python/input/parquet_input.py | 155 + easy_rec/python/model/keep_model/__init__.py | 0 .../model/keep_model/base_model_ps_v2.py | 914 ++++++ easy_rec/python/model/keep_model/dnn.py | 133 + .../python/model/keep_model/model_ps_mmoe.py | 73 + easy_rec/python/model/keep_model/senet.py | 98 + easy_rec/python/model/ppnet_v3m.py | 59 + easy_rec/python/protos/dataset.proto | 1 + easy_rec/python/protos/easy_rec_model.proto | 3 + easy_rec/python/protos/ppnet.proto | 7 + easy_rec/python/utils/estimator_utils.py | 2 +- keep_model.config | 2916 +++++++++++++++++ model_conf.json | 15 + test.parquet | Bin 0 -> 12967073 bytes 16 files changed, 4394 insertions(+), 1 deletion(-) create mode 100644 easy_rec/python/input/parquet_input.py create mode 100644 easy_rec/python/model/keep_model/__init__.py create mode 100644 easy_rec/python/model/keep_model/base_model_ps_v2.py create mode 100644 easy_rec/python/model/keep_model/dnn.py create mode 100644 easy_rec/python/model/keep_model/model_ps_mmoe.py create mode 100644 easy_rec/python/model/keep_model/senet.py create mode 100644 easy_rec/python/model/ppnet_v3m.py create mode 100644 easy_rec/python/protos/ppnet.proto create mode 100644 keep_model.config create mode 100644 model_conf.json create mode 100644 test.parquet diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 23c72923a..f95553892 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -152,6 +152,7 @@ from tensorflow.python.ops import init_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops.ragged import ragged_math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import resource_variable_ops @@ -2138,6 +2139,9 @@ def _get_raw_feature_as_tensor(self, key): ValueError: if the raw feature has rank 0. """ raw_feature = self._features[key] + if 'RaggedTensor' in str(type(raw_feature)): + return raw_feature + feature_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor( raw_feature) @@ -2636,6 +2640,15 @@ def _get_dense_tensor_internal(self, checkpoint_utils.init_from_checkpoint( self.ckpt_to_load_from, {self.tensor_name_in_ckpt: to_restore}) + if 'RaggedTensor' in str(type(sparse_ids)): + assert sparse_weights is None + ragged_embedding = embedding_ops.embedding_lookup_ragged(embedding_weights=embedding_weights, + ragged_ids=sparse_ids, max_norm=self.max_norm, name='%s_weights' % self.name) + if self.combiner == 'sum': + return ragged_math_ops.reduce_sum(ragged_embedding, axis=1) + else: + return ragged_math_ops.reduce_mean(ragged_embedding, axis=1) + # Return embedding lookup result. return embedding_ops.safe_embedding_lookup_sparse( embedding_weights=embedding_weights, diff --git a/easy_rec/python/compat/feature_column/feature_column_v2.py b/easy_rec/python/compat/feature_column/feature_column_v2.py index 1ea1cff30..59341c73a 100644 --- a/easy_rec/python/compat/feature_column/feature_column_v2.py +++ b/easy_rec/python/compat/feature_column/feature_column_v2.py @@ -2543,6 +2543,9 @@ def _to_sparse_input_and_drop_ignore_values(input_tensor, ignore_value=None): Raises: ValueError: when `input_tensor`'s rank is `None`. """ + if 'RaggedTensor' in str(type(input_tensor)): + return input_tensor + input_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor( input_tensor) if isinstance(input_tensor, sparse_tensor_lib.SparseTensor): @@ -4237,6 +4240,9 @@ def _transform_input_tensor(self, input_tensor): if not input_tensor.dtype.is_integer: raise ValueError('Invalid input, not integer. key: {} dtype: {}'.format( self.key, input_tensor.dtype)) + + if 'RaggedTensor' in str(type(input_tensor)): + return input_tensor values = math_ops.cast(input_tensor.values, dtypes.int64, name='values') num_buckets = math_ops.cast( diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py new file mode 100644 index 000000000..6582eb83c --- /dev/null +++ b/easy_rec/python/input/parquet_input.py @@ -0,0 +1,155 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import logging + +import tensorflow as tf +import pandas as pd +import queue +import multiprocessing +import numpy as np +from tensorflow.python.platform import gfile + +from easy_rec.python.input.input import Input + +if tf.__version__ >= '2.0': + tf = tf.compat.v1 + + +class ParquetInput(Input): + + def __init__(self, + data_config, + feature_config, + input_path, + task_index=0, + task_num=1, + check_mode=False, + pipeline_config=None): + super(ParquetInput, + self).__init__(data_config, feature_config, input_path, task_index, + task_num, check_mode, pipeline_config) + if input_path is None: + return + + self._input_files = [] + for sub_path in input_path.strip().split(','): + self._input_files.extend(gfile.Glob(sub_path)) + logging.info('parquet input_path=%s file_num=%d' % (input_path, len(self._input_files))) + self._data_que = multiprocessing.Queue(maxsize=512) + self._file_que = multiprocessing.Queue(maxsize=len(self._input_files)) + for input_file in self._input_files: + self._file_que.put(input_file) + + file_num = len(self._input_files) + num_proc = 8 + if file_num < num_proc: + num_proc = file_num + logging.info('file_num=%d num_proc=%d' % (file_num, num_proc)) + self._proc_arr = [] + for proc_id in range(num_proc): + proc = multiprocessing.Process(target=self._parse_one_file, args=(proc_id,)) + self._proc_arr.append(proc) + + def _parse_one_file(self, proc_id): + all_fields = list(self._label_fields) + list(self._effective_fields) + logging.info('data proc %d start' % proc_id) + num_files = 0 + while True: + try: + input_file = self._file_que.get() + except queue.Empty: + break + num_files += 1 + input_data = pd.read_parquet(input_file, columns=all_fields) + data_len = len(input_data[all_fields[0]]) + batch_num = int(data_len / self._batch_size) + res_num = data_len % self._batch_size + logging.info('proc[%d] read file %s sample_num=%d batch_num=%d res_num=%d' % (proc_id, + input_file, data_len, batch_num, res_num)) + sid = 0 + for batch_id in range(batch_num): + eid = sid + self._batch_size + data_dict = {} + for k in self._label_fields: + data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], dtype=np.float32) + for k in self._effective_fields: + val = input_data[k][sid:eid] + all_lens = np.array([len(x) for x in val], dtype=np.int32) + all_vals = np.concatenate(list(val)) + assert np.sum(all_lens) == len(all_vals), 'len(all_vals)=%d np.sum(all_lens)=%d' % (len(all_vals), np.sum(all_lens)) + data_dict[k] = (all_lens, all_vals) + self._data_que.put(data_dict) + sid += self._batch_size + if res_num > 0: + logging.info('proc[%d] add final sample' % proc_id) + data_dict = {} + for k in self._label_fields: + data_dict[k] = np.array([x[0] for x in input_data[k][sid:]], dtype=np.float32) + for k in self._effective_fields: + val = input_data[k][sid:] + all_lens = np.array([len(x) for x in val], dtype=np.int32) + all_vals = np.concatenate(list(val)) + data_dict[k] = (all_lens, all_vals) + self._data_que.put(data_dict) + self._data_que.put(None) + logging.info('data proc %d done, file_num=%d' % (proc_id, num_files)) + + def _sample_generator(self): + for proc in self._proc_arr: + proc.start() + done_proc_cnt = 0 + fetch_timeout_cnt = 0 + while True: + try: + sample = self._data_que.get(block=False) + if sample is None: + done_proc_cnt += 1 + else: + yield sample + except queue.Empty: + fetch_timeout_cnt += 1 + if done_proc_cnt >= len(self._proc_arr): + logging.info("all sample finished, fetch_timeout_cnt=%d" % fetch_timeout_cnt) + break + for proc in self._proc_arr: + proc.join() + + def _to_fea_dict(self, input_dict): + fea_dict = {} + for fea_name in self._effective_fields: + tmp = input_dict[fea_name][1] % 1000 # 000000 + fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, + input_dict[fea_name][0]) + lbl_dict = {} + for lbl_name in self._label_fields: + lbl_dict[lbl_name] = input_dict[lbl_name] + return {'feature': fea_dict, 'label':lbl_dict} + + def _build(self, mode, params): + out_types = {} + out_shapes = {} + for k in self._label_fields: + out_types[k] = tf.int32 + out_shapes[k] = tf.TensorShape([None]) + for k in self._effective_fields: + out_types[k] = (tf.int64, tf.int32) + out_shapes[k] = (tf.TensorShape([None]), tf.TensorShape([None])) + + dataset = tf.data.Dataset.from_generator( + self._sample_generator, + output_types=out_types, + output_shapes=out_shapes) + num_parallel_calls = self._data_config.num_parallel_calls + dataset = dataset.map( + self._to_fea_dict, num_parallel_calls=num_parallel_calls) + dataset = dataset.prefetch(buffer_size=self._prefetch_size) + # dataset = dataset.map( + # map_func=self._preprocess, num_parallel_calls=num_parallel_calls) + dataset = dataset.prefetch(buffer_size=self._prefetch_size) + + if mode != tf.estimator.ModeKeys.PREDICT: + dataset = dataset.map(lambda x: + (self._get_features(x), self._get_labels(x))) + else: + dataset = dataset.map(lambda x: (self._get_features(x))) + return dataset diff --git a/easy_rec/python/model/keep_model/__init__.py b/easy_rec/python/model/keep_model/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/easy_rec/python/model/keep_model/base_model_ps_v2.py b/easy_rec/python/model/keep_model/base_model_ps_v2.py new file mode 100644 index 000000000..003f56ccc --- /dev/null +++ b/easy_rec/python/model/keep_model/base_model_ps_v2.py @@ -0,0 +1,914 @@ +#!/usr/bin/env python +# encoding: utf-8 +""" +usage +dcn 两输出模型，concat none 特征 +""" +import json +import os +import sys +import time +import logging +import tensorflow as tf +import numpy as np + +from tensorflow.keras.layers import Dense, Concatenate, concatenate, Lambda, Add, add, Activation, \ + Average, BatchNormalization, Embedding, Input, Reshape, Flatten, Add +import pyarrow.parquet as pq +from layers.sequence import AttentionSequencePoolingLayer +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.models import Model +from base.dataset import QDataSet +from base.qbox_wrapper import QWrapper +from base.context import Context, conf_overwrite +from metric.auc import AUCUtil +from metric.bucket_auc import BucketAUCUtil +from utils.mysql_util import MySQLUtils +from utils.basic_utils import * +from layers.dnn import DNN +from utils.ps_deploy import deploy_ps_data + + +# experimental_relax_shapes: True, avoid retracing +@tf.function(experimental_relax_shapes=True) +def train_step(model, input_dic, ctx): + """ + docstring + """ + with tf.GradientTape() as tape: + output_dic = model(input_dic, training=True) + + ps_loss = output_dic['ps_loss'] + ps_grad = tf.gradients(ps_loss, [input_dic["ps_embed"]])[0] + + b1_power = input_dic['b1_power'] + b2_power = input_dic['b2_power'] + #tf.print(b1_power, b2_power) + lr_t = ctx.learning_rate * \ + tf.sqrt(1. - b2_power) / (1. - b1_power) + tmp_m = ctx.adam_beta1 * input_dic["ps_embed_m"] + \ + (1 - ctx.adam_beta1) * ps_grad + tmp_v = ctx.adam_beta2 * input_dic["ps_embed_v"] + \ + (1 - ctx.adam_beta2) * ps_grad * ps_grad + tmp_w = input_dic["ps_embed"] - lr_t * tmp_m / \ + (tf.sqrt(tmp_v) + ctx.adam_epsilon) + + + #tmp_w = input_dic["ps_embed"] - ctx.learning_rate * ps_grad + result = tf.concat([tmp_w, tmp_m, tmp_v], axis=0) + output_dic['ps_grad'] = result + nan_flag = tf.reduce_sum(tf.cast(tf.math.is_nan(result), tf.float32)) + output_dic['nan_flag'] = nan_flag + + loss_value = output_dic['loss'] + grads = tape.gradient(loss_value, model.trainable_weights) + model.optimizer.apply_gradients(zip(grads, model.trainable_weights)) + + return output_dic + + +def make_pos(indice_list, len_list): + """ indice_list: [0, 1, 1, 2, 2, 2] + len_list: [1, 3, 6] + """ + # [6] + num = tf.shape(indice_list)[0] + # [6, 5, 4, 3, 2, 1] + cum_sum = tf.range(start=num, limit=0, delta=-1) + + # [5, 3, 0] + cum_sum_of_segment_sum = num - len_list + # [5, 3, 3, 0, 0, 0] + cum_sum_of_segment_sum = tf.gather(cum_sum_of_segment_sum, indice_list) + + # [1, 2, 1, 3, 2, 1] - 1 = [0, 1, 0, 2, 1, 0] + return cum_sum - cum_sum_of_segment_sum - 1 + +def make_sparse_indice(summed): + """https://stackoverflow.com/questions/54790000/calculate-segment-ids-needed-in-tf-math-segment-sum-by-length-of-segments-in-ten + summed is result of tf.cumsum + + Args: + summed (TYPE): Description + + Returns: + TYPE: Description + """ + return tf.searchsorted( + summed, tf.range(summed[-1]), side='right') + + +def model_inputs_prepare(inputs, ctx): + """Summary + + Args: + inputs (TYPE): Description + ctx (TYPE): Description + + Returns: + TYPE: Description + """ + batch_split = ctx.batch_size // ctx.num_worker + + # slice len + len_arr = [] + off_arr = [0] + size_arr = [] + for idx in range(len(ctx.slot_size_vec)): + len_arr.append(tf.reshape(tf.slice( + inputs["sparse_len"], + [0, ctx.slot_offset_vec[idx]], + [batch_split, ctx.slot_size_vec[idx]]), [-1])) + size_arr.append(len_arr[-1][-1]) + off_arr.append(off_arr[-1] + size_arr[-1]) + + # slice id + tmp_arr = [] + for idx in range(len(ctx.slot_size_vec)): + tmp_arr.append(tf.slice( + inputs["sparse_id"], [off_arr[idx]], [size_arr[idx]])) + + id_arr = [] + for idx in range(len(ctx.slot_size_vec)): + id_arr.append(tf.clip_by_value( + tmp_arr[idx] - inputs["uniq_num_by_dim"][idx], 0, 2**30)) + + # slice embed + embed_arr = [] + for idx in range(len(ctx.embed_dim_vec)): + tmp = tf.slice( + inputs["ps_embed"], + [inputs["uniq_off_by_dim"][idx]], + [inputs["uniq_off_by_dim"][idx + 1] - inputs["uniq_off_by_dim"][idx]]) # noqa + embed_arr.append(tf.reshape(tmp, [-1, ctx.embed_dim_vec[idx]])) + + return embed_arr, id_arr, len_arr, tmp_arr + + +class ExportModel(Model): + def __init__(self, model_conf, indim, name='export_model'): + super(ExportModel, self).__init__(name=name) + self.label_conf_list = model_conf['label'] + self.indim = indim + self._set_inputs([tf.TensorSpec([None] + tmp, tf.float32, name='input' + str(i)) for i, tmp in enumerate(indim)]) + self.dnn = DNN([300, 150, 50], activation='relu', + use_bn=True, output_activation='relu') + self.din_layer = AttentionSequencePoolingLayer() + self.output_layers = [Dense(1, activation='sigmoid', name='output_' + label_conf['input_name']) for label_conf in self.label_conf_list] + + def call(self, embed_input, training=False): + target_fea = embed_input[0] + + listclick = embed_input[1] + listclick_len = tf.reshape(listclick[...,:1], [-1, self.indim[1][0]]) + len_mask = tf.where( + tf.equal(listclick_len, 0), + tf.ones_like(listclick_len, dtype=tf.float32), + tf.zeros_like(listclick_len, dtype=tf.float32)) + listclick_len = tf.cast(tf.reduce_sum(len_mask, axis=1, keepdims=True), tf.int32) + + listcomplete = embed_input[2] + listcomplete_len = tf.reshape(listcomplete[...,:1], [-1, self.indim[2][0]]) + len_mask = tf.where( + tf.equal(listcomplete_len, 0), + tf.ones_like(listcomplete_len, dtype=tf.float32), + tf.zeros_like(listcomplete_len, dtype=tf.float32)) + listcomplete_len = tf.cast(tf.reduce_sum(len_mask, axis=1, keepdims=True), tf.int32) + + + click_att_data = self.din_layer([target_fea, listclick, listclick_len]) + click_att_data = tf.squeeze(click_att_data, axis=[1]) + + complete_att_data = self.din_layer([target_fea, listcomplete, listcomplete_len]) + complete_att_data = tf.squeeze(complete_att_data, axis=[1]) + + + embed = tf.concat([click_att_data, complete_att_data, embed_input[3]], axis=1) + embed = self.dnn(embed) + outputs = [output_layer(embed) for output_layer in self.output_layers] + return outputs + + +class BaseModel: + def __init__(self, conf): + """ + 初始化参数 + :param worker_list: 指定的 worker 节点列表 + :param embed_share: 0 全独立 1 field内共享 2 全共享 + """ + self.monitor_table = 'model_metrics_monitor' + self.mysql_utils = MySQLUtils().master + self.data_conf = conf['data'] + self.model_conf = conf['model'] + self.ps_conf = conf.get('ps', {}) + self.model_name = self.model_conf['model_name'] + self.epochs = self.data_conf.get('epochs', 1) # 总训练轮数 + self.log_step_num = self.data_conf.get('log_step_num', 1) + self.save_path = self.data_conf.get('save_path').rstrip('/') + '/' + self.model_name + '/' + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + self.save_hdfs_path = self.data_conf.get('save_hdfs_path').rstrip('/') + '/' + self.model_name + '/' + self.save_iter = 1 + + self.load_path = self.data_conf.get('load_path') + self.model = None + self.mode = self.model_conf['mode'] + self.profile = self.model_conf.get('profile', False) + self.tb_callback = None + if self.profile: + self.tb_callback = tf.keras.callbacks.TensorBoard(log_dir=self.save_path + '/profile', + profile_batch='10,12') + + if self.mode == 'Update': + self.process_update_conf() + self.feature_conf, self.group_length = self.parse_feature_conf(conf['feature']) + self.custom_group_count = len(self.group_length) - 1 + self.export_model_class = self.get_export_model_classs() + + if self.data_conf.get('deploy'): + shell(f'hadoop fs -mkdir -p {self.save_hdfs_path}') + + def parse_feature_conf(self, conf_list): + group_features = {} + group_len = {} + for conf in conf_list: + group = conf.get('group', 'default') + if group not in group_features: + group_features[group] = [] + group_len[group] = conf.get('length', 1) + group_features[group].append(conf) + + if len(group_features) == 1: + return conf_list, None + return group_features, group_len + + + def get_export_model_classs(self): + return ExportModel + + def process_update_conf(self): + self.mode = 'Train' + self.data_conf['deploy'] = True + model_info_path = self.save_hdfs_path + 'model_info.json' + model_info_local_path = self.save_path + 'model_info.json' + if os.path.exists(model_info_local_path): + shell(f'rm -f {model_info_local_path}') + shell(f'hadoop fs -get {model_info_path} {model_info_local_path}') + recent_date = '' + with open(model_info_local_path, 'r') as target: + model_info = json.load(target) + recent_date = model_info['date'] + + recent_dir = self.save_hdfs_path + recent_date + next_day = get_next_day(recent_date) + self.data_conf['train_end_day'] = next_day + self.data_conf['train_days'] = 1 + self.data_conf['eval_day'] = next_day + self.data_conf['load_path'] = self.save_path + recent_date + self.data_conf['load_embed_only'] = False + self.load_path = self.data_conf['load_path'] + print('download model data', recent_date, self.load_path) + if os.path.exists(self.load_path): + shell(f'rm -rf {self.load_path}') + rst = shell(f"hadoop fs -get {recent_dir} {self.load_path}") + logging.info('download model file rst:' + str(rst)) + logging.info(f'update mode data conf {self.data_conf}') + + + def build_input(self): + """ + 构建模型输入 + """ + ctx = self.ctx + in_dim = np.ma.innerproduct(ctx.slot_size_vec[self.custom_group_count:], ctx.embed_dim_vec[self.custom_group_count:]) + if ctx.use_acc_ctr: + in_dim += ctx.slot_num + self.ctx.net_input_dim = in_dim + input_dic = {} + input_dic["sample_idx"] = Input(dtype=tf.int64, batch_shape=(None,)) + input_dic["label"] = Input(dtype=tf.float32, batch_shape=(None, len(self.model_conf['label']))) + input_dic["sparse_len"] = Input(dtype=tf.int32, batch_shape=(None, + None), name='sparse_len') + input_dic["dense_fea"] = Input(dtype=tf.float32, batch_shape=(None, + None), name='dense_fea') + + input_dic["uniq_num_by_dim"] = Input(dtype=tf.int32, + batch_shape=(None,)) + input_dic["uniq_off_by_dim"] = Input(dtype=tf.int32, + batch_shape=(None,)) + # single worker + input_dic["sparse_id"] = Input(dtype=tf.int32, batch_shape=(None,)) + input_dic["ps_embed"] = Input(dtype=tf.float32, batch_shape=(None,)) + input_dic["ps_embed_m"] = Input(dtype=tf.float32, batch_shape=(None,)) + input_dic["ps_embed_v"] = Input(dtype=tf.float32, batch_shape=(None,)) + input_dic["ps_stat"] = Input(dtype=tf.float32, + batch_shape=(None, 2), name='ps_stat') + input_dic["b1_power"] = Input(dtype=tf.float32, + batch_shape=(1,), name='b1_power') + input_dic["b2_power"] = Input(dtype=tf.float32, + batch_shape=(1,), name='b2_power') + input_dic["test_mask"] = Input(dtype=tf.float32, + batch_shape=(1, in_dim), name='test_mask') + + batch_split = ctx.batch_size // ctx.num_worker + embed_arr, id_arr, len_arr, tmp_arr = model_inputs_prepare(input_dic, ctx) + + fea_dim = 11 if ctx.use_acc_ctr else 10 + gather_arr = [] + inputs_size = [] + + show, click = tf.split(input_dic["ps_stat"], 2, axis=1) + ctr = tf.reshape(click / (show + 10.), [-1, 1]) + + for idx, pair in enumerate(self.group_length.items()): + group, length = pair + if group == 'default': + continue + conf_list = self.feature_conf[group] + fea_size = len(conf_list) + fea_dim_total = fea_size * fea_dim + + indice = make_sparse_indice(len_arr[idx]) + tmp = tf.gather(embed_arr[idx], id_arr[idx]) + if ctx.use_acc_ctr: + acc_ctr = tf.gather(ctr, tmp_arr[idx]) + tmp = tf.concat([tmp, acc_ctr], axis=1) + pos = make_pos(indice, len_arr[idx]) + # non_zero_indice 分别表示第i个样本 第j个pos + non_zero_indice = tf.stack([indice, pos], axis=1) + group_embed = tf.scatter_nd(non_zero_indice, tmp, [batch_split*fea_size, length, fea_dim]) + group_embed = tf.reshape(group_embed, [-1, fea_size, length, fea_dim]) + group_embed = tf.reshape(tf.transpose(group_embed, perm=[0, 2, 1, 3]), [-1, length, fea_dim_total]) + gather_arr.append(group_embed) + inputs_size.append([length, fea_dim_total]) + + for idx in range(self.custom_group_count, len(ctx.embed_dim_vec)): + tmp_seg_ids = make_sparse_indice(len_arr[idx]) + tmp = tf.math.segment_sum( + tf.gather(embed_arr[idx], id_arr[idx]), + tmp_seg_ids) + tmp = tf.reshape(tmp, + [batch_split, ctx.slot_size_vec[idx] * ctx.embed_dim_vec[idx]]) + if ctx.use_acc_ctr: + acc_ctr = tf.reshape(tf.math.segment_mean( + tf.gather(ctr, tmp_arr[idx]), tmp_seg_ids), + [batch_split, ctx.slot_size_vec[idx], 1]) + tmp = tf.reshape(tmp, + [batch_split, ctx.slot_size_vec[idx], ctx.embed_dim_vec[idx]]) + tmp = tf.concat([tmp, acc_ctr], axis=2) + tmp = tf.reshape(tmp, + [batch_split, ctx.slot_size_vec[idx] * (ctx.embed_dim_vec[idx]+1)]) + gather_arr.append(tmp) + + # TODO aucdiff + # embed_input = tf.concat(gather_arr, axis=1, name="embed_input") + # embed_input = embed_input * input_dic["test_mask"][...,item_fea_dim_total:] + inputs_size.append([in_dim]) + + return input_dic, gather_arr, inputs_size + + + def build_and_compile_model(self): + """ + 构建并编译模型 + """ + ctx = self.ctx + input_dic, embed_input, indim = self.build_input() + self.export_model = self.export_model_class(self.model_conf, indim) + # self.export_model.build((None, indim)) + # self.export_model.summary() + + y_pred = self.export_model(embed_input) + y = input_dic["label"] # (bs, label_num) + splited_batch_size = ctx.batch_size // ctx.num_worker + + miss_mask = tf.where( + tf.equal(tf.reshape(input_dic["sample_idx"], [-1]), 0), + tf.zeros([splited_batch_size], dtype=ctx.tfval), + tf.ones([splited_batch_size], dtype=ctx.tfval)) + + loss_object = tf.keras.losses.BinaryCrossentropy() + if type(y_pred) != list: + y_pred = [y_pred] + total_loss = 0 + ps_loss = 0 + ttmp = tf.cast(tf.reduce_sum(miss_mask), tf.int32) + ground_truths = [] + predictions = [] + losses = [] + for label_idx, conf in enumerate(self.model_conf['label']): + # for ground_truth, pred, conf in zip(y, y_pred, self.model_conf['label']): + ground_truth = y[..., label_idx] + pred = y_pred[label_idx] + train_space = conf.get('train_space', -1) + if train_space >= 0: + valid_idx = tf.where(y[..., train_space] > 0) + ground_truth = tf.gather(ground_truth, valid_idx) + pred = tf.gather(pred, valid_idx) + else: + ground_truth = ground_truth[:ttmp] + pred = pred[:ttmp] + ground_truth = tf.reshape(ground_truth, (-1, 1)) + pred = tf.reshape(pred, (-1, 1)) + #cur_loss = tf.nn.sigmoid_cross_entropy_with_logits(ground_truth, logit) + #cur_loss = tf.reduce_mean(cur_loss) + if conf.get('pos_weight'): + pos_weight = conf.get('pos_weight', 1.) + sample_weight = 1. + ground_truth * (pos_weight - 1.) + cur_loss = loss_object(ground_truth, pred, sample_weight=sample_weight) + else: + cur_loss = loss_object(ground_truth, pred) + losses.append(cur_loss) + weight = conf.get('weight', 1.) + embed_weight = conf.get('embed_weight', 1.) + total_loss += cur_loss * weight + ps_loss += cur_loss * embed_weight + + ground_truths.append(ground_truth) + predictions.append(pred) + + net_dic = { + "loss": total_loss, + "ps_loss": ps_loss, + "losses": losses, + "embed_input": embed_input, + "ground_truth": ground_truths, + "prediction": predictions, + "sid": input_dic["sample_idx"][:ttmp] + } + + opt = Adam(0.001) + model = Model(inputs=input_dic, outputs=net_dic) + model.compile(optimizer=opt) + self.checkpoint = tf.train.Checkpoint(step=tf.Variable(1), model=model, optimizer=opt) + return model + + def load_model(self, path): + if not self.data_conf.get('load_embed_only'): + cpt_path = os.path.join(path, 'checkpoint') + if not os.path.exists(cpt_path): + raise Exception(cpt_path + ' not exists.') + self.cpt_manager = tf.train.CheckpointManager(self.checkpoint, + cpt_path, + max_to_keep=1) + logging.info('loading model from:{}'.format(path)) + self.checkpoint.restore(self.cpt_manager.latest_checkpoint) + self.local_db.load() + + + def save_model(self, save_date, model_info, is_deploy=None): + path = self.save_path + save_date + shell(f"rm -rf {path}") + self.cpt_manager = tf.train.CheckpointManager(self.checkpoint, + os.path.join(path, 'checkpoint'), + max_to_keep=1) + save_path = self.cpt_manager.save() + self.local_db.save() + logging.info('saving model to:{}'.format(save_path)) + model_info_path = self.save_path + 'model_info.json' + with open(model_info_path, 'w') as target: + json.dump(model_info, target) + if is_deploy: + #self.export_model.save(path + '/local_model', save_format="tf") + tf.saved_model.save(self.export_model, path + '/local_model') + hdfs_new_model_path = self.save_hdfs_path + save_date + # deploy codis data first, make sure that when server got new version file the codis data is ready. + deploy_ps_data(self.model_name, path, self.save_iter) + shell(f'hadoop fs -rmr {hdfs_new_model_path}') + logging.info('put local model to hdfs %s ' % hdfs_new_model_path) + shell(f'hadoop fs -put {path} {hdfs_new_model_path}') + model_info_remote_path = self.save_hdfs_path + 'model_info.json' + shell(f'hadoop fs -put -f {model_info_path} {model_info_remote_path}') + self.save_iter += 1 + + + def prepare_model(self): + # 编译并构建模型 + self.model = self.build_and_compile_model() + if self.load_path: + self.load_model(self.load_path) + + def log_into_db(self, train_time, metrics): + """log train history to db. + """ + label_name = metrics['Name'] + for metric_name, metric_value in metrics.items(): + if metric_name == 'Name': + continue + metric_name = label_name + '_' + metric_name + metric_time = int(time.mktime(train_time.timetuple())) + data = { + 'model_name': self.model_name, + 'metric_time': metric_time, + 'metric_name': metric_name, + 'metric_value': float(metric_value) + } + where_str = f'model_name="{self.model_name}" and metric_time={metric_time} and metric_name="{metric_name}"' + logging.info(f'log train metric into db: {data}') + + if self.mysql_utils.fetch_one(table=self.monitor_table, where=where_str): + self.mysql_utils.update(self.monitor_table, data, where_str) + else: + self.mysql_utils.insert(self.monitor_table, data) + + def get_batch_input(self, batch, local_db, mask=None): + ctx = self.ctx + ps_stat, ps_embed, tinfo = local_db.pull(batch["task_id"]) + input_dic = {} + # input_dic["scene_index"] = batch["scene_index"] + input_dic["sample_idx"] = batch["sample_idx"] + input_dic["label"] = batch["label"] + input_dic["sparse_len"] = batch["sparse_len"] + input_dic["dense_fea"] = batch["dense_fea"] + + input_dic["uniq_num_by_dim"] = np.cumsum([0] + batch["uniq_num_by_dim"].tolist()).astype(np.int32) # noqa + tmp = np.array(ctx.embed_dim_vec).astype(np.int32) * batch["uniq_num_by_dim"].astype(np.int32) # noqa + input_dic["uniq_off_by_dim"] = np.cumsum([0] + tmp.tolist()).astype(np.int32) # noqa + input_dic["sparse_id"] = batch["sparse_id"][0] + + input_dic["ps_embed"] = ps_embed[0] + input_dic["ps_embed_m"] = np.zeros_like(ps_embed[1]) if self.adam_restart else ps_embed[1] + input_dic["ps_embed_v"] = np.zeros_like(ps_embed[2]) if self.adam_restart else ps_embed[2] + input_dic["ps_stat"] = ps_stat + self.b1_power *= ctx.adam_beta1 + self.b2_power *= ctx.adam_beta2 + input_dic["b1_power"] = self.b1_power + input_dic["b2_power"] = self.b2_power + input_dic['test_mask'] = np.ones([1, self.ctx.net_input_dim], dtype=self.ctx.npval) + # avoid retracing of tf function. + input_dic = {key: tf.constant(input_dic[key]) for key in input_dic} + return input_dic + + def fit_day(self, train_itor, local_db): + ctx = self.ctx + batch_size = self.ctx.batch_size // self.ctx.num_worker + metrics = [AUCUtil(name=conf['input_name'] + '_metric') for conf in self.model_conf['label']] + total_metrics = [BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) for conf in self.model_conf['label']] + step = 0 + while True: + try: + if self.profile: + self.tb_callback.on_train_batch_begin(step) + start_time = time.time() + batch = train_itor.getbatch() + input_dic = self.get_batch_input(batch, local_db) + self.adam_restart = False + output_dic = train_step(self.model, input_dic, ctx) + if output_dic['nan_flag'] > 0: + print('nan and skip') + local_db.push(batch["task_id"], None, None) + continue + + # Update training metric. + for gt, pred, loss, metric, total_metric in zip( + output_dic['ground_truth'], + output_dic['prediction'], + output_dic['losses'], + metrics, + total_metrics + ): + gt = gt.numpy() + pred = pred.numpy() + loss = loss.numpy() + total_metric.add(loss, gt, pred) + if self.log_step_num < 1 or step % self.log_step_num != 0: + continue + #print('pred', pred[:20]) + + metric.add(loss, gt, pred) + print(f'{metric.name} {metric.calc_str()}') + metric.reset() + + ps_grad = output_dic['ps_grad'] + local_db.push(batch["task_id"], ps_grad.numpy(), batch["payload"]) + elapse = time.time() - start_time + if self.log_step_num >= 1 and step % self.log_step_num == 0: + print(f'train step {step}, batch time elapse {elapse}') + print('-' * 50) + step += 1 + if self.profile: + self.tb_callback.on_train_batch_end(step) + if step > 20: + break + #exit() + except EOFError: + # save & terminate + print('EOF, train day end.') + break + rst_list = [] + for metric in total_metrics: + rst = metric.calc() + print(f'{metric.name} {rst}') + rst['Name'] = metric.name + rst_list.append(rst) + print('='*80) + return rst_list + + + def check_day(self, train_itor, local_db): + ctx = self.ctx + batch_size = ctx.batch_size + step = 0 + embed_size = 10 + sample_data = pq.read_table('embed.parquet').to_pandas() + + while True: + try: + start_time = time.time() + batch = train_itor.getbatch() + input_dic = self.get_batch_input(batch, local_db) + output_dic = self.model(input_dic, training=False) + + samples = sample_data[step*batch_size: step*batch_size + batch_size] + online_embeds = samples.embed_input.to_numpy() + + # should add 'embed_input' in ouput_dic first. + embed_input_data = output_dic['embed_input'].numpy() + features = [feature['input_name'] for feature in self.feature_conf] + feature_diff_data = {} + for embed, online_embed in zip( + embed_input_data, + online_embeds + ): + embed_off = embed.tolist() + embed_on = online_embed.tolist() + for idx, fea_name in enumerate(features): + fea_embed_on = embed_on[idx*embed_size: idx*embed_size + embed_size] + fea_embed_off = embed_off[idx*embed_size: idx*embed_size + embed_size] + total_diff = sum([abs(x-y) if abs(x-y) > 1e-7 else 0 for x, y in zip(fea_embed_on, fea_embed_off)]) + if total_diff > 0 or sum(fea_embed_on) == 0: + print(fea_name) + print(fea_embed_on) + print(fea_embed_off) + feature_diff_data[fea_name] = feature_diff_data.get(fea_name, 0) + 1 + print('-'*60) + print('total cnt', len(online_embeds)) + feature_diff_data = [[key, value] for key, value in feature_diff_data.items()] + feature_diff_data.sort(key=lambda x: x[1], reverse=True) + print(feature_diff_data) + + + + # Update training metric. + for gt, pred, conf in zip( + output_dic['ground_truth'], + output_dic['prediction'], + self.model_conf['label'] + ): + gt = gt.numpy() + pred = pred.numpy() + print(conf['input_name']) + thresold = 10e-4 + total = 0 + cnt = 0 + for on, off in zip(gt, pred): + total += 1 + if abs(on - off) > thresold: + cnt += 1 + # print(on, off) + print('diff ratio', cnt / total) + + local_db.push(batch["task_id"]) + elapse = time.time() - start_time + if self.log_step_num >= 1 and step % self.log_step_num == 0: + print(f'train step {step}, batch time elapse {elapse}') + print('-' * 50) + step += 1 + #exit() + except EOFError: + # save & terminate + print('EOF, check day end.') + break + + + def eval(self, train_itor, local_db): + ctx = self.ctx + batch_size = ctx.batch_size + metrics = [AUCUtil(name=conf['input_name'] + '_metric') for conf in self.model_conf['label']] + total_metrics = [BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) for conf in self.model_conf['label']] + step = 0 + + while True: + try: + start_time = time.time() + batch = train_itor.getbatch() + input_dic = self.get_batch_input(batch, local_db) + output_dic = self.model(input_dic, training=False) + + # Update training metric. + for gt, pred, loss, metric, total_metric in zip( + output_dic['ground_truth'], + output_dic['prediction'], + output_dic['losses'], + metrics, + total_metrics + ): + gt = gt.numpy() + pred = pred.numpy() + loss = loss.numpy() + total_metric.add(loss, gt, pred) + if self.log_step_num < 1 or step % self.log_step_num != 0: + continue + metric.add(loss, gt, pred) + print(f'{metric.name} {metric.calc_str()}') + metric.reset() + + local_db.push(batch["task_id"]) + elapse = time.time() - start_time + if self.log_step_num >= 1 and step % self.log_step_num == 0: + print(f'eval step {step}, batch time elapse {elapse}') + print('-' * 50) + step += 1 + #exit() + except EOFError: + # save & terminate + print('EOF, eval end.') + break + rst_list = [] + for metric in total_metrics: + rst = metric.calc() + print(f'{metric.name} {rst}') + rst['Name'] = metric.name + rst_list.append(rst) + return rst_list + + + def aucdiff_run(self, train_itor, local_db): + ctx = self.ctx + batch_size = ctx.batch_size + total_metrics_list = [[BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) for conf in self.model_conf['label']] for _ in range(ctx.origin_slot_num + 1)] + tmask = [np.ones([1, self.ctx.net_input_dim], dtype=self.ctx.npval) for _ in range(self.ctx.origin_slot_num)] # noqa + + step = 0 + embed_offset = 0 + start_arr = [] + end_arr = [] + for mask in range(len(ctx.embed_dim)): + pstart = embed_offset + embed_len = self.ctx.embed_dim[mask] + if ctx.use_acc_ctr: + embed_len += 1 + pend = pstart + embed_len + tmask[mask][:, pstart:pend] = 0. + embed_offset = pend + + tmp_start = [str(pstart)] + tmp_end = [str(pend)] + start_arr.append("/".join(tmp_start)) + end_arr.append("/".join(tmp_end)) + + while True: + try: + batch = train_itor.getbatch() + input_dic = self.get_batch_input(batch, local_db) + origin_mask = input_dic["test_mask"] + + for mask in range(self.ctx.origin_slot_num + 1): + if mask < ctx.origin_slot_num: + input_dic["test_mask"] = tf.constant(tmask[mask]) + else: + input_dic["test_mask"] = origin_mask + + output_dic = self.model(input_dic, training=False) + + for gt, pred, loss, total_metric in zip( + output_dic['ground_truth'], + output_dic['prediction'], + output_dic['losses'], + total_metrics_list[mask] + ): + gt = gt.numpy() + pred = pred.numpy() + loss = loss.numpy() + total_metric.add(loss, gt, pred) + step += 1 + local_db.push(batch["task_id"]) + print('aucdiff check step', step) + + except EOFError: + data = {} + features = [feature['input_name'] for feature in self.feature_conf] + for mask in range(self.ctx.origin_slot_num): + print("# Mask %s from %s to %s." % + (self.ctx.origin_slots.split(",", -1)[mask], + start_arr[mask], end_arr[mask])) + feature = features[mask] + data[feature] = {} + for metric in total_metrics_list[mask]: + rst = metric.calc() + data[feature][metric.name] = rst + print(f'{metric.name} {rst}') + data['base'] = {} + for metric in total_metrics_list[-1]: + print('base metrics') + rst = metric.calc() + data['base'][metric.name] = rst + print(f'{metric.name} {rst}') + with open('aucdiff_rst.json', 'w') as target: + json.dump(data, target) + break + + + def run(self): + conf_overwrite(self, 'job.conf', 'job.conf.tmp') + self.ctx = Context('job.conf.tmp') + self.train_itor = QDataSet('job.conf.tmp', self.ctx, False) + self.local_db = QWrapper('job.conf.tmp') + self.b1_power = 1. + self.b2_power = 1. + self.adam_restart = True + self.prepare_model() + if self.mode.startswith('Train'): + time_range = self.data_conf['train_days'] + data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' + end_day = self.data_conf['train_end_day'] + end_time = datetime.datetime.strptime(f'{end_day} 00:00:00', '%Y-%m-%d %H:%M:%S') + trained_days = 0 + # TODO 尝试add_history、add_progbar + if self.profile: + self.tb_callback.set_model(self.model) + + try: + if self.profile: + self.tb_callback.on_train_begin() + model_info = {} + for epoch in range(self.epochs): + if self.profile: + self.tb_callback.on_epoch_begin(epoch) + print(f'start epoch {epoch}...') + for i in reversed(range(time_range)): + train_time = end_time - datetime.timedelta(days=i) + data_path = train_time.strftime(data_hdfs_path) + # data_date = train_time.strftime('%Y-%m-%d') + files = get_hdfs_files(data_path) + self.train_itor.setfiles(','.join(files)) + print('start train', train_time, data_path) + total_metrics = self.fit_day(self.train_itor, self.local_db) + model_info['metrics'] = total_metrics + if self.data_conf.get('monitor'): + for metrics in total_metrics: + self.log_into_db(train_time, metrics) + self.local_db.clean_nontrain(False) + trained_days += 1 + if trained_days % 5 == 0: + train_date = train_time.strftime('%Y-%m-%d') + self.save_model(train_date, model_info) + if self.profile: + self.tb_callback.on_epoch_end(epoch) + if self.profile: + self.tb_callback.on_train_end() + except Exception as e: + logging.exception(e) + finally: + is_deploy = self.data_conf.get('deploy') + model_info['version'] = int(time.mktime(train_time.timetuple())) + train_date = train_time.strftime('%Y-%m-%d') + model_info['date'] = train_date + self.save_model(train_date, model_info, is_deploy=is_deploy) + self.local_db.terminate() + + if 'Eval' in self.mode: + data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' + eval_day = self.data_conf['eval_day'] + eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', '%Y-%m-%d %H:%M:%S') + + data_path = eval_time.strftime(data_hdfs_path) + files = get_hdfs_files(data_path) + # files = files[::10] + self.train_itor.is_test = True + self.train_itor.setfiles(','.join(files)) + print('start eval', eval_time, data_path) + self.eval(self.train_itor, self.local_db) + self.local_db.terminate() + + + # TODO support accctr + if self.mode == 'AucDiff': + data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' + eval_day = self.data_conf['eval_day'] + eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', '%Y-%m-%d %H:%M:%S') + + data_path = eval_time.strftime(data_hdfs_path) + files = get_hdfs_files(data_path) + # files = files[::10] + self.train_itor.is_test = True + self.train_itor.setfiles(','.join(files)) + print('start check', eval_time, data_path) + self.aucdiff_run(self.train_itor, self.local_db) + self.local_db.terminate() + + # TODO support accctr + if self.mode == 'Check': + data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' + eval_day = self.data_conf['eval_day'] + eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', '%Y-%m-%d %H:%M:%S') + + data_path = eval_time.strftime(data_hdfs_path) + files = get_hdfs_files(data_path) + self.train_itor.is_test = True + self.train_itor.setfiles(','.join(files)) + print('start check', eval_time, data_path) + + total_metrics = self.check_day(self.train_itor, self.local_db) + self.local_db.terminate() + diff --git a/easy_rec/python/model/keep_model/dnn.py b/easy_rec/python/model/keep_model/dnn.py new file mode 100644 index 000000000..5f3e83719 --- /dev/null +++ b/easy_rec/python/model/keep_model/dnn.py @@ -0,0 +1,133 @@ +import tensorflow as tf +from tensorflow.keras import backend as K +from tensorflow.keras.layers import Layer +from tensorflow.keras.initializers import Zeros, glorot_normal +from tensorflow.keras.regularizers import l2 + +def activation_layer(activation): + if isinstance(activation, str): + act_layer = tf.keras.layers.Activation(activation) + elif issubclass(activation, Layer): + act_layer = activation() + else: + raise ValueError( + "Invalid activation,found %s.You should use a str or a Activation Layer Class." % (activation)) + return act_layer + +class DNN(Layer): + """The Multi Layer Percetron + + Input shape + - nD tensor with shape: ``(batch_size, ..., input_dim)``. The most common situation would be a 2D input with shape ``(batch_size, input_dim)``. + + Output shape + - nD tensor with shape: ``(batch_size, ..., hidden_size[-1])``. For instance, for a 2D input with shape ``(batch_size, input_dim)``, the output would have shape ``(batch_size, hidden_size[-1])``. + + Arguments + - **hidden_units**:list of positive integer, the layer number and units in each layer. + + - **activation**: Activation function to use. + + - **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix. + + - **dropout_rate**: float in [0,1). Fraction of the units to dropout. + + - **use_bn**: bool. Whether use BatchNormalization before activation or not. + + - **output_activation**: Activation function to use in the last layer.If ``None``,it will be same as ``activation``. + + - **seed**: A Python integer to use as random seed. + """ + + def __init__(self, hidden_units, activation='relu', l2_reg=0, dropout_rate=0, use_bn=False, output_activation=None, need_ppnet=False, pp_size=0, + seed=1024, **kwargs): + self.hidden_units = hidden_units + self.activation = activation + self.l2_reg = l2_reg + self.dropout_rate = dropout_rate + self.use_bn = use_bn + self.output_activation = output_activation + self.need_ppnet = need_ppnet + self.pp_size = pp_size + self.seed = seed + + super(DNN, self).__init__(**kwargs) + + def build(self, input_shape): + # if len(self.hidden_units) == 0: + # raise ValueError("hidden_units is empty") + input_size = input_shape[-1] + if self.need_ppnet: + input_size -= self.pp_size + hidden_units = [int(input_size)] + list(self.hidden_units) + self.kernels = [self.add_weight(name='kernel' + str(i), + shape=( + hidden_units[i], hidden_units[i + 1]), + initializer=glorot_normal( + seed=self.seed), + regularizer=l2(self.l2_reg), + trainable=True) for i in range(len(self.hidden_units))] + self.bias = [self.add_weight(name='bias' + str(i), + shape=(self.hidden_units[i],), + initializer=Zeros(), + trainable=True) for i in range(len(self.hidden_units))] + if self.use_bn: + self.bn_layers = [tf.keras.layers.BatchNormalization() for _ in range(len(self.hidden_units))] + if self.need_ppnet: + self.pp_gates = [DNN([size, size], activation='relu', use_bn=False, + output_activation='sigmoid') for size in hidden_units[:-1]] + + self.dropout_layers = [tf.keras.layers.Dropout(self.dropout_rate, seed=self.seed + i) for i in + range(len(self.hidden_units))] + + self.activation_layers = [activation_layer(self.activation) for _ in range(len(self.hidden_units) - 1)] + + if self.output_activation: + self.activation_layers += [activation_layer(self.output_activation)] + else: + self.activation_layers += [activation_layer(self.activation)] + + super(DNN, self).build(input_shape) # Be sure to call this somewhere! + + def call(self, inputs, training=None, **kwargs): + + deep_input = inputs + if self.need_ppnet: + deep_input = inputs[..., self.pp_size:] + pp_input = inputs[..., :self.pp_size] + #deep_input_wo_bp = tf.stop_gradient(deep_input) + #pp_input = tf.concat([pp_input, deep_input_wo_bp], axis=-1) + + for i in range(len(self.hidden_units)): + if self.need_ppnet: + deep_input = 2*self.pp_gates[i](pp_input) * deep_input + fc = tf.nn.bias_add(tf.tensordot( + deep_input, self.kernels[i], axes=(-1, 0)), self.bias[i]) + + if self.use_bn: + fc = self.bn_layers[i](fc, training=training) + try: + fc = self.activation_layers[i](fc, training=training) + except TypeError as e: # TypeError: call() got an unexpected keyword argument 'training' + print("make sure the activation function use training flag properly", e) + fc = self.activation_layers[i](fc) + + fc = self.dropout_layers[i](fc, training=training) + deep_input = fc + + return deep_input + + def compute_output_shape(self, input_shape): + if len(self.hidden_units) > 0: + shape = input_shape[:-1] + (self.hidden_units[-1],) + else: + shape = input_shape + + return tuple(shape) + + def get_config(self, ): + config = {'activation': self.activation, 'hidden_units': self.hidden_units, + 'l2_reg': self.l2_reg, 'use_bn': self.use_bn, 'dropout_rate': self.dropout_rate, + 'output_activation': self.output_activation, 'seed': self.seed} + base_config = super(DNN, self).get_config() + return dict(list(base_config.items()) + list(config.items())) diff --git a/easy_rec/python/model/keep_model/model_ps_mmoe.py b/easy_rec/python/model/keep_model/model_ps_mmoe.py new file mode 100644 index 000000000..0d1bb8746 --- /dev/null +++ b/easy_rec/python/model/keep_model/model_ps_mmoe.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python +# encoding: utf-8 +""" +MMOE model +""" +import tensorflow as tf +from tensorflow.keras.layers import Dense, Activation, BatchNormalization, Multiply +from tensorflow.keras.models import Model +# from utils.basic_utils import * +# from model.base_model_ps import BaseModel +from easy_rec.python.model.keep_model.dnn import DNN +from easy_rec.python.model.keep_model.senet import SENETLayer + +class CustomizedModel(Model): + def __init__(self, model_conf, indim, name='export_model'): + super(CustomizedModel, self).__init__(name=name) + self.label_conf_list = [conf for conf in model_conf['label'] if conf.get('weight', 1) > 0] + self.experts = model_conf['experts'] + self.expert_layers = model_conf['expert_layers'] + self.tower_layers = model_conf['tower_layers'] + self.need_senet = model_conf.get('need_senet', False) + if self.need_senet: + self.field_size = model_conf['field_size'] + self.se_layer = SENETLayer(self.field_size) + self.need_ppnet = model_conf.get('need_ppnet', False) + self.pp_feature_cnt = model_conf.get('pp_feature_cnt', 0) + embed_size = 11 + + # self._set_inputs(tf.TensorSpec([None, indim], tf.float32, name='embed_input')) + + self.expert_list = [DNN(self.expert_layers, activation='relu', use_bn=True, output_activation='relu', need_ppnet=self.need_ppnet, pp_size=self.pp_feature_cnt*embed_size) for _ in range(self.experts)] + + self.tower_list = [DNN(self.tower_layers, activation='relu', use_bn=True, output_activation='sigmoid', need_ppnet=self.need_ppnet, pp_size=self.pp_feature_cnt*embed_size) for _ in self.label_conf_list] + + self.gate_list = [DNN([self.experts], activation='relu', use_bn=False, output_activation='linear') for _ in self.label_conf_list] + + + def call(self, embed_input, training=False): + if self.need_senet: + embed_input = self.se_layer(embed_input) + expert_list = [] + for expert_layer in self.expert_list: + # (N, 1, d1) + expert = tf.expand_dims(expert_layer(embed_input), axis=1) + expert_list.append(expert) + # (N, n, d1) + concat_expert = tf.concat(expert_list, axis=1) + + output_list = [] + for i, label_conf in enumerate(self.label_conf_list): + # (N, n, 1) + gate = tf.expand_dims(tf.nn.softmax(self.gate_list[i](embed_input)), axis=2) + # (N, d1) + output = tf.reduce_sum(concat_expert * gate, axis=1) + # (N, 1) with softmax + output = self.tower_list[i](output) + output_list.append(output) + + return output_list + + +# class ExpModel(BaseModel): +# def __init__(self, conf): +# """ +# 初始化参数 +# :param worker_list: 指定的 worker 节点列表 +# :param embed_share: 0 全独立 1 field内共享 2 全共享 +# """ +# super(ExpModel, self).__init__(conf) +# +# def get_export_model_classs(self): +# return CustomizedModel +# diff --git a/easy_rec/python/model/keep_model/senet.py b/easy_rec/python/model/keep_model/senet.py new file mode 100644 index 000000000..19b7288c8 --- /dev/null +++ b/easy_rec/python/model/keep_model/senet.py @@ -0,0 +1,98 @@ +import math +import tensorflow as tf +from tensorflow.python.keras import backend as K +from tensorflow.python.keras.initializers import glorot_normal +from tensorflow.python.keras.layers import Layer + + +class SENETLayer(Layer): + """SENETLayer used in FiBiNET. + Input shape + - tensor with shape: ``(batch_size,field_size*embedding_size)``. + Output shape + - tensor with shape: ``(batch_size,field_size*embedding_size)``. + Arguments + - **reduction_ratio** : Positive integer, dimensionality of the + attention network output space. + - **seed** : A Python integer to use as random seed. + - **norm** : norm function to norm attention factor, l2 or softmax. + - **last_activation** : last act function, relu or identity. + - **field_size** : feature field number. + References + - [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) + """ + + def __init__(self, field_size, norm='l2', last_activation='identity', reduction_ratio=3, seed=1024, **kwargs): + self.field_size = field_size + self.norm = norm + self.last_activation = last_activation + self.reduction_ratio = reduction_ratio + self.seed = seed + super(SENETLayer, self).__init__(**kwargs) + + def tensordot(self, x, y): + return tf.tensordot(x, y, axes=(-1, 0)) + + def build(self, input_shape): + + if len(input_shape) != 2: + raise ValueError('A `SENETLayer` layer should be called ' + 'on a 2D tensor.') + + # if not divisible, raise an exception + if input_shape[1] % self.field_size != 0: + raise ValueError(f'Embed input size must be divisible by field_size, got {input_shape[1]} and {self.field_size}') + self.embedding_size = int(input_shape[1] / self.field_size) + reduction_size = max(1, self.field_size // self.reduction_ratio) + + self.W_1 = self.add_weight(shape=( + self.field_size, reduction_size), + initializer=glorot_normal(seed=self.seed), name="W1", + trainable=True) + self.W_2 = self.add_weight(shape=( + reduction_size, self.field_size), + initializer=glorot_normal(seed=self.seed), name="W2", + trainable=True) + + # zty note:Lambda layer may cause variable not automatically + # added to the variable set for gradient calculation + # DO NOT use Lambda layer if possible. + #self.tensordot = tf.keras.layers.Lambda( + # lambda x: tf.tensordot(x[0], x[1], axes=(-1, 0))) + + # Be sure to call this somewhere! + super(SENETLayer, self).build(input_shape) + + def call(self, inputs, training=None, **kwargs): + + if K.ndim(inputs) != 2: + raise ValueError( + "Unexpected inputs dimensions %d, expect to be 2 dimensions" % (K.ndim(inputs))) + + inputs = tf.reshape(inputs, [-1, self.field_size, self.embedding_size]) + Z = tf.reduce_mean(inputs, axis=-1) + + A_1 = tf.nn.relu(self.tensordot(Z, self.W_1)) + A_2 = self.tensordot(A_1, self.W_2) + if self.last_activation == 'relu': + A_2 = tf.nn.relu(A_2) + + A_2 = tf.nn.l2_normalize(A_2, axis=1) if self.norm == 'l2' else tf.nn.softmax(A_2, axis=1) + A_2 = A_2 * math.sqrt(self.field_size) + output = tf.multiply(inputs, tf.expand_dims(A_2, axis=2)) + output = tf.reshape(output, [-1, self.field_size * self.embedding_size]) + + return output + + def compute_output_shape(self, input_shape): + + return input_shape + + def compute_mask(self, inputs, mask=None): + return [None] * self.field_size + + def get_config(self, ): + config = {'reduction_ratio': self.reduction_ratio, 'seed': self.seed} + base_config = super(SENETLayer, self).get_config() + base_config.update(config) + return base_config diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py new file mode 100644 index 000000000..3e5fdcd2d --- /dev/null +++ b/easy_rec/python/model/ppnet_v3m.py @@ -0,0 +1,59 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import tensorflow as tf + +from easy_rec.python.layers import dnn +from easy_rec.python.layers import mmoe +from easy_rec.python.model.multi_task_model import MultiTaskModel +from easy_rec.python.model.rank_model import RankModel +from easy_rec.python.protos.ppnet_pb2 import PPNet as PPNetConfig +from easy_rec.python.model.keep_model.model_ps_mmoe import CustomizedModel +import json +import logging + +if tf.__version__ >= '2.0': + tf = tf.compat.v1 + + +class PPNetV3M(RankModel): + + def __init__(self, + model_config, + feature_configs, + features, + labels=None, + is_training=False): + super(PPNetV3M, self).__init__(model_config, feature_configs, features, labels, + is_training) + assert self._model_config.WhichOneof('model') == 'ppnet', \ + 'invalid model config: %s' % self._model_config.WhichOneof('model') + self._model_config = self._model_config.ppnet + assert isinstance(self._model_config, PPNetConfig) + + self._features, _ = self._input_layer(self._feature_dict, 'all') + + with open(self._model_config.model_conf, 'r') as fin: + self._model_conf = json.load(fin) + indim = self._features.get_shape()[1] + logging.info('indim = %d' % indim) + self._keras_model = CustomizedModel(self._model_conf, indim) + + def build_predict_graph(self): + # self._add_to_prediction_dict(tower_outputs) + output_list = self._keras_model(self._features) + for lbl_id in range(len(self._model_conf['label'])): + lbl_info = self._model_conf['label'][lbl_id] + lbl_name = lbl_info.get('input_name') + output = output_list[lbl_id] + self._prediction_dict[lbl_name] = output + # return self._prediction_dict + + def build_loss_graph(self): + for lbl_id in range(len(self._model_conf['label'])): + lbl_info = self._model_conf['label'][lbl_id] + lbl_name = lbl_info.get('input_name') + output = self._prediction_dict.get(lbl_name) + loss_obj = tf.keras.losses.BinaryCrossentropy()(self._labels[lbl_name], output) + self._loss_dict[lbl_name] = loss_obj + return self._loss_dict + diff --git a/easy_rec/python/protos/dataset.proto b/easy_rec/python/protos/dataset.proto index 7cdf5e36a..c9bf809f7 100644 --- a/easy_rec/python/protos/dataset.proto +++ b/easy_rec/python/protos/dataset.proto @@ -223,6 +223,7 @@ message DatasetConfig { HiveInput = 16; HiveRTPInput = 17; HiveParquetInput = 18; + ParquetInput = 19; CriteoInput = 1001; } required InputType input_type = 10; diff --git a/easy_rec/python/protos/easy_rec_model.proto b/easy_rec/python/protos/easy_rec_model.proto index b0f79fe0f..ff74a0c48 100644 --- a/easy_rec/python/protos/easy_rec_model.proto +++ b/easy_rec/python/protos/easy_rec_model.proto @@ -27,6 +27,7 @@ import "easy_rec/python/protos/variational_dropout.proto"; import "easy_rec/python/protos/multi_tower_recall.proto"; import "easy_rec/python/protos/tower.proto"; import "easy_rec/python/protos/pdn.proto"; +import "easy_rec/python/protos/ppnet.proto"; // for input performance test message DummyModel { @@ -92,6 +93,8 @@ message EasyRecModel { PLE ple = 305; RocketLaunching rocket_launching = 401; + + PPNet ppnet = 402; } repeated SeqAttGroupConfig seq_att_groups = 7; // implemented in easy_rec/python/model/easy_rec_estimator diff --git a/easy_rec/python/protos/ppnet.proto b/easy_rec/python/protos/ppnet.proto new file mode 100644 index 000000000..cc8201ce4 --- /dev/null +++ b/easy_rec/python/protos/ppnet.proto @@ -0,0 +1,7 @@ +syntax = "proto2"; +package protos; + + +message PPNet { + required string model_conf = 1; +} diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index 3d42b410a..410e6dc88 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -467,7 +467,7 @@ def after_create_session(self, session, coord): self._summary_writer.add_meta_graph(meta_graph_def) # save for step 0 - self._save(session, global_step) + # self._save(session, global_step) self._timer.update_last_triggered_step(global_step) diff --git a/keep_model.config b/keep_model.config new file mode 100644 index 000000000..f11d582e5 --- /dev/null +++ b/keep_model.config @@ -0,0 +1,2916 @@ +train_input_path: 'test.parquet' +eval_input_path: 'test.parquet' +model_dir: "experiments/keep_model/" + +train_config { + optimizer_config: { + adam_optimizer: { + learning_rate: { + constant_learning_rate { + learning_rate: 0.001 + } + } + } + } + log_step_count_steps: 1 + save_checkpoints_steps: 100000 + sync_replicas: True +} + + +data_config { + batch_size: 100 + label_fields: 'is_click' + label_fields: 'is_train' + num_epochs: 1 + prefetch_size: 16 + + # input_type: DummyInput + input_type: ParquetInput + + + input_fields { + input_name: 'is_click' + input_type: INT64 + } + input_fields { + input_name: 'is_train' + input_type: INT64 + } + + input_fields { + input_name: 'user_id' + input_type: INT64 + } + input_fields { + input_name: 'item_id' + input_type: INT64 + } + input_fields { + input_name: 'age' + input_type: INT64 + } + input_fields { + input_name: 'height' + input_type: INT64 + } + input_fields { + input_name: 'weight' + input_type: INT64 + } + input_fields { + input_name: 'gender' + input_type: INT64 + } + input_fields { + input_name: 'vip_status' + input_type: INT64 + } + input_fields { + input_name: 'register_goals' + input_type: STRING + } + input_fields { + input_name: 'kg' + input_type: INT64 + } + input_fields { + input_name: 'bmi' + input_type: INT64 + } + input_fields { + input_name: 'training_duration_total' + input_type: INT64 + } + input_fields { + input_name: 'training_calorie_total' + input_type: INT64 + } + input_fields { + input_name: 'training_days_total' + input_type: INT64 + } + input_fields { + input_name: 'all_calorie_total' + input_type: INT64 + } + input_fields { + input_name: 'running_times_total' + input_type: INT64 + } + input_fields { + input_name: 'hiking_times_total' + input_type: INT64 + } + input_fields { + input_name: 'tags' + input_type: STRING + } + input_fields { + input_name: 'trainingpoints' + input_type: STRING + } + input_fields { + input_name: 'his_no_month_no' + input_type: INT64 + } + input_fields { + input_name: 'his_no_month_yes' + input_type: INT64 + } + input_fields { + input_name: 'his_yes_month_no' + input_type: INT64 + } + input_fields { + input_name: 'his_yes_month_yes' + input_type: INT64 + } + input_fields { + input_name: 'new_7days' + input_type: INT64 + } + input_fields { + input_name: 'is_meditation_ru' + input_type: INT64 + } + input_fields { + input_name: 'is_skipping_ru' + input_type: INT64 + } + input_fields { + input_name: 'is_buyer_ru' + input_type: INT64 + } + input_fields { + input_name: 'network' + input_type: INT64 + } + input_fields { + input_name: 'c_os' + input_type: INT64 + } + input_fields { + input_name: 'recent_plan_list' + input_type: STRING + } + input_fields { + input_name: 'preference' + input_type: STRING + } + input_fields { + input_name: 'interested_types' + input_type: STRING + } + input_fields { + input_name: 'sub_target' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_trainingType' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_subtype' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_dataType' + input_type: INT64 + } + input_fields { + input_name: 'author' + input_type: INT64 + } + input_fields { + input_name: 'plan_difficulty' + input_type: INT64 + } + input_fields { + input_name: 'calorie' + input_type: INT64 + } + input_fields { + input_name: 'average_duration' + input_type: INT64 + } + input_fields { + input_name: 'official' + input_type: INT64 + } + input_fields { + input_name: 'paid_type' + input_type: INT64 + } + input_fields { + input_name: 'course_score' + input_type: INT64 + } + input_fields { + input_name: 'target' + input_type: STRING + } + input_fields { + input_name: 'equipment' + input_type: INT64 + } + input_fields { + input_name: 'data_type' + input_type: INT64 + } + input_fields { + input_name: 'label_ids' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_int_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_int_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_int_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_click_list_int_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_click_list_int_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_click_list_int_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_click_list_int_1' + input_type: INT64 + } + input_fields { + input_name: 'u_plan_train_list_int_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_train_list_int_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_train_list_int_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_train_list_int_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_train_list_int_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_train_list_int_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_train_list_int_1' + input_type: INT64 + } + input_fields { + input_name: 'u_plan_complete_list_int_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_int_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_complete_list_int_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_complete_list_int_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_complete_list_int_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_homepage8_complete_list_int_1' + input_type: INT64 + } + input_fields { + input_name: 'u_train_label_list_50' + input_type: STRING + } + input_fields { + input_name: 'u_train_label_list_10' + input_type: STRING + } + input_fields { + input_name: 'u_train_label_list_3' + input_type: STRING + } + input_fields { + input_name: 'u_train_trainingType_list_10' + input_type: STRING + } + input_fields { + input_name: 'u_train_subtype_list_10' + input_type: STRING + } + input_fields { + input_name: 'u_train_dataType_list_10' + input_type: STRING + } + input_fields { + input_name: 'c_recall_sources' + input_type: STRING + } + input_fields { + input_name: 'u_plan_unclick_list_int_50' + input_type: STRING + } + input_fields { + input_name: 'u_plan_unclick_list_int_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_unclick_list_int_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_author_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_author_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_author_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_dataType_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_dataType_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_dataType_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_difficulty_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_difficulty_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_difficulty_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_official_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_author_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_author_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_author_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_dataType_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_dataType_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_difficulty_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_difficulty_3' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_official_100' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_official_10' + input_type: STRING + } + input_fields { + input_name: 'u_plan_complete_list_official_3' + input_type: STRING + } + input_fields { + input_name: 'training_goals_show_year_0' + input_type: INT64 + } + input_fields { + input_name: 'training_goals_show_year_1' + input_type: INT64 + } + input_fields { + input_name: 'training_goals_show_year_2' + input_type: INT64 + } + input_fields { + input_name: 'training_goals_show_year_4' + input_type: INT64 + } + input_fields { + input_name: 'training_goals_show_year_6' + input_type: INT64 + } + input_fields { + input_name: 'training_goals_show_year_8' + input_type: INT64 + } + input_fields { + input_name: 'trainingpoints_show_year_0' + input_type: INT64 + } + input_fields { + input_name: 'trainingpoints_show_year_3' + input_type: INT64 + } + input_fields { + input_name: 'trainingpoints_show_year_4' + input_type: INT64 + } + input_fields { + input_name: 'trainingpoints_show_year_7' + input_type: INT64 + } + input_fields { + input_name: 'trainingpoints_show_year_9' + input_type: INT64 + } + input_fields { + input_name: 'i_plan_difficulty_show_cnt_01_d' + input_type: INT64 + } + input_fields { + input_name: 'i_plan_difficulty_show_cnt_07_d' + input_type: INT64 + } + input_fields { + input_name: 'i_plan_difficulty_show_cnt_14_d' + input_type: INT64 + } + input_fields { + input_name: 'i_plan_difficulty_show_cnt_28_d' + input_type: INT64 + } + input_fields { + input_name: 'i_plan_difficulty_click_cnt_28_d' + input_type: INT64 + } + input_fields { + input_name: 'i_plan_difficulty_train_cnt_28_d' + input_type: INT64 + } + input_fields { + input_name: 'i_duration_seg_show_cnt_01_d' + input_type: INT64 + } + input_fields { + input_name: 'i_duration_seg_click_cnt_01_d' + input_type: INT64 + } + input_fields { + input_name: 'i_duration_seg_show_cnt_07_d' + input_type: INT64 + } + input_fields { + input_name: 'i_duration_seg_click_cnt_07_d' + input_type: INT64 + } + input_fields { + input_name: 'i_duration_seg_show_cnt_14_d' + input_type: INT64 + } + input_fields { + input_name: 'i_duration_seg_click_cnt_14_d' + input_type: INT64 + } + input_fields { + input_name: 'i_duration_seg_show_cnt_28_d' + input_type: INT64 + } + input_fields { + input_name: 'i_duration_seg_click_cnt_28_d' + input_type: INT64 + } + input_fields { + input_name: 'i_duration_seg_train_cnt_28_d' + input_type: INT64 + } + input_fields { + input_name: 'u_age_seg_train_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_bmi_seg_train_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_difficulty_train_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_gender_train_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_age_seg_show_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_age_seg_click_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_age_seg_complete_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_bmi_seg_show_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_bmi_seg_click_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_bmi_seg_complete_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_difficulty_show_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_difficulty_click_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_difficulty_complete_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_gender_show_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_gender_click_cnt' + input_type: INT64 + } + input_fields { + input_name: 'u_gender_complete_cnt' + input_type: INT64 + } + input_fields { + input_name: 'i_plan_difficulty_train_cvr2_28_d' + input_type: INT64 + } + input_fields { + input_name: 'u_age_seg_ctr' + input_type: INT64 + } + input_fields { + input_name: 'u_age_seg_cvr1' + input_type: INT64 + } + input_fields { + input_name: 'u_bmi_seg_ctr' + input_type: INT64 + } + input_fields { + input_name: 'u_bmi_seg_cvr1' + input_type: INT64 + } + input_fields { + input_name: 'u_bmi_seg_train_cvr2' + input_type: INT64 + } + input_fields { + input_name: 'u_difficulty_ctr' + input_type: INT64 + } + input_fields { + input_name: 'u_difficulty_cvr1' + input_type: INT64 + } + input_fields { + input_name: 'u_difficulty_train_cvr2' + input_type: INT64 + } + input_fields { + input_name: 'u_gender_ctr' + input_type: INT64 + } + input_fields { + input_name: 'u_gender_cvr1' + input_type: INT64 + } + input_fields { + input_name: 'i_total_click' + input_type: INT64 + } + input_fields { + input_name: 'i_decay1_show' + input_type: INT64 + } + input_fields { + input_name: 'i_decay1_click' + input_type: INT64 + } + input_fields { + input_name: 'i_decay1_train' + input_type: INT64 + } + input_fields { + input_name: 'i_decay2_show' + input_type: INT64 + } + input_fields { + input_name: 'i_decay2_click' + input_type: INT64 + } + input_fields { + input_name: 'i_decay2_train' + input_type: INT64 + } + input_fields { + input_name: 'i_decay3_show' + input_type: INT64 + } + input_fields { + input_name: 'i_decay3_click' + input_type: INT64 + } + input_fields { + input_name: 'i_decay3_train' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_distance' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_duration' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_calorie' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_totalSteps' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_averageSpeed' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_accumulativeUpliftedHeight' + input_type: INT64 + } + input_fields { + input_name: 'u_last_train_accumulativeClimbingDistance' + input_type: INT64 + } + input_fields { + input_name: 'u_total_train_distance' + input_type: INT64 + } + input_fields { + input_name: 'u_total_train_totalSteps' + input_type: INT64 + } + input_fields { + input_name: 'u_total_train_accumulativeClimbingDistance' + input_type: INT64 + } + input_fields { + input_name: 'u_plan_click_other_list_int' + input_type: STRING + } + input_fields { + input_name: 'c_hour' + input_type: INT64 + } + input_fields { + input_name: 'content_type' + input_type: INT64 + } + input_fields { + input_name: 'i_total_click_rate_i_total_show' + input_type: INT64 + } + input_fields { + input_name: 'i_total_train_rate_i_total_show' + input_type: INT64 + } + input_fields { + input_name: 'i_decay3_train_rate_i_decay3_show' + input_type: INT64 + } + input_fields { + input_name: 'i_decay3_click_rate_i_decay3_show' + input_type: INT64 + } + input_fields { + input_name: 'gender_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'gender_cross_author' + input_type: INT64 + } + input_fields { + input_name: 'gender_cross_paid_type' + input_type: INT64 + } + input_fields { + input_name: 'age_seg_cross_plan_difficulty' + input_type: INT64 + } + input_fields { + input_name: 'age_seg_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'age_seg_cross_author' + input_type: INT64 + } + input_fields { + input_name: 'bmi_seg_cross_plan_difficulty' + input_type: INT64 + } + input_fields { + input_name: 'bmi_seg_cross_paid_type' + input_type: INT64 + } + input_fields { + input_name: 'his_no_month_no_cross_plan_difficulty' + input_type: INT64 + } + input_fields { + input_name: 'his_no_month_no_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'his_no_month_no_cross_author' + input_type: INT64 + } + input_fields { + input_name: 'his_no_month_yes_cross_plan_difficulty' + input_type: INT64 + } + input_fields { + input_name: 'his_no_month_yes_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'his_no_month_yes_cross_paid_type' + input_type: INT64 + } + input_fields { + input_name: 'his_yes_month_no_cross_plan_difficulty' + input_type: INT64 + } + input_fields { + input_name: 'his_yes_month_no_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'his_yes_month_no_cross_paid_type' + input_type: INT64 + } + input_fields { + input_name: 'his_yes_month_yes_cross_plan_difficulty' + input_type: INT64 + } + input_fields { + input_name: 'his_yes_month_yes_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'his_yes_month_yes_cross_paid_type' + input_type: INT64 + } + input_fields { + input_name: 'vip_status_cross_plan_difficulty' + input_type: INT64 + } + input_fields { + input_name: 'vip_status_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'vip_status_cross_author' + input_type: INT64 + } + input_fields { + input_name: 'vip_status_cross_paid_type' + input_type: INT64 + } + input_fields { + input_name: 'is_meditation_ru_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'is_skipping_ru_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'is_power_ru_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'is_yoga_ru_cross_data_type' + input_type: INT64 + } + input_fields { + input_name: 'is_vip_ru_cross_paid_type' + input_type: INT64 + } + input_fields { + input_name: 'is_buyer_ru_cross_paid_type' + input_type: INT64 + } + input_fields { + input_name: 'network_concat_item_id' + input_type: INT64 + } + input_fields { + input_name: 'network_concat_data_type' + input_type: INT64 + } + input_fields { + input_name: 'c_os_concat_item_id' + input_type: INT64 + } + input_fields { + input_name: 'c_os_concat_data_type' + input_type: INT64 + } + input_fields { + input_name: 'gender_concat_item_id' + input_type: INT64 + } + input_fields { + input_name: 'age_seg_concat_item_id' + input_type: INT64 + } + input_fields { + input_name: 'vip_status_concat_item_id' + input_type: INT64 + } + input_fields { + input_name: 'u_plan_click_list_author_hit_author' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_int_match_u_plan_click_list_author_match_author' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_official_hit_official' + input_type: STRING + } + input_fields { + input_name: 'u_plan_click_list_int_match_u_plan_click_list_official_match_official' + input_type: STRING + } + input_fields { + input_name: 'u_plan_unclick_list_int_hit_item_id_int' + input_type: STRING + } + input_fields { + input_name: 'u_train_label_list_overlap_label_ids' + input_type: INT64 + } + input_fields { + input_name: 'u_train_label_list_hit_label_ids' + input_type: STRING + } + +} + +feature_config { + features: { + input_names: 'user_id' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'item_id' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'age' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'height' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'weight' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'gender' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'vip_status' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'register_goals' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'kg' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'bmi' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'training_duration_total' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'training_calorie_total' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'training_days_total' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'all_calorie_total' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'running_times_total' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'hiking_times_total' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'tags' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'trainingpoints' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_no_month_no' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_no_month_yes' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_yes_month_no' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_yes_month_yes' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'new_7days' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'is_meditation_ru' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'is_skipping_ru' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'is_buyer_ru' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'network' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'c_os' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'recent_plan_list' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'preference' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'interested_types' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'sub_target' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_trainingType' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_subtype' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_dataType' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'author' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'plan_difficulty' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'calorie' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'average_duration' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'official' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'paid_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'course_score' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'target' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'equipment' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'label_ids' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_int_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_int_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_int_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_click_list_int_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_click_list_int_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_click_list_int_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_click_list_int_1' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_train_list_int_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_train_list_int_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_train_list_int_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_train_list_int_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_train_list_int_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_train_list_int_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_train_list_int_1' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_int_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_int_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_complete_list_int_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_complete_list_int_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_complete_list_int_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_homepage8_complete_list_int_1' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_train_label_list_50' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_train_label_list_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_train_label_list_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_train_trainingType_list_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_train_subtype_list_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_train_dataType_list_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'c_recall_sources' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_unclick_list_int_50' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_unclick_list_int_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_unclick_list_int_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_author_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_author_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_author_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_dataType_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_dataType_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_dataType_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_difficulty_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_difficulty_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_difficulty_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_official_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_author_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_author_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_author_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_dataType_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_dataType_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_difficulty_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_difficulty_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_official_100' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_official_10' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_complete_list_official_3' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'training_goals_show_year_0' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'training_goals_show_year_1' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'training_goals_show_year_2' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'training_goals_show_year_4' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'training_goals_show_year_6' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'training_goals_show_year_8' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'trainingpoints_show_year_0' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'trainingpoints_show_year_3' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'trainingpoints_show_year_4' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'trainingpoints_show_year_7' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'trainingpoints_show_year_9' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_plan_difficulty_show_cnt_01_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_plan_difficulty_show_cnt_07_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_plan_difficulty_show_cnt_14_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_plan_difficulty_show_cnt_28_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_plan_difficulty_click_cnt_28_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_plan_difficulty_train_cnt_28_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_duration_seg_show_cnt_01_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_duration_seg_click_cnt_01_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_duration_seg_show_cnt_07_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_duration_seg_click_cnt_07_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_duration_seg_show_cnt_14_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_duration_seg_click_cnt_14_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_duration_seg_show_cnt_28_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_duration_seg_click_cnt_28_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_duration_seg_train_cnt_28_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_age_seg_train_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_bmi_seg_train_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_difficulty_train_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_gender_train_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_age_seg_show_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_age_seg_click_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_age_seg_complete_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_bmi_seg_show_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_bmi_seg_click_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_bmi_seg_complete_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_difficulty_show_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_difficulty_click_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_difficulty_complete_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_gender_show_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_gender_click_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_gender_complete_cnt' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_plan_difficulty_train_cvr2_28_d' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_age_seg_ctr' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_age_seg_cvr1' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_bmi_seg_ctr' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_bmi_seg_cvr1' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_bmi_seg_train_cvr2' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_difficulty_ctr' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_difficulty_cvr1' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_difficulty_train_cvr2' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_gender_ctr' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_gender_cvr1' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_total_click' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay1_show' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay1_click' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay1_train' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay2_show' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay2_click' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay2_train' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay3_show' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay3_click' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay3_train' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_distance' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_duration' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_calorie' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_totalSteps' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_averageSpeed' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_accumulativeUpliftedHeight' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_last_train_accumulativeClimbingDistance' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_total_train_distance' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_total_train_totalSteps' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_total_train_accumulativeClimbingDistance' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_other_list_int' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'c_hour' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'content_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_total_click_rate_i_total_show' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_total_train_rate_i_total_show' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay3_train_rate_i_decay3_show' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'i_decay3_click_rate_i_decay3_show' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'gender_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'gender_cross_author' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'gender_cross_paid_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'age_seg_cross_plan_difficulty' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'age_seg_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'age_seg_cross_author' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'bmi_seg_cross_plan_difficulty' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'bmi_seg_cross_paid_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_no_month_no_cross_plan_difficulty' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_no_month_no_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_no_month_no_cross_author' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_no_month_yes_cross_plan_difficulty' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_no_month_yes_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_no_month_yes_cross_paid_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_yes_month_no_cross_plan_difficulty' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_yes_month_no_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_yes_month_no_cross_paid_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_yes_month_yes_cross_plan_difficulty' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_yes_month_yes_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'his_yes_month_yes_cross_paid_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'vip_status_cross_plan_difficulty' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'vip_status_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'vip_status_cross_author' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'vip_status_cross_paid_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'is_meditation_ru_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'is_skipping_ru_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'is_power_ru_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'is_yoga_ru_cross_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'is_vip_ru_cross_paid_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'is_buyer_ru_cross_paid_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'network_concat_item_id' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'network_concat_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'c_os_concat_item_id' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'c_os_concat_data_type' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'gender_concat_item_id' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'age_seg_concat_item_id' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'vip_status_concat_item_id' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_author_hit_author' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_int_match_u_plan_click_list_author_match_author' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_official_hit_official' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_click_list_int_match_u_plan_click_list_official_match_official' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_plan_unclick_list_int_hit_item_id_int' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_train_label_list_overlap_label_ids' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } + features: { + input_names: 'u_train_label_list_hit_label_ids' + feature_type: TagFeature + embedding_dim: 16 + num_buckets: 2000 + embedding_name: 'all_fea' + max_partitions: 4 + } +} + +model_config { + model_class: 'PPNetV3M' + feature_groups { + group_name: 'all' + feature_names: "user_id" + feature_names: "item_id" + feature_names: "age" + feature_names: "height" + feature_names: "weight" + feature_names: "gender" + feature_names: "vip_status" + feature_names: "register_goals" + feature_names: "kg" + feature_names: "bmi" + feature_names: "training_duration_total" + feature_names: "training_calorie_total" + feature_names: "training_days_total" + feature_names: "all_calorie_total" + feature_names: "running_times_total" + feature_names: "hiking_times_total" + feature_names: "tags" + feature_names: "trainingpoints" + feature_names: "his_no_month_no" + feature_names: "his_no_month_yes" + feature_names: "his_yes_month_no" + feature_names: "his_yes_month_yes" + feature_names: "new_7days" + feature_names: "is_meditation_ru" + feature_names: "is_skipping_ru" + feature_names: "is_buyer_ru" + feature_names: "network" + feature_names: "c_os" + feature_names: "recent_plan_list" + feature_names: "preference" + feature_names: "interested_types" + feature_names: "sub_target" + feature_names: "u_last_train_trainingType" + feature_names: "u_last_train_subtype" + feature_names: "u_last_train_dataType" + feature_names: "author" + feature_names: "plan_difficulty" + feature_names: "calorie" + feature_names: "average_duration" + feature_names: "official" + feature_names: "paid_type" + feature_names: "course_score" + feature_names: "target" + feature_names: "equipment" + feature_names: "data_type" + feature_names: "label_ids" + feature_names: "u_plan_click_list_int_100" + feature_names: "u_plan_click_list_int_10" + feature_names: "u_plan_click_list_int_3" + feature_names: "u_plan_homepage8_click_list_int_100" + feature_names: "u_plan_homepage8_click_list_int_10" + feature_names: "u_plan_homepage8_click_list_int_3" + feature_names: "u_plan_homepage8_click_list_int_1" + feature_names: "u_plan_train_list_int_100" + feature_names: "u_plan_train_list_int_10" + feature_names: "u_plan_train_list_int_3" + feature_names: "u_plan_homepage8_train_list_int_100" + feature_names: "u_plan_homepage8_train_list_int_10" + feature_names: "u_plan_homepage8_train_list_int_3" + feature_names: "u_plan_homepage8_train_list_int_1" + feature_names: "u_plan_complete_list_int_10" + feature_names: "u_plan_complete_list_int_3" + feature_names: "u_plan_homepage8_complete_list_int_100" + feature_names: "u_plan_homepage8_complete_list_int_10" + feature_names: "u_plan_homepage8_complete_list_int_3" + feature_names: "u_plan_homepage8_complete_list_int_1" + feature_names: "u_train_label_list_50" + feature_names: "u_train_label_list_10" + feature_names: "u_train_label_list_3" + feature_names: "u_train_trainingType_list_10" + feature_names: "u_train_subtype_list_10" + feature_names: "u_train_dataType_list_10" + feature_names: "c_recall_sources" + feature_names: "u_plan_unclick_list_int_50" + feature_names: "u_plan_unclick_list_int_10" + feature_names: "u_plan_unclick_list_int_3" + feature_names: "u_plan_click_list_author_100" + feature_names: "u_plan_click_list_author_10" + feature_names: "u_plan_click_list_author_3" + feature_names: "u_plan_click_list_dataType_100" + feature_names: "u_plan_click_list_dataType_10" + feature_names: "u_plan_click_list_dataType_3" + feature_names: "u_plan_click_list_difficulty_100" + feature_names: "u_plan_click_list_difficulty_10" + feature_names: "u_plan_click_list_difficulty_3" + feature_names: "u_plan_click_list_official_10" + feature_names: "u_plan_complete_list_author_100" + feature_names: "u_plan_complete_list_author_10" + feature_names: "u_plan_complete_list_author_3" + feature_names: "u_plan_complete_list_dataType_10" + feature_names: "u_plan_complete_list_dataType_3" + feature_names: "u_plan_complete_list_difficulty_10" + feature_names: "u_plan_complete_list_difficulty_3" + feature_names: "u_plan_complete_list_official_100" + feature_names: "u_plan_complete_list_official_10" + feature_names: "u_plan_complete_list_official_3" + feature_names: "training_goals_show_year_0" + feature_names: "training_goals_show_year_1" + feature_names: "training_goals_show_year_2" + feature_names: "training_goals_show_year_4" + feature_names: "training_goals_show_year_6" + feature_names: "training_goals_show_year_8" + feature_names: "trainingpoints_show_year_0" + feature_names: "trainingpoints_show_year_3" + feature_names: "trainingpoints_show_year_4" + feature_names: "trainingpoints_show_year_7" + feature_names: "trainingpoints_show_year_9" + feature_names: "i_plan_difficulty_show_cnt_01_d" + feature_names: "i_plan_difficulty_show_cnt_07_d" + feature_names: "i_plan_difficulty_show_cnt_14_d" + feature_names: "i_plan_difficulty_show_cnt_28_d" + feature_names: "i_plan_difficulty_click_cnt_28_d" + feature_names: "i_plan_difficulty_train_cnt_28_d" + feature_names: "i_duration_seg_show_cnt_01_d" + feature_names: "i_duration_seg_click_cnt_01_d" + feature_names: "i_duration_seg_show_cnt_07_d" + feature_names: "i_duration_seg_click_cnt_07_d" + feature_names: "i_duration_seg_show_cnt_14_d" + feature_names: "i_duration_seg_click_cnt_14_d" + feature_names: "i_duration_seg_show_cnt_28_d" + feature_names: "i_duration_seg_click_cnt_28_d" + feature_names: "i_duration_seg_train_cnt_28_d" + feature_names: "u_age_seg_train_cnt" + feature_names: "u_bmi_seg_train_cnt" + feature_names: "u_difficulty_train_cnt" + feature_names: "u_gender_train_cnt" + feature_names: "u_age_seg_show_cnt" + feature_names: "u_age_seg_click_cnt" + feature_names: "u_age_seg_complete_cnt" + feature_names: "u_bmi_seg_show_cnt" + feature_names: "u_bmi_seg_click_cnt" + feature_names: "u_bmi_seg_complete_cnt" + feature_names: "u_difficulty_show_cnt" + feature_names: "u_difficulty_click_cnt" + feature_names: "u_difficulty_complete_cnt" + feature_names: "u_gender_show_cnt" + feature_names: "u_gender_click_cnt" + feature_names: "u_gender_complete_cnt" + feature_names: "i_plan_difficulty_train_cvr2_28_d" + feature_names: "u_age_seg_ctr" + feature_names: "u_age_seg_cvr1" + feature_names: "u_bmi_seg_ctr" + feature_names: "u_bmi_seg_cvr1" + feature_names: "u_bmi_seg_train_cvr2" + feature_names: "u_difficulty_ctr" + feature_names: "u_difficulty_cvr1" + feature_names: "u_difficulty_train_cvr2" + feature_names: "u_gender_ctr" + feature_names: "u_gender_cvr1" + feature_names: "i_total_click" + feature_names: "i_decay1_show" + feature_names: "i_decay1_click" + feature_names: "i_decay1_train" + feature_names: "i_decay2_show" + feature_names: "i_decay2_click" + feature_names: "i_decay2_train" + feature_names: "i_decay3_show" + feature_names: "i_decay3_click" + feature_names: "i_decay3_train" + feature_names: "u_last_train_distance" + feature_names: "u_last_train_duration" + feature_names: "u_last_train_calorie" + feature_names: "u_last_train_totalSteps" + feature_names: "u_last_train_averageSpeed" + feature_names: "u_last_train_accumulativeUpliftedHeight" + feature_names: "u_last_train_accumulativeClimbingDistance" + feature_names: "u_total_train_distance" + feature_names: "u_total_train_totalSteps" + feature_names: "u_total_train_accumulativeClimbingDistance" + feature_names: "u_plan_click_other_list_int" + feature_names: "c_hour" + feature_names: "content_type" + feature_names: "i_total_click_rate_i_total_show" + feature_names: "i_total_train_rate_i_total_show" + feature_names: "i_decay3_train_rate_i_decay3_show" + feature_names: "i_decay3_click_rate_i_decay3_show" + feature_names: "gender_cross_data_type" + feature_names: "gender_cross_author" + feature_names: "gender_cross_paid_type" + feature_names: "age_seg_cross_plan_difficulty" + feature_names: "age_seg_cross_data_type" + feature_names: "age_seg_cross_author" + feature_names: "bmi_seg_cross_plan_difficulty" + feature_names: "bmi_seg_cross_paid_type" + feature_names: "his_no_month_no_cross_plan_difficulty" + feature_names: "his_no_month_no_cross_data_type" + feature_names: "his_no_month_no_cross_author" + feature_names: "his_no_month_yes_cross_plan_difficulty" + feature_names: "his_no_month_yes_cross_data_type" + feature_names: "his_no_month_yes_cross_paid_type" + feature_names: "his_yes_month_no_cross_plan_difficulty" + feature_names: "his_yes_month_no_cross_data_type" + feature_names: "his_yes_month_no_cross_paid_type" + feature_names: "his_yes_month_yes_cross_plan_difficulty" + feature_names: "his_yes_month_yes_cross_data_type" + feature_names: "his_yes_month_yes_cross_paid_type" + feature_names: "vip_status_cross_plan_difficulty" + feature_names: "vip_status_cross_data_type" + feature_names: "vip_status_cross_author" + feature_names: "vip_status_cross_paid_type" + feature_names: "is_meditation_ru_cross_data_type" + feature_names: "is_skipping_ru_cross_data_type" + feature_names: "is_power_ru_cross_data_type" + feature_names: "is_yoga_ru_cross_data_type" + feature_names: "is_vip_ru_cross_paid_type" + feature_names: "is_buyer_ru_cross_paid_type" + feature_names: "network_concat_item_id" + feature_names: "network_concat_data_type" + feature_names: "c_os_concat_item_id" + feature_names: "c_os_concat_data_type" + feature_names: "gender_concat_item_id" + feature_names: "age_seg_concat_item_id" + feature_names: "vip_status_concat_item_id" + feature_names: "u_plan_click_list_author_hit_author" + feature_names: "u_plan_click_list_int_match_u_plan_click_list_author_match_author" + feature_names: "u_plan_click_list_official_hit_official" + feature_names: "u_plan_click_list_int_match_u_plan_click_list_official_match_official" + feature_names: "u_plan_unclick_list_int_hit_item_id_int" + feature_names: "u_train_label_list_overlap_label_ids" + feature_names: "u_train_label_list_hit_label_ids" + } + + ppnet { + model_conf: "model_conf.json" + } +} diff --git a/model_conf.json b/model_conf.json new file mode 100644 index 000000000..76f18649d --- /dev/null +++ b/model_conf.json @@ -0,0 +1,15 @@ +{ + "model_name": "PLAN_HOME_PPNET_V3M", + 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245 +++ .../compat/feature_column/feature_column.py | 13 +- .../feature_column/feature_column_v2.py | 2 +- easy_rec/python/input/parquet_input.py | 55 +- easy_rec/python/main.py | 2 +- .../model/keep_model/base_model_ps_v2.py | 1761 +++++++++-------- easy_rec/python/model/keep_model/dnn.py | 285 +-- .../python/model/keep_model/model_ps_mmoe.py | 113 +- easy_rec/python/model/keep_model/senet.py | 194 +- easy_rec/python/model/ppnet_v3m.py | 17 +- easy_rec/python/protos/easy_rec_model.proto | 2 +- keep_model.config | 456 ++--- setup.cfg | 2 +- 13 files changed, 1755 insertions(+), 1392 deletions(-) create mode 100644 easy_rec/python/compat/adam_s.py diff --git a/easy_rec/python/compat/adam_s.py b/easy_rec/python/compat/adam_s.py new file mode 100644 index 000000000..a0ef60b80 --- /dev/null +++ b/easy_rec/python/compat/adam_s.py @@ -0,0 +1,245 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Adam for TensorFlow.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.eager import context +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.training import optimizer +from tensorflow.python.training import training_ops + + +class AdamOptimizerS(optimizer.Optimizer): + """Optimizer that implements the Adam algorithm. + + References: + Adam - A Method for Stochastic Optimization: + [Kingma et al., 2015](https://arxiv.org/abs/1412.6980) + ([pdf](https://arxiv.org/pdf/1412.6980.pdf)) + """ + + def __init__(self, + learning_rate=0.001, + beta1=0.9, + beta2=0.999, + epsilon=1e-8, + use_locking=False, + name='Adam'): + r"""Construct a new Adam optimizer. + + Initialization: + + $$m_0 := 0 \text{(Initialize initial 1st moment vector)}$$ + $$v_0 := 0 \text{(Initialize initial 2nd moment vector)}$$ + $$t := 0 \text{(Initialize timestep)}$$ + + The update rule for `variable` with gradient `g` uses an optimization + described at the end of section 2 of the paper: + + $$t := t + 1$$ + $$\text{lr}_t := \mathrm{learning_rate} * + \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$ + + $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$ + $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$ + $$\text{variable} := \text{variable} - + \text{lr}_t * m_t / (\sqrt{v_t} + \epsilon)$$ + + The default value of 1e-8 for epsilon might not be a good default in + general. For example, when training an Inception network on ImageNet a + current good choice is 1.0 or 0.1. Note that since AdamOptimizerS uses the + formulation just before Section 2.1 of the Kingma and Ba paper rather than + the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon + hat" in the paper. + + The sparse implementation of this algorithm (used when the gradient is an + IndexedSlices object, typically because of `tf.gather` or an embedding + lookup in the forward pass) does apply momentum to variable slices even if + they were not used in the forward pass (meaning they have a gradient equal + to zero). Momentum decay (beta1) is also applied to the entire momentum + accumulator. This means that the sparse behavior is equivalent to the dense + behavior (in contrast to some momentum implementations which ignore momentum + unless a variable slice was actually used). + + Args: + learning_rate: A Tensor or a floating point value. The learning rate. + beta1: A float value or a constant float tensor. The exponential decay + rate for the 1st moment estimates. + beta2: A float value or a constant float tensor. The exponential decay + rate for the 2nd moment estimates. + epsilon: A small constant for numerical stability. This epsilon is + "epsilon hat" in the Kingma and Ba paper (in the formula just before + Section 2.1), not the epsilon in Algorithm 1 of the paper. + use_locking: If True use locks for update operations. + name: Optional name for the operations created when applying gradients. + Defaults to "Adam". + + @compatibility(eager) + When eager execution is enabled, `learning_rate`, `beta1`, `beta2`, and + `epsilon` can each be a callable that takes no arguments and returns the + actual value to use. This can be useful for changing these values across + different invocations of optimizer functions. + @end_compatibility + """ + super(AdamOptimizerS, self).__init__(use_locking, name) + self._lr = learning_rate + self._beta1 = beta1 + self._beta2 = beta2 + self._epsilon = epsilon + + # Tensor versions of the constructor arguments, created in _prepare(). + self._lr_t = None + self._beta1_t = None + self._beta2_t = None + self._epsilon_t = None + + def _get_beta_accumulators(self): + with ops.init_scope(): + if context.executing_eagerly(): + graph = None + else: + graph = ops.get_default_graph() + return (self._get_non_slot_variable('beta1_power', graph=graph), + self._get_non_slot_variable('beta2_power', graph=graph)) + + def _create_slots(self, var_list): + # Create the beta1 and beta2 accumulators on the same device as the first + # variable. Sort the var_list to make sure this device is consistent across + # workers (these need to go on the same PS, otherwise some updates are + # silently ignored). + first_var = min(var_list, key=lambda x: x.name) + self._create_non_slot_variable( + initial_value=self._beta1, name='beta1_power', colocate_with=first_var) + self._create_non_slot_variable( + initial_value=self._beta2, name='beta2_power', colocate_with=first_var) + + # Create slots for the first and second moments. + for v in var_list: + self._zeros_slot(v, 'm', self._name) + self._zeros_slot(v, 'v', self._name) + + def _prepare(self): + lr = self._call_if_callable(self._lr) + beta1 = self._call_if_callable(self._beta1) + beta2 = self._call_if_callable(self._beta2) + epsilon = self._call_if_callable(self._epsilon) + + self._lr_t = ops.convert_to_tensor(lr, name='learning_rate') + self._beta1_t = ops.convert_to_tensor(beta1, name='beta1') + self._beta2_t = ops.convert_to_tensor(beta2, name='beta2') + self._epsilon_t = ops.convert_to_tensor(epsilon, name='epsilon') + + def _apply_dense(self, grad, var): + m = self.get_slot(var, 'm') + v = self.get_slot(var, 'v') + beta1_power, beta2_power = self._get_beta_accumulators() + return training_ops.apply_adam( + var, + m, + v, + math_ops.cast(beta1_power, var.dtype.base_dtype), + math_ops.cast(beta2_power, var.dtype.base_dtype), + math_ops.cast(self._lr_t, var.dtype.base_dtype), + math_ops.cast(self._beta1_t, var.dtype.base_dtype), + math_ops.cast(self._beta2_t, var.dtype.base_dtype), + math_ops.cast(self._epsilon_t, var.dtype.base_dtype), + grad, + use_locking=self._use_locking).op + + def _resource_apply_dense(self, grad, var): + m = self.get_slot(var, 'm') + v = self.get_slot(var, 'v') + beta1_power, beta2_power = self._get_beta_accumulators() + return training_ops.resource_apply_adam( + var.handle, + m.handle, + v.handle, + math_ops.cast(beta1_power, grad.dtype.base_dtype), + math_ops.cast(beta2_power, grad.dtype.base_dtype), + math_ops.cast(self._lr_t, grad.dtype.base_dtype), + math_ops.cast(self._beta1_t, grad.dtype.base_dtype), + math_ops.cast(self._beta2_t, grad.dtype.base_dtype), + math_ops.cast(self._epsilon_t, grad.dtype.base_dtype), + grad, + use_locking=self._use_locking) + + def _apply_sparse_shared(self, grad, var, indices, scatter_add): + beta1_power, beta2_power = self._get_beta_accumulators() + beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) + beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) + lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) + beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) + beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) + epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) + lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power)) + # m_t = beta1 * m + (1 - beta1) * g_t + m = self.get_slot(var, 'm') + m_scaled_g_values = grad * (1 - beta1_t) + # m_t = state_ops.assign(m, m * beta1_t, use_locking=self._use_locking) + m_decay = array_ops.gather(m, indices) * beta1_t + m_part_n = m_scaled_g_values + m_decay + m_t = state_ops.scatter_update(m, indices, m_part_n) + # v_t = beta2 * v + (1 - beta2) * (g_t * g_t) + v = self.get_slot(var, 'v') + v_scaled_g_values = (grad * grad) * (1 - beta2_t) + v_decay = array_ops.gather(v, indices) * beta2_t + v_part_n = v_scaled_g_values + v_decay + v_t = state_ops.scatter_update(v, indices, v_part_n) + # v_sqrt = math_ops.sqrt(v_t) + # var_update = state_ops.assign_sub( + # var, lr * m_t / (v_sqrt + epsilon_t), use_locking=self._use_locking) + v_part_sqrt = math_ops.sqrt(v_part_n) + var_update = scatter_add(var, indices, + -lr * m_part_n / (v_part_sqrt + epsilon_t)) + return control_flow_ops.group(*[var_update, m_t, v_t]) + + def _apply_sparse(self, grad, var): + return self._apply_sparse_shared( + grad.values, + var, + grad.indices, + lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda + x, + i, + v, + use_locking=self._use_locking)) + + def _resource_scatter_add(self, x, i, v): + with ops.control_dependencies( + [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): + return x.value() + + def _resource_apply_sparse(self, grad, var, indices): + return self._apply_sparse_shared(grad, var, indices, + self._resource_scatter_add) + + def _finish(self, update_ops, name_scope): + # Update the power accumulators. + with ops.control_dependencies(update_ops): + beta1_power, beta2_power = self._get_beta_accumulators() + with ops.colocate_with(beta1_power): + update_beta1 = beta1_power.assign( + beta1_power * self._beta1_t, use_locking=self._use_locking) + update_beta2 = beta2_power.assign( + beta2_power * self._beta2_t, use_locking=self._use_locking) + return control_flow_ops.group( + *update_ops + [update_beta1, update_beta2], name=name_scope) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index f95553892..1c04e2e51 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -152,7 +152,6 @@ from tensorflow.python.ops import init_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops.ragged import ragged_math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import resource_variable_ops @@ -161,6 +160,7 @@ from tensorflow.python.ops import template from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables +from tensorflow.python.ops.ragged import ragged_math_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_utils @@ -2253,8 +2253,8 @@ def _normalize_feature_columns(feature_columns): if isinstance(feature_columns, _FeatureColumn): feature_columns = [feature_columns] - if isinstance(feature_columns, collections.Iterator): - feature_columns = list(feature_columns) + # if isinstance(feature_columns, collections.Iterator): + # feature_columns = list(feature_columns) if isinstance(feature_columns, dict): raise ValueError('Expected feature_columns to be iterable, found dict.') @@ -2642,8 +2642,11 @@ def _get_dense_tensor_internal(self, if 'RaggedTensor' in str(type(sparse_ids)): assert sparse_weights is None - ragged_embedding = embedding_ops.embedding_lookup_ragged(embedding_weights=embedding_weights, - ragged_ids=sparse_ids, max_norm=self.max_norm, name='%s_weights' % self.name) + ragged_embedding = embedding_ops.embedding_lookup_ragged( + embedding_weights=embedding_weights, + ragged_ids=sparse_ids, + max_norm=self.max_norm, + name='%s_weights' % self.name) if self.combiner == 'sum': return ragged_math_ops.reduce_sum(ragged_embedding, axis=1) else: diff --git a/easy_rec/python/compat/feature_column/feature_column_v2.py b/easy_rec/python/compat/feature_column/feature_column_v2.py index 59341c73a..f3d3ee4e8 100644 --- a/easy_rec/python/compat/feature_column/feature_column_v2.py +++ b/easy_rec/python/compat/feature_column/feature_column_v2.py @@ -4240,7 +4240,7 @@ def _transform_input_tensor(self, input_tensor): if not input_tensor.dtype.is_integer: raise ValueError('Invalid input, not integer. key: {} dtype: {}'.format( self.key, input_tensor.dtype)) - + if 'RaggedTensor' in str(type(input_tensor)): return input_tensor diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 6582eb83c..a953b8871 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -1,12 +1,12 @@ # -*- encoding:utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. import logging - -import tensorflow as tf -import pandas as pd -import queue import multiprocessing +import queue + import numpy as np +import pandas as pd +import tensorflow as tf from tensorflow.python.platform import gfile from easy_rec.python.input.input import Input @@ -34,8 +34,10 @@ def __init__(self, self._input_files = [] for sub_path in input_path.strip().split(','): self._input_files.extend(gfile.Glob(sub_path)) - logging.info('parquet input_path=%s file_num=%d' % (input_path, len(self._input_files))) - self._data_que = multiprocessing.Queue(maxsize=512) + logging.info('parquet input_path=%s file_num=%d' % + (input_path, len(self._input_files))) + self._data_que = multiprocessing.Queue( + maxsize=self._data_config.prefetch_size) self._file_que = multiprocessing.Queue(maxsize=len(self._input_files)) for input_file in self._input_files: self._file_que.put(input_file) @@ -47,7 +49,8 @@ def __init__(self, logging.info('file_num=%d num_proc=%d' % (file_num, num_proc)) self._proc_arr = [] for proc_id in range(num_proc): - proc = multiprocessing.Process(target=self._parse_one_file, args=(proc_id,)) + proc = multiprocessing.Process( + target=self._parse_one_file, args=(proc_id,)) self._proc_arr.append(proc) def _parse_one_file(self, proc_id): @@ -56,27 +59,31 @@ def _parse_one_file(self, proc_id): num_files = 0 while True: try: - input_file = self._file_que.get() + input_file = self._file_que.get(block=False) except queue.Empty: - break + break num_files += 1 - input_data = pd.read_parquet(input_file, columns=all_fields) + input_data = pd.read_parquet(input_file, columns=all_fields) data_len = len(input_data[all_fields[0]]) batch_num = int(data_len / self._batch_size) res_num = data_len % self._batch_size - logging.info('proc[%d] read file %s sample_num=%d batch_num=%d res_num=%d' % (proc_id, - input_file, data_len, batch_num, res_num)) - sid = 0 + logging.info( + 'proc[%d] read file %s sample_num=%d batch_num=%d res_num=%d' % + (proc_id, input_file, data_len, batch_num, res_num)) + sid = 0 for batch_id in range(batch_num): eid = sid + self._batch_size data_dict = {} for k in self._label_fields: - data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], dtype=np.float32) + data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], + dtype=np.float32) for k in self._effective_fields: val = input_data[k][sid:eid] all_lens = np.array([len(x) for x in val], dtype=np.int32) all_vals = np.concatenate(list(val)) - assert np.sum(all_lens) == len(all_vals), 'len(all_vals)=%d np.sum(all_lens)=%d' % (len(all_vals), np.sum(all_lens)) + assert np.sum(all_lens) == len( + all_vals), 'len(all_vals)=%d np.sum(all_lens)=%d' % ( + len(all_vals), np.sum(all_lens)) data_dict[k] = (all_lens, all_vals) self._data_que.put(data_dict) sid += self._batch_size @@ -84,7 +91,8 @@ def _parse_one_file(self, proc_id): logging.info('proc[%d] add final sample' % proc_id) data_dict = {} for k in self._label_fields: - data_dict[k] = np.array([x[0] for x in input_data[k][sid:]], dtype=np.float32) + data_dict[k] = np.array([x[0] for x in input_data[k][sid:]], + dtype=np.float32) for k in self._effective_fields: val = input_data[k][sid:] all_lens = np.array([len(x) for x in val], dtype=np.int32) @@ -93,7 +101,7 @@ def _parse_one_file(self, proc_id): self._data_que.put(data_dict) self._data_que.put(None) logging.info('data proc %d done, file_num=%d' % (proc_id, num_files)) - + def _sample_generator(self): for proc in self._proc_arr: proc.start() @@ -109,21 +117,22 @@ def _sample_generator(self): except queue.Empty: fetch_timeout_cnt += 1 if done_proc_cnt >= len(self._proc_arr): - logging.info("all sample finished, fetch_timeout_cnt=%d" % fetch_timeout_cnt) + logging.info('all sample finished, fetch_timeout_cnt=%d' % + fetch_timeout_cnt) break for proc in self._proc_arr: - proc.join() + proc.join() def _to_fea_dict(self, input_dict): fea_dict = {} for fea_name in self._effective_fields: - tmp = input_dict[fea_name][1] % 1000 # 000000 - fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, - input_dict[fea_name][0]) + tmp = input_dict[fea_name][1] % 1000 # 000000 + fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths( + tmp, input_dict[fea_name][0]) lbl_dict = {} for lbl_name in self._label_fields: lbl_dict[lbl_name] = input_dict[lbl_name] - return {'feature': fea_dict, 'label':lbl_dict} + return {'feature': fea_dict, 'label': lbl_dict} def _build(self, mode, params): out_types = {} diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index f2b1ad719..0804c9fec 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -104,7 +104,7 @@ def _create_estimator(pipeline_config, distribution=None, params={}): train_config = pipeline_config.train_config gpu_options = GPUOptions(allow_growth=False) - if hvd is not None: + if hvd is not None and pipeline_config.train_config.train_distribute != DistributionStrategy.NoStrategy: gpus = estimator_utils.get_available_gpus() if len(gpus) > 0: local_rnk = hvd.local_rank() diff --git a/easy_rec/python/model/keep_model/base_model_ps_v2.py b/easy_rec/python/model/keep_model/base_model_ps_v2.py index 003f56ccc..be5211dae 100644 --- a/easy_rec/python/model/keep_model/base_model_ps_v2.py +++ b/easy_rec/python/model/keep_model/base_model_ps_v2.py @@ -1,914 +1,941 @@ #!/usr/bin/env python # encoding: utf-8 -""" -usage -dcn 两输出模型，concat none 特征 -""" +"""usage dcn 两输出模型，concat none 特征.""" import json +import logging import os import sys import time -import logging -import tensorflow as tf -import numpy as np -from tensorflow.keras.layers import Dense, Concatenate, concatenate, Lambda, Add, add, Activation, \ - Average, BatchNormalization, Embedding, Input, Reshape, Flatten, Add +import numpy as np import pyarrow.parquet as pq -from layers.sequence import AttentionSequencePoolingLayer -from tensorflow.keras.optimizers import Adam -from tensorflow.keras.models import Model +import tensorflow as tf +from base.context import Context +from base.context import conf_overwrite from base.dataset import QDataSet from base.qbox_wrapper import QWrapper -from base.context import Context, conf_overwrite +from layers.dnn import DNN +from layers.sequence import AttentionSequencePoolingLayer from metric.auc import AUCUtil from metric.bucket_auc import BucketAUCUtil -from utils.mysql_util import MySQLUtils +from tensorflow.keras.layers import Activation +from tensorflow.keras.layers import Add +from tensorflow.keras.layers import Average +from tensorflow.keras.layers import BatchNormalization +from tensorflow.keras.layers import Concatenate +from tensorflow.keras.layers import Dense +from tensorflow.keras.layers import Embedding +from tensorflow.keras.layers import Flatten +from tensorflow.keras.layers import Input +from tensorflow.keras.layers import Lambda +from tensorflow.keras.layers import Reshape +from tensorflow.keras.layers import add +from tensorflow.keras.layers import concatenate +from tensorflow.keras.models import Model +from tensorflow.keras.optimizers import Adam from utils.basic_utils import * -from layers.dnn import DNN +from utils.mysql_util import MySQLUtils from utils.ps_deploy import deploy_ps_data # experimental_relax_shapes: True, avoid retracing @tf.function(experimental_relax_shapes=True) def train_step(model, input_dic, ctx): - """ - docstring - """ - with tf.GradientTape() as tape: - output_dic = model(input_dic, training=True) - - ps_loss = output_dic['ps_loss'] - ps_grad = tf.gradients(ps_loss, [input_dic["ps_embed"]])[0] - - b1_power = input_dic['b1_power'] - b2_power = input_dic['b2_power'] - #tf.print(b1_power, b2_power) - lr_t = ctx.learning_rate * \ - tf.sqrt(1. - b2_power) / (1. - b1_power) - tmp_m = ctx.adam_beta1 * input_dic["ps_embed_m"] + \ - (1 - ctx.adam_beta1) * ps_grad - tmp_v = ctx.adam_beta2 * input_dic["ps_embed_v"] + \ - (1 - ctx.adam_beta2) * ps_grad * ps_grad - tmp_w = input_dic["ps_embed"] - lr_t * tmp_m / \ - (tf.sqrt(tmp_v) + ctx.adam_epsilon) - - - #tmp_w = input_dic["ps_embed"] - ctx.learning_rate * ps_grad - result = tf.concat([tmp_w, tmp_m, tmp_v], axis=0) - output_dic['ps_grad'] = result - nan_flag = tf.reduce_sum(tf.cast(tf.math.is_nan(result), tf.float32)) - output_dic['nan_flag'] = nan_flag - - loss_value = output_dic['loss'] - grads = tape.gradient(loss_value, model.trainable_weights) - model.optimizer.apply_gradients(zip(grads, model.trainable_weights)) - - return output_dic + """docstring.""" + with tf.GradientTape() as tape: + output_dic = model(input_dic, training=True) + + ps_loss = output_dic['ps_loss'] + ps_grad = tf.gradients(ps_loss, [input_dic['ps_embed']])[0] + + b1_power = input_dic['b1_power'] + b2_power = input_dic['b2_power'] + #tf.print(b1_power, b2_power) + lr_t = ctx.learning_rate * \ + tf.sqrt(1. - b2_power) / (1. - b1_power) + tmp_m = ctx.adam_beta1 * input_dic['ps_embed_m'] + \ + (1 - ctx.adam_beta1) * ps_grad + tmp_v = ctx.adam_beta2 * input_dic['ps_embed_v'] + \ + (1 - ctx.adam_beta2) * ps_grad * ps_grad + tmp_w = input_dic['ps_embed'] - lr_t * tmp_m / \ + (tf.sqrt(tmp_v) + ctx.adam_epsilon) + + #tmp_w = input_dic["ps_embed"] - ctx.learning_rate * ps_grad + result = tf.concat([tmp_w, tmp_m, tmp_v], axis=0) + output_dic['ps_grad'] = result + nan_flag = tf.reduce_sum(tf.cast(tf.math.is_nan(result), tf.float32)) + output_dic['nan_flag'] = nan_flag + + loss_value = output_dic['loss'] + grads = tape.gradient(loss_value, model.trainable_weights) + model.optimizer.apply_gradients(zip(grads, model.trainable_weights)) + + return output_dic def make_pos(indice_list, len_list): - """ indice_list: [0, 1, 1, 2, 2, 2] + """ indice_list: [0, 1, 1, 2, 2, 2] len_list: [1, 3, 6] """ - # [6] - num = tf.shape(indice_list)[0] - # [6, 5, 4, 3, 2, 1] - cum_sum = tf.range(start=num, limit=0, delta=-1) + # [6] + num = tf.shape(indice_list)[0] + # [6, 5, 4, 3, 2, 1] + cum_sum = tf.range(start=num, limit=0, delta=-1) + + # [5, 3, 0] + cum_sum_of_segment_sum = num - len_list + # [5, 3, 3, 0, 0, 0] + cum_sum_of_segment_sum = tf.gather(cum_sum_of_segment_sum, indice_list) - # [5, 3, 0] - cum_sum_of_segment_sum = num - len_list - # [5, 3, 3, 0, 0, 0] - cum_sum_of_segment_sum = tf.gather(cum_sum_of_segment_sum, indice_list) + # [1, 2, 1, 3, 2, 1] - 1 = [0, 1, 0, 2, 1, 0] + return cum_sum - cum_sum_of_segment_sum - 1 - # [1, 2, 1, 3, 2, 1] - 1 = [0, 1, 0, 2, 1, 0] - return cum_sum - cum_sum_of_segment_sum - 1 def make_sparse_indice(summed): - """https://stackoverflow.com/questions/54790000/calculate-segment-ids-needed-in-tf-math-segment-sum-by-length-of-segments-in-ten - summed is result of tf.cumsum + """https://stackoverflow.com/questions/54790000/calculate-segment-ids-needed-in-tf-math-segment-sum-by-length-of-segments-in-ten summed is result of tf.cumsum. - Args: - summed (TYPE): Description + Args: + summed (TYPE): Description - Returns: - TYPE: Description - """ - return tf.searchsorted( - summed, tf.range(summed[-1]), side='right') + Returns: + TYPE: Description + """ + return tf.searchsorted(summed, tf.range(summed[-1]), side='right') def model_inputs_prepare(inputs, ctx): - """Summary - - Args: - inputs (TYPE): Description - ctx (TYPE): Description - - Returns: - TYPE: Description - """ - batch_split = ctx.batch_size // ctx.num_worker - - # slice len - len_arr = [] - off_arr = [0] - size_arr = [] - for idx in range(len(ctx.slot_size_vec)): - len_arr.append(tf.reshape(tf.slice( - inputs["sparse_len"], - [0, ctx.slot_offset_vec[idx]], - [batch_split, ctx.slot_size_vec[idx]]), [-1])) - size_arr.append(len_arr[-1][-1]) - off_arr.append(off_arr[-1] + size_arr[-1]) - - # slice id - tmp_arr = [] - for idx in range(len(ctx.slot_size_vec)): - tmp_arr.append(tf.slice( - inputs["sparse_id"], [off_arr[idx]], [size_arr[idx]])) - - id_arr = [] - for idx in range(len(ctx.slot_size_vec)): - id_arr.append(tf.clip_by_value( - tmp_arr[idx] - inputs["uniq_num_by_dim"][idx], 0, 2**30)) - - # slice embed - embed_arr = [] - for idx in range(len(ctx.embed_dim_vec)): - tmp = tf.slice( - inputs["ps_embed"], - [inputs["uniq_off_by_dim"][idx]], - [inputs["uniq_off_by_dim"][idx + 1] - inputs["uniq_off_by_dim"][idx]]) # noqa - embed_arr.append(tf.reshape(tmp, [-1, ctx.embed_dim_vec[idx]])) - - return embed_arr, id_arr, len_arr, tmp_arr + """Summary. + + Args: + inputs (TYPE): Description + ctx (TYPE): Description + + Returns: + TYPE: Description + """ + batch_split = ctx.batch_size // ctx.num_worker + + # slice len + len_arr = [] + off_arr = [0] + size_arr = [] + for idx in range(len(ctx.slot_size_vec)): + len_arr.append( + tf.reshape( + tf.slice(inputs['sparse_len'], [0, ctx.slot_offset_vec[idx]], + [batch_split, ctx.slot_size_vec[idx]]), [-1])) + size_arr.append(len_arr[-1][-1]) + off_arr.append(off_arr[-1] + size_arr[-1]) + + # slice id + tmp_arr = [] + for idx in range(len(ctx.slot_size_vec)): + tmp_arr.append( + tf.slice(inputs['sparse_id'], [off_arr[idx]], [size_arr[idx]])) + + id_arr = [] + for idx in range(len(ctx.slot_size_vec)): + id_arr.append( + tf.clip_by_value(tmp_arr[idx] - inputs['uniq_num_by_dim'][idx], 0, + 2**30)) + + # slice embed + embed_arr = [] + for idx in range(len(ctx.embed_dim_vec)): + tmp = tf.slice( + inputs['ps_embed'], [inputs['uniq_off_by_dim'][idx]], + [inputs['uniq_off_by_dim'][idx + 1] - inputs['uniq_off_by_dim'][idx] + ]) # noqa + embed_arr.append(tf.reshape(tmp, [-1, ctx.embed_dim_vec[idx]])) + + return embed_arr, id_arr, len_arr, tmp_arr class ExportModel(Model): - def __init__(self, model_conf, indim, name='export_model'): - super(ExportModel, self).__init__(name=name) - self.label_conf_list = model_conf['label'] - self.indim = indim - self._set_inputs([tf.TensorSpec([None] + tmp, tf.float32, name='input' + str(i)) for i, tmp in enumerate(indim)]) - self.dnn = DNN([300, 150, 50], activation='relu', - use_bn=True, output_activation='relu') - self.din_layer = AttentionSequencePoolingLayer() - self.output_layers = [Dense(1, activation='sigmoid', name='output_' + label_conf['input_name']) for label_conf in self.label_conf_list] - - def call(self, embed_input, training=False): - target_fea = embed_input[0] - - listclick = embed_input[1] - listclick_len = tf.reshape(listclick[...,:1], [-1, self.indim[1][0]]) - len_mask = tf.where( - tf.equal(listclick_len, 0), - tf.ones_like(listclick_len, dtype=tf.float32), - tf.zeros_like(listclick_len, dtype=tf.float32)) - listclick_len = tf.cast(tf.reduce_sum(len_mask, axis=1, keepdims=True), tf.int32) - - listcomplete = embed_input[2] - listcomplete_len = tf.reshape(listcomplete[...,:1], [-1, self.indim[2][0]]) - len_mask = tf.where( - tf.equal(listcomplete_len, 0), - tf.ones_like(listcomplete_len, dtype=tf.float32), - tf.zeros_like(listcomplete_len, dtype=tf.float32)) - listcomplete_len = tf.cast(tf.reduce_sum(len_mask, axis=1, keepdims=True), tf.int32) - - click_att_data = self.din_layer([target_fea, listclick, listclick_len]) - click_att_data = tf.squeeze(click_att_data, axis=[1]) + def __init__(self, model_conf, indim, name='export_model'): + super(ExportModel, self).__init__(name=name) + self.label_conf_list = model_conf['label'] + self.indim = indim + self._set_inputs([ + tf.TensorSpec([None] + tmp, tf.float32, name='input' + str(i)) + for i, tmp in enumerate(indim) + ]) + self.dnn = DNN([300, 150, 50], + activation='relu', + use_bn=True, + output_activation='relu') + self.din_layer = AttentionSequencePoolingLayer() + self.output_layers = [ + Dense( + 1, activation='sigmoid', name='output_' + label_conf['input_name']) + for label_conf in self.label_conf_list + ] + + def call(self, embed_input, training=False): + target_fea = embed_input[0] + + listclick = embed_input[1] + listclick_len = tf.reshape(listclick[..., :1], [-1, self.indim[1][0]]) + len_mask = tf.where( + tf.equal(listclick_len, 0), + tf.ones_like(listclick_len, dtype=tf.float32), + tf.zeros_like(listclick_len, dtype=tf.float32)) + listclick_len = tf.cast( + tf.reduce_sum(len_mask, axis=1, keepdims=True), tf.int32) + + listcomplete = embed_input[2] + listcomplete_len = tf.reshape(listcomplete[..., :1], [-1, self.indim[2][0]]) + len_mask = tf.where( + tf.equal(listcomplete_len, 0), + tf.ones_like(listcomplete_len, dtype=tf.float32), + tf.zeros_like(listcomplete_len, dtype=tf.float32)) + listcomplete_len = tf.cast( + tf.reduce_sum(len_mask, axis=1, keepdims=True), tf.int32) + + click_att_data = self.din_layer([target_fea, listclick, listclick_len]) + click_att_data = tf.squeeze(click_att_data, axis=[1]) + + complete_att_data = self.din_layer( + [target_fea, listcomplete, listcomplete_len]) + complete_att_data = tf.squeeze(complete_att_data, axis=[1]) + + embed = tf.concat([click_att_data, complete_att_data, embed_input[3]], + axis=1) + embed = self.dnn(embed) + outputs = [output_layer(embed) for output_layer in self.output_layers] + return outputs - complete_att_data = self.din_layer([target_fea, listcomplete, listcomplete_len]) - complete_att_data = tf.squeeze(complete_att_data, axis=[1]) +class BaseModel: - embed = tf.concat([click_att_data, complete_att_data, embed_input[3]], axis=1) - embed = self.dnn(embed) - outputs = [output_layer(embed) for output_layer in self.output_layers] - return outputs + def __init__(self, conf): + """初始化参数. + :param worker_list: 指定的 worker 节点列表 + :param embed_share: 0 全独立 1 field内共享 2 全共享 + """ + self.monitor_table = 'model_metrics_monitor' + self.mysql_utils = MySQLUtils().master + self.data_conf = conf['data'] + self.model_conf = conf['model'] + self.ps_conf = conf.get('ps', {}) + self.model_name = self.model_conf['model_name'] + self.epochs = self.data_conf.get('epochs', 1) # 总训练轮数 + self.log_step_num = self.data_conf.get('log_step_num', 1) + self.save_path = self.data_conf.get('save_path').rstrip( + '/') + '/' + self.model_name + '/' + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + self.save_hdfs_path = self.data_conf.get('save_hdfs_path').rstrip( + '/') + '/' + self.model_name + '/' + self.save_iter = 1 + + self.load_path = self.data_conf.get('load_path') + self.model = None + self.mode = self.model_conf['mode'] + self.profile = self.model_conf.get('profile', False) + self.tb_callback = None + if self.profile: + self.tb_callback = tf.keras.callbacks.TensorBoard( + log_dir=self.save_path + '/profile', profile_batch='10,12') + + if self.mode == 'Update': + self.process_update_conf() + self.feature_conf, self.group_length = self.parse_feature_conf( + conf['feature']) + self.custom_group_count = len(self.group_length) - 1 + self.export_model_class = self.get_export_model_classs() + + if self.data_conf.get('deploy'): + shell(f'hadoop fs -mkdir -p {self.save_hdfs_path}') + + def parse_feature_conf(self, conf_list): + group_features = {} + group_len = {} + for conf in conf_list: + group = conf.get('group', 'default') + if group not in group_features: + group_features[group] = [] + group_len[group] = conf.get('length', 1) + group_features[group].append(conf) + + if len(group_features) == 1: + return conf_list, None + return group_features, group_len + + def get_export_model_classs(self): + return ExportModel + + def process_update_conf(self): + self.mode = 'Train' + self.data_conf['deploy'] = True + model_info_path = self.save_hdfs_path + 'model_info.json' + model_info_local_path = self.save_path + 'model_info.json' + if os.path.exists(model_info_local_path): + shell(f'rm -f {model_info_local_path}') + shell(f'hadoop fs -get {model_info_path} {model_info_local_path}') + recent_date = '' + with open(model_info_local_path, 'r') as target: + model_info = json.load(target) + recent_date = model_info['date'] + + recent_dir = self.save_hdfs_path + recent_date + next_day = get_next_day(recent_date) + self.data_conf['train_end_day'] = next_day + self.data_conf['train_days'] = 1 + self.data_conf['eval_day'] = next_day + self.data_conf['load_path'] = self.save_path + recent_date + self.data_conf['load_embed_only'] = False + self.load_path = self.data_conf['load_path'] + print('download model data', recent_date, self.load_path) + if os.path.exists(self.load_path): + shell(f'rm -rf {self.load_path}') + rst = shell(f'hadoop fs -get {recent_dir} {self.load_path}') + logging.info('download model file rst:' + str(rst)) + logging.info(f'update mode data conf {self.data_conf}') + + def build_input(self): + """构建模型输入.""" + ctx = self.ctx + in_dim = np.ma.innerproduct(ctx.slot_size_vec[self.custom_group_count:], + ctx.embed_dim_vec[self.custom_group_count:]) + if ctx.use_acc_ctr: + in_dim += ctx.slot_num + self.ctx.net_input_dim = in_dim + input_dic = {} + input_dic['sample_idx'] = Input(dtype=tf.int64, batch_shape=(None,)) + input_dic['label'] = Input( + dtype=tf.float32, batch_shape=(None, len(self.model_conf['label']))) + input_dic['sparse_len'] = Input( + dtype=tf.int32, batch_shape=(None, None), name='sparse_len') + input_dic['dense_fea'] = Input( + dtype=tf.float32, batch_shape=(None, None), name='dense_fea') + + input_dic['uniq_num_by_dim'] = Input(dtype=tf.int32, batch_shape=(None,)) + input_dic['uniq_off_by_dim'] = Input(dtype=tf.int32, batch_shape=(None,)) + # single worker + input_dic['sparse_id'] = Input(dtype=tf.int32, batch_shape=(None,)) + input_dic['ps_embed'] = Input(dtype=tf.float32, batch_shape=(None,)) + input_dic['ps_embed_m'] = Input(dtype=tf.float32, batch_shape=(None,)) + input_dic['ps_embed_v'] = Input(dtype=tf.float32, batch_shape=(None,)) + input_dic['ps_stat'] = Input( + dtype=tf.float32, batch_shape=(None, 2), name='ps_stat') + input_dic['b1_power'] = Input( + dtype=tf.float32, batch_shape=(1,), name='b1_power') + input_dic['b2_power'] = Input( + dtype=tf.float32, batch_shape=(1,), name='b2_power') + input_dic['test_mask'] = Input( + dtype=tf.float32, batch_shape=(1, in_dim), name='test_mask') -class BaseModel: - def __init__(self, conf): - """ - 初始化参数 - :param worker_list: 指定的 worker 节点列表 - :param embed_share: 0 全独立 1 field内共享 2 全共享 - """ - self.monitor_table = 'model_metrics_monitor' - self.mysql_utils = MySQLUtils().master - self.data_conf = conf['data'] - self.model_conf = conf['model'] - self.ps_conf = conf.get('ps', {}) - self.model_name = self.model_conf['model_name'] - self.epochs = self.data_conf.get('epochs', 1) # 总训练轮数 - self.log_step_num = self.data_conf.get('log_step_num', 1) - self.save_path = self.data_conf.get('save_path').rstrip('/') + '/' + self.model_name + '/' - if not os.path.exists(self.save_path): - os.makedirs(self.save_path) - self.save_hdfs_path = self.data_conf.get('save_hdfs_path').rstrip('/') + '/' + self.model_name + '/' - self.save_iter = 1 - - self.load_path = self.data_conf.get('load_path') - self.model = None - self.mode = self.model_conf['mode'] - self.profile = self.model_conf.get('profile', False) - self.tb_callback = None + batch_split = ctx.batch_size // ctx.num_worker + embed_arr, id_arr, len_arr, tmp_arr = model_inputs_prepare(input_dic, ctx) + + fea_dim = 11 if ctx.use_acc_ctr else 10 + gather_arr = [] + inputs_size = [] + + show, click = tf.split(input_dic['ps_stat'], 2, axis=1) + ctr = tf.reshape(click / (show + 10.), [-1, 1]) + + for idx, pair in enumerate(self.group_length.items()): + group, length = pair + if group == 'default': + continue + conf_list = self.feature_conf[group] + fea_size = len(conf_list) + fea_dim_total = fea_size * fea_dim + + indice = make_sparse_indice(len_arr[idx]) + tmp = tf.gather(embed_arr[idx], id_arr[idx]) + if ctx.use_acc_ctr: + acc_ctr = tf.gather(ctr, tmp_arr[idx]) + tmp = tf.concat([tmp, acc_ctr], axis=1) + pos = make_pos(indice, len_arr[idx]) + # non_zero_indice 分别表示第i个样本 第j个pos + non_zero_indice = tf.stack([indice, pos], axis=1) + group_embed = tf.scatter_nd(non_zero_indice, tmp, + [batch_split * fea_size, length, fea_dim]) + group_embed = tf.reshape(group_embed, [-1, fea_size, length, fea_dim]) + group_embed = tf.reshape( + tf.transpose(group_embed, perm=[0, 2, 1, 3]), + [-1, length, fea_dim_total]) + gather_arr.append(group_embed) + inputs_size.append([length, fea_dim_total]) + + for idx in range(self.custom_group_count, len(ctx.embed_dim_vec)): + tmp_seg_ids = make_sparse_indice(len_arr[idx]) + tmp = tf.math.segment_sum( + tf.gather(embed_arr[idx], id_arr[idx]), tmp_seg_ids) + tmp = tf.reshape( + tmp, [batch_split, ctx.slot_size_vec[idx] * ctx.embed_dim_vec[idx]]) + if ctx.use_acc_ctr: + acc_ctr = tf.reshape( + tf.math.segment_mean(tf.gather(ctr, tmp_arr[idx]), tmp_seg_ids), + [batch_split, ctx.slot_size_vec[idx], 1]) + tmp = tf.reshape( + tmp, [batch_split, ctx.slot_size_vec[idx], ctx.embed_dim_vec[idx]]) + tmp = tf.concat([tmp, acc_ctr], axis=2) + tmp = tf.reshape(tmp, [ + batch_split, ctx.slot_size_vec[idx] * (ctx.embed_dim_vec[idx] + 1) + ]) + gather_arr.append(tmp) + + # TODO aucdiff + # embed_input = tf.concat(gather_arr, axis=1, name="embed_input") + # embed_input = embed_input * input_dic["test_mask"][...,item_fea_dim_total:] + inputs_size.append([in_dim]) + + return input_dic, gather_arr, inputs_size + + def build_and_compile_model(self): + """构建并编译模型.""" + ctx = self.ctx + input_dic, embed_input, indim = self.build_input() + self.export_model = self.export_model_class(self.model_conf, indim) + # self.export_model.build((None, indim)) + # self.export_model.summary() + + y_pred = self.export_model(embed_input) + y = input_dic['label'] # (bs, label_num) + splited_batch_size = ctx.batch_size // ctx.num_worker + + miss_mask = tf.where( + tf.equal(tf.reshape(input_dic['sample_idx'], [-1]), 0), + tf.zeros([splited_batch_size], dtype=ctx.tfval), + tf.ones([splited_batch_size], dtype=ctx.tfval)) + + loss_object = tf.keras.losses.BinaryCrossentropy() + if type(y_pred) != list: + y_pred = [y_pred] + total_loss = 0 + ps_loss = 0 + ttmp = tf.cast(tf.reduce_sum(miss_mask), tf.int32) + ground_truths = [] + predictions = [] + losses = [] + for label_idx, conf in enumerate(self.model_conf['label']): + # for ground_truth, pred, conf in zip(y, y_pred, self.model_conf['label']): + ground_truth = y[..., label_idx] + pred = y_pred[label_idx] + train_space = conf.get('train_space', -1) + if train_space >= 0: + valid_idx = tf.where(y[..., train_space] > 0) + ground_truth = tf.gather(ground_truth, valid_idx) + pred = tf.gather(pred, valid_idx) + else: + ground_truth = ground_truth[:ttmp] + pred = pred[:ttmp] + ground_truth = tf.reshape(ground_truth, (-1, 1)) + pred = tf.reshape(pred, (-1, 1)) + #cur_loss = tf.nn.sigmoid_cross_entropy_with_logits(ground_truth, logit) + #cur_loss = tf.reduce_mean(cur_loss) + if conf.get('pos_weight'): + pos_weight = conf.get('pos_weight', 1.) + sample_weight = 1. + ground_truth * (pos_weight - 1.) + cur_loss = loss_object(ground_truth, pred, sample_weight=sample_weight) + else: + cur_loss = loss_object(ground_truth, pred) + losses.append(cur_loss) + weight = conf.get('weight', 1.) + embed_weight = conf.get('embed_weight', 1.) + total_loss += cur_loss * weight + ps_loss += cur_loss * embed_weight + + ground_truths.append(ground_truth) + predictions.append(pred) + + net_dic = { + 'loss': total_loss, + 'ps_loss': ps_loss, + 'losses': losses, + 'embed_input': embed_input, + 'ground_truth': ground_truths, + 'prediction': predictions, + 'sid': input_dic['sample_idx'][:ttmp] + } + + opt = Adam(0.001) + model = Model(inputs=input_dic, outputs=net_dic) + model.compile(optimizer=opt) + self.checkpoint = tf.train.Checkpoint( + step=tf.Variable(1), model=model, optimizer=opt) + return model + + def load_model(self, path): + if not self.data_conf.get('load_embed_only'): + cpt_path = os.path.join(path, 'checkpoint') + if not os.path.exists(cpt_path): + raise Exception(cpt_path + ' not exists.') + self.cpt_manager = tf.train.CheckpointManager( + self.checkpoint, cpt_path, max_to_keep=1) + logging.info('loading model from:{}'.format(path)) + self.checkpoint.restore(self.cpt_manager.latest_checkpoint) + self.local_db.load() + + def save_model(self, save_date, model_info, is_deploy=None): + path = self.save_path + save_date + shell(f'rm -rf {path}') + self.cpt_manager = tf.train.CheckpointManager( + self.checkpoint, os.path.join(path, 'checkpoint'), max_to_keep=1) + save_path = self.cpt_manager.save() + self.local_db.save() + logging.info('saving model to:{}'.format(save_path)) + model_info_path = self.save_path + 'model_info.json' + with open(model_info_path, 'w') as target: + json.dump(model_info, target) + if is_deploy: + #self.export_model.save(path + '/local_model', save_format="tf") + tf.saved_model.save(self.export_model, path + '/local_model') + hdfs_new_model_path = self.save_hdfs_path + save_date + # deploy codis data first, make sure that when server got new version file the codis data is ready. + deploy_ps_data(self.model_name, path, self.save_iter) + shell(f'hadoop fs -rmr {hdfs_new_model_path}') + logging.info('put local model to hdfs %s ' % hdfs_new_model_path) + shell(f'hadoop fs -put {path} {hdfs_new_model_path}') + model_info_remote_path = self.save_hdfs_path + 'model_info.json' + shell(f'hadoop fs -put -f {model_info_path} {model_info_remote_path}') + self.save_iter += 1 + + def prepare_model(self): + # 编译并构建模型 + self.model = self.build_and_compile_model() + if self.load_path: + self.load_model(self.load_path) + + def log_into_db(self, train_time, metrics): + """log train history to db.""" + label_name = metrics['Name'] + for metric_name, metric_value in metrics.items(): + if metric_name == 'Name': + continue + metric_name = label_name + '_' + metric_name + metric_time = int(time.mktime(train_time.timetuple())) + data = { + 'model_name': self.model_name, + 'metric_time': metric_time, + 'metric_name': metric_name, + 'metric_value': float(metric_value) + } + where_str = f'model_name="{self.model_name}" and metric_time={metric_time} and metric_name="{metric_name}"' + logging.info(f'log train metric into db: {data}') + + if self.mysql_utils.fetch_one(table=self.monitor_table, where=where_str): + self.mysql_utils.update(self.monitor_table, data, where_str) + else: + self.mysql_utils.insert(self.monitor_table, data) + + def get_batch_input(self, batch, local_db, mask=None): + ctx = self.ctx + ps_stat, ps_embed, tinfo = local_db.pull(batch['task_id']) + input_dic = {} + # input_dic["scene_index"] = batch["scene_index"] + input_dic['sample_idx'] = batch['sample_idx'] + input_dic['label'] = batch['label'] + input_dic['sparse_len'] = batch['sparse_len'] + input_dic['dense_fea'] = batch['dense_fea'] + + input_dic['uniq_num_by_dim'] = np.cumsum( + [0] + batch['uniq_num_by_dim'].tolist()).astype(np.int32) # noqa + tmp = np.array(ctx.embed_dim_vec).astype( + np.int32) * batch['uniq_num_by_dim'].astype(np.int32) # noqa + input_dic['uniq_off_by_dim'] = np.cumsum([0] + tmp.tolist()).astype( + np.int32) # noqa + input_dic['sparse_id'] = batch['sparse_id'][0] + + input_dic['ps_embed'] = ps_embed[0] + input_dic['ps_embed_m'] = np.zeros_like( + ps_embed[1]) if self.adam_restart else ps_embed[1] + input_dic['ps_embed_v'] = np.zeros_like( + ps_embed[2]) if self.adam_restart else ps_embed[2] + input_dic['ps_stat'] = ps_stat + self.b1_power *= ctx.adam_beta1 + self.b2_power *= ctx.adam_beta2 + input_dic['b1_power'] = self.b1_power + input_dic['b2_power'] = self.b2_power + input_dic['test_mask'] = np.ones([1, self.ctx.net_input_dim], + dtype=self.ctx.npval) + # avoid retracing of tf function. + input_dic = {key: tf.constant(input_dic[key]) for key in input_dic} + return input_dic + + def fit_day(self, train_itor, local_db): + ctx = self.ctx + batch_size = self.ctx.batch_size // self.ctx.num_worker + metrics = [ + AUCUtil(name=conf['input_name'] + '_metric') + for conf in self.model_conf['label'] + ] + total_metrics = [ + BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) + for conf in self.model_conf['label'] + ] + step = 0 + while True: + try: if self.profile: - self.tb_callback = tf.keras.callbacks.TensorBoard(log_dir=self.save_path + '/profile', - profile_batch='10,12') - - if self.mode == 'Update': - self.process_update_conf() - self.feature_conf, self.group_length = self.parse_feature_conf(conf['feature']) - self.custom_group_count = len(self.group_length) - 1 - self.export_model_class = self.get_export_model_classs() - - if self.data_conf.get('deploy'): - shell(f'hadoop fs -mkdir -p {self.save_hdfs_path}') - - def parse_feature_conf(self, conf_list): - group_features = {} - group_len = {} - for conf in conf_list: - group = conf.get('group', 'default') - if group not in group_features: - group_features[group] = [] - group_len[group] = conf.get('length', 1) - group_features[group].append(conf) - - if len(group_features) == 1: - return conf_list, None - return group_features, group_len - - - def get_export_model_classs(self): - return ExportModel - - def process_update_conf(self): - self.mode = 'Train' - self.data_conf['deploy'] = True - model_info_path = self.save_hdfs_path + 'model_info.json' - model_info_local_path = self.save_path + 'model_info.json' - if os.path.exists(model_info_local_path): - shell(f'rm -f {model_info_local_path}') - shell(f'hadoop fs -get {model_info_path} {model_info_local_path}') - recent_date = '' - with open(model_info_local_path, 'r') as target: - model_info = json.load(target) - recent_date = model_info['date'] - - recent_dir = self.save_hdfs_path + recent_date - next_day = get_next_day(recent_date) - self.data_conf['train_end_day'] = next_day - self.data_conf['train_days'] = 1 - self.data_conf['eval_day'] = next_day - self.data_conf['load_path'] = self.save_path + recent_date - self.data_conf['load_embed_only'] = False - self.load_path = self.data_conf['load_path'] - print('download model data', recent_date, self.load_path) - if os.path.exists(self.load_path): - shell(f'rm -rf {self.load_path}') - rst = shell(f"hadoop fs -get {recent_dir} {self.load_path}") - logging.info('download model file rst:' + str(rst)) - logging.info(f'update mode data conf {self.data_conf}') - - - def build_input(self): - """ - 构建模型输入 - """ - ctx = self.ctx - in_dim = np.ma.innerproduct(ctx.slot_size_vec[self.custom_group_count:], ctx.embed_dim_vec[self.custom_group_count:]) - if ctx.use_acc_ctr: - in_dim += ctx.slot_num - self.ctx.net_input_dim = in_dim - input_dic = {} - input_dic["sample_idx"] = Input(dtype=tf.int64, batch_shape=(None,)) - input_dic["label"] = Input(dtype=tf.float32, batch_shape=(None, len(self.model_conf['label']))) - input_dic["sparse_len"] = Input(dtype=tf.int32, batch_shape=(None, - None), name='sparse_len') - input_dic["dense_fea"] = Input(dtype=tf.float32, batch_shape=(None, - None), name='dense_fea') - - input_dic["uniq_num_by_dim"] = Input(dtype=tf.int32, - batch_shape=(None,)) - input_dic["uniq_off_by_dim"] = Input(dtype=tf.int32, - batch_shape=(None,)) - # single worker - input_dic["sparse_id"] = Input(dtype=tf.int32, batch_shape=(None,)) - input_dic["ps_embed"] = Input(dtype=tf.float32, batch_shape=(None,)) - input_dic["ps_embed_m"] = Input(dtype=tf.float32, batch_shape=(None,)) - input_dic["ps_embed_v"] = Input(dtype=tf.float32, batch_shape=(None,)) - input_dic["ps_stat"] = Input(dtype=tf.float32, - batch_shape=(None, 2), name='ps_stat') - input_dic["b1_power"] = Input(dtype=tf.float32, - batch_shape=(1,), name='b1_power') - input_dic["b2_power"] = Input(dtype=tf.float32, - batch_shape=(1,), name='b2_power') - input_dic["test_mask"] = Input(dtype=tf.float32, - batch_shape=(1, in_dim), name='test_mask') - - batch_split = ctx.batch_size // ctx.num_worker - embed_arr, id_arr, len_arr, tmp_arr = model_inputs_prepare(input_dic, ctx) - - fea_dim = 11 if ctx.use_acc_ctr else 10 - gather_arr = [] - inputs_size = [] - - show, click = tf.split(input_dic["ps_stat"], 2, axis=1) - ctr = tf.reshape(click / (show + 10.), [-1, 1]) - - for idx, pair in enumerate(self.group_length.items()): - group, length = pair - if group == 'default': - continue - conf_list = self.feature_conf[group] - fea_size = len(conf_list) - fea_dim_total = fea_size * fea_dim - - indice = make_sparse_indice(len_arr[idx]) - tmp = tf.gather(embed_arr[idx], id_arr[idx]) - if ctx.use_acc_ctr: - acc_ctr = tf.gather(ctr, tmp_arr[idx]) - tmp = tf.concat([tmp, acc_ctr], axis=1) - pos = make_pos(indice, len_arr[idx]) - # non_zero_indice 分别表示第i个样本 第j个pos - non_zero_indice = tf.stack([indice, pos], axis=1) - group_embed = tf.scatter_nd(non_zero_indice, tmp, [batch_split*fea_size, length, fea_dim]) - group_embed = tf.reshape(group_embed, [-1, fea_size, length, fea_dim]) - group_embed = tf.reshape(tf.transpose(group_embed, perm=[0, 2, 1, 3]), [-1, length, fea_dim_total]) - gather_arr.append(group_embed) - inputs_size.append([length, fea_dim_total]) - - for idx in range(self.custom_group_count, len(ctx.embed_dim_vec)): - tmp_seg_ids = make_sparse_indice(len_arr[idx]) - tmp = tf.math.segment_sum( - tf.gather(embed_arr[idx], id_arr[idx]), - tmp_seg_ids) - tmp = tf.reshape(tmp, - [batch_split, ctx.slot_size_vec[idx] * ctx.embed_dim_vec[idx]]) - if ctx.use_acc_ctr: - acc_ctr = tf.reshape(tf.math.segment_mean( - tf.gather(ctr, tmp_arr[idx]), tmp_seg_ids), - [batch_split, ctx.slot_size_vec[idx], 1]) - tmp = tf.reshape(tmp, - [batch_split, ctx.slot_size_vec[idx], ctx.embed_dim_vec[idx]]) - tmp = tf.concat([tmp, acc_ctr], axis=2) - tmp = tf.reshape(tmp, - [batch_split, ctx.slot_size_vec[idx] * (ctx.embed_dim_vec[idx]+1)]) - gather_arr.append(tmp) - - # TODO aucdiff - # embed_input = tf.concat(gather_arr, axis=1, name="embed_input") - # embed_input = embed_input * input_dic["test_mask"][...,item_fea_dim_total:] - inputs_size.append([in_dim]) - - return input_dic, gather_arr, inputs_size - - - def build_and_compile_model(self): - """ - 构建并编译模型 - """ - ctx = self.ctx - input_dic, embed_input, indim = self.build_input() - self.export_model = self.export_model_class(self.model_conf, indim) - # self.export_model.build((None, indim)) - # self.export_model.summary() - - y_pred = self.export_model(embed_input) - y = input_dic["label"] # (bs, label_num) - splited_batch_size = ctx.batch_size // ctx.num_worker - - miss_mask = tf.where( - tf.equal(tf.reshape(input_dic["sample_idx"], [-1]), 0), - tf.zeros([splited_batch_size], dtype=ctx.tfval), - tf.ones([splited_batch_size], dtype=ctx.tfval)) - - loss_object = tf.keras.losses.BinaryCrossentropy() - if type(y_pred) != list: - y_pred = [y_pred] - total_loss = 0 - ps_loss = 0 - ttmp = tf.cast(tf.reduce_sum(miss_mask), tf.int32) - ground_truths = [] - predictions = [] - losses = [] - for label_idx, conf in enumerate(self.model_conf['label']): - # for ground_truth, pred, conf in zip(y, y_pred, self.model_conf['label']): - ground_truth = y[..., label_idx] - pred = y_pred[label_idx] - train_space = conf.get('train_space', -1) - if train_space >= 0: - valid_idx = tf.where(y[..., train_space] > 0) - ground_truth = tf.gather(ground_truth, valid_idx) - pred = tf.gather(pred, valid_idx) - else: - ground_truth = ground_truth[:ttmp] - pred = pred[:ttmp] - ground_truth = tf.reshape(ground_truth, (-1, 1)) - pred = tf.reshape(pred, (-1, 1)) - #cur_loss = tf.nn.sigmoid_cross_entropy_with_logits(ground_truth, logit) - #cur_loss = tf.reduce_mean(cur_loss) - if conf.get('pos_weight'): - pos_weight = conf.get('pos_weight', 1.) - sample_weight = 1. + ground_truth * (pos_weight - 1.) - cur_loss = loss_object(ground_truth, pred, sample_weight=sample_weight) - else: - cur_loss = loss_object(ground_truth, pred) - losses.append(cur_loss) - weight = conf.get('weight', 1.) - embed_weight = conf.get('embed_weight', 1.) - total_loss += cur_loss * weight - ps_loss += cur_loss * embed_weight - - ground_truths.append(ground_truth) - predictions.append(pred) - - net_dic = { - "loss": total_loss, - "ps_loss": ps_loss, - "losses": losses, - "embed_input": embed_input, - "ground_truth": ground_truths, - "prediction": predictions, - "sid": input_dic["sample_idx"][:ttmp] - } - - opt = Adam(0.001) - model = Model(inputs=input_dic, outputs=net_dic) - model.compile(optimizer=opt) - self.checkpoint = tf.train.Checkpoint(step=tf.Variable(1), model=model, optimizer=opt) - return model - - def load_model(self, path): - if not self.data_conf.get('load_embed_only'): - cpt_path = os.path.join(path, 'checkpoint') - if not os.path.exists(cpt_path): - raise Exception(cpt_path + ' not exists.') - self.cpt_manager = tf.train.CheckpointManager(self.checkpoint, - cpt_path, - max_to_keep=1) - logging.info('loading model from:{}'.format(path)) - self.checkpoint.restore(self.cpt_manager.latest_checkpoint) - self.local_db.load() - - - def save_model(self, save_date, model_info, is_deploy=None): - path = self.save_path + save_date - shell(f"rm -rf {path}") - self.cpt_manager = tf.train.CheckpointManager(self.checkpoint, - os.path.join(path, 'checkpoint'), - max_to_keep=1) - save_path = self.cpt_manager.save() - self.local_db.save() - logging.info('saving model to:{}'.format(save_path)) - model_info_path = self.save_path + 'model_info.json' - with open(model_info_path, 'w') as target: - json.dump(model_info, target) - if is_deploy: - #self.export_model.save(path + '/local_model', save_format="tf") - tf.saved_model.save(self.export_model, path + '/local_model') - hdfs_new_model_path = self.save_hdfs_path + save_date - # deploy codis data first, make sure that when server got new version file the codis data is ready. - deploy_ps_data(self.model_name, path, self.save_iter) - shell(f'hadoop fs -rmr {hdfs_new_model_path}') - logging.info('put local model to hdfs %s ' % hdfs_new_model_path) - shell(f'hadoop fs -put {path} {hdfs_new_model_path}') - model_info_remote_path = self.save_hdfs_path + 'model_info.json' - shell(f'hadoop fs -put -f {model_info_path} {model_info_remote_path}') - self.save_iter += 1 - - - def prepare_model(self): - # 编译并构建模型 - self.model = self.build_and_compile_model() - if self.load_path: - self.load_model(self.load_path) - - def log_into_db(self, train_time, metrics): - """log train history to db. - """ - label_name = metrics['Name'] - for metric_name, metric_value in metrics.items(): - if metric_name == 'Name': - continue - metric_name = label_name + '_' + metric_name - metric_time = int(time.mktime(train_time.timetuple())) - data = { - 'model_name': self.model_name, - 'metric_time': metric_time, - 'metric_name': metric_name, - 'metric_value': float(metric_value) - } - where_str = f'model_name="{self.model_name}" and metric_time={metric_time} and metric_name="{metric_name}"' - logging.info(f'log train metric into db: {data}') - - if self.mysql_utils.fetch_one(table=self.monitor_table, where=where_str): - self.mysql_utils.update(self.monitor_table, data, where_str) - else: - self.mysql_utils.insert(self.monitor_table, data) - - def get_batch_input(self, batch, local_db, mask=None): - ctx = self.ctx - ps_stat, ps_embed, tinfo = local_db.pull(batch["task_id"]) - input_dic = {} - # input_dic["scene_index"] = batch["scene_index"] - input_dic["sample_idx"] = batch["sample_idx"] - input_dic["label"] = batch["label"] - input_dic["sparse_len"] = batch["sparse_len"] - input_dic["dense_fea"] = batch["dense_fea"] - - input_dic["uniq_num_by_dim"] = np.cumsum([0] + batch["uniq_num_by_dim"].tolist()).astype(np.int32) # noqa - tmp = np.array(ctx.embed_dim_vec).astype(np.int32) * batch["uniq_num_by_dim"].astype(np.int32) # noqa - input_dic["uniq_off_by_dim"] = np.cumsum([0] + tmp.tolist()).astype(np.int32) # noqa - input_dic["sparse_id"] = batch["sparse_id"][0] - - input_dic["ps_embed"] = ps_embed[0] - input_dic["ps_embed_m"] = np.zeros_like(ps_embed[1]) if self.adam_restart else ps_embed[1] - input_dic["ps_embed_v"] = np.zeros_like(ps_embed[2]) if self.adam_restart else ps_embed[2] - input_dic["ps_stat"] = ps_stat - self.b1_power *= ctx.adam_beta1 - self.b2_power *= ctx.adam_beta2 - input_dic["b1_power"] = self.b1_power - input_dic["b2_power"] = self.b2_power - input_dic['test_mask'] = np.ones([1, self.ctx.net_input_dim], dtype=self.ctx.npval) - # avoid retracing of tf function. - input_dic = {key: tf.constant(input_dic[key]) for key in input_dic} - return input_dic - - def fit_day(self, train_itor, local_db): - ctx = self.ctx - batch_size = self.ctx.batch_size // self.ctx.num_worker - metrics = [AUCUtil(name=conf['input_name'] + '_metric') for conf in self.model_conf['label']] - total_metrics = [BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) for conf in self.model_conf['label']] - step = 0 - while True: - try: - if self.profile: - self.tb_callback.on_train_batch_begin(step) - start_time = time.time() - batch = train_itor.getbatch() - input_dic = self.get_batch_input(batch, local_db) - self.adam_restart = False - output_dic = train_step(self.model, input_dic, ctx) - if output_dic['nan_flag'] > 0: - print('nan and skip') - local_db.push(batch["task_id"], None, None) - continue - - # Update training metric. - for gt, pred, loss, metric, total_metric in zip( - output_dic['ground_truth'], - output_dic['prediction'], - output_dic['losses'], - metrics, - total_metrics - ): - gt = gt.numpy() - pred = pred.numpy() - loss = loss.numpy() - total_metric.add(loss, gt, pred) - if self.log_step_num < 1 or step % self.log_step_num != 0: - continue - #print('pred', pred[:20]) - - metric.add(loss, gt, pred) - print(f'{metric.name} {metric.calc_str()}') - metric.reset() - - ps_grad = output_dic['ps_grad'] - local_db.push(batch["task_id"], ps_grad.numpy(), batch["payload"]) - elapse = time.time() - start_time - if self.log_step_num >= 1 and step % self.log_step_num == 0: - print(f'train step {step}, batch time elapse {elapse}') - print('-' * 50) - step += 1 - if self.profile: - self.tb_callback.on_train_batch_end(step) - if step > 20: - break - #exit() - except EOFError: - # save & terminate - print('EOF, train day end.') - break - rst_list = [] - for metric in total_metrics: - rst = metric.calc() - print(f'{metric.name} {rst}') - rst['Name'] = metric.name - rst_list.append(rst) - print('='*80) - return rst_list - - - def check_day(self, train_itor, local_db): - ctx = self.ctx - batch_size = ctx.batch_size - step = 0 - embed_size = 10 - sample_data = pq.read_table('embed.parquet').to_pandas() - - while True: - try: - start_time = time.time() - batch = train_itor.getbatch() - input_dic = self.get_batch_input(batch, local_db) - output_dic = self.model(input_dic, training=False) - - samples = sample_data[step*batch_size: step*batch_size + batch_size] - online_embeds = samples.embed_input.to_numpy() - - # should add 'embed_input' in ouput_dic first. - embed_input_data = output_dic['embed_input'].numpy() - features = [feature['input_name'] for feature in self.feature_conf] - feature_diff_data = {} - for embed, online_embed in zip( - embed_input_data, - online_embeds - ): - embed_off = embed.tolist() - embed_on = online_embed.tolist() - for idx, fea_name in enumerate(features): - fea_embed_on = embed_on[idx*embed_size: idx*embed_size + embed_size] - fea_embed_off = embed_off[idx*embed_size: idx*embed_size + embed_size] - total_diff = sum([abs(x-y) if abs(x-y) > 1e-7 else 0 for x, y in zip(fea_embed_on, fea_embed_off)]) - if total_diff > 0 or sum(fea_embed_on) == 0: - print(fea_name) - print(fea_embed_on) - print(fea_embed_off) - feature_diff_data[fea_name] = feature_diff_data.get(fea_name, 0) + 1 - print('-'*60) - print('total cnt', len(online_embeds)) - feature_diff_data = [[key, value] for key, value in feature_diff_data.items()] - feature_diff_data.sort(key=lambda x: x[1], reverse=True) - print(feature_diff_data) - - - - # Update training metric. - for gt, pred, conf in zip( - output_dic['ground_truth'], - output_dic['prediction'], - self.model_conf['label'] - ): - gt = gt.numpy() - pred = pred.numpy() - print(conf['input_name']) - thresold = 10e-4 - total = 0 - cnt = 0 - for on, off in zip(gt, pred): - total += 1 - if abs(on - off) > thresold: - cnt += 1 - # print(on, off) - print('diff ratio', cnt / total) - - local_db.push(batch["task_id"]) - elapse = time.time() - start_time - if self.log_step_num >= 1 and step % self.log_step_num == 0: - print(f'train step {step}, batch time elapse {elapse}') - print('-' * 50) - step += 1 - #exit() - except EOFError: - # save & terminate - print('EOF, check day end.') - break - - - def eval(self, train_itor, local_db): - ctx = self.ctx - batch_size = ctx.batch_size - metrics = [AUCUtil(name=conf['input_name'] + '_metric') for conf in self.model_conf['label']] - total_metrics = [BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) for conf in self.model_conf['label']] - step = 0 - - while True: - try: - start_time = time.time() - batch = train_itor.getbatch() - input_dic = self.get_batch_input(batch, local_db) - output_dic = self.model(input_dic, training=False) - - # Update training metric. - for gt, pred, loss, metric, total_metric in zip( - output_dic['ground_truth'], - output_dic['prediction'], - output_dic['losses'], - metrics, - total_metrics - ): - gt = gt.numpy() - pred = pred.numpy() - loss = loss.numpy() - total_metric.add(loss, gt, pred) - if self.log_step_num < 1 or step % self.log_step_num != 0: - continue - metric.add(loss, gt, pred) - print(f'{metric.name} {metric.calc_str()}') - metric.reset() - - local_db.push(batch["task_id"]) - elapse = time.time() - start_time - if self.log_step_num >= 1 and step % self.log_step_num == 0: - print(f'eval step {step}, batch time elapse {elapse}') - print('-' * 50) - step += 1 - #exit() - except EOFError: - # save & terminate - print('EOF, eval end.') - break - rst_list = [] - for metric in total_metrics: + self.tb_callback.on_train_batch_begin(step) + start_time = time.time() + batch = train_itor.getbatch() + input_dic = self.get_batch_input(batch, local_db) + self.adam_restart = False + output_dic = train_step(self.model, input_dic, ctx) + if output_dic['nan_flag'] > 0: + print('nan and skip') + local_db.push(batch['task_id'], None, None) + continue + + # Update training metric. + for gt, pred, loss, metric, total_metric in zip( + output_dic['ground_truth'], output_dic['prediction'], + output_dic['losses'], metrics, total_metrics): + gt = gt.numpy() + pred = pred.numpy() + loss = loss.numpy() + total_metric.add(loss, gt, pred) + if self.log_step_num < 1 or step % self.log_step_num != 0: + continue + #print('pred', pred[:20]) + + metric.add(loss, gt, pred) + print(f'{metric.name} {metric.calc_str()}') + metric.reset() + + ps_grad = output_dic['ps_grad'] + local_db.push(batch['task_id'], ps_grad.numpy(), batch['payload']) + elapse = time.time() - start_time + if self.log_step_num >= 1 and step % self.log_step_num == 0: + print(f'train step {step}, batch time elapse {elapse}') + print('-' * 50) + step += 1 + if self.profile: + self.tb_callback.on_train_batch_end(step) + if step > 20: + break + #exit() + except EOFError: + # save & terminate + print('EOF, train day end.') + break + rst_list = [] + for metric in total_metrics: + rst = metric.calc() + print(f'{metric.name} {rst}') + rst['Name'] = metric.name + rst_list.append(rst) + print('=' * 80) + return rst_list + + def check_day(self, train_itor, local_db): + ctx = self.ctx + batch_size = ctx.batch_size + step = 0 + embed_size = 10 + sample_data = pq.read_table('embed.parquet').to_pandas() + + while True: + try: + start_time = time.time() + batch = train_itor.getbatch() + input_dic = self.get_batch_input(batch, local_db) + output_dic = self.model(input_dic, training=False) + + samples = sample_data[step * batch_size:step * batch_size + batch_size] + online_embeds = samples.embed_input.to_numpy() + + # should add 'embed_input' in ouput_dic first. + embed_input_data = output_dic['embed_input'].numpy() + features = [feature['input_name'] for feature in self.feature_conf] + feature_diff_data = {} + for embed, online_embed in zip(embed_input_data, online_embeds): + embed_off = embed.tolist() + embed_on = online_embed.tolist() + for idx, fea_name in enumerate(features): + fea_embed_on = embed_on[idx * embed_size:idx * embed_size + + embed_size] + fea_embed_off = embed_off[idx * embed_size:idx * embed_size + + embed_size] + total_diff = sum([ + abs(x - y) if abs(x - y) > 1e-7 else 0 + for x, y in zip(fea_embed_on, fea_embed_off) + ]) + if total_diff > 0 or sum(fea_embed_on) == 0: + print(fea_name) + print(fea_embed_on) + print(fea_embed_off) + feature_diff_data[fea_name] = feature_diff_data.get(fea_name, + 0) + 1 + print('-' * 60) + print('total cnt', len(online_embeds)) + feature_diff_data = [ + [key, value] for key, value in feature_diff_data.items() + ] + feature_diff_data.sort(key=lambda x: x[1], reverse=True) + print(feature_diff_data) + + # Update training metric. + for gt, pred, conf in zip(output_dic['ground_truth'], + output_dic['prediction'], + self.model_conf['label']): + gt = gt.numpy() + pred = pred.numpy() + print(conf['input_name']) + thresold = 10e-4 + total = 0 + cnt = 0 + for on, off in zip(gt, pred): + total += 1 + if abs(on - off) > thresold: + cnt += 1 + # print(on, off) + print('diff ratio', cnt / total) + + local_db.push(batch['task_id']) + elapse = time.time() - start_time + if self.log_step_num >= 1 and step % self.log_step_num == 0: + print(f'train step {step}, batch time elapse {elapse}') + print('-' * 50) + step += 1 + #exit() + except EOFError: + # save & terminate + print('EOF, check day end.') + break + + def eval(self, train_itor, local_db): + ctx = self.ctx + batch_size = ctx.batch_size + metrics = [ + AUCUtil(name=conf['input_name'] + '_metric') + for conf in self.model_conf['label'] + ] + total_metrics = [ + BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) + for conf in self.model_conf['label'] + ] + step = 0 + + while True: + try: + start_time = time.time() + batch = train_itor.getbatch() + input_dic = self.get_batch_input(batch, local_db) + output_dic = self.model(input_dic, training=False) + + # Update training metric. + for gt, pred, loss, metric, total_metric in zip( + output_dic['ground_truth'], output_dic['prediction'], + output_dic['losses'], metrics, total_metrics): + gt = gt.numpy() + pred = pred.numpy() + loss = loss.numpy() + total_metric.add(loss, gt, pred) + if self.log_step_num < 1 or step % self.log_step_num != 0: + continue + metric.add(loss, gt, pred) + print(f'{metric.name} {metric.calc_str()}') + metric.reset() + + local_db.push(batch['task_id']) + elapse = time.time() - start_time + if self.log_step_num >= 1 and step % self.log_step_num == 0: + print(f'eval step {step}, batch time elapse {elapse}') + print('-' * 50) + step += 1 + #exit() + except EOFError: + # save & terminate + print('EOF, eval end.') + break + rst_list = [] + for metric in total_metrics: + rst = metric.calc() + print(f'{metric.name} {rst}') + rst['Name'] = metric.name + rst_list.append(rst) + return rst_list + + def aucdiff_run(self, train_itor, local_db): + ctx = self.ctx + batch_size = ctx.batch_size + total_metrics_list = [[ + BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) + for conf in self.model_conf['label'] + ] + for _ in range(ctx.origin_slot_num + 1)] + tmask = [ + np.ones([1, self.ctx.net_input_dim], dtype=self.ctx.npval) + for _ in range(self.ctx.origin_slot_num) + ] # noqa + + step = 0 + embed_offset = 0 + start_arr = [] + end_arr = [] + for mask in range(len(ctx.embed_dim)): + pstart = embed_offset + embed_len = self.ctx.embed_dim[mask] + if ctx.use_acc_ctr: + embed_len += 1 + pend = pstart + embed_len + tmask[mask][:, pstart:pend] = 0. + embed_offset = pend + + tmp_start = [str(pstart)] + tmp_end = [str(pend)] + start_arr.append('/'.join(tmp_start)) + end_arr.append('/'.join(tmp_end)) + + while True: + try: + batch = train_itor.getbatch() + input_dic = self.get_batch_input(batch, local_db) + origin_mask = input_dic['test_mask'] + + for mask in range(self.ctx.origin_slot_num + 1): + if mask < ctx.origin_slot_num: + input_dic['test_mask'] = tf.constant(tmask[mask]) + else: + input_dic['test_mask'] = origin_mask + + output_dic = self.model(input_dic, training=False) + + for gt, pred, loss, total_metric in zip(output_dic['ground_truth'], + output_dic['prediction'], + output_dic['losses'], + total_metrics_list[mask]): + gt = gt.numpy() + pred = pred.numpy() + loss = loss.numpy() + total_metric.add(loss, gt, pred) + step += 1 + local_db.push(batch['task_id']) + print('aucdiff check step', step) + + except EOFError: + data = {} + features = [feature['input_name'] for feature in self.feature_conf] + for mask in range(self.ctx.origin_slot_num): + print('# Mask %s from %s to %s.' % (self.ctx.origin_slots.split( + ',', -1)[mask], start_arr[mask], end_arr[mask])) + feature = features[mask] + data[feature] = {} + for metric in total_metrics_list[mask]: rst = metric.calc() + data[feature][metric.name] = rst print(f'{metric.name} {rst}') - rst['Name'] = metric.name - rst_list.append(rst) - return rst_list - - - def aucdiff_run(self, train_itor, local_db): - ctx = self.ctx - batch_size = ctx.batch_size - total_metrics_list = [[BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) for conf in self.model_conf['label']] for _ in range(ctx.origin_slot_num + 1)] - tmask = [np.ones([1, self.ctx.net_input_dim], dtype=self.ctx.npval) for _ in range(self.ctx.origin_slot_num)] # noqa - - step = 0 - embed_offset = 0 - start_arr = [] - end_arr = [] - for mask in range(len(ctx.embed_dim)): - pstart = embed_offset - embed_len = self.ctx.embed_dim[mask] - if ctx.use_acc_ctr: - embed_len += 1 - pend = pstart + embed_len - tmask[mask][:, pstart:pend] = 0. - embed_offset = pend - - tmp_start = [str(pstart)] - tmp_end = [str(pend)] - start_arr.append("/".join(tmp_start)) - end_arr.append("/".join(tmp_end)) - - while True: - try: - batch = train_itor.getbatch() - input_dic = self.get_batch_input(batch, local_db) - origin_mask = input_dic["test_mask"] - - for mask in range(self.ctx.origin_slot_num + 1): - if mask < ctx.origin_slot_num: - input_dic["test_mask"] = tf.constant(tmask[mask]) - else: - input_dic["test_mask"] = origin_mask - - output_dic = self.model(input_dic, training=False) - - for gt, pred, loss, total_metric in zip( - output_dic['ground_truth'], - output_dic['prediction'], - output_dic['losses'], - total_metrics_list[mask] - ): - gt = gt.numpy() - pred = pred.numpy() - loss = loss.numpy() - total_metric.add(loss, gt, pred) - step += 1 - local_db.push(batch["task_id"]) - print('aucdiff check step', step) - - except EOFError: - data = {} - features = [feature['input_name'] for feature in self.feature_conf] - for mask in range(self.ctx.origin_slot_num): - print("# Mask %s from %s to %s." % - (self.ctx.origin_slots.split(",", -1)[mask], - start_arr[mask], end_arr[mask])) - feature = features[mask] - data[feature] = {} - for metric in total_metrics_list[mask]: - rst = metric.calc() - data[feature][metric.name] = rst - print(f'{metric.name} {rst}') - data['base'] = {} - for metric in total_metrics_list[-1]: - print('base metrics') - rst = metric.calc() - data['base'][metric.name] = rst - print(f'{metric.name} {rst}') - with open('aucdiff_rst.json', 'w') as target: - json.dump(data, target) - break - - - def run(self): - conf_overwrite(self, 'job.conf', 'job.conf.tmp') - self.ctx = Context('job.conf.tmp') - self.train_itor = QDataSet('job.conf.tmp', self.ctx, False) - self.local_db = QWrapper('job.conf.tmp') - self.b1_power = 1. - self.b2_power = 1. - self.adam_restart = True - self.prepare_model() - if self.mode.startswith('Train'): - time_range = self.data_conf['train_days'] - data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' - end_day = self.data_conf['train_end_day'] - end_time = datetime.datetime.strptime(f'{end_day} 00:00:00', '%Y-%m-%d %H:%M:%S') - trained_days = 0 - # TODO 尝试add_history、add_progbar - if self.profile: - self.tb_callback.set_model(self.model) - - try: - if self.profile: - self.tb_callback.on_train_begin() - model_info = {} - for epoch in range(self.epochs): - if self.profile: - self.tb_callback.on_epoch_begin(epoch) - print(f'start epoch {epoch}...') - for i in reversed(range(time_range)): - train_time = end_time - datetime.timedelta(days=i) - data_path = train_time.strftime(data_hdfs_path) - # data_date = train_time.strftime('%Y-%m-%d') - files = get_hdfs_files(data_path) - self.train_itor.setfiles(','.join(files)) - print('start train', train_time, data_path) - total_metrics = self.fit_day(self.train_itor, self.local_db) - model_info['metrics'] = total_metrics - if self.data_conf.get('monitor'): - for metrics in total_metrics: - self.log_into_db(train_time, metrics) - self.local_db.clean_nontrain(False) - trained_days += 1 - if trained_days % 5 == 0: - train_date = train_time.strftime('%Y-%m-%d') - self.save_model(train_date, model_info) - if self.profile: - self.tb_callback.on_epoch_end(epoch) - if self.profile: - self.tb_callback.on_train_end() - except Exception as e: - logging.exception(e) - finally: - is_deploy = self.data_conf.get('deploy') - model_info['version'] = int(time.mktime(train_time.timetuple())) - train_date = train_time.strftime('%Y-%m-%d') - model_info['date'] = train_date - self.save_model(train_date, model_info, is_deploy=is_deploy) - self.local_db.terminate() - - if 'Eval' in self.mode: - data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' - eval_day = self.data_conf['eval_day'] - eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', '%Y-%m-%d %H:%M:%S') - - data_path = eval_time.strftime(data_hdfs_path) - files = get_hdfs_files(data_path) - # files = files[::10] - self.train_itor.is_test = True - self.train_itor.setfiles(','.join(files)) - print('start eval', eval_time, data_path) - self.eval(self.train_itor, self.local_db) - self.local_db.terminate() - - - # TODO support accctr - if self.mode == 'AucDiff': - data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' - eval_day = self.data_conf['eval_day'] - eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', '%Y-%m-%d %H:%M:%S') - - data_path = eval_time.strftime(data_hdfs_path) - files = get_hdfs_files(data_path) - # files = files[::10] - self.train_itor.is_test = True - self.train_itor.setfiles(','.join(files)) - print('start check', eval_time, data_path) - self.aucdiff_run(self.train_itor, self.local_db) - self.local_db.terminate() - - # TODO support accctr - if self.mode == 'Check': - data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' - eval_day = self.data_conf['eval_day'] - eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', '%Y-%m-%d %H:%M:%S') - - data_path = eval_time.strftime(data_hdfs_path) + data['base'] = {} + for metric in total_metrics_list[-1]: + print('base metrics') + rst = metric.calc() + data['base'][metric.name] = rst + print(f'{metric.name} {rst}') + with open('aucdiff_rst.json', 'w') as target: + json.dump(data, target) + break + + def run(self): + conf_overwrite(self, 'job.conf', 'job.conf.tmp') + self.ctx = Context('job.conf.tmp') + self.train_itor = QDataSet('job.conf.tmp', self.ctx, False) + self.local_db = QWrapper('job.conf.tmp') + self.b1_power = 1. + self.b2_power = 1. + self.adam_restart = True + self.prepare_model() + if self.mode.startswith('Train'): + time_range = self.data_conf['train_days'] + data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' + end_day = self.data_conf['train_end_day'] + end_time = datetime.datetime.strptime(f'{end_day} 00:00:00', + '%Y-%m-%d %H:%M:%S') + trained_days = 0 + # TODO 尝试add_history、add_progbar + if self.profile: + self.tb_callback.set_model(self.model) + + try: + if self.profile: + self.tb_callback.on_train_begin() + model_info = {} + for epoch in range(self.epochs): + if self.profile: + self.tb_callback.on_epoch_begin(epoch) + print(f'start epoch {epoch}...') + for i in reversed(range(time_range)): + train_time = end_time - datetime.timedelta(days=i) + data_path = train_time.strftime(data_hdfs_path) + # data_date = train_time.strftime('%Y-%m-%d') files = get_hdfs_files(data_path) - self.train_itor.is_test = True self.train_itor.setfiles(','.join(files)) - print('start check', eval_time, data_path) - - total_metrics = self.check_day(self.train_itor, self.local_db) - self.local_db.terminate() - + print('start train', train_time, data_path) + total_metrics = self.fit_day(self.train_itor, self.local_db) + model_info['metrics'] = total_metrics + if self.data_conf.get('monitor'): + for metrics in total_metrics: + self.log_into_db(train_time, metrics) + self.local_db.clean_nontrain(False) + trained_days += 1 + if trained_days % 5 == 0: + train_date = train_time.strftime('%Y-%m-%d') + self.save_model(train_date, model_info) + if self.profile: + self.tb_callback.on_epoch_end(epoch) + if self.profile: + self.tb_callback.on_train_end() + except Exception as e: + logging.exception(e) + finally: + is_deploy = self.data_conf.get('deploy') + model_info['version'] = int(time.mktime(train_time.timetuple())) + train_date = train_time.strftime('%Y-%m-%d') + model_info['date'] = train_date + self.save_model(train_date, model_info, is_deploy=is_deploy) + self.local_db.terminate() + + if 'Eval' in self.mode: + data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' + eval_day = self.data_conf['eval_day'] + eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', + '%Y-%m-%d %H:%M:%S') + + data_path = eval_time.strftime(data_hdfs_path) + files = get_hdfs_files(data_path) + # files = files[::10] + self.train_itor.is_test = True + self.train_itor.setfiles(','.join(files)) + print('start eval', eval_time, data_path) + self.eval(self.train_itor, self.local_db) + self.local_db.terminate() + + # TODO support accctr + if self.mode == 'AucDiff': + data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' + eval_day = self.data_conf['eval_day'] + eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', + '%Y-%m-%d %H:%M:%S') + + data_path = eval_time.strftime(data_hdfs_path) + files = get_hdfs_files(data_path) + # files = files[::10] + self.train_itor.is_test = True + self.train_itor.setfiles(','.join(files)) + print('start check', eval_time, data_path) + self.aucdiff_run(self.train_itor, self.local_db) + self.local_db.terminate() + + # TODO support accctr + if self.mode == 'Check': + data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' + eval_day = self.data_conf['eval_day'] + eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', + '%Y-%m-%d %H:%M:%S') + + data_path = eval_time.strftime(data_hdfs_path) + files = get_hdfs_files(data_path) + self.train_itor.is_test = True + self.train_itor.setfiles(','.join(files)) + print('start check', eval_time, data_path) + + total_metrics = self.check_day(self.train_itor, self.local_db) + self.local_db.terminate() diff --git a/easy_rec/python/model/keep_model/dnn.py b/easy_rec/python/model/keep_model/dnn.py index 5f3e83719..e3c38efff 100644 --- a/easy_rec/python/model/keep_model/dnn.py +++ b/easy_rec/python/model/keep_model/dnn.py @@ -1,133 +1,168 @@ import tensorflow as tf from tensorflow.keras import backend as K +from tensorflow.keras.initializers import Zeros +from tensorflow.keras.initializers import glorot_normal from tensorflow.keras.layers import Layer -from tensorflow.keras.initializers import Zeros, glorot_normal from tensorflow.keras.regularizers import l2 + def activation_layer(activation): - if isinstance(activation, str): - act_layer = tf.keras.layers.Activation(activation) - elif issubclass(activation, Layer): - act_layer = activation() - else: - raise ValueError( - "Invalid activation,found %s.You should use a str or a Activation Layer Class." % (activation)) - return act_layer + if isinstance(activation, str): + act_layer = tf.keras.layers.Activation(activation) + elif issubclass(activation, Layer): + act_layer = activation() + else: + raise ValueError( + 'Invalid activation,found %s.You should use a str or a Activation Layer Class.' + % (activation)) + return act_layer + class DNN(Layer): - """The Multi Layer Percetron - - Input shape - - nD tensor with shape: ``(batch_size, ..., input_dim)``. The most common situation would be a 2D input with shape ``(batch_size, input_dim)``. - - Output shape - - nD tensor with shape: ``(batch_size, ..., hidden_size[-1])``. For instance, for a 2D input with shape ``(batch_size, input_dim)``, the output would have shape ``(batch_size, hidden_size[-1])``. - - Arguments - - **hidden_units**:list of positive integer, the layer number and units in each layer. - - - **activation**: Activation function to use. - - - **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix. - - - **dropout_rate**: float in [0,1). Fraction of the units to dropout. - - - **use_bn**: bool. Whether use BatchNormalization before activation or not. - - - **output_activation**: Activation function to use in the last layer.If ``None``,it will be same as ``activation``. - - - **seed**: A Python integer to use as random seed. - """ - - def __init__(self, hidden_units, activation='relu', l2_reg=0, dropout_rate=0, use_bn=False, output_activation=None, need_ppnet=False, pp_size=0, - seed=1024, **kwargs): - self.hidden_units = hidden_units - self.activation = activation - self.l2_reg = l2_reg - self.dropout_rate = dropout_rate - self.use_bn = use_bn - self.output_activation = output_activation - self.need_ppnet = need_ppnet - self.pp_size = pp_size - self.seed = seed - - super(DNN, self).__init__(**kwargs) - - def build(self, input_shape): - # if len(self.hidden_units) == 0: - # raise ValueError("hidden_units is empty") - input_size = input_shape[-1] - if self.need_ppnet: - input_size -= self.pp_size - hidden_units = [int(input_size)] + list(self.hidden_units) - self.kernels = [self.add_weight(name='kernel' + str(i), - shape=( - hidden_units[i], hidden_units[i + 1]), - initializer=glorot_normal( - seed=self.seed), - regularizer=l2(self.l2_reg), - trainable=True) for i in range(len(self.hidden_units))] - self.bias = [self.add_weight(name='bias' + str(i), - shape=(self.hidden_units[i],), - initializer=Zeros(), - trainable=True) for i in range(len(self.hidden_units))] - if self.use_bn: - self.bn_layers = [tf.keras.layers.BatchNormalization() for _ in range(len(self.hidden_units))] - if self.need_ppnet: - self.pp_gates = [DNN([size, size], activation='relu', use_bn=False, - output_activation='sigmoid') for size in hidden_units[:-1]] - - self.dropout_layers = [tf.keras.layers.Dropout(self.dropout_rate, seed=self.seed + i) for i in - range(len(self.hidden_units))] - - self.activation_layers = [activation_layer(self.activation) for _ in range(len(self.hidden_units) - 1)] - - if self.output_activation: - self.activation_layers += [activation_layer(self.output_activation)] - else: - self.activation_layers += [activation_layer(self.activation)] - - super(DNN, self).build(input_shape) # Be sure to call this somewhere! - - def call(self, inputs, training=None, **kwargs): - - deep_input = inputs - if self.need_ppnet: - deep_input = inputs[..., self.pp_size:] - pp_input = inputs[..., :self.pp_size] - #deep_input_wo_bp = tf.stop_gradient(deep_input) - #pp_input = tf.concat([pp_input, deep_input_wo_bp], axis=-1) - - for i in range(len(self.hidden_units)): - if self.need_ppnet: - deep_input = 2*self.pp_gates[i](pp_input) * deep_input - fc = tf.nn.bias_add(tf.tensordot( - deep_input, self.kernels[i], axes=(-1, 0)), self.bias[i]) - - if self.use_bn: - fc = self.bn_layers[i](fc, training=training) - try: - fc = self.activation_layers[i](fc, training=training) - except TypeError as e: # TypeError: call() got an unexpected keyword argument 'training' - print("make sure the activation function use training flag properly", e) - fc = self.activation_layers[i](fc) - - fc = self.dropout_layers[i](fc, training=training) - deep_input = fc - - return deep_input - - def compute_output_shape(self, input_shape): - if len(self.hidden_units) > 0: - shape = input_shape[:-1] + (self.hidden_units[-1],) - else: - shape = input_shape - - return tuple(shape) - - def get_config(self, ): - config = {'activation': self.activation, 'hidden_units': self.hidden_units, - 'l2_reg': self.l2_reg, 'use_bn': self.use_bn, 'dropout_rate': self.dropout_rate, - 'output_activation': self.output_activation, 'seed': self.seed} - base_config = super(DNN, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + """The Multi Layer Percetron. + + Input shape + - nD tensor with shape: ``(batch_size, ..., input_dim)``. The most common situation would be a 2D input with shape ``(batch_size, input_dim)``. + + Output shape + - nD tensor with shape: ``(batch_size, ..., hidden_size[-1])``. For instance, for a 2D input with shape ``(batch_size, input_dim)``, the output would have shape ``(batch_size, hidden_size[-1])``. + + Arguments + - **hidden_units**:list of positive integer, the layer number and units in each layer. + + - **activation**: Activation function to use. + + - **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix. + + - **dropout_rate**: float in [0,1). Fraction of the units to dropout. + + - **use_bn**: bool. Whether use BatchNormalization before activation or not. + + - **output_activation**: Activation function to use in the last layer.If ``None``,it will be same as ``activation``. + + - **seed**: A Python integer to use as random seed. + """ + + def __init__(self, + hidden_units, + activation='relu', + l2_reg=0, + dropout_rate=0, + use_bn=False, + output_activation=None, + need_ppnet=False, + pp_size=0, + seed=1024, + **kwargs): + self.hidden_units = hidden_units + self.activation = activation + self.l2_reg = l2_reg + self.dropout_rate = dropout_rate + self.use_bn = use_bn + self.output_activation = output_activation + self.need_ppnet = need_ppnet + self.pp_size = pp_size + self.seed = seed + + super(DNN, self).__init__(**kwargs) + + def build(self, input_shape): + # if len(self.hidden_units) == 0: + # raise ValueError("hidden_units is empty") + input_size = input_shape[-1] + if self.need_ppnet: + input_size -= self.pp_size + hidden_units = [int(input_size)] + list(self.hidden_units) + self.kernels = [ + self.add_weight( + name='kernel' + str(i), + shape=(hidden_units[i], hidden_units[i + 1]), + initializer=glorot_normal(seed=self.seed), + regularizer=l2(self.l2_reg), + trainable=True) for i in range(len(self.hidden_units)) + ] + self.bias = [ + self.add_weight( + name='bias' + str(i), + shape=(self.hidden_units[i],), + initializer=Zeros(), + trainable=True) for i in range(len(self.hidden_units)) + ] + if self.use_bn: + self.bn_layers = [ + tf.keras.layers.BatchNormalization() + for _ in range(len(self.hidden_units)) + ] + if self.need_ppnet: + self.pp_gates = [ + DNN([size, size], + activation='relu', + use_bn=False, + output_activation='sigmoid') for size in hidden_units[:-1] + ] + + self.dropout_layers = [ + tf.keras.layers.Dropout(self.dropout_rate, seed=self.seed + i) + for i in range(len(self.hidden_units)) + ] + + self.activation_layers = [ + activation_layer(self.activation) + for _ in range(len(self.hidden_units) - 1) + ] + + if self.output_activation: + self.activation_layers += [activation_layer(self.output_activation)] + else: + self.activation_layers += [activation_layer(self.activation)] + + super(DNN, self).build(input_shape) # Be sure to call this somewhere! + + def call(self, inputs, training=None, **kwargs): + + deep_input = inputs + if self.need_ppnet: + deep_input = inputs[..., self.pp_size:] + pp_input = inputs[..., :self.pp_size] + #deep_input_wo_bp = tf.stop_gradient(deep_input) + #pp_input = tf.concat([pp_input, deep_input_wo_bp], axis=-1) + + for i in range(len(self.hidden_units)): + if self.need_ppnet: + deep_input = 2 * self.pp_gates[i](pp_input) * deep_input + fc = tf.nn.bias_add( + tf.tensordot(deep_input, self.kernels[i], axes=(-1, 0)), self.bias[i]) + + if self.use_bn: + fc = self.bn_layers[i](fc, training=training) + try: + fc = self.activation_layers[i](fc, training=training) + except TypeError as e: # TypeError: call() got an unexpected keyword argument 'training' + print('make sure the activation function use training flag properly', e) + fc = self.activation_layers[i](fc) + + fc = self.dropout_layers[i](fc, training=training) + deep_input = fc + + return deep_input + + def compute_output_shape(self, input_shape): + if len(self.hidden_units) > 0: + shape = input_shape[:-1] + (self.hidden_units[-1],) + else: + shape = input_shape + + return tuple(shape) + + def get_config(self,): + config = { + 'activation': self.activation, + 'hidden_units': self.hidden_units, + 'l2_reg': self.l2_reg, + 'use_bn': self.use_bn, + 'dropout_rate': self.dropout_rate, + 'output_activation': self.output_activation, + 'seed': self.seed + } + base_config = super(DNN, self).get_config() + return dict(list(base_config.items()) + list(config.items())) diff --git a/easy_rec/python/model/keep_model/model_ps_mmoe.py b/easy_rec/python/model/keep_model/model_ps_mmoe.py index 0d1bb8746..274a928b6 100644 --- a/easy_rec/python/model/keep_model/model_ps_mmoe.py +++ b/easy_rec/python/model/keep_model/model_ps_mmoe.py @@ -1,62 +1,89 @@ #!/usr/bin/env python # encoding: utf-8 -""" -MMOE model -""" +"""MMOE model.""" import tensorflow as tf -from tensorflow.keras.layers import Dense, Activation, BatchNormalization, Multiply +from tensorflow.keras.layers import Activation +from tensorflow.keras.layers import BatchNormalization +from tensorflow.keras.layers import Dense +from tensorflow.keras.layers import Multiply from tensorflow.keras.models import Model + # from utils.basic_utils import * # from model.base_model_ps import BaseModel from easy_rec.python.model.keep_model.dnn import DNN from easy_rec.python.model.keep_model.senet import SENETLayer + class CustomizedModel(Model): - def __init__(self, model_conf, indim, name='export_model'): - super(CustomizedModel, self).__init__(name=name) - self.label_conf_list = [conf for conf in model_conf['label'] if conf.get('weight', 1) > 0] - self.experts = model_conf['experts'] - self.expert_layers = model_conf['expert_layers'] - self.tower_layers = model_conf['tower_layers'] - self.need_senet = model_conf.get('need_senet', False) - if self.need_senet: - self.field_size = model_conf['field_size'] - self.se_layer = SENETLayer(self.field_size) - self.need_ppnet = model_conf.get('need_ppnet', False) - self.pp_feature_cnt = model_conf.get('pp_feature_cnt', 0) - embed_size = 11 - # self._set_inputs(tf.TensorSpec([None, indim], tf.float32, name='embed_input')) + def __init__(self, model_conf, indim, name='export_model'): + super(CustomizedModel, self).__init__(name=name) + self.label_conf_list = [ + conf for conf in model_conf['label'] if conf.get('weight', 1) > 0 + ] + self.experts = model_conf['experts'] + self.expert_layers = model_conf['expert_layers'] + self.tower_layers = model_conf['tower_layers'] + self.need_senet = model_conf.get('need_senet', False) + if self.need_senet: + self.field_size = model_conf['field_size'] + self.se_layer = SENETLayer(self.field_size) + self.need_ppnet = model_conf.get('need_ppnet', False) + self.pp_feature_cnt = model_conf.get('pp_feature_cnt', 0) + embed_size = 11 - self.expert_list = [DNN(self.expert_layers, activation='relu', use_bn=True, output_activation='relu', need_ppnet=self.need_ppnet, pp_size=self.pp_feature_cnt*embed_size) for _ in range(self.experts)] + # self._set_inputs(tf.TensorSpec([None, indim], tf.float32, name='embed_input')) - self.tower_list = [DNN(self.tower_layers, activation='relu', use_bn=True, output_activation='sigmoid', need_ppnet=self.need_ppnet, pp_size=self.pp_feature_cnt*embed_size) for _ in self.label_conf_list] + self.expert_list = [ + DNN(self.expert_layers, + activation='relu', + use_bn=True, + output_activation='relu', + need_ppnet=self.need_ppnet, + pp_size=self.pp_feature_cnt * embed_size) + for _ in range(self.experts) + ] - self.gate_list = [DNN([self.experts], activation='relu', use_bn=False, output_activation='linear') for _ in self.label_conf_list] + self.tower_list = [ + DNN(self.tower_layers, + activation='relu', + use_bn=True, + output_activation='sigmoid', + need_ppnet=self.need_ppnet, + pp_size=self.pp_feature_cnt * embed_size) + for _ in self.label_conf_list + ] + self.gate_list = [ + DNN([self.experts], + activation='relu', + use_bn=False, + output_activation='linear') for _ in self.label_conf_list + ] - def call(self, embed_input, training=False): - if self.need_senet: - embed_input = self.se_layer(embed_input) - expert_list = [] - for expert_layer in self.expert_list: - # (N, 1, d1) - expert = tf.expand_dims(expert_layer(embed_input), axis=1) - expert_list.append(expert) - # (N, n, d1) - concat_expert = tf.concat(expert_list, axis=1) + def call(self, embed_input, training=False): + if self.need_senet: + embed_input = self.se_layer(embed_input) + expert_list = [] + for expert_layer in self.expert_list: + # (N, 1, d1) + expert = tf.expand_dims(expert_layer(embed_input), axis=1) + expert_list.append(expert) + # (N, n, d1) + concat_expert = tf.concat(expert_list, axis=1) - output_list = [] - for i, label_conf in enumerate(self.label_conf_list): - # (N, n, 1) - gate = tf.expand_dims(tf.nn.softmax(self.gate_list[i](embed_input)), axis=2) - # (N, d1) - output = tf.reduce_sum(concat_expert * gate, axis=1) - # (N, 1) with softmax - output = self.tower_list[i](output) - output_list.append(output) + output_list = [] + for i, label_conf in enumerate(self.label_conf_list): + # (N, n, 1) + gate = tf.expand_dims( + tf.nn.softmax(self.gate_list[i](embed_input)), axis=2) + # (N, d1) + output = tf.reduce_sum(concat_expert * gate, axis=1) + # (N, 1) with softmax + output = self.tower_list[i](output) + output_list.append(output) - return output_list + return output_list # class ExpModel(BaseModel): @@ -67,7 +94,7 @@ def call(self, embed_input, training=False): # :param embed_share: 0 全独立 1 field内共享 2 全共享 # """ # super(ExpModel, self).__init__(conf) -# +# # def get_export_model_classs(self): # return CustomizedModel -# +# diff --git a/easy_rec/python/model/keep_model/senet.py b/easy_rec/python/model/keep_model/senet.py index 19b7288c8..e48e3328e 100644 --- a/easy_rec/python/model/keep_model/senet.py +++ b/easy_rec/python/model/keep_model/senet.py @@ -1,4 +1,5 @@ import math + import tensorflow as tf from tensorflow.python.keras import backend as K from tensorflow.python.keras.initializers import glorot_normal @@ -6,93 +7,106 @@ class SENETLayer(Layer): - """SENETLayer used in FiBiNET. - Input shape - - tensor with shape: ``(batch_size,field_size*embedding_size)``. - Output shape - - tensor with shape: ``(batch_size,field_size*embedding_size)``. - Arguments - - **reduction_ratio** : Positive integer, dimensionality of the - attention network output space. - - **seed** : A Python integer to use as random seed. - - **norm** : norm function to norm attention factor, l2 or softmax. - - **last_activation** : last act function, relu or identity. - - **field_size** : feature field number. - References - - [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) - """ - - def __init__(self, field_size, norm='l2', last_activation='identity', reduction_ratio=3, seed=1024, **kwargs): - self.field_size = field_size - self.norm = norm - self.last_activation = last_activation - self.reduction_ratio = reduction_ratio - self.seed = seed - super(SENETLayer, self).__init__(**kwargs) - - def tensordot(self, x, y): - return tf.tensordot(x, y, axes=(-1, 0)) - - def build(self, input_shape): - - if len(input_shape) != 2: - raise ValueError('A `SENETLayer` layer should be called ' - 'on a 2D tensor.') - - # if not divisible, raise an exception - if input_shape[1] % self.field_size != 0: - raise ValueError(f'Embed input size must be divisible by field_size, got {input_shape[1]} and {self.field_size}') - self.embedding_size = int(input_shape[1] / self.field_size) - reduction_size = max(1, self.field_size // self.reduction_ratio) - - self.W_1 = self.add_weight(shape=( - self.field_size, reduction_size), - initializer=glorot_normal(seed=self.seed), name="W1", - trainable=True) - self.W_2 = self.add_weight(shape=( - reduction_size, self.field_size), - initializer=glorot_normal(seed=self.seed), name="W2", - trainable=True) - - # zty note:Lambda layer may cause variable not automatically - # added to the variable set for gradient calculation - # DO NOT use Lambda layer if possible. - #self.tensordot = tf.keras.layers.Lambda( - # lambda x: tf.tensordot(x[0], x[1], axes=(-1, 0))) - - # Be sure to call this somewhere! - super(SENETLayer, self).build(input_shape) - - def call(self, inputs, training=None, **kwargs): - - if K.ndim(inputs) != 2: - raise ValueError( - "Unexpected inputs dimensions %d, expect to be 2 dimensions" % (K.ndim(inputs))) - - inputs = tf.reshape(inputs, [-1, self.field_size, self.embedding_size]) - Z = tf.reduce_mean(inputs, axis=-1) - - A_1 = tf.nn.relu(self.tensordot(Z, self.W_1)) - A_2 = self.tensordot(A_1, self.W_2) - if self.last_activation == 'relu': - A_2 = tf.nn.relu(A_2) - - A_2 = tf.nn.l2_normalize(A_2, axis=1) if self.norm == 'l2' else tf.nn.softmax(A_2, axis=1) - A_2 = A_2 * math.sqrt(self.field_size) - output = tf.multiply(inputs, tf.expand_dims(A_2, axis=2)) - output = tf.reshape(output, [-1, self.field_size * self.embedding_size]) - - return output - - def compute_output_shape(self, input_shape): - - return input_shape - - def compute_mask(self, inputs, mask=None): - return [None] * self.field_size - - def get_config(self, ): - config = {'reduction_ratio': self.reduction_ratio, 'seed': self.seed} - base_config = super(SENETLayer, self).get_config() - base_config.update(config) - return base_config + """SENETLayer used in FiBiNET. Input shape. + + - tensor with shape: ``(batch_size,field_size*embedding_size)``. + Output shape + - tensor with shape: ``(batch_size,field_size*embedding_size)``. + Arguments + - **reduction_ratio** : Positive integer, dimensionality of the + attention network output space. + - **seed** : A Python integer to use as random seed. + - **norm** : norm function to norm attention factor, l2 or softmax. + - **last_activation** : last act function, relu or identity. + - **field_size** : feature field number. + References + - [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) + """ + + def __init__(self, + field_size, + norm='l2', + last_activation='identity', + reduction_ratio=3, + seed=1024, + **kwargs): + self.field_size = field_size + self.norm = norm + self.last_activation = last_activation + self.reduction_ratio = reduction_ratio + self.seed = seed + super(SENETLayer, self).__init__(**kwargs) + + def tensordot(self, x, y): + return tf.tensordot(x, y, axes=(-1, 0)) + + def build(self, input_shape): + + if len(input_shape) != 2: + raise ValueError('A `SENETLayer` layer should be called ' + 'on a 2D tensor.') + + # if not divisible, raise an exception + if input_shape[1] % self.field_size != 0: + raise ValueError( + f'Embed input size must be divisible by field_size, got {input_shape[1]} and {self.field_size}' + ) + self.embedding_size = int(input_shape[1] / self.field_size) + reduction_size = max(1, self.field_size // self.reduction_ratio) + + self.W_1 = self.add_weight( + shape=(self.field_size, reduction_size), + initializer=glorot_normal(seed=self.seed), + name='W1', + trainable=True) + self.W_2 = self.add_weight( + shape=(reduction_size, self.field_size), + initializer=glorot_normal(seed=self.seed), + name='W2', + trainable=True) + + # zty note:Lambda layer may cause variable not automatically + # added to the variable set for gradient calculation + # DO NOT use Lambda layer if possible. + #self.tensordot = tf.keras.layers.Lambda( + # lambda x: tf.tensordot(x[0], x[1], axes=(-1, 0))) + + # Be sure to call this somewhere! + super(SENETLayer, self).build(input_shape) + + def call(self, inputs, training=None, **kwargs): + + if K.ndim(inputs) != 2: + raise ValueError( + 'Unexpected inputs dimensions %d, expect to be 2 dimensions' % + (K.ndim(inputs))) + + inputs = tf.reshape(inputs, [-1, self.field_size, self.embedding_size]) + Z = tf.reduce_mean(inputs, axis=-1) + + A_1 = tf.nn.relu(self.tensordot(Z, self.W_1)) + A_2 = self.tensordot(A_1, self.W_2) + if self.last_activation == 'relu': + A_2 = tf.nn.relu(A_2) + + A_2 = tf.nn.l2_normalize( + A_2, axis=1) if self.norm == 'l2' else tf.nn.softmax( + A_2, axis=1) + A_2 = A_2 * math.sqrt(self.field_size) + output = tf.multiply(inputs, tf.expand_dims(A_2, axis=2)) + output = tf.reshape(output, [-1, self.field_size * self.embedding_size]) + + return output + + def compute_output_shape(self, input_shape): + + return input_shape + + def compute_mask(self, inputs, mask=None): + return [None] * self.field_size + + def get_config(self,): + config = {'reduction_ratio': self.reduction_ratio, 'seed': self.seed} + base_config = super(SENETLayer, self).get_config() + base_config.update(config) + return base_config diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index 3e5fdcd2d..8af1e44b8 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -1,15 +1,16 @@ # -*- encoding:utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. +import json +import logging + import tensorflow as tf from easy_rec.python.layers import dnn from easy_rec.python.layers import mmoe +from easy_rec.python.model.keep_model.model_ps_mmoe import CustomizedModel from easy_rec.python.model.multi_task_model import MultiTaskModel from easy_rec.python.model.rank_model import RankModel from easy_rec.python.protos.ppnet_pb2 import PPNet as PPNetConfig -from easy_rec.python.model.keep_model.model_ps_mmoe import CustomizedModel -import json -import logging if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -23,8 +24,8 @@ def __init__(self, features, labels=None, is_training=False): - super(PPNetV3M, self).__init__(model_config, feature_configs, features, labels, - is_training) + super(PPNetV3M, self).__init__(model_config, feature_configs, features, + labels, is_training) assert self._model_config.WhichOneof('model') == 'ppnet', \ 'invalid model config: %s' % self._model_config.WhichOneof('model') self._model_config = self._model_config.ppnet @@ -43,7 +44,7 @@ def build_predict_graph(self): output_list = self._keras_model(self._features) for lbl_id in range(len(self._model_conf['label'])): lbl_info = self._model_conf['label'][lbl_id] - lbl_name = lbl_info.get('input_name') + lbl_name = lbl_info.get('input_name') output = output_list[lbl_id] self._prediction_dict[lbl_name] = output # return self._prediction_dict @@ -53,7 +54,7 @@ def build_loss_graph(self): lbl_info = self._model_conf['label'][lbl_id] lbl_name = lbl_info.get('input_name') output = self._prediction_dict.get(lbl_name) - loss_obj = tf.keras.losses.BinaryCrossentropy()(self._labels[lbl_name], output) + loss_obj = tf.keras.losses.BinaryCrossentropy()(self._labels[lbl_name], + output) self._loss_dict[lbl_name] = loss_obj return self._loss_dict - diff --git a/easy_rec/python/protos/easy_rec_model.proto b/easy_rec/python/protos/easy_rec_model.proto index ff74a0c48..629e03191 100644 --- a/easy_rec/python/protos/easy_rec_model.proto +++ b/easy_rec/python/protos/easy_rec_model.proto @@ -93,7 +93,7 @@ message EasyRecModel { PLE ple = 305; RocketLaunching rocket_launching = 401; - + PPNet ppnet = 402; } repeated SeqAttGroupConfig seq_att_groups = 7; diff --git a/keep_model.config b/keep_model.config index f11d582e5..8bec3a8c9 100644 --- a/keep_model.config +++ b/keep_model.config @@ -1,10 +1,11 @@ -train_input_path: 'test.parquet' +train_input_path: 'test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet' eval_input_path: 'test.parquet' model_dir: "experiments/keep_model/" train_config { optimizer_config: { - adam_optimizer: { + adagrad_optimizer: { + # adam_optimizer: { learning_rate: { constant_learning_rate { learning_rate: 0.001 @@ -12,18 +13,19 @@ train_config { } } } - log_step_count_steps: 1 + log_step_count_steps: 5 save_checkpoints_steps: 100000 sync_replicas: True + # is_profiling: True } data_config { - batch_size: 100 + batch_size: 8192 label_fields: 'is_click' - label_fields: 'is_train' + label_fields: 'is_train' num_epochs: 1 - prefetch_size: 16 + prefetch_size: 32 # input_type: DummyInput input_type: ParquetInput @@ -32,7 +34,7 @@ data_config { input_fields { input_name: 'is_click' input_type: INT64 - } + } input_fields { input_name: 'is_train' input_type: INT64 @@ -926,7 +928,7 @@ feature_config { input_names: 'user_id' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -934,7 +936,7 @@ feature_config { input_names: 'item_id' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -942,7 +944,7 @@ feature_config { input_names: 'age' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -950,7 +952,7 @@ feature_config { input_names: 'height' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -958,7 +960,7 @@ feature_config { input_names: 'weight' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -966,7 +968,7 @@ feature_config { input_names: 'gender' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -974,7 +976,7 @@ feature_config { input_names: 'vip_status' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -982,7 +984,7 @@ feature_config { input_names: 'register_goals' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -990,7 +992,7 @@ feature_config { input_names: 'kg' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -998,7 +1000,7 @@ feature_config { input_names: 'bmi' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1006,7 +1008,7 @@ feature_config { input_names: 'training_duration_total' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1014,7 +1016,7 @@ feature_config { input_names: 'training_calorie_total' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1022,7 +1024,7 @@ feature_config { input_names: 'training_days_total' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1030,7 +1032,7 @@ feature_config { input_names: 'all_calorie_total' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1038,7 +1040,7 @@ feature_config { input_names: 'running_times_total' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1046,7 +1048,7 @@ feature_config { input_names: 'hiking_times_total' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1054,7 +1056,7 @@ feature_config { input_names: 'tags' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1062,7 +1064,7 @@ feature_config { input_names: 'trainingpoints' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1070,7 +1072,7 @@ feature_config { input_names: 'his_no_month_no' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1078,7 +1080,7 @@ feature_config { input_names: 'his_no_month_yes' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1086,7 +1088,7 @@ feature_config { input_names: 'his_yes_month_no' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1094,7 +1096,7 @@ feature_config { input_names: 'his_yes_month_yes' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1102,7 +1104,7 @@ feature_config { input_names: 'new_7days' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1110,7 +1112,7 @@ feature_config { input_names: 'is_meditation_ru' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1118,7 +1120,7 @@ feature_config { input_names: 'is_skipping_ru' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1126,7 +1128,7 @@ feature_config { input_names: 'is_buyer_ru' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1134,7 +1136,7 @@ feature_config { input_names: 'network' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1142,7 +1144,7 @@ feature_config { input_names: 'c_os' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1150,7 +1152,7 @@ feature_config { input_names: 'recent_plan_list' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1158,7 +1160,7 @@ feature_config { input_names: 'preference' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1166,7 +1168,7 @@ feature_config { input_names: 'interested_types' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1174,7 +1176,7 @@ feature_config { input_names: 'sub_target' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1182,7 +1184,7 @@ feature_config { input_names: 'u_last_train_trainingType' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1190,7 +1192,7 @@ feature_config { input_names: 'u_last_train_subtype' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1198,7 +1200,7 @@ feature_config { input_names: 'u_last_train_dataType' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1206,7 +1208,7 @@ feature_config { input_names: 'author' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1214,7 +1216,7 @@ feature_config { input_names: 'plan_difficulty' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1222,7 +1224,7 @@ feature_config { input_names: 'calorie' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1230,7 +1232,7 @@ feature_config { input_names: 'average_duration' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1238,7 +1240,7 @@ feature_config { input_names: 'official' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1246,7 +1248,7 @@ feature_config { input_names: 'paid_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1254,7 +1256,7 @@ feature_config { input_names: 'course_score' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1262,7 +1264,7 @@ feature_config { input_names: 'target' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1270,7 +1272,7 @@ feature_config { input_names: 'equipment' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1278,7 +1280,7 @@ feature_config { input_names: 'data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1286,7 +1288,7 @@ feature_config { input_names: 'label_ids' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1294,7 +1296,7 @@ feature_config { input_names: 'u_plan_click_list_int_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1302,7 +1304,7 @@ feature_config { input_names: 'u_plan_click_list_int_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1310,7 +1312,7 @@ feature_config { input_names: 'u_plan_click_list_int_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1318,7 +1320,7 @@ feature_config { input_names: 'u_plan_homepage8_click_list_int_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1326,7 +1328,7 @@ feature_config { input_names: 'u_plan_homepage8_click_list_int_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1334,7 +1336,7 @@ feature_config { input_names: 'u_plan_homepage8_click_list_int_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1342,7 +1344,7 @@ feature_config { input_names: 'u_plan_homepage8_click_list_int_1' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1350,7 +1352,7 @@ feature_config { input_names: 'u_plan_train_list_int_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1358,7 +1360,7 @@ feature_config { input_names: 'u_plan_train_list_int_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1366,7 +1368,7 @@ feature_config { input_names: 'u_plan_train_list_int_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1374,7 +1376,7 @@ feature_config { input_names: 'u_plan_homepage8_train_list_int_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1382,7 +1384,7 @@ feature_config { input_names: 'u_plan_homepage8_train_list_int_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1390,7 +1392,7 @@ feature_config { input_names: 'u_plan_homepage8_train_list_int_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1398,7 +1400,7 @@ feature_config { input_names: 'u_plan_homepage8_train_list_int_1' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1406,7 +1408,7 @@ feature_config { input_names: 'u_plan_complete_list_int_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1414,7 +1416,7 @@ feature_config { input_names: 'u_plan_complete_list_int_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1422,7 +1424,7 @@ feature_config { input_names: 'u_plan_homepage8_complete_list_int_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1430,7 +1432,7 @@ feature_config { input_names: 'u_plan_homepage8_complete_list_int_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1438,7 +1440,7 @@ feature_config { input_names: 'u_plan_homepage8_complete_list_int_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1446,7 +1448,7 @@ feature_config { input_names: 'u_plan_homepage8_complete_list_int_1' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1454,7 +1456,7 @@ feature_config { input_names: 'u_train_label_list_50' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1462,7 +1464,7 @@ feature_config { input_names: 'u_train_label_list_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1470,7 +1472,7 @@ feature_config { input_names: 'u_train_label_list_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1478,7 +1480,7 @@ feature_config { input_names: 'u_train_trainingType_list_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1486,7 +1488,7 @@ feature_config { input_names: 'u_train_subtype_list_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1494,7 +1496,7 @@ feature_config { input_names: 'u_train_dataType_list_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1502,7 +1504,7 @@ feature_config { input_names: 'c_recall_sources' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1510,7 +1512,7 @@ feature_config { input_names: 'u_plan_unclick_list_int_50' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1518,7 +1520,7 @@ feature_config { input_names: 'u_plan_unclick_list_int_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1526,7 +1528,7 @@ feature_config { input_names: 'u_plan_unclick_list_int_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1534,7 +1536,7 @@ feature_config { input_names: 'u_plan_click_list_author_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1542,7 +1544,7 @@ feature_config { input_names: 'u_plan_click_list_author_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1550,7 +1552,7 @@ feature_config { input_names: 'u_plan_click_list_author_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1558,7 +1560,7 @@ feature_config { input_names: 'u_plan_click_list_dataType_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1566,7 +1568,7 @@ feature_config { input_names: 'u_plan_click_list_dataType_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1574,7 +1576,7 @@ feature_config { input_names: 'u_plan_click_list_dataType_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1582,7 +1584,7 @@ feature_config { input_names: 'u_plan_click_list_difficulty_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1590,7 +1592,7 @@ feature_config { input_names: 'u_plan_click_list_difficulty_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1598,7 +1600,7 @@ feature_config { input_names: 'u_plan_click_list_difficulty_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1606,7 +1608,7 @@ feature_config { input_names: 'u_plan_click_list_official_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1614,7 +1616,7 @@ feature_config { input_names: 'u_plan_complete_list_author_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1622,7 +1624,7 @@ feature_config { input_names: 'u_plan_complete_list_author_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1630,7 +1632,7 @@ feature_config { input_names: 'u_plan_complete_list_author_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1638,7 +1640,7 @@ feature_config { input_names: 'u_plan_complete_list_dataType_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1646,7 +1648,7 @@ feature_config { input_names: 'u_plan_complete_list_dataType_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1654,7 +1656,7 @@ feature_config { input_names: 'u_plan_complete_list_difficulty_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1662,7 +1664,7 @@ feature_config { input_names: 'u_plan_complete_list_difficulty_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1670,7 +1672,7 @@ feature_config { input_names: 'u_plan_complete_list_official_100' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1678,7 +1680,7 @@ feature_config { input_names: 'u_plan_complete_list_official_10' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1686,7 +1688,7 @@ feature_config { input_names: 'u_plan_complete_list_official_3' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1694,7 +1696,7 @@ feature_config { input_names: 'training_goals_show_year_0' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1702,7 +1704,7 @@ feature_config { input_names: 'training_goals_show_year_1' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1710,7 +1712,7 @@ feature_config { input_names: 'training_goals_show_year_2' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1718,7 +1720,7 @@ feature_config { input_names: 'training_goals_show_year_4' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1726,7 +1728,7 @@ feature_config { input_names: 'training_goals_show_year_6' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1734,7 +1736,7 @@ feature_config { input_names: 'training_goals_show_year_8' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1742,7 +1744,7 @@ feature_config { input_names: 'trainingpoints_show_year_0' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1750,7 +1752,7 @@ feature_config { input_names: 'trainingpoints_show_year_3' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1758,7 +1760,7 @@ feature_config { input_names: 'trainingpoints_show_year_4' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1766,7 +1768,7 @@ feature_config { input_names: 'trainingpoints_show_year_7' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1774,7 +1776,7 @@ feature_config { input_names: 'trainingpoints_show_year_9' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1782,7 +1784,7 @@ feature_config { input_names: 'i_plan_difficulty_show_cnt_01_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1790,7 +1792,7 @@ feature_config { input_names: 'i_plan_difficulty_show_cnt_07_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1798,7 +1800,7 @@ feature_config { input_names: 'i_plan_difficulty_show_cnt_14_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1806,7 +1808,7 @@ feature_config { input_names: 'i_plan_difficulty_show_cnt_28_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1814,7 +1816,7 @@ feature_config { input_names: 'i_plan_difficulty_click_cnt_28_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1822,7 +1824,7 @@ feature_config { input_names: 'i_plan_difficulty_train_cnt_28_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1830,7 +1832,7 @@ feature_config { input_names: 'i_duration_seg_show_cnt_01_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1838,7 +1840,7 @@ feature_config { input_names: 'i_duration_seg_click_cnt_01_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1846,7 +1848,7 @@ feature_config { input_names: 'i_duration_seg_show_cnt_07_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1854,7 +1856,7 @@ feature_config { input_names: 'i_duration_seg_click_cnt_07_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1862,7 +1864,7 @@ feature_config { input_names: 'i_duration_seg_show_cnt_14_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1870,7 +1872,7 @@ feature_config { input_names: 'i_duration_seg_click_cnt_14_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1878,7 +1880,7 @@ feature_config { input_names: 'i_duration_seg_show_cnt_28_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1886,7 +1888,7 @@ feature_config { input_names: 'i_duration_seg_click_cnt_28_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1894,7 +1896,7 @@ feature_config { input_names: 'i_duration_seg_train_cnt_28_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1902,7 +1904,7 @@ feature_config { input_names: 'u_age_seg_train_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1910,7 +1912,7 @@ feature_config { input_names: 'u_bmi_seg_train_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1918,7 +1920,7 @@ feature_config { input_names: 'u_difficulty_train_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1926,7 +1928,7 @@ feature_config { input_names: 'u_gender_train_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1934,7 +1936,7 @@ feature_config { input_names: 'u_age_seg_show_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1942,7 +1944,7 @@ feature_config { input_names: 'u_age_seg_click_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1950,7 +1952,7 @@ feature_config { input_names: 'u_age_seg_complete_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1958,7 +1960,7 @@ feature_config { input_names: 'u_bmi_seg_show_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1966,7 +1968,7 @@ feature_config { input_names: 'u_bmi_seg_click_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1974,7 +1976,7 @@ feature_config { input_names: 'u_bmi_seg_complete_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1982,7 +1984,7 @@ feature_config { input_names: 'u_difficulty_show_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1990,7 +1992,7 @@ feature_config { input_names: 'u_difficulty_click_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -1998,7 +2000,7 @@ feature_config { input_names: 'u_difficulty_complete_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2006,7 +2008,7 @@ feature_config { input_names: 'u_gender_show_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2014,7 +2016,7 @@ feature_config { input_names: 'u_gender_click_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2022,7 +2024,7 @@ feature_config { input_names: 'u_gender_complete_cnt' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2030,7 +2032,7 @@ feature_config { input_names: 'i_plan_difficulty_train_cvr2_28_d' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2038,7 +2040,7 @@ feature_config { input_names: 'u_age_seg_ctr' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2046,7 +2048,7 @@ feature_config { input_names: 'u_age_seg_cvr1' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2054,7 +2056,7 @@ feature_config { input_names: 'u_bmi_seg_ctr' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2062,7 +2064,7 @@ feature_config { input_names: 'u_bmi_seg_cvr1' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2070,7 +2072,7 @@ feature_config { input_names: 'u_bmi_seg_train_cvr2' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2078,7 +2080,7 @@ feature_config { input_names: 'u_difficulty_ctr' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2086,7 +2088,7 @@ feature_config { input_names: 'u_difficulty_cvr1' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2094,7 +2096,7 @@ feature_config { input_names: 'u_difficulty_train_cvr2' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2102,7 +2104,7 @@ feature_config { input_names: 'u_gender_ctr' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2110,7 +2112,7 @@ feature_config { input_names: 'u_gender_cvr1' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2118,7 +2120,7 @@ feature_config { input_names: 'i_total_click' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2126,7 +2128,7 @@ feature_config { input_names: 'i_decay1_show' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2134,7 +2136,7 @@ feature_config { input_names: 'i_decay1_click' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2142,7 +2144,7 @@ feature_config { input_names: 'i_decay1_train' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2150,7 +2152,7 @@ feature_config { input_names: 'i_decay2_show' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2158,7 +2160,7 @@ feature_config { input_names: 'i_decay2_click' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2166,7 +2168,7 @@ feature_config { input_names: 'i_decay2_train' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2174,7 +2176,7 @@ feature_config { input_names: 'i_decay3_show' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2182,7 +2184,7 @@ feature_config { input_names: 'i_decay3_click' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2190,7 +2192,7 @@ feature_config { input_names: 'i_decay3_train' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2198,7 +2200,7 @@ feature_config { input_names: 'u_last_train_distance' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2206,7 +2208,7 @@ feature_config { input_names: 'u_last_train_duration' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2214,7 +2216,7 @@ feature_config { input_names: 'u_last_train_calorie' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2222,7 +2224,7 @@ feature_config { input_names: 'u_last_train_totalSteps' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2230,7 +2232,7 @@ feature_config { input_names: 'u_last_train_averageSpeed' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2238,7 +2240,7 @@ feature_config { input_names: 'u_last_train_accumulativeUpliftedHeight' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2246,7 +2248,7 @@ feature_config { input_names: 'u_last_train_accumulativeClimbingDistance' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2254,7 +2256,7 @@ feature_config { input_names: 'u_total_train_distance' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2262,7 +2264,7 @@ feature_config { input_names: 'u_total_train_totalSteps' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2270,7 +2272,7 @@ feature_config { input_names: 'u_total_train_accumulativeClimbingDistance' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2278,7 +2280,7 @@ feature_config { input_names: 'u_plan_click_other_list_int' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2286,7 +2288,7 @@ feature_config { input_names: 'c_hour' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2294,7 +2296,7 @@ feature_config { input_names: 'content_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2302,7 +2304,7 @@ feature_config { input_names: 'i_total_click_rate_i_total_show' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2310,7 +2312,7 @@ feature_config { input_names: 'i_total_train_rate_i_total_show' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2318,7 +2320,7 @@ feature_config { input_names: 'i_decay3_train_rate_i_decay3_show' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2326,7 +2328,7 @@ feature_config { input_names: 'i_decay3_click_rate_i_decay3_show' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2334,7 +2336,7 @@ feature_config { input_names: 'gender_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2342,7 +2344,7 @@ feature_config { input_names: 'gender_cross_author' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2350,7 +2352,7 @@ feature_config { input_names: 'gender_cross_paid_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2358,7 +2360,7 @@ feature_config { input_names: 'age_seg_cross_plan_difficulty' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2366,7 +2368,7 @@ feature_config { input_names: 'age_seg_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2374,7 +2376,7 @@ feature_config { input_names: 'age_seg_cross_author' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2382,7 +2384,7 @@ feature_config { input_names: 'bmi_seg_cross_plan_difficulty' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2390,7 +2392,7 @@ feature_config { input_names: 'bmi_seg_cross_paid_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2398,7 +2400,7 @@ feature_config { input_names: 'his_no_month_no_cross_plan_difficulty' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2406,7 +2408,7 @@ feature_config { input_names: 'his_no_month_no_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2414,7 +2416,7 @@ feature_config { input_names: 'his_no_month_no_cross_author' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2422,7 +2424,7 @@ feature_config { input_names: 'his_no_month_yes_cross_plan_difficulty' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2430,7 +2432,7 @@ feature_config { input_names: 'his_no_month_yes_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2438,7 +2440,7 @@ feature_config { input_names: 'his_no_month_yes_cross_paid_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2446,7 +2448,7 @@ feature_config { input_names: 'his_yes_month_no_cross_plan_difficulty' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2454,7 +2456,7 @@ feature_config { input_names: 'his_yes_month_no_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2462,7 +2464,7 @@ feature_config { input_names: 'his_yes_month_no_cross_paid_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2470,7 +2472,7 @@ feature_config { input_names: 'his_yes_month_yes_cross_plan_difficulty' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2478,7 +2480,7 @@ feature_config { input_names: 'his_yes_month_yes_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2486,7 +2488,7 @@ feature_config { input_names: 'his_yes_month_yes_cross_paid_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2494,7 +2496,7 @@ feature_config { input_names: 'vip_status_cross_plan_difficulty' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2502,7 +2504,7 @@ feature_config { input_names: 'vip_status_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2510,7 +2512,7 @@ feature_config { input_names: 'vip_status_cross_author' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2518,7 +2520,7 @@ feature_config { input_names: 'vip_status_cross_paid_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2526,7 +2528,7 @@ feature_config { input_names: 'is_meditation_ru_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2534,7 +2536,7 @@ feature_config { input_names: 'is_skipping_ru_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2542,7 +2544,7 @@ feature_config { input_names: 'is_power_ru_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2550,7 +2552,7 @@ feature_config { input_names: 'is_yoga_ru_cross_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2558,7 +2560,7 @@ feature_config { input_names: 'is_vip_ru_cross_paid_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2566,7 +2568,7 @@ feature_config { input_names: 'is_buyer_ru_cross_paid_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2574,7 +2576,7 @@ feature_config { input_names: 'network_concat_item_id' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2582,7 +2584,7 @@ feature_config { input_names: 'network_concat_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2590,7 +2592,7 @@ feature_config { input_names: 'c_os_concat_item_id' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2598,7 +2600,7 @@ feature_config { input_names: 'c_os_concat_data_type' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2606,7 +2608,7 @@ feature_config { input_names: 'gender_concat_item_id' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2614,7 +2616,7 @@ feature_config { input_names: 'age_seg_concat_item_id' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2622,7 +2624,7 @@ feature_config { input_names: 'vip_status_concat_item_id' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2630,7 +2632,7 @@ feature_config { input_names: 'u_plan_click_list_author_hit_author' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2638,7 +2640,7 @@ feature_config { input_names: 'u_plan_click_list_int_match_u_plan_click_list_author_match_author' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2646,7 +2648,7 @@ feature_config { input_names: 'u_plan_click_list_official_hit_official' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2654,7 +2656,7 @@ feature_config { input_names: 'u_plan_click_list_int_match_u_plan_click_list_official_match_official' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2662,7 +2664,7 @@ feature_config { input_names: 'u_plan_unclick_list_int_hit_item_id_int' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2670,7 +2672,7 @@ feature_config { input_names: 'u_train_label_list_overlap_label_ids' feature_type: IdFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } @@ -2678,7 +2680,7 @@ feature_config { input_names: 'u_train_label_list_hit_label_ids' feature_type: TagFeature embedding_dim: 16 - num_buckets: 2000 + num_buckets: 1000000000 embedding_name: 'all_fea' max_partitions: 4 } diff --git a/setup.cfg b/setup.cfg index 2303ef802..b0e05ade5 100644 --- a/setup.cfg +++ b/setup.cfg @@ -10,7 +10,7 @@ multi_line_output = 7 force_single_line = true known_standard_library = setuptools known_first_party = easy_rec -known_third_party = absl,common_io,docutils,eas_prediction,future,google,graphlearn,kafka,matplotlib,numpy,oss2,pai,pandas,psutil,six,sklearn,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,yaml +known_third_party = absl,base,common_io,docutils,eas_prediction,future,google,graphlearn,kafka,layers,matplotlib,metric,numpy,oss2,pai,pandas,psutil,pyarrow,six,sklearn,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,utils,yaml no_lines_before = LOCALFOLDER default_section = THIRDPARTY skip = easy_rec/python/protos From ae74f185861ee37ea00d2d570fa4dc62deaf25f2 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Thu, 19 Oct 2023 10:24:41 +0800 Subject: [PATCH 03/67] add sok debug case2 --- .../compat/feature_column/feature_column.py | 133 +- easy_rec/python/compat/optimizers.py | 25 +- easy_rec/python/input/criteo_input.py | 17 +- easy_rec/python/input/dummy_input.py | 34 +- easy_rec/python/input/parquet_input.py | 15 +- easy_rec/python/main.py | 28 +- easy_rec/python/model/easy_rec_estimator.py | 17 +- easy_rec/python/model/keep_model/__init__.py | 0 .../model/keep_model/base_model_ps_v2.py | 941 -- easy_rec/python/model/keep_model/dnn.py | 168 - .../python/model/keep_model/model_ps_mmoe.py | 100 - easy_rec/python/model/keep_model/senet.py | 112 - easy_rec/python/protos/train.proto | 1 + easy_rec/python/train_eval.py | 3 + easy_rec/python/utils/estimator_utils.py | 20 + easy_rec/python/utils/hvd_utils.py | 50 + keep_model.config | 2918 ------ model_conf.json | 15 - .../multi_tower_on_taobao_sok.config | 326 + taobao_train_data_8192 | 8192 +++++++++++++++++ test.parquet | Bin 12967073 -> 0 bytes 21 files changed, 8815 insertions(+), 4300 deletions(-) delete mode 100644 easy_rec/python/model/keep_model/__init__.py delete mode 100644 easy_rec/python/model/keep_model/base_model_ps_v2.py delete mode 100644 easy_rec/python/model/keep_model/dnn.py delete mode 100644 easy_rec/python/model/keep_model/model_ps_mmoe.py delete mode 100644 easy_rec/python/model/keep_model/senet.py create mode 100644 easy_rec/python/utils/hvd_utils.py delete mode 100644 keep_model.config delete mode 100644 model_conf.json create mode 100644 samples/model_config/multi_tower_on_taobao_sok.config create mode 100644 taobao_train_data_8192 delete mode 100644 test.parquet diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 1c04e2e51..7dba71c3c 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -135,6 +135,7 @@ import abc import collections import math +import os import numpy as np import six @@ -168,6 +169,16 @@ from easy_rec.python.compat.feature_column import utils as fc_utils +try: + from sparse_operation_kit import experiment as sok +except Exception: + sok = None + +try: + import horovod.tensorflow as hvd +except Exception: + hvd = None + def _internal_input_layer(features, feature_columns, @@ -222,6 +233,123 @@ def _get_logits(): # pylint: disable=missing-docstring _verify_static_batch_size_equality(output_tensors, ordered_columns) return array_ops.concat(output_tensors, 1) + def _get_logits_with_sok(): # pylint: disable=missing-docstring + assert sok is not None, 'sok is not installed' + assert hvd is not None, 'horovod is not installed' + builder = _LazyBuilder(features) + output_tensors = [] + ordered_columns = [] + + lookup_embeddings = [] + lookup_indices = [] + lookup_combiners = [] + lookup_cols = [] + lookup_output_ids = [] + + lookup_embeddings_with_wgt = [] + lookup_indices_with_wgt = [] + lookup_wgts = [] + lookup_cols_with_wgt = [] + lookup_combiners_with_wgt = [] + lookup_output_ids_with_wgt = [] + shared_weights = {} + for column in sorted(feature_columns, key=lambda x: x.name): + ordered_columns.append(column) + with variable_scope.variable_scope( + None, default_name=column._var_scope_name): # pylint: disable=protected-access + if 'Embedding' not in str(type(column)): + output = column._get_dense_tensor( + builder, weight_collections, trainable=trainable) + output_tensors.append(output) + if cols_to_output_tensors is not None: + cols_to_output_tensors[column] = output + if feature_name_to_output_tensors is not None: + feature_name_to_output_tensors[column.raw_name] = output + continue + num_buckets = column.categorical_column.num_buckets + hvd.size() - 1 + per_worker_buckets = num_buckets // hvd.size() + embedding_shape = (per_worker_buckets, column.dimension) + if 'SharedEmbedding' in str(type(column)): + shared_name = column.shared_embedding_collection_name + if shared_name in shared_weights: + embedding_weights = shared_weights[shared_name] + else: + with ops.device('/gpu:0'): + embedding_weights = variable_scope.get_variable( + name='embedding_weights', + shape=embedding_shape, + dtype=dtypes.float32, + initializer=column.initializer, + trainable=column.trainable and trainable, + partitioner=None, + collections=weight_collections) + shared_weights[shared_name] = embedding_weights + else: + with ops.device('/gpu:0'): + embedding_weights = variable_scope.get_variable( + name='embedding_weights', + shape=embedding_shape, + dtype=dtypes.float32, + initializer=column.initializer, + trainable=column.trainable and trainable, + partitioner=None, + collections=weight_collections) + # required by sok + embedding_weights.target_gpu = -1 + sparse_tensors = column.categorical_column._get_sparse_tensors( + builder, weight_collections=weight_collections, trainable=trainable) + output_id = len(output_tensors) + output_tensors.append(None) + if sparse_tensors.weight_tensor is not None: + lookup_embeddings_with_wgt.append(embedding_weights) + lookup_indices_with_wgt.append(sparse_tensors.id_tensor) + lookup_wgts.append(sparse_tensors.weight_tensor) + lookup_output_ids_with_wgt.append(output_id) + lookup_combiners_with_wgt.append(column.combiner) + lookup_cols_with_wgt.append(column) + else: + lookup_embeddings.append(embedding_weights) + lookup_indices.append(sparse_tensors.id_tensor) + lookup_output_ids.append(output_id) + lookup_combiners.append(column.combiner) + lookup_cols.append(column) + if cols_to_vars is not None: + cols_to_vars[column] = ops.get_collection( + ops.GraphKeys.GLOBAL_VARIABLES, + scope=variable_scope.get_variable_scope().name) + + # do sok lookup + if len(lookup_output_ids) > 0: + outputs = sok.lookup_sparse( + lookup_embeddings, lookup_indices, combiners=lookup_combiners) + for output, output_id, col in zip(outputs, lookup_output_ids, + lookup_cols): + output_tensors[output_id] = output + if cols_to_output_tensors is not None: + cols_to_output_tensors[col] = output + if feature_name_to_output_tensors is not None: + feature_name_to_output_tensors[col.raw_name] = output + elif len(lookup_output_ids_with_wgt) > 0: + outputs = sok.lookup_sparse( + lookup_embeddings_with_wgt, + lookup_indices_with_wgt, + lookup_wgts, + combiners=lookup_combiners_with_wgt) + for output, output_id, col in zip(outputs, lookup_output_ids_with_wgt, + lookup_cols_with_wgt): + output_tensors[output_id] = output + if cols_to_output_tensors is not None: + cols_to_output_tensors[col] = output + if feature_name_to_output_tensors is not None: + feature_name_to_output_tensors[col.raw_name] = output + + if feature_name_to_output_tensors is not None: + for column, output_tensor in zip( + sorted(feature_columns, key=lambda x: x.name), output_tensors): + feature_name_to_output_tensors[column.raw_name] = output_tensor + _verify_static_batch_size_equality(output_tensors, ordered_columns) + return array_ops.concat(output_tensors, 1) + # If we're constructing from the `make_template`, that by default adds a # variable scope with the name of the layer. In that case, we dont want to # add another `variable_scope` as that would break checkpoints. @@ -230,7 +358,10 @@ def _get_logits(): # pylint: disable=missing-docstring else: with variable_scope.variable_scope( scope, default_name='input_layer', values=features.values()): - return _get_logits() + if 'ENABLE_SOK' in os.environ: + return _get_logits_with_sok() + else: + return _get_logits() def input_layer(features, diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index ca2663a18..f458ceef7 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -40,6 +40,11 @@ from easy_rec.python.ops.incr_record import set_sparse_indices from easy_rec.python.utils import estimator_utils +try: + import horovod.tensorflow as hvd +except Exception: + hvd = None + OPTIMIZER_CLS_NAMES = { 'Adagrad': train.AdagradOptimizer, @@ -254,6 +259,24 @@ def optimize_loss(loss, variables, colocate_gradients_with_ops=colocate_gradients_with_ops) + if estimator_utils.has_hvd(): + if not estimator_utils.has_sok(): + reduced_grads = [] + for g, v in gradients: + reduced_grads.append((hvd.allreduce( + g, op=hvd.Average, + compression=hvd.compression.NoneCompressor), v)) + gradients = reduced_grads + else: + reduced_grads = [] + for g, v in gradients: + if '/embedding' not in v.name: + reduced_grads.append((hvd.allreduce( + g, op=hvd.Average, + compression=hvd.compression.NoneCompressor), v)) + else: + reduced_grads.append((g, v)) + # Optionally add gradient noise. if gradient_noise_scale is not None: gradients = _add_scaled_noise_to_gradients(gradients, @@ -304,7 +327,7 @@ def optimize_loss(loss, summary.scalar('global_norm/clipped_gradient_norm', clip_ops.global_norm(list(zip(*gradients))[0])) - task_index, _ = estimator_utils.get_task_index_and_num() + # task_index, _ = estimator_utils.get_task_index_and_num() # Create gradient updates. def _apply_grad(): diff --git a/easy_rec/python/input/criteo_input.py b/easy_rec/python/input/criteo_input.py index 6b65615f2..53f8c37a3 100644 --- a/easy_rec/python/input/criteo_input.py +++ b/easy_rec/python/input/criteo_input.py @@ -75,15 +75,16 @@ def _sample_generator(self): num_epoch += 1 def _to_fea_dict(self, dense, category, labels): - field_dict = {} - for fid in range(1, 14): - fea_name = 'f%d' % fid - field_dict[fea_name] = dense[:, fid - 1] + with tf.device('/gpu:0'): + field_dict = {} + for fid in range(1, 14): + fea_name = 'f%d' % fid + field_dict[fea_name] = tf.identity(dense[:, fid - 1]) - for cid in range(1, 27): - fea_name = 'c%d' % cid - field_dict[fea_name] = category[:, cid - 1] - field_dict['label'] = labels + for cid in range(1, 27): + fea_name = 'c%d' % cid + field_dict[fea_name] = tf.identity(category[:, cid - 1]) + field_dict['label'] = tf.identity(labels) return field_dict def _build(self, mode, params): diff --git a/easy_rec/python/input/dummy_input.py b/easy_rec/python/input/dummy_input.py index f556a3686..37629c5b2 100644 --- a/easy_rec/python/input/dummy_input.py +++ b/easy_rec/python/input/dummy_input.py @@ -41,18 +41,22 @@ def _build(self, mode, params): features tensor dict label tensor dict """ - features = {} - for field, field_type, def_val in zip(self._input_fields, - self._input_field_types, - self._input_field_defaults): - tf_type = get_tf_type(field_type) - def_val = self.get_type_defaults(field_type, default_val=def_val) - - if field in self._input_vals: - tensor = self._input_vals[field] - else: - tensor = tf.constant([def_val] * self._batch_size, dtype=tf_type) - - features[field] = tensor - parse_dict = self._preprocess(features) - return self._get_features(parse_dict), self._get_labels(parse_dict) + # with open('taobao_train_data_1', 'r') as fin: + # for line_str in fin: + # vals = line_str.strip().split(',') + with tf.device('/cpu:0'): + features = {} + for field, field_type, def_val in zip( + self._input_fields, self._input_field_types, + self._input_field_defaults): + tf_type = get_tf_type(field_type) + def_val = self.get_type_defaults(field_type, default_val=def_val) + print('default:', field, field_type, def_val, type(def_val)) + if field in self._input_vals: + tensor = self._input_vals[field] + else: + tensor = tf.constant([def_val] * self._batch_size, dtype=tf_type) + + features[field] = tensor + parse_dict = self._preprocess(features) + return self._get_features(parse_dict), self._get_labels(parse_dict) diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index a953b8871..23409de57 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -125,10 +125,17 @@ def _sample_generator(self): def _to_fea_dict(self, input_dict): fea_dict = {} - for fea_name in self._effective_fields: - tmp = input_dict[fea_name][1] % 1000 # 000000 - fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths( - tmp, input_dict[fea_name][0]) + # for fea_name in self._effective_fields: + # tmp = input_dict[fea_name][1] % 1000 # 000000 + # fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths( + # tmp, input_dict[fea_name][0]) + for fc in self._feature_configs: + if fc.feature_type == fc.IdFeature or fc.feature_type == fc.TagFeature: + input_0 = fc.input_names[0] + fea_name = fc.feature_name if fc.HasField('feature_name') else input_0 + tmp = input_dict[input_0][1] % fc.num_buckets + fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, input_dict[input_0][0]) + lbl_dict = {} for lbl_name in self._label_fields: lbl_dict[lbl_name] = input_dict[lbl_name] diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index 0804c9fec..2833a9145 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -102,16 +102,28 @@ def _get_input_fn(data_config, def _create_estimator(pipeline_config, distribution=None, params={}): model_config = pipeline_config.model_config train_config = pipeline_config.train_config - gpu_options = GPUOptions(allow_growth=False) + gpu_options = GPUOptions(allow_growth=True) # False) + + # if hvd is not None and pipeline_config.train_config.train_distribute != DistributionStrategy.NoStrategy: + # gpus = estimator_utils.get_available_gpus() + # if len(gpus) > 0: + # local_rnk = hvd.local_rank() + # num_gpus_per_worker = pipeline_config.train_config.num_gpus_per_worker + # sid = local_rnk * num_gpus_per_worker + # eid = sid + num_gpus_per_worker + # gpu_options.visible_device_list = ','.join(gpus[sid:eid]) if hvd is not None and pipeline_config.train_config.train_distribute != DistributionStrategy.NoStrategy: - gpus = estimator_utils.get_available_gpus() - if len(gpus) > 0: - local_rnk = hvd.local_rank() - num_gpus_per_worker = pipeline_config.train_config.num_gpus_per_worker - sid = local_rnk * num_gpus_per_worker - eid = sid + num_gpus_per_worker - gpu_options.visible_device_list = ','.join(gpus[sid:eid]) + gpus = tf.config.experimental.list_physical_devices("GPU") + tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], "GPU") + # gpus = estimator_utils.get_available_gpus() + # if len(gpus) > 0: + local_rnk = hvd.local_rank() + # num_gpus_per_worker = pipeline_config.train_config.num_gpus_per_worker + # sid = local_rnk * num_gpus_per_worker + # eid = sid + num_gpus_per_worker + logging.info('local_rnk=%d' % local_rnk) + gpu_options.visible_device_list = str(local_rnk) # ','.join(gpus[sid:eid]) session_config = ConfigProto( gpu_options=gpu_options, diff --git a/easy_rec/python/model/easy_rec_estimator.py b/easy_rec/python/model/easy_rec_estimator.py index 69e578d2b..ea1e1a189 100644 --- a/easy_rec/python/model/easy_rec_estimator.py +++ b/easy_rec/python/model/easy_rec_estimator.py @@ -35,6 +35,7 @@ from easy_rec.python.protos.train_pb2 import DistributionStrategy from easy_rec.python.utils import constant from easy_rec.python.utils import estimator_utils +from easy_rec.python.utils import hvd_utils from easy_rec.python.utils import pai_util from easy_rec.python.utils.multi_optimizer import MultiOptimizer @@ -209,9 +210,7 @@ def _train_model_fn(self, features, labels, run_config): if estimator_utils.has_hvd(): assert not self.train_config.sync_replicas, \ 'sync_replicas should not be set when using horovod' - optimizer = hvd.DistributedOptimizer( - optimizer, backward_passes_per_step=1) - bcast_hook = hvd.BroadcastGlobalVariablesHook(0) + bcast_hook = hvd_utils.BroadcastGlobalVariablesHook(0) hooks.append(bcast_hook) # for distributed and synced training @@ -384,9 +383,9 @@ def _train_model_fn(self, features, labels, run_config): early_stop_var = find_early_stop_var(var_list) var_list = [x for x in var_list if x != early_stop_var] - initialize_var_list = [ - x for x in var_list if 'WorkQueue' not in str(type(x)) - ] + # initialize_var_list = [ + # x for x in var_list if 'WorkQueue' not in str(type(x)) + # ] # incompatiable shape restore will not be saved in checkpoint # but must be able to restore from checkpoint @@ -407,9 +406,9 @@ def _train_model_fn(self, features, labels, run_config): sharded=True, max_to_keep=self.train_config.keep_checkpoint_max, save_relative_paths=True), - local_init_op=tf.group(local_init_ops), - ready_for_local_init_op=tf.report_uninitialized_variables( - var_list=initialize_var_list)) + local_init_op=tf.group(local_init_ops)) + # ready_for_local_init_op=tf.report_uninitialized_variables( + # var_list=initialize_var_list)) # saver hook saver_hook = estimator_utils.CheckpointSaverHook( checkpoint_dir=self.model_dir, diff --git a/easy_rec/python/model/keep_model/__init__.py b/easy_rec/python/model/keep_model/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/easy_rec/python/model/keep_model/base_model_ps_v2.py b/easy_rec/python/model/keep_model/base_model_ps_v2.py deleted file mode 100644 index be5211dae..000000000 --- a/easy_rec/python/model/keep_model/base_model_ps_v2.py +++ /dev/null @@ -1,941 +0,0 @@ -#!/usr/bin/env python -# encoding: utf-8 -"""usage dcn 两输出模型，concat none 特征.""" -import json -import logging -import os -import sys -import time - -import numpy as np -import pyarrow.parquet as pq -import tensorflow as tf -from base.context import Context -from base.context import conf_overwrite -from base.dataset import QDataSet -from base.qbox_wrapper import QWrapper -from layers.dnn import DNN -from layers.sequence import AttentionSequencePoolingLayer -from metric.auc import AUCUtil -from metric.bucket_auc import BucketAUCUtil -from tensorflow.keras.layers import Activation -from tensorflow.keras.layers import Add -from tensorflow.keras.layers import Average -from tensorflow.keras.layers import BatchNormalization -from tensorflow.keras.layers import Concatenate -from tensorflow.keras.layers import Dense -from tensorflow.keras.layers import Embedding -from tensorflow.keras.layers import Flatten -from tensorflow.keras.layers import Input -from tensorflow.keras.layers import Lambda -from tensorflow.keras.layers import Reshape -from tensorflow.keras.layers import add -from tensorflow.keras.layers import concatenate -from tensorflow.keras.models import Model -from tensorflow.keras.optimizers import Adam -from utils.basic_utils import * -from utils.mysql_util import MySQLUtils -from utils.ps_deploy import deploy_ps_data - - -# experimental_relax_shapes: True, avoid retracing -@tf.function(experimental_relax_shapes=True) -def train_step(model, input_dic, ctx): - """docstring.""" - with tf.GradientTape() as tape: - output_dic = model(input_dic, training=True) - - ps_loss = output_dic['ps_loss'] - ps_grad = tf.gradients(ps_loss, [input_dic['ps_embed']])[0] - - b1_power = input_dic['b1_power'] - b2_power = input_dic['b2_power'] - #tf.print(b1_power, b2_power) - lr_t = ctx.learning_rate * \ - tf.sqrt(1. - b2_power) / (1. - b1_power) - tmp_m = ctx.adam_beta1 * input_dic['ps_embed_m'] + \ - (1 - ctx.adam_beta1) * ps_grad - tmp_v = ctx.adam_beta2 * input_dic['ps_embed_v'] + \ - (1 - ctx.adam_beta2) * ps_grad * ps_grad - tmp_w = input_dic['ps_embed'] - lr_t * tmp_m / \ - (tf.sqrt(tmp_v) + ctx.adam_epsilon) - - #tmp_w = input_dic["ps_embed"] - ctx.learning_rate * ps_grad - result = tf.concat([tmp_w, tmp_m, tmp_v], axis=0) - output_dic['ps_grad'] = result - nan_flag = tf.reduce_sum(tf.cast(tf.math.is_nan(result), tf.float32)) - output_dic['nan_flag'] = nan_flag - - loss_value = output_dic['loss'] - grads = tape.gradient(loss_value, model.trainable_weights) - model.optimizer.apply_gradients(zip(grads, model.trainable_weights)) - - return output_dic - - -def make_pos(indice_list, len_list): - """ indice_list: [0, 1, 1, 2, 2, 2] - len_list: [1, 3, 6] - """ - # [6] - num = tf.shape(indice_list)[0] - # [6, 5, 4, 3, 2, 1] - cum_sum = tf.range(start=num, limit=0, delta=-1) - - # [5, 3, 0] - cum_sum_of_segment_sum = num - len_list - # [5, 3, 3, 0, 0, 0] - cum_sum_of_segment_sum = tf.gather(cum_sum_of_segment_sum, indice_list) - - # [1, 2, 1, 3, 2, 1] - 1 = [0, 1, 0, 2, 1, 0] - return cum_sum - cum_sum_of_segment_sum - 1 - - -def make_sparse_indice(summed): - """https://stackoverflow.com/questions/54790000/calculate-segment-ids-needed-in-tf-math-segment-sum-by-length-of-segments-in-ten summed is result of tf.cumsum. - - Args: - summed (TYPE): Description - - Returns: - TYPE: Description - """ - return tf.searchsorted(summed, tf.range(summed[-1]), side='right') - - -def model_inputs_prepare(inputs, ctx): - """Summary. - - Args: - inputs (TYPE): Description - ctx (TYPE): Description - - Returns: - TYPE: Description - """ - batch_split = ctx.batch_size // ctx.num_worker - - # slice len - len_arr = [] - off_arr = [0] - size_arr = [] - for idx in range(len(ctx.slot_size_vec)): - len_arr.append( - tf.reshape( - tf.slice(inputs['sparse_len'], [0, ctx.slot_offset_vec[idx]], - [batch_split, ctx.slot_size_vec[idx]]), [-1])) - size_arr.append(len_arr[-1][-1]) - off_arr.append(off_arr[-1] + size_arr[-1]) - - # slice id - tmp_arr = [] - for idx in range(len(ctx.slot_size_vec)): - tmp_arr.append( - tf.slice(inputs['sparse_id'], [off_arr[idx]], [size_arr[idx]])) - - id_arr = [] - for idx in range(len(ctx.slot_size_vec)): - id_arr.append( - tf.clip_by_value(tmp_arr[idx] - inputs['uniq_num_by_dim'][idx], 0, - 2**30)) - - # slice embed - embed_arr = [] - for idx in range(len(ctx.embed_dim_vec)): - tmp = tf.slice( - inputs['ps_embed'], [inputs['uniq_off_by_dim'][idx]], - [inputs['uniq_off_by_dim'][idx + 1] - inputs['uniq_off_by_dim'][idx] - ]) # noqa - embed_arr.append(tf.reshape(tmp, [-1, ctx.embed_dim_vec[idx]])) - - return embed_arr, id_arr, len_arr, tmp_arr - - -class ExportModel(Model): - - def __init__(self, model_conf, indim, name='export_model'): - super(ExportModel, self).__init__(name=name) - self.label_conf_list = model_conf['label'] - self.indim = indim - self._set_inputs([ - tf.TensorSpec([None] + tmp, tf.float32, name='input' + str(i)) - for i, tmp in enumerate(indim) - ]) - self.dnn = DNN([300, 150, 50], - activation='relu', - use_bn=True, - output_activation='relu') - self.din_layer = AttentionSequencePoolingLayer() - self.output_layers = [ - Dense( - 1, activation='sigmoid', name='output_' + label_conf['input_name']) - for label_conf in self.label_conf_list - ] - - def call(self, embed_input, training=False): - target_fea = embed_input[0] - - listclick = embed_input[1] - listclick_len = tf.reshape(listclick[..., :1], [-1, self.indim[1][0]]) - len_mask = tf.where( - tf.equal(listclick_len, 0), - tf.ones_like(listclick_len, dtype=tf.float32), - tf.zeros_like(listclick_len, dtype=tf.float32)) - listclick_len = tf.cast( - tf.reduce_sum(len_mask, axis=1, keepdims=True), tf.int32) - - listcomplete = embed_input[2] - listcomplete_len = tf.reshape(listcomplete[..., :1], [-1, self.indim[2][0]]) - len_mask = tf.where( - tf.equal(listcomplete_len, 0), - tf.ones_like(listcomplete_len, dtype=tf.float32), - tf.zeros_like(listcomplete_len, dtype=tf.float32)) - listcomplete_len = tf.cast( - tf.reduce_sum(len_mask, axis=1, keepdims=True), tf.int32) - - click_att_data = self.din_layer([target_fea, listclick, listclick_len]) - click_att_data = tf.squeeze(click_att_data, axis=[1]) - - complete_att_data = self.din_layer( - [target_fea, listcomplete, listcomplete_len]) - complete_att_data = tf.squeeze(complete_att_data, axis=[1]) - - embed = tf.concat([click_att_data, complete_att_data, embed_input[3]], - axis=1) - embed = self.dnn(embed) - outputs = [output_layer(embed) for output_layer in self.output_layers] - return outputs - - -class BaseModel: - - def __init__(self, conf): - """初始化参数. - - :param worker_list: 指定的 worker 节点列表 - :param embed_share: 0 全独立 1 field内共享 2 全共享 - """ - self.monitor_table = 'model_metrics_monitor' - self.mysql_utils = MySQLUtils().master - self.data_conf = conf['data'] - self.model_conf = conf['model'] - self.ps_conf = conf.get('ps', {}) - self.model_name = self.model_conf['model_name'] - self.epochs = self.data_conf.get('epochs', 1) # 总训练轮数 - self.log_step_num = self.data_conf.get('log_step_num', 1) - self.save_path = self.data_conf.get('save_path').rstrip( - '/') + '/' + self.model_name + '/' - if not os.path.exists(self.save_path): - os.makedirs(self.save_path) - self.save_hdfs_path = self.data_conf.get('save_hdfs_path').rstrip( - '/') + '/' + self.model_name + '/' - self.save_iter = 1 - - self.load_path = self.data_conf.get('load_path') - self.model = None - self.mode = self.model_conf['mode'] - self.profile = self.model_conf.get('profile', False) - self.tb_callback = None - if self.profile: - self.tb_callback = tf.keras.callbacks.TensorBoard( - log_dir=self.save_path + '/profile', profile_batch='10,12') - - if self.mode == 'Update': - self.process_update_conf() - self.feature_conf, self.group_length = self.parse_feature_conf( - conf['feature']) - self.custom_group_count = len(self.group_length) - 1 - self.export_model_class = self.get_export_model_classs() - - if self.data_conf.get('deploy'): - shell(f'hadoop fs -mkdir -p {self.save_hdfs_path}') - - def parse_feature_conf(self, conf_list): - group_features = {} - group_len = {} - for conf in conf_list: - group = conf.get('group', 'default') - if group not in group_features: - group_features[group] = [] - group_len[group] = conf.get('length', 1) - group_features[group].append(conf) - - if len(group_features) == 1: - return conf_list, None - return group_features, group_len - - def get_export_model_classs(self): - return ExportModel - - def process_update_conf(self): - self.mode = 'Train' - self.data_conf['deploy'] = True - model_info_path = self.save_hdfs_path + 'model_info.json' - model_info_local_path = self.save_path + 'model_info.json' - if os.path.exists(model_info_local_path): - shell(f'rm -f {model_info_local_path}') - shell(f'hadoop fs -get {model_info_path} {model_info_local_path}') - recent_date = '' - with open(model_info_local_path, 'r') as target: - model_info = json.load(target) - recent_date = model_info['date'] - - recent_dir = self.save_hdfs_path + recent_date - next_day = get_next_day(recent_date) - self.data_conf['train_end_day'] = next_day - self.data_conf['train_days'] = 1 - self.data_conf['eval_day'] = next_day - self.data_conf['load_path'] = self.save_path + recent_date - self.data_conf['load_embed_only'] = False - self.load_path = self.data_conf['load_path'] - print('download model data', recent_date, self.load_path) - if os.path.exists(self.load_path): - shell(f'rm -rf {self.load_path}') - rst = shell(f'hadoop fs -get {recent_dir} {self.load_path}') - logging.info('download model file rst:' + str(rst)) - logging.info(f'update mode data conf {self.data_conf}') - - def build_input(self): - """构建模型输入.""" - ctx = self.ctx - in_dim = np.ma.innerproduct(ctx.slot_size_vec[self.custom_group_count:], - ctx.embed_dim_vec[self.custom_group_count:]) - if ctx.use_acc_ctr: - in_dim += ctx.slot_num - self.ctx.net_input_dim = in_dim - input_dic = {} - input_dic['sample_idx'] = Input(dtype=tf.int64, batch_shape=(None,)) - input_dic['label'] = Input( - dtype=tf.float32, batch_shape=(None, len(self.model_conf['label']))) - input_dic['sparse_len'] = Input( - dtype=tf.int32, batch_shape=(None, None), name='sparse_len') - input_dic['dense_fea'] = Input( - dtype=tf.float32, batch_shape=(None, None), name='dense_fea') - - input_dic['uniq_num_by_dim'] = Input(dtype=tf.int32, batch_shape=(None,)) - input_dic['uniq_off_by_dim'] = Input(dtype=tf.int32, batch_shape=(None,)) - # single worker - input_dic['sparse_id'] = Input(dtype=tf.int32, batch_shape=(None,)) - input_dic['ps_embed'] = Input(dtype=tf.float32, batch_shape=(None,)) - input_dic['ps_embed_m'] = Input(dtype=tf.float32, batch_shape=(None,)) - input_dic['ps_embed_v'] = Input(dtype=tf.float32, batch_shape=(None,)) - input_dic['ps_stat'] = Input( - dtype=tf.float32, batch_shape=(None, 2), name='ps_stat') - input_dic['b1_power'] = Input( - dtype=tf.float32, batch_shape=(1,), name='b1_power') - input_dic['b2_power'] = Input( - dtype=tf.float32, batch_shape=(1,), name='b2_power') - input_dic['test_mask'] = Input( - dtype=tf.float32, batch_shape=(1, in_dim), name='test_mask') - - batch_split = ctx.batch_size // ctx.num_worker - embed_arr, id_arr, len_arr, tmp_arr = model_inputs_prepare(input_dic, ctx) - - fea_dim = 11 if ctx.use_acc_ctr else 10 - gather_arr = [] - inputs_size = [] - - show, click = tf.split(input_dic['ps_stat'], 2, axis=1) - ctr = tf.reshape(click / (show + 10.), [-1, 1]) - - for idx, pair in enumerate(self.group_length.items()): - group, length = pair - if group == 'default': - continue - conf_list = self.feature_conf[group] - fea_size = len(conf_list) - fea_dim_total = fea_size * fea_dim - - indice = make_sparse_indice(len_arr[idx]) - tmp = tf.gather(embed_arr[idx], id_arr[idx]) - if ctx.use_acc_ctr: - acc_ctr = tf.gather(ctr, tmp_arr[idx]) - tmp = tf.concat([tmp, acc_ctr], axis=1) - pos = make_pos(indice, len_arr[idx]) - # non_zero_indice 分别表示第i个样本 第j个pos - non_zero_indice = tf.stack([indice, pos], axis=1) - group_embed = tf.scatter_nd(non_zero_indice, tmp, - [batch_split * fea_size, length, fea_dim]) - group_embed = tf.reshape(group_embed, [-1, fea_size, length, fea_dim]) - group_embed = tf.reshape( - tf.transpose(group_embed, perm=[0, 2, 1, 3]), - [-1, length, fea_dim_total]) - gather_arr.append(group_embed) - inputs_size.append([length, fea_dim_total]) - - for idx in range(self.custom_group_count, len(ctx.embed_dim_vec)): - tmp_seg_ids = make_sparse_indice(len_arr[idx]) - tmp = tf.math.segment_sum( - tf.gather(embed_arr[idx], id_arr[idx]), tmp_seg_ids) - tmp = tf.reshape( - tmp, [batch_split, ctx.slot_size_vec[idx] * ctx.embed_dim_vec[idx]]) - if ctx.use_acc_ctr: - acc_ctr = tf.reshape( - tf.math.segment_mean(tf.gather(ctr, tmp_arr[idx]), tmp_seg_ids), - [batch_split, ctx.slot_size_vec[idx], 1]) - tmp = tf.reshape( - tmp, [batch_split, ctx.slot_size_vec[idx], ctx.embed_dim_vec[idx]]) - tmp = tf.concat([tmp, acc_ctr], axis=2) - tmp = tf.reshape(tmp, [ - batch_split, ctx.slot_size_vec[idx] * (ctx.embed_dim_vec[idx] + 1) - ]) - gather_arr.append(tmp) - - # TODO aucdiff - # embed_input = tf.concat(gather_arr, axis=1, name="embed_input") - # embed_input = embed_input * input_dic["test_mask"][...,item_fea_dim_total:] - inputs_size.append([in_dim]) - - return input_dic, gather_arr, inputs_size - - def build_and_compile_model(self): - """构建并编译模型.""" - ctx = self.ctx - input_dic, embed_input, indim = self.build_input() - self.export_model = self.export_model_class(self.model_conf, indim) - # self.export_model.build((None, indim)) - # self.export_model.summary() - - y_pred = self.export_model(embed_input) - y = input_dic['label'] # (bs, label_num) - splited_batch_size = ctx.batch_size // ctx.num_worker - - miss_mask = tf.where( - tf.equal(tf.reshape(input_dic['sample_idx'], [-1]), 0), - tf.zeros([splited_batch_size], dtype=ctx.tfval), - tf.ones([splited_batch_size], dtype=ctx.tfval)) - - loss_object = tf.keras.losses.BinaryCrossentropy() - if type(y_pred) != list: - y_pred = [y_pred] - total_loss = 0 - ps_loss = 0 - ttmp = tf.cast(tf.reduce_sum(miss_mask), tf.int32) - ground_truths = [] - predictions = [] - losses = [] - for label_idx, conf in enumerate(self.model_conf['label']): - # for ground_truth, pred, conf in zip(y, y_pred, self.model_conf['label']): - ground_truth = y[..., label_idx] - pred = y_pred[label_idx] - train_space = conf.get('train_space', -1) - if train_space >= 0: - valid_idx = tf.where(y[..., train_space] > 0) - ground_truth = tf.gather(ground_truth, valid_idx) - pred = tf.gather(pred, valid_idx) - else: - ground_truth = ground_truth[:ttmp] - pred = pred[:ttmp] - ground_truth = tf.reshape(ground_truth, (-1, 1)) - pred = tf.reshape(pred, (-1, 1)) - #cur_loss = tf.nn.sigmoid_cross_entropy_with_logits(ground_truth, logit) - #cur_loss = tf.reduce_mean(cur_loss) - if conf.get('pos_weight'): - pos_weight = conf.get('pos_weight', 1.) - sample_weight = 1. + ground_truth * (pos_weight - 1.) - cur_loss = loss_object(ground_truth, pred, sample_weight=sample_weight) - else: - cur_loss = loss_object(ground_truth, pred) - losses.append(cur_loss) - weight = conf.get('weight', 1.) - embed_weight = conf.get('embed_weight', 1.) - total_loss += cur_loss * weight - ps_loss += cur_loss * embed_weight - - ground_truths.append(ground_truth) - predictions.append(pred) - - net_dic = { - 'loss': total_loss, - 'ps_loss': ps_loss, - 'losses': losses, - 'embed_input': embed_input, - 'ground_truth': ground_truths, - 'prediction': predictions, - 'sid': input_dic['sample_idx'][:ttmp] - } - - opt = Adam(0.001) - model = Model(inputs=input_dic, outputs=net_dic) - model.compile(optimizer=opt) - self.checkpoint = tf.train.Checkpoint( - step=tf.Variable(1), model=model, optimizer=opt) - return model - - def load_model(self, path): - if not self.data_conf.get('load_embed_only'): - cpt_path = os.path.join(path, 'checkpoint') - if not os.path.exists(cpt_path): - raise Exception(cpt_path + ' not exists.') - self.cpt_manager = tf.train.CheckpointManager( - self.checkpoint, cpt_path, max_to_keep=1) - logging.info('loading model from:{}'.format(path)) - self.checkpoint.restore(self.cpt_manager.latest_checkpoint) - self.local_db.load() - - def save_model(self, save_date, model_info, is_deploy=None): - path = self.save_path + save_date - shell(f'rm -rf {path}') - self.cpt_manager = tf.train.CheckpointManager( - self.checkpoint, os.path.join(path, 'checkpoint'), max_to_keep=1) - save_path = self.cpt_manager.save() - self.local_db.save() - logging.info('saving model to:{}'.format(save_path)) - model_info_path = self.save_path + 'model_info.json' - with open(model_info_path, 'w') as target: - json.dump(model_info, target) - if is_deploy: - #self.export_model.save(path + '/local_model', save_format="tf") - tf.saved_model.save(self.export_model, path + '/local_model') - hdfs_new_model_path = self.save_hdfs_path + save_date - # deploy codis data first, make sure that when server got new version file the codis data is ready. - deploy_ps_data(self.model_name, path, self.save_iter) - shell(f'hadoop fs -rmr {hdfs_new_model_path}') - logging.info('put local model to hdfs %s ' % hdfs_new_model_path) - shell(f'hadoop fs -put {path} {hdfs_new_model_path}') - model_info_remote_path = self.save_hdfs_path + 'model_info.json' - shell(f'hadoop fs -put -f {model_info_path} {model_info_remote_path}') - self.save_iter += 1 - - def prepare_model(self): - # 编译并构建模型 - self.model = self.build_and_compile_model() - if self.load_path: - self.load_model(self.load_path) - - def log_into_db(self, train_time, metrics): - """log train history to db.""" - label_name = metrics['Name'] - for metric_name, metric_value in metrics.items(): - if metric_name == 'Name': - continue - metric_name = label_name + '_' + metric_name - metric_time = int(time.mktime(train_time.timetuple())) - data = { - 'model_name': self.model_name, - 'metric_time': metric_time, - 'metric_name': metric_name, - 'metric_value': float(metric_value) - } - where_str = f'model_name="{self.model_name}" and metric_time={metric_time} and metric_name="{metric_name}"' - logging.info(f'log train metric into db: {data}') - - if self.mysql_utils.fetch_one(table=self.monitor_table, where=where_str): - self.mysql_utils.update(self.monitor_table, data, where_str) - else: - self.mysql_utils.insert(self.monitor_table, data) - - def get_batch_input(self, batch, local_db, mask=None): - ctx = self.ctx - ps_stat, ps_embed, tinfo = local_db.pull(batch['task_id']) - input_dic = {} - # input_dic["scene_index"] = batch["scene_index"] - input_dic['sample_idx'] = batch['sample_idx'] - input_dic['label'] = batch['label'] - input_dic['sparse_len'] = batch['sparse_len'] - input_dic['dense_fea'] = batch['dense_fea'] - - input_dic['uniq_num_by_dim'] = np.cumsum( - [0] + batch['uniq_num_by_dim'].tolist()).astype(np.int32) # noqa - tmp = np.array(ctx.embed_dim_vec).astype( - np.int32) * batch['uniq_num_by_dim'].astype(np.int32) # noqa - input_dic['uniq_off_by_dim'] = np.cumsum([0] + tmp.tolist()).astype( - np.int32) # noqa - input_dic['sparse_id'] = batch['sparse_id'][0] - - input_dic['ps_embed'] = ps_embed[0] - input_dic['ps_embed_m'] = np.zeros_like( - ps_embed[1]) if self.adam_restart else ps_embed[1] - input_dic['ps_embed_v'] = np.zeros_like( - ps_embed[2]) if self.adam_restart else ps_embed[2] - input_dic['ps_stat'] = ps_stat - self.b1_power *= ctx.adam_beta1 - self.b2_power *= ctx.adam_beta2 - input_dic['b1_power'] = self.b1_power - input_dic['b2_power'] = self.b2_power - input_dic['test_mask'] = np.ones([1, self.ctx.net_input_dim], - dtype=self.ctx.npval) - # avoid retracing of tf function. - input_dic = {key: tf.constant(input_dic[key]) for key in input_dic} - return input_dic - - def fit_day(self, train_itor, local_db): - ctx = self.ctx - batch_size = self.ctx.batch_size // self.ctx.num_worker - metrics = [ - AUCUtil(name=conf['input_name'] + '_metric') - for conf in self.model_conf['label'] - ] - total_metrics = [ - BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) - for conf in self.model_conf['label'] - ] - step = 0 - while True: - try: - if self.profile: - self.tb_callback.on_train_batch_begin(step) - start_time = time.time() - batch = train_itor.getbatch() - input_dic = self.get_batch_input(batch, local_db) - self.adam_restart = False - output_dic = train_step(self.model, input_dic, ctx) - if output_dic['nan_flag'] > 0: - print('nan and skip') - local_db.push(batch['task_id'], None, None) - continue - - # Update training metric. - for gt, pred, loss, metric, total_metric in zip( - output_dic['ground_truth'], output_dic['prediction'], - output_dic['losses'], metrics, total_metrics): - gt = gt.numpy() - pred = pred.numpy() - loss = loss.numpy() - total_metric.add(loss, gt, pred) - if self.log_step_num < 1 or step % self.log_step_num != 0: - continue - #print('pred', pred[:20]) - - metric.add(loss, gt, pred) - print(f'{metric.name} {metric.calc_str()}') - metric.reset() - - ps_grad = output_dic['ps_grad'] - local_db.push(batch['task_id'], ps_grad.numpy(), batch['payload']) - elapse = time.time() - start_time - if self.log_step_num >= 1 and step % self.log_step_num == 0: - print(f'train step {step}, batch time elapse {elapse}') - print('-' * 50) - step += 1 - if self.profile: - self.tb_callback.on_train_batch_end(step) - if step > 20: - break - #exit() - except EOFError: - # save & terminate - print('EOF, train day end.') - break - rst_list = [] - for metric in total_metrics: - rst = metric.calc() - print(f'{metric.name} {rst}') - rst['Name'] = metric.name - rst_list.append(rst) - print('=' * 80) - return rst_list - - def check_day(self, train_itor, local_db): - ctx = self.ctx - batch_size = ctx.batch_size - step = 0 - embed_size = 10 - sample_data = pq.read_table('embed.parquet').to_pandas() - - while True: - try: - start_time = time.time() - batch = train_itor.getbatch() - input_dic = self.get_batch_input(batch, local_db) - output_dic = self.model(input_dic, training=False) - - samples = sample_data[step * batch_size:step * batch_size + batch_size] - online_embeds = samples.embed_input.to_numpy() - - # should add 'embed_input' in ouput_dic first. - embed_input_data = output_dic['embed_input'].numpy() - features = [feature['input_name'] for feature in self.feature_conf] - feature_diff_data = {} - for embed, online_embed in zip(embed_input_data, online_embeds): - embed_off = embed.tolist() - embed_on = online_embed.tolist() - for idx, fea_name in enumerate(features): - fea_embed_on = embed_on[idx * embed_size:idx * embed_size + - embed_size] - fea_embed_off = embed_off[idx * embed_size:idx * embed_size + - embed_size] - total_diff = sum([ - abs(x - y) if abs(x - y) > 1e-7 else 0 - for x, y in zip(fea_embed_on, fea_embed_off) - ]) - if total_diff > 0 or sum(fea_embed_on) == 0: - print(fea_name) - print(fea_embed_on) - print(fea_embed_off) - feature_diff_data[fea_name] = feature_diff_data.get(fea_name, - 0) + 1 - print('-' * 60) - print('total cnt', len(online_embeds)) - feature_diff_data = [ - [key, value] for key, value in feature_diff_data.items() - ] - feature_diff_data.sort(key=lambda x: x[1], reverse=True) - print(feature_diff_data) - - # Update training metric. - for gt, pred, conf in zip(output_dic['ground_truth'], - output_dic['prediction'], - self.model_conf['label']): - gt = gt.numpy() - pred = pred.numpy() - print(conf['input_name']) - thresold = 10e-4 - total = 0 - cnt = 0 - for on, off in zip(gt, pred): - total += 1 - if abs(on - off) > thresold: - cnt += 1 - # print(on, off) - print('diff ratio', cnt / total) - - local_db.push(batch['task_id']) - elapse = time.time() - start_time - if self.log_step_num >= 1 and step % self.log_step_num == 0: - print(f'train step {step}, batch time elapse {elapse}') - print('-' * 50) - step += 1 - #exit() - except EOFError: - # save & terminate - print('EOF, check day end.') - break - - def eval(self, train_itor, local_db): - ctx = self.ctx - batch_size = ctx.batch_size - metrics = [ - AUCUtil(name=conf['input_name'] + '_metric') - for conf in self.model_conf['label'] - ] - total_metrics = [ - BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) - for conf in self.model_conf['label'] - ] - step = 0 - - while True: - try: - start_time = time.time() - batch = train_itor.getbatch() - input_dic = self.get_batch_input(batch, local_db) - output_dic = self.model(input_dic, training=False) - - # Update training metric. - for gt, pred, loss, metric, total_metric in zip( - output_dic['ground_truth'], output_dic['prediction'], - output_dic['losses'], metrics, total_metrics): - gt = gt.numpy() - pred = pred.numpy() - loss = loss.numpy() - total_metric.add(loss, gt, pred) - if self.log_step_num < 1 or step % self.log_step_num != 0: - continue - metric.add(loss, gt, pred) - print(f'{metric.name} {metric.calc_str()}') - metric.reset() - - local_db.push(batch['task_id']) - elapse = time.time() - start_time - if self.log_step_num >= 1 and step % self.log_step_num == 0: - print(f'eval step {step}, batch time elapse {elapse}') - print('-' * 50) - step += 1 - #exit() - except EOFError: - # save & terminate - print('EOF, eval end.') - break - rst_list = [] - for metric in total_metrics: - rst = metric.calc() - print(f'{metric.name} {rst}') - rst['Name'] = metric.name - rst_list.append(rst) - return rst_list - - def aucdiff_run(self, train_itor, local_db): - ctx = self.ctx - batch_size = ctx.batch_size - total_metrics_list = [[ - BucketAUCUtil(name=conf['input_name'], batch_size=batch_size) - for conf in self.model_conf['label'] - ] - for _ in range(ctx.origin_slot_num + 1)] - tmask = [ - np.ones([1, self.ctx.net_input_dim], dtype=self.ctx.npval) - for _ in range(self.ctx.origin_slot_num) - ] # noqa - - step = 0 - embed_offset = 0 - start_arr = [] - end_arr = [] - for mask in range(len(ctx.embed_dim)): - pstart = embed_offset - embed_len = self.ctx.embed_dim[mask] - if ctx.use_acc_ctr: - embed_len += 1 - pend = pstart + embed_len - tmask[mask][:, pstart:pend] = 0. - embed_offset = pend - - tmp_start = [str(pstart)] - tmp_end = [str(pend)] - start_arr.append('/'.join(tmp_start)) - end_arr.append('/'.join(tmp_end)) - - while True: - try: - batch = train_itor.getbatch() - input_dic = self.get_batch_input(batch, local_db) - origin_mask = input_dic['test_mask'] - - for mask in range(self.ctx.origin_slot_num + 1): - if mask < ctx.origin_slot_num: - input_dic['test_mask'] = tf.constant(tmask[mask]) - else: - input_dic['test_mask'] = origin_mask - - output_dic = self.model(input_dic, training=False) - - for gt, pred, loss, total_metric in zip(output_dic['ground_truth'], - output_dic['prediction'], - output_dic['losses'], - total_metrics_list[mask]): - gt = gt.numpy() - pred = pred.numpy() - loss = loss.numpy() - total_metric.add(loss, gt, pred) - step += 1 - local_db.push(batch['task_id']) - print('aucdiff check step', step) - - except EOFError: - data = {} - features = [feature['input_name'] for feature in self.feature_conf] - for mask in range(self.ctx.origin_slot_num): - print('# Mask %s from %s to %s.' % (self.ctx.origin_slots.split( - ',', -1)[mask], start_arr[mask], end_arr[mask])) - feature = features[mask] - data[feature] = {} - for metric in total_metrics_list[mask]: - rst = metric.calc() - data[feature][metric.name] = rst - print(f'{metric.name} {rst}') - data['base'] = {} - for metric in total_metrics_list[-1]: - print('base metrics') - rst = metric.calc() - data['base'][metric.name] = rst - print(f'{metric.name} {rst}') - with open('aucdiff_rst.json', 'w') as target: - json.dump(data, target) - break - - def run(self): - conf_overwrite(self, 'job.conf', 'job.conf.tmp') - self.ctx = Context('job.conf.tmp') - self.train_itor = QDataSet('job.conf.tmp', self.ctx, False) - self.local_db = QWrapper('job.conf.tmp') - self.b1_power = 1. - self.b2_power = 1. - self.adam_restart = True - self.prepare_model() - if self.mode.startswith('Train'): - time_range = self.data_conf['train_days'] - data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' - end_day = self.data_conf['train_end_day'] - end_time = datetime.datetime.strptime(f'{end_day} 00:00:00', - '%Y-%m-%d %H:%M:%S') - trained_days = 0 - # TODO 尝试add_history、add_progbar - if self.profile: - self.tb_callback.set_model(self.model) - - try: - if self.profile: - self.tb_callback.on_train_begin() - model_info = {} - for epoch in range(self.epochs): - if self.profile: - self.tb_callback.on_epoch_begin(epoch) - print(f'start epoch {epoch}...') - for i in reversed(range(time_range)): - train_time = end_time - datetime.timedelta(days=i) - data_path = train_time.strftime(data_hdfs_path) - # data_date = train_time.strftime('%Y-%m-%d') - files = get_hdfs_files(data_path) - self.train_itor.setfiles(','.join(files)) - print('start train', train_time, data_path) - total_metrics = self.fit_day(self.train_itor, self.local_db) - model_info['metrics'] = total_metrics - if self.data_conf.get('monitor'): - for metrics in total_metrics: - self.log_into_db(train_time, metrics) - self.local_db.clean_nontrain(False) - trained_days += 1 - if trained_days % 5 == 0: - train_date = train_time.strftime('%Y-%m-%d') - self.save_model(train_date, model_info) - if self.profile: - self.tb_callback.on_epoch_end(epoch) - if self.profile: - self.tb_callback.on_train_end() - except Exception as e: - logging.exception(e) - finally: - is_deploy = self.data_conf.get('deploy') - model_info['version'] = int(time.mktime(train_time.timetuple())) - train_date = train_time.strftime('%Y-%m-%d') - model_info['date'] = train_date - self.save_model(train_date, model_info, is_deploy=is_deploy) - self.local_db.terminate() - - if 'Eval' in self.mode: - data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' - eval_day = self.data_conf['eval_day'] - eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', - '%Y-%m-%d %H:%M:%S') - - data_path = eval_time.strftime(data_hdfs_path) - files = get_hdfs_files(data_path) - # files = files[::10] - self.train_itor.is_test = True - self.train_itor.setfiles(','.join(files)) - print('start eval', eval_time, data_path) - self.eval(self.train_itor, self.local_db) - self.local_db.terminate() - - # TODO support accctr - if self.mode == 'AucDiff': - data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' - eval_day = self.data_conf['eval_day'] - eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', - '%Y-%m-%d %H:%M:%S') - - data_path = eval_time.strftime(data_hdfs_path) - files = get_hdfs_files(data_path) - # files = files[::10] - self.train_itor.is_test = True - self.train_itor.setfiles(','.join(files)) - print('start check', eval_time, data_path) - self.aucdiff_run(self.train_itor, self.local_db) - self.local_db.terminate() - - # TODO support accctr - if self.mode == 'Check': - data_hdfs_path = self.data_conf['data_hdfs_path'].rstrip('/') + '/' - eval_day = self.data_conf['eval_day'] - eval_time = datetime.datetime.strptime(f'{eval_day} 00:00:00', - '%Y-%m-%d %H:%M:%S') - - data_path = eval_time.strftime(data_hdfs_path) - files = get_hdfs_files(data_path) - self.train_itor.is_test = True - self.train_itor.setfiles(','.join(files)) - print('start check', eval_time, data_path) - - total_metrics = self.check_day(self.train_itor, self.local_db) - self.local_db.terminate() diff --git a/easy_rec/python/model/keep_model/dnn.py b/easy_rec/python/model/keep_model/dnn.py deleted file mode 100644 index e3c38efff..000000000 --- a/easy_rec/python/model/keep_model/dnn.py +++ /dev/null @@ -1,168 +0,0 @@ -import tensorflow as tf -from tensorflow.keras import backend as K -from tensorflow.keras.initializers import Zeros -from tensorflow.keras.initializers import glorot_normal -from tensorflow.keras.layers import Layer -from tensorflow.keras.regularizers import l2 - - -def activation_layer(activation): - if isinstance(activation, str): - act_layer = tf.keras.layers.Activation(activation) - elif issubclass(activation, Layer): - act_layer = activation() - else: - raise ValueError( - 'Invalid activation,found %s.You should use a str or a Activation Layer Class.' - % (activation)) - return act_layer - - -class DNN(Layer): - """The Multi Layer Percetron. - - Input shape - - nD tensor with shape: ``(batch_size, ..., input_dim)``. The most common situation would be a 2D input with shape ``(batch_size, input_dim)``. - - Output shape - - nD tensor with shape: ``(batch_size, ..., hidden_size[-1])``. For instance, for a 2D input with shape ``(batch_size, input_dim)``, the output would have shape ``(batch_size, hidden_size[-1])``. - - Arguments - - **hidden_units**:list of positive integer, the layer number and units in each layer. - - - **activation**: Activation function to use. - - - **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix. - - - **dropout_rate**: float in [0,1). Fraction of the units to dropout. - - - **use_bn**: bool. Whether use BatchNormalization before activation or not. - - - **output_activation**: Activation function to use in the last layer.If ``None``,it will be same as ``activation``. - - - **seed**: A Python integer to use as random seed. - """ - - def __init__(self, - hidden_units, - activation='relu', - l2_reg=0, - dropout_rate=0, - use_bn=False, - output_activation=None, - need_ppnet=False, - pp_size=0, - seed=1024, - **kwargs): - self.hidden_units = hidden_units - self.activation = activation - self.l2_reg = l2_reg - self.dropout_rate = dropout_rate - self.use_bn = use_bn - self.output_activation = output_activation - self.need_ppnet = need_ppnet - self.pp_size = pp_size - self.seed = seed - - super(DNN, self).__init__(**kwargs) - - def build(self, input_shape): - # if len(self.hidden_units) == 0: - # raise ValueError("hidden_units is empty") - input_size = input_shape[-1] - if self.need_ppnet: - input_size -= self.pp_size - hidden_units = [int(input_size)] + list(self.hidden_units) - self.kernels = [ - self.add_weight( - name='kernel' + str(i), - shape=(hidden_units[i], hidden_units[i + 1]), - initializer=glorot_normal(seed=self.seed), - regularizer=l2(self.l2_reg), - trainable=True) for i in range(len(self.hidden_units)) - ] - self.bias = [ - self.add_weight( - name='bias' + str(i), - shape=(self.hidden_units[i],), - initializer=Zeros(), - trainable=True) for i in range(len(self.hidden_units)) - ] - if self.use_bn: - self.bn_layers = [ - tf.keras.layers.BatchNormalization() - for _ in range(len(self.hidden_units)) - ] - if self.need_ppnet: - self.pp_gates = [ - DNN([size, size], - activation='relu', - use_bn=False, - output_activation='sigmoid') for size in hidden_units[:-1] - ] - - self.dropout_layers = [ - tf.keras.layers.Dropout(self.dropout_rate, seed=self.seed + i) - for i in range(len(self.hidden_units)) - ] - - self.activation_layers = [ - activation_layer(self.activation) - for _ in range(len(self.hidden_units) - 1) - ] - - if self.output_activation: - self.activation_layers += [activation_layer(self.output_activation)] - else: - self.activation_layers += [activation_layer(self.activation)] - - super(DNN, self).build(input_shape) # Be sure to call this somewhere! - - def call(self, inputs, training=None, **kwargs): - - deep_input = inputs - if self.need_ppnet: - deep_input = inputs[..., self.pp_size:] - pp_input = inputs[..., :self.pp_size] - #deep_input_wo_bp = tf.stop_gradient(deep_input) - #pp_input = tf.concat([pp_input, deep_input_wo_bp], axis=-1) - - for i in range(len(self.hidden_units)): - if self.need_ppnet: - deep_input = 2 * self.pp_gates[i](pp_input) * deep_input - fc = tf.nn.bias_add( - tf.tensordot(deep_input, self.kernels[i], axes=(-1, 0)), self.bias[i]) - - if self.use_bn: - fc = self.bn_layers[i](fc, training=training) - try: - fc = self.activation_layers[i](fc, training=training) - except TypeError as e: # TypeError: call() got an unexpected keyword argument 'training' - print('make sure the activation function use training flag properly', e) - fc = self.activation_layers[i](fc) - - fc = self.dropout_layers[i](fc, training=training) - deep_input = fc - - return deep_input - - def compute_output_shape(self, input_shape): - if len(self.hidden_units) > 0: - shape = input_shape[:-1] + (self.hidden_units[-1],) - else: - shape = input_shape - - return tuple(shape) - - def get_config(self,): - config = { - 'activation': self.activation, - 'hidden_units': self.hidden_units, - 'l2_reg': self.l2_reg, - 'use_bn': self.use_bn, - 'dropout_rate': self.dropout_rate, - 'output_activation': self.output_activation, - 'seed': self.seed - } - base_config = super(DNN, self).get_config() - return dict(list(base_config.items()) + list(config.items())) diff --git a/easy_rec/python/model/keep_model/model_ps_mmoe.py b/easy_rec/python/model/keep_model/model_ps_mmoe.py deleted file mode 100644 index 274a928b6..000000000 --- a/easy_rec/python/model/keep_model/model_ps_mmoe.py +++ /dev/null @@ -1,100 +0,0 @@ -#!/usr/bin/env python -# encoding: utf-8 -"""MMOE model.""" -import tensorflow as tf -from tensorflow.keras.layers import Activation -from tensorflow.keras.layers import BatchNormalization -from tensorflow.keras.layers import Dense -from tensorflow.keras.layers import Multiply -from tensorflow.keras.models import Model - -# from utils.basic_utils import * -# from model.base_model_ps import BaseModel -from easy_rec.python.model.keep_model.dnn import DNN -from easy_rec.python.model.keep_model.senet import SENETLayer - - -class CustomizedModel(Model): - - def __init__(self, model_conf, indim, name='export_model'): - super(CustomizedModel, self).__init__(name=name) - self.label_conf_list = [ - conf for conf in model_conf['label'] if conf.get('weight', 1) > 0 - ] - self.experts = model_conf['experts'] - self.expert_layers = model_conf['expert_layers'] - self.tower_layers = model_conf['tower_layers'] - self.need_senet = model_conf.get('need_senet', False) - if self.need_senet: - self.field_size = model_conf['field_size'] - self.se_layer = SENETLayer(self.field_size) - self.need_ppnet = model_conf.get('need_ppnet', False) - self.pp_feature_cnt = model_conf.get('pp_feature_cnt', 0) - embed_size = 11 - - # self._set_inputs(tf.TensorSpec([None, indim], tf.float32, name='embed_input')) - - self.expert_list = [ - DNN(self.expert_layers, - activation='relu', - use_bn=True, - output_activation='relu', - need_ppnet=self.need_ppnet, - pp_size=self.pp_feature_cnt * embed_size) - for _ in range(self.experts) - ] - - self.tower_list = [ - DNN(self.tower_layers, - activation='relu', - use_bn=True, - output_activation='sigmoid', - need_ppnet=self.need_ppnet, - pp_size=self.pp_feature_cnt * embed_size) - for _ in self.label_conf_list - ] - - self.gate_list = [ - DNN([self.experts], - activation='relu', - use_bn=False, - output_activation='linear') for _ in self.label_conf_list - ] - - def call(self, embed_input, training=False): - if self.need_senet: - embed_input = self.se_layer(embed_input) - expert_list = [] - for expert_layer in self.expert_list: - # (N, 1, d1) - expert = tf.expand_dims(expert_layer(embed_input), axis=1) - expert_list.append(expert) - # (N, n, d1) - concat_expert = tf.concat(expert_list, axis=1) - - output_list = [] - for i, label_conf in enumerate(self.label_conf_list): - # (N, n, 1) - gate = tf.expand_dims( - tf.nn.softmax(self.gate_list[i](embed_input)), axis=2) - # (N, d1) - output = tf.reduce_sum(concat_expert * gate, axis=1) - # (N, 1) with softmax - output = self.tower_list[i](output) - output_list.append(output) - - return output_list - - -# class ExpModel(BaseModel): -# def __init__(self, conf): -# """ -# 初始化参数 -# :param worker_list: 指定的 worker 节点列表 -# :param embed_share: 0 全独立 1 field内共享 2 全共享 -# """ -# super(ExpModel, self).__init__(conf) -# -# def get_export_model_classs(self): -# return CustomizedModel -# diff --git a/easy_rec/python/model/keep_model/senet.py b/easy_rec/python/model/keep_model/senet.py deleted file mode 100644 index e48e3328e..000000000 --- a/easy_rec/python/model/keep_model/senet.py +++ /dev/null @@ -1,112 +0,0 @@ -import math - -import tensorflow as tf -from tensorflow.python.keras import backend as K -from tensorflow.python.keras.initializers import glorot_normal -from tensorflow.python.keras.layers import Layer - - -class SENETLayer(Layer): - """SENETLayer used in FiBiNET. Input shape. - - - tensor with shape: ``(batch_size,field_size*embedding_size)``. - Output shape - - tensor with shape: ``(batch_size,field_size*embedding_size)``. - Arguments - - **reduction_ratio** : Positive integer, dimensionality of the - attention network output space. - - **seed** : A Python integer to use as random seed. - - **norm** : norm function to norm attention factor, l2 or softmax. - - **last_activation** : last act function, relu or identity. - - **field_size** : feature field number. - References - - [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) - """ - - def __init__(self, - field_size, - norm='l2', - last_activation='identity', - reduction_ratio=3, - seed=1024, - **kwargs): - self.field_size = field_size - self.norm = norm - self.last_activation = last_activation - self.reduction_ratio = reduction_ratio - self.seed = seed - super(SENETLayer, self).__init__(**kwargs) - - def tensordot(self, x, y): - return tf.tensordot(x, y, axes=(-1, 0)) - - def build(self, input_shape): - - if len(input_shape) != 2: - raise ValueError('A `SENETLayer` layer should be called ' - 'on a 2D tensor.') - - # if not divisible, raise an exception - if input_shape[1] % self.field_size != 0: - raise ValueError( - f'Embed input size must be divisible by field_size, got {input_shape[1]} and {self.field_size}' - ) - self.embedding_size = int(input_shape[1] / self.field_size) - reduction_size = max(1, self.field_size // self.reduction_ratio) - - self.W_1 = self.add_weight( - shape=(self.field_size, reduction_size), - initializer=glorot_normal(seed=self.seed), - name='W1', - trainable=True) - self.W_2 = self.add_weight( - shape=(reduction_size, self.field_size), - initializer=glorot_normal(seed=self.seed), - name='W2', - trainable=True) - - # zty note:Lambda layer may cause variable not automatically - # added to the variable set for gradient calculation - # DO NOT use Lambda layer if possible. - #self.tensordot = tf.keras.layers.Lambda( - # lambda x: tf.tensordot(x[0], x[1], axes=(-1, 0))) - - # Be sure to call this somewhere! - super(SENETLayer, self).build(input_shape) - - def call(self, inputs, training=None, **kwargs): - - if K.ndim(inputs) != 2: - raise ValueError( - 'Unexpected inputs dimensions %d, expect to be 2 dimensions' % - (K.ndim(inputs))) - - inputs = tf.reshape(inputs, [-1, self.field_size, self.embedding_size]) - Z = tf.reduce_mean(inputs, axis=-1) - - A_1 = tf.nn.relu(self.tensordot(Z, self.W_1)) - A_2 = self.tensordot(A_1, self.W_2) - if self.last_activation == 'relu': - A_2 = tf.nn.relu(A_2) - - A_2 = tf.nn.l2_normalize( - A_2, axis=1) if self.norm == 'l2' else tf.nn.softmax( - A_2, axis=1) - A_2 = A_2 * math.sqrt(self.field_size) - output = tf.multiply(inputs, tf.expand_dims(A_2, axis=2)) - output = tf.reshape(output, [-1, self.field_size * self.embedding_size]) - - return output - - def compute_output_shape(self, input_shape): - - return input_shape - - def compute_mask(self, inputs, mask=None): - return [None] * self.field_size - - def get_config(self,): - config = {'reduction_ratio': self.reduction_ratio, 'seed': self.seed} - base_config = super(SENETLayer, self).get_config() - base_config.update(config) - return base_config diff --git a/easy_rec/python/protos/train.proto b/easy_rec/python/protos/train.proto index 24d35bc6a..83cf09814 100644 --- a/easy_rec/python/protos/train.proto +++ b/easy_rec/python/protos/train.proto @@ -21,6 +21,7 @@ enum DistributionStrategy { MultiWorkerMirroredStrategy = 5; // use horovod strategy HorovodStrategy = 6; + SokStrategy = 7; } message IncrementSaveConfig { diff --git a/easy_rec/python/train_eval.py b/easy_rec/python/train_eval.py index a7ebca5db..c7c75c26c 100644 --- a/easy_rec/python/train_eval.py +++ b/easy_rec/python/train_eval.py @@ -164,6 +164,9 @@ if pipeline_config.train_config.train_distribute == DistributionStrategy.HorovodStrategy: estimator_utils.init_hvd() + elif pipeline_config.train_config.train_distribute == DistributionStrategy.SokStrategy: + estimator_utils.init_hvd() + estimator_utils.init_sok() if args.hpo_param_path: with gfile.GFile(args.hpo_param_path, 'r') as fin: diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index 410e6dc88..006ee06eb 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -38,6 +38,11 @@ except Exception: hvd = None +try: + from sparse_operation_kit import experiment as sok +except Exception: + sok = None + try: from kafka import KafkaProducer, KafkaAdminClient from kafka.admin import NewTopic @@ -614,6 +619,7 @@ def after_run(self, run_context, run_values): def _save(self, session, step): """Saves the latest checkpoint, returns should_stop.""" logging.info('Saving checkpoints for %d into %s.', step, self._save_path) + return False for l in self._listeners: # noqa: E741 l.before_save(session, step) @@ -998,6 +1004,10 @@ def has_hvd(): return hvd is not None and 'HOROVOD_RANK' in os.environ +def has_sok(): + return sok is not None and 'ENABLE_SOK' in os.environ + + def init_hvd(): if hvd is None: logging.error( @@ -1009,6 +1019,16 @@ def init_hvd(): os.environ['HOROVOD_RANK'] = str(hvd.rank()) +def init_sok(): + try: + sok.init() + os.environ['ENABLE_SOK'] = '1' + return True + except Exception: + logging.error('sok is not installed') + return False + + def get_available_gpus(): local_device_protos = device_lib.list_local_devices() return [x.name for x in local_device_protos if x.device_type == 'GPU'] diff --git a/easy_rec/python/utils/hvd_utils.py b/easy_rec/python/utils/hvd_utils.py new file mode 100644 index 000000000..c2362063c --- /dev/null +++ b/easy_rec/python/utils/hvd_utils.py @@ -0,0 +1,50 @@ +# -*- encoding: utf-8 -*- +import logging + +import tensorflow as tf +from tensorflow.python.training import session_run_hook + +# from horovod.tensorflow.compression import Compression +try: + from horovod.tensorflow.functions import broadcast_variables +except Exception: + pass + +if tf.__version__ >= '2.0': + tf = tf.compat.v1 + + +class BroadcastGlobalVariablesHook(session_run_hook.SessionRunHook): + """SessionRunHook that will broadcast all global variables from root rank to all other processes during initialization. + + This is necessary to ensure consistent initialization of all workers when + training is started with random weights or restored from a checkpoint. + """ # noqa: E501 + + def __init__(self, root_rank, device=''): + """Construct a new BroadcastGlobalVariablesHook that will broadcast all global variables from root rank to all other processes during initialization. + + Args: + root_rank: + Rank that will send data, other ranks will receive data. + device: + Device to be used for broadcasting. Uses GPU by default + if Horovod was built with HOROVOD_GPU_OPERATIONS. + """ # noqa: E501 + super(BroadcastGlobalVariablesHook, self).__init__() + self.root_rank = root_rank + self.bcast_op = None + self.device = device + + def begin(self): + bcast_vars = [] + for x in tf.global_variables(): + if '/embedding' not in x.name: + bcast_vars.append(x) + logging.info('will broadcast variable: %s' % x.name) + if not self.bcast_op or self.bcast_op.graph != tf.get_default_graph(): + with tf.device(self.device): + self.bcast_op = broadcast_variables(bcast_vars, self.root_rank) + + def after_create_session(self, session, coord): + session.run(self.bcast_op) diff --git a/keep_model.config b/keep_model.config deleted file mode 100644 index 8bec3a8c9..000000000 --- a/keep_model.config +++ /dev/null @@ -1,2918 +0,0 @@ -train_input_path: 'test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet,test.parquet' -eval_input_path: 'test.parquet' -model_dir: "experiments/keep_model/" - -train_config { - optimizer_config: { - adagrad_optimizer: { - # adam_optimizer: { - learning_rate: { - constant_learning_rate { - learning_rate: 0.001 - } - } - } - } - log_step_count_steps: 5 - save_checkpoints_steps: 100000 - sync_replicas: True - # is_profiling: True -} - - -data_config { - batch_size: 8192 - label_fields: 'is_click' - label_fields: 'is_train' - num_epochs: 1 - prefetch_size: 32 - - # input_type: DummyInput - input_type: ParquetInput - - - input_fields { - input_name: 'is_click' - input_type: INT64 - } - input_fields { - input_name: 'is_train' - input_type: INT64 - } - - input_fields { - input_name: 'user_id' - input_type: INT64 - } - input_fields { - input_name: 'item_id' - input_type: INT64 - } - input_fields { - input_name: 'age' - input_type: INT64 - } - input_fields { - input_name: 'height' - input_type: INT64 - } - input_fields { - input_name: 'weight' - input_type: INT64 - } - input_fields { - input_name: 'gender' - input_type: INT64 - } - input_fields { - input_name: 'vip_status' - input_type: INT64 - } - input_fields { - input_name: 'register_goals' - input_type: STRING - } - input_fields { - input_name: 'kg' - input_type: INT64 - } - input_fields { - input_name: 'bmi' - input_type: INT64 - } - input_fields { - input_name: 'training_duration_total' - input_type: INT64 - } - input_fields { - input_name: 'training_calorie_total' - input_type: INT64 - } - input_fields { - input_name: 'training_days_total' - input_type: INT64 - } - input_fields { - input_name: 'all_calorie_total' - input_type: INT64 - } - input_fields { - input_name: 'running_times_total' - input_type: INT64 - } - input_fields { - input_name: 'hiking_times_total' - input_type: INT64 - } - input_fields { - input_name: 'tags' - input_type: STRING - } - input_fields { - input_name: 'trainingpoints' - input_type: STRING - } - input_fields { - input_name: 'his_no_month_no' - input_type: INT64 - } - input_fields { - input_name: 'his_no_month_yes' - input_type: INT64 - } - input_fields { - input_name: 'his_yes_month_no' - input_type: INT64 - } - input_fields { - input_name: 'his_yes_month_yes' - input_type: INT64 - } - input_fields { - input_name: 'new_7days' - input_type: INT64 - } - input_fields { - input_name: 'is_meditation_ru' - input_type: INT64 - } - input_fields { - input_name: 'is_skipping_ru' - input_type: INT64 - } - input_fields { - input_name: 'is_buyer_ru' - input_type: INT64 - } - input_fields { - input_name: 'network' - input_type: INT64 - } - input_fields { - input_name: 'c_os' - input_type: INT64 - } - input_fields { - input_name: 'recent_plan_list' - input_type: STRING - } - input_fields { - input_name: 'preference' - input_type: STRING - } - input_fields { - input_name: 'interested_types' - input_type: STRING - } - input_fields { - input_name: 'sub_target' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_trainingType' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_subtype' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_dataType' - input_type: INT64 - } - input_fields { - input_name: 'author' - input_type: INT64 - } - input_fields { - input_name: 'plan_difficulty' - input_type: INT64 - } - input_fields { - input_name: 'calorie' - input_type: INT64 - } - input_fields { - input_name: 'average_duration' - input_type: INT64 - } - input_fields { - input_name: 'official' - input_type: INT64 - } - input_fields { - input_name: 'paid_type' - input_type: INT64 - } - input_fields { - input_name: 'course_score' - input_type: INT64 - } - input_fields { - input_name: 'target' - input_type: STRING - } - input_fields { - input_name: 'equipment' - input_type: INT64 - } - input_fields { - input_name: 'data_type' - input_type: INT64 - } - input_fields { - input_name: 'label_ids' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_int_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_int_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_int_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_click_list_int_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_click_list_int_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_click_list_int_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_click_list_int_1' - input_type: INT64 - } - input_fields { - input_name: 'u_plan_train_list_int_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_train_list_int_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_train_list_int_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_train_list_int_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_train_list_int_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_train_list_int_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_train_list_int_1' - input_type: INT64 - } - input_fields { - input_name: 'u_plan_complete_list_int_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_int_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_complete_list_int_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_complete_list_int_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_complete_list_int_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_homepage8_complete_list_int_1' - input_type: INT64 - } - input_fields { - input_name: 'u_train_label_list_50' - input_type: STRING - } - input_fields { - input_name: 'u_train_label_list_10' - input_type: STRING - } - input_fields { - input_name: 'u_train_label_list_3' - input_type: STRING - } - input_fields { - input_name: 'u_train_trainingType_list_10' - input_type: STRING - } - input_fields { - input_name: 'u_train_subtype_list_10' - input_type: STRING - } - input_fields { - input_name: 'u_train_dataType_list_10' - input_type: STRING - } - input_fields { - input_name: 'c_recall_sources' - input_type: STRING - } - input_fields { - input_name: 'u_plan_unclick_list_int_50' - input_type: STRING - } - input_fields { - input_name: 'u_plan_unclick_list_int_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_unclick_list_int_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_author_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_author_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_author_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_dataType_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_dataType_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_dataType_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_difficulty_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_difficulty_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_difficulty_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_official_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_author_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_author_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_author_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_dataType_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_dataType_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_difficulty_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_difficulty_3' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_official_100' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_official_10' - input_type: STRING - } - input_fields { - input_name: 'u_plan_complete_list_official_3' - input_type: STRING - } - input_fields { - input_name: 'training_goals_show_year_0' - input_type: INT64 - } - input_fields { - input_name: 'training_goals_show_year_1' - input_type: INT64 - } - input_fields { - input_name: 'training_goals_show_year_2' - input_type: INT64 - } - input_fields { - input_name: 'training_goals_show_year_4' - input_type: INT64 - } - input_fields { - input_name: 'training_goals_show_year_6' - input_type: INT64 - } - input_fields { - input_name: 'training_goals_show_year_8' - input_type: INT64 - } - input_fields { - input_name: 'trainingpoints_show_year_0' - input_type: INT64 - } - input_fields { - input_name: 'trainingpoints_show_year_3' - input_type: INT64 - } - input_fields { - input_name: 'trainingpoints_show_year_4' - input_type: INT64 - } - input_fields { - input_name: 'trainingpoints_show_year_7' - input_type: INT64 - } - input_fields { - input_name: 'trainingpoints_show_year_9' - input_type: INT64 - } - input_fields { - input_name: 'i_plan_difficulty_show_cnt_01_d' - input_type: INT64 - } - input_fields { - input_name: 'i_plan_difficulty_show_cnt_07_d' - input_type: INT64 - } - input_fields { - input_name: 'i_plan_difficulty_show_cnt_14_d' - input_type: INT64 - } - input_fields { - input_name: 'i_plan_difficulty_show_cnt_28_d' - input_type: INT64 - } - input_fields { - input_name: 'i_plan_difficulty_click_cnt_28_d' - input_type: INT64 - } - input_fields { - input_name: 'i_plan_difficulty_train_cnt_28_d' - input_type: INT64 - } - input_fields { - input_name: 'i_duration_seg_show_cnt_01_d' - input_type: INT64 - } - input_fields { - input_name: 'i_duration_seg_click_cnt_01_d' - input_type: INT64 - } - input_fields { - input_name: 'i_duration_seg_show_cnt_07_d' - input_type: INT64 - } - input_fields { - input_name: 'i_duration_seg_click_cnt_07_d' - input_type: INT64 - } - input_fields { - input_name: 'i_duration_seg_show_cnt_14_d' - input_type: INT64 - } - input_fields { - input_name: 'i_duration_seg_click_cnt_14_d' - input_type: INT64 - } - input_fields { - input_name: 'i_duration_seg_show_cnt_28_d' - input_type: INT64 - } - input_fields { - input_name: 'i_duration_seg_click_cnt_28_d' - input_type: INT64 - } - input_fields { - input_name: 'i_duration_seg_train_cnt_28_d' - input_type: INT64 - } - input_fields { - input_name: 'u_age_seg_train_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_bmi_seg_train_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_difficulty_train_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_gender_train_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_age_seg_show_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_age_seg_click_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_age_seg_complete_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_bmi_seg_show_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_bmi_seg_click_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_bmi_seg_complete_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_difficulty_show_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_difficulty_click_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_difficulty_complete_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_gender_show_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_gender_click_cnt' - input_type: INT64 - } - input_fields { - input_name: 'u_gender_complete_cnt' - input_type: INT64 - } - input_fields { - input_name: 'i_plan_difficulty_train_cvr2_28_d' - input_type: INT64 - } - input_fields { - input_name: 'u_age_seg_ctr' - input_type: INT64 - } - input_fields { - input_name: 'u_age_seg_cvr1' - input_type: INT64 - } - input_fields { - input_name: 'u_bmi_seg_ctr' - input_type: INT64 - } - input_fields { - input_name: 'u_bmi_seg_cvr1' - input_type: INT64 - } - input_fields { - input_name: 'u_bmi_seg_train_cvr2' - input_type: INT64 - } - input_fields { - input_name: 'u_difficulty_ctr' - input_type: INT64 - } - input_fields { - input_name: 'u_difficulty_cvr1' - input_type: INT64 - } - input_fields { - input_name: 'u_difficulty_train_cvr2' - input_type: INT64 - } - input_fields { - input_name: 'u_gender_ctr' - input_type: INT64 - } - input_fields { - input_name: 'u_gender_cvr1' - input_type: INT64 - } - input_fields { - input_name: 'i_total_click' - input_type: INT64 - } - input_fields { - input_name: 'i_decay1_show' - input_type: INT64 - } - input_fields { - input_name: 'i_decay1_click' - input_type: INT64 - } - input_fields { - input_name: 'i_decay1_train' - input_type: INT64 - } - input_fields { - input_name: 'i_decay2_show' - input_type: INT64 - } - input_fields { - input_name: 'i_decay2_click' - input_type: INT64 - } - input_fields { - input_name: 'i_decay2_train' - input_type: INT64 - } - input_fields { - input_name: 'i_decay3_show' - input_type: INT64 - } - input_fields { - input_name: 'i_decay3_click' - input_type: INT64 - } - input_fields { - input_name: 'i_decay3_train' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_distance' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_duration' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_calorie' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_totalSteps' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_averageSpeed' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_accumulativeUpliftedHeight' - input_type: INT64 - } - input_fields { - input_name: 'u_last_train_accumulativeClimbingDistance' - input_type: INT64 - } - input_fields { - input_name: 'u_total_train_distance' - input_type: INT64 - } - input_fields { - input_name: 'u_total_train_totalSteps' - input_type: INT64 - } - input_fields { - input_name: 'u_total_train_accumulativeClimbingDistance' - input_type: INT64 - } - input_fields { - input_name: 'u_plan_click_other_list_int' - input_type: STRING - } - input_fields { - input_name: 'c_hour' - input_type: INT64 - } - input_fields { - input_name: 'content_type' - input_type: INT64 - } - input_fields { - input_name: 'i_total_click_rate_i_total_show' - input_type: INT64 - } - input_fields { - input_name: 'i_total_train_rate_i_total_show' - input_type: INT64 - } - input_fields { - input_name: 'i_decay3_train_rate_i_decay3_show' - input_type: INT64 - } - input_fields { - input_name: 'i_decay3_click_rate_i_decay3_show' - input_type: INT64 - } - input_fields { - input_name: 'gender_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'gender_cross_author' - input_type: INT64 - } - input_fields { - input_name: 'gender_cross_paid_type' - input_type: INT64 - } - input_fields { - input_name: 'age_seg_cross_plan_difficulty' - input_type: INT64 - } - input_fields { - input_name: 'age_seg_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'age_seg_cross_author' - input_type: INT64 - } - input_fields { - input_name: 'bmi_seg_cross_plan_difficulty' - input_type: INT64 - } - input_fields { - input_name: 'bmi_seg_cross_paid_type' - input_type: INT64 - } - input_fields { - input_name: 'his_no_month_no_cross_plan_difficulty' - input_type: INT64 - } - input_fields { - input_name: 'his_no_month_no_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'his_no_month_no_cross_author' - input_type: INT64 - } - input_fields { - input_name: 'his_no_month_yes_cross_plan_difficulty' - input_type: INT64 - } - input_fields { - input_name: 'his_no_month_yes_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'his_no_month_yes_cross_paid_type' - input_type: INT64 - } - input_fields { - input_name: 'his_yes_month_no_cross_plan_difficulty' - input_type: INT64 - } - input_fields { - input_name: 'his_yes_month_no_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'his_yes_month_no_cross_paid_type' - input_type: INT64 - } - input_fields { - input_name: 'his_yes_month_yes_cross_plan_difficulty' - input_type: INT64 - } - input_fields { - input_name: 'his_yes_month_yes_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'his_yes_month_yes_cross_paid_type' - input_type: INT64 - } - input_fields { - input_name: 'vip_status_cross_plan_difficulty' - input_type: INT64 - } - input_fields { - input_name: 'vip_status_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'vip_status_cross_author' - input_type: INT64 - } - input_fields { - input_name: 'vip_status_cross_paid_type' - input_type: INT64 - } - input_fields { - input_name: 'is_meditation_ru_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'is_skipping_ru_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'is_power_ru_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'is_yoga_ru_cross_data_type' - input_type: INT64 - } - input_fields { - input_name: 'is_vip_ru_cross_paid_type' - input_type: INT64 - } - input_fields { - input_name: 'is_buyer_ru_cross_paid_type' - input_type: INT64 - } - input_fields { - input_name: 'network_concat_item_id' - input_type: INT64 - } - input_fields { - input_name: 'network_concat_data_type' - input_type: INT64 - } - input_fields { - input_name: 'c_os_concat_item_id' - input_type: INT64 - } - input_fields { - input_name: 'c_os_concat_data_type' - input_type: INT64 - } - input_fields { - input_name: 'gender_concat_item_id' - input_type: INT64 - } - input_fields { - input_name: 'age_seg_concat_item_id' - input_type: INT64 - } - input_fields { - input_name: 'vip_status_concat_item_id' - input_type: INT64 - } - input_fields { - input_name: 'u_plan_click_list_author_hit_author' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_int_match_u_plan_click_list_author_match_author' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_official_hit_official' - input_type: STRING - } - input_fields { - input_name: 'u_plan_click_list_int_match_u_plan_click_list_official_match_official' - input_type: STRING - } - input_fields { - input_name: 'u_plan_unclick_list_int_hit_item_id_int' - input_type: STRING - } - input_fields { - input_name: 'u_train_label_list_overlap_label_ids' - input_type: INT64 - } - input_fields { - input_name: 'u_train_label_list_hit_label_ids' - input_type: STRING - } - -} - -feature_config { - features: { - input_names: 'user_id' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'item_id' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'age' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'height' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'weight' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'gender' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'vip_status' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'register_goals' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'kg' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'bmi' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'training_duration_total' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'training_calorie_total' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'training_days_total' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'all_calorie_total' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'running_times_total' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'hiking_times_total' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'tags' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'trainingpoints' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_no_month_no' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_no_month_yes' - 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input_names: 'is_buyer_ru' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'network' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'c_os' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'recent_plan_list' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'preference' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'interested_types' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'sub_target' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_trainingType' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_subtype' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_dataType' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'author' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'plan_difficulty' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - 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max_partitions: 4 - } - features: { - input_names: 'equipment' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'label_ids' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_click_list_int_100' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_click_list_int_10' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_click_list_int_3' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_click_list_int_100' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_click_list_int_10' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_click_list_int_3' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_click_list_int_1' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_train_list_int_100' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_train_list_int_10' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_train_list_int_3' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_train_list_int_100' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_train_list_int_10' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_train_list_int_3' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_train_list_int_1' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_complete_list_int_10' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_complete_list_int_3' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_complete_list_int_100' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_complete_list_int_10' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_homepage8_complete_list_int_3' - feature_type: 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max_partitions: 4 - } - features: { - input_names: 'u_train_subtype_list_10' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_train_dataType_list_10' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'c_recall_sources' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_unclick_list_int_50' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_unclick_list_int_10' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_unclick_list_int_3' - feature_type: 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max_partitions: 4 - } - features: { - input_names: 'i_duration_seg_show_cnt_01_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_duration_seg_click_cnt_01_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_duration_seg_show_cnt_07_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_duration_seg_click_cnt_07_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_duration_seg_show_cnt_14_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_duration_seg_click_cnt_14_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_duration_seg_show_cnt_28_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_duration_seg_click_cnt_28_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_duration_seg_train_cnt_28_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_age_seg_train_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_bmi_seg_train_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_difficulty_train_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_gender_train_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_age_seg_show_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_age_seg_click_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_age_seg_complete_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_bmi_seg_show_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_bmi_seg_click_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_bmi_seg_complete_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_difficulty_show_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_difficulty_click_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_difficulty_complete_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_gender_show_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_gender_click_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_gender_complete_cnt' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_plan_difficulty_train_cvr2_28_d' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_age_seg_ctr' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_age_seg_cvr1' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_bmi_seg_ctr' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_bmi_seg_cvr1' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_bmi_seg_train_cvr2' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_difficulty_ctr' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_difficulty_cvr1' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_difficulty_train_cvr2' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_gender_ctr' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_gender_cvr1' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_total_click' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay1_show' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay1_click' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay1_train' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay2_show' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay2_click' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay2_train' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay3_show' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay3_click' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay3_train' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_distance' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_duration' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_calorie' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_totalSteps' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_averageSpeed' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_accumulativeUpliftedHeight' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_last_train_accumulativeClimbingDistance' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_total_train_distance' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_total_train_totalSteps' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_total_train_accumulativeClimbingDistance' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_click_other_list_int' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'c_hour' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'content_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_total_click_rate_i_total_show' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_total_train_rate_i_total_show' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay3_train_rate_i_decay3_show' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'i_decay3_click_rate_i_decay3_show' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'gender_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'gender_cross_author' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'gender_cross_paid_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'age_seg_cross_plan_difficulty' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'age_seg_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'age_seg_cross_author' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'bmi_seg_cross_plan_difficulty' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'bmi_seg_cross_paid_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_no_month_no_cross_plan_difficulty' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_no_month_no_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_no_month_no_cross_author' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_no_month_yes_cross_plan_difficulty' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_no_month_yes_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_no_month_yes_cross_paid_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_yes_month_no_cross_plan_difficulty' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_yes_month_no_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_yes_month_no_cross_paid_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_yes_month_yes_cross_plan_difficulty' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_yes_month_yes_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'his_yes_month_yes_cross_paid_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'vip_status_cross_plan_difficulty' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'vip_status_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'vip_status_cross_author' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'vip_status_cross_paid_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'is_meditation_ru_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'is_skipping_ru_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'is_power_ru_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'is_yoga_ru_cross_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'is_vip_ru_cross_paid_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'is_buyer_ru_cross_paid_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'network_concat_item_id' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'network_concat_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'c_os_concat_item_id' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'c_os_concat_data_type' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'gender_concat_item_id' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'age_seg_concat_item_id' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'vip_status_concat_item_id' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_click_list_author_hit_author' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_click_list_int_match_u_plan_click_list_author_match_author' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_click_list_official_hit_official' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_click_list_int_match_u_plan_click_list_official_match_official' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_plan_unclick_list_int_hit_item_id_int' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_train_label_list_overlap_label_ids' - feature_type: IdFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } - features: { - input_names: 'u_train_label_list_hit_label_ids' - feature_type: TagFeature - embedding_dim: 16 - num_buckets: 1000000000 - embedding_name: 'all_fea' - max_partitions: 4 - } -} - -model_config { - model_class: 'PPNetV3M' - feature_groups { - group_name: 'all' - feature_names: "user_id" - feature_names: "item_id" - feature_names: "age" - feature_names: "height" - feature_names: "weight" - feature_names: "gender" - feature_names: "vip_status" - feature_names: "register_goals" - feature_names: "kg" - feature_names: "bmi" - feature_names: "training_duration_total" - feature_names: "training_calorie_total" - feature_names: "training_days_total" - feature_names: "all_calorie_total" - feature_names: "running_times_total" - feature_names: "hiking_times_total" - feature_names: "tags" - feature_names: "trainingpoints" - feature_names: "his_no_month_no" - feature_names: "his_no_month_yes" - feature_names: "his_yes_month_no" - feature_names: "his_yes_month_yes" - feature_names: "new_7days" - feature_names: "is_meditation_ru" - feature_names: "is_skipping_ru" - feature_names: "is_buyer_ru" - feature_names: "network" - feature_names: "c_os" - feature_names: "recent_plan_list" - feature_names: "preference" - feature_names: "interested_types" - feature_names: "sub_target" - feature_names: "u_last_train_trainingType" - feature_names: "u_last_train_subtype" - feature_names: "u_last_train_dataType" - feature_names: "author" - feature_names: "plan_difficulty" - feature_names: "calorie" - feature_names: "average_duration" - feature_names: "official" - feature_names: "paid_type" - feature_names: "course_score" - feature_names: "target" - feature_names: "equipment" - feature_names: "data_type" - feature_names: "label_ids" - feature_names: "u_plan_click_list_int_100" - feature_names: "u_plan_click_list_int_10" - feature_names: "u_plan_click_list_int_3" - feature_names: "u_plan_homepage8_click_list_int_100" - feature_names: "u_plan_homepage8_click_list_int_10" - feature_names: "u_plan_homepage8_click_list_int_3" - feature_names: "u_plan_homepage8_click_list_int_1" - feature_names: "u_plan_train_list_int_100" - feature_names: "u_plan_train_list_int_10" - feature_names: "u_plan_train_list_int_3" - feature_names: "u_plan_homepage8_train_list_int_100" - feature_names: "u_plan_homepage8_train_list_int_10" - feature_names: "u_plan_homepage8_train_list_int_3" - feature_names: "u_plan_homepage8_train_list_int_1" - feature_names: "u_plan_complete_list_int_10" - feature_names: "u_plan_complete_list_int_3" - feature_names: "u_plan_homepage8_complete_list_int_100" - feature_names: "u_plan_homepage8_complete_list_int_10" - feature_names: "u_plan_homepage8_complete_list_int_3" - feature_names: "u_plan_homepage8_complete_list_int_1" - feature_names: "u_train_label_list_50" - feature_names: "u_train_label_list_10" - feature_names: "u_train_label_list_3" - feature_names: "u_train_trainingType_list_10" - feature_names: "u_train_subtype_list_10" - feature_names: "u_train_dataType_list_10" - feature_names: "c_recall_sources" - feature_names: "u_plan_unclick_list_int_50" - feature_names: "u_plan_unclick_list_int_10" - feature_names: "u_plan_unclick_list_int_3" - feature_names: "u_plan_click_list_author_100" - feature_names: "u_plan_click_list_author_10" - feature_names: "u_plan_click_list_author_3" - feature_names: "u_plan_click_list_dataType_100" - feature_names: "u_plan_click_list_dataType_10" - feature_names: "u_plan_click_list_dataType_3" - feature_names: "u_plan_click_list_difficulty_100" - feature_names: "u_plan_click_list_difficulty_10" - feature_names: "u_plan_click_list_difficulty_3" - feature_names: "u_plan_click_list_official_10" - feature_names: "u_plan_complete_list_author_100" - feature_names: "u_plan_complete_list_author_10" - feature_names: "u_plan_complete_list_author_3" - feature_names: "u_plan_complete_list_dataType_10" - feature_names: "u_plan_complete_list_dataType_3" - feature_names: "u_plan_complete_list_difficulty_10" - feature_names: "u_plan_complete_list_difficulty_3" - feature_names: "u_plan_complete_list_official_100" - feature_names: "u_plan_complete_list_official_10" - feature_names: "u_plan_complete_list_official_3" - feature_names: "training_goals_show_year_0" - feature_names: "training_goals_show_year_1" - feature_names: "training_goals_show_year_2" - feature_names: "training_goals_show_year_4" - feature_names: "training_goals_show_year_6" - feature_names: "training_goals_show_year_8" - feature_names: "trainingpoints_show_year_0" - feature_names: "trainingpoints_show_year_3" - feature_names: "trainingpoints_show_year_4" - feature_names: "trainingpoints_show_year_7" - feature_names: "trainingpoints_show_year_9" - feature_names: "i_plan_difficulty_show_cnt_01_d" - feature_names: "i_plan_difficulty_show_cnt_07_d" - feature_names: "i_plan_difficulty_show_cnt_14_d" - feature_names: "i_plan_difficulty_show_cnt_28_d" - feature_names: "i_plan_difficulty_click_cnt_28_d" - feature_names: "i_plan_difficulty_train_cnt_28_d" - feature_names: "i_duration_seg_show_cnt_01_d" - feature_names: "i_duration_seg_click_cnt_01_d" - feature_names: "i_duration_seg_show_cnt_07_d" - feature_names: "i_duration_seg_click_cnt_07_d" - feature_names: "i_duration_seg_show_cnt_14_d" - feature_names: "i_duration_seg_click_cnt_14_d" - feature_names: "i_duration_seg_show_cnt_28_d" - feature_names: "i_duration_seg_click_cnt_28_d" - feature_names: "i_duration_seg_train_cnt_28_d" - feature_names: "u_age_seg_train_cnt" - feature_names: "u_bmi_seg_train_cnt" - feature_names: "u_difficulty_train_cnt" - feature_names: "u_gender_train_cnt" - feature_names: "u_age_seg_show_cnt" - feature_names: "u_age_seg_click_cnt" - feature_names: "u_age_seg_complete_cnt" - feature_names: "u_bmi_seg_show_cnt" - feature_names: "u_bmi_seg_click_cnt" - feature_names: "u_bmi_seg_complete_cnt" - feature_names: "u_difficulty_show_cnt" - feature_names: "u_difficulty_click_cnt" - feature_names: "u_difficulty_complete_cnt" - feature_names: "u_gender_show_cnt" - feature_names: "u_gender_click_cnt" - feature_names: "u_gender_complete_cnt" - feature_names: "i_plan_difficulty_train_cvr2_28_d" - feature_names: "u_age_seg_ctr" - feature_names: "u_age_seg_cvr1" - feature_names: "u_bmi_seg_ctr" - feature_names: "u_bmi_seg_cvr1" - feature_names: "u_bmi_seg_train_cvr2" - feature_names: "u_difficulty_ctr" - feature_names: "u_difficulty_cvr1" - feature_names: "u_difficulty_train_cvr2" - feature_names: "u_gender_ctr" - feature_names: "u_gender_cvr1" - feature_names: "i_total_click" - feature_names: "i_decay1_show" - feature_names: "i_decay1_click" - feature_names: "i_decay1_train" - feature_names: "i_decay2_show" - feature_names: "i_decay2_click" - feature_names: "i_decay2_train" - feature_names: "i_decay3_show" - feature_names: "i_decay3_click" - feature_names: "i_decay3_train" - feature_names: "u_last_train_distance" - feature_names: "u_last_train_duration" - feature_names: "u_last_train_calorie" - feature_names: "u_last_train_totalSteps" - feature_names: "u_last_train_averageSpeed" - feature_names: "u_last_train_accumulativeUpliftedHeight" - feature_names: "u_last_train_accumulativeClimbingDistance" - feature_names: "u_total_train_distance" - feature_names: "u_total_train_totalSteps" - feature_names: "u_total_train_accumulativeClimbingDistance" - feature_names: "u_plan_click_other_list_int" - feature_names: "c_hour" - feature_names: "content_type" - feature_names: "i_total_click_rate_i_total_show" - feature_names: "i_total_train_rate_i_total_show" - feature_names: "i_decay3_train_rate_i_decay3_show" - feature_names: "i_decay3_click_rate_i_decay3_show" - feature_names: "gender_cross_data_type" - feature_names: "gender_cross_author" - feature_names: "gender_cross_paid_type" - feature_names: "age_seg_cross_plan_difficulty" - feature_names: "age_seg_cross_data_type" - feature_names: "age_seg_cross_author" - feature_names: "bmi_seg_cross_plan_difficulty" - feature_names: "bmi_seg_cross_paid_type" - feature_names: "his_no_month_no_cross_plan_difficulty" - feature_names: "his_no_month_no_cross_data_type" - feature_names: "his_no_month_no_cross_author" - feature_names: "his_no_month_yes_cross_plan_difficulty" - feature_names: "his_no_month_yes_cross_data_type" - feature_names: "his_no_month_yes_cross_paid_type" - feature_names: "his_yes_month_no_cross_plan_difficulty" - feature_names: "his_yes_month_no_cross_data_type" - feature_names: "his_yes_month_no_cross_paid_type" - feature_names: "his_yes_month_yes_cross_plan_difficulty" - feature_names: "his_yes_month_yes_cross_data_type" - feature_names: "his_yes_month_yes_cross_paid_type" - feature_names: "vip_status_cross_plan_difficulty" - feature_names: "vip_status_cross_data_type" - feature_names: "vip_status_cross_author" - feature_names: "vip_status_cross_paid_type" - feature_names: "is_meditation_ru_cross_data_type" - feature_names: "is_skipping_ru_cross_data_type" - feature_names: "is_power_ru_cross_data_type" - feature_names: "is_yoga_ru_cross_data_type" - feature_names: "is_vip_ru_cross_paid_type" - feature_names: "is_buyer_ru_cross_paid_type" - feature_names: "network_concat_item_id" - feature_names: "network_concat_data_type" - feature_names: "c_os_concat_item_id" - feature_names: "c_os_concat_data_type" - feature_names: "gender_concat_item_id" - feature_names: "age_seg_concat_item_id" - feature_names: "vip_status_concat_item_id" - feature_names: "u_plan_click_list_author_hit_author" - feature_names: "u_plan_click_list_int_match_u_plan_click_list_author_match_author" - feature_names: "u_plan_click_list_official_hit_official" - feature_names: "u_plan_click_list_int_match_u_plan_click_list_official_match_official" - feature_names: "u_plan_unclick_list_int_hit_item_id_int" - feature_names: "u_train_label_list_overlap_label_ids" - feature_names: "u_train_label_list_hit_label_ids" - } - - ppnet { - model_conf: "model_conf.json" - } -} diff --git a/model_conf.json b/model_conf.json deleted file mode 100644 index 76f18649d..000000000 --- a/model_conf.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "model_name": "PLAN_HOME_PPNET_V3M", - "label": [ - { "input_name": "is_click", "weight": 1.0}, - { "input_name": "is_train", "weight": 0.5} - ], - "model_file_suffix": "ps_mmoe", - "expert_layers": [256, 128, 64], - "experts": 3, - "tower_layers": [64, 32, 1], - "need_senet": true, - "field_size": 220, - "need_ppnet": true, - "pp_feature_cnt": 2 -} diff --git a/samples/model_config/multi_tower_on_taobao_sok.config b/samples/model_config/multi_tower_on_taobao_sok.config new file mode 100644 index 000000000..df425cc51 --- /dev/null +++ b/samples/model_config/multi_tower_on_taobao_sok.config @@ -0,0 +1,326 @@ +train_input_path: "taobao_train_data_8192" +# train_input_path: "data/test/tb_data/taobao_train_data" +eval_input_path: "data/test/tb_data/taobao_test_data" +model_dir: "experiments/multi_tower_taobao_ckpt" + +train_config { + log_step_count_steps: 100 + optimizer_config: { + adam_optimizer: { + learning_rate: { + exponential_decay_learning_rate { + initial_learning_rate: 0.001 + decay_steps: 1000 + decay_factor: 0.5 + min_learning_rate: 0.00001 + } + } + } + use_moving_average: false + } + save_checkpoints_steps: 1000000 + log_step_count_steps: 10 + sync_replicas: False + train_distribute: SokStrategy + num_steps: 200000 + # is_profiling: true +} + +eval_config { + metrics_set: { + auc {} + } +} + +data_config { + input_fields { + input_name:'clk' + input_type: INT32 + } + input_fields { + input_name:'buy' + input_type: INT32 + } + input_fields { + input_name: 'pid' + input_type: STRING + # default_val: "430548_1007" + } + input_fields { + input_name: 'adgroup_id' + input_type: STRING + # default_val: "620392" + } + input_fields { + input_name: 'cate_id' + input_type: STRING + # default_val: "6519" + } + input_fields { + input_name: 'campaign_id' + input_type: STRING + # default_val: "336811" + } + input_fields { + input_name: 'customer' + input_type: STRING + # default_val: "252462" + } + input_fields { + input_name: 'brand' + input_type: STRING + # default_val: "247789" + } + input_fields { + input_name: 'user_id' + input_type: STRING + # default_val: "100002" + } + input_fields { + input_name: 'cms_segid' + input_type: STRING + # default_val: "5" + } + input_fields { + input_name: 'cms_group_id' + input_type: STRING + # default_val: "2" + } + input_fields { + input_name: 'final_gender_code' + input_type: STRING + # default_val: "2" + } + input_fields { + input_name: 'age_level' + input_type: STRING + # default_val: "2" + } + input_fields { + input_name: 'pvalue_level' + input_type: STRING + # default_val: "1" + } + input_fields { + input_name: 'shopping_level' + input_type: STRING + # default_val: "3" + } + input_fields { + input_name: 'occupation' + input_type: STRING + # default_val: "1" + } + input_fields { + input_name: 'new_user_class_level' + input_type: STRING + # default_val: "2" + } + input_fields { + input_name: 'tag_category_list' + input_type: STRING + # default_val: '4281|4281|4526' + } + input_fields { + input_name: 'tag_brand_list' + input_type: STRING + # default_val: '283837|283837|367594' + } + input_fields { + input_name: 'price' + input_type: INT32 + # default_val: '23' + } + + label_fields: 'clk' + batch_size: 8192 + num_epochs: 1000000 + prefetch_size: 64 + input_type: CSVInput + # input_type: DummyInput +} + +feature_config: { + features: { + input_names: 'pid' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'adgroup_id' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100000 + } + features: { + input_names: 'cate_id' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10000 + } + features: { + input_names: 'campaign_id' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100000 + } + features: { + input_names: 'customer' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100000 + } + features: { + input_names: 'brand' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100000 + } + features: { + input_names: 'user_id' + feature_type: IdFeature + embedding_dim: 32 + hash_bucket_size: 100000 + } + features: { + input_names: 'cms_segid' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100 + } + features: { + input_names: 'cms_group_id' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100 + } + features: { + input_names: 'final_gender_code' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'age_level' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'pvalue_level' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'shopping_level' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'occupation' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'new_user_class_level' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'tag_category_list' + feature_type: TagFeature + separator: '|' + hash_bucket_size: 100000 + embedding_dim: 16 + } + features: { + input_names: 'tag_brand_list' + feature_type: TagFeature + separator: '|' + hash_bucket_size: 100000 + embedding_dim: 16 + } + features: { + input_names: 'price' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 50 + } +} +model_config: { + model_class: 'MultiTower' + feature_groups: { + group_name: 'user' + feature_names: 'user_id' + feature_names: 'cms_segid' + feature_names: 'cms_group_id' + feature_names: 'age_level' + feature_names: 'pvalue_level' + feature_names: 'shopping_level' + feature_names: 'occupation' + feature_names: 'new_user_class_level' + wide_deep: DEEP + } + feature_groups: { + group_name: 'item' + feature_names: 'adgroup_id' + feature_names: 'cate_id' + feature_names: 'campaign_id' + feature_names: 'customer' + feature_names: 'brand' + feature_names: 'price' + wide_deep: DEEP + } + feature_groups: { + group_name: 'combo' + feature_names: 'pid' + feature_names: 'tag_category_list' + feature_names: 'tag_brand_list' + wide_deep: DEEP + } + losses { + loss_type: F1_REWEIGHTED_LOSS + weight: 1.0 + f1_reweighted_loss { + f1_beta_square: 2.25 + } + } + losses { + loss_type: PAIR_WISE_LOSS + weight: 1.0 + } + multi_tower { + towers { + input: "user" + dnn { + hidden_units: [256, 128, 96, 64] + } + } + towers { + input: "item" + dnn { + hidden_units: [256, 128, 96, 64] + } + } + towers { + input: "combo" + dnn { + hidden_units: [128, 96, 64, 32] + } + } + final_dnn { + hidden_units: [128, 96, 64, 32, 16] + } + l2_regularization: 1e-6 + } + embedding_regularization: 1e-4 +} + +export_config { + multi_placeholder: false +} diff --git a/taobao_train_data_8192 b/taobao_train_data_8192 new file mode 100644 index 000000000..850f4725d --- /dev/null +++ b/taobao_train_data_8192 @@ -0,0 +1,8192 @@ +0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 +0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 +0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 +0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 +0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 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zpq^!sX!8vdQo(i(w`(?pX(^&W>ej$l|1ke6&Z&g9jWR-3=d=p-E}S~O$<5nB*@Jc| z@@F={g2E}j>XDh`z(kjL8X)dPsch=p0fRGDc`pbis!lsvg=eJ!+&;=q*LAQu8*SFm z4liZ_B>n~E{;isKQ0w_v80nVNZh9x%PrY=8-P%Yv1Vc>1nB@|{^(xt@utSFrt5eg8T`oXLN z+|igWdjt=~8Zc5obvBSc{~RFm)u~QUuTrYx$-o+NYF4h!kA>iv6n&p$&Bg(#%b^IK zcwLGEJ#@N{r>9RaTn?Eg)9Hl_waYN<@~hK#S8PL!Le{|a2xPDc99z6xo4V~Co?vV`sO)Y!kNQ^hK3_3uV5ME>yP+bE zOP6a8JMKAqNqGvYYmcf&1Kz#Reb_jt18UJxy8P%|P?9dCe#{bV7>}&FAGU!8zQ|dI z4+JrLI{U(1#Rj3gWR_uGmQ7ubl#eb-I^7hY6QsJ2sptEw8}dp+eJ5QFaw-jrCQGTH z+wrIpY~K)0G{uDATsZ10m(CRx_zpTh$l(l=22qNIYZCT)eUyZXLviMxb+25oVAY&E zD%Ol{RXgs>HK1juGIuy#7eAp+V|mzAJtJ3|rTqsXVJ%jv;>1~q+O zsY&Q`j;Q+!bW1!ZHUxCMVX7(kx;Oj+oLzMq&7c@~g40x8Pko}Y@f0@aZJbcg8!2lnkVb%1R8fB}8`PU|(G*MNbt w{=HlVPPHFs-)pLD>a^b7I@Fa)EE_fYzyJSV5B#s~fkp={B#nL;`cv=!2Ox$-Jpcdz From bbfe530a31b03126266010f848a22ddd383ecfa5 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Fri, 20 Oct 2023 10:07:48 +0800 Subject: [PATCH 04/67] add drop_remainder option as required by sok --- .../compat/feature_column/feature_column.py | 37 ++++++++---- easy_rec/python/compat/optimizers.py | 33 +++++++++-- easy_rec/python/input/input.py | 4 +- easy_rec/python/input/parquet_input.py | 59 ++++++++++++++++--- easy_rec/python/main.py | 8 +++ easy_rec/python/model/easy_rec_estimator.py | 10 ++++ easy_rec/python/protos/dataset.proto | 2 +- easy_rec/python/utils/hvd_utils.py | 2 +- .../multi_tower_on_taobao_sok.config | 6 +- 9 files changed, 128 insertions(+), 33 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 7dba71c3c..13f1f9df2 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -275,6 +275,27 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring embedding_weights = shared_weights[shared_name] else: with ops.device('/gpu:0'): + if column.ev_params is not None: + embedding_weights = sok.DynamicVariable(name='embedding_weights', + dimension=column.dimension, initializer='random', #column.initializer, + trainable=column.trainable and trainable, dtype=dtypes.float32) + else: + embedding_weights = variable_scope.get_variable( + name='embedding_weights', + shape=embedding_shape, + dtype=dtypes.float32, + initializer=column.initializer, + trainable=column.trainable and trainable, + partitioner=None, + collections=weight_collections) + shared_weights[shared_name] = embedding_weights + else: + with ops.device('/gpu:0'): + if column.ev_params is not None: + embedding_weights = sok.DynamicVariable(name='embedding_weights', + dimension=column.dimension, initializer='random', #column.initializer, + trainable=column.trainable and trainable, dtype=dtypes.float32) + else: embedding_weights = variable_scope.get_variable( name='embedding_weights', shape=embedding_shape, @@ -283,19 +304,10 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring trainable=column.trainable and trainable, partitioner=None, collections=weight_collections) - shared_weights[shared_name] = embedding_weights - else: - with ops.device('/gpu:0'): - embedding_weights = variable_scope.get_variable( - name='embedding_weights', - shape=embedding_shape, - dtype=dtypes.float32, - initializer=column.initializer, - trainable=column.trainable and trainable, - partitioner=None, - collections=weight_collections) # required by sok - embedding_weights.target_gpu = -1 + if 'DynamicVariable' not in str(type(embedding_weights)): + embedding_weights.target_gpu = -1 + # SparseTensor RaggedTensor sparse_tensors = column.categorical_column._get_sparse_tensors( builder, weight_collections=weight_collections, trainable=trainable) output_id = len(output_tensors) @@ -332,6 +344,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring elif len(lookup_output_ids_with_wgt) > 0: outputs = sok.lookup_sparse( lookup_embeddings_with_wgt, + # RaggedTensor .values .row_lengths lookup_indices_with_wgt, lookup_wgts, combiners=lookup_combiners_with_wgt) diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index f458ceef7..87c08420c 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -244,17 +244,23 @@ def optimize_loss(loss, raise ValueError('Unrecognized optimizer: function should return ' 'subclass of Optimizer. Got %s.' % str(opt)) else: - raise ValueError('Unrecognized optimizer: should be string, ' - 'subclass of Optimizer, instance of ' - 'subclass of Optimizer or function with one argument. ' - 'Got %s.' % str(optimizer)) + opt = optimizer + # raise ValueError('Unrecognized optimizer: should be string, ' + # 'subclass of Optimizer, instance of ' + # 'subclass of Optimizer or function with one argument. ' + # 'Got %s.' % str(optimizer)) # All trainable variables, if specific variables are not specified. if variables is None: variables = vars_.trainable_variables() # Compute gradients. - gradients = opt.compute_gradients( + if 'compute_gradients' not in dir(opt): + import tensorflow as tf + gradients = tf.gradients(loss, variables) + gradients = list(zip(gradients, variables)) + else: + gradients = opt.compute_gradients( loss, variables, colocate_gradients_with_ops=colocate_gradients_with_ops) @@ -331,7 +337,22 @@ def optimize_loss(loss, # Create gradient updates. def _apply_grad(): - grad_updates = opt.apply_gradients( + if 'compute_gradients' not in dir(opt): + sparse_vars = [ x for x in gradients if 'DynamicVariable' in str(type(x[1])) ] + dense_vars = [ x for x in gradients if 'DynamicVariable' not in str(type(x[1])) ] + sparse_grad_updates = opt.apply_gradients(sparse_vars) + dense_grad_updates = opt._optimizer.apply_gradients( + dense_vars, + global_step=global_step if increment_global_step else None, + name='train') + if sparse_grad_updates is not None and dense_grad_updates is not None: + grad_updates = tf.group([sparse_grad_updates, dense_grad_updates]) + elif sparse_grad_updates is not None: + grad_updates = sparse_grad_updates + elif dense_grad_updates is not None: + grad_updates = dense_grad_updates + else: + grad_updates = opt.apply_gradients( gradients, global_step=global_step if increment_global_step else None, name='train') diff --git a/easy_rec/python/input/input.py b/easy_rec/python/input/input.py index 3e2785018..632c5cf42 100644 --- a/easy_rec/python/input/input.py +++ b/easy_rec/python/input/input.py @@ -43,7 +43,8 @@ def __init__(self, task_index=0, task_num=1, check_mode=False, - pipeline_config=None): + pipeline_config=None, + **kwargs): self._pipeline_config = pipeline_config self._data_config = data_config self._check_mode = check_mode @@ -51,6 +52,7 @@ def __init__(self, # tf.estimator.ModeKeys.*, only available before # calling self._build self._mode = None + self._has_ev = 'ev_params' in kwargs if self._data_config.auto_expand_input_fields: input_fields = [x for x in self._data_config.input_fields] diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 23409de57..93012a753 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -24,10 +24,11 @@ def __init__(self, task_index=0, task_num=1, check_mode=False, - pipeline_config=None): + pipeline_config=None, + **kwargs): super(ParquetInput, self).__init__(data_config, feature_config, input_path, task_index, - task_num, check_mode, pipeline_config) + task_num, check_mode, pipeline_config, **kwargs) if input_path is None: return @@ -50,13 +51,14 @@ def __init__(self, self._proc_arr = [] for proc_id in range(num_proc): proc = multiprocessing.Process( - target=self._parse_one_file, args=(proc_id,)) + target=self._load_data_proc, args=(proc_id,)) self._proc_arr.append(proc) - def _parse_one_file(self, proc_id): + def _load_data_proc(self, proc_id): all_fields = list(self._label_fields) + list(self._effective_fields) logging.info('data proc %d start' % proc_id) num_files = 0 + part_data_dict = {} while True: try: input_file = self._file_que.get(block=False) @@ -88,17 +90,53 @@ def _parse_one_file(self, proc_id): self._data_que.put(data_dict) sid += self._batch_size if res_num > 0: - logging.info('proc[%d] add final sample' % proc_id) + accum_res_num = 0 data_dict = {} + part_data_dict_n = {} for k in self._label_fields: - data_dict[k] = np.array([x[0] for x in input_data[k][sid:]], + tmp_lbls = np.array([x[0] for x in input_data[k][sid:]], dtype=np.float32) + if part_data_dict is not None and k in part_data_dict: + tmp_lbls = np.concatenate([part_data_dict[k], tmp_lbls], axis=0) + if len(tmp_lbls) > self._batch_size: + data_dict[k] = tmp_lbls[:self._batch_size] + part_data_dict_n[k] = tmp_lbls[self._batch_size:] + elif len(tmp_lbls) == self._batch_size: + data_dict[k] = tmp_lbls + else: + part_data_dict_n[k] = tmp_lbls + else: + part_data_dict_n[k] = tmp_lbls for k in self._effective_fields: val = input_data[k][sid:] all_lens = np.array([len(x) for x in val], dtype=np.int32) all_vals = np.concatenate(list(val)) - data_dict[k] = (all_lens, all_vals) - self._data_que.put(data_dict) + if part_data_dict is not None and k in part_data_dict: + tmp_lens = np.concatenate([part_data_dict[k][0], all_lens], axis=0) + tmp_vals = np.concatenate([part_data_dict[k][1], all_vals], axis=0) + if len(tmp_lens) > self._batch_size: + tmp_res_lens = tmp_lens[self._batch_size:] + tmp_lens = tmp_lens[:self._batch_size] + tmp_num_elems = np.sum(tmp_lens) + tmp_res_vals = tmp_vals[tmp_num_elems:] + tmp_vals = tmp_vals[:tmp_num_elems] + part_data_dict_n[k] = (tmp_res_lens, tmp_res_vals) + data_dict[k] = (tmp_lens, tmp_vals) + elif len(tmp_lens) == self._batch_size: + data_dict[k] = (tmp_lens, tmp_vals) + else: + part_data_dict_n[k] = (tmp_lens, tmp_vals) + else: + part_data_dict_n[k] = (all_lens, all_vals) + if len(data_dict) > 0: + self._data_que.put(data_dict) + part_data_dict = part_data_dict_n + if len(part_data_dict) > 0: + if not self._data_config.drop_remainder: + self._data_que.put(part_data_dict) + else: + logging.warning('drop remain %d samples as drop_remainder is set' % \ + len(part_data_dict[self._label_fields[0]])) self._data_que.put(None) logging.info('data proc %d done, file_num=%d' % (proc_id, num_files)) @@ -133,7 +171,10 @@ def _to_fea_dict(self, input_dict): if fc.feature_type == fc.IdFeature or fc.feature_type == fc.TagFeature: input_0 = fc.input_names[0] fea_name = fc.feature_name if fc.HasField('feature_name') else input_0 - tmp = input_dict[input_0][1] % fc.num_buckets + if not self._has_ev: + tmp = input_dict[input_0][1] % fc.num_buckets + else: + tmp = input_dict[input_0][1] fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, input_dict[input_0][0]) lbl_dict = {} diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index 2833a9145..ba26000ed 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -342,6 +342,8 @@ def _train_and_evaluate_impl(pipeline_config, input_fn_kwargs = {'pipeline_config': pipeline_config} if data_config.input_type == data_config.InputType.OdpsRTPInputV2: input_fn_kwargs['fg_json_path'] = pipeline_config.fg_json_path + if pipeline_config.model_config.HasField('ev_params'): + input_fn_kwargs['ev_params'] = pipeline_config.model_config.ev_params # create train input train_input_fn = _get_input_fn( @@ -779,6 +781,8 @@ def export(export_dir, input_fn_kwargs = {'pipeline_config': pipeline_config} if data_config.input_type == data_config.InputType.OdpsRTPInputV2: input_fn_kwargs['fg_json_path'] = pipeline_config.fg_json_path + if pipeline_config.model_config.HasField('ev_params'): + input_fn_kwargs['ev_params'] = pipeline_config.model_config.ev_params serving_input_fn = _get_input_fn(data_config, feature_configs, None, export_config, **input_fn_kwargs) ckpt_path = _get_ckpt_path(pipeline_config, checkpoint_path) @@ -841,6 +845,10 @@ def export_checkpoint(pipeline_config=None, if data_config.input_type == data_config.InputType.OdpsRTPInputV2: input_fn_kwargs['fg_json_path'] = pipeline_config.fg_json_path + if pipeline_config.model_config.HasField('ev_params'): + input_fn_kwargs['ev_params'] = pipeline_config.model_config.ev_params + + # create estimator params = {'log_device_placement': verbose} if asset_files: diff --git a/easy_rec/python/model/easy_rec_estimator.py b/easy_rec/python/model/easy_rec_estimator.py index ea1e1a189..6e711e7d2 100644 --- a/easy_rec/python/model/easy_rec_estimator.py +++ b/easy_rec/python/model/easy_rec_estimator.py @@ -44,6 +44,13 @@ except Exception: hvd = None +try: + from sparse_operation_kit import experiment as sok +except Exception: + sok = None + + + if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -206,6 +213,9 @@ def _train_model_fn(self, features, labels, run_config): % (len(grouped_vars), len(optimizer_config)) optimizer = MultiOptimizer(all_opts, grouped_vars) + if self.train_config.train_distribute == DistributionStrategy.SokStrategy: + optimizer = sok.OptimizerWrapper(optimizer) + hooks = [] if estimator_utils.has_hvd(): assert not self.train_config.sync_replicas, \ diff --git a/easy_rec/python/protos/dataset.proto b/easy_rec/python/protos/dataset.proto index c9bf809f7..a4def5cd8 100644 --- a/easy_rec/python/protos/dataset.proto +++ b/easy_rec/python/protos/dataset.proto @@ -298,5 +298,5 @@ message DatasetConfig { } optional uint32 eval_batch_size = 1001 [default = 4096]; - + optional bool drop_remainder = 1002 [default = false]; } diff --git a/easy_rec/python/utils/hvd_utils.py b/easy_rec/python/utils/hvd_utils.py index c2362063c..8ef425d0f 100644 --- a/easy_rec/python/utils/hvd_utils.py +++ b/easy_rec/python/utils/hvd_utils.py @@ -39,7 +39,7 @@ def __init__(self, root_rank, device=''): def begin(self): bcast_vars = [] for x in tf.global_variables(): - if '/embedding' not in x.name: + if '/embedding' not in x.name and 'DynamicVariable' not in str(type(x)): bcast_vars.append(x) logging.info('will broadcast variable: %s' % x.name) if not self.bcast_op or self.bcast_op.graph != tf.get_default_graph(): diff --git a/samples/model_config/multi_tower_on_taobao_sok.config b/samples/model_config/multi_tower_on_taobao_sok.config index df425cc51..d2a3402bb 100644 --- a/samples/model_config/multi_tower_on_taobao_sok.config +++ b/samples/model_config/multi_tower_on_taobao_sok.config @@ -23,7 +23,7 @@ train_config { sync_replicas: False train_distribute: SokStrategy num_steps: 200000 - # is_profiling: true + is_profiling: true } eval_config { @@ -136,8 +136,8 @@ data_config { batch_size: 8192 num_epochs: 1000000 prefetch_size: 64 - input_type: CSVInput - # input_type: DummyInput + # input_type: CSVInput + input_type: DummyInput } feature_config: { From 5477cb261f333a79616828a247d5d8b5eb0da838 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Fri, 20 Oct 2023 15:00:02 +0800 Subject: [PATCH 05/67] add adam sparse --- easy_rec/python/builders/optimizer_builder.py | 10 +++++++++ .../compat/feature_column/feature_column.py | 21 +++++++++++++++++-- easy_rec/python/compat/optimizers.py | 19 ++++++++++------- easy_rec/python/protos/optimizer.proto | 7 +++++++ requirements/runtime.txt | 1 + .../multi_tower_on_taobao_sok.config | 2 +- 6 files changed, 50 insertions(+), 10 deletions(-) diff --git a/easy_rec/python/builders/optimizer_builder.py b/easy_rec/python/builders/optimizer_builder.py index c7e3aca49..ad791371b 100644 --- a/easy_rec/python/builders/optimizer_builder.py +++ b/easy_rec/python/builders/optimizer_builder.py @@ -88,6 +88,16 @@ def build(optimizer_config): beta1=config.beta1, beta2=config.beta2) + if optimizer_type == 'lazy_adam_optimizer': + config = optimizer_config.lazy_adam_optimizer + learning_rate = _create_learning_rate(config.learning_rate) + summary_vars.append(learning_rate) + from easy_rec.python.compat.adam_s import AdamOptimizerS + optimizer = AdamOptimizerS( + learning_rate =learning_rate, + beta1=config.beta1, + beta2=config.beta2) + if optimizer_type == 'momentumw_optimizer': config = optimizer_config.momentumw_optimizer learning_rate = _create_learning_rate(config.learning_rate) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 13f1f9df2..350a320f8 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -150,6 +150,18 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import embedding_ops + +try: + from tensorflow.python.ops.ragged import ragged_embedding_ops + def embedding_lookup_ragged(embedding_weights, ragged_ids, max_norm=None, name=None): + return ragged_embedding_ops.embedding_lookup(params=embedding_weights, + ids=ragged_ids, max_norm=max_norm, name=name) +except: + if hasattr(embedding_ops, 'embedding_lookup_ragged'): + embedding_lookup_ragged = embedding_ops.embedding_lookup_ragged + else: + embedding_lookup_ragged = None + from tensorflow.python.ops import init_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops @@ -161,7 +173,12 @@ from tensorflow.python.ops import template from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables -from tensorflow.python.ops.ragged import ragged_math_ops + +try: + from tensorflow.python.ops.ragged import ragged_math_ops +except: + pass + from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_utils @@ -2786,7 +2803,7 @@ def _get_dense_tensor_internal(self, if 'RaggedTensor' in str(type(sparse_ids)): assert sparse_weights is None - ragged_embedding = embedding_ops.embedding_lookup_ragged( + ragged_embedding = embedding_lookup_ragged( embedding_weights=embedding_weights, ragged_ids=sparse_ids, max_norm=self.max_norm, diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index 87c08420c..dddf175cc 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -40,6 +40,11 @@ from easy_rec.python.ops.incr_record import set_sparse_indices from easy_rec.python.utils import estimator_utils +try: + from tensorflow.python.framework import indexed_slices +except Exception as ex: + indexed_slices = ops + try: import horovod.tensorflow as hvd except Exception: @@ -315,7 +320,7 @@ def optimize_loss(loss, # Add histograms for variables, gradients and gradient norms. for gradient, variable in gradients: - if isinstance(gradient, ops.IndexedSlices): + if isinstance(gradient, indexed_slices.IndexedSlices): grad_values = gradient.values else: grad_values = gradient @@ -360,7 +365,7 @@ def _apply_grad(): incr_save_ops = [] if incr_save: for grad, var in gradients: - if isinstance(grad, ops.IndexedSlices): + if isinstance(grad, indexed_slices.IndexedSlices): indices = grad.indices with ops.colocate_with(var), ops.control_dependencies( [grad_updates]): @@ -477,9 +482,9 @@ def gradient_clipping(grads_and_vars): for grad in grads: if grad is None: clipped_grads.append(None) - elif isinstance(grad, ops.IndexedSlices): + elif isinstance(grad, indexed_slices.IndexedSlices): clipped_grads.append( - ops.IndexedSlices(grad.values * factor, grad.indices, + indexed_slices.IndexedSlices(grad.values * factor, grad.indices, grad.dense_shape)) else: clipped_grads.append(grad * factor) @@ -497,7 +502,7 @@ def _add_scaled_noise_to_gradients(grads_and_vars, gradient_noise_scale): if gradient is None: noisy_gradients.append(None) continue - if isinstance(gradient, ops.IndexedSlices): + if isinstance(gradient, indexed_slices.IndexedSlices): gradient_shape = gradient.dense_shape else: gradient_shape = gradient.get_shape() @@ -514,9 +519,9 @@ def _multiply_gradients(grads_and_vars, gradient_multipliers): (var in gradient_multipliers or var.name in gradient_multipliers)): key = var if var in gradient_multipliers else var.name multiplier = gradient_multipliers[key] - if isinstance(grad, ops.IndexedSlices): + if isinstance(grad, indexed_slices.IndexedSlices): grad_values = grad.values * multiplier - grad = ops.IndexedSlices(grad_values, grad.indices, grad.dense_shape) + grad = indexed_slices.IndexedSlices(grad_values, grad.indices, grad.dense_shape) else: grad *= math_ops.cast(multiplier, grad.dtype) multiplied_grads_and_vars.append((grad, var)) diff --git a/easy_rec/python/protos/optimizer.proto b/easy_rec/python/protos/optimizer.proto index ee10500cd..b825149f9 100644 --- a/easy_rec/python/protos/optimizer.proto +++ b/easy_rec/python/protos/optimizer.proto @@ -15,6 +15,7 @@ message Optimizer { AdagradOptimizer adagrad_optimizer = 107; FtrlOptimizer ftrl_optimizer = 108; AdamAsyncWOptimizer adam_asyncw_optimizer = 109; + LazyAdamOptimizer lazy_adam_optimizer = 110; } optional bool use_moving_average = 5 [default = false]; optional float moving_average_decay = 6 [default = 0.9999]; @@ -65,6 +66,12 @@ message AdamAsyncWOptimizer { optional float beta2 = 4 [default = 0.999]; } +message LazyAdamOptimizer { + optional LearningRate learning_rate = 1; + optional float beta1 = 3 [default = 0.9]; + optional float beta2 = 4 [default = 0.999]; +} + // Configuration message for the AdagradOptimizer // See: https://www.tensorflow.org/api_docs/python/tf/train/AdagradOptimizer message AdagradOptimizer { diff --git a/requirements/runtime.txt b/requirements/runtime.txt index 0bfc70146..437e61134 100644 --- a/requirements/runtime.txt +++ b/requirements/runtime.txt @@ -5,6 +5,7 @@ oss2 pandas psutil pyodps +pyarrow PyYAML scikit-learn xlrd >= 0.9.0 diff --git a/samples/model_config/multi_tower_on_taobao_sok.config b/samples/model_config/multi_tower_on_taobao_sok.config index d2a3402bb..05d98ce7f 100644 --- a/samples/model_config/multi_tower_on_taobao_sok.config +++ b/samples/model_config/multi_tower_on_taobao_sok.config @@ -23,7 +23,7 @@ train_config { sync_replicas: False train_distribute: SokStrategy num_steps: 200000 - is_profiling: true + # is_profiling: true } eval_config { From 23a9fba1caf10ca27c438e9e70bd4cb203872a6b Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Sat, 21 Oct 2023 23:30:40 +0800 Subject: [PATCH 06/67] fix parquet input bugs --- .../compat/feature_column/feature_column.py | 68 +++++++++++++++---- easy_rec/python/input/parquet_input.py | 34 ++++++++-- easy_rec/python/model/ppnet_v3m.py | 4 +- easy_rec/python/utils/estimator_utils.py | 2 + 4 files changed, 84 insertions(+), 24 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 350a320f8..0be29c778 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -151,16 +151,6 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import embedding_ops -try: - from tensorflow.python.ops.ragged import ragged_embedding_ops - def embedding_lookup_ragged(embedding_weights, ragged_ids, max_norm=None, name=None): - return ragged_embedding_ops.embedding_lookup(params=embedding_weights, - ids=ragged_ids, max_norm=max_norm, name=name) -except: - if hasattr(embedding_ops, 'embedding_lookup_ragged'): - embedding_lookup_ragged = embedding_ops.embedding_lookup_ragged - else: - embedding_lookup_ragged = None from tensorflow.python.ops import init_ops from tensorflow.python.ops import lookup_ops @@ -196,6 +186,56 @@ def embedding_lookup_ragged(embedding_weights, ragged_ids, max_norm=None, name=N except Exception: hvd = None +def embedding_lookup_ragged(embedding_weights, ragged_ids, ragged_weights, + combiner, max_norm=None, name=None): + segment_ids = ragged_ids.value_rowids() + ids = ragged_ids.flat_values + ids, idx = array_ops.unique(ids) + embeddings = embedding_ops.embedding_lookup(embedding_weights, ids, + partition_strategy='mod', max_norm=max_norm) + if ragged_weights is not None: + weights = ragged_weights.flat_values + embeddings = array_ops.gather(embeddings, idx) + original_dtype = embeddings.dtype + if embeddings.dtype in (dtypes.float16, dtypes.bfloat16): + # Cast low-precision embeddings to float32 during the computation to + # avoid numerical issues. + embeddings = math_ops.cast(embeddings, dtypes.float32) + if weights.dtype != embeddings.dtype: + weights = math_ops.cast(weights, embeddings.dtype) + weights = array_ops.expand_dims(weights, len(embeddings.get_shape())) + embeddings = embeddings * weights + if combiner == 'sum': + return math_ops.segment_sum(embeddings, segment_ids, name=name) + elif combiner == 'mean': + embeddings = math_ops.segment_sum(embeddings, segment_ids) + weight_sum = math_ops.segment_sum(weights, segment_ids) + embeddings = math_ops.div_no_nan(embeddings, weight_sum, name=name) + elif combiner == "sqrtn": + embeddings = math_ops.segment_sum(embeddings, segment_ids) + weights_squared = math_ops.pow(weights, 2) + weight_sum = math_ops.segment_sum(weights_squared, segment_ids) + weight_sum_sqrt = math_ops.sqrt(weight_sum) + embeddings = math_ops.div_no_nan(embeddings, weight_sum_sqrt, name=name) + else: + assert False, "Unrecognized combiner" + if embeddings.dtype != original_dtype: + embeddings = math_ops.cast(embeddings, original_dtype) + return embeddings + else: + assert idx is not None + if combiner == "sum": + embeddings = math_ops.sparse_segment_sum( + embeddings, idx, segment_ids, name=name) + elif combiner == "mean": + embeddings = math_ops.sparse_segment_mean( + embeddings, idx, segment_ids, name=name) + elif combiner == "sqrtn": + embeddings = math_ops.sparse_segment_sqrt_n( + embeddings, idx, segment_ids, name=name) + else: + assert False, "Unrecognized combiner" + return embeddings def _internal_input_layer(features, feature_columns, @@ -2803,15 +2843,13 @@ def _get_dense_tensor_internal(self, if 'RaggedTensor' in str(type(sparse_ids)): assert sparse_weights is None - ragged_embedding = embedding_lookup_ragged( + return embedding_lookup_ragged( embedding_weights=embedding_weights, ragged_ids=sparse_ids, + ragged_weights=sparse_weights, + combiner=self.combiner, max_norm=self.max_norm, name='%s_weights' % self.name) - if self.combiner == 'sum': - return ragged_math_ops.reduce_sum(ragged_embedding, axis=1) - else: - return ragged_math_ops.reduce_mean(ragged_embedding, axis=1) # Return embedding lookup result. return embedding_ops.safe_embedding_lookup_sparse( diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 93012a753..542d65174 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -39,15 +39,24 @@ def __init__(self, (input_path, len(self._input_files))) self._data_que = multiprocessing.Queue( maxsize=self._data_config.prefetch_size) - self._file_que = multiprocessing.Queue(maxsize=len(self._input_files)) - for input_file in self._input_files: - self._file_que.put(input_file) + file_num = len(self._input_files) + logging.info('[task_index=%d] total_file_num=%d task_num=%d' % (task_index, file_num, task_num)) + avg_file_num = int(file_num / task_num) + res_file_num = file_num % task_num + file_sid = task_index * avg_file_num + min(res_file_num, task_index) + file_eid = (task_index + 1) * avg_file_num + min(res_file_num, task_index+1) + self._my_files = self._input_files[file_sid:file_eid] + + logging.info('[task_index=%d] task_file_num=%d' % (task_index, len(self._my_files))) + self._file_que = multiprocessing.Queue() + num_proc = 8 if file_num < num_proc: num_proc = file_num - logging.info('file_num=%d num_proc=%d' % (file_num, num_proc)) + + self._proc_start = False self._proc_arr = [] for proc_id in range(num_proc): proc = multiprocessing.Process( @@ -141,8 +150,11 @@ def _load_data_proc(self, proc_id): logging.info('data proc %d done, file_num=%d' % (proc_id, num_files)) def _sample_generator(self): - for proc in self._proc_arr: - proc.start() + if not self._proc_start: + self._proc_start = True + for proc in self._proc_arr: + proc.start() + done_proc_cnt = 0 fetch_timeout_cnt = 0 while True: @@ -183,6 +195,14 @@ def _to_fea_dict(self, input_dict): return {'feature': fea_dict, 'label': lbl_dict} def _build(self, mode, params): + if mode == tf.estimator.ModeKeys.TRAIN and self._data_config.num_epochs > 1: + logging.info('will repeat train data for %d epochs' % self._data_config.num_epochs) + my_files = self._my_files * self._data_config.num_epochs + else: + my_files = self._my_files + for input_file in my_files: + self._file_que.put(input_file) + out_types = {} out_shapes = {} for k in self._label_fields: @@ -202,7 +222,7 @@ def _build(self, mode, params): dataset = dataset.prefetch(buffer_size=self._prefetch_size) # dataset = dataset.map( # map_func=self._preprocess, num_parallel_calls=num_parallel_calls) - dataset = dataset.prefetch(buffer_size=self._prefetch_size) + # dataset = dataset.prefetch(buffer_size=self._prefetch_size) if mode != tf.estimator.ModeKeys.PREDICT: dataset = dataset.map(lambda x: diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index 8af1e44b8..e82a439b9 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -54,7 +54,7 @@ def build_loss_graph(self): lbl_info = self._model_conf['label'][lbl_id] lbl_name = lbl_info.get('input_name') output = self._prediction_dict.get(lbl_name) - loss_obj = tf.keras.losses.BinaryCrossentropy()(self._labels[lbl_name], - output) + loss_obj = tf.keras.losses.BinaryCrossentropy(reduction='sum')( + self._labels[lbl_name], output) self._loss_dict[lbl_name] = loss_obj return self._loss_dict diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index 006ee06eb..72567546a 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -852,6 +852,8 @@ def parse_tf_config(): def get_task_index_and_num(): + if hvd is not None and 'HOROVOD_RANK' in os.environ: + return hvd.rank(), hvd.size() cluster, task_type, task_index = parse_tf_config() if 'worker' not in cluster: return 0, 1 From 4ce52299f0adf3f44ca0f64458535036f2dfb34c Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Sun, 22 Oct 2023 01:55:13 +0800 Subject: [PATCH 07/67] add all2all impl --- .../compat/feature_column/feature_column.py | 88 ++++++++++++++----- 1 file changed, 65 insertions(+), 23 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 0be29c778..ec782b692 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -163,9 +163,11 @@ from tensorflow.python.ops import template from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables +from tensorflow.python.ops import data_flow_ops try: from tensorflow.python.ops.ragged import ragged_math_ops + from tensorflow.python.ops.ragged import ragged_concat_ops except: pass @@ -389,8 +391,35 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # do sok lookup if len(lookup_output_ids) > 0: - outputs = sok.lookup_sparse( - lookup_embeddings, lookup_indices, combiners=lookup_combiners) + # first concat all the ids and unique + all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) + all_uniq_ids, uniq_idx = array_ops.unique(all_ids.flat_values) + + # dynamic partition + np = hvd.size() + # from sparse_operation_kit.experiment import raw_ops + p_assignments = all_uniq_ids % np + gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, p_assignments, np) + # all2all + split_sizes = array_ops.concat([ array_ops.shape(x) for x in gather_ids ], axis=0) + send_ids = array_ops.concat(gather_ids, axis=0) + recv_ids, recv_lens = hvd.alltoall(send_ids, split_sizes) + + logging.info('recv_lens = %s' % str(recv_lens)) + + # read embedding from dynamic variable + send_embed = lookup_embeddings[0].sparse_read(recv_ids) + + embed_dim = lookup_embeddings[0]._dimension + # all2all + recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) + + # all_embed = array_ops.gather(recv_embeddings, uniq_idx) + segment_ids = all_ids.value_rowids() + embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') + output_tensor = array_ops.reshape(embeddings, [len(lookup_output_ids), -1, lookup_embeddings[0]._dimension]) + + outputs = array_ops.split(output_tensor, num_or_size_splits=len(lookup_output_ids), axis=0) for output, output_id, col in zip(outputs, lookup_output_ids, lookup_cols): output_tensors[output_id] = output @@ -398,27 +427,40 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring cols_to_output_tensors[col] = output if feature_name_to_output_tensors is not None: feature_name_to_output_tensors[col.raw_name] = output - elif len(lookup_output_ids_with_wgt) > 0: - outputs = sok.lookup_sparse( - lookup_embeddings_with_wgt, - # RaggedTensor .values .row_lengths - lookup_indices_with_wgt, - lookup_wgts, - combiners=lookup_combiners_with_wgt) - for output, output_id, col in zip(outputs, lookup_output_ids_with_wgt, - lookup_cols_with_wgt): - output_tensors[output_id] = output - if cols_to_output_tensors is not None: - cols_to_output_tensors[col] = output - if feature_name_to_output_tensors is not None: - feature_name_to_output_tensors[col.raw_name] = output - - if feature_name_to_output_tensors is not None: - for column, output_tensor in zip( - sorted(feature_columns, key=lambda x: x.name), output_tensors): - feature_name_to_output_tensors[column.raw_name] = output_tensor - _verify_static_batch_size_equality(output_tensors, ordered_columns) - return array_ops.concat(output_tensors, 1) + return array_ops.reshape(array_ops.transpose(output_tensor, perm=[1, 0, 2]), + [-1, len(lookup_output_ids) * output_tensor.get_shape()[-1]]) + + + # outputs = sok.lookup_sparse( + # lookup_embeddings, lookup_indices, combiners=lookup_combiners) + # for output, output_id, col in zip(outputs, lookup_output_ids, + # lookup_cols): + # output_tensors[output_id] = output + # if cols_to_output_tensors is not None: + # cols_to_output_tensors[col] = output + # if feature_name_to_output_tensors is not None: + # feature_name_to_output_tensors[col.raw_name] = output + # elif len(lookup_output_ids_with_wgt) > 0: + # outputs = sok.lookup_sparse( + # lookup_embeddings_with_wgt, + # # RaggedTensor .values .row_lengths + # lookup_indices_with_wgt, + # lookup_wgts, + # combiners=lookup_combiners_with_wgt) + # for output, output_id, col in zip(outputs, lookup_output_ids_with_wgt, + # lookup_cols_with_wgt): + # output_tensors[output_id] = output + # if cols_to_output_tensors is not None: + # cols_to_output_tensors[col] = output + # if feature_name_to_output_tensors is not None: + # feature_name_to_output_tensors[col.raw_name] = output + + # if feature_name_to_output_tensors is not None: + # for column, output_tensor in zip( + # sorted(feature_columns, key=lambda x: x.name), output_tensors): + # feature_name_to_output_tensors[column.raw_name] = output_tensor + # _verify_static_batch_size_equality(output_tensors, ordered_columns) + # return array_ops.concat(output_tensors, 1) # If we're constructing from the `make_template`, that by default adds a # variable scope with the name of the layer. In that case, we dont want to From 628674ac6b78e1d9e3091edcd08f1afcf6a98f10 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Thu, 26 Oct 2023 10:04:02 +0800 Subject: [PATCH 08/67] add saver for sok --- .../compat/feature_column/feature_column.py | 99 +++++---- easy_rec/python/compat/optimizers.py | 3 +- easy_rec/python/input/input.py | 3 + easy_rec/python/input/parquet_input.py | 205 ++++++++---------- easy_rec/python/layers/input_layer.py | 23 +- easy_rec/python/main.py | 16 +- easy_rec/python/model/easy_rec_estimator.py | 17 +- easy_rec/python/model/easy_rec_model.py | 12 +- easy_rec/python/model/ppnet_v3m.py | 13 +- easy_rec/python/utils/estimator_utils.py | 69 +++++- 10 files changed, 281 insertions(+), 179 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index ec782b692..74190c573 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -337,6 +337,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring if column.ev_params is not None: embedding_weights = sok.DynamicVariable(name='embedding_weights', dimension=column.dimension, initializer='random', #column.initializer, + # var_type='hybrid', trainable=column.trainable and trainable, dtype=dtypes.float32) else: embedding_weights = variable_scope.get_variable( @@ -363,27 +364,32 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring trainable=column.trainable and trainable, partitioner=None, collections=weight_collections) + lookup_embeddings.append(embedding_weights) + output_id = len(output_tensors) + output_tensors.append(None) + lookup_output_ids.append(output_id) + lookup_cols.append(column) # required by sok if 'DynamicVariable' not in str(type(embedding_weights)): embedding_weights.target_gpu = -1 - # SparseTensor RaggedTensor - sparse_tensors = column.categorical_column._get_sparse_tensors( - builder, weight_collections=weight_collections, trainable=trainable) - output_id = len(output_tensors) - output_tensors.append(None) - if sparse_tensors.weight_tensor is not None: - lookup_embeddings_with_wgt.append(embedding_weights) - lookup_indices_with_wgt.append(sparse_tensors.id_tensor) - lookup_wgts.append(sparse_tensors.weight_tensor) - lookup_output_ids_with_wgt.append(output_id) - lookup_combiners_with_wgt.append(column.combiner) - lookup_cols_with_wgt.append(column) - else: - lookup_embeddings.append(embedding_weights) - lookup_indices.append(sparse_tensors.id_tensor) - lookup_output_ids.append(output_id) - lookup_combiners.append(column.combiner) - lookup_cols.append(column) + # # SparseTensor RaggedTensor + # sparse_tensors = column.categorical_column._get_sparse_tensors( + # builder, weight_collections=weight_collections, trainable=trainable) + # output_id = len(output_tensors) + # output_tensors.append(None) + # if sparse_tensors.weight_tensor is not None: + # lookup_embeddings_with_wgt.append(embedding_weights) + # lookup_indices_with_wgt.append(sparse_tensors.id_tensor) + # lookup_wgts.append(sparse_tensors.weight_tensor) + # lookup_output_ids_with_wgt.append(output_id) + # lookup_combiners_with_wgt.append(column.combiner) + # lookup_cols_with_wgt.append(column) + # else: + # lookup_embeddings.append(embedding_weights) + # lookup_indices.append(sparse_tensors.id_tensor) + # lookup_output_ids.append(output_id) + # lookup_combiners.append(column.combiner) + # lookup_cols.append(column) if cols_to_vars is not None: cols_to_vars[column] = ops.get_collection( ops.GraphKeys.GLOBAL_VARIABLES, @@ -392,32 +398,43 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # do sok lookup if len(lookup_output_ids) > 0: # first concat all the ids and unique - all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) - all_uniq_ids, uniq_idx = array_ops.unique(all_ids.flat_values) - - # dynamic partition + # all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) + all_ids = features['feature'] # ragged_concat_ops.concat(lookup_indices, axis=0) np = hvd.size() - # from sparse_operation_kit.experiment import raw_ops - p_assignments = all_uniq_ids % np - gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, p_assignments, np) - # all2all - split_sizes = array_ops.concat([ array_ops.shape(x) for x in gather_ids ], axis=0) - send_ids = array_ops.concat(gather_ids, axis=0) - recv_ids, recv_lens = hvd.alltoall(send_ids, split_sizes) - - logging.info('recv_lens = %s' % str(recv_lens)) + # embed_dim = lookup_embeddings[0]._dimension - # read embedding from dynamic variable - send_embed = lookup_embeddings[0].sparse_read(recv_ids) - - embed_dim = lookup_embeddings[0]._dimension - # all2all - recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) - - # all_embed = array_ops.gather(recv_embeddings, uniq_idx) segment_ids = all_ids.value_rowids() - embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') - output_tensor = array_ops.reshape(embeddings, [len(lookup_output_ids), -1, lookup_embeddings[0]._dimension]) + if np > 1: + all_uniq_ids, uniq_idx = array_ops.unique(all_ids.flat_values) + + # dynamic partition + # from sparse_operation_kit.experiment import raw_ops + p_assignments = all_uniq_ids % np + gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, p_assignments, np) + # all2all + split_sizes = array_ops.concat([ array_ops.shape(x) for x in gather_ids ], axis=0) + send_ids = array_ops.concat(gather_ids, axis=0) + recv_ids, recv_lens = hvd.alltoall(send_ids, split_sizes) + + # read embedding from dynamic variable + send_embed = lookup_embeddings[0].sparse_read(recv_ids) + + # all2all + recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) + embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') + else: + all_uniq_ids, uniq_idx = array_ops.unique(all_ids.flat_values) + if isinstance(lookup_embeddings[0], sok.DynamicVariable): + recv_embeddings = lookup_embeddings[0].sparse_read(all_uniq_ids) + else: + recv_embeddings = array_ops.gather(lookup_embeddings[0], all_uniq_ids) + embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') + # all_embed = array_ops.gather(recv_embeddings, uniq_idx) + if isinstance(lookup_embeddings[0], sok.DynamicVariable): + output_tensor = array_ops.reshape(embeddings, [len(lookup_output_ids), -1, lookup_embeddings[0]._dimension]) + else: + embed_dim = lookup_embeddings[0].get_shape()[-1] + output_tensor = array_ops.reshape(embeddings, [len(lookup_output_ids), -1, embed_dim]) outputs = array_ops.split(output_tensor, num_or_size_splits=len(lookup_output_ids), axis=0) for output, output_id, col in zip(outputs, lookup_output_ids, diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index dddf175cc..1dd7925e5 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -270,7 +270,7 @@ def optimize_loss(loss, variables, colocate_gradients_with_ops=colocate_gradients_with_ops) - if estimator_utils.has_hvd(): + if estimator_utils.has_hvd() and hvd.size() > 1: if not estimator_utils.has_sok(): reduced_grads = [] for g, v in gradients: @@ -287,6 +287,7 @@ def optimize_loss(loss, compression=hvd.compression.NoneCompressor), v)) else: reduced_grads.append((g, v)) + gradients = reduced_grads # Optionally add gradient noise. if gradient_noise_scale is not None: diff --git a/easy_rec/python/input/input.py b/easy_rec/python/input/input.py index 632c5cf42..d1f1ce717 100644 --- a/easy_rec/python/input/input.py +++ b/easy_rec/python/input/input.py @@ -988,6 +988,9 @@ def _pre_build(self, mode, params): def restore(self, checkpoint_path): pass + def stop(self): + pass + def _safe_shard(self, dataset): if self._data_config.chief_redundant: return dataset.shard( diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 542d65174..43f82b88f 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -3,6 +3,7 @@ import logging import multiprocessing import queue +import time import numpy as np import pandas as pd @@ -11,10 +12,12 @@ from easy_rec.python.input.input import Input +from easy_rec.python.input import load_parquet +from easy_rec.python.compat import queues + if tf.__version__ >= '2.0': tf = tf.compat.v1 - class ParquetInput(Input): def __init__(self, @@ -37,12 +40,14 @@ def __init__(self, self._input_files.extend(gfile.Glob(sub_path)) logging.info('parquet input_path=%s file_num=%d' % (input_path, len(self._input_files))) - self._data_que = multiprocessing.Queue( + mp_ctxt = multiprocessing.get_context('spawn') + self._data_que = queues.Queue(name='data_que', ctx=mp_ctxt, maxsize=self._data_config.prefetch_size) - + # self._data_que._name = 'data_que' file_num = len(self._input_files) - logging.info('[task_index=%d] total_file_num=%d task_num=%d' % (task_index, file_num, task_num)) + logging.info('[task_index=%d] total_file_num=%d task_num=%d' % (task_index, + file_num, task_num)) avg_file_num = int(file_num / task_num) res_file_num = file_num % task_num file_sid = task_index * avg_file_num + min(res_file_num, task_index) @@ -50,144 +55,118 @@ def __init__(self, self._my_files = self._input_files[file_sid:file_eid] logging.info('[task_index=%d] task_file_num=%d' % (task_index, len(self._my_files))) - self._file_que = multiprocessing.Queue() + self._file_que = queues.Queue(name='file_que', ctx=mp_ctxt) num_proc = 8 if file_num < num_proc: num_proc = file_num self._proc_start = False - self._proc_arr = [] - for proc_id in range(num_proc): - proc = multiprocessing.Process( - target=self._load_data_proc, args=(proc_id,)) - self._proc_arr.append(proc) - - def _load_data_proc(self, proc_id): - all_fields = list(self._label_fields) + list(self._effective_fields) - logging.info('data proc %d start' % proc_id) - num_files = 0 - part_data_dict = {} - while True: - try: - input_file = self._file_que.get(block=False) - except queue.Empty: - break - num_files += 1 - input_data = pd.read_parquet(input_file, columns=all_fields) - data_len = len(input_data[all_fields[0]]) - batch_num = int(data_len / self._batch_size) - res_num = data_len % self._batch_size - logging.info( - 'proc[%d] read file %s sample_num=%d batch_num=%d res_num=%d' % - (proc_id, input_file, data_len, batch_num, res_num)) - sid = 0 - for batch_id in range(batch_num): - eid = sid + self._batch_size - data_dict = {} - for k in self._label_fields: - data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], - dtype=np.float32) - for k in self._effective_fields: - val = input_data[k][sid:eid] - all_lens = np.array([len(x) for x in val], dtype=np.int32) - all_vals = np.concatenate(list(val)) - assert np.sum(all_lens) == len( - all_vals), 'len(all_vals)=%d np.sum(all_lens)=%d' % ( - len(all_vals), np.sum(all_lens)) - data_dict[k] = (all_lens, all_vals) - self._data_que.put(data_dict) - sid += self._batch_size - if res_num > 0: - accum_res_num = 0 - data_dict = {} - part_data_dict_n = {} - for k in self._label_fields: - tmp_lbls = np.array([x[0] for x in input_data[k][sid:]], - dtype=np.float32) - if part_data_dict is not None and k in part_data_dict: - tmp_lbls = np.concatenate([part_data_dict[k], tmp_lbls], axis=0) - if len(tmp_lbls) > self._batch_size: - data_dict[k] = tmp_lbls[:self._batch_size] - part_data_dict_n[k] = tmp_lbls[self._batch_size:] - elif len(tmp_lbls) == self._batch_size: - data_dict[k] = tmp_lbls - else: - part_data_dict_n[k] = tmp_lbls - else: - part_data_dict_n[k] = tmp_lbls - for k in self._effective_fields: - val = input_data[k][sid:] - all_lens = np.array([len(x) for x in val], dtype=np.int32) - all_vals = np.concatenate(list(val)) - if part_data_dict is not None and k in part_data_dict: - tmp_lens = np.concatenate([part_data_dict[k][0], all_lens], axis=0) - tmp_vals = np.concatenate([part_data_dict[k][1], all_vals], axis=0) - if len(tmp_lens) > self._batch_size: - tmp_res_lens = tmp_lens[self._batch_size:] - tmp_lens = tmp_lens[:self._batch_size] - tmp_num_elems = np.sum(tmp_lens) - tmp_res_vals = tmp_vals[tmp_num_elems:] - tmp_vals = tmp_vals[:tmp_num_elems] - part_data_dict_n[k] = (tmp_res_lens, tmp_res_vals) - data_dict[k] = (tmp_lens, tmp_vals) - elif len(tmp_lens) == self._batch_size: - data_dict[k] = (tmp_lens, tmp_vals) - else: - part_data_dict_n[k] = (tmp_lens, tmp_vals) - else: - part_data_dict_n[k] = (all_lens, all_vals) - if len(data_dict) > 0: - self._data_que.put(data_dict) - part_data_dict = part_data_dict_n - if len(part_data_dict) > 0: - if not self._data_config.drop_remainder: - self._data_que.put(part_data_dict) - else: - logging.warning('drop remain %d samples as drop_remainder is set' % \ - len(part_data_dict[self._label_fields[0]])) - self._data_que.put(None) - logging.info('data proc %d done, file_num=%d' % (proc_id, num_files)) + self._proc_start_que = queues.Queue(name='proc_start_que', ctx=mp_ctxt) + self._proc_stop = False + self._proc_stop_que = queues.Queue(name='proc_stop_que', ctx=mp_ctxt) + self._proc_arr = load_parquet.start_data_proc(task_index, num_proc, self._file_que, + self._data_que, self._proc_start_que, self._proc_stop_que, self._batch_size, + self._label_fields, self._effective_fields, self._data_config.drop_remainder) def _sample_generator(self): if not self._proc_start: self._proc_start = True - for proc in self._proc_arr: - proc.start() + for proc in (self._proc_arr): + self._proc_start_que.put(True) + logging.info('task[%s] data_proc=%s is_alive=%s' % (self._task_index, + proc, proc.is_alive())) done_proc_cnt = 0 fetch_timeout_cnt = 0 + + # for mock purpose + ## all_samples = [] + ## while len(all_samples) < 64: + ## try: + ## sample = self._data_que.get(block=False) + ## all_samples.append(sample) + ## except queue.Empty: + ## continue + ## sid = 0 + ## while True: + ## yield all_samples[sid] + ## sid += 1 + ## if sid >= len(all_samples): + ## sid = 0 + + fetch_good_cnt = 0 while True: try: - sample = self._data_que.get(block=False) + sample = self._data_que.get(timeout=1) if sample is None: done_proc_cnt += 1 else: + fetch_good_cnt += 1 yield sample + if fetch_good_cnt % 200 == 0: + logging.info('task[%d] fetch_good_cnt=%d, fetch_timeout_cnt=%d, qsize=%d' % ( + self._task_index, fetch_good_cnt, fetch_timeout_cnt, self._data_que.qsize())) except queue.Empty: fetch_timeout_cnt += 1 if done_proc_cnt >= len(self._proc_arr): logging.info('all sample finished, fetch_timeout_cnt=%d' % fetch_timeout_cnt) break + except Exception as ex: + logging.warning('task[%d] get from data_que exception: %s' % (self._task_index, str(ex))) for proc in self._proc_arr: proc.join() + def stop(self): + logging.info("task[%d] will stop dataset procs, proc_num=%d" % (self._task_index, + len(self._proc_arr))) + self._file_que.close() + if self._proc_start: + logging.info("try close data que") + for _ in range(len(self._proc_arr)): + self._proc_stop_que.put(1) + self._proc_stop_que.close() + + # to ensure the sender part of the stupid python Queue could exit properly + for _ in range(5): + while not self._data_que.empty(): + try: + self._data_que.get() + except: + pass + time.sleep(1) + self._data_que.close() + logging.info("data que closed") + # import time + # time.sleep(10) + for proc in self._proc_arr: + # proc.terminate() + proc.join() + logging.info("join proc done") + self._proc_start = False + def _to_fea_dict(self, input_dict): fea_dict = {} # for fea_name in self._effective_fields: # tmp = input_dict[fea_name][1] % 1000 # 000000 # fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths( # tmp, input_dict[fea_name][0]) - for fc in self._feature_configs: - if fc.feature_type == fc.IdFeature or fc.feature_type == fc.TagFeature: - input_0 = fc.input_names[0] - fea_name = fc.feature_name if fc.HasField('feature_name') else input_0 - if not self._has_ev: - tmp = input_dict[input_0][1] % fc.num_buckets - else: - tmp = input_dict[input_0][1] - fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, input_dict[input_0][0]) + # for fc in self._feature_configs: + # if fc.feature_type == fc.IdFeature or fc.feature_type == fc.TagFeature: + # input_0 = fc.input_names[0] + # fea_name = fc.feature_name if fc.HasField('feature_name') else input_0 + # if not self._has_ev: + # tmp = input_dict[input_0][1] % fc.num_buckets + # else: + # tmp = input_dict[input_0][1] + # fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, input_dict[input_0][0]) + if self._has_ev: + fea_dict['feature'] = tf.RaggedTensor.from_row_lengths(input_dict['feature'][1], + input_dict['feature'][0]) + else: + fea_dict['feature'] = tf.RaggedTensor.from_row_lengths(input_dict['feature'][1] % self._feature_configs[0].num_buckets, + input_dict['feature'][0]) lbl_dict = {} for lbl_name in self._label_fields: @@ -202,15 +181,18 @@ def _build(self, mode, params): my_files = self._my_files for input_file in my_files: self._file_que.put(input_file) + logging.info('add input_files to file_que, qsize=%d' % self._file_que.qsize()) out_types = {} out_shapes = {} for k in self._label_fields: out_types[k] = tf.int32 out_shapes[k] = tf.TensorShape([None]) - for k in self._effective_fields: - out_types[k] = (tf.int64, tf.int32) - out_shapes[k] = (tf.TensorShape([None]), tf.TensorShape([None])) + # for k in self._effective_fields: + # out_types[k] = (tf.int64, tf.int32) + # out_shapes[k] = (tf.TensorShape([None]), tf.TensorShape([None])) + out_types['feature'] = (tf.int64, tf.int32) + out_shapes['feature'] = (tf.TensorShape([None]), tf.TensorShape([None])) dataset = tf.data.Dataset.from_generator( self._sample_generator, @@ -229,4 +211,5 @@ def _build(self, mode, params): (self._get_features(x), self._get_labels(x))) else: dataset = dataset.map(lambda x: (self._get_features(x))) + # dataset = dataset.prefetch(buffer_size=self._prefetch_size) return dataset diff --git a/easy_rec/python/layers/input_layer.py b/easy_rec/python/layers/input_layer.py index 914f977fa..a19ce75b8 100644 --- a/easy_rec/python/layers/input_layer.py +++ b/easy_rec/python/layers/input_layer.py @@ -140,7 +140,9 @@ def get_plain_feature(self, features, group_name): for col, val in cols_to_output_tensors.items(): if is_embedding_column(col): embedding_reg_lst.append(val) - regularizers.apply_regularization( + + if self._embedding_regularizer is not None: + regularizers.apply_regularization( self._embedding_regularizer, weights_list=embedding_reg_lst) return output_features, group_features @@ -178,8 +180,10 @@ def get_sequence_feature(self, features, group_name): tmp_embedding, tmp_seq_len, fc.max_seq_length) seq_features.append((tmp_embedding, tmp_seq_len)) embedding_reg_lst.append(tmp_embedding) - regularizers.apply_regularization( - self._embedding_regularizer, weights_list=embedding_reg_lst) + + if self._embedding_regularizer is not None: + regularizers.apply_regularization( + self._embedding_regularizer, weights_list=embedding_reg_lst) return seq_features def __call__(self, features, group_name, is_combine=True, is_dict=False): @@ -305,12 +309,13 @@ def single_call_input_layer(self, group_features = [cols_to_output_tensors[x] for x in group_columns] + \ [cols_to_output_tensors[x] for x in group_seq_columns] - for fc, val in cols_to_output_tensors.items(): - if is_embedding_column(fc): - embedding_reg_lst.append(val) - if embedding_reg_lst: - regularizers.apply_regularization( - self._embedding_regularizer, weights_list=embedding_reg_lst) + if self._embedding_regularizer is not None: + for fc, val in cols_to_output_tensors.items(): + if is_embedding_column(fc): + embedding_reg_lst.append(val) + if embedding_reg_lst: + regularizers.apply_regularization( + self._embedding_regularizer, weights_list=embedding_reg_lst) return concat_features, group_features def get_wide_deep_dict(self): diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index ba26000ed..bfe2fa361 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -355,11 +355,19 @@ def _train_and_evaluate_impl(pipeline_config, # Currently only a single Eval Spec is allowed. train_spec = tf.estimator.TrainSpec( input_fn=train_input_fn, max_steps=train_steps) - # create eval spec - eval_spec = _create_eval_export_spec( + if estimator_utils.has_sok(): + estimator.train( + input_fn=train_input_fn, + max_steps=train_spec.max_steps, + hooks=list(train_spec.hooks), + saving_listeners=train_spec.saving_listeners) + train_input_fn.input_creator.stop() + else: + from easy_rec.python.compat import estimator_train + # create eval spec + eval_spec = _create_eval_export_spec( pipeline_config, eval_data, check_mode=check_mode) - from easy_rec.python.compat import estimator_train - estimator_train.train_and_evaluate(estimator, train_spec, eval_spec) + estimator_train.train_and_evaluate(estimator, train_spec, eval_spec) logging.info('Train and evaluate finish') if fit_on_eval and (not estimator_utils.is_evaluator()): tf.reset_default_graph() diff --git a/easy_rec/python/model/easy_rec_estimator.py b/easy_rec/python/model/easy_rec_estimator.py index 6e711e7d2..66e337246 100644 --- a/easy_rec/python/model/easy_rec_estimator.py +++ b/easy_rec/python/model/easy_rec_estimator.py @@ -18,6 +18,7 @@ from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import saver +from easy_rec.python.compat.saver import SaverV2 from easy_rec.python.builders import optimizer_builder from easy_rec.python.compat import optimizers @@ -410,8 +411,19 @@ def _train_model_fn(self, features, labels, run_config): local_init_ops.append( tf.initializers.variables(incompatiable_shape_restore)) + sok_vars = [] + other_vars = [] + for tmp_var in var_list: + if 'Dynamic' in str(type(tmp_var)): + sok_vars.append(tmp_var) + else: + other_vars.append(tmp_var) + # var_list = other_vars + + scaffold = tf.train.Scaffold( - saver=tf.train.Saver( + # saver=tf.train.Saver( + saver=SaverV2( var_list=var_list, sharded=True, max_to_keep=self.train_config.keep_checkpoint_max, @@ -427,7 +439,8 @@ def _train_model_fn(self, features, labels, run_config): scaffold=scaffold, write_graph=self.train_config.write_graph, data_offset_var=data_offset_var, - increment_save_config=self.incr_save_config) + increment_save_config=self.incr_save_config, + sok_dynamic_vars=sok_vars) hooks.append(saver_hook) if estimator_utils.is_chief(): hooks.append( diff --git a/easy_rec/python/model/easy_rec_model.py b/easy_rec/python/model/easy_rec_model.py index 37249949b..434d31dc3 100644 --- a/easy_rec/python/model/easy_rec_model.py +++ b/easy_rec/python/model/easy_rec_model.py @@ -45,8 +45,16 @@ def __init__(self, if model_config.HasField('ev_params'): self._global_ev_params = model_config.ev_params - self._emb_reg = regularizers.l2_regularizer(self.embedding_regularization) - self._l2_reg = regularizers.l2_regularizer(self.l2_regularization) + if self.embedding_regularization > 0: + self._emb_reg = regularizers.l2_regularizer(self.embedding_regularization) + else: + self._emb_reg = None + + if self.l2_regularization > 0: + self._l2_reg = regularizers.l2_regularizer(self.l2_regularization) + else: + self._l2_reg = None + # only used by model with wide feature groups, e.g. WideAndDeep self._wide_output_dim = -1 if self.has_backbone: diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index e82a439b9..5700f553f 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -4,6 +4,7 @@ import logging import tensorflow as tf +from tensorflow.python.framework import ops from easy_rec.python.layers import dnn from easy_rec.python.layers import mmoe @@ -41,7 +42,17 @@ def __init__(self, def build_predict_graph(self): # self._add_to_prediction_dict(tower_outputs) - output_list = self._keras_model(self._features) + output_list = self._keras_model(self._features, self._is_training) + trainable_variables = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES) + for var in self._keras_model.trainable_variables: + if var not in trainable_variables: + ops.add_to_collection(ops.GraphKeys.TRAINABLE_VARIABLES, var) + + # update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS) + # for var in self._keras_model.updates: + # if var not in update_ops: + # ops.add_to_collection(ops.GraphKeys.UPDATE_OPS, var) + for lbl_id in range(len(self._model_conf['label'])): lbl_info = self._model_conf['label'][lbl_id] lbl_name = lbl_info.get('input_name') diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index 72567546a..27d9ae1da 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -317,7 +317,8 @@ def __init__(self, listeners=None, write_graph=True, data_offset_var=None, - increment_save_config=None): + increment_save_config=None, + sok_dynamic_vars=None): """Initializes a `CheckpointSaverHook`. Args: @@ -346,9 +347,22 @@ def __init__(self, checkpoint_basename=checkpoint_basename, scaffold=scaffold, listeners=listeners) + self._steps_per_run = 1 self._write_graph = write_graph self._data_offset_var = data_offset_var + self._task_idx, self._task_num = get_task_index_and_num() + + if sok_dynamic_vars is not None: + self._sok_dynamic_vars = sok_dynamic_vars + from sparse_operation_kit.experiment import raw_ops as dynamic_variable_ops + self._dyn_export = {} + for dyn_var in self._sok_dynamic_vars: + indices, values = dynamic_variable_ops.dummy_var_export( + dyn_var.handle, key_type=dyn_var.key_type, dtype=dyn_var.handle_dtype + ) + self._dyn_export[dyn_var.name] = (indices, values) + if increment_save_config is not None: self._kafka_timeout_ms = os.environ.get('KAFKA_TIMEOUT', 600) * 1000 logging.info('KAFKA_TIMEOUT: %dms' % self._kafka_timeout_ms) @@ -455,6 +469,10 @@ def __init__(self, def after_create_session(self, session, coord): if not is_chief(): + if has_sok(): + global_step = session.run(self._global_step_tensor) + self._save_sok(session, global_step) + self._timer.update_last_triggered_step(global_step) return global_step = session.run(self._global_step_tensor) @@ -472,7 +490,7 @@ def after_create_session(self, session, coord): self._summary_writer.add_meta_graph(meta_graph_def) # save for step 0 - # self._save(session, global_step) + self._save(session, global_step) self._timer.update_last_triggered_step(global_step) @@ -597,6 +615,14 @@ def _send_sparse(self, global_step, session): def after_run(self, run_context, run_values): if not is_chief(): + if has_sok(): + stale_global_step = run_values.results + global_step = stale_global_step + self._steps_per_run + if self._timer.should_trigger_for_step(global_step): + logging.info('worker[%d] global_step=%d, save sok dynamic vars' % ( + self._task_idx, global_step)) + self._save_sok(run_context.session, global_step) + self._timer.update_last_triggered_step(global_step) return super(CheckpointSaverHook, self).after_run(run_context, run_values) @@ -616,10 +642,31 @@ def after_run(self, run_context, run_values): self._sparse_timer.update_last_triggered_step(global_step) self._send_sparse(global_step, run_context.session) + def _save_sok(self, session, step): + if self._dyn_export is None: + return + + # get self._dyn_export and save + dyn_save_dir = os.path.join(self._checkpoint_dir, 'model.ckpt-%d-sok' % step) + gfile.MakeDirs(dyn_save_dir) + dyn_idx_vals = session.run(self._dyn_export) + for var_name in dyn_idx_vals: + idx, vals = dyn_idx_vals[var_name] + var_name = var_name.replace('/', '__') + dyn_save_path_keys = os.path.join(dyn_save_dir, 'embed-%s-part-%d.keys' % ( + var_name, self._task_idx)) + with gfile.GFile(dyn_save_path_keys, 'wb') as fout: + fout.write(idx.tobytes()) + dyn_save_path_vals = os.path.join(dyn_save_dir, 'embed-%s-part-%d.vals' % ( + var_name, self._task_idx)) + with gfile.GFile(dyn_save_path_vals, 'wb') as fout: + fout.write(vals.tobytes()) + logging.info('save %s[len=%d] to %s and %s' % (var_name, len(idx), + dyn_save_path_keys, dyn_save_path_vals)) + def _save(self, session, step): """Saves the latest checkpoint, returns should_stop.""" logging.info('Saving checkpoints for %d into %s.', step, self._save_path) - return False for l in self._listeners: # noqa: E741 l.before_save(session, step) @@ -630,8 +677,7 @@ def _save(self, session, step): for x in save_data_offset: if x: data_offset_json.update(json.loads(x)) - save_dir, _ = os.path.split(self._save_path) - save_offset_path = os.path.join(save_dir, 'model.ckpt-%d.offset' % step) + save_offset_path = os.path.join(self._checkpoint_dir, 'model.ckpt-%d.offset' % step) with gfile.GFile(save_offset_path, 'w') as fout: json.dump(data_offset_json, fout) @@ -641,6 +687,9 @@ def _save(self, session, step): global_step=step, write_meta_graph=self._write_graph) + # save sok + self._save_sok(session, step) + self._summary_writer.add_session_log( tf.SessionLog( status=tf.SessionLog.CHECKPOINT, checkpoint_path=self._save_path), @@ -656,10 +705,13 @@ def _save(self, session, step): return should_stop def end(self, session): + global_step = session.run(self._global_step_tensor) if not is_chief(): + if has_sok(): + self._save_sok(session, global_step) return + super(CheckpointSaverHook, self).end(session) - global_step = session.run(self._global_step_tensor) if self._dense_timer is not None and \ global_step != self._dense_timer.last_triggered_step(): self._dense_timer.update_last_triggered_step(global_step) @@ -977,12 +1029,13 @@ def is_ps(): def is_chief(): + if has_hvd(): + return hvd.rank() == 0 + if 'TF_CONFIG' in os.environ: tf_config = json.loads(os.environ['TF_CONFIG']) if 'task' in tf_config: return tf_config['task']['type'] in ['chief', 'master'] - elif has_hvd(): - return hvd.rank() == 0 return True From a1032da0ec2489cc8c75d7aa1ae1ddd7b14ce625 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Thu, 26 Oct 2023 14:35:40 +0800 Subject: [PATCH 09/67] add missing files --- easy_rec/python/compat/adam_async.py | 248 +++++++++++++++++++++ easy_rec/python/compat/queues.py | 308 ++++++++++++++++++++++++++ easy_rec/python/compat/saver.py | 121 ++++++++++ easy_rec/python/input/load_parquet.py | 202 +++++++++++++++++ 4 files changed, 879 insertions(+) create mode 100644 easy_rec/python/compat/adam_async.py create mode 100644 easy_rec/python/compat/queues.py create mode 100644 easy_rec/python/compat/saver.py create mode 100644 easy_rec/python/input/load_parquet.py diff --git a/easy_rec/python/compat/adam_async.py b/easy_rec/python/compat/adam_async.py new file mode 100644 index 000000000..def3a4d34 --- /dev/null +++ b/easy_rec/python/compat/adam_async.py @@ -0,0 +1,248 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""AdamAsync for TensorFlow.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.eager import context +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +# from tensorflow.python.ops import gen_hash_training_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import resource_variable_ops +# from tensorflow.python.ops import kv_variable_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.training import slot_creator +from tensorflow.python.training import optimizer +from tensorflow.python.training import training_ops +from tensorflow.python.training import training_util +from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.framework.load_library import load_op_library + +import os +curr_dir, _ = os.path.split(__file__) +adam_async_ops = load_op_library(os.path.join(curr_dir, 'libadam_async_op.so')) + +@tf_export("train.AdamAsyncOptimizer") +class AdamAsyncOptimizer(optimizer.Optimizer): + """Optimizer that implements the Adam algorithm. + + See [Kingma et al., 2014](http://arxiv.org/abs/1412.6980) + ([pdf](http://arxiv.org/pdf/1412.6980.pdf)). + """ + + def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, + use_locking=False, apply_sparse_rmsprop=False, name="Adam"): + """Construct a new Adam optimizer for training asynchronous. + + Initialization: + + ``` + m_0 <- 0 (Initialize initial 1st moment vector) + v_0 <- 0 (Initialize initial 2nd moment vector) + t <- 0 (Initialize timestep) + ``` + + The update rule for `variable` with gradient `g` uses an optimization + described at the end of section2 of the paper: + + ``` + t <- t + 1 + lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) + + m_t <- beta1 * m_{t-1} + (1 - beta1) * g + v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g + variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon) + ``` + + The default value of 1e-8 for epsilon might not be a good default in + general. For example, when training an Inception network on ImageNet a + current good choice is 1.0 or 0.1. Note that since AdamOptimizer uses the + formulation just before Section 2.1 of the Kingma and Ba paper rather than + the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon + hat" in the paper. + + The sparse implementation of this algorithm (used when the gradient is an + IndexedSlices object, typically because of `tf.gather` or an embedding + lookup in the forward pass) does apply momentum to variable slices even if + they were not used in the forward pass (meaning they have a gradient equal + to zero). Momentum decay (beta1) is also applied to the entire momentum + accumulator. This means that the sparse behavior is equivalent to the dense + behavior (in contrast to some momentum implementations which ignore momentum + unless a variable slice was actually used). + + Args: + learning_rate: A Tensor or a floating point value. The learning rate. + beta1: A float value or a constant float tensor. + The exponential decay rate for the 1st moment estimates. + beta2: A float value or a constant float tensor. + The exponential decay rate for the 2nd moment estimates. + epsilon: A small constant for numerical stability. This epsilon is + "epsilon hat" in the Kingma and Ba paper (in the formula just before + Section 2.1), not the epsilon in Algorithm 1 of the paper. + use_locking: If True use locks for update operations. + apply_sparse_rmsprop: If True use rmsprop optimizer for sparse apply. + name: Optional name for the operations created when applying gradients. + Defaults to "AdamASync". + """ + super(AdamAsyncOptimizer, self).__init__(use_locking, name) + self._lr = learning_rate + self._beta1 = beta1 + self._beta2 = beta2 + self._epsilon = epsilon + + # Tensor versions of the constructor arguments, created in _prepare(). + self._lr_t = None + self._beta1_t = None + self._beta2_t = None + self._epsilon_t = None + + # Created in SparseApply if needed. + self._updated_lr = None + + self._apply_sparse_rmsprop = apply_sparse_rmsprop + + def _get_beta_accumulators(self): + with ops.init_scope(): + if context.executing_eagerly(): + graph = None + else: + graph = ops.get_default_graph() + return (self._get_non_slot_variable("beta1_power", graph=graph), + self._get_non_slot_variable("beta2_power", graph=graph)) + + def _create_slots(self, var_list): + # When training asynchronous, we create the beta1 and beta2 accumulators for + # each variable to avoid communication bottlenecks. + first_var = min(var_list, key=lambda x: x.name) + self._create_non_slot_variable( + initial_value=self._beta1, name="beta1_power", colocate_with=first_var) + self._create_non_slot_variable( + initial_value=self._beta2, name="beta2_power", colocate_with=first_var) + + # Create slots for the moments. + for v in var_list: + with ops.colocate_with(v): + self._zeros_slot(v, "m", self._name) + self._zeros_slot(v, "v", self._name) + # self._get_or_make_slot(v, + # ops.convert_to_tensor(self._beta1, dtype=v.dtype.base_dtype), + # "beta1_power", self._name) + # self._get_or_make_slot(v, + # ops.convert_to_tensor(self._beta2, dtype=v.dtype.base_dtype), + # "beta2_power", self._name) + + def _prepare(self): + self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate") + self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1") + self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2") + self._epsilon_t = ops.convert_to_tensor(self._epsilon, name="epsilon") + + def _apply_dense(self, grad, var): + m = self.get_slot(var, "m") + v = self.get_slot(var, "v") + # beta1_power = self.get_slot(var, 'beta1_power') + # beta2_power = self.get_slot(var, 'beta2_power') + beta1_power, beta2_power = self._get_beta_accumulators() + return adam_async_ops.apply_adam_async( + var, m, v, beta1_power, beta2_power, + math_ops.cast(self._lr_t, var.dtype.base_dtype), + math_ops.cast(self._beta1_t, var.dtype.base_dtype), + math_ops.cast(self._beta2_t, var.dtype.base_dtype), + math_ops.cast(self._epsilon_t, var.dtype.base_dtype), + grad, use_locking=self._use_locking).op + + def _resource_apply_dense(self, grad, var): + m = self.get_slot(var, "m") + v = self.get_slot(var, "v") + # beta1_power = self.get_slot(var, 'beta1_power') + # beta2_power = self.get_slot(var, 'beta2_power') + beta1_power, beta2_power = self._get_beta_accumulators() + return adam_async_ops.resource_apply_adam_async( + var.handle, m.handle, v.handle, beta1_power.handle, beta2_power.handle, + math_ops.cast(self._lr_t, grad.dtype.base_dtype), + math_ops.cast(self._beta1_t, grad.dtype.base_dtype), + math_ops.cast(self._beta2_t, grad.dtype.base_dtype), + math_ops.cast(self._epsilon_t, grad.dtype.base_dtype), + grad, use_locking=self._use_locking) + + def _apply_sparse(self, grad, var): + m = self.get_slot(var, "m") + v = self.get_slot(var, "v") + # beta1_power = self.get_slot(var, 'beta1_power') + # beta2_power = self.get_slot(var, 'beta2_power') + beta1_power, beta2_power = self._get_beta_accumulators() + return adam_async_ops.sparse_apply_adam_async( + var, m, v, beta1_power, beta2_power, + math_ops.cast(self._lr_t, var.dtype.base_dtype), + math_ops.cast(self._beta1_t, var.dtype.base_dtype), + math_ops.cast(self._beta2_t, var.dtype.base_dtype), + math_ops.cast(self._epsilon_t, var.dtype.base_dtype), + grad.values, grad.indices, use_locking=self._use_locking, + apply_sparse_rmsprop=self._apply_sparse_rmsprop) + + # def _hash_table_apply_sparse(self, grad, var, indices): + # m = self.get_slot(var, "m") + # v = self.get_slot(var, "v") + # beta1_power = self.get_slot(var, "beta1_power") + # beta2_power = self.get_slot(var, "beta2_power") + # update_op = gen_hash_training_ops.tensible_variable_apply_adam( + # var.handle, m.handle, v.handle, + # math_ops.cast(beta1_power, grad.dtype.base_dtype), + # math_ops.cast(beta2_power, grad.dtype.base_dtype), + # math_ops.cast(self._lr_t, grad.dtype.base_dtype), + # math_ops.cast(self._beta1_t, grad.dtype.base_dtype), + # math_ops.cast(self._beta2_t, grad.dtype.base_dtype), + # math_ops.cast(self._epsilon_t, grad.dtype.base_dtype), + # grad, indices, use_locking=self._use_locking) + # with ops.control_dependencies([update_op]): + # update_beta1 = beta1_power.assign( + # beta1_power * math_ops.cast(self._beta1_t, var.dtype.base_dtype), + # use_locking=self._use_locking) + # update_beta2 = beta2_power.assign( + # beta2_power * math_ops.cast(self._beta2_t, var.dtype.base_dtype), + # use_locking=self._use_locking) + # return control_flow_ops.group(update_beta1, update_beta2) + + def _resource_apply_sparse(self, grad, var, indices): + m = self.get_slot(var, "m") + v = self.get_slot(var, "v") + # beta1_power = self.get_slot(var, 'beta1_power') + # beta2_power = self.get_slot(var, 'beta2_power') + beta1_power, beta2_power = self._get_beta_accumulators() + return adam_async_ops.resource_sparse_apply_adam_async( + var.handle, m.handle, v.handle, beta1_power.handle, beta2_power.handle, + math_ops.cast(self._lr_t, grad.dtype), + math_ops.cast(self._beta1_t, grad.dtype), + math_ops.cast(self._beta2_t, grad.dtype), + math_ops.cast(self._epsilon_t, grad.dtype), + grad, indices, use_locking=self._use_locking, + apply_sparse_rmsprop=self._apply_sparse_rmsprop) + + def _finish(self, update_ops, name_scope): + # Update the power accumulators. + with ops.control_dependencies(update_ops): + beta1_power, beta2_power = self._get_beta_accumulators() + with ops.colocate_with(beta1_power): + update_beta1 = beta1_power.assign( + beta1_power * self._beta1_t, use_locking=self._use_locking) + update_beta2 = beta2_power.assign( + beta2_power * self._beta2_t, use_locking=self._use_locking) + return control_flow_ops.group( + *update_ops + [update_beta1, update_beta2], name=name_scope) diff --git a/easy_rec/python/compat/queues.py b/easy_rec/python/compat/queues.py new file mode 100644 index 000000000..98c7ec0cb --- /dev/null +++ b/easy_rec/python/compat/queues.py @@ -0,0 +1,308 @@ +# +# Module implementing queues +# +# multiprocessing/queues.py +# +# Copyright (c) 2006-2008, R Oudkerk +# Licensed to PSF under a Contributor Agreement. +# + +import sys +import os +import threading +import collections +import time +import types +import weakref +import errno + +from queue import Empty, Full + +from multiprocessing import connection +from multiprocessing import context +_ForkingPickler = context.reduction.ForkingPickler + +from multiprocessing.util import Finalize, register_after_fork, is_exiting +import logging + +# +# Queue type using a pipe, buffer and thread +# + +class Queue(object): + + _sentinel = object() + + def __init__(self, maxsize=0, name="", *, ctx): + if maxsize <= 0: + # Can raise ImportError (see issues #3770 and #23400) + from multiprocessing.synchronize import SEM_VALUE_MAX as maxsize + self._maxsize = maxsize + self._reader, self._writer = connection.Pipe(duplex=False) + self._rlock = ctx.Lock() + self._opid = os.getpid() + if sys.platform == 'win32': + self._wlock = None + else: + self._wlock = ctx.Lock() + self._sem = ctx.BoundedSemaphore(maxsize) + # For use by concurrent.futures + self._ignore_epipe = False + self._reset() + self._name = name + self._run = True + + if sys.platform != 'win32': + register_after_fork(self, Queue._after_fork) + + def __getstate__(self): + context.assert_spawning(self) + return (self._ignore_epipe, self._maxsize, self._reader, self._writer, + self._rlock, self._wlock, self._sem, self._opid, self._name, self._run) + + def __setstate__(self, state): + (self._ignore_epipe, self._maxsize, self._reader, self._writer, + self._rlock, self._wlock, self._sem, self._opid, self._name, self._run) = state + self._reset() + + def _after_fork(self): + logging.debug('Queue._after_fork()') + self._reset(after_fork=True) + + def _reset(self, after_fork=False): + if after_fork: + self._notempty._at_fork_reinit() + else: + self._notempty = threading.Condition(threading.Lock()) + self._buffer = collections.deque() + self._thread = None + self._jointhread = None + self._joincancelled = False + self._closed = False + self._close = None + self._send_bytes = self._writer.send_bytes + self._recv_bytes = self._reader.recv_bytes + self._poll = self._reader.poll + + def put(self, obj, block=True, timeout=None): + if self._closed: + raise ValueError(f"Queue {self!r} is closed") + if not self._sem.acquire(block, timeout): + raise Full + + with self._notempty: + if self._thread is None: + self._start_thread() + self._buffer.append(obj) + self._notempty.notify() + + def get(self, block=True, timeout=None): + if self._closed: + raise ValueError(f"Queue {self!r} is closed") + if block and timeout is None: + with self._rlock: + res = self._recv_bytes() + self._sem.release() + else: + if block: + deadline = time.monotonic() + timeout + if not self._rlock.acquire(block, timeout): + raise Empty + try: + if block: + timeout = deadline - time.monotonic() + if not self._poll(timeout): + raise Empty + elif not self._poll(): + raise Empty + res = self._recv_bytes() + self._sem.release() + finally: + self._rlock.release() + # unserialize the data after having released the lock + return _ForkingPickler.loads(res) + + def qsize(self): + # Raises NotImplementedError on Mac OSX because of broken sem_getvalue() + return self._maxsize - self._sem._semlock._get_value() + + def empty(self): + return not self._poll() + + def full(self): + return self._sem._semlock._is_zero() + + def get_nowait(self): + return self.get(False) + + def put_nowait(self, obj): + return self.put(obj, False) + + def close(self, wait_send_finish=True): + self._closed = True + close = self._close + if not wait_send_finish and self._thread is not None and self._thread.is_alive(): + try: + if self._reader is not None: + self._reader.close() + except Exception: + pass + try: + if self._wlock is not None: + self._wlock.release() + except: + pass + # logging.info('Queue[name=' + self._name + '] set self._run to false') + self._run = False + # clear queue + # with self._rlock: + # while self._thread.is_alive() and self._poll(1): + # res = self._recv_bytes() + # logging.info('Queue[name=' + self._name + '] clear one elem') + # logging.info('Queue[name=' + self._name + '] clear queue done') + if close: + self._close = None + close() + + def join_thread(self): + logging.debug('Queue.join_thread()') + assert self._closed, "Queue {0!r} not closed".format(self) + if self._jointhread: + self._jointhread() + + def cancel_join_thread(self): + logging.debug('Queue.cancel_join_thread()') + self._joincancelled = True + try: + self._jointhread.cancel() + except AttributeError: + pass + + def _start_thread(self): + logging.debug('Queue._start_thread()') + + # Start thread which transfers data from buffer to pipe + self._buffer.clear() + self._thread = threading.Thread( + target=self._feed, + args=(self._buffer, self._notempty, self._send_bytes, + self._wlock, self._reader.close, self._writer.close, + self._ignore_epipe, self._on_queue_feeder_error, + self._sem), + name='QueueFeederThread' + ) + self._thread.daemon = True + + logging.debug('doing self._thread.start()') + self._thread.start() + logging.debug('... done self._thread.start()') + + if not self._joincancelled: + self._jointhread = Finalize( + self._thread, Queue._finalize_join, + [weakref.ref(self._thread)], + exitpriority=-5 + ) + + # Send sentinel to the thread queue object when garbage collected + self._close = Finalize( + self, Queue._finalize_close, + [self._buffer, self._notempty], + exitpriority=10 + ) + + @staticmethod + def _finalize_join(twr): + logging.debug('joining queue thread') + thread = twr() + if thread is not None: + thread.join() + logging.debug('... queue thread joined') + else: + logging.debug('... queue thread already dead') + + @staticmethod + def _finalize_close(buffer, notempty): + logging.debug('telling queue thread to quit') + with notempty: + buffer.append(Queue._sentinel) + notempty.notify() + + def _feed(self, buffer, notempty, send_bytes, writelock, reader_close, + writer_close, ignore_epipe, onerror, queue_sem): + logging.debug('starting thread to feed data to pipe') + nacquire = notempty.acquire + nrelease = notempty.release + nwait = notempty.wait + bpopleft = buffer.popleft + sentinel = Queue._sentinel + if sys.platform != 'win32': + wacquire = writelock.acquire + wrelease = writelock.release + else: + wacquire = None + + pid = os.getpid() + name = self._name + while self._run: + try: + nacquire() + try: + if not buffer: + nwait() + finally: + nrelease() + try: + while self._run: + obj = bpopleft() + if obj is sentinel: + # logging.info('Queue[' + self._name + '] feeder thread got sentinel -- exiting: ' + str(self._run)) + reader_close() + writer_close() + return + + # serialize the data before acquiring the lock + obj = _ForkingPickler.dumps(obj) + if wacquire is None: + send_bytes(obj) + else: + wacquire() + try: + send_bytes(obj) + finally: + wrelease() + except IndexError: + pass + except Exception as e: + if ignore_epipe and getattr(e, 'errno', 0) == errno.EPIPE: + logging.warning('Queue[' + name + '] exception: pid=' + str(pid) + + ' run=' + str(self._run) + ' e=' + str(e)) + return + # Since this runs in a daemon thread the resources it uses + # may be become unusable while the process is cleaning up. + # We ignore errors which happen after the process has + # started to cleanup. + if is_exiting(): + logging.warning('Queue[' + name + '] thread error in exiting: pid=' + + str(pid) + ' run=' + str(self._run) + ' e=' + str(e)) + return + else: + # Since the object has not been sent in the queue, we need + # to decrease the size of the queue. The error acts as + # if the object had been silently removed from the queue + # and this step is necessary to have a properly working + # queue. + queue_sem.release() + onerror(e, obj) + # logging.info('Queue[' + name + '] send thread finish: pid=' + str(pid) + # + ' run=' + str(self._run)) + + @staticmethod + def _on_queue_feeder_error(e, obj): + """ + Private API hook called when feeding data in the background thread + raises an exception. For overriding by concurrent.futures. + """ + import traceback + traceback.print_exc() + diff --git a/easy_rec/python/compat/saver.py b/easy_rec/python/compat/saver.py new file mode 100644 index 000000000..f1d0b1659 --- /dev/null +++ b/easy_rec/python/compat/saver.py @@ -0,0 +1,121 @@ +# -*- encoding:utf-8 -*- + +import logging +import sys +import os +import numpy as np + +from tensorflow.python.training import saver +from tensorflow.core.protobuf import saver_pb2 +from tensorflow.python.framework import dtypes, ops +from tensorflow.python.ops import script_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.platform import gfile + +try: + import horovod.tensorflow as hvd + from sparse_operation_kit.experiment import raw_ops as dynamic_variable_ops + from sparse_operation_kit import experiment as sok +except: + dynamic_variable_ops = None + sok = None + +class SaverV2(saver.Saver): + def __init__(self, + var_list=None, + reshape=False, + sharded=False, + max_to_keep=5, + keep_checkpoint_every_n_hours=10000.0, + name=None, + restore_sequentially=False, + saver_def=None, + builder=None, + defer_build=False, + allow_empty=False, + write_version=saver_pb2.SaverDef.V2, + pad_step_number=False, + save_relative_paths=False, + filename=None): + self._sok_vars = [] + tf_vars = [] + if sok is None: + tf_vars = var_list + else: + for var in var_list: + if isinstance(var, sok.DynamicVariable): + self._sok_vars.append(var) + else: + tf_vars.append(var) + super(SaverV2, self).__init__(tf_vars, reshape=reshape, sharded=sharded, + max_to_keep=max_to_keep, + keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, + name=name, restore_sequentially=restore_sequentially, + saver_def=saver_def, builder=builder, + defer_build=defer_build, + allow_empty=allow_empty, + write_version=write_version, + pad_step_number=pad_step_number, + save_relative_paths=save_relative_paths, + filename=filename) + self._is_build = False + + def _save_sok_embedding(self, sok_var): + pass + + def _load_sok_embedding(self, sok_var): + def _load_key_vals(filename, var_name): + var_name = var_name.decode('utf-8').replace('/', '__') + filename = filename.decode('utf-8') + key_file_pattern = filename + '-sok/embed-' + var_name + '-part-*.keys' + logging.info('key_file_pattern=%s filename=%s var_name=%s var=%s' % (key_file_pattern, + filename, var_name, str(sok_var))) + key_files = gfile.Glob(key_file_pattern) + logging.info('key_file_pattern=%s file_num=%d' % (key_file_pattern, len(key_files))) + all_keys = [] + all_vals = [] + for key_file in key_files: + with gfile.GFile(key_file, 'rb') as fin: + tmp_keys = np.frombuffer(fin.read(), dtype=np.int64) + tmp_ids = tmp_keys % hvd.size() + tmp_ids = np.where(tmp_ids == hvd.rank())[0] + all_keys.append(tmp_keys.take(tmp_ids, axis=0)) + logging.info('tmp_keys.shape=%s %s %s' % (str(tmp_keys.shape), + str(tmp_ids.shape), str(all_keys[-1].shape))) + + val_file = key_file[:-4] + 'vals' + with gfile.GFile(val_file, 'rb') as fin: + tmp_vals = np.frombuffer(fin.read(), dtype=np.float32).reshape([-1, sok_var._dimension]) + all_vals.append(tmp_vals.take(tmp_ids, axis=0)) + logging.info('tmp_vals.shape=%s %s %s' % (str(tmp_vals.shape), + str(tmp_ids.shape), str(all_vals[-1].shape))) + + all_keys = np.concatenate(all_keys, axis=0) + all_vals = np.concatenate(all_vals, axis=0) + return all_keys, all_vals + file_name = ops.get_default_graph().get_tensor_by_name( + self.saver_def.filename_tensor_name) + keys, vals = script_ops.py_func(_load_key_vals, [file_name, sok_var.name], + (dtypes.int64, dtypes.float32)) + with ops.control_dependencies([sok_var._initializer_op]): + return dynamic_variable_ops.dummy_var_assign(sok_var.handle, keys, vals) + # return dynamic_variable_ops.dummy_var_scatter_update(sok_var.handle, keys, vals) + + + def build(self): + if self._is_built: + return + super(SaverV2, self).build() + if self.saver_def.restore_op_name and len(self._sok_vars) > 0: + # load data from the model + restore_ops = [] + for sok_var in self._sok_vars: + restore_ops.append(self._load_sok_embedding(sok_var)) + old_restore_op = ops.get_default_graph().get_operation_by_name(self.saver_def.restore_op_name) + restore_ops.append(old_restore_op) + restore_op_n = control_flow_ops.group(restore_ops) + self.saver_def.restore_op_name = restore_op_n.name + # if self.saver_def.save_tensor_name: + # save_sok_ops = [] + # for sok_var in self._sok_vars: + # save_sok_ops. diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py new file mode 100644 index 000000000..0b811b3ab --- /dev/null +++ b/easy_rec/python/input/load_parquet.py @@ -0,0 +1,202 @@ +import sys +import logging +import multiprocessing +import queue +import numpy as np +import pandas as pd + +def start_data_proc(task_index, num_proc, file_que, data_que, proc_start_que, proc_stop_que, + batch_size, label_fields, effective_fields, drop_remainder): + mp_ctxt = multiprocessing.get_context('spawn') + proc_arr = [] + for proc_id in range(num_proc): + proc = mp_ctxt.Process( + target=load_data_proc, args=(proc_id, file_que, data_que, proc_start_que, + proc_stop_que, batch_size, label_fields, effective_fields, + drop_remainder), name='task_%d_data_proc_%d' % (task_index, proc_id)) + proc.start() + proc_arr.append(proc) + return proc_arr + +def _should_stop(proc_stop_que): + try: + proc_stop_que.get(block=False) + logging.info('data_proc stop signal received') + proc_stop_que.close() + return True + except queue.Empty: + return False + except ValueError: + return True + except AssertionError: + return True + +def _add_to_que(data_dict, data_que, proc_stop_que): + while True: + try: + data_que.put(data_dict, timeout=5) + return True + except queue.Full: + logging.warning('data_que is full') + if _should_stop(proc_stop_que): + return False + except ValueError: + logging.warning('data_que is closed') + return False + except AssertionError: + logging.warning('data_que is closed') + return False + +def _get_one_file(file_que, proc_stop_que): + if _should_stop(proc_stop_que): + return None + while True: + try: + input_file = file_que.get(block=False) + return input_file + except queue.Empty: + if file_que.empty() and file_que.qsize() == 0: + logging.info('file_que is empty: %d' % file_que.qsize()) + return None + return input_file + +def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, + batch_size, label_fields, effective_fields, drop_remainder): + logging.info('data proc %d start, proc_start_que=%s' % (proc_id, proc_start_que.qsize())) + proc_start_que.get() + all_fields = list(label_fields) + list(effective_fields) + logging.info('data proc %d start, file_que.qsize=%d' % (proc_id, file_que.qsize())) + num_files = 0 + part_data_dict = {} + + is_good = True + while is_good: + input_file = _get_one_file(file_que, proc_stop_que) + if input_file is None: + logging.info('input_file is none') + is_good = False + break + num_files += 1 + input_data = pd.read_parquet(input_file, columns=all_fields) + data_len = len(input_data[all_fields[0]]) + batch_num = int(data_len / batch_size) + res_num = data_len % batch_size + # logging.info( + # 'proc[%d] read file %s sample_num=%d batch_num=%d res_num=%d' % + # (proc_id, input_file, data_len, batch_num, res_num)) + sid = 0 + for batch_id in range(batch_num): + eid = sid + batch_size + data_dict = {} + for k in label_fields: + data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], + dtype=np.float32) + for k in effective_fields: + val = input_data[k][sid:eid] + all_lens = np.array([len(x) for x in val], dtype=np.int32) + all_vals = np.concatenate(list(val)) + assert np.sum(all_lens) == len( + all_vals), 'len(all_vals)=%d np.sum(all_lens)=%d' % ( + len(all_vals), np.sum(all_lens)) + data_dict[k] = (all_lens, all_vals) + + fea_val_arr = [] + fea_len_arr = [] + for fea_name in effective_fields: + fea_val_arr.append(data_dict[fea_name][1]) + fea_len_arr.append(data_dict[fea_name][0]) + del data_dict[fea_name] + fea_lens = np.concatenate(fea_len_arr, axis=0) + fea_vals = np.concatenate(fea_val_arr, axis=0) + data_dict['feature'] = (fea_lens, fea_vals) + if not _add_to_que(data_dict, data_que, proc_stop_que): + logging.info('add to que failed') + is_good = False + break + sid += batch_size + if res_num > 0 and is_good: + accum_res_num = 0 + data_dict = {} + part_data_dict_n = {} + for k in label_fields: + tmp_lbls = np.array([x[0] for x in input_data[k][sid:]], + dtype=np.float32) + if part_data_dict is not None and k in part_data_dict: + tmp_lbls = np.concatenate([part_data_dict[k], tmp_lbls], axis=0) + if len(tmp_lbls) > batch_size: + data_dict[k] = tmp_lbls[:batch_size] + part_data_dict_n[k] = tmp_lbls[batch_size:] + elif len(tmp_lbls) == batch_size: + data_dict[k] = tmp_lbls + else: + part_data_dict_n[k] = tmp_lbls + else: + part_data_dict_n[k] = tmp_lbls + for k in effective_fields: + val = input_data[k][sid:] + all_lens = np.array([len(x) for x in val], dtype=np.int32) + all_vals = np.concatenate(list(val)) + if part_data_dict is not None and k in part_data_dict: + tmp_lens = np.concatenate([part_data_dict[k][0], all_lens], axis=0) + tmp_vals = np.concatenate([part_data_dict[k][1], all_vals], axis=0) + if len(tmp_lens) > batch_size: + tmp_res_lens = tmp_lens[batch_size:] + tmp_lens = tmp_lens[:batch_size] + tmp_num_elems = np.sum(tmp_lens) + tmp_res_vals = tmp_vals[tmp_num_elems:] + tmp_vals = tmp_vals[:tmp_num_elems] + part_data_dict_n[k] = (tmp_res_lens, tmp_res_vals) + data_dict[k] = (tmp_lens, tmp_vals) + elif len(tmp_lens) == batch_size: + data_dict[k] = (tmp_lens, tmp_vals) + else: + part_data_dict_n[k] = (tmp_lens, tmp_vals) + else: + part_data_dict_n[k] = (all_lens, all_vals) + if len(data_dict) > 0: + fea_val_arr = [] + fea_len_arr = [] + for fea_name in effective_fields: + fea_val_arr.append(data_dict[fea_name][1]) + fea_len_arr.append(data_dict[fea_name][0]) + del data_dict[fea_name] + fea_lens = np.concatenate(fea_len_arr, axis=0) + fea_vals = np.concatenate(fea_val_arr, axis=0) + data_dict['feature'] = (fea_lens, fea_vals) + if not _add_to_que(data_dict, data_que, proc_stop_que): + logging.info('add to que failed') + is_good = False + break + + part_data_dict = part_data_dict_n + if len(part_data_dict) > 0 and is_good: + if not drop_remainder: + _add_to_que(part_data_dict, data_que, proc_stop_que) + else: + logging.warning('drop remain %d samples as drop_remainder is set' % \ + len(part_data_dict[label_fields[0]])) + if is_good: + is_good = _add_to_que(None, data_que, proc_stop_que) + logging.info('data_proc_id=%d, is_good = %s' % (proc_id, is_good)) + data_que.close(wait_send_finish=is_good) + + if proc_id == 0: + logging.info('data_que.qsize=%d' % data_que.qsize()) + while data_que.qsize() > 0: + try: + logging.info('data_que try to get one') + data_que.get(timeout=5) + logging.info('data_que get one') + except queue.Empty: + logging.warning('data_que.get timeout') + pass + logging.info('after clean: data_que.qsize=%d' % data_que.qsize()) + + if proc_id == 0: + while file_que.qsize() > 0: + try: + file_que.get(timeout=5) + except queue.Empty: + pass + file_que.close() + logging.info('data proc %d done, file_num=%d' % (proc_id, num_files)) From 7f33ddb058883d3de98d81fe34e0b63348a99aea Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Fri, 27 Oct 2023 01:13:04 +0800 Subject: [PATCH 10/67] add support for eval --- easy_rec/python/compat/optimizers.py | 15 +++++++- easy_rec/python/eval.py | 8 ++++ easy_rec/python/input/load_parquet.py | 41 +++++++-------------- easy_rec/python/input/parquet_input.py | 3 ++ easy_rec/python/main.py | 1 + easy_rec/python/model/easy_rec_estimator.py | 10 +++++ easy_rec/python/utils/distribution_utils.py | 3 +- 7 files changed, 51 insertions(+), 30 deletions(-) diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index 1dd7925e5..e6ded32f3 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import logging_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as vars_ @@ -288,7 +289,19 @@ def optimize_loss(loss, else: reduced_grads.append((g, v)) gradients = reduced_grads - + #else: + # tmp_grads = [] + # for g, v in gradients: + # if '/embedding' in v.name: + # import logging + # logging.info('do nothing to sok gradients: %s' % str(v)) + # with ops.control_dependencies([logging_ops.Print(g.indices, [array_ops.shape(g.indices), array_ops.shape(g.values), math_ops.reduce_min(g.indices), math_ops.reduce_max(g.indices), math_ops.reduce_min(g.values), math_ops.reduce_max(g.values)], message='grad_%s' % v.name)]): + # g = indexed_slices.IndexedSlices(array_ops.identity(g.values), array_ops.identity(g.indices)) + # else: + # g = logging_ops.Print(g, [array_ops.shape(g), math_ops.reduce_min(g), math_ops.reduce_max(g), math_ops.reduce_mean(g)], message='grad_%s' % v.name) + # tmp_grads.append((g, v)) + # gradients=tmp_grads + # Optionally add gradient noise. if gradient_noise_scale is not None: gradients = _add_scaled_noise_to_gradients(gradients, diff --git a/easy_rec/python/eval.py b/easy_rec/python/eval.py index 90038b0dc..f2ca0b6a0 100644 --- a/easy_rec/python/eval.py +++ b/easy_rec/python/eval.py @@ -11,7 +11,9 @@ from easy_rec.python.main import distribute_evaluate from easy_rec.python.main import evaluate from easy_rec.python.utils import config_util +from easy_rec.python.utils import estimator_utils from easy_rec.python.utils import ds_util +from easy_rec.python.protos.train_pb2 import DistributionStrategy from easy_rec.python.utils.distribution_utils import set_tf_config_and_get_distribute_eval_worker_num_on_ds # NOQA if tf.__version__ >= '2.0': @@ -65,6 +67,12 @@ def main(argv): if FLAGS.model_dir: pipeline_config.model_dir = FLAGS.model_dir + if pipeline_config.train_config.train_distribute == DistributionStrategy.HorovodStrategy: + estimator_utils.init_hvd() + elif pipeline_config.train_config.train_distribute == DistributionStrategy.SokStrategy: + estimator_utils.init_hvd() + estimator_utils.init_sok() + if FLAGS.distribute_eval: os.environ['distribute_eval'] = 'True' eval_result = distribute_evaluate(pipeline_config, FLAGS.checkpoint_path, diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index 0b811b3ab..185244b6c 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -48,17 +48,13 @@ def _add_to_que(data_dict, data_que, proc_stop_que): return False def _get_one_file(file_que, proc_stop_que): - if _should_stop(proc_stop_que): - return None while True: try: - input_file = file_que.get(block=False) + input_file = file_que.get(timeout=1) return input_file except queue.Empty: - if file_que.empty() and file_que.qsize() == 0: - logging.info('file_que is empty: %d' % file_que.qsize()) - return None - return input_file + pass + return None def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, batch_size, label_fields, effective_fields, drop_remainder): @@ -71,10 +67,11 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, is_good = True while is_good: + if _should_stop(proc_stop_que): + is_good = False + break input_file = _get_one_file(file_que, proc_stop_que) if input_file is None: - logging.info('input_file is none') - is_good = False break num_files += 1 input_data = pd.read_parquet(input_file, columns=all_fields) @@ -180,23 +177,11 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, logging.info('data_proc_id=%d, is_good = %s' % (proc_id, is_good)) data_que.close(wait_send_finish=is_good) - if proc_id == 0: - logging.info('data_que.qsize=%d' % data_que.qsize()) - while data_que.qsize() > 0: - try: - logging.info('data_que try to get one') - data_que.get(timeout=5) - logging.info('data_que get one') - except queue.Empty: - logging.warning('data_que.get timeout') - pass - logging.info('after clean: data_que.qsize=%d' % data_que.qsize()) - - if proc_id == 0: - while file_que.qsize() > 0: - try: - file_que.get(timeout=5) - except queue.Empty: - pass - file_que.close() + while not is_good: + try: + if file_que.get(timeout=1) is None: + break + except queue.Empty: + pass + file_que.close() logging.info('data proc %d done, file_num=%d' % (proc_id, num_files)) diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 43f82b88f..75797fd14 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -181,6 +181,9 @@ def _build(self, mode, params): my_files = self._my_files for input_file in my_files: self._file_que.put(input_file) + # add end signal + for proc in self._proc_arr: + self._file_que.put(None) logging.info('add input_files to file_que, qsize=%d' % self._file_que.qsize()) out_types = {} diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index bfe2fa361..b1986fa15 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -495,6 +495,7 @@ def evaluate(pipeline_config, # worker_device='/job:master/task:0', cluster=cluster)): eval_result = estimator.evaluate( eval_spec.input_fn, eval_spec.steps, checkpoint_path=ckpt_path) + eval_spec.input_fn.input_creator.stop() logging.info('Evaluate finish') print('eval_result = ', eval_result) diff --git a/easy_rec/python/model/easy_rec_estimator.py b/easy_rec/python/model/easy_rec_estimator.py index 66e337246..f93cdd225 100644 --- a/easy_rec/python/model/easy_rec_estimator.py +++ b/easy_rec/python/model/easy_rec_estimator.py @@ -480,11 +480,21 @@ def _eval_model_fn(self, features, labels, run_config): metric_dict['loss/loss/' + loss_key] = tf.metrics.mean(loss_tensor) tf.logging.info('metric_dict keys: %s' % metric_dict.keys()) + var_list = ( + ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + + ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS)) + scaffold = tf.train.Scaffold( + # saver=tf.train.Saver( + saver=SaverV2( + var_list=var_list, + sharded=True, + save_relative_paths=True)) end = time.time() tf.logging.info('eval graph construct finished. Time %.3fs' % (end - start)) return tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.EVAL, loss=loss, + scaffold=scaffold, predictions=predict_dict, eval_metric_ops=metric_dict) diff --git a/easy_rec/python/utils/distribution_utils.py b/easy_rec/python/utils/distribution_utils.py index 6ddcbf3ec..22ae9ecf8 100644 --- a/easy_rec/python/utils/distribution_utils.py +++ b/easy_rec/python/utils/distribution_utils.py @@ -216,7 +216,8 @@ def set_tf_config_and_get_train_worker_num( def set_tf_config_and_get_train_worker_num_on_ds(): - assert 'TF_CONFIG' in os.environ, "'TF_CONFIG' must in os.environ" + if 'TF_CONFIG' not in os.environ: + return tf_config = json.loads(os.environ['TF_CONFIG']) if 'cluster' in tf_config and 'ps' in tf_config['cluster'] and ( 'evaluator' not in tf_config['cluster']): From 5d858bee96412ba7cc331eebc1c5a314d904dc10 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Sat, 28 Oct 2023 11:05:25 +0800 Subject: [PATCH 11/67] add load kv embed op --- easy_rec/__init__.py | 2 + easy_rec/python/compat/queues.py | 6 -- easy_rec/python/compat/saver.py | 26 ++++++++- easy_rec/python/export.py | 11 ++++ easy_rec/python/input/parquet_input.py | 59 +++++++++++++++++--- easy_rec/python/model/easy_rec_estimator.py | 12 +++- easy_rec/python/model/ppnet_v3m.py | 3 + easy_rec/python/ops/2.12/libload_embed.so | Bin 0 -> 60232 bytes 8 files changed, 102 insertions(+), 17 deletions(-) create mode 100755 easy_rec/python/ops/2.12/libload_embed.so diff --git a/easy_rec/__init__.py b/easy_rec/__init__.py index ccb5b80bd..26ba8e032 100644 --- a/easy_rec/__init__.py +++ b/easy_rec/__init__.py @@ -29,6 +29,8 @@ ops_dir = os.path.join(ops_dir, 'DeepRec') else: ops_dir = os.path.join(ops_dir, '1.15') + elif tf.__version__.startswith('2.12'): + ops_dir = os.path.join(ops_dir, '2.12') else: ops_dir = None else: diff --git a/easy_rec/python/compat/queues.py b/easy_rec/python/compat/queues.py index 98c7ec0cb..98c1bd46d 100644 --- a/easy_rec/python/compat/queues.py +++ b/easy_rec/python/compat/queues.py @@ -147,12 +147,6 @@ def close(self, wait_send_finish=True): self._reader.close() except Exception: pass - try: - if self._wlock is not None: - self._wlock.release() - except: - pass - # logging.info('Queue[name=' + self._name + '] set self._run to false') self._run = False # clear queue # with self._rlock: diff --git a/easy_rec/python/compat/saver.py b/easy_rec/python/compat/saver.py index f1d0b1659..e9bb55dd5 100644 --- a/easy_rec/python/compat/saver.py +++ b/easy_rec/python/compat/saver.py @@ -20,6 +20,15 @@ dynamic_variable_ops = None sok = None +try: + from tensorflow.python.framework.load_library import load_op_library + import easy_rec + load_embed_lib_path = os.path.join(easy_rec.ops_dir, 'libload_embed.so') + load_embed_lib = load_op_library(load_embed_lib_path) +except Exception as ex: + logging.warning('load libload_embed.so failed: %s' % str(ex)) + + class SaverV2(saver.Saver): def __init__(self, var_list=None, @@ -79,6 +88,8 @@ def _load_key_vals(filename, var_name): tmp_keys = np.frombuffer(fin.read(), dtype=np.int64) tmp_ids = tmp_keys % hvd.size() tmp_ids = np.where(tmp_ids == hvd.rank())[0] + if len(tmp_ids) == 0: + break all_keys.append(tmp_keys.take(tmp_ids, axis=0)) logging.info('tmp_keys.shape=%s %s %s' % (str(tmp_keys.shape), str(tmp_ids.shape), str(all_keys[-1].shape))) @@ -92,11 +103,22 @@ def _load_key_vals(filename, var_name): all_keys = np.concatenate(all_keys, axis=0) all_vals = np.concatenate(all_vals, axis=0) + + shuffle_ids = np.array(range(len(all_keys))) + np.random.shuffle(shuffle_ids) + all_keys = all_keys.take(shuffle_ids, axis=0) + all_vals = all_vals.take(shuffle_ids, axis=0) + print(len(all_keys), all_vals.shape, np.min(all_keys), np.max(all_keys), np.min(all_vals), + np.max(all_vals)) return all_keys, all_vals file_name = ops.get_default_graph().get_tensor_by_name( self.saver_def.filename_tensor_name) - keys, vals = script_ops.py_func(_load_key_vals, [file_name, sok_var.name], - (dtypes.int64, dtypes.float32)) + # keys, vals = script_ops.py_func(_load_key_vals, [file_name, sok_var.name], + # (dtypes.int64, dtypes.float32)) + keys, vals = load_embed_lib.load_kv_embed(task_index=hvd.rank(), + task_num=hvd.size(), embed_dim=sok_var._dimension, + var_name='embed-' + sok_var.name.replace('/', '__'), + ckpt_path=file_name) with ops.control_dependencies([sok_var._initializer_op]): return dynamic_variable_ops.dummy_var_assign(sok_var.handle, keys, vals) # return dynamic_variable_ops.dummy_var_scatter_update(sok_var.handle, keys, vals) diff --git a/easy_rec/python/export.py b/easy_rec/python/export.py index 0c01f2eed..fe092ecae 100644 --- a/easy_rec/python/export.py +++ b/easy_rec/python/export.py @@ -7,6 +7,9 @@ from tensorflow.python.lib.io import file_io from easy_rec.python.main import export +from easy_rec.python.utils import config_util +from easy_rec.python.utils import estimator_utils +from easy_rec.python.protos.train_pb2 import DistributionStrategy if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -105,6 +108,14 @@ def main(argv): if FLAGS.oss_embedding_version: extra_params['oss_embedding_version'] = FLAGS.oss_embedding_version + pipeline_config = config_util.get_configs_from_pipeline_file( + pipeline_config_path) + if pipeline_config.train_config.train_distribute == DistributionStrategy.HorovodStrategy: + estimator_utils.init_hvd() + elif pipeline_config.train_config.train_distribute == DistributionStrategy.SokStrategy: + estimator_utils.init_hvd() + estimator_utils.init_sok() + export(FLAGS.export_dir, pipeline_config_path, FLAGS.checkpoint_path, FLAGS.asset_files, FLAGS.verbose, **extra_params) diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 75797fd14..92aba99ca 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -115,6 +115,7 @@ def _sample_generator(self): break except Exception as ex: logging.warning('task[%d] get from data_que exception: %s' % (self._task_index, str(ex))) + break for proc in self._proc_arr: proc.join() @@ -128,14 +129,19 @@ def stop(self): self._proc_stop_que.put(1) self._proc_stop_que.close() + def _any_alive(): + for proc in self._proc_arr: + if proc.is_alive(): + return True + return False + # to ensure the sender part of the stupid python Queue could exit properly - for _ in range(5): - while not self._data_que.empty(): - try: - self._data_que.get() - except: - pass - time.sleep(1) + while _any_alive(): + try: + self._data_que.get(timeout=1) + except: + pass + time.sleep(1) self._data_que.close() logging.info("data que closed") # import time @@ -216,3 +222,42 @@ def _build(self, mode, params): dataset = dataset.map(lambda x: (self._get_features(x))) # dataset = dataset.prefetch(buffer_size=self._prefetch_size) return dataset + + def create_input(self, export_config=None): + + def _input_fn(mode=None, params=None, config=None): + """Build input_fn for estimator. + + Args: + mode: tf.estimator.ModeKeys.(TRAIN, EVAL, PREDICT) + params: `dict` of hyper parameters, from Estimator + config: tf.estimator.RunConfig instance + + Return: + if mode is not None, return: + features: inputs to the model. + labels: groundtruth + else, return: + tf.estimator.export.ServingInputReceiver instance + """ + if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, + tf.estimator.ModeKeys.PREDICT): + # build dataset from self._config.input_path + self._mode = mode + dataset = self._build(mode, params) + return dataset + elif mode is None: # serving_input_receiver_fn for export SavedModel + sparse_ids = tf.placeholder(tf.int64, [None], name='sparse_ids') + sparse_lens = tf.placeholder(tf.int32, [None], name='sparse_lens') + inputs = {'sparse_ids': sparse_ids, 'sparse_lens': sparse_lens} + if self._has_ev: + features = {'feature':tf.RaggedTensor.from_row_lengths(sparse_ids, + sparse_lens)} + else: + features = {'feature':tf.RaggedTensor.from_row_lengths( + sparse_ids % self._feature_configs[0].num_buckets, + sparse_lens)} + return tf.estimator.export.ServingInputReceiver(features, inputs) + + _input_fn.input_creator = self + return _input_fn diff --git a/easy_rec/python/model/easy_rec_estimator.py b/easy_rec/python/model/easy_rec_estimator.py index f93cdd225..bd5275112 100644 --- a/easy_rec/python/model/easy_rec_estimator.py +++ b/easy_rec/python/model/easy_rec_estimator.py @@ -422,7 +422,6 @@ def _train_model_fn(self, features, labels, run_config): scaffold = tf.train.Scaffold( - # saver=tf.train.Saver( saver=SaverV2( var_list=var_list, sharded=True, @@ -484,7 +483,6 @@ def _eval_model_fn(self, features, labels, run_config): ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS)) scaffold = tf.train.Scaffold( - # saver=tf.train.Saver( saver=SaverV2( var_list=var_list, sharded=True, @@ -628,9 +626,19 @@ def _export_model_fn(self, features, labels, run_config, params): tf.GraphKeys.ASSET_FILEPATHS, tf.constant(fg_path, dtype=tf.string, name='fg.json')) + var_list = ( + ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + + ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS)) + scaffold = tf.train.Scaffold( + saver=SaverV2( + var_list=var_list, + sharded=True, + save_relative_paths=True)) + return tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.PREDICT, loss=None, + scaffold=scaffold, predictions=outputs, export_outputs=export_outputs) diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index 5700f553f..60a9262ce 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -69,3 +69,6 @@ def build_loss_graph(self): self._labels[lbl_name], output) self._loss_dict[lbl_name] = loss_obj return self._loss_dict + + def get_outputs(self): + return [x['input_name'] for x in self._model_conf['label']] diff --git a/easy_rec/python/ops/2.12/libload_embed.so 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z)!*mjKENxee2VIt{ZQevQJ-k3&8YXR$BX)EJ8F62RsWnX+BfTwcvtIBHsICz`c&0@ zeNkDozlW3q5B+Z7e0)@XRQrdYM`*m`ReRLmISi>kI1o8%yJ|tYyQubE#Zfx`bq>+r LlF>f~Q2PG>Wsbkg literal 0 HcmV?d00001 From b5a143e3b0b88a1b5e6711f6d024a0ff31ff752d Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Sun, 29 Oct 2023 21:38:15 +0800 Subject: [PATCH 12/67] fix bug --- .../compat/feature_column/feature_column.py | 13 +- easy_rec/python/compat/optimizers.py | 3 +- .../python/inference/parquet_predictor.py | 149 ++++++ easy_rec/python/inference/predictor.py | 492 +----------------- easy_rec/python/input/load_parquet.py | 110 ++-- easy_rec/python/input/parquet_input.py | 113 +++- easy_rec/python/ops/2.12/libload_embed.so | Bin 60232 -> 60296 bytes easy_rec/python/predict.py | 16 +- easy_rec/python/protos/pipeline.proto | 2 + easy_rec/python/utils/input_utils.py | 26 + 10 files changed, 360 insertions(+), 564 deletions(-) create mode 100644 easy_rec/python/inference/parquet_predictor.py diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 74190c573..26f61b667 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -155,6 +155,7 @@ from tensorflow.python.ops import init_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import logging_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import resource_variable_ops @@ -399,7 +400,10 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring if len(lookup_output_ids) > 0: # first concat all the ids and unique # all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) - all_ids = features['feature'] # ragged_concat_ops.concat(lookup_indices, axis=0) + if isinstance(features, dict) and 'sparse_fea' in features.keys(): + all_ids = features['sparse_fea'] + else: + all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) np = hvd.size() # embed_dim = lookup_embeddings[0]._dimension @@ -421,9 +425,14 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # all2all recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) - embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') + # embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') else: all_uniq_ids, uniq_idx = array_ops.unique(all_ids.flat_values) + # all_uniq_ids = logging_ops.Print(all_uniq_ids, [math_ops.reduce_min(all_uniq_ids), + # math_ops.reduce_max(all_uniq_ids), array_ops.shape(all_uniq_ids), + # array_ops.shape(all_ids.flat_values), array_ops.shape(segment_ids), + # math_ops.reduce_min(segment_ids), math_ops.reduce_max(segment_ids)], + # message='debug_all_uniq_ids') if isinstance(lookup_embeddings[0], sok.DynamicVariable): recv_embeddings = lookup_embeddings[0].sparse_read(all_uniq_ids) else: diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index e6ded32f3..ec173ebf5 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -359,7 +359,8 @@ def _apply_grad(): if 'compute_gradients' not in dir(opt): sparse_vars = [ x for x in gradients if 'DynamicVariable' in str(type(x[1])) ] dense_vars = [ x for x in gradients if 'DynamicVariable' not in str(type(x[1])) ] - sparse_grad_updates = opt.apply_gradients(sparse_vars) + with ops.control_dependencies([array_ops.identity(loss)]): + sparse_grad_updates = opt.apply_gradients(sparse_vars) dense_grad_updates = opt._optimizer.apply_gradients( dense_vars, global_step=global_step if increment_global_step else None, diff --git a/easy_rec/python/inference/parquet_predictor.py b/easy_rec/python/inference/parquet_predictor.py new file mode 100644 index 000000000..5733b4263 --- /dev/null +++ b/easy_rec/python/inference/parquet_predictor.py @@ -0,0 +1,149 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import abc +import json +import logging +import math +import os +import time + +import numpy as np +import pandas as pd +import six +import tensorflow as tf +from tensorflow.core.protobuf import meta_graph_pb2 +from tensorflow.python.platform import gfile +from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import signature_constants + +from easy_rec.python.protos.dataset_pb2 import DatasetConfig +from easy_rec.python.utils import config_util +from easy_rec.python.utils import numpy_utils +from easy_rec.python.utils import tf_utils +from easy_rec.python.utils.check_utils import check_split +from easy_rec.python.utils.config_util import get_configs_from_pipeline_file +from easy_rec.python.utils.config_util import get_input_name_from_fg_json +from easy_rec.python.utils.config_util import search_fg_json +from easy_rec.python.utils.hive_utils import HiveUtils +from easy_rec.python.utils import input_utils +from easy_rec.python.utils.load_class import get_register_class_meta +from easy_rec.python.utils.tf_utils import get_tf_type +from easy_rec.python.inference.predictor import Predictor +from easy_rec.python.input.parquet_input import ParquetInput + +if tf.__version__ >= '2.0': + tf = tf.compat.v1 + +try: + from tensorflow.python.framework.load_library import load_op_library + import easy_rec + load_embed_lib_path = os.path.join(easy_rec.ops_dir, 'libload_embed.so') + load_embed_lib = load_op_library(load_embed_lib_path) +except Exception as ex: + logging.warning('load libload_embed.so failed: %s' % str(ex)) + + +class ParquetPredictor(Predictor): + + def __init__(self, + model_path, + data_config, + ds_vector_recall=False, + fg_json_path=None, + profiling_file=None, + selected_cols=None, + output_sep=chr(1), + pipeline_config=None): + super(ParquetPredictor, self).__init__(model_path, profiling_file, fg_json_path) + self._output_sep = output_sep + self._ds_vector_recall = ds_vector_recall + input_type = DatasetConfig.InputType.Name(data_config.input_type).lower() + self.pipeline_config=pipeline_config + + if 'rtp' in input_type: + self._is_rtp = True + self._input_sep = data_config.rtp_separator + else: + self._is_rtp = False + self._input_sep = data_config.separator + + if selected_cols and not ds_vector_recall: + self._selected_cols = [int(x) for x in selected_cols.split(',')] + elif ds_vector_recall: + self._selected_cols = selected_cols.split(',') + else: + self._selected_cols = None + + def _get_reserved_cols(self, reserved_cols): + if reserved_cols == 'ALL_COLUMNS': + if self._is_rtp: + if self._with_header: + reserved_cols = self._all_fields + else: + idx = 0 + reserved_cols = [] + for x in range(len(self._record_defaults) - 1): + if not self._selected_cols or x in self._selected_cols[:-1]: + reserved_cols.append(self._input_fields[idx]) + idx += 1 + else: + reserved_cols.append('no_used_%d' % x) + reserved_cols.append(SINGLE_PLACEHOLDER_FEATURE_KEY) + else: + reserved_cols = self._all_fields + else: + reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] + return reserved_cols + + def _parse_line(self, line): + out_dict = {} + for key in line['feature']: + out_dict[key] = line['feature'][key] + if 'reserve' in line: + for key in line['reserve']: + out_dict[key] = line['reserve'][key] + return out_dict + + def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, + slice_id): + feature_configs = config_util.get_compatible_feature_configs(self.pipeline_config) + kwargs = {} + if self.pipeline_config.model_config.HasField('ev_params'): + kwargs['ev_params'] = self.pipeline_config.model_config.ev_params + if self._reserved_cols is not None and len(self._reserved_cols) > 0: + kwargs['reserve_fields'] = self._reserved_cols + parquet_file = gfile.Glob(input_path.split(',')[0])[0] + kwargs['reserve_types'] = input_utils.get_tf_type_from_parquet_file( + self._reserved_cols, parquet_file) + parquet_input = ParquetInput(self.pipeline_config.data_config, feature_configs, + input_path, task_index=slice_id, task_num=slice_num, + pipeline_config=self.pipeline_config, + **kwargs) + return parquet_input._build(tf.estimator.ModeKeys.PREDICT, {}) + + def _get_writer(self, output_path, slice_id): + if not gfile.Exists(output_path): + gfile.MakeDirs(output_path) + res_path = os.path.join(output_path, 'part-%d.csv' % slice_id) + table_writer = gfile.GFile(res_path, 'w') + table_writer.write( + self._output_sep.join(self._output_cols + self._reserved_cols) + '\n') + return table_writer + + def _write_lines(self, table_writer, outputs): + outputs = '\n'.join( + [self._output_sep.join([str(i) for i in output]) for output in outputs]) + table_writer.write(outputs + '\n') + + def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): + reserve_vals = [outputs[x] for x in output_cols] + \ + [all_vals[k] for k in reserved_cols] + return reserve_vals + + @property + def out_of_range_exception(self): + return (tf.errors.OutOfRangeError) diff --git a/easy_rec/python/inference/predictor.py b/easy_rec/python/inference/predictor.py index 340523bfd..6eb891a9a 100644 --- a/easy_rec/python/inference/predictor.py +++ b/easy_rec/python/inference/predictor.py @@ -469,13 +469,13 @@ def predict_impl( with tf.Graph().as_default(), tf.Session() as sess: num_parallel_calls = 8 + self._reserved_cols = self._get_reserved_cols(reserved_cols) dataset = self._get_dataset(input_path, num_parallel_calls, batch_size, slice_num, slice_id) dataset = dataset.map( self._parse_line, num_parallel_calls=num_parallel_calls) iterator = dataset.make_one_shot_iterator() all_dict = iterator.get_next() - self._reserved_cols = self._get_reserved_cols(reserved_cols) input_names = self._predictor_impl.input_names table_writer = self._get_writer(output_path, slice_id) @@ -612,174 +612,6 @@ def batch(self, data_list): return batch_input -class CSVPredictor(Predictor): - - def __init__(self, - model_path, - data_config, - ds_vector_recall=False, - fg_json_path=None, - profiling_file=None, - selected_cols=None, - output_sep=chr(1)): - super(CSVPredictor, self).__init__(model_path, profiling_file, fg_json_path) - self._output_sep = output_sep - self._ds_vector_recall = ds_vector_recall - input_type = DatasetConfig.InputType.Name(data_config.input_type).lower() - self._with_header = data_config.with_header - - if 'rtp' in input_type: - self._is_rtp = True - self._input_sep = data_config.rtp_separator - else: - self._is_rtp = False - self._input_sep = data_config.separator - - if selected_cols and not ds_vector_recall: - self._selected_cols = [int(x) for x in selected_cols.split(',')] - elif ds_vector_recall: - self._selected_cols = selected_cols.split(',') - else: - self._selected_cols = None - - def _get_reserved_cols(self, reserved_cols): - if reserved_cols == 'ALL_COLUMNS': - if self._is_rtp: - if self._with_header: - reserved_cols = self._all_fields - else: - idx = 0 - reserved_cols = [] - for x in range(len(self._record_defaults) - 1): - if not self._selected_cols or x in self._selected_cols[:-1]: - reserved_cols.append(self._input_fields[idx]) - idx += 1 - else: - reserved_cols.append('no_used_%d' % x) - reserved_cols.append(SINGLE_PLACEHOLDER_FEATURE_KEY) - else: - reserved_cols = self._all_fields - else: - reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] - return reserved_cols - - def _parse_line(self, line): - check_list = [ - tf.py_func( - check_split, [line, self._input_sep, - len(self._record_defaults)], - Tout=tf.bool) - ] - with tf.control_dependencies(check_list): - fields = tf.decode_csv( - line, - field_delim=self._input_sep, - record_defaults=self._record_defaults, - name='decode_csv') - if self._is_rtp: - if self._with_header: - inputs = dict(zip(self._all_fields, fields)) - else: - inputs = {} - idx = 0 - for x in range(len(self._record_defaults) - 1): - if not self._selected_cols or x in self._selected_cols[:-1]: - inputs[self._input_fields[idx]] = fields[x] - idx += 1 - else: - inputs['no_used_%d' % x] = fields[x] - inputs[SINGLE_PLACEHOLDER_FEATURE_KEY] = fields[-1] - else: - inputs = {self._all_fields[x]: fields[x] for x in range(len(fields))} - return inputs - - def _get_num_cols(self, file_paths): - # try to figure out number of fields from one file - num_cols = -1 - with tf.gfile.GFile(file_paths[0], 'r') as fin: - num_lines = 0 - for line_str in fin: - line_tok = line_str.strip().split(self._input_sep) - if num_cols != -1: - assert num_cols == len(line_tok), ( - 'num selected cols is %d, not equal to %d, current line is: %s, please check input_sep and data.' - % (num_cols, len(line_tok), line_str)) - num_cols = len(line_tok) - num_lines += 1 - if num_lines > 10: - break - logging.info('num selected cols = %d' % num_cols) - return num_cols - - def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, - slice_id): - file_paths = [] - for path in input_path.split(','): - for x in gfile.Glob(path): - if not x.endswith('_SUCCESS'): - file_paths.append(x) - assert len(file_paths) > 0, 'match no files with %s' % input_path - - if self._with_header: - with tf.gfile.GFile(file_paths[0], 'r') as fin: - for line_str in fin: - line_str = line_str.strip() - self._field_names = line_str.split(self._input_sep) - break - print('field_names: %s' % ','.join(self._field_names)) - self._all_fields = self._field_names - elif self._ds_vector_recall: - self._all_fields = self._selected_cols - else: - self._all_fields = self._input_fields - if self._is_rtp: - num_cols = self._get_num_cols(file_paths) - self._record_defaults = ['' for _ in range(num_cols)] - if not self._selected_cols: - self._selected_cols = list(range(num_cols)) - for col_idx in self._selected_cols[:-1]: - col_name = self._input_fields[col_idx] - default_val = self._get_defaults(col_name) - self._record_defaults[col_idx] = default_val - else: - self._record_defaults = [ - self._get_defaults(col_name) for col_name in self._all_fields - ] - - dataset = tf.data.Dataset.from_tensor_slices(file_paths) - parallel_num = min(num_parallel_calls, len(file_paths)) - dataset = dataset.interleave( - lambda x: tf.data.TextLineDataset(x).skip(int(self._with_header)), - cycle_length=parallel_num, - num_parallel_calls=parallel_num) - dataset = dataset.shard(slice_num, slice_id) - dataset = dataset.batch(batch_size) - dataset = dataset.prefetch(buffer_size=64) - return dataset - - def _get_writer(self, output_path, slice_id): - if not gfile.Exists(output_path): - gfile.MakeDirs(output_path) - res_path = os.path.join(output_path, 'part-%d.csv' % slice_id) - table_writer = gfile.GFile(res_path, 'w') - table_writer.write( - self._output_sep.join(self._output_cols + self._reserved_cols) + '\n') - return table_writer - - def _write_lines(self, table_writer, outputs): - outputs = '\n'.join( - [self._output_sep.join([str(i) for i in output]) for output in outputs]) - table_writer.write(outputs + '\n') - - def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): - reserve_vals = [outputs[x] for x in output_cols] + \ - [all_vals[k] for k in reserved_cols] - return reserve_vals - - @property - def out_of_range_exception(self): - return (tf.errors.OutOfRangeError) - class ODPSPredictor(Predictor): @@ -842,326 +674,4 @@ def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): return reserve_vals -class HivePredictor(Predictor): - def __init__(self, - model_path, - data_config, - hive_config, - fg_json_path=None, - profiling_file=None, - output_sep=chr(1), - all_cols=None, - all_col_types=None): - super(HivePredictor, self).__init__(model_path, profiling_file, - fg_json_path) - - self._data_config = data_config - self._hive_config = hive_config - self._output_sep = output_sep - input_type = DatasetConfig.InputType.Name(data_config.input_type).lower() - if 'rtp' in input_type: - self._is_rtp = True - else: - self._is_rtp = False - self._all_cols = [x.strip() for x in all_cols if x != ''] - self._all_col_types = [x.strip() for x in all_col_types if x != ''] - self._record_defaults = [ - self._get_defaults(col_name, col_type) - for col_name, col_type in zip(self._all_cols, self._all_col_types) - ] - - def _get_reserved_cols(self, reserved_cols): - if reserved_cols == 'ALL_COLUMNS': - reserved_cols = self._all_cols - else: - reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] - return reserved_cols - - def _parse_line(self, line): - field_delim = self._data_config.rtp_separator if self._is_rtp else self._data_config.separator - fields = tf.decode_csv( - line, - field_delim=field_delim, - record_defaults=self._record_defaults, - name='decode_csv') - inputs = {self._all_cols[x]: fields[x] for x in range(len(fields))} - return inputs - - def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, - slice_id): - self._hive_util = HiveUtils( - data_config=self._data_config, hive_config=self._hive_config) - self._input_hdfs_path = self._hive_util.get_table_location(input_path) - file_paths = tf.gfile.Glob(os.path.join(self._input_hdfs_path, '*')) - assert len(file_paths) > 0, 'match no files with %s' % input_path - - dataset = tf.data.Dataset.from_tensor_slices(file_paths) - parallel_num = min(num_parallel_calls, len(file_paths)) - dataset = dataset.interleave( - tf.data.TextLineDataset, - cycle_length=parallel_num, - num_parallel_calls=parallel_num) - dataset = dataset.shard(slice_num, slice_id) - dataset = dataset.batch(batch_size) - dataset = dataset.prefetch(buffer_size=64) - return dataset - - def get_table_info(self, output_path): - partition_name, partition_val = None, None - if len(output_path.split('/')) == 2: - table_name, partition = output_path.split('/') - partition_name, partition_val = partition.split('=') - else: - table_name = output_path - return table_name, partition_name, partition_val - - def _get_writer(self, output_path, slice_id): - table_name, partition_name, partition_val = self.get_table_info(output_path) - is_exist = self._hive_util.is_table_or_partition_exist( - table_name, partition_name, partition_val) - assert not is_exist, '%s is already exists. Please drop it.' % output_path - - output_path = output_path.replace('.', '/') - self._hdfs_path = 'hdfs://%s:9000/user/easy_rec/%s_tmp' % ( - self._hive_config.host, output_path) - if not gfile.Exists(self._hdfs_path): - gfile.MakeDirs(self._hdfs_path) - res_path = os.path.join(self._hdfs_path, 'part-%d.csv' % slice_id) - table_writer = gfile.GFile(res_path, 'w') - return table_writer - - def _write_lines(self, table_writer, outputs): - outputs = '\n'.join( - [self._output_sep.join([str(i) for i in output]) for output in outputs]) - table_writer.write(outputs + '\n') - - def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): - reserve_vals = [outputs[x] for x in output_cols] + \ - [all_vals[k] for k in reserved_cols] - return reserve_vals - - def load_to_table(self, output_path, slice_num, slice_id): - res_path = os.path.join(self._hdfs_path, 'SUCCESS-%s' % slice_id) - success_writer = gfile.GFile(res_path, 'w') - success_writer.write('') - success_writer.close() - - if slice_id != 0: - return - - for id in range(slice_num): - res_path = os.path.join(self._hdfs_path, 'SUCCESS-%s' % id) - while not gfile.Exists(res_path): - time.sleep(10) - - table_name, partition_name, partition_val = self.get_table_info(output_path) - schema = '' - for output_col_name in self._output_cols: - tf_type = self._predictor_impl._outputs_map[output_col_name].dtype - col_type = tf_utils.get_col_type(tf_type) - schema += output_col_name + ' ' + col_type + ',' - - for output_col_name in self._reserved_cols: - assert output_col_name in self._all_cols, 'Column: %s not exists.' % output_col_name - idx = self._all_cols.index(output_col_name) - output_col_types = self._all_col_types[idx] - schema += output_col_name + ' ' + output_col_types + ',' - schema = schema.rstrip(',') - - if partition_name and partition_val: - sql = 'create table if not exists %s (%s) PARTITIONED BY (%s string)' % \ - (table_name, schema, partition_name) - self._hive_util.run_sql(sql) - sql = "LOAD DATA INPATH '%s/*' INTO TABLE %s PARTITION (%s=%s)" % \ - (self._hdfs_path, table_name, partition_name, partition_val) - self._hive_util.run_sql(sql) - else: - sql = 'create table if not exists %s (%s)' % \ - (table_name, schema) - self._hive_util.run_sql(sql) - sql = "LOAD DATA INPATH '%s/*' INTO TABLE %s" % \ - (self._hdfs_path, table_name) - self._hive_util.run_sql(sql) - - @property - def out_of_range_exception(self): - return (tf.errors.OutOfRangeError) - - -class HiveParquetPredictor(Predictor): - - def __init__(self, - model_path, - data_config, - hive_config, - fg_json_path=None, - profiling_file=None, - output_sep=chr(1), - all_cols=None, - all_col_types=None): - super(HiveParquetPredictor, self).__init__(model_path, profiling_file, - fg_json_path) - - self._data_config = data_config - self._hive_config = hive_config - self._output_sep = output_sep - input_type = DatasetConfig.InputType.Name(data_config.input_type).lower() - if 'rtp' in input_type: - self._is_rtp = True - else: - self._is_rtp = False - self._all_cols = [x.strip() for x in all_cols if x != ''] - self._all_col_types = [x.strip() for x in all_col_types if x != ''] - self._record_defaults = [ - self._get_defaults(col_name, col_type) - for col_name, col_type in zip(self._all_cols, self._all_col_types) - ] - - def _get_reserved_cols(self, reserved_cols): - if reserved_cols == 'ALL_COLUMNS': - reserved_cols = self._all_cols - else: - reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] - return reserved_cols - - def _parse_line(self, *fields): - fields = list(fields) - field_dict = {self._all_cols[i]: fields[i] for i in range(len(fields))} - return field_dict - - def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, - slice_id): - self._hive_util = HiveUtils( - data_config=self._data_config, hive_config=self._hive_config) - hdfs_path = self._hive_util.get_table_location(input_path) - self._input_hdfs_path = tf.gfile.Glob(os.path.join(hdfs_path, '*')) - assert len(self._input_hdfs_path) > 0, 'match no files with %s' % input_path - - list_type = [] - input_field_type_map = { - x.input_name: x.input_type for x in self._data_config.input_fields - } - type_2_tftype = { - 'string': tf.string, - 'double': tf.double, - 'float': tf.float32, - 'bigint': tf.int32, - 'boolean': tf.bool - } - for col_name, col_type in zip(self._all_cols, self._all_col_types): - if col_name in input_field_type_map: - list_type.append(get_tf_type(input_field_type_map[col_name])) - else: - list_type.append(type_2_tftype[col_type.lower()]) - list_type = tuple(list_type) - list_shapes = [tf.TensorShape([None]) for x in range(0, len(list_type))] - list_shapes = tuple(list_shapes) - - def parquet_read(): - for input_path in self._input_hdfs_path: - if input_path.endswith('SUCCESS'): - continue - df = pd.read_parquet(input_path, engine='pyarrow') - - df.replace('', np.nan, inplace=True) - df.replace('NULL', np.nan, inplace=True) - total_records_num = len(df) - - for k, v in zip(self._all_cols, self._record_defaults): - df[k].fillna(v, inplace=True) - - for start_idx in range(0, total_records_num, batch_size): - end_idx = min(total_records_num, start_idx + batch_size) - batch_data = df[start_idx:end_idx] - inputs = [] - for k in self._all_cols: - inputs.append(batch_data[k].to_numpy()) - yield tuple(inputs) - - dataset = tf.data.Dataset.from_generator( - parquet_read, output_types=list_type, output_shapes=list_shapes) - dataset = dataset.shard(slice_num, slice_id) - dataset = dataset.prefetch(buffer_size=64) - return dataset - - def get_table_info(self, output_path): - partition_name, partition_val = None, None - if len(output_path.split('/')) == 2: - table_name, partition = output_path.split('/') - partition_name, partition_val = partition.split('=') - else: - table_name = output_path - return table_name, partition_name, partition_val - - def _get_writer(self, output_path, slice_id): - table_name, partition_name, partition_val = self.get_table_info(output_path) - is_exist = self._hive_util.is_table_or_partition_exist( - table_name, partition_name, partition_val) - assert not is_exist, '%s is already exists. Please drop it.' % output_path - - output_path = output_path.replace('.', '/') - self._hdfs_path = 'hdfs://%s:9000/user/easy_rec/%s_tmp' % ( - self._hive_config.host, output_path) - if not gfile.Exists(self._hdfs_path): - gfile.MakeDirs(self._hdfs_path) - res_path = os.path.join(self._hdfs_path, 'part-%d.csv' % slice_id) - table_writer = gfile.GFile(res_path, 'w') - return table_writer - - def _write_lines(self, table_writer, outputs): - outputs = '\n'.join( - [self._output_sep.join([str(i) for i in output]) for output in outputs]) - table_writer.write(outputs + '\n') - - def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): - reserve_vals = [outputs[x] for x in output_cols] + \ - [all_vals[k] for k in reserved_cols] - return reserve_vals - - def load_to_table(self, output_path, slice_num, slice_id): - res_path = os.path.join(self._hdfs_path, 'SUCCESS-%s' % slice_id) - success_writer = gfile.GFile(res_path, 'w') - success_writer.write('') - success_writer.close() - - if slice_id != 0: - return - - for id in range(slice_num): - res_path = os.path.join(self._hdfs_path, 'SUCCESS-%s' % id) - while not gfile.Exists(res_path): - time.sleep(10) - - table_name, partition_name, partition_val = self.get_table_info(output_path) - schema = '' - for output_col_name in self._output_cols: - tf_type = self._predictor_impl._outputs_map[output_col_name].dtype - col_type = tf_utils.get_col_type(tf_type) - schema += output_col_name + ' ' + col_type + ',' - - for output_col_name in self._reserved_cols: - assert output_col_name in self._all_cols, 'Column: %s not exists.' % output_col_name - idx = self._all_cols.index(output_col_name) - output_col_types = self._all_col_types[idx] - schema += output_col_name + ' ' + output_col_types + ',' - schema = schema.rstrip(',') - - if partition_name and partition_val: - sql = 'create table if not exists %s (%s) PARTITIONED BY (%s string)' % \ - (table_name, schema, partition_name) - self._hive_util.run_sql(sql) - sql = "LOAD DATA INPATH '%s/*' INTO TABLE %s PARTITION (%s=%s)" % \ - (self._hdfs_path, table_name, partition_name, partition_val) - self._hive_util.run_sql(sql) - else: - sql = 'create table if not exists %s (%s)' % \ - (table_name, schema) - self._hive_util.run_sql(sql) - sql = "LOAD DATA INPATH '%s/*' INTO TABLE %s" % \ - (self._hdfs_path, table_name) - self._hive_util.run_sql(sql) - - @property - def out_of_range_exception(self): - return (tf.errors.OutOfRangeError) diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index 185244b6c..e36e2f6a4 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -6,14 +6,15 @@ import pandas as pd def start_data_proc(task_index, num_proc, file_que, data_que, proc_start_que, proc_stop_que, - batch_size, label_fields, effective_fields, drop_remainder): + batch_size, label_fields, effective_fields, reserve_fields, drop_remainder): mp_ctxt = multiprocessing.get_context('spawn') proc_arr = [] for proc_id in range(num_proc): proc = mp_ctxt.Process( target=load_data_proc, args=(proc_id, file_que, data_que, proc_start_que, proc_stop_que, batch_size, label_fields, effective_fields, - drop_remainder), name='task_%d_data_proc_%d' % (task_index, proc_id)) + reserve_fields, drop_remainder), + name='task_%d_data_proc_%d' % (task_index, proc_id)) proc.start() proc_arr.append(proc) return proc_arr @@ -56,11 +57,26 @@ def _get_one_file(file_que, proc_stop_que): pass return None +def _pack_sparse_feas(data_dict, effective_fields): + fea_val_arr = [] + fea_len_arr = [] + for fea_name in effective_fields: + fea_len_arr.append(data_dict[fea_name][0]) + fea_val_arr.append(data_dict[fea_name][1]) + del data_dict[fea_name] + fea_lens = np.concatenate(fea_len_arr, axis=0) + fea_vals = np.concatenate(fea_val_arr, axis=0) + data_dict['sparse_fea'] = (fea_lens, fea_vals) + def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, - batch_size, label_fields, effective_fields, drop_remainder): + batch_size, label_fields, effective_fields, reserve_fields, drop_remainder): logging.info('data proc %d start, proc_start_que=%s' % (proc_id, proc_start_que.qsize())) proc_start_que.get() - all_fields = list(label_fields) + list(effective_fields) + all_fields = list(effective_fields) + if label_fields is not None: + all_fields = all_fields + label_fields + if reserve_fields is not None: + all_fields = all_fields + reserve_fields logging.info('data proc %d start, file_que.qsize=%d' % (proc_id, file_que.qsize())) num_files = 0 part_data_dict = {} @@ -85,9 +101,19 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, for batch_id in range(batch_num): eid = sid + batch_size data_dict = {} - for k in label_fields: - data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], - dtype=np.float32) + + if label_fields is not None and len(label_fields) > 0: + for k in label_fields: + data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], + dtype=np.float32) + if reserve_fields is not None and len(reserve_fields) > 0: + for k in reserve_fields: + np_dtype = type(input_data[k][0]) + if np_dtype == object: + np_dtype = np.str + data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], + dtype=np_dtype) + for k in effective_fields: val = input_data[k][sid:eid] all_lens = np.array([len(x) for x in val], dtype=np.int32) @@ -97,15 +123,7 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, len(all_vals), np.sum(all_lens)) data_dict[k] = (all_lens, all_vals) - fea_val_arr = [] - fea_len_arr = [] - for fea_name in effective_fields: - fea_val_arr.append(data_dict[fea_name][1]) - fea_len_arr.append(data_dict[fea_name][0]) - del data_dict[fea_name] - fea_lens = np.concatenate(fea_len_arr, axis=0) - fea_vals = np.concatenate(fea_val_arr, axis=0) - data_dict['feature'] = (fea_lens, fea_vals) + _pack_sparse_feas(data_dict, effective_fields) if not _add_to_que(data_dict, data_que, proc_stop_que): logging.info('add to que failed') is_good = False @@ -115,20 +133,41 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, accum_res_num = 0 data_dict = {} part_data_dict_n = {} - for k in label_fields: - tmp_lbls = np.array([x[0] for x in input_data[k][sid:]], - dtype=np.float32) - if part_data_dict is not None and k in part_data_dict: - tmp_lbls = np.concatenate([part_data_dict[k], tmp_lbls], axis=0) - if len(tmp_lbls) > batch_size: - data_dict[k] = tmp_lbls[:batch_size] - part_data_dict_n[k] = tmp_lbls[batch_size:] - elif len(tmp_lbls) == batch_size: - data_dict[k] = tmp_lbls + + if label_fields is not None and len(label_fields) > 0: + for k in label_fields: + tmp_lbls = np.array([x[0] for x in input_data[k][sid:]], + dtype=np.float32) + if part_data_dict is not None and k in part_data_dict: + tmp_lbls = np.concatenate([part_data_dict[k], tmp_lbls], axis=0) + if len(tmp_lbls) > batch_size: + data_dict[k] = tmp_lbls[:batch_size] + part_data_dict_n[k] = tmp_lbls[batch_size:] + elif len(tmp_lbls) == batch_size: + data_dict[k] = tmp_lbls + else: + part_data_dict_n[k] = tmp_lbls else: part_data_dict_n[k] = tmp_lbls - else: - part_data_dict_n[k] = tmp_lbls + + if reserve_fields is not None and len(reserve_fields) > 0: + for k in reserve_fields: + np_dtype = type(input_data[k][0]) + if np_dtype == object: + np_dtype = np.str + tmp_r = np.array([x[0] for x in input_data[k][sid:]], dtype=np_dtype) + if part_data_dict is not None and k in part_data_dict: + tmp_r = np.concatenate([part_data_dict[k], tmp_r], axis=0) + if len(tmp_r) > batch_size: + data_dict[k] = tmp_r[:batch_size] + part_data_dict_n[k] = tmp_r[batch_size:] + elif len(tmp_r) == batch_size: + data_dict[k] = tmp_r + else: + part_data_dict_n[k] = tmp_r + else: + part_data_dict_n[k] = tmp_r + for k in effective_fields: val = input_data[k][sid:] all_lens = np.array([len(x) for x in val], dtype=np.int32) @@ -151,15 +190,7 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, else: part_data_dict_n[k] = (all_lens, all_vals) if len(data_dict) > 0: - fea_val_arr = [] - fea_len_arr = [] - for fea_name in effective_fields: - fea_val_arr.append(data_dict[fea_name][1]) - fea_len_arr.append(data_dict[fea_name][0]) - del data_dict[fea_name] - fea_lens = np.concatenate(fea_len_arr, axis=0) - fea_vals = np.concatenate(fea_val_arr, axis=0) - data_dict['feature'] = (fea_lens, fea_vals) + _pack_sparse_feas(data_dict, effective_fields) if not _add_to_que(data_dict, data_que, proc_stop_que): logging.info('add to que failed') is_good = False @@ -168,10 +199,11 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, part_data_dict = part_data_dict_n if len(part_data_dict) > 0 and is_good: if not drop_remainder: + _pack_sparse_feas(part_data_dict, effective_fields) _add_to_que(part_data_dict, data_que, proc_stop_que) else: - logging.warning('drop remain %d samples as drop_remainder is set' % \ - len(part_data_dict[label_fields[0]])) + logging.warning('drop remain %d samples as drop_remainder is set' % + len(part_data_dict[effective_fields[0]])) if is_good: is_good = _add_to_que(None, data_que, proc_stop_que) logging.info('data_proc_id=%d, is_good = %s' % (proc_id, is_good)) diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 92aba99ca..2da108359 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -9,6 +9,7 @@ import pandas as pd import tensorflow as tf from tensorflow.python.platform import gfile +from tensorflow.python.ops import math_ops, array_ops, logging_ops from easy_rec.python.input.input import Input @@ -57,17 +58,22 @@ def __init__(self, logging.info('[task_index=%d] task_file_num=%d' % (task_index, len(self._my_files))) self._file_que = queues.Queue(name='file_que', ctx=mp_ctxt) - num_proc = 8 - if file_num < num_proc: - num_proc = file_num + self._num_proc = 8 + if file_num < self._num_proc: + self._num_proc = file_num self._proc_start = False self._proc_start_que = queues.Queue(name='proc_start_que', ctx=mp_ctxt) self._proc_stop = False self._proc_stop_que = queues.Queue(name='proc_stop_que', ctx=mp_ctxt) - self._proc_arr = load_parquet.start_data_proc(task_index, num_proc, self._file_que, - self._data_que, self._proc_start_que, self._proc_stop_que, self._batch_size, - self._label_fields, self._effective_fields, self._data_config.drop_remainder) + + self._reserve_fields = None + self._reserve_types = None + if 'reserve_fields' in kwargs and 'reserve_types' in kwargs: + self._reserve_fields = kwargs['reserve_fields'] + self._reserve_types = kwargs['reserve_types'] + + self._proc_arr = None def _sample_generator(self): if not self._proc_start: @@ -168,16 +174,38 @@ def _to_fea_dict(self, input_dict): # tmp = input_dict[input_0][1] # fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, input_dict[input_0][0]) if self._has_ev: - fea_dict['feature'] = tf.RaggedTensor.from_row_lengths(input_dict['feature'][1], - input_dict['feature'][0]) + tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict['sparse_fea'][0] + # tmp_vals = logging_ops.Print(tmp_vals, [ + # array_ops.shape(tmp_vals), math_ops.reduce_min(tmp_vals), + # math_ops.reduce_max(tmp_vals), + # array_ops.shape(tmp_lens), + # math_ops.reduce_min(tmp_lens), + # math_ops.reduce_max(tmp_lens)], message='debug_sparse_fea') + fea_dict['sparse_fea'] = tf.RaggedTensor.from_row_lengths( + values=tmp_vals, row_lengths=tmp_lens) + # values=input_dict['sparse_fea'][1], + # row_lengths=input_dict['sparse_fea'][0]) else: - fea_dict['feature'] = tf.RaggedTensor.from_row_lengths(input_dict['feature'][1] % self._feature_configs[0].num_buckets, - input_dict['feature'][0]) + fea_dict['sparse_fea'] = tf.RaggedTensor.from_row_lengths( + values=input_dict['sparse_fea'][1] % self._feature_configs[0].num_buckets, + row_lengths=input_dict['sparse_fea'][0]) + + output_dict = {'feature': fea_dict} lbl_dict = {} for lbl_name in self._label_fields: - lbl_dict[lbl_name] = input_dict[lbl_name] - return {'feature': fea_dict, 'label': lbl_dict} + if lbl_name in input_dict: + lbl_dict[lbl_name] = input_dict[lbl_name] + + if len(lbl_dict) > 0: + output_dict['label'] = lbl_dict + + if self._reserve_fields is not None: + reserve_dict = {} + for tmp_name in self._reserve_fields: + reserve_dict[tmp_name] = input_dict[tmp_name] + output_dict['reserve'] = reserve_dict + return output_dict def _build(self, mode, params): if mode == tf.estimator.ModeKeys.TRAIN and self._data_config.num_epochs > 1: @@ -185,8 +213,20 @@ def _build(self, mode, params): my_files = self._my_files * self._data_config.num_epochs else: my_files = self._my_files + + if mode != tf.estimator.ModeKeys.PREDICT: + self._proc_arr = load_parquet.start_data_proc(self._task_index, self._num_proc, + self._file_que, self._data_que, self._proc_start_que, self._proc_stop_que, + self._batch_size, self._label_fields, self._effective_fields, + self._reserve_fields, self._data_config.drop_remainder) + else: + self._proc_arr = load_parquet.start_data_proc(self._task_index, self._num_proc, + self._file_que, self._data_que, self._proc_start_que, self._proc_stop_que, + self._batch_size, None, self._effective_fields, self._reserve_fields, False) + for input_file in my_files: self._file_que.put(input_file) + # add end signal for proc in self._proc_arr: self._file_que.put(None) @@ -194,14 +234,23 @@ def _build(self, mode, params): out_types = {} out_shapes = {} - for k in self._label_fields: - out_types[k] = tf.int32 - out_shapes[k] = tf.TensorShape([None]) + + if mode != tf.estimator.ModeKeys.PREDICT: + for k in self._label_fields: + out_types[k] = tf.float32 + out_shapes[k] = tf.TensorShape([None]) + + if self._reserve_fields is not None: + for k, t in zip(self._reserve_fields, self._reserve_types): + out_types[k] = t + out_shapes[k] = tf.TensorShape([None]) + # for k in self._effective_fields: # out_types[k] = (tf.int64, tf.int32) # out_shapes[k] = (tf.TensorShape([None]), tf.TensorShape([None])) - out_types['feature'] = (tf.int64, tf.int32) - out_shapes['feature'] = (tf.TensorShape([None]), tf.TensorShape([None])) + out_types['sparse_fea'] = (tf.int32, tf.int64) + # out_types['sparse_fea'] = (tf.int64, tf.int32) + out_shapes['sparse_fea'] = (tf.TensorShape([None]), tf.TensorShape([None])) dataset = tf.data.Dataset.from_generator( self._sample_generator, @@ -219,8 +268,18 @@ def _build(self, mode, params): dataset = dataset.map(lambda x: (self._get_features(x), self._get_labels(x))) else: - dataset = dataset.map(lambda x: (self._get_features(x))) - # dataset = dataset.prefetch(buffer_size=self._prefetch_size) + if self._reserve_fields is not None: + # for predictor with saved model + def _get_with_reserve(fea_dict): + out_dict = {'feature': { + 'ragged_ids': fea_dict['feature']['sparse_fea'].flat_values, + 'ragged_lens': fea_dict['feature']['sparse_fea'].row_lengths() } } + out_dict['reserve'] = fea_dict['reserve'] + return out_dict + dataset = dataset.map(_get_with_reserve) + else: + dataset = dataset.map(lambda x : self._get_features(x)) + dataset = dataset.prefetch(buffer_size=self._prefetch_size) return dataset def create_input(self, export_config=None): @@ -247,16 +306,16 @@ def _input_fn(mode=None, params=None, config=None): dataset = self._build(mode, params) return dataset elif mode is None: # serving_input_receiver_fn for export SavedModel - sparse_ids = tf.placeholder(tf.int64, [None], name='sparse_ids') - sparse_lens = tf.placeholder(tf.int32, [None], name='sparse_lens') - inputs = {'sparse_ids': sparse_ids, 'sparse_lens': sparse_lens} + ragged_ids = tf.placeholder(tf.int64, [None], name='ragged_ids') + ragged_lens = tf.placeholder(tf.int32, [None], name='ragged_lens') + inputs = {'ragged_ids': ragged_ids, 'ragged_lens': ragged_lens} if self._has_ev: - features = {'feature':tf.RaggedTensor.from_row_lengths(sparse_ids, - sparse_lens)} + features = {'sparse_fea':tf.RaggedTensor.from_row_lengths( + ragged_ids, ragged_lens)} else: - features = {'feature':tf.RaggedTensor.from_row_lengths( - sparse_ids % self._feature_configs[0].num_buckets, - sparse_lens)} + features = {'sparse_fea':tf.RaggedTensor.from_row_lengths( + ragged_ids % self._feature_configs[0].num_buckets, + ragged_lens)} return tf.estimator.export.ServingInputReceiver(features, inputs) _input_fn.input_creator = self diff --git a/easy_rec/python/ops/2.12/libload_embed.so b/easy_rec/python/ops/2.12/libload_embed.so index 85607148d168dca29a85060666fb79691ce622a5..99b4945846ab0fc26f13913623bcd8aadd771a29 100755 GIT binary patch delta 11926 zcmZ`<33yEB`afrqi6jy;u}ucqkRp~Owj`2})RI9$qOIyexR%nZrJA6vcH$txIiZC_*h!YW~0XJ7>}~_xYdanQwXb_x;{)IcL6_ z?*kV95U{+fytuquN~n}7sZVP8v%Nx-$Euy=vE}de3P}#rdgcF449N;oI~IzN??hC* zr}i3ZGVAt4MLDFPWlN%`X5X+6XcHH)=)kC~M5c?tZz|o=C1y@l*`(>HpT+Mv{9eFs 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easy_rec.python.inference.predictor import CSVPredictor -from easy_rec.python.inference.predictor import HiveParquetPredictor -from easy_rec.python.inference.predictor import HivePredictor +from easy_rec.python.inference.csv_predictor import CSVPredictor +from easy_rec.python.inference.hive_parquet_predictor import HiveParquetPredictor +from easy_rec.python.inference.hive_predictor import HivePredictor +from easy_rec.python.inference.parquet_predictor import ParquetPredictor from easy_rec.python.main import predict from easy_rec.python.protos.dataset_pb2 import DatasetConfig from easy_rec.python.utils import config_util @@ -90,7 +91,14 @@ def main(argv): output_sep=FLAGS.output_sep, all_cols=all_cols, all_col_types=all_col_types) - + elif pipeline_config.WhichOneof('train_path') == 'parquet_train_input': + predictor = ParquetPredictor(FLAGS.saved_model_dir, + pipeline_config.data_config, + ds_vector_recall=FLAGS.ds_vector_recall, + fg_json_path=FLAGS.fg_json_path, + selected_cols=FLAGS.selected_cols, + output_sep=FLAGS.output_sep, + pipeline_config=pipeline_config) else: predictor = CSVPredictor( FLAGS.saved_model_dir, diff --git a/easy_rec/python/protos/pipeline.proto b/easy_rec/python/protos/pipeline.proto index 301b9bcfa..1030bb31d 100644 --- a/easy_rec/python/protos/pipeline.proto +++ b/easy_rec/python/protos/pipeline.proto @@ -19,6 +19,7 @@ message EasyRecConfig { DatahubServer datahub_train_input = 12; HiveConfig hive_train_input = 101; BinaryDataInput binary_train_input = 102; + string parquet_train_input = 103; } oneof eval_path { string eval_input_path = 3; @@ -26,6 +27,7 @@ message EasyRecConfig { DatahubServer datahub_eval_input = 13; HiveConfig hive_eval_input= 201; BinaryDataInput binary_eval_input = 202; + string parquet_eval_input = 203; } required string model_dir = 5; diff --git a/easy_rec/python/utils/input_utils.py b/easy_rec/python/utils/input_utils.py index d42127bc3..3dd5b8f2c 100644 --- a/easy_rec/python/utils/input_utils.py +++ b/easy_rec/python/utils/input_utils.py @@ -2,6 +2,7 @@ # Copyright (c) Alibaba, Inc. and its affiliates. import numpy as np import tensorflow as tf +import pandas as pd from easy_rec.python.protos.dataset_pb2 import DatasetConfig @@ -72,3 +73,28 @@ def string_to_number(field, ftype, default_value, name=''): else: assert False, 'invalid types: %s' % str(ftype) return tmp_field + + +def get_tf_type_from_parquet_file(cols, parquet_file): + input_data = pd.read_parquet(parquet_file, columns=cols) + tf_types = [] + for col in cols: + obj = input_data[col][0] + if isinstance(obj, list): + data_type = type(obj[0]) + elif isinstance(obj, np.ndarray): + data_type = type(obj[0]) + else: + data_type = type(obj) + if data_type in (int, np.int32): + tf_types.append(tf.int32) + elif data_type in (np.str, str): + tf_types.append(tf.string) + elif data_type in (np.float, np.float32, float): + tf_types.append(tf.float32) + elif data_type in (np.double, ): + tf_types.append(tf.float64) + else: + tf_types.append(tf.string) + return tf_types + From d0910e3c430f91f4175a0b4e937c2853b4630782 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Sun, 29 Oct 2023 22:18:42 +0800 Subject: [PATCH 13/67] add export sok embed --- easy_rec/python/main.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index b1986fa15..46215e545 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -818,6 +818,19 @@ def export(export_dir, checkpoint_path=ckpt_path, strip_default_attrs=True) + sok_embed_path = ckpt_path + '-sok/' + if gfile.Exists(sok_embed_path): + if isinstance(final_export_dir, bytes): + final_export_dir = final_export_dir.decode('utf-8') + export_sok_embed_dir = os.path.join(final_export_dir, 'variables/variables-sok/') + gfile.MkDir(export_sok_embed_dir) + for tmp_file in gfile.Glob(sok_embed_path + '*'): + if (tmp_file.endswith('.keys') or tmp_file.endswith('.vals')) \ + and 'DynamicSlot' not in tmp_file: + _, tmp_name = os.path.split(tmp_file) + dst_file = os.path.join(export_sok_embed_dir, tmp_name) + gfile.Copy(tmp_file, dst_file) + # add export ts as version info saved_model = saved_model_pb2.SavedModel() if type(final_export_dir) not in [type(''), type(u'')]: From e8441692747c018ff98635263812d506a9d5c6b4 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Mon, 30 Oct 2023 09:47:53 +0800 Subject: [PATCH 14/67] fix sok optimizer wrapper call _finish two times bug --- easy_rec/python/compat/optimizers.py | 59 +-- easy_rec/python/compat/sok_optimizer.py | 439 ++++++++++++++++++++ easy_rec/python/model/easy_rec_estimator.py | 3 +- 3 files changed, 471 insertions(+), 30 deletions(-) create mode 100644 easy_rec/python/compat/sok_optimizer.py diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index ec173ebf5..70ffcbd23 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -261,15 +261,15 @@ def optimize_loss(loss, variables = vars_.trainable_variables() # Compute gradients. - if 'compute_gradients' not in dir(opt): - import tensorflow as tf - gradients = tf.gradients(loss, variables) - gradients = list(zip(gradients, variables)) - else: - gradients = opt.compute_gradients( - loss, - variables, - colocate_gradients_with_ops=colocate_gradients_with_ops) + # if 'compute_gradients' not in dir(opt): + # import tensorflow as tf + # gradients = tf.gradients(loss, variables) + # gradients = list(zip(gradients, variables)) + # else: + gradients = opt.compute_gradients( + loss, + variables, + colocate_gradients_with_ops=colocate_gradients_with_ops) if estimator_utils.has_hvd() and hvd.size() > 1: if not estimator_utils.has_sok(): @@ -356,26 +356,27 @@ def optimize_loss(loss, # Create gradient updates. def _apply_grad(): - if 'compute_gradients' not in dir(opt): - sparse_vars = [ x for x in gradients if 'DynamicVariable' in str(type(x[1])) ] - dense_vars = [ x for x in gradients if 'DynamicVariable' not in str(type(x[1])) ] - with ops.control_dependencies([array_ops.identity(loss)]): - sparse_grad_updates = opt.apply_gradients(sparse_vars) - dense_grad_updates = opt._optimizer.apply_gradients( - dense_vars, - global_step=global_step if increment_global_step else None, - name='train') - if sparse_grad_updates is not None and dense_grad_updates is not None: - grad_updates = tf.group([sparse_grad_updates, dense_grad_updates]) - elif sparse_grad_updates is not None: - grad_updates = sparse_grad_updates - elif dense_grad_updates is not None: - grad_updates = dense_grad_updates - else: - grad_updates = opt.apply_gradients( - gradients, - global_step=global_step if increment_global_step else None, - name='train') + # if 'compute_gradients' not in dir(opt): + # sparse_vars = [ x for x in gradients if 'DynamicVariable' in str(type(x[1])) ] + # dense_vars = [ x for x in gradients if 'DynamicVariable' not in str(type(x[1])) ] + # opt._op + # with ops.control_dependencies([array_ops.identity(loss)]): + # sparse_grad_updates = opt.apply_gradients(sparse_vars) + # dense_grad_updates = opt._optimizer.apply_gradients( + # dense_vars, + # global_step=global_step if increment_global_step else None, + # name='train') + # if sparse_grad_updates is not None and dense_grad_updates is not None: + # grad_updates = tf.group([sparse_grad_updates, dense_grad_updates]) + # elif sparse_grad_updates is not None: + # grad_updates = sparse_grad_updates + # elif dense_grad_updates is not None: + # grad_updates = dense_grad_updates + # else: + grad_updates = opt.apply_gradients( + gradients, + global_step=global_step if increment_global_step else None, + name='train') incr_save_ops = [] if incr_save: diff --git a/easy_rec/python/compat/sok_optimizer.py b/easy_rec/python/compat/sok_optimizer.py new file mode 100644 index 000000000..222923d1a --- /dev/null +++ b/easy_rec/python/compat/sok_optimizer.py @@ -0,0 +1,439 @@ +# +# Copyright (c) 2022, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import tensorflow as tf +from tensorflow.python.eager import context +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import resource_variable_ops + +from sparse_operation_kit.experiment.dynamic_variable import DynamicVariable + + +def OptimizerWrapper(optimizer): + """ + Abbreviated as ``sok.experiment.OptimizerWrapper``. + + This is a wrapper for tensorflow optimizer so that it can update + sok.DynamicVariable. + + Parameters + ---------- + optimizer: tensorflow optimizer + The original tensorflow optimizer. + + Example + ------- + .. code-block:: python + + import numpy as np + import tensorflow as tf + import horovod.tensorflow as hvd + from sparse_operation_kit import experiment as sok + + v = sok.DynamicVariable(dimension=3, initializer="13") + + indices = tf.convert_to_tensor([0, 1, 2**40], dtype=tf.int64) + + with tf.GradientTape() as tape: + embedding = tf.nn.embedding_lookup(v, indices) + print("embedding:", embedding) + loss = tf.reduce_sum(embedding) + + grads = tape.gradient(loss, [v]) + + optimizer = tf.keras.optimizers.SGD(learning_rate=1.0) + optimizer = sok.OptimizerWrapper(optimizer) + optimizer.apply_gradients(zip(grads, [v])) + + embedding = tf.nn.embedding_lookup(v, indices) + print("embedding:", embedding) + """ + + # a specific code path for dl framework tf2.11.0 + try: + if isinstance(optimizer, tf.keras.optimizers.legacy.Optimizer): + return OptimizerWrapperV2(optimizer) + except: + pass + + if isinstance(optimizer, tf.keras.optimizers.Optimizer): + return OptimizerWrapperV2(optimizer) + else: + return OptimizerWrapperV1(optimizer) + + +class OptimizerWrapperV1(object): + def __init__(self, optimizer): + self._optimizer = optimizer + # slots + unused = tf.Variable([0.0], dtype=tf.float32, name="unused", trainable=False) + self._optimizer._create_slots([unused]) + names, slots = [], [] + for name in self._optimizer.get_slot_names(): + names.append(name) + slots.append(self._optimizer.get_slot(unused, name)) + unused_key = self._var_key(unused) + for name in names: + assert unused_key in self._optimizer._slots[name] + self._optimizer._slots[name].pop(unused_key) + self._initial_vals = {} + for i, name in enumerate(names): + self._initial_vals[name] = slots[i] + # non-slots + self._optimizer._prepare() + self._non_slot_dict = {} + for name, v in self._optimizer._non_slot_dict.items(): + # self._non_slot_dict[name] = tf.Variable(v, trainable=False, name='SokOptimizerWrapper/' + name[0]) + self._non_slot_dict[name] = tf.compat.v1.get_variable(shape=[], trainable=False, name='SokOptimizerWrapper/' + name[0]) + + def compute_gradients(self, loss, var_list=None, + aggregation_method=None, + colocate_gradients_with_ops=False, + grad_loss=None): + self._loss = loss + import tensorflow as tf + gradients = tf.gradients(loss, var_list) + return list(zip(gradients, var_list)) + # return self._optimizer.compute_gradients(loss=loss, var_list=var_list, + # gate_gradients=gate_gradients, + # aggregation_method=aggregation_method, + # colocate_gradients_with_ops=colocate_gradients_with_ops, + # grad_loss=grad_loss) + + def _var_key(self, var): + if hasattr(var, "op"): + return (var.op.graph, var.op.name) + return var._unique_id + + def _create_slots(self, vars): + for var in vars: + if isinstance(var, DynamicVariable): + self._create_slots_dynamic(var) + else: + self._optimizer._create_slots(var) + + def _create_slots_dynamic(self, var): + key = self._var_key(var) + for slot_name in self._initial_vals: + if key not in self._optimizer._slots[slot_name]: + if var.backend_type == "hbm": + slot = DynamicVariable( + dimension=var.dimension, + initializer=self._initial_vals[slot_name], + name="DynamicSlot", + trainable=False, + ) + else: + tmp_config = var.config_dict + tmp_initializer = var.initializer_str + slot = DynamicVariable( + dimension=var.dimension, + initializer=self._initial_vals[slot_name], + var_type=var.backend_type, + name="DynamicSlot", + trainable=False, + **tmp_config + ) + + self._optimizer._slots[slot_name][key] = slot + + def get_slot_names(self): + return self._optimizer.get_slot_names() + + def get_slot(self, var, name): + return self._optimizer.get_slot(var, name) + + @property + def _slots(self): + return self._optimizer._slots + + def apply_gradients(self, grads_and_vars, global_step=None, name=None): + gradients = grads_and_vars + sparse_vars = [ x for x in gradients if 'DynamicVariable' in str(type(x[1])) ] + dense_vars = [ x for x in gradients if 'DynamicVariable' not in str(type(x[1])) ] + + def _dummy_finish(update_ops, name_scope): + return update_ops + + finish_func = self._optimizer._finish + self._optimizer._finish = _dummy_finish + with ops.control_dependencies([array_ops.identity(self._loss)]): + sparse_grad_updates = self.apply_sparse_gradients(sparse_vars, name=name) + + dense_grad_updates = self._optimizer.apply_gradients( + dense_vars, + global_step=None, + name=name) + if sparse_grad_updates is not None and dense_grad_updates is not None: + grad_updates = sparse_grad_updates + dense_grad_updates + elif sparse_grad_updates is not None: + grad_updates = sparse_grad_updates + elif dense_grad_updates is not None: + grad_updates = dense_grad_updates + + assert global_step is not None + with ops.control_dependencies([finish_func(grad_updates, "update")]): + with ops.colocate_with(global_step): + if isinstance( + global_step, resource_variable_ops.BaseResourceVariable): + # TODO(apassos): the implicit read in assign_add is slow; consider + # making it less so. + apply_updates = resource_variable_ops.assign_add_variable_op( + global_step.handle, + ops.convert_to_tensor(1, dtype=global_step.dtype), + name=name) + else: + apply_updates = state_ops.assign_add(global_step, 1, name=name) + + if not context.executing_eagerly(): + if isinstance(apply_updates, ops.Tensor): + apply_updates = apply_updates.op + train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) + if apply_updates not in train_op: + train_op.append(apply_updates) + + return apply_updates + + def apply_sparse_gradients(self, grads_and_vars, global_step=None, name=None): + # 1. Create slots and do sparse_read + to_static_ops = [] + grad_list, var_list = [], [] + for g, v in grads_and_vars: + if g is not None: + unique, indices = tf.unique(g.indices) + grad_list.append(ops.IndexedSlices(g.values, indices, g.dense_shape)) + # TODO: Check multi-thread safety of DET + with tf.control_dependencies([g.values]): + to_static_ops.append(v.to_static(unique)) + var_list.append(v) + key = self._var_key(v) + for slot_name in self._initial_vals: + if key not in self._optimizer._slots[slot_name]: + if v.backend_type == "hbm": + slot = DynamicVariable( + dimension=v.dimension, + initializer=self._initial_vals[slot_name], + name="DynamicSlot", + trainable=False, + ) + else: + tmp_config = v.config_dict + tmp_initializer = v.initializer_str + slot = DynamicVariable( + dimension=v.dimension, + initializer=self._initial_vals[slot_name], + var_type=v.backend_type, + name="DynamicSlot", + trainable=False, + **tmp_config + ) + + self._optimizer._slots[slot_name][key] = slot + else: + slot = self._optimizer._slots[slot_name][key] + to_static_ops.append(slot.to_static(unique)) + + if len(grad_list) == 0: + return + + # 2. Switch non_slot_dict + non_slot_dict = self._optimizer._non_slot_dict + self._optimizer._non_slot_dict = self._non_slot_dict + + # 3. Call tf-optimizer + with ops.control_dependencies(to_static_ops): + train_op = self._optimizer.apply_gradients( + zip(grad_list, var_list), global_step=global_step, name=name + ) + + # 4. Switch non_slot_dict + self._optimizer._non_slot_dict = non_slot_dict + + # 5. Write buffer back to dynamic variables + to_dynamic_ops = [] + if not isinstance(train_op, list): + train_op = [train_op] + with ops.control_dependencies(train_op): + for v in var_list: + key = self._var_key(v) + to_dynamic_ops.append(v.to_dynamic()) + for name in self._initial_vals: + slot = self._optimizer._slots[name][key] + to_dynamic_ops.append(slot.to_dynamic()) + + return to_dynamic_ops + + +class OptimizerWrapperV2(object): + def __init__(self, optimizer): + self._optimizer = optimizer + # slots + if tf.__version__[0] == "1": + unused = tf.Variable([0.0], name="unused", trainable=False, use_resource=True) + else: + unused = tf.Variable([0.0], name="unused", trainable=False) + self._optimizer._create_slots([unused]) + names, slots = [], [] + for name in self._optimizer.get_slot_names(): + names.append(name) + slots.append(self._optimizer.get_slot(unused, name)) + unused_key = self._var_key(unused) + if unused_key in self._optimizer._slots: + self._optimizer._slots.pop(unused_key) + self._initial_vals = {} + for i, name in enumerate(names): + self._initial_vals[name] = slots[i] + self._iterations = tf.Variable(0) + + @property + def lr(self): + return self._optimizer.lr + + def _create_slots(self, vars): + for tmp_var in vars: + if isinstance(tmp_var, DynamicVariable): + self._create_slots_dynamic(tmp_var) + else: + self._optimizer._create_slots(tmp_var) + + def _create_slots_dynamic(self, var): + key = self._var_key(var) + if key not in self._optimizer._slots: + self._optimizer._slots[key] = {} + for slot_name in self._initial_vals: + if slot_name not in self._optimizer._slots[key]: + if var.backend_type == "hbm": + slot = DynamicVariable( + dimension=var.dimension, + initializer=self._initial_vals[slot_name], + name="DynamicSlot", + trainable=False, + ) + else: + tmp_config = var.config_dict + tmp_initializer = var.initializer_str + slot = DynamicVariable( + dimension=var.dimension, + initializer=self._initial_vals[slot_name], + var_type=var.backend_type, + name="DynamicSlot", + trainable=False, + **tmp_config + ) + self._optimizer._slots[key][slot_name] = slot + + def _var_key(self, var): + if hasattr(var, "_distributed_container"): + var = var._distributed_container() + if var._in_graph_mode: + return var._shared_name + return var._unique_id + + def get_slot_names(self): + return self._optimizer.get_slot_names() + + def get_slot(self, var, name): + return self._optimizer.get_slot(var, name) + + @property + def _slots(self): + return self._optimizer._slots + + def apply_gradients(self, grads_and_vars, global_step=None, name=None): + # 1. Create slots and do sparse_read + to_static_ops = [] + grad_list, var_list = [], [] + for g, v in grads_and_vars: + if g is not None: + unique, indices = tf.unique(g.indices) + grad_list.append(ops.IndexedSlices(g.values, indices, g.dense_shape)) + # TODO: Check multi-thread safety of DET + # with tf.control_dependencies([g.values]): + to_static_ops.append(v.to_static(unique)) + var_list.append(v) + key = self._var_key(v) + if key not in self._optimizer._slots: + self._optimizer._slots[key] = {} + for slot_name in self._initial_vals: + if slot_name not in self._optimizer._slots[key]: + if v.backend_type == "hbm": + slot = DynamicVariable( + dimension=v.dimension, + initializer=self._initial_vals[slot_name], + name="DynamicSlot", + trainable=False, + ) + else: + tmp_config = v.config_dict + tmp_initializer = v.initializer_str + slot = DynamicVariable( + dimension=v.dimension, + initializer=self._initial_vals[slot_name], + var_type=v.backend_type, + name="DynamicSlot", + trainable=False, + **tmp_config + ) + + self._optimizer._slots[key][slot_name] = slot + else: + slot = self._optimizer._slots[key][slot_name] + to_static_ops.append(slot.to_static(unique)) + + if len(grad_list) == 0: + return + + # 2. Switch iterations + iterations = self._optimizer._iterations + self._optimizer._iterations = self._iterations + + # 3. Call tf-optimizer + with tf.control_dependencies(to_static_ops): + train_op = self._optimizer.apply_gradients(zip(grad_list, var_list), name=name) + + # 4. Switch iterations + self._optimizer._iterations = iterations + + # 5. Write buffer back to dynamic variables + to_dynamic_ops = [] + with tf.control_dependencies([train_op]): + for v in var_list: + key = self._var_key(v) + to_dynamic_ops.append(v.to_dynamic()) + for name in self._initial_vals: + slot = self._optimizer._slots[key][name] + to_dynamic_ops.append(slot.to_dynamic()) + return tf.group(to_dynamic_ops) + + +class SGD(object): + def __init__(self, lr): + self._lr = tf.Variable(lr) + + @property + def lr(self): + return self._lr + + def apply_gradients(self, grads_and_vars, global_step=None, name=None): + train_ops = [] + for g, v in grads_and_vars: + if g is not None: + scaled_g = ops.IndexedSlices(g.values * self._lr, g.indices, g.dense_shape) + train_ops.append(v.scatter_sub(scaled_g)) + return tf.group(train_ops) diff --git a/easy_rec/python/model/easy_rec_estimator.py b/easy_rec/python/model/easy_rec_estimator.py index bd5275112..a5025ca66 100644 --- a/easy_rec/python/model/easy_rec_estimator.py +++ b/easy_rec/python/model/easy_rec_estimator.py @@ -47,6 +47,7 @@ try: from sparse_operation_kit import experiment as sok + from easy_rec.python.compat import sok_optimizer except Exception: sok = None @@ -215,7 +216,7 @@ def _train_model_fn(self, features, labels, run_config): optimizer = MultiOptimizer(all_opts, grouped_vars) if self.train_config.train_distribute == DistributionStrategy.SokStrategy: - optimizer = sok.OptimizerWrapper(optimizer) + optimizer = sok_optimizer.OptimizerWrapper(optimizer) hooks = [] if estimator_utils.has_hvd(): From b60e3bad2d4aa697dfeceb5ff7064669b26151c2 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Mon, 30 Oct 2023 10:32:40 +0800 Subject: [PATCH 15/67] no need to switch non slot --- easy_rec/python/compat/sok_optimizer.py | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/easy_rec/python/compat/sok_optimizer.py b/easy_rec/python/compat/sok_optimizer.py index 222923d1a..771355c71 100644 --- a/easy_rec/python/compat/sok_optimizer.py +++ b/easy_rec/python/compat/sok_optimizer.py @@ -95,12 +95,7 @@ def __init__(self, optimizer): self._initial_vals = {} for i, name in enumerate(names): self._initial_vals[name] = slots[i] - # non-slots - self._optimizer._prepare() - self._non_slot_dict = {} - for name, v in self._optimizer._non_slot_dict.items(): - # self._non_slot_dict[name] = tf.Variable(v, trainable=False, name='SokOptimizerWrapper/' + name[0]) - self._non_slot_dict[name] = tf.compat.v1.get_variable(shape=[], trainable=False, name='SokOptimizerWrapper/' + name[0]) + # self._optimizer._prepare() def compute_gradients(self, loss, var_list=None, aggregation_method=None, @@ -252,19 +247,12 @@ def apply_sparse_gradients(self, grads_and_vars, global_step=None, name=None): if len(grad_list) == 0: return - # 2. Switch non_slot_dict - non_slot_dict = self._optimizer._non_slot_dict - self._optimizer._non_slot_dict = self._non_slot_dict - # 3. Call tf-optimizer with ops.control_dependencies(to_static_ops): train_op = self._optimizer.apply_gradients( zip(grad_list, var_list), global_step=global_step, name=name ) - # 4. Switch non_slot_dict - self._optimizer._non_slot_dict = non_slot_dict - # 5. Write buffer back to dynamic variables to_dynamic_ops = [] if not isinstance(train_op, list): From 4124a166e08f412ce59e8edd8114e63c04f0674d Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Mon, 30 Oct 2023 22:37:47 +0800 Subject: [PATCH 16/67] add support for lookup only --- .../compat/feature_column/feature_column.py | 19 ++++++++++++------- easy_rec/python/compat/sok_optimizer.py | 2 ++ easy_rec/python/layers/input_layer.py | 6 ++++-- 3 files changed, 18 insertions(+), 9 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 26f61b667..e00338a40 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -248,7 +248,8 @@ def _internal_input_layer(features, scope=None, cols_to_output_tensors=None, from_template=False, - feature_name_to_output_tensors=None): + feature_name_to_output_tensors=None, + is_training=True): """See input_layer, `scope` is a name or variable scope to use.""" feature_columns = _normalize_feature_columns(feature_columns) for column in feature_columns: @@ -338,8 +339,10 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring if column.ev_params is not None: embedding_weights = sok.DynamicVariable(name='embedding_weights', dimension=column.dimension, initializer='random', #column.initializer, - # var_type='hybrid', - trainable=column.trainable and trainable, dtype=dtypes.float32) + var_type='hybrid', + trainable=column.trainable and trainable, dtype=dtypes.float32, + init_capacity=1024 * 1024 * 8, + max_capacity=1024 * 1024 * 16) else: embedding_weights = variable_scope.get_variable( name='embedding_weights', @@ -421,7 +424,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring recv_ids, recv_lens = hvd.alltoall(send_ids, split_sizes) # read embedding from dynamic variable - send_embed = lookup_embeddings[0].sparse_read(recv_ids) + send_embed = lookup_embeddings[0].sparse_read(recv_ids, lookup_only=(not is_training)) # all2all recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) @@ -434,7 +437,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # math_ops.reduce_min(segment_ids), math_ops.reduce_max(segment_ids)], # message='debug_all_uniq_ids') if isinstance(lookup_embeddings[0], sok.DynamicVariable): - recv_embeddings = lookup_embeddings[0].sparse_read(all_uniq_ids) + recv_embeddings = lookup_embeddings[0].sparse_read(all_uniq_ids, lookup_only=(not is_training)) else: recv_embeddings = array_ops.gather(lookup_embeddings[0], all_uniq_ids) embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') @@ -508,7 +511,8 @@ def input_layer(features, trainable=True, cols_to_vars=None, cols_to_output_tensors=None, - feature_name_to_output_tensors=None): + feature_name_to_output_tensors=None, + is_training=True): """Returns a dense `Tensor` as input layer based on given `feature_columns`. Generally a single example in training data is described with FeatureColumns. @@ -572,7 +576,8 @@ def input_layer(features, trainable=trainable, cols_to_vars=cols_to_vars, cols_to_output_tensors=cols_to_output_tensors, - feature_name_to_output_tensors=feature_name_to_output_tensors) + feature_name_to_output_tensors=feature_name_to_output_tensors, + is_training=is_training) # TODO(akshayka): InputLayer should be a subclass of Layer, and it diff --git a/easy_rec/python/compat/sok_optimizer.py b/easy_rec/python/compat/sok_optimizer.py index 771355c71..48ba74585 100644 --- a/easy_rec/python/compat/sok_optimizer.py +++ b/easy_rec/python/compat/sok_optimizer.py @@ -249,6 +249,8 @@ def apply_sparse_gradients(self, grads_and_vars, global_step=None, name=None): # 3. Call tf-optimizer with ops.control_dependencies(to_static_ops): + import pdb + pdb.set_trace() train_op = self._optimizer.apply_gradients( zip(grad_list, var_list), global_step=global_step, name=name ) diff --git a/easy_rec/python/layers/input_layer.py b/easy_rec/python/layers/input_layer.py index a19ce75b8..276cc8e9c 100644 --- a/easy_rec/python/layers/input_layer.py +++ b/easy_rec/python/layers/input_layer.py @@ -133,7 +133,8 @@ def get_plain_feature(self, features, group_name): cols_to_output_tensors = OrderedDict() output_features = feature_column.input_layer( - features, group_columns, cols_to_output_tensors=cols_to_output_tensors) + features, group_columns, cols_to_output_tensors=cols_to_output_tensors, + is_training=self._is_training) group_features = [cols_to_output_tensors[x] for x in group_columns] embedding_reg_lst = [] @@ -247,7 +248,8 @@ def single_call_input_layer(self, features, group_columns, cols_to_output_tensors=cols_to_output_tensors, - feature_name_to_output_tensors=feature_name_to_output_tensors) + feature_name_to_output_tensors=feature_name_to_output_tensors, + is_training=self._is_training) embedding_reg_lst = [] builder = feature_column._LazyBuilder(features) From 40fd1a73d3f06fe304018c6a80a0f557ccfb71af Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Tue, 31 Oct 2023 14:23:12 +0800 Subject: [PATCH 17/67] remove debug code --- easy_rec/python/compat/feature_column/feature_column.py | 2 +- easy_rec/python/compat/sok_optimizer.py | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index e00338a40..42e3170e3 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -339,7 +339,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring if column.ev_params is not None: embedding_weights = sok.DynamicVariable(name='embedding_weights', dimension=column.dimension, initializer='random', #column.initializer, - var_type='hybrid', + # var_type='hybrid', trainable=column.trainable and trainable, dtype=dtypes.float32, init_capacity=1024 * 1024 * 8, max_capacity=1024 * 1024 * 16) diff --git a/easy_rec/python/compat/sok_optimizer.py b/easy_rec/python/compat/sok_optimizer.py index 48ba74585..771355c71 100644 --- a/easy_rec/python/compat/sok_optimizer.py +++ b/easy_rec/python/compat/sok_optimizer.py @@ -249,8 +249,6 @@ def apply_sparse_gradients(self, grads_and_vars, global_step=None, name=None): # 3. Call tf-optimizer with ops.control_dependencies(to_static_ops): - import pdb - pdb.set_trace() train_op = self._optimizer.apply_gradients( zip(grad_list, var_list), global_step=global_step, name=name ) From 592f7bd26738212adcb273ec529e48efc8f29feb Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Tue, 31 Oct 2023 00:20:41 +0800 Subject: [PATCH 18/67] fix dynamic partition bug --- easy_rec/python/compat/feature_column/feature_column.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 42e3170e3..24833a456 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -416,7 +416,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # dynamic partition # from sparse_operation_kit.experiment import raw_ops - p_assignments = all_uniq_ids % np + p_assignments = math_ops.cast(all_uniq_ids % np, dtypes.int32) gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, p_assignments, np) # all2all split_sizes = array_ops.concat([ array_ops.shape(x) for x in gather_ids ], axis=0) @@ -428,7 +428,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # all2all recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) - # embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') + embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') else: all_uniq_ids, uniq_idx = array_ops.unique(all_ids.flat_values) # all_uniq_ids = logging_ops.Print(all_uniq_ids, [math_ops.reduce_min(all_uniq_ids), From a1909cb83ba24cffda53ce6d7a571ed6190772e0 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Mon, 30 Oct 2023 22:50:37 +0800 Subject: [PATCH 19/67] add load embed to whl package --- MANIFEST.in | 1 + 1 file changed, 1 insertion(+) diff --git a/MANIFEST.in b/MANIFEST.in index 5576404e2..cee9c7a9c 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,2 +1,3 @@ include easy_rec/python/ops/1.12/*.so* include easy_rec/python/ops/1.15/*.so* +include easy_rec/python/ops/2.12/*.so* From dc3e45b1f479387831f26455091f6ac331e37a36 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Wed, 1 Nov 2023 15:58:10 +0800 Subject: [PATCH 20/67] optimize segment_ids performance --- .../compat/feature_column/feature_column.py | 20 ++++++-- easy_rec/python/compat/sok_optimizer.py | 2 +- easy_rec/python/inference/predictor.py | 3 +- easy_rec/python/input/load_parquet.py | 1 + easy_rec/python/input/parquet_input.py | 48 ++++++++++++------- easy_rec/python/utils/estimator_utils.py | 24 ++++++++++ 6 files changed, 75 insertions(+), 23 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 24833a456..894e83299 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -150,6 +150,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import embedding_ops +from tensorflow.python.ops.ragged import ragged_util from tensorflow.python.ops import init_ops @@ -165,6 +166,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.ops import data_flow_ops +from tensorflow.python.ops.ragged import ragged_tensor try: from tensorflow.python.ops.ragged import ragged_math_ops @@ -404,15 +406,25 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # first concat all the ids and unique # all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) if isinstance(features, dict) and 'sparse_fea' in features.keys(): - all_ids = features['sparse_fea'] + # all_uniq_ids, uniq_idx, segment_lens = features['sparse_fea'] + all_ids, segment_lens = features['sparse_fea'] + all_uniq_ids, uniq_idx = array_ops.unique(all_ids) + cumsum_lens = math_ops.cumsum(segment_lens) + segment_ids = array_ops.searchsorted(cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') + elif isinstance(features, dict) and 'ragged_ids' in features.keys() and 'ragged_lens' in features.keys(): + all_ids, segment_lens = features['ragged_ids'], features['ragged_lens'] + all_uniq_ids, uniq_idx = array_ops.unique(all_ids) + cumsum_lens = math_ops.cumsum(segment_lens) + segment_ids = array_ops.searchsorted(cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') else: all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) + segment_ids = all_ids.value_rowids() + flat_vals = all_ids.flat_vals() np = hvd.size() # embed_dim = lookup_embeddings[0]._dimension - segment_ids = all_ids.value_rowids() if np > 1: - all_uniq_ids, uniq_idx = array_ops.unique(all_ids.flat_values) + # all_uniq_ids, uniq_idx = array_ops.unique(flat_vals) # dynamic partition # from sparse_operation_kit.experiment import raw_ops @@ -430,7 +442,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') else: - all_uniq_ids, uniq_idx = array_ops.unique(all_ids.flat_values) + # all_uniq_ids, uniq_idx = array_ops.unique(flat_vals) # all_uniq_ids = logging_ops.Print(all_uniq_ids, [math_ops.reduce_min(all_uniq_ids), # math_ops.reduce_max(all_uniq_ids), array_ops.shape(all_uniq_ids), # array_ops.shape(all_ids.flat_values), array_ops.shape(segment_ids), diff --git a/easy_rec/python/compat/sok_optimizer.py b/easy_rec/python/compat/sok_optimizer.py index 771355c71..45f26d4bb 100644 --- a/easy_rec/python/compat/sok_optimizer.py +++ b/easy_rec/python/compat/sok_optimizer.py @@ -215,7 +215,7 @@ def apply_sparse_gradients(self, grads_and_vars, global_step=None, name=None): grad_list.append(ops.IndexedSlices(g.values, indices, g.dense_shape)) # TODO: Check multi-thread safety of DET with tf.control_dependencies([g.values]): - to_static_ops.append(v.to_static(unique)) + to_static_ops.append(v.to_static(unique, False)) var_list.append(v) key = self._var_key(v) for slot_name in self._initial_vals: diff --git a/easy_rec/python/inference/predictor.py b/easy_rec/python/inference/predictor.py index 6eb891a9a..473b024f3 100644 --- a/easy_rec/python/inference/predictor.py +++ b/easy_rec/python/inference/predictor.py @@ -474,7 +474,8 @@ def predict_impl( slice_num, slice_id) dataset = dataset.map( self._parse_line, num_parallel_calls=num_parallel_calls) - iterator = dataset.make_one_shot_iterator() + # iterator = dataset.make_one_shot_iterator() + iterator = tf.data.make_one_shot_iterator(dataset) all_dict = iterator.get_next() input_names = self._predictor_impl.input_names table_writer = self._get_writer(output_path, slice_id) diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index e36e2f6a4..15ab894ca 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -15,6 +15,7 @@ def start_data_proc(task_index, num_proc, file_que, data_que, proc_start_que, pr proc_stop_que, batch_size, label_fields, effective_fields, reserve_fields, drop_remainder), name='task_%d_data_proc_%d' % (task_index, proc_id)) + proc.daemon = True proc.start() proc_arr.append(proc) return proc_arr diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 2da108359..ca82eafcd 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -9,15 +9,17 @@ import pandas as pd import tensorflow as tf from tensorflow.python.platform import gfile +from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops, array_ops, logging_ops +from tensorflow.python.ops.ragged import ragged_util from easy_rec.python.input.input import Input from easy_rec.python.input import load_parquet from easy_rec.python.compat import queues -if tf.__version__ >= '2.0': - tf = tf.compat.v1 +# if tf.__version__ >= '2.0': +# tf = tf.compat.v1 class ParquetInput(Input): @@ -126,6 +128,8 @@ def _sample_generator(self): proc.join() def stop(self): + if self._proc_arr is None or len(self._proc_arr) == 0: + return logging.info("task[%d] will stop dataset procs, proc_num=%d" % (self._task_index, len(self._proc_arr))) self._file_que.close() @@ -181,14 +185,21 @@ def _to_fea_dict(self, input_dict): # array_ops.shape(tmp_lens), # math_ops.reduce_min(tmp_lens), # math_ops.reduce_max(tmp_lens)], message='debug_sparse_fea') - fea_dict['sparse_fea'] = tf.RaggedTensor.from_row_lengths( - values=tmp_vals, row_lengths=tmp_lens) - # values=input_dict['sparse_fea'][1], - # row_lengths=input_dict['sparse_fea'][0]) + print('tmp_vals=%s tmp_lens=%s' % (tmp_vals, tmp_lens)) + # all_uniq_ids, uniq_idx = array_ops.unique(tmp_vals) + # uniq_idx = math_ops.cast(uniq_idx, tf.int32) + + fea_dict['sparse_fea'] = (tmp_vals, tmp_lens) + # tf.RaggedTensor.from_row_lengths( + # values=tmp_vals, row_lengths=tmp_lens) + # values=input_dict['sparse_fea'][1], + # row_lengths=input_dict['sparse_fea'][0]) else: - fea_dict['sparse_fea'] = tf.RaggedTensor.from_row_lengths( - values=input_dict['sparse_fea'][1] % self._feature_configs[0].num_buckets, - row_lengths=input_dict['sparse_fea'][0]) + tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict['sparse_fea'][0] + fea_dict['sparse_fea'] = (tmp_vals % self._feature_configs[0].num_buckets, tmp_lens) + # fea_dict['sparse_fea'] = tf.RaggedTensor.from_row_lengths( + # values=input_dict['sparse_fea'][1] % self._feature_configs[0].num_buckets, + # row_lengths=input_dict['sparse_fea'][0]) output_dict = {'feature': fea_dict} @@ -205,6 +216,7 @@ def _to_fea_dict(self, input_dict): for tmp_name in self._reserve_fields: reserve_dict[tmp_name] = input_dict[tmp_name] output_dict['reserve'] = reserve_dict + return output_dict def _build(self, mode, params): @@ -267,13 +279,16 @@ def _build(self, mode, params): if mode != tf.estimator.ModeKeys.PREDICT: dataset = dataset.map(lambda x: (self._get_features(x), self._get_labels(x))) + # dataset = dataset.apply(tf.data.experimental.prefetch_to_device('/gpu:0')) + # dataset = dataset.prefetch(32) else: if self._reserve_fields is not None: # for predictor with saved model def _get_with_reserve(fea_dict): + print('fea_dict=%s' % fea_dict) out_dict = {'feature': { - 'ragged_ids': fea_dict['feature']['sparse_fea'].flat_values, - 'ragged_lens': fea_dict['feature']['sparse_fea'].row_lengths() } } + 'ragged_ids': fea_dict['feature']['sparse_fea'][0], + 'ragged_lens': fea_dict['feature']['sparse_fea'][1] } } out_dict['reserve'] = fea_dict['reserve'] return out_dict dataset = dataset.map(_get_with_reserve) @@ -306,16 +321,15 @@ def _input_fn(mode=None, params=None, config=None): dataset = self._build(mode, params) return dataset elif mode is None: # serving_input_receiver_fn for export SavedModel - ragged_ids = tf.placeholder(tf.int64, [None], name='ragged_ids') - ragged_lens = tf.placeholder(tf.int32, [None], name='ragged_lens') + ragged_ids = tf.compat.v1.placeholder(tf.int64, [None], name='ragged_ids') + ragged_lens = tf.compat.v1.placeholder(tf.int32, [None], name='ragged_lens') inputs = {'ragged_ids': ragged_ids, 'ragged_lens': ragged_lens} if self._has_ev: - features = {'sparse_fea':tf.RaggedTensor.from_row_lengths( - ragged_ids, ragged_lens)} + features = {'ragged_ids': ragged_ids, 'ragged_lens': ragged_lens} else: - features = {'sparse_fea':tf.RaggedTensor.from_row_lengths( + features = { 'ragged_ids': ragged_ids % self._feature_configs[0].num_buckets, - ragged_lens)} + 'ragged_lens': ragged_lens} return tf.estimator.export.ServingInputReceiver(features, inputs) _input_fn.input_creator = self diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index 27d9ae1da..3901b9aa5 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -304,6 +304,8 @@ def end(self, session): self._progress_file.close() +import ctypes +_cudart = ctypes.CDLL('libcudart.so') class CheckpointSaverHook(CheckpointSaverHook): """Saves checkpoints every N steps or seconds.""" @@ -347,6 +349,8 @@ def __init__(self, checkpoint_basename=checkpoint_basename, scaffold=scaffold, listeners=listeners) + self._cuda_profile_start = 0 + self._cuda_profile_stop = 0 self._steps_per_run = 1 self._write_graph = write_graph self._data_offset_var = data_offset_var @@ -614,6 +618,25 @@ def _send_sparse(self, global_step, session): % (global_step, msg_num, len(bytes_buf))) def after_run(self, run_context, run_values): + #stale_global_step = run_values.results + #if stale_global_step > 10 and not self._cuda_profile_start: + # self._cuda_profile_start = 1 + # ret = _cudart.cudaProfilerStart() + # if ret != 0: + # logging.error('cudaProfilerStart failed') + # else: + # logging.info('cudaProfilerStart good') + # return + #if stale_global_step > 20 and not self._cuda_profile_stop: + # self._cuda_profile_stop = 1 + # ret = _cudart.cudaProfilerStop() + # if ret != 0: + # logging.error('cudaProfilerStop failed') + # else: + # logging.info('cudaProfilerStop finish') + # return + #return + if not is_chief(): if has_sok(): stale_global_step = run_values.results @@ -643,6 +666,7 @@ def after_run(self, run_context, run_values): self._send_sparse(global_step, run_context.session) def _save_sok(self, session, step): + return if self._dyn_export is None: return From 528fa3b4db0d1c7447879ab5785577f4a36df55c Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Wed, 1 Nov 2023 16:01:16 +0800 Subject: [PATCH 21/67] add predictor files --- easy_rec/python/inference/csv_predictor.py | 207 +++++++++++++++++ .../inference/hive_parquet_predictor.py | 214 ++++++++++++++++++ easy_rec/python/inference/hive_predictor.py | 183 +++++++++++++++ 3 files changed, 604 insertions(+) create mode 100644 easy_rec/python/inference/csv_predictor.py create mode 100644 easy_rec/python/inference/hive_parquet_predictor.py create mode 100644 easy_rec/python/inference/hive_predictor.py diff --git a/easy_rec/python/inference/csv_predictor.py b/easy_rec/python/inference/csv_predictor.py new file mode 100644 index 000000000..5ae273853 --- /dev/null +++ b/easy_rec/python/inference/csv_predictor.py @@ -0,0 +1,207 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import abc +import json +import logging +import math +import os +import time + +import numpy as np +import pandas as pd +import six +import tensorflow as tf +from tensorflow.core.protobuf import meta_graph_pb2 +from tensorflow.python.platform import gfile +from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import signature_constants + +from easy_rec.python.protos.dataset_pb2 import DatasetConfig +from easy_rec.python.utils import numpy_utils +from easy_rec.python.utils import tf_utils +from easy_rec.python.utils.check_utils import check_split +from easy_rec.python.utils.config_util import get_configs_from_pipeline_file +from easy_rec.python.utils.config_util import get_input_name_from_fg_json +from easy_rec.python.utils.config_util import search_fg_json +from easy_rec.python.utils.hive_utils import HiveUtils +from easy_rec.python.utils.input_utils import get_type_defaults +from easy_rec.python.utils.load_class import get_register_class_meta +from easy_rec.python.utils.tf_utils import get_tf_type +from easy_rec.python.inference.predictor import Predictor + +if tf.__version__ >= '2.0': + tf = tf.compat.v1 + +class CSVPredictor(Predictor): + + def __init__(self, + model_path, + data_config, + ds_vector_recall=False, + fg_json_path=None, + profiling_file=None, + selected_cols=None, + output_sep=chr(1)): + super(CSVPredictor, self).__init__(model_path, profiling_file, fg_json_path) + self._output_sep = output_sep + self._ds_vector_recall = ds_vector_recall + input_type = DatasetConfig.InputType.Name(data_config.input_type).lower() + self._with_header = data_config.with_header + + if 'rtp' in input_type: + self._is_rtp = True + self._input_sep = data_config.rtp_separator + else: + self._is_rtp = False + self._input_sep = data_config.separator + + if selected_cols and not ds_vector_recall: + self._selected_cols = [int(x) for x in selected_cols.split(',')] + elif ds_vector_recall: + self._selected_cols = selected_cols.split(',') + else: + self._selected_cols = None + + def _get_reserved_cols(self, reserved_cols): + if reserved_cols == 'ALL_COLUMNS': + if self._is_rtp: + if self._with_header: + reserved_cols = self._all_fields + else: + idx = 0 + reserved_cols = [] + for x in range(len(self._record_defaults) - 1): + if not self._selected_cols or x in self._selected_cols[:-1]: + reserved_cols.append(self._input_fields[idx]) + idx += 1 + else: + reserved_cols.append('no_used_%d' % x) + reserved_cols.append(SINGLE_PLACEHOLDER_FEATURE_KEY) + else: + reserved_cols = self._all_fields + else: + reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] + return reserved_cols + + def _parse_line(self, line): + check_list = [ + tf.py_func( + check_split, [line, self._input_sep, + len(self._record_defaults)], + Tout=tf.bool) + ] + with tf.control_dependencies(check_list): + fields = tf.decode_csv( + line, + field_delim=self._input_sep, + record_defaults=self._record_defaults, + name='decode_csv') + if self._is_rtp: + if self._with_header: + inputs = dict(zip(self._all_fields, fields)) + else: + inputs = {} + idx = 0 + for x in range(len(self._record_defaults) - 1): + if not self._selected_cols or x in self._selected_cols[:-1]: + inputs[self._input_fields[idx]] = fields[x] + idx += 1 + else: + inputs['no_used_%d' % x] = fields[x] + inputs[SINGLE_PLACEHOLDER_FEATURE_KEY] = fields[-1] + else: + inputs = {self._all_fields[x]: fields[x] for x in range(len(fields))} + return inputs + + def _get_num_cols(self, file_paths): + # try to figure out number of fields from one file + num_cols = -1 + with tf.gfile.GFile(file_paths[0], 'r') as fin: + num_lines = 0 + for line_str in fin: + line_tok = line_str.strip().split(self._input_sep) + if num_cols != -1: + assert num_cols == len(line_tok), ( + 'num selected cols is %d, not equal to %d, current line is: %s, please check input_sep and data.' + % (num_cols, len(line_tok), line_str)) + num_cols = len(line_tok) + num_lines += 1 + if num_lines > 10: + break + logging.info('num selected cols = %d' % num_cols) + return num_cols + + def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, + slice_id): + file_paths = [] + for path in input_path.split(','): + for x in gfile.Glob(path): + if not x.endswith('_SUCCESS'): + file_paths.append(x) + assert len(file_paths) > 0, 'match no files with %s' % input_path + + if self._with_header: + with tf.gfile.GFile(file_paths[0], 'r') as fin: + for line_str in fin: + line_str = line_str.strip() + self._field_names = line_str.split(self._input_sep) + break + print('field_names: %s' % ','.join(self._field_names)) + self._all_fields = self._field_names + elif self._ds_vector_recall: + self._all_fields = self._selected_cols + else: + self._all_fields = self._input_fields + if self._is_rtp: + num_cols = self._get_num_cols(file_paths) + self._record_defaults = ['' for _ in range(num_cols)] + if not self._selected_cols: + self._selected_cols = list(range(num_cols)) + for col_idx in self._selected_cols[:-1]: + col_name = self._input_fields[col_idx] + default_val = self._get_defaults(col_name) + self._record_defaults[col_idx] = default_val + else: + self._record_defaults = [ + self._get_defaults(col_name) for col_name in self._all_fields + ] + + dataset = tf.data.Dataset.from_tensor_slices(file_paths) + parallel_num = min(num_parallel_calls, len(file_paths)) + dataset = dataset.interleave( + lambda x: tf.data.TextLineDataset(x).skip(int(self._with_header)), + cycle_length=parallel_num, + num_parallel_calls=parallel_num) + dataset = dataset.shard(slice_num, slice_id) + dataset = dataset.batch(batch_size) + dataset = dataset.prefetch(buffer_size=64) + return dataset + + def _get_writer(self, output_path, slice_id): + if not gfile.Exists(output_path): + gfile.MakeDirs(output_path) + res_path = os.path.join(output_path, 'part-%d.csv' % slice_id) + table_writer = gfile.GFile(res_path, 'w') + table_writer.write( + self._output_sep.join(self._output_cols + self._reserved_cols) + '\n') + return table_writer + + def _write_lines(self, table_writer, outputs): + outputs = '\n'.join( + [self._output_sep.join([str(i) for i in output]) for output in outputs]) + table_writer.write(outputs + '\n') + + def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): + reserve_vals = [outputs[x] for x in output_cols] + \ + [all_vals[k] for k in reserved_cols] + return reserve_vals + + @property + def out_of_range_exception(self): + return (tf.errors.OutOfRangeError) + + diff --git a/easy_rec/python/inference/hive_parquet_predictor.py b/easy_rec/python/inference/hive_parquet_predictor.py new file mode 100644 index 000000000..1b6cdede3 --- /dev/null +++ b/easy_rec/python/inference/hive_parquet_predictor.py @@ -0,0 +1,214 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import abc +import json +import logging +import math +import os +import time + +import numpy as np +import pandas as pd +import six +import tensorflow as tf +from tensorflow.core.protobuf import meta_graph_pb2 +from tensorflow.python.platform import gfile +from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import signature_constants + +from easy_rec.python.protos.dataset_pb2 import DatasetConfig +from easy_rec.python.utils import numpy_utils +from easy_rec.python.utils import tf_utils +from easy_rec.python.utils.check_utils import check_split +from easy_rec.python.utils.config_util import get_configs_from_pipeline_file +from easy_rec.python.utils.config_util import get_input_name_from_fg_json +from easy_rec.python.utils.config_util import search_fg_json +from easy_rec.python.utils.hive_utils import HiveUtils +from easy_rec.python.utils.input_utils import get_type_defaults +from easy_rec.python.utils.load_class import get_register_class_meta +from easy_rec.python.utils.tf_utils import get_tf_type +from easy_rec.python.inference.predictor import Predictor + +if tf.__version__ >= '2.0': + tf = tf.compat.v1 + +class HiveParquetPredictor(Predictor): + + def __init__(self, + model_path, + data_config, + hive_config, + fg_json_path=None, + profiling_file=None, + output_sep=chr(1), + all_cols=None, + all_col_types=None): + super(HiveParquetPredictor, self).__init__(model_path, profiling_file, + fg_json_path) + + self._data_config = data_config + self._hive_config = hive_config + self._output_sep = output_sep + input_type = DatasetConfig.InputType.Name(data_config.input_type).lower() + if 'rtp' in input_type: + self._is_rtp = True + else: + self._is_rtp = False + self._all_cols = [x.strip() for x in all_cols if x != ''] + self._all_col_types = [x.strip() for x in all_col_types if x != ''] + self._record_defaults = [ + self._get_defaults(col_name, col_type) + for col_name, col_type in zip(self._all_cols, self._all_col_types) + ] + + def _get_reserved_cols(self, reserved_cols): + if reserved_cols == 'ALL_COLUMNS': + reserved_cols = self._all_cols + else: + reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] + return reserved_cols + + def _parse_line(self, *fields): + fields = list(fields) + field_dict = {self._all_cols[i]: fields[i] for i in range(len(fields))} + return field_dict + + def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, + slice_id): + self._hive_util = HiveUtils( + data_config=self._data_config, hive_config=self._hive_config) + hdfs_path = self._hive_util.get_table_location(input_path) + self._input_hdfs_path = gfile.Glob(os.path.join(hdfs_path, '*')) + assert len(self._input_hdfs_path) > 0, 'match no files with %s' % input_path + + list_type = [] + input_field_type_map = { + x.input_name: x.input_type for x in self._data_config.input_fields + } + type_2_tftype = { + 'string': tf.string, + 'double': tf.double, + 'float': tf.float32, + 'bigint': tf.int32, + 'boolean': tf.bool + } + for col_name, col_type in zip(self._all_cols, self._all_col_types): + if col_name in input_field_type_map: + list_type.append(get_tf_type(input_field_type_map[col_name])) + else: + list_type.append(type_2_tftype[col_type.lower()]) + list_type = tuple(list_type) + list_shapes = [tf.TensorShape([None]) for x in range(0, len(list_type))] + list_shapes = tuple(list_shapes) + + def parquet_read(): + for input_path in self._input_hdfs_path: + if input_path.endswith('SUCCESS'): + continue + df = pd.read_parquet(input_path, engine='pyarrow') + + df.replace('', np.nan, inplace=True) + df.replace('NULL', np.nan, inplace=True) + total_records_num = len(df) + + for k, v in zip(self._all_cols, self._record_defaults): + df[k].fillna(v, inplace=True) + + for start_idx in range(0, total_records_num, batch_size): + end_idx = min(total_records_num, start_idx + batch_size) + batch_data = df[start_idx:end_idx] + inputs = [] + for k in self._all_cols: + inputs.append(batch_data[k].to_numpy()) + yield tuple(inputs) + + dataset = tf.data.Dataset.from_generator( + parquet_read, output_types=list_type, output_shapes=list_shapes) + dataset = dataset.shard(slice_num, slice_id) + dataset = dataset.prefetch(buffer_size=64) + return dataset + + def get_table_info(self, output_path): + partition_name, partition_val = None, None + if len(output_path.split('/')) == 2: + table_name, partition = output_path.split('/') + partition_name, partition_val = partition.split('=') + else: + table_name = output_path + return table_name, partition_name, partition_val + + def _get_writer(self, output_path, slice_id): + table_name, partition_name, partition_val = self.get_table_info(output_path) + is_exist = self._hive_util.is_table_or_partition_exist( + table_name, partition_name, partition_val) + assert not is_exist, '%s is already exists. Please drop it.' % output_path + + output_path = output_path.replace('.', '/') + self._hdfs_path = 'hdfs://%s:9000/user/easy_rec/%s_tmp' % ( + self._hive_config.host, output_path) + if not gfile.Exists(self._hdfs_path): + gfile.MakeDirs(self._hdfs_path) + res_path = os.path.join(self._hdfs_path, 'part-%d.csv' % slice_id) + table_writer = gfile.GFile(res_path, 'w') + return table_writer + + def _write_lines(self, table_writer, outputs): + outputs = '\n'.join( + [self._output_sep.join([str(i) for i in output]) for output in outputs]) + table_writer.write(outputs + '\n') + + def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): + reserve_vals = [outputs[x] for x in output_cols] + \ + [all_vals[k] for k in reserved_cols] + return reserve_vals + + def load_to_table(self, output_path, slice_num, slice_id): + res_path = os.path.join(self._hdfs_path, 'SUCCESS-%s' % slice_id) + success_writer = gfile.GFile(res_path, 'w') + success_writer.write('') + success_writer.close() + + if slice_id != 0: + return + + for id in range(slice_num): + res_path = os.path.join(self._hdfs_path, 'SUCCESS-%s' % id) + while not gfile.Exists(res_path): + time.sleep(10) + + table_name, partition_name, partition_val = self.get_table_info(output_path) + schema = '' + for output_col_name in self._output_cols: + tf_type = self._predictor_impl._outputs_map[output_col_name].dtype + col_type = tf_utils.get_col_type(tf_type) + schema += output_col_name + ' ' + col_type + ',' + + for output_col_name in self._reserved_cols: + assert output_col_name in self._all_cols, 'Column: %s not exists.' % output_col_name + idx = self._all_cols.index(output_col_name) + output_col_types = self._all_col_types[idx] + schema += output_col_name + ' ' + output_col_types + ',' + schema = schema.rstrip(',') + + if partition_name and partition_val: + sql = 'create table if not exists %s (%s) PARTITIONED BY (%s string)' % \ + (table_name, schema, partition_name) + self._hive_util.run_sql(sql) + sql = "LOAD DATA INPATH '%s/*' INTO TABLE %s PARTITION (%s=%s)" % \ + (self._hdfs_path, table_name, partition_name, partition_val) + self._hive_util.run_sql(sql) + else: + sql = 'create table if not exists %s (%s)' % \ + (table_name, schema) + self._hive_util.run_sql(sql) + sql = "LOAD DATA INPATH '%s/*' INTO TABLE %s" % \ + (self._hdfs_path, table_name) + self._hive_util.run_sql(sql) + + @property + def out_of_range_exception(self): + return (tf.errors.OutOfRangeError) diff --git a/easy_rec/python/inference/hive_predictor.py b/easy_rec/python/inference/hive_predictor.py new file mode 100644 index 000000000..7c0cd2008 --- /dev/null +++ b/easy_rec/python/inference/hive_predictor.py @@ -0,0 +1,183 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import abc +import json +import logging +import math +import os +import time + +import numpy as np +import pandas as pd +import six +import tensorflow as tf +from tensorflow.core.protobuf import meta_graph_pb2 +from tensorflow.python.platform import gfile +from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import signature_constants + +from easy_rec.python.protos.dataset_pb2 import DatasetConfig +from easy_rec.python.utils import numpy_utils +from easy_rec.python.utils import tf_utils +from easy_rec.python.utils.check_utils import check_split +from easy_rec.python.utils.config_util import get_configs_from_pipeline_file +from easy_rec.python.utils.config_util import get_input_name_from_fg_json +from easy_rec.python.utils.config_util import search_fg_json +from easy_rec.python.utils.hive_utils import HiveUtils +from easy_rec.python.utils.input_utils import get_type_defaults +from easy_rec.python.utils.load_class import get_register_class_meta +from easy_rec.python.utils.tf_utils import get_tf_type +from easy_rec.python.inference.predictor import Predictor + +if tf.__version__ >= '2.0': + tf = tf.compat.v1 + +class HivePredictor(Predictor): + + def __init__(self, + model_path, + data_config, + hive_config, + fg_json_path=None, + profiling_file=None, + output_sep=chr(1), + all_cols=None, + all_col_types=None): + super(HivePredictor, self).__init__(model_path, profiling_file, + fg_json_path) + + self._data_config = data_config + self._hive_config = hive_config + self._output_sep = output_sep + input_type = DatasetConfig.InputType.Name(data_config.input_type).lower() + if 'rtp' in input_type: + self._is_rtp = True + else: + self._is_rtp = False + self._all_cols = [x.strip() for x in all_cols if x != ''] + self._all_col_types = [x.strip() for x in all_col_types if x != ''] + self._record_defaults = [ + self._get_defaults(col_name, col_type) + for col_name, col_type in zip(self._all_cols, self._all_col_types) + ] + + def _get_reserved_cols(self, reserved_cols): + if reserved_cols == 'ALL_COLUMNS': + reserved_cols = self._all_cols + else: + reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] + return reserved_cols + + def _parse_line(self, line): + field_delim = self._data_config.rtp_separator if self._is_rtp else self._data_config.separator + fields = tf.decode_csv( + line, + field_delim=field_delim, + record_defaults=self._record_defaults, + name='decode_csv') + inputs = {self._all_cols[x]: fields[x] for x in range(len(fields))} + return inputs + + def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, + slice_id): + self._hive_util = HiveUtils( + data_config=self._data_config, hive_config=self._hive_config) + self._input_hdfs_path = self._hive_util.get_table_location(input_path) + file_paths = tf.gfile.Glob(os.path.join(self._input_hdfs_path, '*')) + assert len(file_paths) > 0, 'match no files with %s' % input_path + + dataset = tf.data.Dataset.from_tensor_slices(file_paths) + parallel_num = min(num_parallel_calls, len(file_paths)) + dataset = dataset.interleave( + tf.data.TextLineDataset, + cycle_length=parallel_num, + num_parallel_calls=parallel_num) + dataset = dataset.shard(slice_num, slice_id) + dataset = dataset.batch(batch_size) + dataset = dataset.prefetch(buffer_size=64) + return dataset + + def get_table_info(self, output_path): + partition_name, partition_val = None, None + if len(output_path.split('/')) == 2: + table_name, partition = output_path.split('/') + partition_name, partition_val = partition.split('=') + else: + table_name = output_path + return table_name, partition_name, partition_val + + def _get_writer(self, output_path, slice_id): + table_name, partition_name, partition_val = self.get_table_info(output_path) + is_exist = self._hive_util.is_table_or_partition_exist( + table_name, partition_name, partition_val) + assert not is_exist, '%s is already exists. Please drop it.' % output_path + + output_path = output_path.replace('.', '/') + self._hdfs_path = 'hdfs://%s:9000/user/easy_rec/%s_tmp' % ( + self._hive_config.host, output_path) + if not gfile.Exists(self._hdfs_path): + gfile.MakeDirs(self._hdfs_path) + res_path = os.path.join(self._hdfs_path, 'part-%d.csv' % slice_id) + table_writer = gfile.GFile(res_path, 'w') + return table_writer + + def _write_lines(self, table_writer, outputs): + outputs = '\n'.join( + [self._output_sep.join([str(i) for i in output]) for output in outputs]) + table_writer.write(outputs + '\n') + + def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): + reserve_vals = [outputs[x] for x in output_cols] + \ + [all_vals[k] for k in reserved_cols] + return reserve_vals + + def load_to_table(self, output_path, slice_num, slice_id): + res_path = os.path.join(self._hdfs_path, 'SUCCESS-%s' % slice_id) + success_writer = gfile.GFile(res_path, 'w') + success_writer.write('') + success_writer.close() + + if slice_id != 0: + return + + for id in range(slice_num): + res_path = os.path.join(self._hdfs_path, 'SUCCESS-%s' % id) + while not gfile.Exists(res_path): + time.sleep(10) + + table_name, partition_name, partition_val = self.get_table_info(output_path) + schema = '' + for output_col_name in self._output_cols: + tf_type = self._predictor_impl._outputs_map[output_col_name].dtype + col_type = tf_utils.get_col_type(tf_type) + schema += output_col_name + ' ' + col_type + ',' + + for output_col_name in self._reserved_cols: + assert output_col_name in self._all_cols, 'Column: %s not exists.' % output_col_name + idx = self._all_cols.index(output_col_name) + output_col_types = self._all_col_types[idx] + schema += output_col_name + ' ' + output_col_types + ',' + schema = schema.rstrip(',') + + if partition_name and partition_val: + sql = 'create table if not exists %s (%s) PARTITIONED BY (%s string)' % \ + (table_name, schema, partition_name) + self._hive_util.run_sql(sql) + sql = "LOAD DATA INPATH '%s/*' INTO TABLE %s PARTITION (%s=%s)" % \ + (self._hdfs_path, table_name, partition_name, partition_val) + self._hive_util.run_sql(sql) + else: + sql = 'create table if not exists %s (%s)' % \ + (table_name, schema) + self._hive_util.run_sql(sql) + sql = "LOAD DATA INPATH '%s/*' INTO TABLE %s" % \ + (self._hdfs_path, table_name) + self._hive_util.run_sql(sql) + + @property + def out_of_range_exception(self): + return (tf.errors.OutOfRangeError) From 34938de3c1c321af76afa72543547bab06c31b46 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Wed, 1 Nov 2023 17:46:57 +0800 Subject: [PATCH 22/67] fix predict bug --- easy_rec/python/builders/optimizer_builder.py | 4 +- easy_rec/python/compat/adam_async.py | 109 ++- .../compat/feature_column/feature_column.py | 122 +-- easy_rec/python/compat/optimizers.py | 23 +- easy_rec/python/compat/queues.py | 553 +++++++------ easy_rec/python/compat/saver.py | 72 +- easy_rec/python/compat/sok_optimizer.py | 751 +++++++++--------- easy_rec/python/eval.py | 4 +- easy_rec/python/export.py | 2 +- easy_rec/python/inference/csv_predictor.py | 5 +- .../inference/hive_parquet_predictor.py | 3 +- easy_rec/python/inference/hive_predictor.py | 3 +- .../python/inference/parquet_predictor.py | 30 +- easy_rec/python/inference/predictor.py | 4 - easy_rec/python/input/dummy_input.py | 6 +- easy_rec/python/input/load_parquet.py | 48 +- easy_rec/python/input/parquet_input.py | 155 ++-- easy_rec/python/layers/input_layer.py | 6 +- easy_rec/python/main.py | 10 +- easy_rec/python/model/easy_rec_estimator.py | 13 +- easy_rec/python/predict.py | 18 +- easy_rec/python/utils/estimator_utils.py | 70 +- easy_rec/python/utils/input_utils.py | 9 +- requirements/runtime.txt | 2 +- setup.cfg | 2 +- 25 files changed, 1065 insertions(+), 959 deletions(-) diff --git a/easy_rec/python/builders/optimizer_builder.py b/easy_rec/python/builders/optimizer_builder.py index ad791371b..8b9a8251b 100644 --- a/easy_rec/python/builders/optimizer_builder.py +++ b/easy_rec/python/builders/optimizer_builder.py @@ -94,9 +94,7 @@ def build(optimizer_config): summary_vars.append(learning_rate) from easy_rec.python.compat.adam_s import AdamOptimizerS optimizer = AdamOptimizerS( - learning_rate =learning_rate, - beta1=config.beta1, - beta2=config.beta2) + learning_rate=learning_rate, beta1=config.beta1, beta2=config.beta2) if optimizer_type == 'momentumw_optimizer': config = optimizer_config.momentumw_optimizer diff --git a/easy_rec/python/compat/adam_async.py b/easy_rec/python/compat/adam_async.py index def3a4d34..58ef98fe7 100644 --- a/easy_rec/python/compat/adam_async.py +++ b/easy_rec/python/compat/adam_async.py @@ -12,34 +12,35 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """AdamAsync for TensorFlow.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os + from tensorflow.python.eager import context from tensorflow.python.framework import ops +from tensorflow.python.framework.load_library import load_op_library +# from tensorflow.python.ops import kv_variable_ops +# from tensorflow.python.ops import gen_hash_training_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops -# from tensorflow.python.ops import gen_hash_training_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops -# from tensorflow.python.ops import kv_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope -from tensorflow.python.training import slot_creator from tensorflow.python.training import optimizer +from tensorflow.python.training import slot_creator from tensorflow.python.training import training_ops from tensorflow.python.training import training_util from tensorflow.python.util.tf_export import tf_export -from tensorflow.python.framework.load_library import load_op_library -import os curr_dir, _ = os.path.split(__file__) adam_async_ops = load_op_library(os.path.join(curr_dir, 'libadam_async_op.so')) -@tf_export("train.AdamAsyncOptimizer") + +@tf_export('train.AdamAsyncOptimizer') class AdamAsyncOptimizer(optimizer.Optimizer): """Optimizer that implements the Adam algorithm. @@ -47,8 +48,14 @@ class AdamAsyncOptimizer(optimizer.Optimizer): ([pdf](http://arxiv.org/pdf/1412.6980.pdf)). """ - def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, - use_locking=False, apply_sparse_rmsprop=False, name="Adam"): + def __init__(self, + learning_rate=0.001, + beta1=0.9, + beta2=0.999, + epsilon=1e-8, + use_locking=False, + apply_sparse_rmsprop=False, + name='Adam'): """Construct a new Adam optimizer for training asynchronous. Initialization: @@ -124,23 +131,23 @@ def _get_beta_accumulators(self): graph = None else: graph = ops.get_default_graph() - return (self._get_non_slot_variable("beta1_power", graph=graph), - self._get_non_slot_variable("beta2_power", graph=graph)) + return (self._get_non_slot_variable('beta1_power', graph=graph), + self._get_non_slot_variable('beta2_power', graph=graph)) def _create_slots(self, var_list): # When training asynchronous, we create the beta1 and beta2 accumulators for # each variable to avoid communication bottlenecks. first_var = min(var_list, key=lambda x: x.name) self._create_non_slot_variable( - initial_value=self._beta1, name="beta1_power", colocate_with=first_var) + initial_value=self._beta1, name='beta1_power', colocate_with=first_var) self._create_non_slot_variable( - initial_value=self._beta2, name="beta2_power", colocate_with=first_var) + initial_value=self._beta2, name='beta2_power', colocate_with=first_var) # Create slots for the moments. for v in var_list: with ops.colocate_with(v): - self._zeros_slot(v, "m", self._name) - self._zeros_slot(v, "v", self._name) + self._zeros_slot(v, 'm', self._name) + self._zeros_slot(v, 'v', self._name) # self._get_or_make_slot(v, # ops.convert_to_tensor(self._beta1, dtype=v.dtype.base_dtype), # "beta1_power", self._name) @@ -149,52 +156,68 @@ def _create_slots(self, var_list): # "beta2_power", self._name) def _prepare(self): - self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate") - self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1") - self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2") - self._epsilon_t = ops.convert_to_tensor(self._epsilon, name="epsilon") + self._lr_t = ops.convert_to_tensor(self._lr, name='learning_rate') + self._beta1_t = ops.convert_to_tensor(self._beta1, name='beta1') + self._beta2_t = ops.convert_to_tensor(self._beta2, name='beta2') + self._epsilon_t = ops.convert_to_tensor(self._epsilon, name='epsilon') def _apply_dense(self, grad, var): - m = self.get_slot(var, "m") - v = self.get_slot(var, "v") + m = self.get_slot(var, 'm') + v = self.get_slot(var, 'v') # beta1_power = self.get_slot(var, 'beta1_power') # beta2_power = self.get_slot(var, 'beta2_power') beta1_power, beta2_power = self._get_beta_accumulators() return adam_async_ops.apply_adam_async( - var, m, v, beta1_power, beta2_power, + var, + m, + v, + beta1_power, + beta2_power, math_ops.cast(self._lr_t, var.dtype.base_dtype), math_ops.cast(self._beta1_t, var.dtype.base_dtype), math_ops.cast(self._beta2_t, var.dtype.base_dtype), math_ops.cast(self._epsilon_t, var.dtype.base_dtype), - grad, use_locking=self._use_locking).op + grad, + use_locking=self._use_locking).op def _resource_apply_dense(self, grad, var): - m = self.get_slot(var, "m") - v = self.get_slot(var, "v") + m = self.get_slot(var, 'm') + v = self.get_slot(var, 'v') # beta1_power = self.get_slot(var, 'beta1_power') # beta2_power = self.get_slot(var, 'beta2_power') beta1_power, beta2_power = self._get_beta_accumulators() return adam_async_ops.resource_apply_adam_async( - var.handle, m.handle, v.handle, beta1_power.handle, beta2_power.handle, + var.handle, + m.handle, + v.handle, + beta1_power.handle, + beta2_power.handle, math_ops.cast(self._lr_t, grad.dtype.base_dtype), math_ops.cast(self._beta1_t, grad.dtype.base_dtype), math_ops.cast(self._beta2_t, grad.dtype.base_dtype), math_ops.cast(self._epsilon_t, grad.dtype.base_dtype), - grad, use_locking=self._use_locking) + grad, + use_locking=self._use_locking) def _apply_sparse(self, grad, var): - m = self.get_slot(var, "m") - v = self.get_slot(var, "v") + m = self.get_slot(var, 'm') + v = self.get_slot(var, 'v') # beta1_power = self.get_slot(var, 'beta1_power') # beta2_power = self.get_slot(var, 'beta2_power') beta1_power, beta2_power = self._get_beta_accumulators() return adam_async_ops.sparse_apply_adam_async( - var, m, v, beta1_power, beta2_power, + var, + m, + v, + beta1_power, + beta2_power, math_ops.cast(self._lr_t, var.dtype.base_dtype), math_ops.cast(self._beta1_t, var.dtype.base_dtype), math_ops.cast(self._beta2_t, var.dtype.base_dtype), math_ops.cast(self._epsilon_t, var.dtype.base_dtype), - grad.values, grad.indices, use_locking=self._use_locking, + grad.values, + grad.indices, + use_locking=self._use_locking, apply_sparse_rmsprop=self._apply_sparse_rmsprop) # def _hash_table_apply_sparse(self, grad, var, indices): @@ -221,19 +244,25 @@ def _apply_sparse(self, grad, var): # return control_flow_ops.group(update_beta1, update_beta2) def _resource_apply_sparse(self, grad, var, indices): - m = self.get_slot(var, "m") - v = self.get_slot(var, "v") + m = self.get_slot(var, 'm') + v = self.get_slot(var, 'v') # beta1_power = self.get_slot(var, 'beta1_power') # beta2_power = self.get_slot(var, 'beta2_power') beta1_power, beta2_power = self._get_beta_accumulators() return adam_async_ops.resource_sparse_apply_adam_async( - var.handle, m.handle, v.handle, beta1_power.handle, beta2_power.handle, - math_ops.cast(self._lr_t, grad.dtype), - math_ops.cast(self._beta1_t, grad.dtype), - math_ops.cast(self._beta2_t, grad.dtype), - math_ops.cast(self._epsilon_t, grad.dtype), - grad, indices, use_locking=self._use_locking, - apply_sparse_rmsprop=self._apply_sparse_rmsprop) + var.handle, + m.handle, + v.handle, + beta1_power.handle, + beta2_power.handle, + math_ops.cast(self._lr_t, grad.dtype), + math_ops.cast(self._beta1_t, grad.dtype), + math_ops.cast(self._beta2_t, grad.dtype), + math_ops.cast(self._epsilon_t, grad.dtype), + grad, + indices, + use_locking=self._use_locking, + apply_sparse_rmsprop=self._apply_sparse_rmsprop) def _finish(self, update_ops, name_scope): # Update the power accumulators. diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 894e83299..37207ca77 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -149,14 +149,12 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import embedding_ops -from tensorflow.python.ops.ragged import ragged_util - - from tensorflow.python.ops import init_ops +from tensorflow.python.ops import logging_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import logging_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import resource_variable_ops @@ -165,15 +163,8 @@ from tensorflow.python.ops import template from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables -from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops.ragged import ragged_tensor - -try: - from tensorflow.python.ops.ragged import ragged_math_ops - from tensorflow.python.ops.ragged import ragged_concat_ops -except: - pass - +from tensorflow.python.ops.ragged import ragged_util from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_utils @@ -181,6 +172,12 @@ from easy_rec.python.compat.feature_column import utils as fc_utils +try: + from tensorflow.python.ops.ragged import ragged_math_ops + from tensorflow.python.ops.ragged import ragged_concat_ops +except: + pass + try: from sparse_operation_kit import experiment as sok except Exception: @@ -191,13 +188,18 @@ except Exception: hvd = None -def embedding_lookup_ragged(embedding_weights, ragged_ids, ragged_weights, - combiner, max_norm=None, name=None): + +def embedding_lookup_ragged(embedding_weights, + ragged_ids, + ragged_weights, + combiner, + max_norm=None, + name=None): segment_ids = ragged_ids.value_rowids() ids = ragged_ids.flat_values ids, idx = array_ops.unique(ids) - embeddings = embedding_ops.embedding_lookup(embedding_weights, ids, - partition_strategy='mod', max_norm=max_norm) + embeddings = embedding_ops.embedding_lookup( + embedding_weights, ids, partition_strategy='mod', max_norm=max_norm) if ragged_weights is not None: weights = ragged_weights.flat_values embeddings = array_ops.gather(embeddings, idx) @@ -216,32 +218,33 @@ def embedding_lookup_ragged(embedding_weights, ragged_ids, ragged_weights, embeddings = math_ops.segment_sum(embeddings, segment_ids) weight_sum = math_ops.segment_sum(weights, segment_ids) embeddings = math_ops.div_no_nan(embeddings, weight_sum, name=name) - elif combiner == "sqrtn": + elif combiner == 'sqrtn': embeddings = math_ops.segment_sum(embeddings, segment_ids) weights_squared = math_ops.pow(weights, 2) weight_sum = math_ops.segment_sum(weights_squared, segment_ids) weight_sum_sqrt = math_ops.sqrt(weight_sum) embeddings = math_ops.div_no_nan(embeddings, weight_sum_sqrt, name=name) else: - assert False, "Unrecognized combiner" + assert False, 'Unrecognized combiner' if embeddings.dtype != original_dtype: - embeddings = math_ops.cast(embeddings, original_dtype) + embeddings = math_ops.cast(embeddings, original_dtype) return embeddings else: assert idx is not None - if combiner == "sum": + if combiner == 'sum': embeddings = math_ops.sparse_segment_sum( embeddings, idx, segment_ids, name=name) - elif combiner == "mean": + elif combiner == 'mean': embeddings = math_ops.sparse_segment_mean( embeddings, idx, segment_ids, name=name) - elif combiner == "sqrtn": + elif combiner == 'sqrtn': embeddings = math_ops.sparse_segment_sqrt_n( embeddings, idx, segment_ids, name=name) else: - assert False, "Unrecognized combiner" + assert False, 'Unrecognized combiner' return embeddings + def _internal_input_layer(features, feature_columns, weight_collections=None, @@ -339,10 +342,13 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring else: with ops.device('/gpu:0'): if column.ev_params is not None: - embedding_weights = sok.DynamicVariable(name='embedding_weights', - dimension=column.dimension, initializer='random', #column.initializer, + embedding_weights = sok.DynamicVariable( + name='embedding_weights', + dimension=column.dimension, + initializer='random', #column.initializer, # var_type='hybrid', - trainable=column.trainable and trainable, dtype=dtypes.float32, + trainable=column.trainable and trainable, + dtype=dtypes.float32, init_capacity=1024 * 1024 * 8, max_capacity=1024 * 1024 * 16) else: @@ -358,9 +364,12 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring else: with ops.device('/gpu:0'): if column.ev_params is not None: - embedding_weights = sok.DynamicVariable(name='embedding_weights', - dimension=column.dimension, initializer='random', #column.initializer, - trainable=column.trainable and trainable, dtype=dtypes.float32) + embedding_weights = sok.DynamicVariable( + name='embedding_weights', + dimension=column.dimension, + initializer='random', #column.initializer, + trainable=column.trainable and trainable, + dtype=dtypes.float32) else: embedding_weights = variable_scope.get_variable( name='embedding_weights', @@ -410,12 +419,15 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring all_ids, segment_lens = features['sparse_fea'] all_uniq_ids, uniq_idx = array_ops.unique(all_ids) cumsum_lens = math_ops.cumsum(segment_lens) - segment_ids = array_ops.searchsorted(cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') - elif isinstance(features, dict) and 'ragged_ids' in features.keys() and 'ragged_lens' in features.keys(): + segment_ids = array_ops.searchsorted( + cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') + elif isinstance(features, dict) and 'ragged_ids' in features.keys( + ) and 'ragged_lens' in features.keys(): all_ids, segment_lens = features['ragged_ids'], features['ragged_lens'] all_uniq_ids, uniq_idx = array_ops.unique(all_ids) cumsum_lens = math_ops.cumsum(segment_lens) - segment_ids = array_ops.searchsorted(cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') + segment_ids = array_ops.searchsorted( + cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') else: all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) segment_ids = all_ids.value_rowids() @@ -424,43 +436,53 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # embed_dim = lookup_embeddings[0]._dimension if np > 1: - # all_uniq_ids, uniq_idx = array_ops.unique(flat_vals) - - # dynamic partition + # all_uniq_ids, uniq_idx = array_ops.unique(flat_vals) + + # dynamic partition # from sparse_operation_kit.experiment import raw_ops p_assignments = math_ops.cast(all_uniq_ids % np, dtypes.int32) - gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, p_assignments, np) + gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, + p_assignments, np) # all2all - split_sizes = array_ops.concat([ array_ops.shape(x) for x in gather_ids ], axis=0) + split_sizes = array_ops.concat([array_ops.shape(x) for x in gather_ids], + axis=0) send_ids = array_ops.concat(gather_ids, axis=0) recv_ids, recv_lens = hvd.alltoall(send_ids, split_sizes) # read embedding from dynamic variable - send_embed = lookup_embeddings[0].sparse_read(recv_ids, lookup_only=(not is_training)) + send_embed = lookup_embeddings[0].sparse_read( + recv_ids, lookup_only=(not is_training)) # all2all recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) - embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') + embeddings = math_ops.sparse_segment_sum( + recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') else: - # all_uniq_ids, uniq_idx = array_ops.unique(flat_vals) + # all_uniq_ids, uniq_idx = array_ops.unique(flat_vals) # all_uniq_ids = logging_ops.Print(all_uniq_ids, [math_ops.reduce_min(all_uniq_ids), # math_ops.reduce_max(all_uniq_ids), array_ops.shape(all_uniq_ids), - # array_ops.shape(all_ids.flat_values), array_ops.shape(segment_ids), + # array_ops.shape(all_ids.flat_values), array_ops.shape(segment_ids), # math_ops.reduce_min(segment_ids), math_ops.reduce_max(segment_ids)], # message='debug_all_uniq_ids') if isinstance(lookup_embeddings[0], sok.DynamicVariable): - recv_embeddings = lookup_embeddings[0].sparse_read(all_uniq_ids, lookup_only=(not is_training)) - else: + recv_embeddings = lookup_embeddings[0].sparse_read( + all_uniq_ids, lookup_only=(not is_training)) + else: recv_embeddings = array_ops.gather(lookup_embeddings[0], all_uniq_ids) - embeddings = math_ops.sparse_segment_sum(recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') + embeddings = math_ops.sparse_segment_sum( + recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') # all_embed = array_ops.gather(recv_embeddings, uniq_idx) if isinstance(lookup_embeddings[0], sok.DynamicVariable): - output_tensor = array_ops.reshape(embeddings, [len(lookup_output_ids), -1, lookup_embeddings[0]._dimension]) + output_tensor = array_ops.reshape( + embeddings, + [len(lookup_output_ids), -1, lookup_embeddings[0]._dimension]) else: embed_dim = lookup_embeddings[0].get_shape()[-1] - output_tensor = array_ops.reshape(embeddings, [len(lookup_output_ids), -1, embed_dim]) + output_tensor = array_ops.reshape( + embeddings, [len(lookup_output_ids), -1, embed_dim]) - outputs = array_ops.split(output_tensor, num_or_size_splits=len(lookup_output_ids), axis=0) + outputs = array_ops.split( + output_tensor, num_or_size_splits=len(lookup_output_ids), axis=0) for output, output_id, col in zip(outputs, lookup_output_ids, lookup_cols): output_tensors[output_id] = output @@ -468,10 +490,10 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring cols_to_output_tensors[col] = output if feature_name_to_output_tensors is not None: feature_name_to_output_tensors[col.raw_name] = output - return array_ops.reshape(array_ops.transpose(output_tensor, perm=[1, 0, 2]), - [-1, len(lookup_output_ids) * output_tensor.get_shape()[-1]]) + return array_ops.reshape( + array_ops.transpose(output_tensor, perm=[1, 0, 2]), + [-1, len(lookup_output_ids) * output_tensor.get_shape()[-1]]) - # outputs = sok.lookup_sparse( # lookup_embeddings, lookup_indices, combiners=lookup_combiners) # for output, output_id, col in zip(outputs, lookup_output_ids, diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index 70ffcbd23..1f4f36be9 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -28,8 +28,8 @@ from tensorflow.python.ops import clip_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops -from tensorflow.python.ops import math_ops from tensorflow.python.ops import logging_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as vars_ @@ -267,9 +267,9 @@ def optimize_loss(loss, # gradients = list(zip(gradients, variables)) # else: gradients = opt.compute_gradients( - loss, - variables, - colocate_gradients_with_ops=colocate_gradients_with_ops) + loss, + variables, + colocate_gradients_with_ops=colocate_gradients_with_ops) if estimator_utils.has_hvd() and hvd.size() > 1: if not estimator_utils.has_sok(): @@ -301,7 +301,7 @@ def optimize_loss(loss, # g = logging_ops.Print(g, [array_ops.shape(g), math_ops.reduce_min(g), math_ops.reduce_max(g), math_ops.reduce_mean(g)], message='grad_%s' % v.name) # tmp_grads.append((g, v)) # gradients=tmp_grads - + # Optionally add gradient noise. if gradient_noise_scale is not None: gradients = _add_scaled_noise_to_gradients(gradients, @@ -365,7 +365,7 @@ def _apply_grad(): # dense_grad_updates = opt._optimizer.apply_gradients( # dense_vars, # global_step=global_step if increment_global_step else None, - # name='train') + # name='train') # if sparse_grad_updates is not None and dense_grad_updates is not None: # grad_updates = tf.group([sparse_grad_updates, dense_grad_updates]) # elif sparse_grad_updates is not None: @@ -374,9 +374,9 @@ def _apply_grad(): # grad_updates = dense_grad_updates # else: grad_updates = opt.apply_gradients( - gradients, - global_step=global_step if increment_global_step else None, - name='train') + gradients, + global_step=global_step if increment_global_step else None, + name='train') incr_save_ops = [] if incr_save: @@ -501,7 +501,7 @@ def gradient_clipping(grads_and_vars): elif isinstance(grad, indexed_slices.IndexedSlices): clipped_grads.append( indexed_slices.IndexedSlices(grad.values * factor, grad.indices, - grad.dense_shape)) + grad.dense_shape)) else: clipped_grads.append(grad * factor) @@ -537,7 +537,8 @@ def _multiply_gradients(grads_and_vars, gradient_multipliers): multiplier = gradient_multipliers[key] if isinstance(grad, indexed_slices.IndexedSlices): grad_values = grad.values * multiplier - grad = indexed_slices.IndexedSlices(grad_values, grad.indices, grad.dense_shape) + grad = indexed_slices.IndexedSlices(grad_values, grad.indices, + grad.dense_shape) else: grad *= math_ops.cast(multiplier, grad.dtype) multiplied_grads_and_vars.append((grad, var)) diff --git a/easy_rec/python/compat/queues.py b/easy_rec/python/compat/queues.py index 98c1bd46d..3ff549b7e 100644 --- a/easy_rec/python/compat/queues.py +++ b/easy_rec/python/compat/queues.py @@ -7,296 +7,295 @@ # Licensed to PSF under a Contributor Agreement. # -import sys +import collections +import errno +import logging import os +import sys import threading -import collections import time import types import weakref -import errno - -from queue import Empty, Full - from multiprocessing import connection from multiprocessing import context -_ForkingPickler = context.reduction.ForkingPickler +from multiprocessing.util import Finalize +from multiprocessing.util import is_exiting +from multiprocessing.util import register_after_fork +from queue import Empty +from queue import Full -from multiprocessing.util import Finalize, register_after_fork, is_exiting -import logging +_ForkingPickler = context.reduction.ForkingPickler # # Queue type using a pipe, buffer and thread # + class Queue(object): - - _sentinel = object() - - def __init__(self, maxsize=0, name="", *, ctx): - if maxsize <= 0: - # Can raise ImportError (see issues #3770 and #23400) - from multiprocessing.synchronize import SEM_VALUE_MAX as maxsize - self._maxsize = maxsize - self._reader, self._writer = connection.Pipe(duplex=False) - self._rlock = ctx.Lock() - self._opid = os.getpid() - if sys.platform == 'win32': - self._wlock = None - else: - self._wlock = ctx.Lock() - self._sem = ctx.BoundedSemaphore(maxsize) - # For use by concurrent.futures - self._ignore_epipe = False - self._reset() - self._name = name - self._run = True - - if sys.platform != 'win32': - register_after_fork(self, Queue._after_fork) - - def __getstate__(self): - context.assert_spawning(self) - return (self._ignore_epipe, self._maxsize, self._reader, self._writer, - self._rlock, self._wlock, self._sem, self._opid, self._name, self._run) - - def __setstate__(self, state): - (self._ignore_epipe, self._maxsize, self._reader, self._writer, - self._rlock, self._wlock, self._sem, self._opid, self._name, self._run) = state - self._reset() - - def _after_fork(self): - logging.debug('Queue._after_fork()') - self._reset(after_fork=True) - - def _reset(self, after_fork=False): - if after_fork: - self._notempty._at_fork_reinit() - else: - self._notempty = threading.Condition(threading.Lock()) - self._buffer = collections.deque() - self._thread = None - self._jointhread = None - self._joincancelled = False - self._closed = False - self._close = None - self._send_bytes = self._writer.send_bytes - self._recv_bytes = self._reader.recv_bytes - self._poll = self._reader.poll - - def put(self, obj, block=True, timeout=None): - if self._closed: - raise ValueError(f"Queue {self!r} is closed") - if not self._sem.acquire(block, timeout): - raise Full - - with self._notempty: - if self._thread is None: - self._start_thread() - self._buffer.append(obj) - self._notempty.notify() - - def get(self, block=True, timeout=None): - if self._closed: - raise ValueError(f"Queue {self!r} is closed") - if block and timeout is None: - with self._rlock: - res = self._recv_bytes() - self._sem.release() - else: - if block: - deadline = time.monotonic() + timeout - if not self._rlock.acquire(block, timeout): - raise Empty - try: - if block: - timeout = deadline - time.monotonic() - if not self._poll(timeout): - raise Empty - elif not self._poll(): - raise Empty - res = self._recv_bytes() - self._sem.release() - finally: - self._rlock.release() - # unserialize the data after having released the lock - return _ForkingPickler.loads(res) - - def qsize(self): - # Raises NotImplementedError on Mac OSX because of broken sem_getvalue() - return self._maxsize - self._sem._semlock._get_value() - - def empty(self): - return not self._poll() - - def full(self): - return self._sem._semlock._is_zero() - - def get_nowait(self): - return self.get(False) - - def put_nowait(self, obj): - return self.put(obj, False) - - def close(self, wait_send_finish=True): - self._closed = True - close = self._close - if not wait_send_finish and self._thread is not None and self._thread.is_alive(): - try: - if self._reader is not None: - self._reader.close() - except Exception: - pass - self._run = False - # clear queue - # with self._rlock: - # while self._thread.is_alive() and self._poll(1): - # res = self._recv_bytes() - # logging.info('Queue[name=' + self._name + '] clear one elem') - # logging.info('Queue[name=' + self._name + '] clear queue done') - if close: - self._close = None - close() - - def join_thread(self): - logging.debug('Queue.join_thread()') - assert self._closed, "Queue {0!r} not closed".format(self) - if self._jointhread: - self._jointhread() - - def cancel_join_thread(self): - logging.debug('Queue.cancel_join_thread()') - self._joincancelled = True + + _sentinel = object() + + def __init__(self, maxsize=0, name='', *, ctx): + if maxsize <= 0: + # Can raise ImportError (see issues #3770 and #23400) + from multiprocessing.synchronize import SEM_VALUE_MAX as maxsize + self._maxsize = maxsize + self._reader, self._writer = connection.Pipe(duplex=False) + self._rlock = ctx.Lock() + self._opid = os.getpid() + if sys.platform == 'win32': + self._wlock = None + else: + self._wlock = ctx.Lock() + self._sem = ctx.BoundedSemaphore(maxsize) + # For use by concurrent.futures + self._ignore_epipe = False + self._reset() + self._name = name + self._run = True + + if sys.platform != 'win32': + register_after_fork(self, Queue._after_fork) + + def __getstate__(self): + context.assert_spawning(self) + return (self._ignore_epipe, self._maxsize, self._reader, self._writer, + self._rlock, self._wlock, self._sem, self._opid, self._name, + self._run) + + def __setstate__(self, state): + (self._ignore_epipe, self._maxsize, self._reader, self._writer, self._rlock, + self._wlock, self._sem, self._opid, self._name, self._run) = state + self._reset() + + def _after_fork(self): + logging.debug('Queue._after_fork()') + self._reset(after_fork=True) + + def _reset(self, after_fork=False): + if after_fork: + self._notempty._at_fork_reinit() + else: + self._notempty = threading.Condition(threading.Lock()) + self._buffer = collections.deque() + self._thread = None + self._jointhread = None + self._joincancelled = False + self._closed = False + self._close = None + self._send_bytes = self._writer.send_bytes + self._recv_bytes = self._reader.recv_bytes + self._poll = self._reader.poll + + def put(self, obj, block=True, timeout=None): + if self._closed: + raise ValueError(f'Queue {self!r} is closed') + if not self._sem.acquire(block, timeout): + raise Full + + with self._notempty: + if self._thread is None: + self._start_thread() + self._buffer.append(obj) + self._notempty.notify() + + def get(self, block=True, timeout=None): + if self._closed: + raise ValueError(f'Queue {self!r} is closed') + if block and timeout is None: + with self._rlock: + res = self._recv_bytes() + self._sem.release() + else: + if block: + deadline = time.monotonic() + timeout + if not self._rlock.acquire(block, timeout): + raise Empty + try: + if block: + timeout = deadline - time.monotonic() + if not self._poll(timeout): + raise Empty + elif not self._poll(): + raise Empty + res = self._recv_bytes() + self._sem.release() + finally: + self._rlock.release() + # unserialize the data after having released the lock + return _ForkingPickler.loads(res) + + def qsize(self): + # Raises NotImplementedError on Mac OSX because of broken sem_getvalue() + return self._maxsize - self._sem._semlock._get_value() + + def empty(self): + return not self._poll() + + def full(self): + return self._sem._semlock._is_zero() + + def get_nowait(self): + return self.get(False) + + def put_nowait(self, obj): + return self.put(obj, False) + + def close(self, wait_send_finish=True): + self._closed = True + close = self._close + if not wait_send_finish and self._thread is not None and self._thread.is_alive( + ): + try: + if self._reader is not None: + self._reader.close() + except Exception: + pass + self._run = False + # clear queue + # with self._rlock: + # while self._thread.is_alive() and self._poll(1): + # res = self._recv_bytes() + # logging.info('Queue[name=' + self._name + '] clear one elem') + # logging.info('Queue[name=' + self._name + '] clear queue done') + if close: + self._close = None + close() + + def join_thread(self): + logging.debug('Queue.join_thread()') + assert self._closed, 'Queue {0!r} not closed'.format(self) + if self._jointhread: + self._jointhread() + + def cancel_join_thread(self): + logging.debug('Queue.cancel_join_thread()') + self._joincancelled = True + try: + self._jointhread.cancel() + except AttributeError: + pass + + def _start_thread(self): + logging.debug('Queue._start_thread()') + + # Start thread which transfers data from buffer to pipe + self._buffer.clear() + self._thread = threading.Thread( + target=self._feed, + args=(self._buffer, self._notempty, self._send_bytes, self._wlock, + self._reader.close, self._writer.close, self._ignore_epipe, + self._on_queue_feeder_error, self._sem), + name='QueueFeederThread') + self._thread.daemon = True + + logging.debug('doing self._thread.start()') + self._thread.start() + logging.debug('... done self._thread.start()') + + if not self._joincancelled: + self._jointhread = Finalize( + self._thread, + Queue._finalize_join, [weakref.ref(self._thread)], + exitpriority=-5) + + # Send sentinel to the thread queue object when garbage collected + self._close = Finalize( + self, + Queue._finalize_close, [self._buffer, self._notempty], + exitpriority=10) + + @staticmethod + def _finalize_join(twr): + logging.debug('joining queue thread') + thread = twr() + if thread is not None: + thread.join() + logging.debug('... queue thread joined') + else: + logging.debug('... queue thread already dead') + + @staticmethod + def _finalize_close(buffer, notempty): + logging.debug('telling queue thread to quit') + with notempty: + buffer.append(Queue._sentinel) + notempty.notify() + + def _feed(self, buffer, notempty, send_bytes, writelock, reader_close, + writer_close, ignore_epipe, onerror, queue_sem): + logging.debug('starting thread to feed data to pipe') + nacquire = notempty.acquire + nrelease = notempty.release + nwait = notempty.wait + bpopleft = buffer.popleft + sentinel = Queue._sentinel + if sys.platform != 'win32': + wacquire = writelock.acquire + wrelease = writelock.release + else: + wacquire = None + + pid = os.getpid() + name = self._name + while self._run: + try: + nacquire() try: - self._jointhread.cancel() - except AttributeError: - pass - - def _start_thread(self): - logging.debug('Queue._start_thread()') - - # Start thread which transfers data from buffer to pipe - self._buffer.clear() - self._thread = threading.Thread( - target=self._feed, - args=(self._buffer, self._notempty, self._send_bytes, - self._wlock, self._reader.close, self._writer.close, - self._ignore_epipe, self._on_queue_feeder_error, - self._sem), - name='QueueFeederThread' - ) - self._thread.daemon = True - - logging.debug('doing self._thread.start()') - self._thread.start() - logging.debug('... done self._thread.start()') - - if not self._joincancelled: - self._jointhread = Finalize( - self._thread, Queue._finalize_join, - [weakref.ref(self._thread)], - exitpriority=-5 - ) - - # Send sentinel to the thread queue object when garbage collected - self._close = Finalize( - self, Queue._finalize_close, - [self._buffer, self._notempty], - exitpriority=10 - ) - - @staticmethod - def _finalize_join(twr): - logging.debug('joining queue thread') - thread = twr() - if thread is not None: - thread.join() - logging.debug('... queue thread joined') - else: - logging.debug('... queue thread already dead') - - @staticmethod - def _finalize_close(buffer, notempty): - logging.debug('telling queue thread to quit') - with notempty: - buffer.append(Queue._sentinel) - notempty.notify() - - def _feed(self, buffer, notempty, send_bytes, writelock, reader_close, - writer_close, ignore_epipe, onerror, queue_sem): - logging.debug('starting thread to feed data to pipe') - nacquire = notempty.acquire - nrelease = notempty.release - nwait = notempty.wait - bpopleft = buffer.popleft - sentinel = Queue._sentinel - if sys.platform != 'win32': - wacquire = writelock.acquire - wrelease = writelock.release + if not buffer: + nwait() + finally: + nrelease() + try: + while self._run: + obj = bpopleft() + if obj is sentinel: + # logging.info('Queue[' + self._name + '] feeder thread got sentinel -- exiting: ' + str(self._run)) + reader_close() + writer_close() + return + + # serialize the data before acquiring the lock + obj = _ForkingPickler.dumps(obj) + if wacquire is None: + send_bytes(obj) + else: + wacquire() + try: + send_bytes(obj) + finally: + wrelease() + except IndexError: + pass + except Exception as e: + if ignore_epipe and getattr(e, 'errno', 0) == errno.EPIPE: + logging.warning('Queue[' + name + '] exception: pid=' + str(pid) + + ' run=' + str(self._run) + ' e=' + str(e)) + return + # Since this runs in a daemon thread the resources it uses + # may be become unusable while the process is cleaning up. + # We ignore errors which happen after the process has + # started to cleanup. + if is_exiting(): + logging.warning('Queue[' + name + '] thread error in exiting: pid=' + + str(pid) + ' run=' + str(self._run) + ' e=' + str(e)) + return else: - wacquire = None - - pid = os.getpid() - name = self._name - while self._run: - try: - nacquire() - try: - if not buffer: - nwait() - finally: - nrelease() - try: - while self._run: - obj = bpopleft() - if obj is sentinel: - # logging.info('Queue[' + self._name + '] feeder thread got sentinel -- exiting: ' + str(self._run)) - reader_close() - writer_close() - return - - # serialize the data before acquiring the lock - obj = _ForkingPickler.dumps(obj) - if wacquire is None: - send_bytes(obj) - else: - wacquire() - try: - send_bytes(obj) - finally: - wrelease() - except IndexError: - pass - except Exception as e: - if ignore_epipe and getattr(e, 'errno', 0) == errno.EPIPE: - logging.warning('Queue[' + name + '] exception: pid=' + str(pid) - + ' run=' + str(self._run) + ' e=' + str(e)) - return - # Since this runs in a daemon thread the resources it uses - # may be become unusable while the process is cleaning up. - # We ignore errors which happen after the process has - # started to cleanup. - if is_exiting(): - logging.warning('Queue[' + name + '] thread error in exiting: pid=' - + str(pid) + ' run=' + str(self._run) + ' e=' + str(e)) - return - else: - # Since the object has not been sent in the queue, we need - # to decrease the size of the queue. The error acts as - # if the object had been silently removed from the queue - # and this step is necessary to have a properly working - # queue. - queue_sem.release() - onerror(e, obj) - # logging.info('Queue[' + name + '] send thread finish: pid=' + str(pid) - # + ' run=' + str(self._run)) - - @staticmethod - def _on_queue_feeder_error(e, obj): - """ - Private API hook called when feeding data in the background thread - raises an exception. For overriding by concurrent.futures. - """ - import traceback - traceback.print_exc() - + # Since the object has not been sent in the queue, we need + # to decrease the size of the queue. The error acts as + # if the object had been silently removed from the queue + # and this step is necessary to have a properly working + # queue. + queue_sem.release() + onerror(e, obj) + # logging.info('Queue[' + name + '] send thread finish: pid=' + str(pid) + # + ' run=' + str(self._run)) + + @staticmethod + def _on_queue_feeder_error(e, obj): + """Private API hook called when feeding data in the background thread raises an exception. + + For overriding by concurrent.futures. + """ + import traceback + traceback.print_exc() diff --git a/easy_rec/python/compat/saver.py b/easy_rec/python/compat/saver.py index e9bb55dd5..3eea40258 100644 --- a/easy_rec/python/compat/saver.py +++ b/easy_rec/python/compat/saver.py @@ -1,16 +1,17 @@ # -*- encoding:utf-8 -*- import logging -import sys import os -import numpy as np +import sys -from tensorflow.python.training import saver +import numpy as np from tensorflow.core.protobuf import saver_pb2 -from tensorflow.python.framework import dtypes, ops -from tensorflow.python.ops import script_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import script_ops from tensorflow.python.platform import gfile +from tensorflow.python.training import saver try: import horovod.tensorflow as hvd @@ -20,7 +21,7 @@ dynamic_variable_ops = None sok = None -try: +try: from tensorflow.python.framework.load_library import load_op_library import easy_rec load_embed_lib_path = os.path.join(easy_rec.ops_dir, 'libload_embed.so') @@ -30,6 +31,7 @@ class SaverV2(saver.Saver): + def __init__(self, var_list=None, reshape=False, @@ -56,31 +58,38 @@ def __init__(self, self._sok_vars.append(var) else: tf_vars.append(var) - super(SaverV2, self).__init__(tf_vars, reshape=reshape, sharded=sharded, + super(SaverV2, self).__init__( + tf_vars, + reshape=reshape, + sharded=sharded, max_to_keep=max_to_keep, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, - name=name, restore_sequentially=restore_sequentially, - saver_def=saver_def, builder=builder, + name=name, + restore_sequentially=restore_sequentially, + saver_def=saver_def, + builder=builder, defer_build=defer_build, allow_empty=allow_empty, write_version=write_version, pad_step_number=pad_step_number, save_relative_paths=save_relative_paths, - filename=filename) + filename=filename) self._is_build = False def _save_sok_embedding(self, sok_var): pass def _load_sok_embedding(self, sok_var): + def _load_key_vals(filename, var_name): var_name = var_name.decode('utf-8').replace('/', '__') filename = filename.decode('utf-8') key_file_pattern = filename + '-sok/embed-' + var_name + '-part-*.keys' - logging.info('key_file_pattern=%s filename=%s var_name=%s var=%s' % (key_file_pattern, - filename, var_name, str(sok_var))) + logging.info('key_file_pattern=%s filename=%s var_name=%s var=%s' % + (key_file_pattern, filename, var_name, str(sok_var))) key_files = gfile.Glob(key_file_pattern) - logging.info('key_file_pattern=%s file_num=%d' % (key_file_pattern, len(key_files))) + logging.info('key_file_pattern=%s file_num=%d' % + (key_file_pattern, len(key_files))) all_keys = [] all_vals = [] for key_file in key_files: @@ -91,15 +100,16 @@ def _load_key_vals(filename, var_name): if len(tmp_ids) == 0: break all_keys.append(tmp_keys.take(tmp_ids, axis=0)) - logging.info('tmp_keys.shape=%s %s %s' % (str(tmp_keys.shape), - str(tmp_ids.shape), str(all_keys[-1].shape))) + logging.info('tmp_keys.shape=%s %s %s' % (str( + tmp_keys.shape), str(tmp_ids.shape), str(all_keys[-1].shape))) val_file = key_file[:-4] + 'vals' with gfile.GFile(val_file, 'rb') as fin: - tmp_vals = np.frombuffer(fin.read(), dtype=np.float32).reshape([-1, sok_var._dimension]) + tmp_vals = np.frombuffer( + fin.read(), dtype=np.float32).reshape([-1, sok_var._dimension]) all_vals.append(tmp_vals.take(tmp_ids, axis=0)) - logging.info('tmp_vals.shape=%s %s %s' % (str(tmp_vals.shape), - str(tmp_ids.shape), str(all_vals[-1].shape))) + logging.info('tmp_vals.shape=%s %s %s' % (str( + tmp_vals.shape), str(tmp_ids.shape), str(all_vals[-1].shape))) all_keys = np.concatenate(all_keys, axis=0) all_vals = np.concatenate(all_vals, axis=0) @@ -108,36 +118,40 @@ def _load_key_vals(filename, var_name): np.random.shuffle(shuffle_ids) all_keys = all_keys.take(shuffle_ids, axis=0) all_vals = all_vals.take(shuffle_ids, axis=0) - print(len(all_keys), all_vals.shape, np.min(all_keys), np.max(all_keys), np.min(all_vals), - np.max(all_vals)) + print( + len(all_keys), all_vals.shape, np.min(all_keys), np.max(all_keys), + np.min(all_vals), np.max(all_vals)) return all_keys, all_vals + file_name = ops.get_default_graph().get_tensor_by_name( - self.saver_def.filename_tensor_name) + self.saver_def.filename_tensor_name) # keys, vals = script_ops.py_func(_load_key_vals, [file_name, sok_var.name], # (dtypes.int64, dtypes.float32)) - keys, vals = load_embed_lib.load_kv_embed(task_index=hvd.rank(), - task_num=hvd.size(), embed_dim=sok_var._dimension, + keys, vals = load_embed_lib.load_kv_embed( + task_index=hvd.rank(), + task_num=hvd.size(), + embed_dim=sok_var._dimension, var_name='embed-' + sok_var.name.replace('/', '__'), ckpt_path=file_name) with ops.control_dependencies([sok_var._initializer_op]): return dynamic_variable_ops.dummy_var_assign(sok_var.handle, keys, vals) # return dynamic_variable_ops.dummy_var_scatter_update(sok_var.handle, keys, vals) - def build(self): if self._is_built: - return - super(SaverV2, self).build() + return + super(SaverV2, self).build() if self.saver_def.restore_op_name and len(self._sok_vars) > 0: - # load data from the model + # load data from the model restore_ops = [] for sok_var in self._sok_vars: restore_ops.append(self._load_sok_embedding(sok_var)) - old_restore_op = ops.get_default_graph().get_operation_by_name(self.saver_def.restore_op_name) + old_restore_op = ops.get_default_graph().get_operation_by_name( + self.saver_def.restore_op_name) restore_ops.append(old_restore_op) restore_op_n = control_flow_ops.group(restore_ops) self.saver_def.restore_op_name = restore_op_n.name # if self.saver_def.save_tensor_name: # save_sok_ops = [] # for sok_var in self._sok_vars: - # save_sok_ops. + # save_sok_ops. diff --git a/easy_rec/python/compat/sok_optimizer.py b/easy_rec/python/compat/sok_optimizer.py index 45f26d4bb..de03bb611 100644 --- a/easy_rec/python/compat/sok_optimizer.py +++ b/easy_rec/python/compat/sok_optimizer.py @@ -15,413 +15,418 @@ # import tensorflow as tf +from sparse_operation_kit.experiment.dynamic_variable import DynamicVariable from tensorflow.python.eager import context +from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops -from tensorflow.python.framework import dtypes from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import resource_variable_ops -from sparse_operation_kit.experiment.dynamic_variable import DynamicVariable - def OptimizerWrapper(optimizer): - """ - Abbreviated as ``sok.experiment.OptimizerWrapper``. + """Abbreviated as ``sok.experiment.OptimizerWrapper``. - This is a wrapper for tensorflow optimizer so that it can update - sok.DynamicVariable. + This is a wrapper for tensorflow optimizer so that it can update + sok.DynamicVariable. - Parameters - ---------- - optimizer: tensorflow optimizer - The original tensorflow optimizer. + Parameters + ---------- + optimizer: tensorflow optimizer + The original tensorflow optimizer. - Example - ------- - .. code-block:: python + Example + ------- + .. code-block:: python - import numpy as np - import tensorflow as tf - import horovod.tensorflow as hvd - from sparse_operation_kit import experiment as sok + import numpy as np + import tensorflow as tf + import horovod.tensorflow as hvd + from sparse_operation_kit import experiment as sok - v = sok.DynamicVariable(dimension=3, initializer="13") + v = sok.DynamicVariable(dimension=3, initializer="13") - indices = tf.convert_to_tensor([0, 1, 2**40], dtype=tf.int64) + indices = tf.convert_to_tensor([0, 1, 2**40], dtype=tf.int64) - with tf.GradientTape() as tape: - embedding = tf.nn.embedding_lookup(v, indices) - print("embedding:", embedding) - loss = tf.reduce_sum(embedding) + with tf.GradientTape() as tape: + embedding = tf.nn.embedding_lookup(v, indices) + print("embedding:", embedding) + loss = tf.reduce_sum(embedding) - grads = tape.gradient(loss, [v]) + grads = tape.gradient(loss, [v]) - optimizer = tf.keras.optimizers.SGD(learning_rate=1.0) - optimizer = sok.OptimizerWrapper(optimizer) - optimizer.apply_gradients(zip(grads, [v])) + optimizer = tf.keras.optimizers.SGD(learning_rate=1.0) + optimizer = sok.OptimizerWrapper(optimizer) + optimizer.apply_gradients(zip(grads, [v])) - embedding = tf.nn.embedding_lookup(v, indices) - print("embedding:", embedding) - """ + embedding = tf.nn.embedding_lookup(v, indices) + print("embedding:", embedding) + """ - # a specific code path for dl framework tf2.11.0 - try: - if isinstance(optimizer, tf.keras.optimizers.legacy.Optimizer): - return OptimizerWrapperV2(optimizer) - except: - pass + # a specific code path for dl framework tf2.11.0 + try: + if isinstance(optimizer, tf.keras.optimizers.legacy.Optimizer): + return OptimizerWrapperV2(optimizer) + except: + pass - if isinstance(optimizer, tf.keras.optimizers.Optimizer): - return OptimizerWrapperV2(optimizer) - else: - return OptimizerWrapperV1(optimizer) + if isinstance(optimizer, tf.keras.optimizers.Optimizer): + return OptimizerWrapperV2(optimizer) + else: + return OptimizerWrapperV1(optimizer) class OptimizerWrapperV1(object): - def __init__(self, optimizer): - self._optimizer = optimizer - # slots - unused = tf.Variable([0.0], dtype=tf.float32, name="unused", trainable=False) - self._optimizer._create_slots([unused]) - names, slots = [], [] - for name in self._optimizer.get_slot_names(): - names.append(name) - slots.append(self._optimizer.get_slot(unused, name)) - unused_key = self._var_key(unused) - for name in names: - assert unused_key in self._optimizer._slots[name] - self._optimizer._slots[name].pop(unused_key) - self._initial_vals = {} - for i, name in enumerate(names): - self._initial_vals[name] = slots[i] - # self._optimizer._prepare() - - def compute_gradients(self, loss, var_list=None, + + def __init__(self, optimizer): + self._optimizer = optimizer + # slots + unused = tf.Variable([0.0], + dtype=tf.float32, + name='unused', + trainable=False) + self._optimizer._create_slots([unused]) + names, slots = [], [] + for name in self._optimizer.get_slot_names(): + names.append(name) + slots.append(self._optimizer.get_slot(unused, name)) + unused_key = self._var_key(unused) + for name in names: + assert unused_key in self._optimizer._slots[name] + self._optimizer._slots[name].pop(unused_key) + self._initial_vals = {} + for i, name in enumerate(names): + self._initial_vals[name] = slots[i] + # self._optimizer._prepare() + + def compute_gradients(self, + loss, + var_list=None, aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None): - self._loss = loss - import tensorflow as tf - gradients = tf.gradients(loss, var_list) - return list(zip(gradients, var_list)) - # return self._optimizer.compute_gradients(loss=loss, var_list=var_list, - # gate_gradients=gate_gradients, - # aggregation_method=aggregation_method, - # colocate_gradients_with_ops=colocate_gradients_with_ops, - # grad_loss=grad_loss) - - def _var_key(self, var): - if hasattr(var, "op"): - return (var.op.graph, var.op.name) - return var._unique_id - - def _create_slots(self, vars): - for var in vars: - if isinstance(var, DynamicVariable): - self._create_slots_dynamic(var) - else: - self._optimizer._create_slots(var) - - def _create_slots_dynamic(self, var): - key = self._var_key(var) + self._loss = loss + import tensorflow as tf + gradients = tf.gradients(loss, var_list) + return list(zip(gradients, var_list)) + # return self._optimizer.compute_gradients(loss=loss, var_list=var_list, + # gate_gradients=gate_gradients, + # aggregation_method=aggregation_method, + # colocate_gradients_with_ops=colocate_gradients_with_ops, + # grad_loss=grad_loss) + + def _var_key(self, var): + if hasattr(var, 'op'): + return (var.op.graph, var.op.name) + return var._unique_id + + def _create_slots(self, vars): + for var in vars: + if isinstance(var, DynamicVariable): + self._create_slots_dynamic(var) + else: + self._optimizer._create_slots(var) + + def _create_slots_dynamic(self, var): + key = self._var_key(var) + for slot_name in self._initial_vals: + if key not in self._optimizer._slots[slot_name]: + if var.backend_type == 'hbm': + slot = DynamicVariable( + dimension=var.dimension, + initializer=self._initial_vals[slot_name], + name='DynamicSlot', + trainable=False, + ) + else: + tmp_config = var.config_dict + tmp_initializer = var.initializer_str + slot = DynamicVariable( + dimension=var.dimension, + initializer=self._initial_vals[slot_name], + var_type=var.backend_type, + name='DynamicSlot', + trainable=False, + **tmp_config) + + self._optimizer._slots[slot_name][key] = slot + + def get_slot_names(self): + return self._optimizer.get_slot_names() + + def get_slot(self, var, name): + return self._optimizer.get_slot(var, name) + + @property + def _slots(self): + return self._optimizer._slots + + def apply_gradients(self, grads_and_vars, global_step=None, name=None): + gradients = grads_and_vars + sparse_vars = [x for x in gradients if 'DynamicVariable' in str(type(x[1]))] + dense_vars = [ + x for x in gradients if 'DynamicVariable' not in str(type(x[1])) + ] + + def _dummy_finish(update_ops, name_scope): + return update_ops + + finish_func = self._optimizer._finish + self._optimizer._finish = _dummy_finish + with ops.control_dependencies([array_ops.identity(self._loss)]): + sparse_grad_updates = self.apply_sparse_gradients(sparse_vars, name=name) + + dense_grad_updates = self._optimizer.apply_gradients( + dense_vars, global_step=None, name=name) + if sparse_grad_updates is not None and dense_grad_updates is not None: + grad_updates = sparse_grad_updates + dense_grad_updates + elif sparse_grad_updates is not None: + grad_updates = sparse_grad_updates + elif dense_grad_updates is not None: + grad_updates = dense_grad_updates + + assert global_step is not None + with ops.control_dependencies([finish_func(grad_updates, 'update')]): + with ops.colocate_with(global_step): + if isinstance(global_step, resource_variable_ops.BaseResourceVariable): + # TODO(apassos): the implicit read in assign_add is slow; consider + # making it less so. + apply_updates = resource_variable_ops.assign_add_variable_op( + global_step.handle, + ops.convert_to_tensor(1, dtype=global_step.dtype), + name=name) + else: + apply_updates = state_ops.assign_add(global_step, 1, name=name) + + if not context.executing_eagerly(): + if isinstance(apply_updates, ops.Tensor): + apply_updates = apply_updates.op + train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) + if apply_updates not in train_op: + train_op.append(apply_updates) + + return apply_updates + + def apply_sparse_gradients(self, grads_and_vars, global_step=None, name=None): + # 1. Create slots and do sparse_read + to_static_ops = [] + grad_list, var_list = [], [] + for g, v in grads_and_vars: + if g is not None: + unique, indices = tf.unique(g.indices) + grad_list.append(ops.IndexedSlices(g.values, indices, g.dense_shape)) + # TODO: Check multi-thread safety of DET + with tf.control_dependencies([g.values]): + to_static_ops.append(v.to_static(unique, False)) + var_list.append(v) + key = self._var_key(v) for slot_name in self._initial_vals: - if key not in self._optimizer._slots[slot_name]: - if var.backend_type == "hbm": - slot = DynamicVariable( - dimension=var.dimension, - initializer=self._initial_vals[slot_name], - name="DynamicSlot", - trainable=False, - ) - else: - tmp_config = var.config_dict - tmp_initializer = var.initializer_str - slot = DynamicVariable( - dimension=var.dimension, - initializer=self._initial_vals[slot_name], - var_type=var.backend_type, - name="DynamicSlot", - trainable=False, - **tmp_config - ) - - self._optimizer._slots[slot_name][key] = slot - - def get_slot_names(self): - return self._optimizer.get_slot_names() - - def get_slot(self, var, name): - return self._optimizer.get_slot(var, name) - - @property - def _slots(self): - return self._optimizer._slots - - def apply_gradients(self, grads_and_vars, global_step=None, name=None): - gradients = grads_and_vars - sparse_vars = [ x for x in gradients if 'DynamicVariable' in str(type(x[1])) ] - dense_vars = [ x for x in gradients if 'DynamicVariable' not in str(type(x[1])) ] - - def _dummy_finish(update_ops, name_scope): - return update_ops - - finish_func = self._optimizer._finish - self._optimizer._finish = _dummy_finish - with ops.control_dependencies([array_ops.identity(self._loss)]): - sparse_grad_updates = self.apply_sparse_gradients(sparse_vars, name=name) - - dense_grad_updates = self._optimizer.apply_gradients( - dense_vars, - global_step=None, - name=name) - if sparse_grad_updates is not None and dense_grad_updates is not None: - grad_updates = sparse_grad_updates + dense_grad_updates - elif sparse_grad_updates is not None: - grad_updates = sparse_grad_updates - elif dense_grad_updates is not None: - grad_updates = dense_grad_updates - - assert global_step is not None - with ops.control_dependencies([finish_func(grad_updates, "update")]): - with ops.colocate_with(global_step): - if isinstance( - global_step, resource_variable_ops.BaseResourceVariable): - # TODO(apassos): the implicit read in assign_add is slow; consider - # making it less so. - apply_updates = resource_variable_ops.assign_add_variable_op( - global_step.handle, - ops.convert_to_tensor(1, dtype=global_step.dtype), - name=name) + if key not in self._optimizer._slots[slot_name]: + if v.backend_type == 'hbm': + slot = DynamicVariable( + dimension=v.dimension, + initializer=self._initial_vals[slot_name], + name='DynamicSlot', + trainable=False, + ) else: - apply_updates = state_ops.assign_add(global_step, 1, name=name) - - if not context.executing_eagerly(): - if isinstance(apply_updates, ops.Tensor): - apply_updates = apply_updates.op - train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) - if apply_updates not in train_op: - train_op.append(apply_updates) - - return apply_updates - - def apply_sparse_gradients(self, grads_and_vars, global_step=None, name=None): - # 1. Create slots and do sparse_read - to_static_ops = [] - grad_list, var_list = [], [] - for g, v in grads_and_vars: - if g is not None: - unique, indices = tf.unique(g.indices) - grad_list.append(ops.IndexedSlices(g.values, indices, g.dense_shape)) - # TODO: Check multi-thread safety of DET - with tf.control_dependencies([g.values]): - to_static_ops.append(v.to_static(unique, False)) - var_list.append(v) - key = self._var_key(v) - for slot_name in self._initial_vals: - if key not in self._optimizer._slots[slot_name]: - if v.backend_type == "hbm": - slot = DynamicVariable( - dimension=v.dimension, - initializer=self._initial_vals[slot_name], - name="DynamicSlot", - trainable=False, - ) - else: - tmp_config = v.config_dict - tmp_initializer = v.initializer_str - slot = DynamicVariable( - dimension=v.dimension, - initializer=self._initial_vals[slot_name], - var_type=v.backend_type, - name="DynamicSlot", - trainable=False, - **tmp_config - ) - - self._optimizer._slots[slot_name][key] = slot - else: - slot = self._optimizer._slots[slot_name][key] - to_static_ops.append(slot.to_static(unique)) - - if len(grad_list) == 0: - return - - # 3. Call tf-optimizer - with ops.control_dependencies(to_static_ops): - train_op = self._optimizer.apply_gradients( - zip(grad_list, var_list), global_step=global_step, name=name - ) - - # 5. Write buffer back to dynamic variables - to_dynamic_ops = [] - if not isinstance(train_op, list): - train_op = [train_op] - with ops.control_dependencies(train_op): - for v in var_list: - key = self._var_key(v) - to_dynamic_ops.append(v.to_dynamic()) - for name in self._initial_vals: - slot = self._optimizer._slots[name][key] - to_dynamic_ops.append(slot.to_dynamic()) - - return to_dynamic_ops + tmp_config = v.config_dict + tmp_initializer = v.initializer_str + slot = DynamicVariable( + dimension=v.dimension, + initializer=self._initial_vals[slot_name], + var_type=v.backend_type, + name='DynamicSlot', + trainable=False, + **tmp_config) + + self._optimizer._slots[slot_name][key] = slot + else: + slot = self._optimizer._slots[slot_name][key] + to_static_ops.append(slot.to_static(unique)) + + if len(grad_list) == 0: + return + + # 3. Call tf-optimizer + with ops.control_dependencies(to_static_ops): + train_op = self._optimizer.apply_gradients( + zip(grad_list, var_list), global_step=global_step, name=name) + + # 5. Write buffer back to dynamic variables + to_dynamic_ops = [] + if not isinstance(train_op, list): + train_op = [train_op] + with ops.control_dependencies(train_op): + for v in var_list: + key = self._var_key(v) + to_dynamic_ops.append(v.to_dynamic()) + for name in self._initial_vals: + slot = self._optimizer._slots[name][key] + to_dynamic_ops.append(slot.to_dynamic()) + + return to_dynamic_ops class OptimizerWrapperV2(object): - def __init__(self, optimizer): - self._optimizer = optimizer - # slots - if tf.__version__[0] == "1": - unused = tf.Variable([0.0], name="unused", trainable=False, use_resource=True) - else: - unused = tf.Variable([0.0], name="unused", trainable=False) - self._optimizer._create_slots([unused]) - names, slots = [], [] - for name in self._optimizer.get_slot_names(): - names.append(name) - slots.append(self._optimizer.get_slot(unused, name)) - unused_key = self._var_key(unused) - if unused_key in self._optimizer._slots: - self._optimizer._slots.pop(unused_key) - self._initial_vals = {} - for i, name in enumerate(names): - self._initial_vals[name] = slots[i] - self._iterations = tf.Variable(0) - - @property - def lr(self): - return self._optimizer.lr - - def _create_slots(self, vars): - for tmp_var in vars: - if isinstance(tmp_var, DynamicVariable): - self._create_slots_dynamic(tmp_var) - else: - self._optimizer._create_slots(tmp_var) - def _create_slots_dynamic(self, var): - key = self._var_key(var) + def __init__(self, optimizer): + self._optimizer = optimizer + # slots + if tf.__version__[0] == '1': + unused = tf.Variable([0.0], + name='unused', + trainable=False, + use_resource=True) + else: + unused = tf.Variable([0.0], name='unused', trainable=False) + self._optimizer._create_slots([unused]) + names, slots = [], [] + for name in self._optimizer.get_slot_names(): + names.append(name) + slots.append(self._optimizer.get_slot(unused, name)) + unused_key = self._var_key(unused) + if unused_key in self._optimizer._slots: + self._optimizer._slots.pop(unused_key) + self._initial_vals = {} + for i, name in enumerate(names): + self._initial_vals[name] = slots[i] + self._iterations = tf.Variable(0) + + @property + def lr(self): + return self._optimizer.lr + + def _create_slots(self, vars): + for tmp_var in vars: + if isinstance(tmp_var, DynamicVariable): + self._create_slots_dynamic(tmp_var) + else: + self._optimizer._create_slots(tmp_var) + + def _create_slots_dynamic(self, var): + key = self._var_key(var) + if key not in self._optimizer._slots: + self._optimizer._slots[key] = {} + for slot_name in self._initial_vals: + if slot_name not in self._optimizer._slots[key]: + if var.backend_type == 'hbm': + slot = DynamicVariable( + dimension=var.dimension, + initializer=self._initial_vals[slot_name], + name='DynamicSlot', + trainable=False, + ) + else: + tmp_config = var.config_dict + tmp_initializer = var.initializer_str + slot = DynamicVariable( + dimension=var.dimension, + initializer=self._initial_vals[slot_name], + var_type=var.backend_type, + name='DynamicSlot', + trainable=False, + **tmp_config) + self._optimizer._slots[key][slot_name] = slot + + def _var_key(self, var): + if hasattr(var, '_distributed_container'): + var = var._distributed_container() + if var._in_graph_mode: + return var._shared_name + return var._unique_id + + def get_slot_names(self): + return self._optimizer.get_slot_names() + + def get_slot(self, var, name): + return self._optimizer.get_slot(var, name) + + @property + def _slots(self): + return self._optimizer._slots + + def apply_gradients(self, grads_and_vars, global_step=None, name=None): + # 1. Create slots and do sparse_read + to_static_ops = [] + grad_list, var_list = [], [] + for g, v in grads_and_vars: + if g is not None: + unique, indices = tf.unique(g.indices) + grad_list.append(ops.IndexedSlices(g.values, indices, g.dense_shape)) + # TODO: Check multi-thread safety of DET + # with tf.control_dependencies([g.values]): + to_static_ops.append(v.to_static(unique)) + var_list.append(v) + key = self._var_key(v) if key not in self._optimizer._slots: - self._optimizer._slots[key] = {} + self._optimizer._slots[key] = {} for slot_name in self._initial_vals: - if slot_name not in self._optimizer._slots[key]: - if var.backend_type == "hbm": - slot = DynamicVariable( - dimension=var.dimension, - initializer=self._initial_vals[slot_name], - name="DynamicSlot", - trainable=False, - ) - else: - tmp_config = var.config_dict - tmp_initializer = var.initializer_str - slot = DynamicVariable( - dimension=var.dimension, - initializer=self._initial_vals[slot_name], - var_type=var.backend_type, - name="DynamicSlot", - trainable=False, - **tmp_config - ) - self._optimizer._slots[key][slot_name] = slot - - def _var_key(self, var): - if hasattr(var, "_distributed_container"): - var = var._distributed_container() - if var._in_graph_mode: - return var._shared_name - return var._unique_id - - def get_slot_names(self): - return self._optimizer.get_slot_names() - - def get_slot(self, var, name): - return self._optimizer.get_slot(var, name) - - @property - def _slots(self): - return self._optimizer._slots - - def apply_gradients(self, grads_and_vars, global_step=None, name=None): - # 1. Create slots and do sparse_read - to_static_ops = [] - grad_list, var_list = [], [] - for g, v in grads_and_vars: - if g is not None: - unique, indices = tf.unique(g.indices) - grad_list.append(ops.IndexedSlices(g.values, indices, g.dense_shape)) - # TODO: Check multi-thread safety of DET - # with tf.control_dependencies([g.values]): - to_static_ops.append(v.to_static(unique)) - var_list.append(v) - key = self._var_key(v) - if key not in self._optimizer._slots: - self._optimizer._slots[key] = {} - for slot_name in self._initial_vals: - if slot_name not in self._optimizer._slots[key]: - if v.backend_type == "hbm": - slot = DynamicVariable( - dimension=v.dimension, - initializer=self._initial_vals[slot_name], - name="DynamicSlot", - trainable=False, - ) - else: - tmp_config = v.config_dict - tmp_initializer = v.initializer_str - slot = DynamicVariable( - dimension=v.dimension, - initializer=self._initial_vals[slot_name], - var_type=v.backend_type, - name="DynamicSlot", - trainable=False, - **tmp_config - ) - - self._optimizer._slots[key][slot_name] = slot - else: - slot = self._optimizer._slots[key][slot_name] - to_static_ops.append(slot.to_static(unique)) - - if len(grad_list) == 0: - return - - # 2. Switch iterations - iterations = self._optimizer._iterations - self._optimizer._iterations = self._iterations - - # 3. Call tf-optimizer - with tf.control_dependencies(to_static_ops): - train_op = self._optimizer.apply_gradients(zip(grad_list, var_list), name=name) - - # 4. Switch iterations - self._optimizer._iterations = iterations - - # 5. Write buffer back to dynamic variables - to_dynamic_ops = [] - with tf.control_dependencies([train_op]): - for v in var_list: - key = self._var_key(v) - to_dynamic_ops.append(v.to_dynamic()) - for name in self._initial_vals: - slot = self._optimizer._slots[key][name] - to_dynamic_ops.append(slot.to_dynamic()) - return tf.group(to_dynamic_ops) + if slot_name not in self._optimizer._slots[key]: + if v.backend_type == 'hbm': + slot = DynamicVariable( + dimension=v.dimension, + initializer=self._initial_vals[slot_name], + name='DynamicSlot', + trainable=False, + ) + else: + tmp_config = v.config_dict + tmp_initializer = v.initializer_str + slot = DynamicVariable( + dimension=v.dimension, + initializer=self._initial_vals[slot_name], + var_type=v.backend_type, + name='DynamicSlot', + trainable=False, + **tmp_config) + + self._optimizer._slots[key][slot_name] = slot + else: + slot = self._optimizer._slots[key][slot_name] + to_static_ops.append(slot.to_static(unique)) + + if len(grad_list) == 0: + return + + # 2. Switch iterations + iterations = self._optimizer._iterations + self._optimizer._iterations = self._iterations + + # 3. Call tf-optimizer + with tf.control_dependencies(to_static_ops): + train_op = self._optimizer.apply_gradients( + zip(grad_list, var_list), name=name) + + # 4. Switch iterations + self._optimizer._iterations = iterations + + # 5. Write buffer back to dynamic variables + to_dynamic_ops = [] + with tf.control_dependencies([train_op]): + for v in var_list: + key = self._var_key(v) + to_dynamic_ops.append(v.to_dynamic()) + for name in self._initial_vals: + slot = self._optimizer._slots[key][name] + to_dynamic_ops.append(slot.to_dynamic()) + return tf.group(to_dynamic_ops) class SGD(object): - def __init__(self, lr): - self._lr = tf.Variable(lr) - - @property - def lr(self): - return self._lr - - def apply_gradients(self, grads_and_vars, global_step=None, name=None): - train_ops = [] - for g, v in grads_and_vars: - if g is not None: - scaled_g = ops.IndexedSlices(g.values * self._lr, g.indices, g.dense_shape) - train_ops.append(v.scatter_sub(scaled_g)) - return tf.group(train_ops) + + def __init__(self, lr): + self._lr = tf.Variable(lr) + + @property + def lr(self): + return self._lr + + def apply_gradients(self, grads_and_vars, global_step=None, name=None): + train_ops = [] + for g, v in grads_and_vars: + if g is not None: + scaled_g = ops.IndexedSlices(g.values * self._lr, g.indices, + g.dense_shape) + train_ops.append(v.scatter_sub(scaled_g)) + return tf.group(train_ops) diff --git a/easy_rec/python/eval.py b/easy_rec/python/eval.py index f2ca0b6a0..c85827181 100644 --- a/easy_rec/python/eval.py +++ b/easy_rec/python/eval.py @@ -10,10 +10,10 @@ from easy_rec.python.main import distribute_evaluate from easy_rec.python.main import evaluate +from easy_rec.python.protos.train_pb2 import DistributionStrategy from easy_rec.python.utils import config_util -from easy_rec.python.utils import estimator_utils from easy_rec.python.utils import ds_util -from easy_rec.python.protos.train_pb2 import DistributionStrategy +from easy_rec.python.utils import estimator_utils from easy_rec.python.utils.distribution_utils import set_tf_config_and_get_distribute_eval_worker_num_on_ds # NOQA if tf.__version__ >= '2.0': diff --git a/easy_rec/python/export.py b/easy_rec/python/export.py index fe092ecae..3e493ab6f 100644 --- a/easy_rec/python/export.py +++ b/easy_rec/python/export.py @@ -7,9 +7,9 @@ from tensorflow.python.lib.io import file_io from easy_rec.python.main import export +from easy_rec.python.protos.train_pb2 import DistributionStrategy from easy_rec.python.utils import config_util from easy_rec.python.utils import estimator_utils -from easy_rec.python.protos.train_pb2 import DistributionStrategy if tf.__version__ >= '2.0': tf = tf.compat.v1 diff --git a/easy_rec/python/inference/csv_predictor.py b/easy_rec/python/inference/csv_predictor.py index 5ae273853..f3b3ebf2d 100644 --- a/easy_rec/python/inference/csv_predictor.py +++ b/easy_rec/python/inference/csv_predictor.py @@ -20,6 +20,7 @@ from tensorflow.python.saved_model import constants from tensorflow.python.saved_model import signature_constants +from easy_rec.python.inference.predictor import Predictor from easy_rec.python.protos.dataset_pb2 import DatasetConfig from easy_rec.python.utils import numpy_utils from easy_rec.python.utils import tf_utils @@ -31,11 +32,11 @@ from easy_rec.python.utils.input_utils import get_type_defaults from easy_rec.python.utils.load_class import get_register_class_meta from easy_rec.python.utils.tf_utils import get_tf_type -from easy_rec.python.inference.predictor import Predictor if tf.__version__ >= '2.0': tf = tf.compat.v1 + class CSVPredictor(Predictor): def __init__(self, @@ -203,5 +204,3 @@ def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): @property def out_of_range_exception(self): return (tf.errors.OutOfRangeError) - - diff --git a/easy_rec/python/inference/hive_parquet_predictor.py b/easy_rec/python/inference/hive_parquet_predictor.py index 1b6cdede3..572eaff8c 100644 --- a/easy_rec/python/inference/hive_parquet_predictor.py +++ b/easy_rec/python/inference/hive_parquet_predictor.py @@ -20,6 +20,7 @@ from tensorflow.python.saved_model import constants from tensorflow.python.saved_model import signature_constants +from easy_rec.python.inference.predictor import Predictor from easy_rec.python.protos.dataset_pb2 import DatasetConfig from easy_rec.python.utils import numpy_utils from easy_rec.python.utils import tf_utils @@ -31,11 +32,11 @@ from easy_rec.python.utils.input_utils import get_type_defaults from easy_rec.python.utils.load_class import get_register_class_meta from easy_rec.python.utils.tf_utils import get_tf_type -from easy_rec.python.inference.predictor import Predictor if tf.__version__ >= '2.0': tf = tf.compat.v1 + class HiveParquetPredictor(Predictor): def __init__(self, diff --git a/easy_rec/python/inference/hive_predictor.py b/easy_rec/python/inference/hive_predictor.py index 7c0cd2008..3289db3dd 100644 --- a/easy_rec/python/inference/hive_predictor.py +++ b/easy_rec/python/inference/hive_predictor.py @@ -20,6 +20,7 @@ from tensorflow.python.saved_model import constants from tensorflow.python.saved_model import signature_constants +from easy_rec.python.inference.predictor import Predictor from easy_rec.python.protos.dataset_pb2 import DatasetConfig from easy_rec.python.utils import numpy_utils from easy_rec.python.utils import tf_utils @@ -31,11 +32,11 @@ from easy_rec.python.utils.input_utils import get_type_defaults from easy_rec.python.utils.load_class import get_register_class_meta from easy_rec.python.utils.tf_utils import get_tf_type -from easy_rec.python.inference.predictor import Predictor if tf.__version__ >= '2.0': tf = tf.compat.v1 + class HivePredictor(Predictor): def __init__(self, diff --git a/easy_rec/python/inference/parquet_predictor.py b/easy_rec/python/inference/parquet_predictor.py index 5733b4263..92dbfd83a 100644 --- a/easy_rec/python/inference/parquet_predictor.py +++ b/easy_rec/python/inference/parquet_predictor.py @@ -20,8 +20,11 @@ from tensorflow.python.saved_model import constants from tensorflow.python.saved_model import signature_constants +from easy_rec.python.inference.predictor import Predictor +from easy_rec.python.input.parquet_input import ParquetInput from easy_rec.python.protos.dataset_pb2 import DatasetConfig from easy_rec.python.utils import config_util +from easy_rec.python.utils import input_utils from easy_rec.python.utils import numpy_utils from easy_rec.python.utils import tf_utils from easy_rec.python.utils.check_utils import check_split @@ -29,16 +32,13 @@ from easy_rec.python.utils.config_util import get_input_name_from_fg_json from easy_rec.python.utils.config_util import search_fg_json from easy_rec.python.utils.hive_utils import HiveUtils -from easy_rec.python.utils import input_utils from easy_rec.python.utils.load_class import get_register_class_meta from easy_rec.python.utils.tf_utils import get_tf_type -from easy_rec.python.inference.predictor import Predictor -from easy_rec.python.input.parquet_input import ParquetInput if tf.__version__ >= '2.0': tf = tf.compat.v1 -try: +try: from tensorflow.python.framework.load_library import load_op_library import easy_rec load_embed_lib_path = os.path.join(easy_rec.ops_dir, 'libload_embed.so') @@ -58,11 +58,12 @@ def __init__(self, selected_cols=None, output_sep=chr(1), pipeline_config=None): - super(ParquetPredictor, self).__init__(model_path, profiling_file, fg_json_path) + super(ParquetPredictor, self).__init__(model_path, profiling_file, + fg_json_path) self._output_sep = output_sep self._ds_vector_recall = ds_vector_recall input_type = DatasetConfig.InputType.Name(data_config.input_type).lower() - self.pipeline_config=pipeline_config + self.pipeline_config = pipeline_config if 'rtp' in input_type: self._is_rtp = True @@ -102,15 +103,16 @@ def _get_reserved_cols(self, reserved_cols): def _parse_line(self, line): out_dict = {} for key in line['feature']: - out_dict[key] = line['feature'][key] + out_dict[key] = line['feature'][key] if 'reserve' in line: for key in line['reserve']: - out_dict[key] = line['reserve'][key] + out_dict[key] = line['reserve'][key] return out_dict def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, slice_id): - feature_configs = config_util.get_compatible_feature_configs(self.pipeline_config) + feature_configs = config_util.get_compatible_feature_configs( + self.pipeline_config) kwargs = {} if self.pipeline_config.model_config.HasField('ev_params'): kwargs['ev_params'] = self.pipeline_config.model_config.ev_params @@ -119,10 +121,14 @@ def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, parquet_file = gfile.Glob(input_path.split(',')[0])[0] kwargs['reserve_types'] = input_utils.get_tf_type_from_parquet_file( self._reserved_cols, parquet_file) - parquet_input = ParquetInput(self.pipeline_config.data_config, feature_configs, - input_path, task_index=slice_id, task_num=slice_num, + parquet_input = ParquetInput( + self.pipeline_config.data_config, + feature_configs, + input_path, + task_index=slice_id, + task_num=slice_num, pipeline_config=self.pipeline_config, - **kwargs) + **kwargs) return parquet_input._build(tf.estimator.ModeKeys.PREDICT, {}) def _get_writer(self, output_path, slice_id): diff --git a/easy_rec/python/inference/predictor.py b/easy_rec/python/inference/predictor.py index 473b024f3..f1f027de3 100644 --- a/easy_rec/python/inference/predictor.py +++ b/easy_rec/python/inference/predictor.py @@ -613,7 +613,6 @@ def batch(self, data_list): return batch_input - class ODPSPredictor(Predictor): def __init__(self, @@ -673,6 +672,3 @@ def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): reserve_vals = [all_vals[k] for k in reserved_cols] + \ [outputs[x] for x in output_cols] return reserve_vals - - - diff --git a/easy_rec/python/input/dummy_input.py b/easy_rec/python/input/dummy_input.py index 37629c5b2..d07814f7b 100644 --- a/easy_rec/python/input/dummy_input.py +++ b/easy_rec/python/input/dummy_input.py @@ -46,9 +46,9 @@ def _build(self, mode, params): # vals = line_str.strip().split(',') with tf.device('/cpu:0'): features = {} - for field, field_type, def_val in zip( - self._input_fields, self._input_field_types, - self._input_field_defaults): + for field, field_type, def_val in zip(self._input_fields, + self._input_field_types, + self._input_field_defaults): tf_type = get_tf_type(field_type) def_val = self.get_type_defaults(field_type, default_val=def_val) print('default:', field, field_type, def_val, type(def_val)) diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index 15ab894ca..410e55d90 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -1,25 +1,30 @@ -import sys import logging import multiprocessing import queue +import sys + import numpy as np import pandas as pd -def start_data_proc(task_index, num_proc, file_que, data_que, proc_start_que, proc_stop_que, - batch_size, label_fields, effective_fields, reserve_fields, drop_remainder): + +def start_data_proc(task_index, num_proc, file_que, data_que, proc_start_que, + proc_stop_que, batch_size, label_fields, effective_fields, + reserve_fields, drop_remainder): mp_ctxt = multiprocessing.get_context('spawn') proc_arr = [] for proc_id in range(num_proc): proc = mp_ctxt.Process( - target=load_data_proc, args=(proc_id, file_que, data_que, proc_start_que, - proc_stop_que, batch_size, label_fields, effective_fields, - reserve_fields, drop_remainder), - name='task_%d_data_proc_%d' % (task_index, proc_id)) + target=load_data_proc, + args=(proc_id, file_que, data_que, proc_start_que, proc_stop_que, + batch_size, label_fields, effective_fields, reserve_fields, + drop_remainder), + name='task_%d_data_proc_%d' % (task_index, proc_id)) proc.daemon = True proc.start() proc_arr.append(proc) return proc_arr + def _should_stop(proc_stop_que): try: proc_stop_que.get(block=False) @@ -33,6 +38,7 @@ def _should_stop(proc_stop_que): except AssertionError: return True + def _add_to_que(data_dict, data_que, proc_stop_que): while True: try: @@ -49,6 +55,7 @@ def _add_to_que(data_dict, data_que, proc_stop_que): logging.warning('data_que is closed') return False + def _get_one_file(file_que, proc_stop_que): while True: try: @@ -56,10 +63,11 @@ def _get_one_file(file_que, proc_stop_que): return input_file except queue.Empty: pass - return None + return None + def _pack_sparse_feas(data_dict, effective_fields): - fea_val_arr = [] + fea_val_arr = [] fea_len_arr = [] for fea_name in effective_fields: fea_len_arr.append(data_dict[fea_name][0]) @@ -69,16 +77,20 @@ def _pack_sparse_feas(data_dict, effective_fields): fea_vals = np.concatenate(fea_val_arr, axis=0) data_dict['sparse_fea'] = (fea_lens, fea_vals) + def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, - batch_size, label_fields, effective_fields, reserve_fields, drop_remainder): - logging.info('data proc %d start, proc_start_que=%s' % (proc_id, proc_start_que.qsize())) + batch_size, label_fields, effective_fields, reserve_fields, + drop_remainder): + logging.info('data proc %d start, proc_start_que=%s' % + (proc_id, proc_start_que.qsize())) proc_start_que.get() all_fields = list(effective_fields) if label_fields is not None: all_fields = all_fields + label_fields if reserve_fields is not None: all_fields = all_fields + reserve_fields - logging.info('data proc %d start, file_que.qsize=%d' % (proc_id, file_que.qsize())) + logging.info('data proc %d start, file_que.qsize=%d' % + (proc_id, file_que.qsize())) num_files = 0 part_data_dict = {} @@ -113,7 +125,7 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, if np_dtype == object: np_dtype = np.str data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], - dtype=np_dtype) + dtype=np_dtype) for k in effective_fields: val = input_data[k][sid:eid] @@ -138,11 +150,11 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, if label_fields is not None and len(label_fields) > 0: for k in label_fields: tmp_lbls = np.array([x[0] for x in input_data[k][sid:]], - dtype=np.float32) + dtype=np.float32) if part_data_dict is not None and k in part_data_dict: tmp_lbls = np.concatenate([part_data_dict[k], tmp_lbls], axis=0) if len(tmp_lbls) > batch_size: - data_dict[k] = tmp_lbls[:batch_size] + data_dict[k] = tmp_lbls[:batch_size] part_data_dict_n[k] = tmp_lbls[batch_size:] elif len(tmp_lbls) == batch_size: data_dict[k] = tmp_lbls @@ -160,7 +172,7 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, if part_data_dict is not None and k in part_data_dict: tmp_r = np.concatenate([part_data_dict[k], tmp_r], axis=0) if len(tmp_r) > batch_size: - data_dict[k] = tmp_r[:batch_size] + data_dict[k] = tmp_r[:batch_size] part_data_dict_n[k] = tmp_r[batch_size:] elif len(tmp_r) == batch_size: data_dict[k] = tmp_r @@ -200,11 +212,11 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, part_data_dict = part_data_dict_n if len(part_data_dict) > 0 and is_good: if not drop_remainder: - _pack_sparse_feas(part_data_dict, effective_fields) + _pack_sparse_feas(part_data_dict, effective_fields) _add_to_que(part_data_dict, data_que, proc_stop_que) else: logging.warning('drop remain %d samples as drop_remainder is set' % - len(part_data_dict[effective_fields[0]])) + len(part_data_dict[effective_fields[0]])) if is_good: is_good = _add_to_que(None, data_que, proc_stop_que) logging.info('data_proc_id=%d, is_good = %s' % (proc_id, is_good)) diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index ca82eafcd..f537c8b37 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -8,19 +8,21 @@ import numpy as np import pandas as pd import tensorflow as tf -from tensorflow.python.platform import gfile from tensorflow.python.framework import ops -from tensorflow.python.ops import math_ops, array_ops, logging_ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import logging_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops.ragged import ragged_util +from tensorflow.python.platform import gfile -from easy_rec.python.input.input import Input - +from easy_rec.python.compat import queues from easy_rec.python.input import load_parquet -from easy_rec.python.compat import queues +from easy_rec.python.input.input import Input # if tf.__version__ >= '2.0': # tf = tf.compat.v1 + class ParquetInput(Input): def __init__(self, @@ -44,20 +46,22 @@ def __init__(self, logging.info('parquet input_path=%s file_num=%d' % (input_path, len(self._input_files))) mp_ctxt = multiprocessing.get_context('spawn') - self._data_que = queues.Queue(name='data_que', ctx=mp_ctxt, - maxsize=self._data_config.prefetch_size) + self._data_que = queues.Queue( + name='data_que', ctx=mp_ctxt, maxsize=self._data_config.prefetch_size) # self._data_que._name = 'data_que' file_num = len(self._input_files) - logging.info('[task_index=%d] total_file_num=%d task_num=%d' % (task_index, - file_num, task_num)) + logging.info('[task_index=%d] total_file_num=%d task_num=%d' % + (task_index, file_num, task_num)) avg_file_num = int(file_num / task_num) res_file_num = file_num % task_num file_sid = task_index * avg_file_num + min(res_file_num, task_index) - file_eid = (task_index + 1) * avg_file_num + min(res_file_num, task_index+1) - self._my_files = self._input_files[file_sid:file_eid] + file_eid = (task_index + 1) * avg_file_num + min(res_file_num, + task_index + 1) + self._my_files = self._input_files[file_sid:file_eid] - logging.info('[task_index=%d] task_file_num=%d' % (task_index, len(self._my_files))) + logging.info('[task_index=%d] task_file_num=%d' % + (task_index, len(self._my_files))) self._file_que = queues.Queue(name='file_que', ctx=mp_ctxt) self._num_proc = 8 @@ -74,34 +78,34 @@ def __init__(self, if 'reserve_fields' in kwargs and 'reserve_types' in kwargs: self._reserve_fields = kwargs['reserve_fields'] self._reserve_types = kwargs['reserve_types'] - - self._proc_arr = None + + self._proc_arr = None def _sample_generator(self): if not self._proc_start: - self._proc_start = True + self._proc_start = True for proc in (self._proc_arr): self._proc_start_que.put(True) - logging.info('task[%s] data_proc=%s is_alive=%s' % (self._task_index, - proc, proc.is_alive())) + logging.info('task[%s] data_proc=%s is_alive=%s' % + (self._task_index, proc, proc.is_alive())) done_proc_cnt = 0 fetch_timeout_cnt = 0 - # for mock purpose - ## all_samples = [] - ## while len(all_samples) < 64: - ## try: - ## sample = self._data_que.get(block=False) - ## all_samples.append(sample) - ## except queue.Empty: - ## continue - ## sid = 0 - ## while True: - ## yield all_samples[sid] - ## sid += 1 - ## if sid >= len(all_samples): - ## sid = 0 + # # for mock purpose + # all_samples = [] + # while len(all_samples) < 64: + # try: + # sample = self._data_que.get(block=False) + # all_samples.append(sample) + # except queue.Empty: + # continue + # sid = 0 + # while True: + # yield all_samples[sid] + # sid += 1 + # if sid >= len(all_samples): + # sid = 0 fetch_good_cnt = 0 while True: @@ -113,8 +117,10 @@ def _sample_generator(self): fetch_good_cnt += 1 yield sample if fetch_good_cnt % 200 == 0: - logging.info('task[%d] fetch_good_cnt=%d, fetch_timeout_cnt=%d, qsize=%d' % ( - self._task_index, fetch_good_cnt, fetch_timeout_cnt, self._data_que.qsize())) + logging.info( + 'task[%d] fetch_good_cnt=%d, fetch_timeout_cnt=%d, qsize=%d' % + (self._task_index, fetch_good_cnt, fetch_timeout_cnt, + self._data_que.qsize())) except queue.Empty: fetch_timeout_cnt += 1 if done_proc_cnt >= len(self._proc_arr): @@ -122,7 +128,8 @@ def _sample_generator(self): fetch_timeout_cnt) break except Exception as ex: - logging.warning('task[%d] get from data_que exception: %s' % (self._task_index, str(ex))) + logging.warning('task[%d] get from data_que exception: %s' % + (self._task_index, str(ex))) break for proc in self._proc_arr: proc.join() @@ -130,11 +137,11 @@ def _sample_generator(self): def stop(self): if self._proc_arr is None or len(self._proc_arr) == 0: return - logging.info("task[%d] will stop dataset procs, proc_num=%d" % (self._task_index, - len(self._proc_arr))) + logging.info('task[%d] will stop dataset procs, proc_num=%d' % + (self._task_index, len(self._proc_arr))) self._file_que.close() if self._proc_start: - logging.info("try close data que") + logging.info('try close data que') for _ in range(len(self._proc_arr)): self._proc_stop_que.put(1) self._proc_stop_que.close() @@ -147,21 +154,21 @@ def _any_alive(): # to ensure the sender part of the stupid python Queue could exit properly while _any_alive(): - try: + try: self._data_que.get(timeout=1) except: pass time.sleep(1) self._data_que.close() - logging.info("data que closed") + logging.info('data que closed') # import time # time.sleep(10) for proc in self._proc_arr: # proc.terminate() proc.join() - logging.info("join proc done") + logging.info('join proc done') self._proc_start = False - + def _to_fea_dict(self, input_dict): fea_dict = {} # for fea_name in self._effective_fields: @@ -178,7 +185,8 @@ def _to_fea_dict(self, input_dict): # tmp = input_dict[input_0][1] # fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, input_dict[input_0][0]) if self._has_ev: - tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict['sparse_fea'][0] + tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict[ + 'sparse_fea'][0] # tmp_vals = logging_ops.Print(tmp_vals, [ # array_ops.shape(tmp_vals), math_ops.reduce_min(tmp_vals), # math_ops.reduce_max(tmp_vals), @@ -186,17 +194,19 @@ def _to_fea_dict(self, input_dict): # math_ops.reduce_min(tmp_lens), # math_ops.reduce_max(tmp_lens)], message='debug_sparse_fea') print('tmp_vals=%s tmp_lens=%s' % (tmp_vals, tmp_lens)) - # all_uniq_ids, uniq_idx = array_ops.unique(tmp_vals) + # all_uniq_ids, uniq_idx = array_ops.unique(tmp_vals) # uniq_idx = math_ops.cast(uniq_idx, tf.int32) fea_dict['sparse_fea'] = (tmp_vals, tmp_lens) - # tf.RaggedTensor.from_row_lengths( - # values=tmp_vals, row_lengths=tmp_lens) - # values=input_dict['sparse_fea'][1], - # row_lengths=input_dict['sparse_fea'][0]) + # tf.RaggedTensor.from_row_lengths( + # values=tmp_vals, row_lengths=tmp_lens) + # values=input_dict['sparse_fea'][1], + # row_lengths=input_dict['sparse_fea'][0]) else: - tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict['sparse_fea'][0] - fea_dict['sparse_fea'] = (tmp_vals % self._feature_configs[0].num_buckets, tmp_lens) + tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict[ + 'sparse_fea'][0] + fea_dict['sparse_fea'] = (tmp_vals % self._feature_configs[0].num_buckets, + tmp_lens) # fea_dict['sparse_fea'] = tf.RaggedTensor.from_row_lengths( # values=input_dict['sparse_fea'][1] % self._feature_configs[0].num_buckets, # row_lengths=input_dict['sparse_fea'][0]) @@ -221,20 +231,23 @@ def _to_fea_dict(self, input_dict): def _build(self, mode, params): if mode == tf.estimator.ModeKeys.TRAIN and self._data_config.num_epochs > 1: - logging.info('will repeat train data for %d epochs' % self._data_config.num_epochs) + logging.info('will repeat train data for %d epochs' % + self._data_config.num_epochs) my_files = self._my_files * self._data_config.num_epochs else: my_files = self._my_files if mode != tf.estimator.ModeKeys.PREDICT: - self._proc_arr = load_parquet.start_data_proc(self._task_index, self._num_proc, - self._file_que, self._data_que, self._proc_start_que, self._proc_stop_que, - self._batch_size, self._label_fields, self._effective_fields, - self._reserve_fields, self._data_config.drop_remainder) + self._proc_arr = load_parquet.start_data_proc( + self._task_index, self._num_proc, self._file_que, self._data_que, + self._proc_start_que, self._proc_stop_que, self._batch_size, + self._label_fields, self._effective_fields, self._reserve_fields, + self._data_config.drop_remainder) else: - self._proc_arr = load_parquet.start_data_proc(self._task_index, self._num_proc, - self._file_que, self._data_que, self._proc_start_que, self._proc_stop_que, - self._batch_size, None, self._effective_fields, self._reserve_fields, False) + self._proc_arr = load_parquet.start_data_proc( + self._task_index, self._num_proc, self._file_que, self._data_que, + self._proc_start_que, self._proc_stop_que, self._batch_size, None, + self._effective_fields, self._reserve_fields, False) for input_file in my_files: self._file_que.put(input_file) @@ -242,7 +255,8 @@ def _build(self, mode, params): # add end signal for proc in self._proc_arr: self._file_que.put(None) - logging.info('add input_files to file_que, qsize=%d' % self._file_que.qsize()) + logging.info('add input_files to file_que, qsize=%d' % + self._file_que.qsize()) out_types = {} out_shapes = {} @@ -286,14 +300,18 @@ def _build(self, mode, params): # for predictor with saved model def _get_with_reserve(fea_dict): print('fea_dict=%s' % fea_dict) - out_dict = {'feature': { - 'ragged_ids': fea_dict['feature']['sparse_fea'][0], - 'ragged_lens': fea_dict['feature']['sparse_fea'][1] } } + out_dict = { + 'feature': { + 'ragged_ids': fea_dict['feature']['sparse_fea'][0], + 'ragged_lens': fea_dict['feature']['sparse_fea'][1] + } + } out_dict['reserve'] = fea_dict['reserve'] return out_dict + dataset = dataset.map(_get_with_reserve) else: - dataset = dataset.map(lambda x : self._get_features(x)) + dataset = dataset.map(lambda x: self._get_features(x)) dataset = dataset.prefetch(buffer_size=self._prefetch_size) return dataset @@ -321,15 +339,18 @@ def _input_fn(mode=None, params=None, config=None): dataset = self._build(mode, params) return dataset elif mode is None: # serving_input_receiver_fn for export SavedModel - ragged_ids = tf.compat.v1.placeholder(tf.int64, [None], name='ragged_ids') - ragged_lens = tf.compat.v1.placeholder(tf.int32, [None], name='ragged_lens') + ragged_ids = tf.compat.v1.placeholder( + tf.int64, [None], name='ragged_ids') + ragged_lens = tf.compat.v1.placeholder( + tf.int32, [None], name='ragged_lens') inputs = {'ragged_ids': ragged_ids, 'ragged_lens': ragged_lens} if self._has_ev: features = {'ragged_ids': ragged_ids, 'ragged_lens': ragged_lens} else: - features = { 'ragged_ids': - ragged_ids % self._feature_configs[0].num_buckets, - 'ragged_lens': ragged_lens} + features = { + 'ragged_ids': ragged_ids % self._feature_configs[0].num_buckets, + 'ragged_lens': ragged_lens + } return tf.estimator.export.ServingInputReceiver(features, inputs) _input_fn.input_creator = self diff --git a/easy_rec/python/layers/input_layer.py b/easy_rec/python/layers/input_layer.py index 276cc8e9c..44bf71f5f 100644 --- a/easy_rec/python/layers/input_layer.py +++ b/easy_rec/python/layers/input_layer.py @@ -133,7 +133,9 @@ def get_plain_feature(self, features, group_name): cols_to_output_tensors = OrderedDict() output_features = feature_column.input_layer( - features, group_columns, cols_to_output_tensors=cols_to_output_tensors, + features, + group_columns, + cols_to_output_tensors=cols_to_output_tensors, is_training=self._is_training) group_features = [cols_to_output_tensors[x] for x in group_columns] @@ -144,7 +146,7 @@ def get_plain_feature(self, features, group_name): if self._embedding_regularizer is not None: regularizers.apply_regularization( - self._embedding_regularizer, weights_list=embedding_reg_lst) + self._embedding_regularizer, weights_list=embedding_reg_lst) return output_features, group_features def get_sequence_feature(self, features, group_name): diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index 46215e545..ef0eec625 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -114,8 +114,8 @@ def _create_estimator(pipeline_config, distribution=None, params={}): # gpu_options.visible_device_list = ','.join(gpus[sid:eid]) if hvd is not None and pipeline_config.train_config.train_distribute != DistributionStrategy.NoStrategy: - gpus = tf.config.experimental.list_physical_devices("GPU") - tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], "GPU") + gpus = tf.config.experimental.list_physical_devices('GPU') + tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU') # gpus = estimator_utils.get_available_gpus() # if len(gpus) > 0: local_rnk = hvd.local_rank() @@ -366,7 +366,7 @@ def _train_and_evaluate_impl(pipeline_config, from easy_rec.python.compat import estimator_train # create eval spec eval_spec = _create_eval_export_spec( - pipeline_config, eval_data, check_mode=check_mode) + pipeline_config, eval_data, check_mode=check_mode) estimator_train.train_and_evaluate(estimator, train_spec, eval_spec) logging.info('Train and evaluate finish') if fit_on_eval and (not estimator_utils.is_evaluator()): @@ -822,7 +822,8 @@ def export(export_dir, if gfile.Exists(sok_embed_path): if isinstance(final_export_dir, bytes): final_export_dir = final_export_dir.decode('utf-8') - export_sok_embed_dir = os.path.join(final_export_dir, 'variables/variables-sok/') + export_sok_embed_dir = os.path.join(final_export_dir, + 'variables/variables-sok/') gfile.MkDir(export_sok_embed_dir) for tmp_file in gfile.Glob(sok_embed_path + '*'): if (tmp_file.endswith('.keys') or tmp_file.endswith('.vals')) \ @@ -870,7 +871,6 @@ def export_checkpoint(pipeline_config=None, if pipeline_config.model_config.HasField('ev_params'): input_fn_kwargs['ev_params'] = pipeline_config.model_config.ev_params - # create estimator params = {'log_device_placement': verbose} if asset_files: diff --git a/easy_rec/python/model/easy_rec_estimator.py b/easy_rec/python/model/easy_rec_estimator.py index a5025ca66..ec1f71933 100644 --- a/easy_rec/python/model/easy_rec_estimator.py +++ b/easy_rec/python/model/easy_rec_estimator.py @@ -18,7 +18,6 @@ from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import saver -from easy_rec.python.compat.saver import SaverV2 from easy_rec.python.builders import optimizer_builder from easy_rec.python.compat import optimizers @@ -30,6 +29,7 @@ from easy_rec.python.compat.early_stopping import stop_if_no_decrease_hook from easy_rec.python.compat.early_stopping import stop_if_no_increase_hook from easy_rec.python.compat.ops import GraphKeys +from easy_rec.python.compat.saver import SaverV2 from easy_rec.python.input.input import Input from easy_rec.python.layers.utils import _tensor_to_tensorinfo from easy_rec.python.protos.pipeline_pb2 import EasyRecConfig @@ -51,8 +51,6 @@ except Exception: sok = None - - if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -421,7 +419,6 @@ def _train_model_fn(self, features, labels, run_config): other_vars.append(tmp_var) # var_list = other_vars - scaffold = tf.train.Scaffold( saver=SaverV2( var_list=var_list, @@ -485,9 +482,7 @@ def _eval_model_fn(self, features, labels, run_config): ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS)) scaffold = tf.train.Scaffold( saver=SaverV2( - var_list=var_list, - sharded=True, - save_relative_paths=True)) + var_list=var_list, sharded=True, save_relative_paths=True)) end = time.time() tf.logging.info('eval graph construct finished. Time %.3fs' % (end - start)) return tf.estimator.EstimatorSpec( @@ -632,9 +627,7 @@ def _export_model_fn(self, features, labels, run_config, params): ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS)) scaffold = tf.train.Scaffold( saver=SaverV2( - var_list=var_list, - sharded=True, - save_relative_paths=True)) + var_list=var_list, sharded=True, save_relative_paths=True)) return tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.PREDICT, diff --git a/easy_rec/python/predict.py b/easy_rec/python/predict.py index 046b1ab05..2e61685ce 100644 --- a/easy_rec/python/predict.py +++ b/easy_rec/python/predict.py @@ -9,7 +9,6 @@ from tensorflow.python.lib.io import file_io from easy_rec.python.inference.csv_predictor import CSVPredictor -from easy_rec.python.inference.hive_parquet_predictor import HiveParquetPredictor from easy_rec.python.inference.hive_predictor import HivePredictor from easy_rec.python.inference.parquet_predictor import ParquetPredictor from easy_rec.python.main import predict @@ -18,6 +17,8 @@ from easy_rec.python.utils import numpy_utils from easy_rec.python.utils.hive_utils import HiveUtils +from easy_rec.python.inference.hive_parquet_predictor import HiveParquetPredictor # NOQA + if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -92,13 +93,14 @@ def main(argv): all_cols=all_cols, all_col_types=all_col_types) elif pipeline_config.WhichOneof('train_path') == 'parquet_train_input': - predictor = ParquetPredictor(FLAGS.saved_model_dir, - pipeline_config.data_config, - ds_vector_recall=FLAGS.ds_vector_recall, - fg_json_path=FLAGS.fg_json_path, - selected_cols=FLAGS.selected_cols, - output_sep=FLAGS.output_sep, - pipeline_config=pipeline_config) + predictor = ParquetPredictor( + FLAGS.saved_model_dir, + pipeline_config.data_config, + ds_vector_recall=FLAGS.ds_vector_recall, + fg_json_path=FLAGS.fg_json_path, + selected_cols=FLAGS.selected_cols, + output_sep=FLAGS.output_sep, + pipeline_config=pipeline_config) else: predictor = CSVPredictor( FLAGS.saved_model_dir, diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index 3901b9aa5..e5869ed29 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -4,6 +4,7 @@ from __future__ import division from __future__ import print_function +import ctypes import json import logging import os @@ -304,8 +305,9 @@ def end(self, session): self._progress_file.close() -import ctypes _cudart = ctypes.CDLL('libcudart.so') + + class CheckpointSaverHook(CheckpointSaverHook): """Saves checkpoints every N steps or seconds.""" @@ -363,8 +365,9 @@ def __init__(self, self._dyn_export = {} for dyn_var in self._sok_dynamic_vars: indices, values = dynamic_variable_ops.dummy_var_export( - dyn_var.handle, key_type=dyn_var.key_type, dtype=dyn_var.handle_dtype - ) + dyn_var.handle, + key_type=dyn_var.key_type, + dtype=dyn_var.handle_dtype) self._dyn_export[dyn_var.name] = (indices, values) if increment_save_config is not None: @@ -618,32 +621,32 @@ def _send_sparse(self, global_step, session): % (global_step, msg_num, len(bytes_buf))) def after_run(self, run_context, run_values): - #stale_global_step = run_values.results - #if stale_global_step > 10 and not self._cuda_profile_start: - # self._cuda_profile_start = 1 - # ret = _cudart.cudaProfilerStart() - # if ret != 0: - # logging.error('cudaProfilerStart failed') - # else: - # logging.info('cudaProfilerStart good') - # return - #if stale_global_step > 20 and not self._cuda_profile_stop: - # self._cuda_profile_stop = 1 - # ret = _cudart.cudaProfilerStop() - # if ret != 0: - # logging.error('cudaProfilerStop failed') - # else: - # logging.info('cudaProfilerStop finish') - # return - #return + # stale_global_step = run_values.results + # if stale_global_step > 10 and not self._cuda_profile_start: + # self._cuda_profile_start = 1 + # ret = _cudart.cudaProfilerStart() + # if ret != 0: + # logging.error('cudaProfilerStart failed') + # else: + # logging.info('cudaProfilerStart good') + # return + # if stale_global_step > 20 and not self._cuda_profile_stop: + # self._cuda_profile_stop = 1 + # ret = _cudart.cudaProfilerStop() + # if ret != 0: + # logging.error('cudaProfilerStop failed') + # else: + # logging.info('cudaProfilerStop finish') + # return + # return if not is_chief(): if has_sok(): stale_global_step = run_values.results global_step = stale_global_step + self._steps_per_run if self._timer.should_trigger_for_step(global_step): - logging.info('worker[%d] global_step=%d, save sok dynamic vars' % ( - self._task_idx, global_step)) + logging.info('worker[%d] global_step=%d, save sok dynamic vars' % + (self._task_idx, global_step)) self._save_sok(run_context.session, global_step) self._timer.update_last_triggered_step(global_step) return @@ -666,27 +669,27 @@ def after_run(self, run_context, run_values): self._send_sparse(global_step, run_context.session) def _save_sok(self, session, step): - return if self._dyn_export is None: return # get self._dyn_export and save - dyn_save_dir = os.path.join(self._checkpoint_dir, 'model.ckpt-%d-sok' % step) + dyn_save_dir = os.path.join(self._checkpoint_dir, + 'model.ckpt-%d-sok' % step) gfile.MakeDirs(dyn_save_dir) dyn_idx_vals = session.run(self._dyn_export) for var_name in dyn_idx_vals: idx, vals = dyn_idx_vals[var_name] var_name = var_name.replace('/', '__') - dyn_save_path_keys = os.path.join(dyn_save_dir, 'embed-%s-part-%d.keys' % ( - var_name, self._task_idx)) + dyn_save_path_keys = os.path.join( + dyn_save_dir, 'embed-%s-part-%d.keys' % (var_name, self._task_idx)) with gfile.GFile(dyn_save_path_keys, 'wb') as fout: fout.write(idx.tobytes()) - dyn_save_path_vals = os.path.join(dyn_save_dir, 'embed-%s-part-%d.vals' % ( - var_name, self._task_idx)) + dyn_save_path_vals = os.path.join( + dyn_save_dir, 'embed-%s-part-%d.vals' % (var_name, self._task_idx)) with gfile.GFile(dyn_save_path_vals, 'wb') as fout: fout.write(vals.tobytes()) - logging.info('save %s[len=%d] to %s and %s' % (var_name, len(idx), - dyn_save_path_keys, dyn_save_path_vals)) + logging.info('save %s[len=%d] to %s and %s' % + (var_name, len(idx), dyn_save_path_keys, dyn_save_path_vals)) def _save(self, session, step): """Saves the latest checkpoint, returns should_stop.""" @@ -701,7 +704,8 @@ def _save(self, session, step): for x in save_data_offset: if x: data_offset_json.update(json.loads(x)) - save_offset_path = os.path.join(self._checkpoint_dir, 'model.ckpt-%d.offset' % step) + save_offset_path = os.path.join(self._checkpoint_dir, + 'model.ckpt-%d.offset' % step) with gfile.GFile(save_offset_path, 'w') as fout: json.dump(data_offset_json, fout) @@ -713,7 +717,7 @@ def _save(self, session, step): # save sok self._save_sok(session, step) - + self._summary_writer.add_session_log( tf.SessionLog( status=tf.SessionLog.CHECKPOINT, checkpoint_path=self._save_path), diff --git a/easy_rec/python/utils/input_utils.py b/easy_rec/python/utils/input_utils.py index 3dd5b8f2c..26abea1b7 100644 --- a/easy_rec/python/utils/input_utils.py +++ b/easy_rec/python/utils/input_utils.py @@ -1,8 +1,8 @@ # -*- encoding:utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. import numpy as np -import tensorflow as tf import pandas as pd +import tensorflow as tf from easy_rec.python.protos.dataset_pb2 import DatasetConfig @@ -76,7 +76,7 @@ def string_to_number(field, ftype, default_value, name=''): def get_tf_type_from_parquet_file(cols, parquet_file): - input_data = pd.read_parquet(parquet_file, columns=cols) + input_data = pd.read_parquet(parquet_file, columns=cols) tf_types = [] for col in cols: obj = input_data[col][0] @@ -88,13 +88,14 @@ def get_tf_type_from_parquet_file(cols, parquet_file): data_type = type(obj) if data_type in (int, np.int32): tf_types.append(tf.int32) + elif data_type in (np.int64,): + tf_types.append(tf.int64) elif data_type in (np.str, str): tf_types.append(tf.string) elif data_type in (np.float, np.float32, float): tf_types.append(tf.float32) - elif data_type in (np.double, ): + elif data_type in (np.double,): tf_types.append(tf.float64) else: tf_types.append(tf.string) return tf_types - diff --git a/requirements/runtime.txt b/requirements/runtime.txt index 437e61134..56d86d2f6 100644 --- a/requirements/runtime.txt +++ b/requirements/runtime.txt @@ -4,8 +4,8 @@ matplotlib oss2 pandas psutil -pyodps pyarrow +pyodps PyYAML scikit-learn xlrd >= 0.9.0 diff --git a/setup.cfg b/setup.cfg index b0e05ade5..66190f744 100644 --- a/setup.cfg +++ b/setup.cfg @@ -10,7 +10,7 @@ multi_line_output = 7 force_single_line = true known_standard_library = setuptools known_first_party = easy_rec -known_third_party = absl,base,common_io,docutils,eas_prediction,future,google,graphlearn,kafka,layers,matplotlib,metric,numpy,oss2,pai,pandas,psutil,pyarrow,six,sklearn,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,utils,yaml +known_third_party = absl,common_io,docutils,eas_prediction,future,google,graphlearn,kafka,matplotlib,numpy,oss2,pai,pandas,psutil,six,sklearn,sparse_operation_kit,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,yaml no_lines_before = LOCALFOLDER default_section = THIRDPARTY skip = easy_rec/python/protos From 03fb4d9544b997ecf640e7ad92e83f864d856721 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Wed, 1 Nov 2023 21:47:00 +0800 Subject: [PATCH 23/67] fix bug --- easy_rec/python/input/load_parquet.py | 2 +- easy_rec/python/input/parquet_input.py | 2 +- easy_rec/python/model/ppnet_v3m.py | 27 ++++++++++++++++++++------ easy_rec/python/utils/config_util.py | 5 +++++ 4 files changed, 28 insertions(+), 8 deletions(-) diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index 410e55d90..28350cf81 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -118,7 +118,7 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, if label_fields is not None and len(label_fields) > 0: for k in label_fields: data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], - dtype=np.float32) + dtype=np.int32) if reserve_fields is not None and len(reserve_fields) > 0: for k in reserve_fields: np_dtype = type(input_data[k][0]) diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index f537c8b37..415707cfe 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -263,7 +263,7 @@ def _build(self, mode, params): if mode != tf.estimator.ModeKeys.PREDICT: for k in self._label_fields: - out_types[k] = tf.float32 + out_types[k] = tf.int32 out_shapes[k] = tf.TensorShape([None]) if self._reserve_fields is not None: diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index 60a9262ce..5ae5199cb 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -37,7 +37,7 @@ def __init__(self, with open(self._model_config.model_conf, 'r') as fin: self._model_conf = json.load(fin) indim = self._features.get_shape()[1] - logging.info('indim = %d' % indim) + logging.info('ppnetv3m indim = %d' % indim) self._keras_model = CustomizedModel(self._model_conf, indim) def build_predict_graph(self): @@ -48,10 +48,10 @@ def build_predict_graph(self): if var not in trainable_variables: ops.add_to_collection(ops.GraphKeys.TRAINABLE_VARIABLES, var) - # update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS) - # for var in self._keras_model.updates: - # if var not in update_ops: - # ops.add_to_collection(ops.GraphKeys.UPDATE_OPS, var) + update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS) + for var in self._keras_model.updates: + if var not in update_ops: + ops.add_to_collection(ops.GraphKeys.UPDATE_OPS, var) for lbl_id in range(len(self._model_conf['label'])): lbl_info = self._model_conf['label'][lbl_id] @@ -60,12 +60,27 @@ def build_predict_graph(self): self._prediction_dict[lbl_name] = output # return self._prediction_dict + def build_metric_graph(self, eval_config): + metric_dict = {} + from easy_rec.python.core.easyrec_metrics import metrics_tf + for lbl_id in range(len(self._model_conf['label'])): + lbl_info = self._model_conf['label'][lbl_id] + lbl_name = lbl_info.get('input_name') + output = self._prediction_dict.get(lbl_name) + metric_dict['auc_' + lbl_name] = metrics_tf.auc(self._labels[lbl_name], + output, num_thresholds=1000) + return metric_dict + + def build_loss_graph(self): for lbl_id in range(len(self._model_conf['label'])): lbl_info = self._model_conf['label'][lbl_id] lbl_name = lbl_info.get('input_name') output = self._prediction_dict.get(lbl_name) - loss_obj = tf.keras.losses.BinaryCrossentropy(reduction='sum')( + tf.summary.scalar('label/%s' % lbl_name, + tf.reduce_mean(tf.to_float(self._labels[lbl_name]))) + tf.summary.scalar('predict/%s' % lbl_name, tf.reduce_mean(output)) + loss_obj = tf.keras.losses.BinaryCrossentropy(reduction='sum_over_batch_size')( self._labels[lbl_name], output) self._loss_dict[lbl_name] = loss_obj return self._loss_dict diff --git a/easy_rec/python/utils/config_util.py b/easy_rec/python/utils/config_util.py index b63a02f71..4e7005bca 100644 --- a/easy_rec/python/utils/config_util.py +++ b/easy_rec/python/utils/config_util.py @@ -509,6 +509,11 @@ def set_eval_input_path(pipeline_config, eval_input_path): pipeline_config.hive_eval_input.table_name = eval_input_path logging.info('update hive_eval_input.table_name to %s' % pipeline_config.hive_eval_input.table_name) + elif pipeline_config.WhichOneof('eval_path') == 'parquet_eval_input': + if isinstance(eval_input_path, list): + pipeline_config.parquet_eval_input = ','.join(eval_input_path) + else: + pipeline_config.parquet_eval_input = eval_input_path elif pipeline_config.WhichOneof('eval_path') == 'kafka_eval_input': if isinstance(eval_input_path, list): pipeline_config.kafka_eval_input = ','.join(eval_input_path) From e3613f399569565482407c90f05de4473e259766 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Thu, 2 Nov 2023 12:29:20 +0800 Subject: [PATCH 24/67] fix eval bug and dense grad bug --- .../compat/feature_column/feature_column.py | 27 +++++++++---------- easy_rec/python/compat/optimizers.py | 3 ++- easy_rec/python/compat/saver.py | 4 ++- easy_rec/python/input/input.py | 5 +++- easy_rec/python/input/load_parquet.py | 1 - easy_rec/python/input/parquet_input.py | 7 ++--- easy_rec/python/model/ppnet_v3m.py | 6 +++++ 7 files changed, 29 insertions(+), 24 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 37207ca77..83b542895 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -429,20 +429,22 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring segment_ids = array_ops.searchsorted( cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') else: - all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) - segment_ids = all_ids.value_rowids() - flat_vals = all_ids.flat_vals() - np = hvd.size() - # embed_dim = lookup_embeddings[0]._dimension + all_ids = array_ops.concat([x[0] for x in lookup_indices], axis=0) + segment_lens = array_ops.concat([x[1] for x in lookup_lens], axis=0) + all_uniq_ids, uniq_idx = array_ops.unique(all_ids) + cumsum_lens = math_ops.cumsum(segment_lens) + segment_ids = array_ops.searchsorted( + cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') - if np > 1: - # all_uniq_ids, uniq_idx = array_ops.unique(flat_vals) + num_parts = hvd.size() + # embed_dim = lookup_embeddings[0]._dimension + if num_parts > 1: # dynamic partition # from sparse_operation_kit.experiment import raw_ops - p_assignments = math_ops.cast(all_uniq_ids % np, dtypes.int32) + p_assignments = math_ops.cast(all_uniq_ids % num_parts, dtypes.int32) gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, - p_assignments, np) + p_assignments, num_parts) # all2all split_sizes = array_ops.concat([array_ops.shape(x) for x in gather_ids], axis=0) @@ -458,12 +460,6 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring embeddings = math_ops.sparse_segment_sum( recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') else: - # all_uniq_ids, uniq_idx = array_ops.unique(flat_vals) - # all_uniq_ids = logging_ops.Print(all_uniq_ids, [math_ops.reduce_min(all_uniq_ids), - # math_ops.reduce_max(all_uniq_ids), array_ops.shape(all_uniq_ids), - # array_ops.shape(all_ids.flat_values), array_ops.shape(segment_ids), - # math_ops.reduce_min(segment_ids), math_ops.reduce_max(segment_ids)], - # message='debug_all_uniq_ids') if isinstance(lookup_embeddings[0], sok.DynamicVariable): recv_embeddings = lookup_embeddings[0].sparse_read( all_uniq_ids, lookup_only=(not is_training)) @@ -490,6 +486,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring cols_to_output_tensors[col] = output if feature_name_to_output_tensors is not None: feature_name_to_output_tensors[col.raw_name] = output + return array_ops.reshape( array_ops.transpose(output_tensor, perm=[1, 0, 2]), [-1, len(lookup_output_ids) * output_tensor.get_shape()[-1]]) diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index 1f4f36be9..84cf91f74 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -275,8 +275,9 @@ def optimize_loss(loss, if not estimator_utils.has_sok(): reduced_grads = [] for g, v in gradients: + # the gradients for embeddings from different workers are also summed together reduced_grads.append((hvd.allreduce( - g, op=hvd.Average, + g, op=hvd.Sum, compression=hvd.compression.NoneCompressor), v)) gradients = reduced_grads else: diff --git a/easy_rec/python/compat/saver.py b/easy_rec/python/compat/saver.py index 3eea40258..337f8700b 100644 --- a/easy_rec/python/compat/saver.py +++ b/easy_rec/python/compat/saver.py @@ -10,6 +10,9 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import script_ops +from tensorflow.python.ops import logging_ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops from tensorflow.python.platform import gfile from tensorflow.python.training import saver @@ -135,7 +138,6 @@ def _load_key_vals(filename, var_name): ckpt_path=file_name) with ops.control_dependencies([sok_var._initializer_op]): return dynamic_variable_ops.dummy_var_assign(sok_var.handle, keys, vals) - # return dynamic_variable_ops.dummy_var_scatter_update(sok_var.handle, keys, vals) def build(self): if self._is_built: diff --git a/easy_rec/python/input/input.py b/easy_rec/python/input/input.py index d1f1ce717..0920cd682 100644 --- a/easy_rec/python/input/input.py +++ b/easy_rec/python/input/input.py @@ -52,7 +52,10 @@ def __init__(self, # tf.estimator.ModeKeys.*, only available before # calling self._build self._mode = None - self._has_ev = 'ev_params' in kwargs + if pipeline_config.model_config.HasField('ev_params'): + self._has_ev = True + else: + self._has_ev = False if self._data_config.auto_expand_input_fields: input_fields = [x for x in self._data_config.input_fields] diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index 28350cf81..081c7565d 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -65,7 +65,6 @@ def _get_one_file(file_que, proc_stop_que): pass return None - def _pack_sparse_feas(data_dict, effective_fields): fea_val_arr = [] fea_len_arr = [] diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 415707cfe..1a2e2d9df 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -185,15 +185,13 @@ def _to_fea_dict(self, input_dict): # tmp = input_dict[input_0][1] # fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, input_dict[input_0][0]) if self._has_ev: - tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict[ - 'sparse_fea'][0] + tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict['sparse_fea'][0] # tmp_vals = logging_ops.Print(tmp_vals, [ # array_ops.shape(tmp_vals), math_ops.reduce_min(tmp_vals), # math_ops.reduce_max(tmp_vals), # array_ops.shape(tmp_lens), # math_ops.reduce_min(tmp_lens), # math_ops.reduce_max(tmp_lens)], message='debug_sparse_fea') - print('tmp_vals=%s tmp_lens=%s' % (tmp_vals, tmp_lens)) # all_uniq_ids, uniq_idx = array_ops.unique(tmp_vals) # uniq_idx = math_ops.cast(uniq_idx, tf.int32) @@ -203,8 +201,7 @@ def _to_fea_dict(self, input_dict): # values=input_dict['sparse_fea'][1], # row_lengths=input_dict['sparse_fea'][0]) else: - tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict[ - 'sparse_fea'][0] + tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict['sparse_fea'][0] fea_dict['sparse_fea'] = (tmp_vals % self._feature_configs[0].num_buckets, tmp_lens) # fea_dict['sparse_fea'] = tf.RaggedTensor.from_row_lengths( diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index 5ae5199cb..fe60399fa 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -67,6 +67,10 @@ def build_metric_graph(self, eval_config): lbl_info = self._model_conf['label'][lbl_id] lbl_name = lbl_info.get('input_name') output = self._prediction_dict.get(lbl_name) + # output = tf.Print(output, [tf.reduce_min(output), tf.reduce_max(output), + # tf.reduce_mean(output), tf.reduce_min(self._labels[lbl_name]), + # tf.reduce_max(self._labels[lbl_name]), + # tf.reduce_mean(self._labels[lbl_name])], message='output') metric_dict['auc_' + lbl_name] = metrics_tf.auc(self._labels[lbl_name], output, num_thresholds=1000) return metric_dict @@ -82,6 +86,8 @@ def build_loss_graph(self): tf.summary.scalar('predict/%s' % lbl_name, tf.reduce_mean(output)) loss_obj = tf.keras.losses.BinaryCrossentropy(reduction='sum_over_batch_size')( self._labels[lbl_name], output) + # loss_obj = tf.Print(loss_obj, [tf.reduce_min(loss_obj), tf.reduce_max(loss_obj), + # tf.reduce_mean(loss_obj), tf.shape(loss_obj)], message='loss_obj') self._loss_dict[lbl_name] = loss_obj return self._loss_dict From 3292707e1fa9d4823166442a027a0d7e6ed1c430 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Thu, 2 Nov 2023 12:44:19 +0800 Subject: [PATCH 25/67] add support for dense lookup in distributed mode --- easy_rec/python/compat/feature_column/feature_column.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 83b542895..24204fbbc 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -452,8 +452,11 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring recv_ids, recv_lens = hvd.alltoall(send_ids, split_sizes) # read embedding from dynamic variable - send_embed = lookup_embeddings[0].sparse_read( - recv_ids, lookup_only=(not is_training)) + if isinstance(lookup_embeddings[0], sok.DynamicVariable): + send_embed = lookup_embeddings[0].sparse_read( + recv_ids, lookup_only=(not is_training)) + else: + send_embed = array_ops.gather(lookup_embeddings[0], recv_ids) # all2all recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) From bf0edb0f5505bc3a9fa71b1412bb399405ea467e Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Thu, 2 Nov 2023 17:55:31 +0800 Subject: [PATCH 26/67] refactor code style --- easy_rec/python/compat/adam_async.py | 18 +++-- .../compat/feature_column/feature_column.py | 69 +++++++----------- easy_rec/python/compat/optimizers.py | 28 +++----- easy_rec/python/compat/queues.py | 2 +- easy_rec/python/compat/saver.py | 33 ++++----- easy_rec/python/compat/sok_optimizer.py | 16 ++--- easy_rec/python/inference/csv_predictor.py | 24 +------ .../inference/hive_parquet_predictor.py | 15 ---- easy_rec/python/inference/hive_predictor.py | 18 ----- easy_rec/python/inference/odps_predictor.py | 70 +++++++++++++++++++ .../python/inference/parquet_predictor.py | 23 +----- easy_rec/python/inference/predictor.py | 67 ------------------ easy_rec/python/input/load_parquet.py | 3 +- easy_rec/python/input/parquet_input.py | 59 ++++------------ easy_rec/python/main.py | 7 -- easy_rec/python/model/ppnet_v3m.py | 16 ++--- 16 files changed, 163 insertions(+), 305 deletions(-) create mode 100644 easy_rec/python/inference/odps_predictor.py diff --git a/easy_rec/python/compat/adam_async.py b/easy_rec/python/compat/adam_async.py index 58ef98fe7..41cbd8c21 100644 --- a/easy_rec/python/compat/adam_async.py +++ b/easy_rec/python/compat/adam_async.py @@ -22,25 +22,23 @@ from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.framework.load_library import load_op_library -# from tensorflow.python.ops import kv_variable_ops # from tensorflow.python.ops import gen_hash_training_ops -from tensorflow.python.ops import array_ops +# from tensorflow.python.ops import array_ops +# from tensorflow.python.ops import resource_variable_ops +# from tensorflow.python.ops import state_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import resource_variable_ops -from tensorflow.python.ops import state_ops -from tensorflow.python.ops import variable_scope +# from tensorflow.python.ops import variable_scope from tensorflow.python.training import optimizer -from tensorflow.python.training import slot_creator -from tensorflow.python.training import training_ops -from tensorflow.python.training import training_util -from tensorflow.python.util.tf_export import tf_export + +# from tensorflow.python.training import slot_creator +# from tensorflow.python.training import training_ops +# from tensorflow.python.training import training_util curr_dir, _ = os.path.split(__file__) adam_async_ops = load_op_library(os.path.join(curr_dir, 'libadam_async_op.so')) -@tf_export('train.AdamAsyncOptimizer') class AdamAsyncOptimizer(optimizer.Optimizer): """Optimizer that implements the Adam algorithm. diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 24204fbbc..453f5f3ee 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -146,13 +146,13 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.keras.engine import training from tensorflow.python.layers import base +# from tensorflow.python.ops import logging_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import init_ops -from tensorflow.python.ops import logging_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops @@ -163,8 +163,8 @@ from tensorflow.python.ops import template from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables -from tensorflow.python.ops.ragged import ragged_tensor -from tensorflow.python.ops.ragged import ragged_util +# from tensorflow.python.ops.ragged import ragged_tensor +# from tensorflow.python.ops.ragged import ragged_util from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_utils @@ -172,12 +172,6 @@ from easy_rec.python.compat.feature_column import utils as fc_utils -try: - from tensorflow.python.ops.ragged import ragged_math_ops - from tensorflow.python.ops.ragged import ragged_concat_ops -except: - pass - try: from sparse_operation_kit import experiment as sok except Exception: @@ -311,15 +305,10 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring lookup_combiners = [] lookup_cols = [] lookup_output_ids = [] - - lookup_embeddings_with_wgt = [] - lookup_indices_with_wgt = [] lookup_wgts = [] - lookup_cols_with_wgt = [] - lookup_combiners_with_wgt = [] - lookup_output_ids_with_wgt = [] + shared_weights = {} - for column in sorted(feature_columns, key=lambda x: x.name): + for column in feature_columns: ordered_columns.append(column) with variable_scope.variable_scope( None, default_name=column._var_scope_name): # pylint: disable=protected-access @@ -345,7 +334,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring embedding_weights = sok.DynamicVariable( name='embedding_weights', dimension=column.dimension, - initializer='random', #column.initializer, + initializer='random', # column.initializer, # var_type='hybrid', trainable=column.trainable and trainable, dtype=dtypes.float32, @@ -367,7 +356,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring embedding_weights = sok.DynamicVariable( name='embedding_weights', dimension=column.dimension, - initializer='random', #column.initializer, + initializer='random', # column.initializer, trainable=column.trainable and trainable, dtype=dtypes.float32) else: @@ -384,27 +373,23 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring output_tensors.append(None) lookup_output_ids.append(output_id) lookup_cols.append(column) + lookup_combiners.append(column.combiner) # required by sok if 'DynamicVariable' not in str(type(embedding_weights)): embedding_weights.target_gpu = -1 - # # SparseTensor RaggedTensor - # sparse_tensors = column.categorical_column._get_sparse_tensors( - # builder, weight_collections=weight_collections, trainable=trainable) - # output_id = len(output_tensors) - # output_tensors.append(None) - # if sparse_tensors.weight_tensor is not None: - # lookup_embeddings_with_wgt.append(embedding_weights) - # lookup_indices_with_wgt.append(sparse_tensors.id_tensor) - # lookup_wgts.append(sparse_tensors.weight_tensor) - # lookup_output_ids_with_wgt.append(output_id) - # lookup_combiners_with_wgt.append(column.combiner) - # lookup_cols_with_wgt.append(column) - # else: - # lookup_embeddings.append(embedding_weights) - # lookup_indices.append(sparse_tensors.id_tensor) - # lookup_output_ids.append(output_id) - # lookup_combiners.append(column.combiner) - # lookup_cols.append(column) + # SparseTensor RaggedTensor + # features are not gathered into one, may have + # performance issues + if 'sparse_fea' not in features.keys( + ) and 'ragged_ids' not in features.keys(): + with ops.device('/cpu:0'): + sparse_tensors = column.categorical_column._get_sparse_tensors( + builder, + weight_collections=weight_collections, + trainable=trainable) + lookup_indices.append(sparse_tensors.id_tensor) + if sparse_tensors.weight_tensor is not None: + lookup_wgts.append(sparse_tensors.weight_tensor) if cols_to_vars is not None: cols_to_vars[column] = ops.get_collection( ops.GraphKeys.GLOBAL_VARIABLES, @@ -413,7 +398,6 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # do sok lookup if len(lookup_output_ids) > 0: # first concat all the ids and unique - # all_ids = ragged_concat_ops.concat(lookup_indices, axis=0) if isinstance(features, dict) and 'sparse_fea' in features.keys(): # all_uniq_ids, uniq_idx, segment_lens = features['sparse_fea'] all_ids, segment_lens = features['sparse_fea'] @@ -429,16 +413,13 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring segment_ids = array_ops.searchsorted( cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') else: - all_ids = array_ops.concat([x[0] for x in lookup_indices], axis=0) - segment_lens = array_ops.concat([x[1] for x in lookup_lens], axis=0) + with ops.device('/cpu:0'): + all_ids = array_ops.concat([x.values for x in lookup_indices], axis=0) + segment_ids = array_ops.concat( + [x.indices[:, 0] for x in lookup_indices], axis=0) all_uniq_ids, uniq_idx = array_ops.unique(all_ids) - cumsum_lens = math_ops.cumsum(segment_lens) - segment_ids = array_ops.searchsorted( - cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') num_parts = hvd.size() - # embed_dim = lookup_embeddings[0]._dimension - if num_parts > 1: # dynamic partition # from sparse_operation_kit.experiment import raw_ops diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index 84cf91f74..2cfa526d6 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -24,11 +24,11 @@ # from tensorflow.contrib import framework as contrib_framework from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +# from tensorflow.python.ops import logging_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops -from tensorflow.python.ops import logging_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope as vs @@ -43,7 +43,7 @@ try: from tensorflow.python.framework import indexed_slices -except Exception as ex: +except Exception: indexed_slices = ops try: @@ -273,35 +273,25 @@ def optimize_loss(loss, if estimator_utils.has_hvd() and hvd.size() > 1: if not estimator_utils.has_sok(): + # embedding parameters not partitioned reduced_grads = [] for g, v in gradients: - # the gradients for embeddings from different workers are also summed together reduced_grads.append((hvd.allreduce( - g, op=hvd.Sum, - compression=hvd.compression.NoneCompressor), v)) + g, op=hvd.Sum, compression=hvd.compression.NoneCompressor), v)) gradients = reduced_grads else: + # embedding parameters partitioned: + # the gradients for embeddings from different workers are + # already summed together in the backward pass through + # hvd.alltoall reduced_grads = [] for g, v in gradients: if '/embedding' not in v.name: reduced_grads.append((hvd.allreduce( - g, op=hvd.Average, - compression=hvd.compression.NoneCompressor), v)) + g, op=hvd.Sum, compression=hvd.compression.NoneCompressor), v)) else: reduced_grads.append((g, v)) gradients = reduced_grads - #else: - # tmp_grads = [] - # for g, v in gradients: - # if '/embedding' in v.name: - # import logging - # logging.info('do nothing to sok gradients: %s' % str(v)) - # with ops.control_dependencies([logging_ops.Print(g.indices, [array_ops.shape(g.indices), array_ops.shape(g.values), math_ops.reduce_min(g.indices), math_ops.reduce_max(g.indices), math_ops.reduce_min(g.values), math_ops.reduce_max(g.values)], message='grad_%s' % v.name)]): - # g = indexed_slices.IndexedSlices(array_ops.identity(g.values), array_ops.identity(g.indices)) - # else: - # g = logging_ops.Print(g, [array_ops.shape(g), math_ops.reduce_min(g), math_ops.reduce_max(g), math_ops.reduce_mean(g)], message='grad_%s' % v.name) - # tmp_grads.append((g, v)) - # gradients=tmp_grads # Optionally add gradient noise. if gradient_noise_scale is not None: diff --git a/easy_rec/python/compat/queues.py b/easy_rec/python/compat/queues.py index 3ff549b7e..3c93801ea 100644 --- a/easy_rec/python/compat/queues.py +++ b/easy_rec/python/compat/queues.py @@ -14,7 +14,7 @@ import sys import threading import time -import types +# import types import weakref from multiprocessing import connection from multiprocessing import context diff --git a/easy_rec/python/compat/saver.py b/easy_rec/python/compat/saver.py index 337f8700b..f9fda6e92 100644 --- a/easy_rec/python/compat/saver.py +++ b/easy_rec/python/compat/saver.py @@ -2,17 +2,16 @@ import logging import os -import sys import numpy as np from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +# from tensorflow.python.ops import logging_ops +# from tensorflow.python.ops import math_ops +# from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import script_ops -from tensorflow.python.ops import logging_ops -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops from tensorflow.python.platform import gfile from tensorflow.python.training import saver @@ -20,7 +19,7 @@ import horovod.tensorflow as hvd from sparse_operation_kit.experiment import raw_ops as dynamic_variable_ops from sparse_operation_kit import experiment as sok -except: +except Exception: dynamic_variable_ops = None sok = None @@ -31,6 +30,7 @@ load_embed_lib = load_op_library(load_embed_lib_path) except Exception as ex: logging.warning('load libload_embed.so failed: %s' % str(ex)) + load_embed_lib = None class SaverV2(saver.Saver): @@ -121,21 +121,21 @@ def _load_key_vals(filename, var_name): np.random.shuffle(shuffle_ids) all_keys = all_keys.take(shuffle_ids, axis=0) all_vals = all_vals.take(shuffle_ids, axis=0) - print( - len(all_keys), all_vals.shape, np.min(all_keys), np.max(all_keys), - np.min(all_vals), np.max(all_vals)) return all_keys, all_vals file_name = ops.get_default_graph().get_tensor_by_name( self.saver_def.filename_tensor_name) - # keys, vals = script_ops.py_func(_load_key_vals, [file_name, sok_var.name], - # (dtypes.int64, dtypes.float32)) - keys, vals = load_embed_lib.load_kv_embed( - task_index=hvd.rank(), - task_num=hvd.size(), - embed_dim=sok_var._dimension, - var_name='embed-' + sok_var.name.replace('/', '__'), - ckpt_path=file_name) + if load_embed_lib is not None: + keys, vals = load_embed_lib.load_kv_embed( + task_index=hvd.rank(), + task_num=hvd.size(), + embed_dim=sok_var._dimension, + var_name='embed-' + sok_var.name.replace('/', '__'), + ckpt_path=file_name) + else: + logging.warning('libload_embed.so not loaded, will use python script_ops') + keys, vals = script_ops.py_func(_load_key_vals, [file_name, sok_var.name], + (dtypes.int64, dtypes.float32)) with ops.control_dependencies([sok_var._initializer_op]): return dynamic_variable_ops.dummy_var_assign(sok_var.handle, keys, vals) @@ -153,6 +153,7 @@ def build(self): restore_ops.append(old_restore_op) restore_op_n = control_flow_ops.group(restore_ops) self.saver_def.restore_op_name = restore_op_n.name + # TODO # if self.saver_def.save_tensor_name: # save_sok_ops = [] # for sok_var in self._sok_vars: diff --git a/easy_rec/python/compat/sok_optimizer.py b/easy_rec/python/compat/sok_optimizer.py index de03bb611..50a6d0a83 100644 --- a/easy_rec/python/compat/sok_optimizer.py +++ b/easy_rec/python/compat/sok_optimizer.py @@ -17,11 +17,12 @@ import tensorflow as tf from sparse_operation_kit.experiment.dynamic_variable import DynamicVariable from tensorflow.python.eager import context -from tensorflow.python.framework import dtypes +# from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +# from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import array_ops -from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import state_ops def OptimizerWrapper(optimizer): @@ -62,12 +63,11 @@ def OptimizerWrapper(optimizer): embedding = tf.nn.embedding_lookup(v, indices) print("embedding:", embedding) """ - # a specific code path for dl framework tf2.11.0 try: if isinstance(optimizer, tf.keras.optimizers.legacy.Optimizer): return OptimizerWrapperV2(optimizer) - except: + except Exception: pass if isinstance(optimizer, tf.keras.optimizers.Optimizer): @@ -140,7 +140,7 @@ def _create_slots_dynamic(self, var): ) else: tmp_config = var.config_dict - tmp_initializer = var.initializer_str + # tmp_initializer = var.initializer_str slot = DynamicVariable( dimension=var.dimension, initializer=self._initial_vals[slot_name], @@ -231,7 +231,7 @@ def apply_sparse_gradients(self, grads_and_vars, global_step=None, name=None): ) else: tmp_config = v.config_dict - tmp_initializer = v.initializer_str + # tmp_initializer = v.initializer_str slot = DynamicVariable( dimension=v.dimension, initializer=self._initial_vals[slot_name], @@ -319,7 +319,7 @@ def _create_slots_dynamic(self, var): ) else: tmp_config = var.config_dict - tmp_initializer = var.initializer_str + # tmp_initializer = var.initializer_str slot = DynamicVariable( dimension=var.dimension, initializer=self._initial_vals[slot_name], @@ -372,7 +372,7 @@ def apply_gradients(self, grads_and_vars, global_step=None, name=None): ) else: tmp_config = v.config_dict - tmp_initializer = v.initializer_str + # tmp_initializer = v.initializer_str slot = DynamicVariable( dimension=v.dimension, initializer=self._initial_vals[slot_name], diff --git a/easy_rec/python/inference/csv_predictor.py b/easy_rec/python/inference/csv_predictor.py index f3b3ebf2d..018f9fb4f 100644 --- a/easy_rec/python/inference/csv_predictor.py +++ b/easy_rec/python/inference/csv_predictor.py @@ -4,34 +4,16 @@ from __future__ import division from __future__ import print_function -import abc -import json import logging -import math import os -import time -import numpy as np -import pandas as pd -import six import tensorflow as tf -from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.python.platform import gfile -from tensorflow.python.saved_model import constants -from tensorflow.python.saved_model import signature_constants +from easy_rec.python.inference.predictor import SINGLE_PLACEHOLDER_FEATURE_KEY from easy_rec.python.inference.predictor import Predictor from easy_rec.python.protos.dataset_pb2 import DatasetConfig -from easy_rec.python.utils import numpy_utils -from easy_rec.python.utils import tf_utils from easy_rec.python.utils.check_utils import check_split -from easy_rec.python.utils.config_util import get_configs_from_pipeline_file -from easy_rec.python.utils.config_util import get_input_name_from_fg_json -from easy_rec.python.utils.config_util import search_fg_json -from easy_rec.python.utils.hive_utils import HiveUtils -from easy_rec.python.utils.input_utils import get_type_defaults -from easy_rec.python.utils.load_class import get_register_class_meta -from easy_rec.python.utils.tf_utils import get_tf_type if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -121,7 +103,7 @@ def _parse_line(self, line): def _get_num_cols(self, file_paths): # try to figure out number of fields from one file num_cols = -1 - with tf.gfile.GFile(file_paths[0], 'r') as fin: + with gfile.GFile(file_paths[0], 'r') as fin: num_lines = 0 for line_str in fin: line_tok = line_str.strip().split(self._input_sep) @@ -146,7 +128,7 @@ def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, assert len(file_paths) > 0, 'match no files with %s' % input_path if self._with_header: - with tf.gfile.GFile(file_paths[0], 'r') as fin: + with gfile.GFile(file_paths[0], 'r') as fin: for line_str in fin: line_str = line_str.strip() self._field_names = line_str.split(self._input_sep) diff --git a/easy_rec/python/inference/hive_parquet_predictor.py b/easy_rec/python/inference/hive_parquet_predictor.py index 572eaff8c..bd5178fe7 100644 --- a/easy_rec/python/inference/hive_parquet_predictor.py +++ b/easy_rec/python/inference/hive_parquet_predictor.py @@ -4,33 +4,18 @@ from __future__ import division from __future__ import print_function -import abc -import json -import logging -import math import os import time import numpy as np import pandas as pd -import six import tensorflow as tf -from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.python.platform import gfile -from tensorflow.python.saved_model import constants -from tensorflow.python.saved_model import signature_constants from easy_rec.python.inference.predictor import Predictor from easy_rec.python.protos.dataset_pb2 import DatasetConfig -from easy_rec.python.utils import numpy_utils from easy_rec.python.utils import tf_utils -from easy_rec.python.utils.check_utils import check_split -from easy_rec.python.utils.config_util import get_configs_from_pipeline_file -from easy_rec.python.utils.config_util import get_input_name_from_fg_json -from easy_rec.python.utils.config_util import search_fg_json from easy_rec.python.utils.hive_utils import HiveUtils -from easy_rec.python.utils.input_utils import get_type_defaults -from easy_rec.python.utils.load_class import get_register_class_meta from easy_rec.python.utils.tf_utils import get_tf_type if tf.__version__ >= '2.0': diff --git a/easy_rec/python/inference/hive_predictor.py b/easy_rec/python/inference/hive_predictor.py index 3289db3dd..f2923f8a3 100644 --- a/easy_rec/python/inference/hive_predictor.py +++ b/easy_rec/python/inference/hive_predictor.py @@ -4,34 +4,16 @@ from __future__ import division from __future__ import print_function -import abc -import json -import logging -import math import os import time -import numpy as np -import pandas as pd -import six import tensorflow as tf -from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.python.platform import gfile -from tensorflow.python.saved_model import constants -from tensorflow.python.saved_model import signature_constants from easy_rec.python.inference.predictor import Predictor from easy_rec.python.protos.dataset_pb2 import DatasetConfig -from easy_rec.python.utils import numpy_utils from easy_rec.python.utils import tf_utils -from easy_rec.python.utils.check_utils import check_split -from easy_rec.python.utils.config_util import get_configs_from_pipeline_file -from easy_rec.python.utils.config_util import get_input_name_from_fg_json -from easy_rec.python.utils.config_util import search_fg_json from easy_rec.python.utils.hive_utils import HiveUtils -from easy_rec.python.utils.input_utils import get_type_defaults -from easy_rec.python.utils.load_class import get_register_class_meta -from easy_rec.python.utils.tf_utils import get_tf_type if tf.__version__ >= '2.0': tf = tf.compat.v1 diff --git a/easy_rec/python/inference/odps_predictor.py b/easy_rec/python/inference/odps_predictor.py new file mode 100644 index 000000000..36c3f9da3 --- /dev/null +++ b/easy_rec/python/inference/odps_predictor.py @@ -0,0 +1,70 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from easy_rec.python.inference import Predictor + + +class ODPSPredictor(Predictor): + + def __init__(self, + model_path, + fg_json_path=None, + profiling_file=None, + all_cols='', + all_col_types=''): + super(ODPSPredictor, self).__init__(model_path, profiling_file, + fg_json_path) + self._all_cols = [x.strip() for x in all_cols.split(',') if x != ''] + self._all_col_types = [ + x.strip() for x in all_col_types.split(',') if x != '' + ] + self._record_defaults = [ + self._get_defaults(col_name, col_type) + for col_name, col_type in zip(self._all_cols, self._all_col_types) + ] + + def _get_reserved_cols(self, reserved_cols): + reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] + return reserved_cols + + def _parse_line(self, *fields): + fields = list(fields) + field_dict = {self._all_cols[i]: fields[i] for i in range(len(fields))} + return field_dict + + def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, + slice_id): + input_list = input_path.split(',') + dataset = tf.data.TableRecordDataset( + input_list, + record_defaults=self._record_defaults, + slice_id=slice_id, + slice_count=slice_num, + selected_cols=','.join(self._all_cols)) + dataset = dataset.batch(batch_size) + dataset = dataset.prefetch(buffer_size=64) + return dataset + + def _get_writer(self, output_path, slice_id): + import common_io + table_writer = common_io.table.TableWriter(output_path, slice_id=slice_id) + return table_writer + + def _write_lines(self, table_writer, outputs): + assert len(outputs) > 0 + indices = list(range(0, len(outputs[0]))) + table_writer.write(outputs, indices, allow_type_cast=False) + + @property + def out_of_range_exception(self): + return (tf.python_io.OutOfRangeException, tf.errors.OutOfRangeError) + + def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): + reserve_vals = [all_vals[k] for k in reserved_cols] + \ + [outputs[x] for x in output_cols] + return reserve_vals diff --git a/easy_rec/python/inference/parquet_predictor.py b/easy_rec/python/inference/parquet_predictor.py index 92dbfd83a..cde1e81d7 100644 --- a/easy_rec/python/inference/parquet_predictor.py +++ b/easy_rec/python/inference/parquet_predictor.py @@ -4,39 +4,18 @@ from __future__ import division from __future__ import print_function -import abc -import json import logging -import math import os -import time -import numpy as np -import pandas as pd -import six import tensorflow as tf -from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.python.platform import gfile -from tensorflow.python.saved_model import constants -from tensorflow.python.saved_model import signature_constants +from easy_rec.python.inference.predictor import SINGLE_PLACEHOLDER_FEATURE_KEY from easy_rec.python.inference.predictor import Predictor from easy_rec.python.input.parquet_input import ParquetInput from easy_rec.python.protos.dataset_pb2 import DatasetConfig from easy_rec.python.utils import config_util from easy_rec.python.utils import input_utils -from easy_rec.python.utils import numpy_utils -from easy_rec.python.utils import tf_utils -from easy_rec.python.utils.check_utils import check_split -from easy_rec.python.utils.config_util import get_configs_from_pipeline_file -from easy_rec.python.utils.config_util import get_input_name_from_fg_json -from easy_rec.python.utils.config_util import search_fg_json -from easy_rec.python.utils.hive_utils import HiveUtils -from easy_rec.python.utils.load_class import get_register_class_meta -from easy_rec.python.utils.tf_utils import get_tf_type - -if tf.__version__ >= '2.0': - tf = tf.compat.v1 try: from tensorflow.python.framework.load_library import load_op_library diff --git a/easy_rec/python/inference/predictor.py b/easy_rec/python/inference/predictor.py index f1f027de3..5700955ba 100644 --- a/easy_rec/python/inference/predictor.py +++ b/easy_rec/python/inference/predictor.py @@ -12,7 +12,6 @@ import time import numpy as np -import pandas as pd import six import tensorflow as tf from tensorflow.core.protobuf import meta_graph_pb2 @@ -20,17 +19,12 @@ from tensorflow.python.saved_model import constants from tensorflow.python.saved_model import signature_constants -from easy_rec.python.protos.dataset_pb2 import DatasetConfig from easy_rec.python.utils import numpy_utils -from easy_rec.python.utils import tf_utils -from easy_rec.python.utils.check_utils import check_split from easy_rec.python.utils.config_util import get_configs_from_pipeline_file from easy_rec.python.utils.config_util import get_input_name_from_fg_json from easy_rec.python.utils.config_util import search_fg_json -from easy_rec.python.utils.hive_utils import HiveUtils from easy_rec.python.utils.input_utils import get_type_defaults from easy_rec.python.utils.load_class import get_register_class_meta -from easy_rec.python.utils.tf_utils import get_tf_type if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -611,64 +605,3 @@ def batch(self, data_list): for key in batch_input: batch_input[key] = np.array(batch_input[key]) return batch_input - - -class ODPSPredictor(Predictor): - - def __init__(self, - model_path, - fg_json_path=None, - profiling_file=None, - all_cols='', - all_col_types=''): - super(ODPSPredictor, self).__init__(model_path, profiling_file, - fg_json_path) - self._all_cols = [x.strip() for x in all_cols.split(',') if x != ''] - self._all_col_types = [ - x.strip() for x in all_col_types.split(',') if x != '' - ] - self._record_defaults = [ - self._get_defaults(col_name, col_type) - for col_name, col_type in zip(self._all_cols, self._all_col_types) - ] - - def _get_reserved_cols(self, reserved_cols): - reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] - return reserved_cols - - def _parse_line(self, *fields): - fields = list(fields) - field_dict = {self._all_cols[i]: fields[i] for i in range(len(fields))} - return field_dict - - def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, - slice_id): - input_list = input_path.split(',') - dataset = tf.data.TableRecordDataset( - input_list, - record_defaults=self._record_defaults, - slice_id=slice_id, - slice_count=slice_num, - selected_cols=','.join(self._all_cols)) - dataset = dataset.batch(batch_size) - dataset = dataset.prefetch(buffer_size=64) - return dataset - - def _get_writer(self, output_path, slice_id): - import common_io - table_writer = common_io.table.TableWriter(output_path, slice_id=slice_id) - return table_writer - - def _write_lines(self, table_writer, outputs): - assert len(outputs) > 0 - indices = list(range(0, len(outputs[0]))) - table_writer.write(outputs, indices, allow_type_cast=False) - - @property - def out_of_range_exception(self): - return (tf.python_io.OutOfRangeException, tf.errors.OutOfRangeError) - - def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): - reserve_vals = [all_vals[k] for k in reserved_cols] + \ - [outputs[x] for x in output_cols] - return reserve_vals diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index 081c7565d..fdefb21c5 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -1,7 +1,6 @@ import logging import multiprocessing import queue -import sys import numpy as np import pandas as pd @@ -65,6 +64,7 @@ def _get_one_file(file_que, proc_stop_que): pass return None + def _pack_sparse_feas(data_dict, effective_fields): fea_val_arr = [] fea_len_arr = [] @@ -142,7 +142,6 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, break sid += batch_size if res_num > 0 and is_good: - accum_res_num = 0 data_dict = {} part_data_dict_n = {} diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 1a2e2d9df..b795dd7e6 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -5,23 +5,19 @@ import queue import time -import numpy as np -import pandas as pd +# import numpy as np +# import pandas as pd import tensorflow as tf -from tensorflow.python.framework import ops +# from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops -from tensorflow.python.ops import logging_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops.ragged import ragged_util +# from tensorflow.python.ops import logging_ops +# from tensorflow.python.ops import math_ops from tensorflow.python.platform import gfile from easy_rec.python.compat import queues from easy_rec.python.input import load_parquet from easy_rec.python.input.input import Input -# if tf.__version__ >= '2.0': -# tf = tf.compat.v1 - class ParquetInput(Input): @@ -48,7 +44,6 @@ def __init__(self, mp_ctxt = multiprocessing.get_context('spawn') self._data_que = queues.Queue( name='data_que', ctx=mp_ctxt, maxsize=self._data_config.prefetch_size) - # self._data_que._name = 'data_que' file_num = len(self._input_files) logging.info('[task_index=%d] total_file_num=%d task_num=%d' % @@ -152,11 +147,11 @@ def _any_alive(): return True return False - # to ensure the sender part of the stupid python Queue could exit properly + # to ensure the sender part of the python Queue could exit while _any_alive(): try: self._data_que.get(timeout=1) - except: + except Exception: pass time.sleep(1) self._data_que.close() @@ -171,42 +166,17 @@ def _any_alive(): def _to_fea_dict(self, input_dict): fea_dict = {} - # for fea_name in self._effective_fields: - # tmp = input_dict[fea_name][1] % 1000 # 000000 - # fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths( - # tmp, input_dict[fea_name][0]) - # for fc in self._feature_configs: - # if fc.feature_type == fc.IdFeature or fc.feature_type == fc.TagFeature: - # input_0 = fc.input_names[0] - # fea_name = fc.feature_name if fc.HasField('feature_name') else input_0 - # if not self._has_ev: - # tmp = input_dict[input_0][1] % fc.num_buckets - # else: - # tmp = input_dict[input_0][1] - # fea_dict[fea_name] = tf.RaggedTensor.from_row_lengths(tmp, input_dict[input_0][0]) + if self._has_ev: - tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict['sparse_fea'][0] - # tmp_vals = logging_ops.Print(tmp_vals, [ - # array_ops.shape(tmp_vals), math_ops.reduce_min(tmp_vals), - # math_ops.reduce_max(tmp_vals), - # array_ops.shape(tmp_lens), - # math_ops.reduce_min(tmp_lens), - # math_ops.reduce_max(tmp_lens)], message='debug_sparse_fea') - # all_uniq_ids, uniq_idx = array_ops.unique(tmp_vals) - # uniq_idx = math_ops.cast(uniq_idx, tf.int32) + tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict[ + 'sparse_fea'][0] fea_dict['sparse_fea'] = (tmp_vals, tmp_lens) - # tf.RaggedTensor.from_row_lengths( - # values=tmp_vals, row_lengths=tmp_lens) - # values=input_dict['sparse_fea'][1], - # row_lengths=input_dict['sparse_fea'][0]) else: - tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict['sparse_fea'][0] + tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict[ + 'sparse_fea'][0] fea_dict['sparse_fea'] = (tmp_vals % self._feature_configs[0].num_buckets, tmp_lens) - # fea_dict['sparse_fea'] = tf.RaggedTensor.from_row_lengths( - # values=input_dict['sparse_fea'][1] % self._feature_configs[0].num_buckets, - # row_lengths=input_dict['sparse_fea'][0]) output_dict = {'feature': fea_dict} @@ -336,9 +306,8 @@ def _input_fn(mode=None, params=None, config=None): dataset = self._build(mode, params) return dataset elif mode is None: # serving_input_receiver_fn for export SavedModel - ragged_ids = tf.compat.v1.placeholder( - tf.int64, [None], name='ragged_ids') - ragged_lens = tf.compat.v1.placeholder( + ragged_ids = array_ops.placeholder(tf.int64, [None], name='ragged_ids') + ragged_lens = array_ops.placeholder( tf.int32, [None], name='ragged_lens') inputs = {'ragged_ids': ragged_ids, 'ragged_lens': ragged_lens} if self._has_ev: diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index ef0eec625..a47132813 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -342,8 +342,6 @@ def _train_and_evaluate_impl(pipeline_config, input_fn_kwargs = {'pipeline_config': pipeline_config} if data_config.input_type == data_config.InputType.OdpsRTPInputV2: input_fn_kwargs['fg_json_path'] = pipeline_config.fg_json_path - if pipeline_config.model_config.HasField('ev_params'): - input_fn_kwargs['ev_params'] = pipeline_config.model_config.ev_params # create train input train_input_fn = _get_input_fn( @@ -790,8 +788,6 @@ def export(export_dir, input_fn_kwargs = {'pipeline_config': pipeline_config} if data_config.input_type == data_config.InputType.OdpsRTPInputV2: input_fn_kwargs['fg_json_path'] = pipeline_config.fg_json_path - if pipeline_config.model_config.HasField('ev_params'): - input_fn_kwargs['ev_params'] = pipeline_config.model_config.ev_params serving_input_fn = _get_input_fn(data_config, feature_configs, None, export_config, **input_fn_kwargs) ckpt_path = _get_ckpt_path(pipeline_config, checkpoint_path) @@ -868,9 +864,6 @@ def export_checkpoint(pipeline_config=None, if data_config.input_type == data_config.InputType.OdpsRTPInputV2: input_fn_kwargs['fg_json_path'] = pipeline_config.fg_json_path - if pipeline_config.model_config.HasField('ev_params'): - input_fn_kwargs['ev_params'] = pipeline_config.model_config.ev_params - # create estimator params = {'log_device_placement': verbose} if asset_files: diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index fe60399fa..8687bba53 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -6,10 +6,7 @@ import tensorflow as tf from tensorflow.python.framework import ops -from easy_rec.python.layers import dnn -from easy_rec.python.layers import mmoe from easy_rec.python.model.keep_model.model_ps_mmoe import CustomizedModel -from easy_rec.python.model.multi_task_model import MultiTaskModel from easy_rec.python.model.rank_model import RankModel from easy_rec.python.protos.ppnet_pb2 import PPNet as PPNetConfig @@ -71,21 +68,20 @@ def build_metric_graph(self, eval_config): # tf.reduce_mean(output), tf.reduce_min(self._labels[lbl_name]), # tf.reduce_max(self._labels[lbl_name]), # tf.reduce_mean(self._labels[lbl_name])], message='output') - metric_dict['auc_' + lbl_name] = metrics_tf.auc(self._labels[lbl_name], - output, num_thresholds=1000) + metric_dict['auc_' + lbl_name] = metrics_tf.auc( + self._labels[lbl_name], output, num_thresholds=1000) return metric_dict - def build_loss_graph(self): for lbl_id in range(len(self._model_conf['label'])): lbl_info = self._model_conf['label'][lbl_id] lbl_name = lbl_info.get('input_name') output = self._prediction_dict.get(lbl_name) - tf.summary.scalar('label/%s' % lbl_name, - tf.reduce_mean(tf.to_float(self._labels[lbl_name]))) + tf.summary.scalar('label/%s' % lbl_name, + tf.reduce_mean(tf.to_float(self._labels[lbl_name]))) tf.summary.scalar('predict/%s' % lbl_name, tf.reduce_mean(output)) - loss_obj = tf.keras.losses.BinaryCrossentropy(reduction='sum_over_batch_size')( - self._labels[lbl_name], output) + loss_obj = tf.keras.losses.BinaryCrossentropy( + reduction='sum_over_batch_size')(self._labels[lbl_name], output) # loss_obj = tf.Print(loss_obj, [tf.reduce_min(loss_obj), tf.reduce_max(loss_obj), # tf.reduce_mean(loss_obj), tf.shape(loss_obj)], message='loss_obj') self._loss_dict[lbl_name] = loss_obj From 76937dc5473694b1ea65562f44ed52ea86a540ee Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Thu, 2 Nov 2023 19:43:03 +0800 Subject: [PATCH 27/67] add support for finetune --- easy_rec/python/model/easy_rec_model.py | 30 +++++++++++++++++++++++++ easy_rec/python/utils/config_util.py | 5 +++++ 2 files changed, 35 insertions(+) diff --git a/easy_rec/python/model/easy_rec_model.py b/easy_rec/python/model/easy_rec_model.py index 434d31dc3..212370868 100644 --- a/easy_rec/python/model/easy_rec_model.py +++ b/easy_rec/python/model/easy_rec_model.py @@ -2,11 +2,13 @@ # Copyright (c) Alibaba, Inc. and its affiliates. import logging +import os import re from abc import abstractmethod import six import tensorflow as tf +from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import variables @@ -18,6 +20,23 @@ from easy_rec.python.utils import restore_filter from easy_rec.python.utils.load_class import get_register_class_meta +try: + import horovod.tensorflow as hvd + from sparse_operation_kit.experiment import raw_ops as dynamic_variable_ops + from sparse_operation_kit import experiment as sok +except Exception: + dynamic_variable_ops = None + sok = None + +try: + from tensorflow.python.framework.load_library import load_op_library + import easy_rec + load_embed_lib_path = os.path.join(easy_rec.ops_dir, 'libload_embed.so') + load_embed_lib = load_op_library(load_embed_lib_path) +except Exception as ex: + logging.warning('load libload_embed.so failed: %s' % str(ex)) + load_embed_lib = None + if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -296,6 +315,17 @@ def restore(self, saveable_objects.append(s) init_op = saveable_objects[0].restore([ckpt_path], None) part_var._initializer_op = init_op + elif isinstance(variable, sok.DynamicVariable): + print('restore dynamic_variable %s' % variable_name) + keys, vals = load_embed_lib.load_kv_embed( + task_index=hvd.rank(), + task_num=hvd.size(), + embed_dim=variable._dimension, + var_name='embed-' + variable.name.replace('/', '__'), + ckpt_path=ckpt_path) + with ops.control_dependencies([variable._initializer_op]): + variable._initializer_op = dynamic_variable_ops.dummy_var_assign( + variable.handle, keys, vals) else: fail_restore_vars.append(variable_name) for variable_name in fail_restore_vars: diff --git a/easy_rec/python/utils/config_util.py b/easy_rec/python/utils/config_util.py index 4e7005bca..526b9a0fb 100644 --- a/easy_rec/python/utils/config_util.py +++ b/easy_rec/python/utils/config_util.py @@ -485,6 +485,11 @@ def set_train_input_path(pipeline_config, train_input_path): pipeline_config.kafka_train_input = ','.join(train_input_path) else: pipeline_config.kafka_train_input = train_input_path + elif pipeline_config.WhichOneof('train_path') == 'parquet_train_input': + if isinstance(train_input_path, list): + pipeline_config.parquet_train_input = ','.join(train_input_path) + else: + pipeline_config.parquet_train_input = train_input_path else: if isinstance(train_input_path, list): pipeline_config.train_input_path = ','.join(train_input_path) From e3b91a5590bf1550f20268022cfd7f9d06189905 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Mon, 6 Nov 2023 11:25:12 +0800 Subject: [PATCH 28/67] add support for ALL_COLUMNS in parquet predictor --- .../compat/feature_column/feature_column.py | 12 ++-- .../python/feature_column/feature_column.py | 7 +- .../python/inference/parquet_predictor.py | 72 ++++++++++--------- easy_rec/python/inference/predictor.py | 5 +- easy_rec/python/input/load_parquet.py | 29 +++++--- easy_rec/python/input/parquet_input.py | 11 ++- easy_rec/python/protos/feature_config.proto | 5 ++ easy_rec/python/utils/input_utils.py | 1 + 8 files changed, 86 insertions(+), 56 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 453f5f3ee..352aebd4f 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -335,11 +335,12 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring name='embedding_weights', dimension=column.dimension, initializer='random', # column.initializer, - # var_type='hybrid', + var_type=None + if not column.ev_params.use_cache else 'hybrid', trainable=column.trainable and trainable, dtype=dtypes.float32, - init_capacity=1024 * 1024 * 8, - max_capacity=1024 * 1024 * 16) + init_capacity=column.ev_params.init_capacity, + max_capacity=column.ev_params.max_capacity) else: embedding_weights = variable_scope.get_variable( name='embedding_weights', @@ -357,8 +358,11 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring name='embedding_weights', dimension=column.dimension, initializer='random', # column.initializer, + var_type=None if not column.ev_params.use_cache else 'hybrid', trainable=column.trainable and trainable, - dtype=dtypes.float32) + dtype=dtypes.float32, + init_capacity=column.ev_params.init_capacity, + max_capacity=column.ev_params.max_capacity) else: embedding_weights = variable_scope.get_variable( name='embedding_weights', diff --git a/easy_rec/python/feature_column/feature_column.py b/easy_rec/python/feature_column/feature_column.py index 9487b4bc6..306460fa2 100644 --- a/easy_rec/python/feature_column/feature_column.py +++ b/easy_rec/python/feature_column/feature_column.py @@ -32,7 +32,9 @@ def __init__(self, embedding_name, index, sequence_combiner=None): self.sequence_combiner = sequence_combiner -EVParams = collections.namedtuple('EVParams', ['filter_freq', 'steps_to_live']) +EVParams = collections.namedtuple('EVParams', [ + 'filter_freq', 'steps_to_live', 'use_cache', 'init_capacity', 'max_capacity' +]) class FeatureColumnParser(object): @@ -651,5 +653,6 @@ def _build_ev_params(self, ev_params): """Build embedding_variables params.""" ev_params = EVParams( ev_params.filter_freq, - ev_params.steps_to_live if ev_params.steps_to_live > 0 else None) + ev_params.steps_to_live if ev_params.steps_to_live > 0 else None, + ev_params.use_cache, ev_params.init_capacity, ev_params.max_capacity) return ev_params diff --git a/easy_rec/python/inference/parquet_predictor.py b/easy_rec/python/inference/parquet_predictor.py index cde1e81d7..003f5581f 100644 --- a/easy_rec/python/inference/parquet_predictor.py +++ b/easy_rec/python/inference/parquet_predictor.py @@ -7,10 +7,11 @@ import logging import os +import numpy as np +import pandas as pd import tensorflow as tf from tensorflow.python.platform import gfile -from easy_rec.python.inference.predictor import SINGLE_PLACEHOLDER_FEATURE_KEY from easy_rec.python.inference.predictor import Predictor from easy_rec.python.input.parquet_input import ParquetInput from easy_rec.python.protos.dataset_pb2 import DatasetConfig @@ -58,48 +59,48 @@ def __init__(self, else: self._selected_cols = None - def _get_reserved_cols(self, reserved_cols): - if reserved_cols == 'ALL_COLUMNS': - if self._is_rtp: - if self._with_header: - reserved_cols = self._all_fields - else: - idx = 0 - reserved_cols = [] - for x in range(len(self._record_defaults) - 1): - if not self._selected_cols or x in self._selected_cols[:-1]: - reserved_cols.append(self._input_fields[idx]) - idx += 1 - else: - reserved_cols.append('no_used_%d' % x) - reserved_cols.append(SINGLE_PLACEHOLDER_FEATURE_KEY) - else: - reserved_cols = self._all_fields - else: - reserved_cols = [x.strip() for x in reserved_cols.split(',') if x != ''] - return reserved_cols - def _parse_line(self, line): out_dict = {} for key in line['feature']: out_dict[key] = line['feature'][key] if 'reserve' in line: - for key in line['reserve']: - out_dict[key] = line['reserve'][key] + out_dict['reserve'] = line['reserve'] + # for key in line['reserve']: + # out_dict[key] = line['reserve'][key] return out_dict + def _get_reserved_cols(self, reserved_cols): + if reserved_cols == 'ALL_COLUMNS': + return reserved_cols + else: + return [x.strip() for x in reserved_cols.split(',')] + def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, slice_id): feature_configs = config_util.get_compatible_feature_configs( self.pipeline_config) + kwargs = {} - if self.pipeline_config.model_config.HasField('ev_params'): - kwargs['ev_params'] = self.pipeline_config.model_config.ev_params if self._reserved_cols is not None and len(self._reserved_cols) > 0: - kwargs['reserve_fields'] = self._reserved_cols - parquet_file = gfile.Glob(input_path.split(',')[0])[0] - kwargs['reserve_types'] = input_utils.get_tf_type_from_parquet_file( - self._reserved_cols, parquet_file) + if self._reserved_cols == 'ALL_COLUMNS': + parquet_file = gfile.Glob(input_path.split(',')[0])[0] + # gfile not supported, read_parquet requires random access + all_data = pd.read_parquet(parquet_file) + all_cols = list(all_data.columns) + kwargs['reserve_fields'] = all_cols + self._all_fields = all_cols + self._reserved_cols = all_cols + kwargs['reserve_types'] = input_utils.get_tf_type_from_parquet_file( + all_cols, parquet_file) + else: + kwargs['reserve_fields'] = self._reserved_cols + parquet_file = gfile.Glob(input_path.split(',')[0])[0] + kwargs['reserve_types'] = input_utils.get_tf_type_from_parquet_file( + self._reserved_cols, parquet_file) + logging.info('reserve_fields=%s reserve_types=%s' % + (','.join(self._reserved_cols), ','.join( + [str(x) for x in kwargs['reserve_types']]))) + parquet_input = ParquetInput( self.pipeline_config.data_config, feature_configs, @@ -125,8 +126,15 @@ def _write_lines(self, table_writer, outputs): table_writer.write(outputs + '\n') def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): - reserve_vals = [outputs[x] for x in output_cols] + \ - [all_vals[k] for k in reserved_cols] + reserve_vals = [] + for x in outputs: + tmp_val = outputs[x] + reserve_vals.append(tmp_val) + for k in reserved_cols: + tmp_val = all_vals['reserve'][k] + if tmp_val.dtype == np.object: + tmp_val = tmp_val[0].decode('utf-8') + reserve_vals.append(tmp_val) return reserve_vals @property diff --git a/easy_rec/python/inference/predictor.py b/easy_rec/python/inference/predictor.py index 5700955ba..0d3cfb1e0 100644 --- a/easy_rec/python/inference/predictor.py +++ b/easy_rec/python/inference/predictor.py @@ -515,13 +515,16 @@ def _parse_value(all_vals): for x in self._output_cols: if outputs[x].dtype == np.object: outputs[x] = [val.decode('utf-8') for val in outputs[x]] + elif len(outputs[x].shape) == 2 and outputs[x].shape[1] == 1: + # automatic flatten only one element array + outputs[x] = [val[0] for val in outputs[x]] elif len(outputs[x].shape) > 1: outputs[x] = [ json.dumps(val, cls=numpy_utils.NumpyEncoder) for val in outputs[x] ] for k in self._reserved_cols: - if all_vals[k].dtype == np.object: + if k in all_vals and all_vals[k].dtype == np.object: all_vals[k] = [val.decode('utf-8') for val in all_vals[k]] ts2 = time.time() diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index fdefb21c5..350d1b040 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -87,7 +87,9 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, if label_fields is not None: all_fields = all_fields + label_fields if reserve_fields is not None: - all_fields = all_fields + reserve_fields + for tmp in reserve_fields: + if tmp not in all_fields: + all_fields.append(tmp) logging.info('data proc %d start, file_que.qsize=%d' % (proc_id, file_que.qsize())) num_files = 0 @@ -119,12 +121,13 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], dtype=np.int32) if reserve_fields is not None and len(reserve_fields) > 0: + data_dict['reserve'] = {} for k in reserve_fields: np_dtype = type(input_data[k][0]) if np_dtype == object: np_dtype = np.str - data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], - dtype=np_dtype) + data_dict['reserve'][k] = np.array( + [x[0] for x in input_data[k][sid:eid]], dtype=np_dtype) for k in effective_fields: val = input_data[k][sid:eid] @@ -162,22 +165,26 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, part_data_dict_n[k] = tmp_lbls if reserve_fields is not None and len(reserve_fields) > 0: + data_dict['reserve'] = {} + part_data_dict_n['reserve'] = {} for k in reserve_fields: np_dtype = type(input_data[k][0]) if np_dtype == object: np_dtype = np.str tmp_r = np.array([x[0] for x in input_data[k][sid:]], dtype=np_dtype) - if part_data_dict is not None and k in part_data_dict: - tmp_r = np.concatenate([part_data_dict[k], tmp_r], axis=0) + if part_data_dict is not None and 'reserve' in part_data_dict and \ + k in part_data_dict['reserve']: + tmp_r = np.concatenate([part_data_dict['reserve'][k], tmp_r], + axis=0) if len(tmp_r) > batch_size: - data_dict[k] = tmp_r[:batch_size] - part_data_dict_n[k] = tmp_r[batch_size:] + data_dict['reserve'][k] = tmp_r[:batch_size] + part_data_dict_n['reserve'][k] = tmp_r[batch_size:] elif len(tmp_r) == batch_size: - data_dict[k] = tmp_r + data_dict['reserve'][k] = tmp_r else: - part_data_dict_n[k] = tmp_r + part_data_dict_n['reserve'][k] = tmp_r else: - part_data_dict_n[k] = tmp_r + part_data_dict_n['reserve'][k] = tmp_r for k in effective_fields: val = input_data[k][sid:] @@ -200,7 +207,7 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, part_data_dict_n[k] = (tmp_lens, tmp_vals) else: part_data_dict_n[k] = (all_lens, all_vals) - if len(data_dict) > 0: + if 'sparse_fea' in data_dict: _pack_sparse_feas(data_dict, effective_fields) if not _add_to_que(data_dict, data_que, proc_stop_que): logging.info('add to que failed') diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index b795dd7e6..d1d8106df 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -189,10 +189,7 @@ def _to_fea_dict(self, input_dict): output_dict['label'] = lbl_dict if self._reserve_fields is not None: - reserve_dict = {} - for tmp_name in self._reserve_fields: - reserve_dict[tmp_name] = input_dict[tmp_name] - output_dict['reserve'] = reserve_dict + output_dict['reserve'] = input_dict['reserve'] return output_dict @@ -234,9 +231,11 @@ def _build(self, mode, params): out_shapes[k] = tf.TensorShape([None]) if self._reserve_fields is not None: + out_types['reserve'] = {} + out_shapes['reserve'] = {} for k, t in zip(self._reserve_fields, self._reserve_types): - out_types[k] = t - out_shapes[k] = tf.TensorShape([None]) + out_types['reserve'][k] = t + out_shapes['reserve'][k] = tf.TensorShape([None]) # for k in self._effective_fields: # out_types[k] = (tf.int64, tf.int32) diff --git a/easy_rec/python/protos/feature_config.proto b/easy_rec/python/protos/feature_config.proto index 2f1627f29..dca184936 100644 --- a/easy_rec/python/protos/feature_config.proto +++ b/easy_rec/python/protos/feature_config.proto @@ -31,6 +31,11 @@ message SequenceCombiner { message EVParams { optional uint64 filter_freq = 1 [default=0]; optional uint64 steps_to_live = 2 [default=0]; + // use embedding cache + optional bool use_cache = 3 [default=false]; + // for hybrid key value embedding + optional uint64 init_capacity = 4 [default=8388608]; + optional uint64 max_capacity = 5 [default=16777216]; } message FeatureConfig { diff --git a/easy_rec/python/utils/input_utils.py b/easy_rec/python/utils/input_utils.py index 26abea1b7..ad386cf5b 100644 --- a/easy_rec/python/utils/input_utils.py +++ b/easy_rec/python/utils/input_utils.py @@ -76,6 +76,7 @@ def string_to_number(field, ftype, default_value, name=''): def get_tf_type_from_parquet_file(cols, parquet_file): + # gfile not supported, read_parquet requires random access input_data = pd.read_parquet(parquet_file, columns=cols) tf_types = [] for col in cols: From 62ef27dafd81097bd8f29cb927e23f429c1a0a37 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Fri, 10 Nov 2023 10:31:02 +0800 Subject: [PATCH 29/67] fix predict bug --- easy_rec/python/inference/parquet_predictor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/easy_rec/python/inference/parquet_predictor.py b/easy_rec/python/inference/parquet_predictor.py index 003f5581f..17d657129 100644 --- a/easy_rec/python/inference/parquet_predictor.py +++ b/easy_rec/python/inference/parquet_predictor.py @@ -133,7 +133,7 @@ def _get_reserve_vals(self, reserved_cols, output_cols, all_vals, outputs): for k in reserved_cols: tmp_val = all_vals['reserve'][k] if tmp_val.dtype == np.object: - tmp_val = tmp_val[0].decode('utf-8') + tmp_val = [x.decode('utf-8') for x in tmp_val] reserve_vals.append(tmp_val) return reserve_vals From 7c55ff7de854b1a5d3628cef6f2ec7d4de1aeb6e Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Fri, 10 Nov 2023 10:40:08 +0800 Subject: [PATCH 30/67] fix numpy version --- requirements/runtime.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements/runtime.txt b/requirements/runtime.txt index 56d86d2f6..8e6fa5616 100644 --- a/requirements/runtime.txt +++ b/requirements/runtime.txt @@ -1,6 +1,7 @@ eas_prediction future matplotlib +numpy <= 1.23 oss2 pandas psutil From 77efe8aebadf6a452e63f45ba0939bc9f93fdfd7 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Fri, 10 Nov 2023 10:46:34 +0800 Subject: [PATCH 31/67] make batch_size work for parquet predictor --- easy_rec/python/inference/parquet_predictor.py | 1 + 1 file changed, 1 insertion(+) diff --git a/easy_rec/python/inference/parquet_predictor.py b/easy_rec/python/inference/parquet_predictor.py index 17d657129..cca42b252 100644 --- a/easy_rec/python/inference/parquet_predictor.py +++ b/easy_rec/python/inference/parquet_predictor.py @@ -100,6 +100,7 @@ def _get_dataset(self, input_path, num_parallel_calls, batch_size, slice_num, logging.info('reserve_fields=%s reserve_types=%s' % (','.join(self._reserved_cols), ','.join( [str(x) for x in kwargs['reserve_types']]))) + self.pipeline_config.data_config.batch_size = batch_size parquet_input = ParquetInput( self.pipeline_config.data_config, From 2b34fda9d3383070a10a10fcc67b5e7a5c384507 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Fri, 10 Nov 2023 22:46:45 +0800 Subject: [PATCH 32/67] fix multiproc bug --- .../compat/feature_column/feature_column.py | 17 ++- easy_rec/python/compat/optimizers.py | 109 ++++++++++-------- easy_rec/python/input/load_parquet.py | 20 +++- easy_rec/python/input/parquet_input.py | 32 ++--- requirements/runtime.txt | 1 + 5 files changed, 111 insertions(+), 68 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 352aebd4f..4b78a9ca4 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -334,7 +334,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring embedding_weights = sok.DynamicVariable( name='embedding_weights', dimension=column.dimension, - initializer='random', # column.initializer, + initializer='0.0001', # 'random', # column.initializer, var_type=None if not column.ev_params.use_cache else 'hybrid', trainable=column.trainable and trainable, @@ -430,10 +430,17 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring p_assignments = math_ops.cast(all_uniq_ids % num_parts, dtypes.int32) gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, p_assignments, num_parts) + original_ids = math_ops.range(array_ops.size(all_uniq_ids)) + original_part_ids = data_flow_ops.dynamic_partition( + original_ids, p_assignments, num_parts) # all2all split_sizes = array_ops.concat([array_ops.shape(x) for x in gather_ids], axis=0) send_ids = array_ops.concat(gather_ids, axis=0) + # send_ids = logging_ops.Print(send_ids, [array_ops.shape(send_ids), + # array_ops.shape(p_assignments) ] + + # [ array_ops.shape(x) for x in gather_ids] + # , message='send_ids_dbg_%d' % num_parts) recv_ids, recv_lens = hvd.alltoall(send_ids, split_sizes) # read embedding from dynamic variable @@ -441,10 +448,18 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring send_embed = lookup_embeddings[0].sparse_read( recv_ids, lookup_only=(not is_training)) else: + # find in subarray position + # 0 2 4 6 8 10 ... + # 1 3 5 7 9 11 ... + recv_ids = recv_ids / num_parts send_embed = array_ops.gather(lookup_embeddings[0], recv_ids) # all2all recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) + recv_embeddings = array_ops.split( + recv_embeddings, num_or_size_splits=split_sizes) + recv_embeddings = data_flow_ops.parallel_dynamic_stitch( + original_part_ids, recv_embeddings, name='parallel_dynamic_stitch') embeddings = math_ops.sparse_segment_sum( recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') else: diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index 2cfa526d6..99098073a 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops @@ -261,11 +262,6 @@ def optimize_loss(loss, variables = vars_.trainable_variables() # Compute gradients. - # if 'compute_gradients' not in dir(opt): - # import tensorflow as tf - # gradients = tf.gradients(loss, variables) - # gradients = list(zip(gradients, variables)) - # else: gradients = opt.compute_gradients( loss, variables, @@ -277,20 +273,24 @@ def optimize_loss(loss, reduced_grads = [] for g, v in gradients: reduced_grads.append((hvd.allreduce( - g, op=hvd.Sum, compression=hvd.compression.NoneCompressor), v)) + g, op=hvd.Average, + compression=hvd.compression.NoneCompressor), v)) gradients = reduced_grads else: # embedding parameters partitioned: # the gradients for embeddings from different workers are # already summed together in the backward pass through - # hvd.alltoall + # hvd.alltoall, as the loss are not divided, the gradients + # need to be normalized, divide by worker number reduced_grads = [] for g, v in gradients: if '/embedding' not in v.name: reduced_grads.append((hvd.allreduce( - g, op=hvd.Sum, compression=hvd.compression.NoneCompressor), v)) + g, op=hvd.Average, + compression=hvd.compression.NoneCompressor), v)) else: - reduced_grads.append((g, v)) + reduced_grads.append((indexed_slices.IndexedSlices( + indices=g.indices, values=g.values / hvd.size()), v)) gradients = reduced_grads # Optionally add gradient noise. @@ -306,13 +306,22 @@ def optimize_loss(loss, 'Empty list of (gradient, var) pairs encountered. This is most ' 'likely to be caused by an improper value of gradient_multipliers.') - if 'global_gradient_norm' in summaries or 'gradient_norm' in summaries: - summary.scalar('global_norm/gradient_norm', - clip_ops.global_norm(list(zip(*gradients))[0])) + # if 'global_gradient_norm' in summaries or 'gradient_norm' in summaries: + # summary.scalar('global_norm/gradient_norm', + # clip_ops.global_norm(list(zip(*gradients))[0])) # Optionally clip gradients by global norm. if isinstance(clip_gradients, float): - gradients = _clip_gradients_by_norm(gradients, clip_gradients) + # gradients = _clip_gradients_by_norm(gradients, clip_gradients) + sparse_norm, dense_norm, grad_norm = _get_grad_norm(gradients) + summary.scalar('global_norm/sparse_grad', sparse_norm) + summary.scalar('global_norm/dense_grad', dense_norm) + summary.scalar('global_norm/gradient_norm', grad_norm) + grads = [x[0] for x in gradients] + vars = [x[1] for x in gradients] + clipped_grads, _ = clip_ops.clip_by_global_norm( + grads, clip_gradients, use_norm=grad_norm) + gradients = list(zip(clipped_grads, vars)) elif callable(clip_gradients): gradients = clip_gradients(gradients) elif clip_gradients is not None: @@ -324,46 +333,29 @@ def optimize_loss(loss, summary.scalar('loss', loss) # Add histograms for variables, gradients and gradient norms. - for gradient, variable in gradients: - if isinstance(gradient, indexed_slices.IndexedSlices): - grad_values = gradient.values - else: - grad_values = gradient - - if grad_values is not None: - var_name = variable.name.replace(':', '_') - if 'gradients' in summaries: - summary.histogram('gradients/%s' % var_name, grad_values) - if 'gradient_norm' in summaries: - summary.scalar('gradient_norm/%s' % var_name, - clip_ops.global_norm([grad_values])) + # for gradient, variable in gradients: + # if isinstance(gradient, indexed_slices.IndexedSlices): + # grad_values = gradient.values + # else: + # grad_values = gradient + + # if grad_values is not None: + # var_name = variable.name.replace(':', '_') + # if 'gradients' in summaries: + # summary.histogram('gradients/%s' % var_name, grad_values) + # if 'gradient_norm' in summaries: + # summary.scalar('gradient_norm/%s' % var_name, + # clip_ops.global_norm([grad_values])) if clip_gradients is not None and ('global_gradient_norm' in summaries or 'gradient_norm' in summaries): - summary.scalar('global_norm/clipped_gradient_norm', - clip_ops.global_norm(list(zip(*gradients))[0])) - - # task_index, _ = estimator_utils.get_task_index_and_num() + sparse_norm, dense_norm, grad_norm = _get_grad_norm(gradients) + summary.scalar('global_norm/clipped_sparse_grad', sparse_norm) + summary.scalar('global_norm/clipped_dense_grad', dense_norm) + summary.scalar('global_norm/clipped_gradient_norm', grad_norm) # Create gradient updates. def _apply_grad(): - # if 'compute_gradients' not in dir(opt): - # sparse_vars = [ x for x in gradients if 'DynamicVariable' in str(type(x[1])) ] - # dense_vars = [ x for x in gradients if 'DynamicVariable' not in str(type(x[1])) ] - # opt._op - # with ops.control_dependencies([array_ops.identity(loss)]): - # sparse_grad_updates = opt.apply_gradients(sparse_vars) - # dense_grad_updates = opt._optimizer.apply_gradients( - # dense_vars, - # global_step=global_step if increment_global_step else None, - # name='train') - # if sparse_grad_updates is not None and dense_grad_updates is not None: - # grad_updates = tf.group([sparse_grad_updates, dense_grad_updates]) - # elif sparse_grad_updates is not None: - # grad_updates = sparse_grad_updates - # elif dense_grad_updates is not None: - # grad_updates = dense_grad_updates - # else: grad_updates = opt.apply_gradients( gradients, global_step=global_step if increment_global_step else None, @@ -395,9 +387,32 @@ def _apply_grad(): return train_tensor +def _get_grad_norm(grads_and_vars): + sparse_norms = [] + dense_norms = [] + for grad, var in grads_and_vars: + if isinstance(grad, indexed_slices.IndexedSlices): + sparse_norms.append( + hvd.allreduce( + gen_nn_ops.l2_loss(grad.values), + op=hvd.Sum, + compression=hvd.compression.NoneCompressor)) + else: + dense_norms.append(gen_nn_ops.l2_loss(grad)) + all_norms = sparse_norms + dense_norms + sparse_norm = math_ops.sqrt( + math_ops.reduce_sum(array_ops.stack(sparse_norms) * 2.0)) + dense_norm = math_ops.sqrt( + math_ops.reduce_sum(array_ops.stack(dense_norms) * 2.0)) + grad_norm = math_ops.sqrt( + math_ops.reduce_sum(array_ops.stack(all_norms)) * 2.0) + return sparse_norm, dense_norm, grad_norm + + def _clip_gradients_by_norm(grads_and_vars, clip_gradients): """Clips gradients by global norm.""" gradients, variables = zip(*grads_and_vars) + clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients, clip_gradients) return list(zip(clipped_gradients, variables)) diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index 350d1b040..320632dae 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -6,9 +6,9 @@ import pandas as pd -def start_data_proc(task_index, num_proc, file_que, data_que, proc_start_que, - proc_stop_que, batch_size, label_fields, effective_fields, - reserve_fields, drop_remainder): +def start_data_proc(task_index, task_num, num_proc, file_que, data_que, + proc_start_que, proc_stop_que, batch_size, label_fields, + effective_fields, reserve_fields, drop_remainder): mp_ctxt = multiprocessing.get_context('spawn') proc_arr = [] for proc_id in range(num_proc): @@ -16,7 +16,7 @@ def start_data_proc(task_index, num_proc, file_que, data_que, proc_start_que, target=load_data_proc, args=(proc_id, file_que, data_que, proc_start_que, proc_stop_que, batch_size, label_fields, effective_fields, reserve_fields, - drop_remainder), + drop_remainder, task_index, task_num), name='task_%d_data_proc_%d' % (task_index, proc_id)) proc.daemon = True proc.start() @@ -79,7 +79,7 @@ def _pack_sparse_feas(data_dict, effective_fields): def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, batch_size, label_fields, effective_fields, reserve_fields, - drop_remainder): + drop_remainder, task_index, task_num): logging.info('data proc %d start, proc_start_que=%s' % (proc_id, proc_start_que.qsize())) proc_start_que.get() @@ -111,11 +111,16 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, # logging.info( # 'proc[%d] read file %s sample_num=%d batch_num=%d res_num=%d' % # (proc_id, input_file, data_len, batch_num, res_num)) + # sub_batch_size = int(batch_size / task_num) sid = 0 for batch_id in range(batch_num): eid = sid + batch_size data_dict = {} + # sid_stub = sid + # sid = sid + sub_batch_size * task_index + # eid = sid + sub_batch_size + if label_fields is not None and len(label_fields) > 0: for k in label_fields: data_dict[k] = np.array([x[0] for x in input_data[k][sid:eid]], @@ -139,11 +144,16 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, data_dict[k] = (all_lens, all_vals) _pack_sparse_feas(data_dict, effective_fields) + # logging.info('task_index=%d sid=%d eid=%d total_len=%d' % (task_index, sid, eid, + # len(data_dict['sparse_fea'][1]))) if not _add_to_que(data_dict, data_que, proc_stop_que): logging.info('add to que failed') is_good = False break sid += batch_size + # sid = batch_size + sid_stub + # return + if res_num > 0 and is_good: data_dict = {} part_data_dict_n = {} diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index d1d8106df..2211893d0 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -3,6 +3,7 @@ import logging import multiprocessing import queue +# import threading import time # import numpy as np @@ -48,12 +49,12 @@ def __init__(self, file_num = len(self._input_files) logging.info('[task_index=%d] total_file_num=%d task_num=%d' % (task_index, file_num, task_num)) - avg_file_num = int(file_num / task_num) - res_file_num = file_num % task_num - file_sid = task_index * avg_file_num + min(res_file_num, task_index) - file_eid = (task_index + 1) * avg_file_num + min(res_file_num, - task_index + 1) - self._my_files = self._input_files[file_sid:file_eid] + + self._my_files = [] + for file_id in range(file_num): + if (file_id % task_num) == task_index: + self._my_files.append(self._input_files[file_id]) + # self._my_files = self._input_files logging.info('[task_index=%d] task_file_num=%d' % (task_index, len(self._my_files))) @@ -126,8 +127,8 @@ def _sample_generator(self): logging.warning('task[%d] get from data_que exception: %s' % (self._task_index, str(ex))) break - for proc in self._proc_arr: - proc.join() + # for proc in self._proc_arr: + # proc.join() def stop(self): if self._proc_arr is None or len(self._proc_arr) == 0: @@ -203,15 +204,16 @@ def _build(self, mode, params): if mode != tf.estimator.ModeKeys.PREDICT: self._proc_arr = load_parquet.start_data_proc( - self._task_index, self._num_proc, self._file_que, self._data_que, - self._proc_start_que, self._proc_stop_que, self._batch_size, - self._label_fields, self._effective_fields, self._reserve_fields, - self._data_config.drop_remainder) + self._task_index, self._task_num, self._num_proc, self._file_que, + self._data_que, self._proc_start_que, self._proc_stop_que, + self._batch_size, self._label_fields, self._effective_fields, + self._reserve_fields, self._data_config.drop_remainder) else: self._proc_arr = load_parquet.start_data_proc( - self._task_index, self._num_proc, self._file_que, self._data_que, - self._proc_start_que, self._proc_stop_que, self._batch_size, None, - self._effective_fields, self._reserve_fields, False) + self._task_index, self._task_num, self._num_proc, self._file_que, + self._data_que, self._proc_start_que, self._proc_stop_que, + self._batch_size, None, self._effective_fields, self._reserve_fields, + False) for input_file in my_files: self._file_que.put(input_file) diff --git a/requirements/runtime.txt b/requirements/runtime.txt index 8e6fa5616..37302e7d5 100644 --- a/requirements/runtime.txt +++ b/requirements/runtime.txt @@ -2,6 +2,7 @@ eas_prediction future matplotlib numpy <= 1.23 +numpy <= 1.23 oss2 pandas psutil From f606c01b0791bade32e0299dcaae3f55de2509b9 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Mon, 13 Nov 2023 10:34:32 +0800 Subject: [PATCH 33/67] make read parquet fast --- easy_rec/python/input/load_parquet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index 320632dae..7f0ae583e 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -137,7 +137,7 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, for k in effective_fields: val = input_data[k][sid:eid] all_lens = np.array([len(x) for x in val], dtype=np.int32) - all_vals = np.concatenate(list(val)) + all_vals = np.concatenate(val.to_numpy()) assert np.sum(all_lens) == len( all_vals), 'len(all_vals)=%d np.sum(all_lens)=%d' % ( len(all_vals), np.sum(all_lens)) @@ -199,7 +199,7 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, for k in effective_fields: val = input_data[k][sid:] all_lens = np.array([len(x) for x in val], dtype=np.int32) - all_vals = np.concatenate(list(val)) + all_vals = np.concatenate(val.to_numpy()) if part_data_dict is not None and k in part_data_dict: tmp_lens = np.concatenate([part_data_dict[k][0], all_lens], axis=0) tmp_vals = np.concatenate([part_data_dict[k][1], all_vals], axis=0) From fab2dad554b623970b6081b0bea7026d999f2e75 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Sun, 19 Nov 2023 23:15:40 +0800 Subject: [PATCH 34/67] fix dataset miss padding data bug --- easy_rec/python/input/load_parquet.py | 20 ++++++++++++++------ easy_rec/python/input/parquet_input.py | 15 +++++++++------ 2 files changed, 23 insertions(+), 12 deletions(-) diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index 7f0ae583e..b68886662 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -96,6 +96,8 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, part_data_dict = {} is_good = True + total_batch_cnt = 0 + total_sample_cnt = 0 while is_good: if _should_stop(proc_stop_que): is_good = False @@ -106,6 +108,7 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, num_files += 1 input_data = pd.read_parquet(input_file, columns=all_fields) data_len = len(input_data[all_fields[0]]) + total_sample_cnt += data_len batch_num = int(data_len / batch_size) res_num = data_len % batch_size # logging.info( @@ -150,9 +153,8 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, logging.info('add to que failed') is_good = False break + total_batch_cnt += 1 sid += batch_size - # sid = batch_size + sid_stub - # return if res_num > 0 and is_good: data_dict = {} @@ -217,24 +219,30 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, part_data_dict_n[k] = (tmp_lens, tmp_vals) else: part_data_dict_n[k] = (all_lens, all_vals) - if 'sparse_fea' in data_dict: + if effective_fields[0] in data_dict: _pack_sparse_feas(data_dict, effective_fields) if not _add_to_que(data_dict, data_que, proc_stop_que): logging.info('add to que failed') is_good = False break - + total_batch_cnt += 1 part_data_dict = part_data_dict_n if len(part_data_dict) > 0 and is_good: + batch_len = len(part_data_dict[effective_fields[0]][0]) if not drop_remainder: _pack_sparse_feas(part_data_dict, effective_fields) + logging.info('remainder batch: %s sample_num=%d' % + (','.join(part_data_dict.keys()), batch_len)) _add_to_que(part_data_dict, data_que, proc_stop_que) + total_batch_cnt += 1 else: logging.warning('drop remain %d samples as drop_remainder is set' % - len(part_data_dict[effective_fields[0]])) + batch_len) if is_good: is_good = _add_to_que(None, data_que, proc_stop_que) - logging.info('data_proc_id=%d, is_good = %s' % (proc_id, is_good)) + logging.info( + 'data_proc_id[%d]: is_good = %s, total_batch_cnt=%d, total_sample_cnt=%d' + % (proc_id, is_good, total_batch_cnt, total_sample_cnt)) data_que.close(wait_send_finish=is_good) while not is_good: diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 2211893d0..84cfc025f 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -114,7 +114,7 @@ def _sample_generator(self): yield sample if fetch_good_cnt % 200 == 0: logging.info( - 'task[%d] fetch_good_cnt=%d, fetch_timeout_cnt=%d, qsize=%d' % + 'task[%d] fetch_batch_cnt=%d, fetch_timeout_cnt=%d, qsize=%d' % (self._task_index, fetch_good_cnt, fetch_timeout_cnt, self._data_que.qsize())) except queue.Empty: @@ -127,8 +127,8 @@ def _sample_generator(self): logging.warning('task[%d] get from data_que exception: %s' % (self._task_index, str(ex))) break - # for proc in self._proc_arr: - # proc.join() + logging.info('task[%d] sample_generator: total_batches=%d' % + (self._task_index, fetch_good_cnt)) def stop(self): if self._proc_arr is None or len(self._proc_arr) == 0: @@ -202,18 +202,21 @@ def _build(self, mode, params): else: my_files = self._my_files - if mode != tf.estimator.ModeKeys.PREDICT: + if mode == tf.estimator.ModeKeys.TRAIN: self._proc_arr = load_parquet.start_data_proc( self._task_index, self._task_num, self._num_proc, self._file_que, self._data_que, self._proc_start_que, self._proc_stop_que, self._batch_size, self._label_fields, self._effective_fields, self._reserve_fields, self._data_config.drop_remainder) else: + lbl_fields = self._label_fields + if mode == tf.estimator.ModeKeys.PREDICT: + lbl_fields = None self._proc_arr = load_parquet.start_data_proc( self._task_index, self._task_num, self._num_proc, self._file_que, self._data_que, self._proc_start_que, self._proc_stop_que, - self._batch_size, None, self._effective_fields, self._reserve_fields, - False) + self._batch_size, lbl_fields, self._effective_fields, + self._reserve_fields, False) for input_file in my_files: self._file_que.put(input_file) From 9f903e743d009b5fb55779587ade43e6ac2ef733 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Sun, 19 Nov 2023 23:26:36 +0800 Subject: [PATCH 35/67] add loss weight update --- easy_rec/python/model/ppnet_v3m.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index 8687bba53..c9079361a 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -76,6 +76,8 @@ def build_loss_graph(self): for lbl_id in range(len(self._model_conf['label'])): lbl_info = self._model_conf['label'][lbl_id] lbl_name = lbl_info.get('input_name') + lbl_weight = lbl_info.get('weight') + logging.info('target_name=%s target_weight=%.3f' % (lbl_name, lbl_weight)) output = self._prediction_dict.get(lbl_name) tf.summary.scalar('label/%s' % lbl_name, tf.reduce_mean(tf.to_float(self._labels[lbl_name]))) @@ -84,7 +86,7 @@ def build_loss_graph(self): reduction='sum_over_batch_size')(self._labels[lbl_name], output) # loss_obj = tf.Print(loss_obj, [tf.reduce_min(loss_obj), tf.reduce_max(loss_obj), # tf.reduce_mean(loss_obj), tf.shape(loss_obj)], message='loss_obj') - self._loss_dict[lbl_name] = loss_obj + self._loss_dict[lbl_name] = loss_obj * lbl_weight return self._loss_dict def get_outputs(self): From cb1a9316426d5c08c980ba8f3f39fcac9fc0836d Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Tue, 21 Nov 2023 11:30:07 +0800 Subject: [PATCH 36/67] update load_embed to support large embedding tables --- easy_rec/python/ops/2.12/libload_embed.so | Bin 60296 -> 60360 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git 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zDOSYmFZ5KlchJMAuM^w6m8ra&q=$_6lJ%6KTuBalHA8WA)%oLo=}fYZT%Vo^^8DppjsF8UWz*Hq-p^dZaf5{6mx=#V=j3VgfBN2S{turM z;zp))I=b14-NYRg8{5&yPAxEN!h7ezA82P;2#Hb*&G}*l$4pwYh_3)N+N=g(_46SeEWr}fGz!}1S)gf z*#ARM7^~1#V59eGSWow&LB-foir!)p)s(g+(OlzpkVdH}E8R3kTD;}q-2e}EJFtRp z*U*<+5=Akc*|Gt4+RFF9=7C!i@z2Q1TNx6)ZGf(BQrVPkjf8IP^E7k&3IN59S}Yz{ zcbM=Oz5mW!UG_wpvh#yrzTeUCE=r!f#&lTbCn)D@lV~At3-y%wqnsz-#eo^~HoOC3 zhB$^dyoZu5?;hpIe2&b&miYyl|0(lOc`2%&%xB1a1Lp&e8uh;wLaV^B^ybT z+zc-VPLI3c4ew$trJ%hg@eSwt^PrLbOx9t&k@n5hxIMSgGTd_B^GL zCZA9A_v@h1`q8@cDgN8tt?hK?d`dIVBI+qsKZ?Jw2y4lv3yJhu(Uu~W0CEH}bk>K-qfa&fl~D^=X3M1Rk1CnJr!l;ZEXEf_`XF7?4ub>>o^CfQ{g af1QEiFL(FPa4)98H2HFhc#_s#ZvX!`1f75Y From 3ac74945b504f5c3099386abf53a930aeb853f2f Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Wed, 22 Nov 2023 16:47:50 +0800 Subject: [PATCH 37/67] support old training path --- .../compat/feature_column/feature_column.py | 3 +- easy_rec/python/input/load_parquet.py | 29 +- easy_rec/python/input/parquet_input_v2.py | 354 ++++++++++++++++++ easy_rec/python/main.py | 26 +- easy_rec/python/model/ppnet_v3m.py | 5 +- easy_rec/python/protos/dataset.proto | 1 + easy_rec/python/utils/estimator_utils.py | 4 +- 7 files changed, 397 insertions(+), 25 deletions(-) create mode 100644 easy_rec/python/input/parquet_input_v2.py diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 4b78a9ca4..5679b97ca 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -536,7 +536,8 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring if 'ENABLE_SOK' in os.environ: return _get_logits_with_sok() else: - return _get_logits() + with ops.device('/CPU:0'): + return _get_logits() def input_layer(features, diff --git a/easy_rec/python/input/load_parquet.py b/easy_rec/python/input/load_parquet.py index b68886662..5dba9cabd 100644 --- a/easy_rec/python/input/load_parquet.py +++ b/easy_rec/python/input/load_parquet.py @@ -6,9 +6,19 @@ import pandas as pd -def start_data_proc(task_index, task_num, num_proc, file_que, data_que, - proc_start_que, proc_stop_que, batch_size, label_fields, - effective_fields, reserve_fields, drop_remainder): +def start_data_proc(task_index, + task_num, + num_proc, + file_que, + data_que, + proc_start_que, + proc_stop_que, + batch_size, + label_fields, + effective_fields, + reserve_fields, + drop_remainder, + need_pack=True): mp_ctxt = multiprocessing.get_context('spawn') proc_arr = [] for proc_id in range(num_proc): @@ -16,7 +26,7 @@ def start_data_proc(task_index, task_num, num_proc, file_que, data_que, target=load_data_proc, args=(proc_id, file_que, data_que, proc_start_que, proc_stop_que, batch_size, label_fields, effective_fields, reserve_fields, - drop_remainder, task_index, task_num), + drop_remainder, task_index, task_num, need_pack), name='task_%d_data_proc_%d' % (task_index, proc_id)) proc.daemon = True proc.start() @@ -79,7 +89,7 @@ def _pack_sparse_feas(data_dict, effective_fields): def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, batch_size, label_fields, effective_fields, reserve_fields, - drop_remainder, task_index, task_num): + drop_remainder, task_index, task_num, need_pack): logging.info('data proc %d start, proc_start_que=%s' % (proc_id, proc_start_que.qsize())) proc_start_que.get() @@ -146,7 +156,8 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, len(all_vals), np.sum(all_lens)) data_dict[k] = (all_lens, all_vals) - _pack_sparse_feas(data_dict, effective_fields) + if need_pack: + _pack_sparse_feas(data_dict, effective_fields) # logging.info('task_index=%d sid=%d eid=%d total_len=%d' % (task_index, sid, eid, # len(data_dict['sparse_fea'][1]))) if not _add_to_que(data_dict, data_que, proc_stop_que): @@ -220,7 +231,8 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, else: part_data_dict_n[k] = (all_lens, all_vals) if effective_fields[0] in data_dict: - _pack_sparse_feas(data_dict, effective_fields) + if need_pack: + _pack_sparse_feas(data_dict, effective_fields) if not _add_to_que(data_dict, data_que, proc_stop_que): logging.info('add to que failed') is_good = False @@ -230,7 +242,8 @@ def load_data_proc(proc_id, file_que, data_que, proc_start_que, proc_stop_que, if len(part_data_dict) > 0 and is_good: batch_len = len(part_data_dict[effective_fields[0]][0]) if not drop_remainder: - _pack_sparse_feas(part_data_dict, effective_fields) + if need_pack: + _pack_sparse_feas(part_data_dict, effective_fields) logging.info('remainder batch: %s sample_num=%d' % (','.join(part_data_dict.keys()), batch_len)) _add_to_que(part_data_dict, data_que, proc_stop_que) diff --git a/easy_rec/python/input/parquet_input_v2.py b/easy_rec/python/input/parquet_input_v2.py new file mode 100644 index 000000000..634ac8610 --- /dev/null +++ b/easy_rec/python/input/parquet_input_v2.py @@ -0,0 +1,354 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import logging +import multiprocessing +import queue +# import threading +import time + +# import numpy as np +# import pandas as pd +import tensorflow as tf +# from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +# from tensorflow.python.ops import logging_ops +# from tensorflow.python.ops import math_ops +from tensorflow.python.platform import gfile + +from easy_rec.python.compat import queues +from easy_rec.python.input import load_parquet +from easy_rec.python.input.input import Input + + +class ParquetInputV2(Input): + + def __init__(self, + data_config, + feature_config, + input_path, + task_index=0, + task_num=1, + check_mode=False, + pipeline_config=None, + **kwargs): + super(ParquetInputV2, + self).__init__(data_config, feature_config, input_path, task_index, + task_num, check_mode, pipeline_config, **kwargs) + if input_path is None: + return + + self._input_files = [] + for sub_path in input_path.strip().split(','): + self._input_files.extend(gfile.Glob(sub_path)) + logging.info('parquet input_path=%s file_num=%d' % + (input_path, len(self._input_files))) + mp_ctxt = multiprocessing.get_context('spawn') + self._data_que = queues.Queue( + name='data_que', ctx=mp_ctxt, maxsize=self._data_config.prefetch_size) + + file_num = len(self._input_files) + logging.info('[task_index=%d] total_file_num=%d task_num=%d' % + (task_index, file_num, task_num)) + + self._my_files = [] + for file_id in range(file_num): + if (file_id % task_num) == task_index: + self._my_files.append(self._input_files[file_id]) + # self._my_files = self._input_files + + logging.info('[task_index=%d] task_file_num=%d' % + (task_index, len(self._my_files))) + self._file_que = queues.Queue(name='file_que', ctx=mp_ctxt) + + self._num_proc = 8 + if file_num < self._num_proc: + self._num_proc = file_num + + self._proc_start = False + self._proc_start_que = queues.Queue(name='proc_start_que', ctx=mp_ctxt) + self._proc_stop = False + self._proc_stop_que = queues.Queue(name='proc_stop_que', ctx=mp_ctxt) + + self._reserve_fields = None + self._reserve_types = None + if 'reserve_fields' in kwargs and 'reserve_types' in kwargs: + self._reserve_fields = kwargs['reserve_fields'] + self._reserve_types = kwargs['reserve_types'] + + self._proc_arr = None + + def _sample_generator(self): + if not self._proc_start: + self._proc_start = True + for proc in (self._proc_arr): + self._proc_start_que.put(True) + logging.info('task[%s] data_proc=%s is_alive=%s' % + (self._task_index, proc, proc.is_alive())) + + done_proc_cnt = 0 + fetch_timeout_cnt = 0 + + # # for mock purpose + # all_samples = [] + # while len(all_samples) < 64: + # try: + # sample = self._data_que.get(block=False) + # all_samples.append(sample) + # except queue.Empty: + # continue + # sid = 0 + # while True: + # yield all_samples[sid] + # sid += 1 + # if sid >= len(all_samples): + # sid = 0 + + fetch_good_cnt = 0 + while True: + try: + sample = self._data_que.get(timeout=1) + if sample is None: + done_proc_cnt += 1 + else: + fetch_good_cnt += 1 + yield sample + if fetch_good_cnt % 200 == 0: + logging.info( + 'task[%d] fetch_batch_cnt=%d, fetch_timeout_cnt=%d, qsize=%d' % + (self._task_index, fetch_good_cnt, fetch_timeout_cnt, + self._data_que.qsize())) + except queue.Empty: + fetch_timeout_cnt += 1 + if done_proc_cnt >= len(self._proc_arr): + logging.info('all sample finished, fetch_timeout_cnt=%d' % + fetch_timeout_cnt) + break + except Exception as ex: + logging.warning('task[%d] get from data_que exception: %s' % + (self._task_index, str(ex))) + break + logging.info('task[%d] sample_generator: total_batches=%d' % + (self._task_index, fetch_good_cnt)) + + def stop(self): + if self._proc_arr is None or len(self._proc_arr) == 0: + return + logging.info('task[%d] will stop dataset procs, proc_num=%d' % + (self._task_index, len(self._proc_arr))) + self._file_que.close() + if self._proc_start: + logging.info('try close data que') + for _ in range(len(self._proc_arr)): + self._proc_stop_que.put(1) + self._proc_stop_que.close() + + def _any_alive(): + for proc in self._proc_arr: + if proc.is_alive(): + return True + return False + + # to ensure the sender part of the python Queue could exit + while _any_alive(): + try: + self._data_que.get(timeout=1) + except Exception: + pass + time.sleep(1) + self._data_que.close() + logging.info('data que closed') + # import time + # time.sleep(10) + for proc in self._proc_arr: + # proc.terminate() + proc.join() + logging.info('join proc done') + self._proc_start = False + + def _to_fea_dict(self, input_dict): + fea_dict = {} + + # if self._has_ev: + # tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict[ + # 'sparse_fea'][0] + + # fea_dict['sparse_fea'] = (tmp_vals, tmp_lens) + # else: + # tmp_vals, tmp_lens = input_dict['sparse_fea'][1], input_dict[ + # 'sparse_fea'][0] + # fea_dict['sparse_fea'] = (tmp_vals % self._feature_configs[0].num_buckets, + # tmp_lens) + + for fea in self._effective_fields: + tmp_lens, tmp_vals = input_dict[fea] + if self._has_ev: + fea_dict[fea] = tf.RaggedTensor.from_row_lengths( + values=tmp_vals, row_lengths=tmp_lens) + else: + fea_dict[fea] = tf.RaggedTensor.from_row_lengths( + values=(tmp_vals % self._feature_configs[0].num_buckets), + row_lengths=tmp_lens) + + output_dict = {'feature': fea_dict} + + lbl_dict = {} + for lbl_name in self._label_fields: + if lbl_name in input_dict: + lbl_dict[lbl_name] = input_dict[lbl_name] + + if len(lbl_dict) > 0: + output_dict['label'] = lbl_dict + + if self._reserve_fields is not None: + output_dict['reserve'] = input_dict['reserve'] + + return output_dict + + def _build(self, mode, params): + if mode == tf.estimator.ModeKeys.TRAIN and self._data_config.num_epochs > 1: + logging.info('will repeat train data for %d epochs' % + self._data_config.num_epochs) + my_files = self._my_files * self._data_config.num_epochs + else: + my_files = self._my_files + + if mode == tf.estimator.ModeKeys.TRAIN: + self._proc_arr = load_parquet.start_data_proc( + self._task_index, + self._task_num, + self._num_proc, + self._file_que, + self._data_que, + self._proc_start_que, + self._proc_stop_que, + self._batch_size, + self._label_fields, + self._effective_fields, + self._reserve_fields, + self._data_config.drop_remainder, + need_pack=False) + else: + lbl_fields = self._label_fields + if mode == tf.estimator.ModeKeys.PREDICT: + lbl_fields = None + self._proc_arr = load_parquet.start_data_proc( + self._task_index, + self._task_num, + self._num_proc, + self._file_que, + self._data_que, + self._proc_start_que, + self._proc_stop_que, + self._batch_size, + lbl_fields, + self._effective_fields, + self._reserve_fields, + False, + need_pack=False) + + for input_file in my_files: + self._file_que.put(input_file) + + # add end signal + for proc in self._proc_arr: + self._file_que.put(None) + logging.info('add input_files to file_que, qsize=%d' % + self._file_que.qsize()) + + out_types = {} + out_shapes = {} + + if mode != tf.estimator.ModeKeys.PREDICT: + for k in self._label_fields: + out_types[k] = tf.int32 + out_shapes[k] = tf.TensorShape([None]) + + if self._reserve_fields is not None: + out_types['reserve'] = {} + out_shapes['reserve'] = {} + for k, t in zip(self._reserve_fields, self._reserve_types): + out_types['reserve'][k] = t + out_shapes['reserve'][k] = tf.TensorShape([None]) + + for k in self._effective_fields: + out_types[k] = (tf.int32, tf.int64) + out_shapes[k] = (tf.TensorShape([None]), tf.TensorShape([None])) + # out_types['sparse_fea'] = (tf.int32, tf.int64) + # out_shapes['sparse_fea'] = (tf.TensorShape([None]), tf.TensorShape([None])) + + dataset = tf.data.Dataset.from_generator( + self._sample_generator, + output_types=out_types, + output_shapes=out_shapes) + num_parallel_calls = self._data_config.num_parallel_calls + dataset = dataset.map( + self._to_fea_dict, num_parallel_calls=num_parallel_calls) + dataset = dataset.prefetch(buffer_size=self._prefetch_size) + # dataset = dataset.map( + # map_func=self._preprocess, num_parallel_calls=num_parallel_calls) + # dataset = dataset.prefetch(buffer_size=self._prefetch_size) + + if mode != tf.estimator.ModeKeys.PREDICT: + dataset = dataset.map(lambda x: + (self._get_features(x), self._get_labels(x))) + # dataset = dataset.apply(tf.data.experimental.prefetch_to_device('/gpu:0')) + # dataset = dataset.prefetch(32) + else: + if self._reserve_fields is not None: + # for predictor with saved model + def _get_with_reserve(fea_dict): + print('fea_dict=%s' % fea_dict) + out_dict = { + 'feature': { + 'ragged_ids': fea_dict['feature']['sparse_fea'][0], + 'ragged_lens': fea_dict['feature']['sparse_fea'][1] + } + } + out_dict['reserve'] = fea_dict['reserve'] + return out_dict + + dataset = dataset.map(_get_with_reserve) + else: + dataset = dataset.map(lambda x: self._get_features(x)) + dataset = dataset.prefetch(buffer_size=self._prefetch_size) + return dataset + + def create_input(self, export_config=None): + + def _input_fn(mode=None, params=None, config=None): + """Build input_fn for estimator. + + Args: + mode: tf.estimator.ModeKeys.(TRAIN, EVAL, PREDICT) + params: `dict` of hyper parameters, from Estimator + config: tf.estimator.RunConfig instance + + Return: + if mode is not None, return: + features: inputs to the model. + labels: groundtruth + else, return: + tf.estimator.export.ServingInputReceiver instance + """ + if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, + tf.estimator.ModeKeys.PREDICT): + # build dataset from self._config.input_path + self._mode = mode + dataset = self._build(mode, params) + return dataset + elif mode is None: # serving_input_receiver_fn for export SavedModel + ragged_ids = array_ops.placeholder(tf.int64, [None], name='ragged_ids') + ragged_lens = array_ops.placeholder( + tf.int32, [None], name='ragged_lens') + inputs = {'ragged_ids': ragged_ids, 'ragged_lens': ragged_lens} + if self._has_ev: + features = {'ragged_ids': ragged_ids, 'ragged_lens': ragged_lens} + else: + features = { + 'ragged_ids': ragged_ids % self._feature_configs[0].num_buckets, + 'ragged_lens': ragged_lens + } + return tf.estimator.export.ServingInputReceiver(features, inputs) + + _input_fn.input_creator = self + return _input_fn diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index a47132813..3871cce4f 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -17,6 +17,7 @@ import easy_rec from easy_rec.python.builders import strategy_builder +from easy_rec.python.compat import estimator_train from easy_rec.python.compat import exporter from easy_rec.python.input.input import Input from easy_rec.python.model.easy_rec_estimator import EasyRecEstimator @@ -353,19 +354,18 @@ def _train_and_evaluate_impl(pipeline_config, # Currently only a single Eval Spec is allowed. train_spec = tf.estimator.TrainSpec( input_fn=train_input_fn, max_steps=train_steps) - if estimator_utils.has_sok(): - estimator.train( - input_fn=train_input_fn, - max_steps=train_spec.max_steps, - hooks=list(train_spec.hooks), - saving_listeners=train_spec.saving_listeners) - train_input_fn.input_creator.stop() - else: - from easy_rec.python.compat import estimator_train - # create eval spec - eval_spec = _create_eval_export_spec( - pipeline_config, eval_data, check_mode=check_mode) - estimator_train.train_and_evaluate(estimator, train_spec, eval_spec) + # if estimator_utils.has_sok(): + estimator.train( + input_fn=train_input_fn, + max_steps=train_spec.max_steps, + hooks=list(train_spec.hooks), + saving_listeners=train_spec.saving_listeners) + train_input_fn.input_creator.stop() + # else: + # # create eval spec + # eval_spec = _create_eval_export_spec( + # pipeline_config, eval_data, check_mode=check_mode) + # estimator_train.train_and_evaluate(estimator, train_spec, eval_spec) logging.info('Train and evaluate finish') if fit_on_eval and (not estimator_utils.is_evaluator()): tf.reset_default_graph() diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index c9079361a..37dbc5954 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -68,8 +68,9 @@ def build_metric_graph(self, eval_config): # tf.reduce_mean(output), tf.reduce_min(self._labels[lbl_name]), # tf.reduce_max(self._labels[lbl_name]), # tf.reduce_mean(self._labels[lbl_name])], message='output') - metric_dict['auc_' + lbl_name] = metrics_tf.auc( - self._labels[lbl_name], output, num_thresholds=1000) + with ops.device('/cpu:0'): + metric_dict['auc_' + lbl_name] = metrics_tf.auc( + self._labels[lbl_name], output, num_thresholds=100000) return metric_dict def build_loss_graph(self): diff --git a/easy_rec/python/protos/dataset.proto b/easy_rec/python/protos/dataset.proto index a4def5cd8..c5226017c 100644 --- a/easy_rec/python/protos/dataset.proto +++ b/easy_rec/python/protos/dataset.proto @@ -224,6 +224,7 @@ message DatasetConfig { HiveRTPInput = 17; HiveParquetInput = 18; ParquetInput = 19; + ParquetInputV2 = 20; CriteoInput = 1001; } required InputType input_type = 10; diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index e5869ed29..ff35b5da7 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -359,7 +359,7 @@ def __init__(self, self._task_idx, self._task_num = get_task_index_and_num() - if sok_dynamic_vars is not None: + if sok_dynamic_vars is not None and len(sok_dynamic_vars) > 0: self._sok_dynamic_vars = sok_dynamic_vars from sparse_operation_kit.experiment import raw_ops as dynamic_variable_ops self._dyn_export = {} @@ -369,6 +369,8 @@ def __init__(self, key_type=dyn_var.key_type, dtype=dyn_var.handle_dtype) self._dyn_export[dyn_var.name] = (indices, values) + else: + self._dyn_export = None if increment_save_config is not None: self._kafka_timeout_ms = os.environ.get('KAFKA_TIMEOUT', 600) * 1000 From 4fd07b995faf60919c9f565111058ecf31a419fc Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Mon, 27 Nov 2023 22:42:44 +0800 Subject: [PATCH 38/67] support rebuild queue --- .../compat/feature_column/feature_column.py | 10 ++++---- easy_rec/python/input/parquet_input_v2.py | 12 ++++++++++ easy_rec/python/main.py | 24 +++++++++---------- easy_rec/python/model/ppnet_v3m.py | 6 ++--- easy_rec/python/utils/estimator_utils.py | 4 ++++ easy_rec/version.py | 2 +- 6 files changed, 36 insertions(+), 22 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 5679b97ca..488565273 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -266,9 +266,8 @@ def _internal_input_layer(features, def _get_logits(): # pylint: disable=missing-docstring builder = _LazyBuilder(features) output_tensors = [] - ordered_columns = [] - for column in sorted(feature_columns, key=lambda x: x.name): - ordered_columns.append(column) + for column in feature_columns: + # for column in sorted(feature_columns, key=lambda x: x.name): with variable_scope.variable_scope( None, default_name=column._var_scope_name): # pylint: disable=protected-access tensor = column._get_dense_tensor( # pylint: disable=protected-access @@ -290,7 +289,6 @@ def _get_logits(): # pylint: disable=missing-docstring cols_to_output_tensors[column] = output_tensor if feature_name_to_output_tensors is not None: feature_name_to_output_tensors[column.raw_name] = output_tensor - _verify_static_batch_size_equality(output_tensors, ordered_columns) return array_ops.concat(output_tensors, 1) def _get_logits_with_sok(): # pylint: disable=missing-docstring @@ -334,7 +332,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring embedding_weights = sok.DynamicVariable( name='embedding_weights', dimension=column.dimension, - initializer='0.0001', # 'random', # column.initializer, + initializer='random {"stddev":0.0025}', # column.initializer, var_type=None if not column.ev_params.use_cache else 'hybrid', trainable=column.trainable and trainable, @@ -357,7 +355,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring embedding_weights = sok.DynamicVariable( name='embedding_weights', dimension=column.dimension, - initializer='random', # column.initializer, + initializer='random {"stddev":0.0025}', # column.initializer, var_type=None if not column.ev_params.use_cache else 'hybrid', trainable=column.trainable and trainable, dtype=dtypes.float32, diff --git a/easy_rec/python/input/parquet_input_v2.py b/easy_rec/python/input/parquet_input_v2.py index 634ac8610..3cf838b4c 100644 --- a/easy_rec/python/input/parquet_input_v2.py +++ b/easy_rec/python/input/parquet_input_v2.py @@ -77,6 +77,14 @@ def __init__(self, self._proc_arr = None + def _rebuild_que(self): + mp_ctxt = multiprocessing.get_context('spawn') + self._data_que = queues.Queue( + name='data_que', ctx=mp_ctxt, maxsize=self._data_config.prefetch_size) + self._file_que = queues.Queue(name='file_que', ctx=mp_ctxt) + self._proc_start_que = queues.Queue(name='proc_start_que', ctx=mp_ctxt) + self._proc_stop_que = queues.Queue(name='proc_stop_que', ctx=mp_ctxt) + def _sample_generator(self): if not self._proc_start: self._proc_start = True @@ -163,7 +171,11 @@ def _any_alive(): # proc.terminate() proc.join() logging.info('join proc done') + + self._rebuild_que() + self._proc_arr = None self._proc_start = False + self._proc_stop = False def _to_fea_dict(self, input_dict): fea_dict = {} diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index 3871cce4f..6bab5a320 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -354,18 +354,18 @@ def _train_and_evaluate_impl(pipeline_config, # Currently only a single Eval Spec is allowed. train_spec = tf.estimator.TrainSpec( input_fn=train_input_fn, max_steps=train_steps) - # if estimator_utils.has_sok(): - estimator.train( - input_fn=train_input_fn, - max_steps=train_spec.max_steps, - hooks=list(train_spec.hooks), - saving_listeners=train_spec.saving_listeners) - train_input_fn.input_creator.stop() - # else: - # # create eval spec - # eval_spec = _create_eval_export_spec( - # pipeline_config, eval_data, check_mode=check_mode) - # estimator_train.train_and_evaluate(estimator, train_spec, eval_spec) + if estimator_utils.has_sok(): + estimator.train( + input_fn=train_input_fn, + max_steps=train_spec.max_steps, + hooks=list(train_spec.hooks), + saving_listeners=train_spec.saving_listeners) + train_input_fn.input_creator.stop() + else: + # create eval spec + eval_spec = _create_eval_export_spec( + pipeline_config, eval_data, check_mode=check_mode) + estimator_train.train_and_evaluate(estimator, train_spec, eval_spec) logging.info('Train and evaluate finish') if fit_on_eval and (not estimator_utils.is_evaluator()): tf.reset_default_graph() diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index 37dbc5954..9828749f2 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -68,9 +68,9 @@ def build_metric_graph(self, eval_config): # tf.reduce_mean(output), tf.reduce_min(self._labels[lbl_name]), # tf.reduce_max(self._labels[lbl_name]), # tf.reduce_mean(self._labels[lbl_name])], message='output') - with ops.device('/cpu:0'): - metric_dict['auc_' + lbl_name] = metrics_tf.auc( - self._labels[lbl_name], output, num_thresholds=100000) + # with ops.device('/cpu:0'): + metric_dict['auc_' + lbl_name] = metrics_tf.auc( + self._labels[lbl_name], output, num_thresholds=20000) return metric_dict def build_loss_graph(self): diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index ff35b5da7..6a84d9a42 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -695,6 +695,10 @@ def _save_sok(self, session, step): def _save(self, session, step): """Saves the latest checkpoint, returns should_stop.""" + if step < 25000 and step % 5000 != 0: + logging.info('ignore saving step: %d' % step) + return + logging.info('Saving checkpoints for %d into %s.', step, self._save_path) for l in self._listeners: # noqa: E741 diff --git a/easy_rec/version.py b/easy_rec/version.py index edc79a5a5..8d913a2ee 100644 --- a/easy_rec/version.py +++ b/easy_rec/version.py @@ -1,3 +1,3 @@ # -*- encoding:utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. -__version__ = '0.7.5' +__version__ = '0.7.5rc9' From 9eab301b5e3a5ff79b209d50e3b809f792adec27 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Tue, 28 Nov 2023 14:33:59 +0800 Subject: [PATCH 39/67] drop old code --- .../compat/feature_column/feature_column.py | 31 ------------------- 1 file changed, 31 deletions(-) diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index 488565273..b644ee7f9 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -492,37 +492,6 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring array_ops.transpose(output_tensor, perm=[1, 0, 2]), [-1, len(lookup_output_ids) * output_tensor.get_shape()[-1]]) - # outputs = sok.lookup_sparse( - # lookup_embeddings, lookup_indices, combiners=lookup_combiners) - # for output, output_id, col in zip(outputs, lookup_output_ids, - # lookup_cols): - # output_tensors[output_id] = output - # if cols_to_output_tensors is not None: - # cols_to_output_tensors[col] = output - # if feature_name_to_output_tensors is not None: - # feature_name_to_output_tensors[col.raw_name] = output - # elif len(lookup_output_ids_with_wgt) > 0: - # outputs = sok.lookup_sparse( - # lookup_embeddings_with_wgt, - # # RaggedTensor .values .row_lengths - # lookup_indices_with_wgt, - # lookup_wgts, - # combiners=lookup_combiners_with_wgt) - # for output, output_id, col in zip(outputs, lookup_output_ids_with_wgt, - # lookup_cols_with_wgt): - # output_tensors[output_id] = output - # if cols_to_output_tensors is not None: - # cols_to_output_tensors[col] = output - # if feature_name_to_output_tensors is not None: - # feature_name_to_output_tensors[col.raw_name] = output - - # if feature_name_to_output_tensors is not None: - # for column, output_tensor in zip( - # sorted(feature_columns, key=lambda x: x.name), output_tensors): - # feature_name_to_output_tensors[column.raw_name] = output_tensor - # _verify_static_batch_size_equality(output_tensors, ordered_columns) - # return array_ops.concat(output_tensors, 1) - # If we're constructing from the `make_template`, that by default adds a # variable scope with the name of the layer. In that case, we dont want to # add another `variable_scope` as that would break checkpoints. From 41df7ce38dfd7a4e6a5bd50cdfa83589f2023137 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Wed, 29 Nov 2023 09:18:54 +0800 Subject: [PATCH 40/67] add fast auc calculate --- easy_rec/python/core/metrics.py | 55 +++++++++++++++++++-- easy_rec/python/input/parquet_input.py | 17 +++++-- easy_rec/python/input/parquet_input_v2.py | 3 +- easy_rec/python/model/easy_rec_estimator.py | 7 ++- easy_rec/python/model/ppnet_v3m.py | 18 +++---- easy_rec/python/utils/estimator_utils.py | 4 -- 6 files changed, 81 insertions(+), 23 deletions(-) diff --git a/easy_rec/python/core/metrics.py b/easy_rec/python/core/metrics.py index e83bb0b3c..ee506b7da 100644 --- a/easy_rec/python/core/metrics.py +++ b/easy_rec/python/core/metrics.py @@ -7,6 +7,10 @@ import numpy as np import tensorflow as tf from sklearn import metrics as sklearn_metrics +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope from easy_rec.python.utils.estimator_utils import get_task_index_and_num from easy_rec.python.utils.io_util import read_data_from_json_path @@ -103,6 +107,50 @@ def value_pyfunc(): return value_op, update_op +def fast_auc(labels, predictions, name, num_thresholds=1e5): + num_thresholds = int(num_thresholds) + + def value_pyfunc(pos_neg_arr, total_pos_neg): + partial_sum_pos = 0 + auc = 0 + total_neg = total_pos_neg[0] + total_pos = total_pos_neg[1] + for i in range(num_thresholds + 1): + auc += (total_pos - partial_sum_pos) * pos_neg_arr[0][i] + partial_sum_pos += pos_neg_arr[1][i] + auc = np.double(auc) / np.double(total_pos * total_neg) + return np.float32(auc) + + with variable_scope.variable_scope(name_or_scope=name), tf.name_scope(name): + neg_pos_var = variable_scope.get_variable( + name='neg_pos_cnt', + shape=[2, num_thresholds + 1], + trainable=False, + collections=[tf.GraphKeys.METRIC_VARIABLES], + initializer=tf.zeros_initializer(), + dtype=tf.int64) + total_var = variable_scope.get_variable( + name='total_cnt', + shape=[2], + trainable=False, + collections=[tf.GraphKeys.METRIC_VARIABLES], + initializer=tf.zeros_initializer(), + dtype=tf.int64) + pred_bins = math_ops.cast(predictions * num_thresholds, dtype=tf.int32) + labels = math_ops.cast(labels, dtype=tf.int32) + labels = array_ops.reshape(labels, [-1, 1]) + pred_bins = array_ops.reshape(pred_bins, [-1, 1]) + update_op0 = state_ops.scatter_nd_add( + neg_pos_var, tf.concat([labels, pred_bins], axis=1), + array_ops.ones(tf.shape(labels)[0], dtype=tf.int64)) + total_pos = math_ops.reduce_sum(labels) + total_neg = array_ops.shape(labels)[0] - total_pos + total_add = math_ops.cast(tf.stack([total_neg, total_pos]), dtype=tf.int64) + update_op1 = state_ops.assign_add(total_var, total_add) + return tf.py_func(value_pyfunc, [neg_pos_var, total_var], + tf.float32), tf.group([update_op0, update_op1]) + + def _distribute_separated_auc_impl(labels, predictions, keys, @@ -275,8 +323,9 @@ def metric_learning_recall_at_k(k, tf.expand_dims(session_ids, 0), tf.expand_dims(session_ids, 1)) labels_equal = tf.logical_and(sessions_equal, labels_equal) mask = tf.logical_and(indices_not_equal, labels_equal) - mask_pos = tf.where(mask, sim_mat, - -tf.ones_like(sim_mat)) # shape: (batch_size, batch_size) + mask_pos = tf.where( + mask, sim_mat, + -array_ops.ones_like(sim_mat)) # shape: (batch_size, batch_size) if isinstance(k, int): _, pos_top_k_idx = tf.nn.top_k(mask_pos, k) # shape: (batch_size, k) return metrics_tf.recall_at_k( @@ -342,7 +391,7 @@ def _get_matrix_mask_indices(matrix, num_rows=None): result = tf.gather(indices[:, 1], idx) # replace invalid elements with -1 result = tf.where( - tf.expand_dims(elem_per_row, 1) > r, result, -tf.ones_like(result)) + tf.expand_dims(elem_per_row, 1) > r, result, -array_ops.ones_like(result)) max_index_per_row = tf.reduce_max(result, axis=1, keepdims=True) max_index_per_row = tf.tile(max_index_per_row, [1, max_elem_per_row]) result = tf.where(result >= 0, result, max_index_per_row) diff --git a/easy_rec/python/input/parquet_input.py b/easy_rec/python/input/parquet_input.py index 84cfc025f..85e5fa47d 100644 --- a/easy_rec/python/input/parquet_input.py +++ b/easy_rec/python/input/parquet_input.py @@ -77,6 +77,14 @@ def __init__(self, self._proc_arr = None + def _rebuild_que(self): + mp_ctxt = multiprocessing.get_context('spawn') + self._data_que = queues.Queue( + name='data_que', ctx=mp_ctxt, maxsize=self._data_config.prefetch_size) + self._file_que = queues.Queue(name='file_que', ctx=mp_ctxt) + self._proc_start_que = queues.Queue(name='proc_start_que', ctx=mp_ctxt) + self._proc_stop_que = queues.Queue(name='proc_stop_que', ctx=mp_ctxt) + def _sample_generator(self): if not self._proc_start: self._proc_start = True @@ -163,7 +171,12 @@ def _any_alive(): # proc.terminate() proc.join() logging.info('join proc done') + + # rebuild for next run, which is necessary for evaluation + self._rebuild_que() + self._proc_arr = None self._proc_start = False + self._proc_stop = False def _to_fea_dict(self, input_dict): fea_dict = {} @@ -242,11 +255,7 @@ def _build(self, mode, params): out_types['reserve'][k] = t out_shapes['reserve'][k] = tf.TensorShape([None]) - # for k in self._effective_fields: - # out_types[k] = (tf.int64, tf.int32) - # out_shapes[k] = (tf.TensorShape([None]), tf.TensorShape([None])) out_types['sparse_fea'] = (tf.int32, tf.int64) - # out_types['sparse_fea'] = (tf.int64, tf.int32) out_shapes['sparse_fea'] = (tf.TensorShape([None]), tf.TensorShape([None])) dataset = tf.data.Dataset.from_generator( diff --git a/easy_rec/python/input/parquet_input_v2.py b/easy_rec/python/input/parquet_input_v2.py index 3cf838b4c..b00558449 100644 --- a/easy_rec/python/input/parquet_input_v2.py +++ b/easy_rec/python/input/parquet_input_v2.py @@ -172,6 +172,7 @@ def _any_alive(): proc.join() logging.info('join proc done') + # rebuild for next run, which is necessary for evaluation self._rebuild_que() self._proc_arr = None self._proc_start = False @@ -285,8 +286,6 @@ def _build(self, mode, params): for k in self._effective_fields: out_types[k] = (tf.int32, tf.int64) out_shapes[k] = (tf.TensorShape([None]), tf.TensorShape([None])) - # out_types['sparse_fea'] = (tf.int32, tf.int64) - # out_shapes['sparse_fea'] = (tf.TensorShape([None]), tf.TensorShape([None])) dataset = tf.data.Dataset.from_generator( self._sample_generator, diff --git a/easy_rec/python/model/easy_rec_estimator.py b/easy_rec/python/model/easy_rec_estimator.py index ec1f71933..2877cc33c 100644 --- a/easy_rec/python/model/easy_rec_estimator.py +++ b/easy_rec/python/model/easy_rec_estimator.py @@ -470,6 +470,7 @@ def _eval_model_fn(self, features, labels, run_config): loss_dict = model.build_loss_graph() loss = tf.add_n(list(loss_dict.values())) loss_dict['total_loss'] = loss + metric_dict = model.build_metric_graph(self.eval_config) for loss_key in loss_dict.keys(): loss_tensor = loss_dict[loss_key] @@ -480,9 +481,13 @@ def _eval_model_fn(self, features, labels, run_config): var_list = ( ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS)) + + metric_variables = ops.get_collection(ops.GraphKeys.METRIC_VARIABLES) + model_ready_for_local_init_op = tf.variables_initializer(metric_variables) scaffold = tf.train.Scaffold( saver=SaverV2( - var_list=var_list, sharded=True, save_relative_paths=True)) + var_list=var_list, sharded=True, save_relative_paths=True), + ready_for_local_init_op=model_ready_for_local_init_op) end = time.time() tf.logging.info('eval graph construct finished. Time %.3fs' % (end - start)) return tf.estimator.EstimatorSpec( diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py index 9828749f2..c77352508 100644 --- a/easy_rec/python/model/ppnet_v3m.py +++ b/easy_rec/python/model/ppnet_v3m.py @@ -6,6 +6,8 @@ import tensorflow as tf from tensorflow.python.framework import ops +from easy_rec.python.core import metrics +# from easy_rec.python.core.easyrec_metrics import metrics_tf from easy_rec.python.model.keep_model.model_ps_mmoe import CustomizedModel from easy_rec.python.model.rank_model import RankModel from easy_rec.python.protos.ppnet_pb2 import PPNet as PPNetConfig @@ -59,18 +61,18 @@ def build_predict_graph(self): def build_metric_graph(self, eval_config): metric_dict = {} - from easy_rec.python.core.easyrec_metrics import metrics_tf for lbl_id in range(len(self._model_conf['label'])): lbl_info = self._model_conf['label'][lbl_id] lbl_name = lbl_info.get('input_name') output = self._prediction_dict.get(lbl_name) - # output = tf.Print(output, [tf.reduce_min(output), tf.reduce_max(output), - # tf.reduce_mean(output), tf.reduce_min(self._labels[lbl_name]), - # tf.reduce_max(self._labels[lbl_name]), - # tf.reduce_mean(self._labels[lbl_name])], message='output') # with ops.device('/cpu:0'): - metric_dict['auc_' + lbl_name] = metrics_tf.auc( - self._labels[lbl_name], output, num_thresholds=20000) + # metric_dict['auc_' + lbl_name] = metrics_tf.auc( + # self._labels[lbl_name], output, num_thresholds=20000) + metric_dict['auc_' + lbl_name] = metrics.fast_auc( + self._labels[lbl_name], + output, + num_thresholds=1e5, + name='auc_' + lbl_name) return metric_dict def build_loss_graph(self): @@ -85,8 +87,6 @@ def build_loss_graph(self): tf.summary.scalar('predict/%s' % lbl_name, tf.reduce_mean(output)) loss_obj = tf.keras.losses.BinaryCrossentropy( reduction='sum_over_batch_size')(self._labels[lbl_name], output) - # loss_obj = tf.Print(loss_obj, [tf.reduce_min(loss_obj), tf.reduce_max(loss_obj), - # tf.reduce_mean(loss_obj), tf.shape(loss_obj)], message='loss_obj') self._loss_dict[lbl_name] = loss_obj * lbl_weight return self._loss_dict diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index 6a84d9a42..ff35b5da7 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -695,10 +695,6 @@ def _save_sok(self, session, step): def _save(self, session, step): """Saves the latest checkpoint, returns should_stop.""" - if step < 25000 and step % 5000 != 0: - logging.info('ignore saving step: %d' % step) - return - logging.info('Saving checkpoints for %d into %s.', step, self._save_path) for l in self._listeners: # noqa: E741 From 51b306c527fb7e62505e850e9579fa991395d44b Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Wed, 29 Nov 2023 10:04:13 +0800 Subject: [PATCH 41/67] add debug info --- easy_rec/python/core/metrics.py | 3 +++ easy_rec/version.py | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/easy_rec/python/core/metrics.py b/easy_rec/python/core/metrics.py index ee506b7da..87206e5b3 100644 --- a/easy_rec/python/core/metrics.py +++ b/easy_rec/python/core/metrics.py @@ -1,6 +1,7 @@ # -*- encoding:utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. import json +import logging import os from collections import defaultdict @@ -119,6 +120,8 @@ def value_pyfunc(pos_neg_arr, total_pos_neg): auc += (total_pos - partial_sum_pos) * pos_neg_arr[0][i] partial_sum_pos += pos_neg_arr[1][i] auc = np.double(auc) / np.double(total_pos * total_neg) + logging.info('fast_auc[%s]: total_pos=%d total_neg=%d total=%d' % + (name, total_pos, total_neg, total_pos + total_neg)) return np.float32(auc) with variable_scope.variable_scope(name_or_scope=name), tf.name_scope(name): diff --git a/easy_rec/version.py b/easy_rec/version.py index 8d913a2ee..5808909e8 100644 --- a/easy_rec/version.py +++ b/easy_rec/version.py @@ -1,3 +1,3 @@ # -*- encoding:utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. -__version__ = '0.7.5rc9' +__version__ = '0.7.5rc10' From 23a379f0a002ff55c57577f881f2b4bb0841c622 Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Sat, 9 Dec 2023 22:43:04 +0800 Subject: [PATCH 42/67] merge master and remove extra files --- .../python/compat/embedding_parallel_saver.py | 301 + .../compat/feature_column/feature_column.py | 243 +- easy_rec/python/compat/optimizers.py | 50 +- easy_rec/python/compat/saver.py | 160 - easy_rec/python/model/easy_rec_estimator.py | 43 +- easy_rec/python/model/ppnet_v3m.py | 94 + easy_rec/python/protos/easy_rec_model.proto | 2 + easy_rec/python/protos/ppnet.proto | 7 + easy_rec/python/protos/train.proto | 1 + easy_rec/python/test/train_eval_test.py | 14 + easy_rec/python/utils/estimator_utils.py | 42 +- taobao_train_data_8192 | 8192 ----------------- 12 files changed, 652 insertions(+), 8497 deletions(-) create mode 100644 easy_rec/python/compat/embedding_parallel_saver.py delete mode 100644 easy_rec/python/compat/saver.py create mode 100644 easy_rec/python/model/ppnet_v3m.py create mode 100644 easy_rec/python/protos/ppnet.proto delete mode 100644 taobao_train_data_8192 diff --git a/easy_rec/python/compat/embedding_parallel_saver.py b/easy_rec/python/compat/embedding_parallel_saver.py new file mode 100644 index 000000000..d4ac05886 --- /dev/null +++ b/easy_rec/python/compat/embedding_parallel_saver.py @@ -0,0 +1,301 @@ +# -*- encoding:utf-8 -*- + +import logging +import os + +import numpy as np +from tensorflow.core.protobuf import saver_pb2 +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +# from tensorflow.python.ops import math_ops +# from tensorflow.python.ops import logging_ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import script_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.platform import gfile +from tensorflow.python.training import saver + +try: + import horovod.tensorflow as hvd + from sparse_operation_kit.experiment import raw_ops as dynamic_variable_ops + from sparse_operation_kit import experiment as sok +except Exception: + dynamic_variable_ops = None + sok = None + +try: + from tensorflow.python.framework.load_library import load_op_library + import easy_rec + load_embed_lib_path = os.path.join(easy_rec.ops_dir, 'libload_embed.so') + load_embed_lib = load_op_library(load_embed_lib_path) +except Exception as ex: + logging.warning('load libload_embed.so failed: %s' % str(ex)) + load_embed_lib = None + + +def _get_embed_part_id(embed_file): + embed_file = embed_file.split('/')[-1] + embed_file = embed_file.split('.')[0] + embed_id = embed_file.split('-')[-1] + return int(embed_id) + + +class EmbeddingParallelSaver(saver.Saver): + + def __init__(self, + var_list=None, + reshape=False, + sharded=False, + max_to_keep=5, + keep_checkpoint_every_n_hours=10000.0, + name=None, + restore_sequentially=False, + saver_def=None, + builder=None, + defer_build=False, + allow_empty=False, + write_version=saver_pb2.SaverDef.V2, + pad_step_number=False, + save_relative_paths=False, + filename=None): + self._kv_vars = [] + self._embed_vars = [] + tf_vars = [] + for var in var_list: + if isinstance(var, sok.DynamicVariable): + self._kv_vars.append(var) + elif '/embedding_weights' in var.name: + logging.info('get one dense embedding: %s' % var.name) + self._embed_vars.append(var) + else: + tf_vars.append(var) + super(EmbeddingParallelSaver, self).__init__( + tf_vars, + reshape=reshape, + sharded=sharded, + max_to_keep=max_to_keep, + keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, + name=name, + restore_sequentially=restore_sequentially, + saver_def=saver_def, + builder=builder, + defer_build=defer_build, + allow_empty=allow_empty, + write_version=write_version, + pad_step_number=pad_step_number, + save_relative_paths=save_relative_paths, + filename=filename) + self._is_build = False + + def _has_embed_vars(self): + return (len(self._kv_vars) + len(self._embed_vars)) > 0 + + def _save_dense_embedding(self, embed_var): + logging.info('task[%d] save_dense_embed: %s' % (hvd.rank(), embed_var.name)) + + def _save_embed(embed, filename, var_name): + task_id = hvd.rank() + filename = filename.decode('utf-8') + var_name = var_name.decode('utf-8').replace('/', '__') + embed_dir = filename + '-embedding/' + logging.info('task[%d] save_dense_embed: %s to %s' % + (task_id, var_name, embed_dir)) + if not gfile.Exists(embed_dir): + gfile.MakeDirs(embed_dir) + embed_file = filename + '-embedding/embed-' + var_name + '_part-%d.bin' % task_id + with gfile.GFile(embed_file, 'wb') as fout: + fout.write(embed.tobytes()) + + if task_id == 0: + # clear old embedding tables + embed_pattern = filename + '-embedding/embed-' + var_name + '_part-*.bin' + embed_files = gfile.Glob(embed_pattern) + for embed_file in embed_files: + embed_id = _get_embed_part_id(embed_file) + if embed_id >= hvd.size(): + gfile.DeleteRecursively(embed_file) + return np.asarray([embed_file], order='C', dtype=np.object) + + file_name = ops.get_default_graph().get_tensor_by_name( + self.saver_def.filename_tensor_name) + save_paths = script_ops.py_func(_save_embed, + [embed_var, file_name, embed_var.name], + dtypes.string) + return save_paths + + def _load_dense_embedding(self, embed_var): + file_name = ops.get_default_graph().get_tensor_by_name( + self.saver_def.filename_tensor_name) + embed_dim = embed_var.get_shape()[-1] + embed_part_size = embed_var.get_shape()[0] + + def _load_embed(embed, embed_dim, embed_part_size, part_id, part_num, + filename, var_name): + filename = filename.decode('utf-8') + var_name = var_name.decode('utf-8').replace('/', '__') + embed_pattern = filename + '-embedding/embed-' + var_name + '_part-*.bin' + embed_files = gfile.Glob(embed_pattern) + + embed_files.sort(key=_get_embed_part_id) + + logging.info('task[%d] embed_files=%s embed_dim=%d embed_part_size=%d' % + (part_id, ','.join(embed_files), embed_dim, embed_part_size)) + + part_embed_vals = np.zeros([embed_part_size, embed_dim], dtype=np.float32) + part_update_cnt = 0 + for embed_file in embed_files: + part_id_o = _get_embed_part_id(embed_file) + with gfile.GFile(embed_file, 'rb') as fin: + embed_val = np.frombuffer(fin.read(), np.float32) + embed_val = embed_val.reshape([-1, embed_dim]) + embed_ids_o = np.arange(len(embed_val)) + embed_ids_o = part_id_o + embed_ids_o * len(embed_files) + sel_ids = np.where( + np.logical_and((embed_ids_o % part_num) == part_id, + embed_ids_o < embed_part_size * part_num))[0] + part_update_cnt += len(sel_ids) + embed_ids = embed_ids_o[sel_ids] + embed_ids_n = np.array(embed_ids / part_num, dtype=np.int64) + part_embed_vals[embed_ids_n] = embed_val[sel_ids] + logging.info('task[%d] load_part_cnt=%d' % (part_id, part_update_cnt)) + return part_embed_vals + + with ops.control_dependencies([embed_var._initializer_op]): + embed_val = script_ops.py_func(_load_embed, [ + embed_var, embed_dim, embed_part_size, + hvd.rank(), + hvd.size(), file_name, embed_var.name + ], dtypes.float32) + embed_val.set_shape(embed_var.get_shape()) + return state_ops.assign(embed_var, embed_val) + + def _save_kv_embedding(self, sok_var): + indices, values = dynamic_variable_ops.dummy_var_export( + sok_var.handle, key_type=sok_var.key_type, dtype=sok_var.handle_dtype) + file_name = ops.get_default_graph().get_tensor_by_name( + self.saver_def.filename_tensor_name) + + def _save_key_vals(indices, values, filename, var_name): + var_name = var_name.decode('utf-8').replace('/', '__') + filename = filename.decode('utf-8') + sok_dir = filename + '-embedding/' + if gfile.Exists(sok_dir): + gfile.DeleteRecursively(sok_dir) + gfile.MakeDirs(sok_dir) + task_id = hvd.rank() + key_file = filename + '-embedding/embed-' + var_name + '-part-%d.key' % task_id + with gfile.GFile(key_file, 'wb') as fout: + fout.write(indices.tobytes()) + val_file = filename + '-embedding/embed-' + var_name + '-part-%d.val' % task_id + with gfile.GFile(val_file, 'wb') as fout: + fout.write(values.tobytes()) + + if task_id == 0: + key_file_pattern = filename + '-embedding/embed-' + var_name + '-part-*.key' + key_files = gfile.Glob(key_file_pattern) + for key_file in key_files: + embed_id = _get_embed_part_id(key_file) + if embed_id >= hvd.size(): + gfile.DeleteRecursively(key_file) + val_file = key_file[:-4] + '.val' + + return np.asarray([key_file, val_file], order='C', dtype=np.object) + + save_paths = script_ops.py_func(_save_key_vals, + [indices, values, file_name, sok_var.name], + dtypes.string) + return save_paths + + def _load_kv_embedding(self, sok_var): + + def _load_key_vals(filename, var_name): + var_name = var_name.decode('utf-8').replace('/', '__') + filename = filename.decode('utf-8') + key_file_pattern = filename + '-embedding/embed-' + var_name + '-part-*.key' + logging.info('key_file_pattern=%s filename=%s var_name=%s var=%s' % + (key_file_pattern, filename, var_name, str(sok_var))) + key_files = gfile.Glob(key_file_pattern) + logging.info('key_file_pattern=%s file_num=%d' % + (key_file_pattern, len(key_files))) + all_keys = [] + all_vals = [] + for key_file in key_files: + with gfile.GFile(key_file, 'rb') as fin: + tmp_keys = np.frombuffer(fin.read(), dtype=np.int64) + tmp_ids = tmp_keys % hvd.size() + tmp_ids = np.where(tmp_ids == hvd.rank())[0] + if len(tmp_ids) == 0: + break + all_keys.append(tmp_keys.take(tmp_ids, axis=0)) + logging.info('part_keys.shape=%s %s %s' % (str( + tmp_keys.shape), str(tmp_ids.shape), str(all_keys[-1].shape))) + + val_file = key_file[:-4] + 'vals' + with gfile.GFile(val_file, 'rb') as fin: + tmp_vals = np.frombuffer( + fin.read(), dtype=np.float32).reshape([-1, sok_var._dimension]) + all_vals.append(tmp_vals.take(tmp_ids, axis=0)) + logging.info('part_vals.shape=%s %s %s' % (str( + tmp_vals.shape), str(tmp_ids.shape), str(all_vals[-1].shape))) + + all_keys = np.concatenate(all_keys, axis=0) + all_vals = np.concatenate(all_vals, axis=0) + + shuffle_ids = np.array(range(len(all_keys))) + np.random.shuffle(shuffle_ids) + all_keys = all_keys.take(shuffle_ids, axis=0) + all_vals = all_vals.take(shuffle_ids, axis=0) + return all_keys, all_vals + + file_name = ops.get_default_graph().get_tensor_by_name( + self.saver_def.filename_tensor_name) + if load_embed_lib is not None: + keys, vals = load_embed_lib.load_kv_embed( + task_index=hvd.rank(), + task_num=hvd.size(), + embed_dim=sok_var._dimension, + var_name='embed-' + sok_var.name.replace('/', '__'), + ckpt_path=file_name) + else: + logging.warning('libload_embed.so not loaded, will use python script_ops') + keys, vals = script_ops.py_func(_load_key_vals, [file_name, sok_var.name], + (dtypes.int64, dtypes.float32)) + with ops.control_dependencies([sok_var._initializer_op]): + return dynamic_variable_ops.dummy_var_assign(sok_var.handle, keys, vals) + + def build(self): + if self._is_built: + return + super(EmbeddingParallelSaver, self).build() + if self.saver_def.restore_op_name and self._has_embed_vars(): + # load data from the model + restore_ops = [] + for sok_var in self._kv_vars: + restore_ops.append(self._load_kv_embedding(sok_var)) + for embed_var in self._embed_vars: + restore_ops.append(self._load_dense_embedding(embed_var)) + old_restore_op = ops.get_default_graph().get_operation_by_name( + self.saver_def.restore_op_name) + restore_ops.append(old_restore_op) + restore_op_n = control_flow_ops.group(restore_ops) + self.saver_def.restore_op_name = restore_op_n.name + + if self.saver_def.save_tensor_name and self._has_embed_vars(): + file_name = ops.get_default_graph().get_tensor_by_name( + self.saver_def.filename_tensor_name) + save_part_ops = [] + for sok_var in self._kv_vars: + save_part_op = self._save_kv_embedding(sok_var) + save_part_ops.append(save_part_op) + for embed_var in self._embed_vars: + save_part_op = self._save_dense_embedding(embed_var) + save_part_ops.append(save_part_op) + old_save_op = ops.get_default_graph().get_tensor_by_name( + self.saver_def.save_tensor_name) + # only the first worker needs to save non embedding variables + if hvd.rank() == 0: + save_part_ops.append(old_save_op) + with ops.control_dependencies(save_part_ops): + save_op_n = array_ops.identity(file_name) + self.saver_def.save_tensor_name = save_op_n.name diff --git a/easy_rec/python/compat/feature_column/feature_column.py b/easy_rec/python/compat/feature_column/feature_column.py index b644ee7f9..edf4e6bf0 100644 --- a/easy_rec/python/compat/feature_column/feature_column.py +++ b/easy_rec/python/compat/feature_column/feature_column.py @@ -239,6 +239,96 @@ def embedding_lookup_ragged(embedding_weights, return embeddings +# model parallel embedding lookup +def embedding_parallel_lookup(embedding, + lookup_indices, + output_ids, + is_training, + output_tensors=None): + N = len(output_ids) + # first concat all the ids and unique + if isinstance(lookup_indices, dict) and 'sparse_fea' in lookup_indices.keys(): + # all_uniq_ids, uniq_idx, segment_lens = features['sparse_fea'] + all_ids, segment_lens = lookup_indices['sparse_fea'] + all_uniq_ids, uniq_idx = array_ops.unique(all_ids) + cumsum_lens = math_ops.cumsum(segment_lens) + segment_ids = array_ops.searchsorted( + cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') + elif isinstance(lookup_indices, dict) and 'ragged_ids' in lookup_indices.keys( + ) and 'ragged_lens' in lookup_indices.keys(): + all_ids, segment_lens = lookup_indices['ragged_ids'], lookup_indices[ + 'ragged_lens'] + all_uniq_ids, uniq_idx = array_ops.unique(all_ids) + cumsum_lens = math_ops.cumsum(segment_lens) + segment_ids = array_ops.searchsorted( + cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') + else: + with ops.device('/cpu:0'): + all_ids = array_ops.concat([x.values for x in lookup_indices], axis=0) + segment_ids = array_ops.concat([x.indices[:, 0] for x in lookup_indices], + axis=0) + all_uniq_ids, uniq_idx = array_ops.unique(all_ids) + + num_parts = hvd.size() + if num_parts > 1: + # dynamic partition + p_assignments = math_ops.cast(all_uniq_ids % num_parts, dtypes.int32) + gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, p_assignments, + num_parts) + original_ids = math_ops.range(array_ops.size(all_uniq_ids)) + original_part_ids = data_flow_ops.dynamic_partition(original_ids, + p_assignments, + num_parts) + # all2all + split_sizes = array_ops.concat([array_ops.shape(x) for x in gather_ids], + axis=0) + send_ids = array_ops.concat(gather_ids, axis=0) + recv_ids, recv_lens = hvd.alltoall(send_ids, split_sizes) + + # read embedding from dynamic variable + if isinstance(embedding, sok.DynamicVariable): + send_embed = embedding.sparse_read( + recv_ids, lookup_only=(not is_training)) + else: + # find in subarray position + # 0 2 4 6 8 10 ... + # 1 3 5 7 9 11 ... + recv_ids = math_ops.cast(recv_ids / num_parts, dtypes.int64) + send_embed = array_ops.gather(embedding, recv_ids) + + # all2all + recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) + recv_embeddings = array_ops.split( + recv_embeddings, num_or_size_splits=split_sizes) + recv_embeddings = data_flow_ops.parallel_dynamic_stitch( + original_part_ids, recv_embeddings, name='parallel_dynamic_stitch') + embeddings = math_ops.sparse_segment_sum( + recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') + else: + if isinstance(embedding, sok.DynamicVariable): + recv_embeddings = embedding.sparse_read( + all_uniq_ids, lookup_only=(not is_training)) + else: + recv_embeddings = array_ops.gather(embedding, all_uniq_ids) + embeddings = math_ops.sparse_segment_sum( + recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') + # all_embed = array_ops.gather(recv_embeddings, uniq_idx) + if isinstance(embedding, sok.DynamicVariable): + embed_dim = embedding._dimension + output_tensor = array_ops.reshape(embeddings, [N, -1, embed_dim]) + else: + embed_dim = embedding.get_shape()[-1] + output_tensor = array_ops.reshape(embeddings, [N, -1, embed_dim]) + + if output_tensors is not None: + outputs = array_ops.split(output_tensor, num_or_size_splits=N, axis=0) + for output, output_id in zip(outputs, output_ids): + output_tensors[output_id] = array_ops.squeeze(output, axis=0) + + return array_ops.reshape( + array_ops.transpose(output_tensor, perm=[1, 0, 2]), [-1, N * embed_dim]) + + def _internal_input_layer(features, feature_columns, weight_collections=None, @@ -292,24 +382,25 @@ def _get_logits(): # pylint: disable=missing-docstring return array_ops.concat(output_tensors, 1) def _get_logits_with_sok(): # pylint: disable=missing-docstring - assert sok is not None, 'sok is not installed' assert hvd is not None, 'horovod is not installed' builder = _LazyBuilder(features) output_tensors = [] ordered_columns = [] lookup_embeddings = [] - lookup_indices = [] + lookup_indices = None lookup_combiners = [] lookup_cols = [] lookup_output_ids = [] lookup_wgts = [] shared_weights = {} + dense_cnt = 0 for column in feature_columns: ordered_columns.append(column) with variable_scope.variable_scope( None, default_name=column._var_scope_name): # pylint: disable=protected-access + # for features which does not require embedding if 'Embedding' not in str(type(column)): output = column._get_dense_tensor( builder, weight_collections, trainable=trainable) @@ -318,7 +409,10 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring cols_to_output_tensors[column] = output if feature_name_to_output_tensors is not None: feature_name_to_output_tensors[column.raw_name] = output + dense_cnt += 1 continue + + # for features require embedding num_buckets = column.categorical_column.num_buckets + hvd.size() - 1 per_worker_buckets = num_buckets // hvd.size() embedding_shape = (per_worker_buckets, column.dimension) @@ -329,6 +423,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring else: with ops.device('/gpu:0'): if column.ev_params is not None: + assert sok is not None, 'sok is not installed' embedding_weights = sok.DynamicVariable( name='embedding_weights', dimension=column.dimension, @@ -352,6 +447,7 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring else: with ops.device('/gpu:0'): if column.ev_params is not None: + assert sok is not None, 'sok is not installed' embedding_weights = sok.DynamicVariable( name='embedding_weights', dimension=column.dimension, @@ -382,8 +478,20 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring # SparseTensor RaggedTensor # features are not gathered into one, may have # performance issues - if 'sparse_fea' not in features.keys( - ) and 'ragged_ids' not in features.keys(): + if 'sparse_fea' in features.keys(): + if lookup_indices is None: + lookup_indices = {'sparse_fea': features['sparse_fea']} + elif 'ragged_ids' in features.keys(): + if lookup_indices is None: + lookup_indices = { + 'ragged_ids': features['ragged_ids'], + 'ragged_lens': features['ragged_lens'] + } + if 'ragged_wgts' in features: + lookup_indices['ragged_wgts'] = features['ragged_wgts'] + else: + if lookup_indices is None: + lookup_indices = [] with ops.device('/cpu:0'): sparse_tensors = column.categorical_column._get_sparse_tensors( builder, @@ -397,100 +505,49 @@ def _get_logits_with_sok(): # pylint: disable=missing-docstring ops.GraphKeys.GLOBAL_VARIABLES, scope=variable_scope.get_variable_scope().name) - # do sok lookup + # do embedding parallel lookup if len(lookup_output_ids) > 0: - # first concat all the ids and unique - if isinstance(features, dict) and 'sparse_fea' in features.keys(): - # all_uniq_ids, uniq_idx, segment_lens = features['sparse_fea'] - all_ids, segment_lens = features['sparse_fea'] - all_uniq_ids, uniq_idx = array_ops.unique(all_ids) - cumsum_lens = math_ops.cumsum(segment_lens) - segment_ids = array_ops.searchsorted( - cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') - elif isinstance(features, dict) and 'ragged_ids' in features.keys( - ) and 'ragged_lens' in features.keys(): - all_ids, segment_lens = features['ragged_ids'], features['ragged_lens'] - all_uniq_ids, uniq_idx = array_ops.unique(all_ids) - cumsum_lens = math_ops.cumsum(segment_lens) - segment_ids = array_ops.searchsorted( - cumsum_lens, math_ops.range(cumsum_lens[-1]), side='right') + packed_input = ('sparse_fea' in features or 'ragged_ids' in features) + if packed_input: + uniq_embed_cnt = len(set(lookup_embeddings)) + assert uniq_embed_cnt == 1, 'only one uniq embed is support for packed inputs' + outputs = embedding_parallel_lookup(lookup_embeddings[0], + lookup_indices, lookup_output_ids, + is_training, output_tensors) else: - with ops.device('/cpu:0'): - all_ids = array_ops.concat([x.values for x in lookup_indices], axis=0) - segment_ids = array_ops.concat( - [x.indices[:, 0] for x in lookup_indices], axis=0) - all_uniq_ids, uniq_idx = array_ops.unique(all_ids) - - num_parts = hvd.size() - if num_parts > 1: - # dynamic partition - # from sparse_operation_kit.experiment import raw_ops - p_assignments = math_ops.cast(all_uniq_ids % num_parts, dtypes.int32) - gather_ids = data_flow_ops.dynamic_partition(all_uniq_ids, - p_assignments, num_parts) - original_ids = math_ops.range(array_ops.size(all_uniq_ids)) - original_part_ids = data_flow_ops.dynamic_partition( - original_ids, p_assignments, num_parts) - # all2all - split_sizes = array_ops.concat([array_ops.shape(x) for x in gather_ids], - axis=0) - send_ids = array_ops.concat(gather_ids, axis=0) - # send_ids = logging_ops.Print(send_ids, [array_ops.shape(send_ids), - # array_ops.shape(p_assignments) ] + - # [ array_ops.shape(x) for x in gather_ids] - # , message='send_ids_dbg_%d' % num_parts) - recv_ids, recv_lens = hvd.alltoall(send_ids, split_sizes) - - # read embedding from dynamic variable - if isinstance(lookup_embeddings[0], sok.DynamicVariable): - send_embed = lookup_embeddings[0].sparse_read( - recv_ids, lookup_only=(not is_training)) - else: - # find in subarray position - # 0 2 4 6 8 10 ... - # 1 3 5 7 9 11 ... - recv_ids = recv_ids / num_parts - send_embed = array_ops.gather(lookup_embeddings[0], recv_ids) - - # all2all - recv_embeddings, _ = hvd.alltoall(send_embed, recv_lens) - recv_embeddings = array_ops.split( - recv_embeddings, num_or_size_splits=split_sizes) - recv_embeddings = data_flow_ops.parallel_dynamic_stitch( - original_part_ids, recv_embeddings, name='parallel_dynamic_stitch') - embeddings = math_ops.sparse_segment_sum( - recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') - else: - if isinstance(lookup_embeddings[0], sok.DynamicVariable): - recv_embeddings = lookup_embeddings[0].sparse_read( - all_uniq_ids, lookup_only=(not is_training)) - else: - recv_embeddings = array_ops.gather(lookup_embeddings[0], all_uniq_ids) - embeddings = math_ops.sparse_segment_sum( - recv_embeddings, uniq_idx, segment_ids, name='sparse_segment_sum') - # all_embed = array_ops.gather(recv_embeddings, uniq_idx) - if isinstance(lookup_embeddings[0], sok.DynamicVariable): - output_tensor = array_ops.reshape( - embeddings, - [len(lookup_output_ids), -1, lookup_embeddings[0]._dimension]) - else: - embed_dim = lookup_embeddings[0].get_shape()[-1] - output_tensor = array_ops.reshape( - embeddings, [len(lookup_output_ids), -1, embed_dim]) + # group lookup_embeddings + grouped_inputs = {} + for embedding, lookup_indice, output_id in zip(lookup_embeddings, + lookup_indices, + lookup_output_ids): + if embedding not in grouped_inputs: + grouped_inputs[embedding] = { + 'lookup_indice': [lookup_indice], + 'output_id': [output_id] + } + else: + grouped_inputs[embedding]['lookup_indice'].append(lookup_indice) + grouped_inputs[embedding]['output_id'].append(output_id) - outputs = array_ops.split( - output_tensor, num_or_size_splits=len(lookup_output_ids), axis=0) - for output, output_id, col in zip(outputs, lookup_output_ids, - lookup_cols): - output_tensors[output_id] = output - if cols_to_output_tensors is not None: - cols_to_output_tensors[col] = output + for embedding in grouped_inputs: + lookup_indices = grouped_inputs[embedding]['lookup_indice'] + output_ids = grouped_inputs[embedding]['output_id'] + outputs = embedding_parallel_lookup(embedding, lookup_indices, + output_ids, is_training, + output_tensors) + + for output_tensor, col in zip(output_tensors, feature_columns): if feature_name_to_output_tensors is not None: - feature_name_to_output_tensors[col.raw_name] = output + feature_name_to_output_tensors[col.raw_name] = output_tensor + if cols_to_output_tensors is not None: + cols_to_output_tensors[col] = output_tensor - return array_ops.reshape( - array_ops.transpose(output_tensor, perm=[1, 0, 2]), - [-1, len(lookup_output_ids) * output_tensor.get_shape()[-1]]) + if packed_input and dense_cnt == 0: + return outputs + else: + return array_ops.concat(output_tensors, axis=1) + else: + return array_ops.concat(output_tensors, axis=1) # If we're constructing from the `make_template`, that by default adds a # variable scope with the name of the layer. In that case, we dont want to diff --git a/easy_rec/python/compat/optimizers.py b/easy_rec/python/compat/optimizers.py index 99098073a..57686abdf 100644 --- a/easy_rec/python/compat/optimizers.py +++ b/easy_rec/python/compat/optimizers.py @@ -92,7 +92,8 @@ def optimize_loss(loss, colocate_gradients_with_ops=False, not_apply_grad_after_first_step=False, increment_global_step=True, - incr_save=False): + incr_save=False, + embedding_parallel=False): """Given loss and parameters for optimizer, returns a training op. Various ways of passing optimizers include: @@ -164,6 +165,8 @@ def optimize_loss(loss, different parts of the model), use this arg to avoid incrementing `global_step` more times than necessary. incr_save: increment dump checkpoints. + embedding_parallel: whether to shard embedding and place embedding parts on + different works. Returns: Training op. @@ -268,7 +271,7 @@ def optimize_loss(loss, colocate_gradients_with_ops=colocate_gradients_with_ops) if estimator_utils.has_hvd() and hvd.size() > 1: - if not estimator_utils.has_sok(): + if not (estimator_utils.has_hvd() and embedding_parallel): # embedding parameters not partitioned reduced_grads = [] for g, v in gradients: @@ -333,23 +336,25 @@ def optimize_loss(loss, summary.scalar('loss', loss) # Add histograms for variables, gradients and gradient norms. - # for gradient, variable in gradients: - # if isinstance(gradient, indexed_slices.IndexedSlices): - # grad_values = gradient.values - # else: - # grad_values = gradient - - # if grad_values is not None: - # var_name = variable.name.replace(':', '_') - # if 'gradients' in summaries: - # summary.histogram('gradients/%s' % var_name, grad_values) - # if 'gradient_norm' in summaries: - # summary.scalar('gradient_norm/%s' % var_name, - # clip_ops.global_norm([grad_values])) + if not embedding_parallel: + for gradient, variable in gradients: + if isinstance(gradient, indexed_slices.IndexedSlices): + grad_values = gradient.values + else: + grad_values = gradient + + if grad_values is not None: + var_name = variable.name.replace(':', '_') + if 'gradients' in summaries: + summary.histogram('gradients/%s' % var_name, grad_values) + if 'gradient_norm' in summaries: + summary.scalar('gradient_norm/%s' % var_name, + clip_ops.global_norm([grad_values])) if clip_gradients is not None and ('global_gradient_norm' in summaries or 'gradient_norm' in summaries): - sparse_norm, dense_norm, grad_norm = _get_grad_norm(gradients) + sparse_norm, dense_norm, grad_norm = _get_grad_norm( + gradients, embedding_parallel) summary.scalar('global_norm/clipped_sparse_grad', sparse_norm) summary.scalar('global_norm/clipped_dense_grad', dense_norm) summary.scalar('global_norm/clipped_gradient_norm', grad_norm) @@ -392,11 +397,14 @@ def _get_grad_norm(grads_and_vars): dense_norms = [] for grad, var in grads_and_vars: if isinstance(grad, indexed_slices.IndexedSlices): - sparse_norms.append( - hvd.allreduce( - gen_nn_ops.l2_loss(grad.values), - op=hvd.Sum, - compression=hvd.compression.NoneCompressor)) + if hvd is not None: + sparse_norms.append( + hvd.allreduce( + gen_nn_ops.l2_loss(grad.values), + op=hvd.Sum, + compression=hvd.compression.NoneCompressor)) + else: + sparse_norms.append(gen_nn_ops.l2_loss(grad.values)) else: dense_norms.append(gen_nn_ops.l2_loss(grad)) all_norms = sparse_norms + dense_norms diff --git a/easy_rec/python/compat/saver.py b/easy_rec/python/compat/saver.py deleted file mode 100644 index f9fda6e92..000000000 --- a/easy_rec/python/compat/saver.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- encoding:utf-8 -*- - -import logging -import os - -import numpy as np -from tensorflow.core.protobuf import saver_pb2 -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -# from tensorflow.python.ops import logging_ops -# from tensorflow.python.ops import math_ops -# from tensorflow.python.ops import array_ops -from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import script_ops -from tensorflow.python.platform import gfile -from tensorflow.python.training import saver - -try: - import horovod.tensorflow as hvd - from sparse_operation_kit.experiment import raw_ops as dynamic_variable_ops - from sparse_operation_kit import experiment as sok -except Exception: - dynamic_variable_ops = None - sok = None - -try: - from tensorflow.python.framework.load_library import load_op_library - import easy_rec - load_embed_lib_path = os.path.join(easy_rec.ops_dir, 'libload_embed.so') - load_embed_lib = load_op_library(load_embed_lib_path) -except Exception as ex: - logging.warning('load libload_embed.so failed: %s' % str(ex)) - load_embed_lib = None - - -class SaverV2(saver.Saver): - - def __init__(self, - var_list=None, - reshape=False, - sharded=False, - max_to_keep=5, - keep_checkpoint_every_n_hours=10000.0, - name=None, - restore_sequentially=False, - saver_def=None, - builder=None, - defer_build=False, - allow_empty=False, - write_version=saver_pb2.SaverDef.V2, - pad_step_number=False, - save_relative_paths=False, - filename=None): - self._sok_vars = [] - tf_vars = [] - if sok is None: - tf_vars = var_list - else: - for var in var_list: - if isinstance(var, sok.DynamicVariable): - self._sok_vars.append(var) - else: - tf_vars.append(var) - super(SaverV2, self).__init__( - tf_vars, - reshape=reshape, - sharded=sharded, - max_to_keep=max_to_keep, - keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, - name=name, - restore_sequentially=restore_sequentially, - saver_def=saver_def, - builder=builder, - defer_build=defer_build, - allow_empty=allow_empty, - write_version=write_version, - pad_step_number=pad_step_number, - save_relative_paths=save_relative_paths, - filename=filename) - self._is_build = False - - def _save_sok_embedding(self, sok_var): - pass - - def _load_sok_embedding(self, sok_var): - - def _load_key_vals(filename, var_name): - var_name = var_name.decode('utf-8').replace('/', '__') - filename = filename.decode('utf-8') - key_file_pattern = filename + '-sok/embed-' + var_name + '-part-*.keys' - logging.info('key_file_pattern=%s filename=%s var_name=%s var=%s' % - (key_file_pattern, filename, var_name, str(sok_var))) - key_files = gfile.Glob(key_file_pattern) - logging.info('key_file_pattern=%s file_num=%d' % - (key_file_pattern, len(key_files))) - all_keys = [] - all_vals = [] - for key_file in key_files: - with gfile.GFile(key_file, 'rb') as fin: - tmp_keys = np.frombuffer(fin.read(), dtype=np.int64) - tmp_ids = tmp_keys % hvd.size() - tmp_ids = np.where(tmp_ids == hvd.rank())[0] - if len(tmp_ids) == 0: - break - all_keys.append(tmp_keys.take(tmp_ids, axis=0)) - logging.info('tmp_keys.shape=%s %s %s' % (str( - tmp_keys.shape), str(tmp_ids.shape), str(all_keys[-1].shape))) - - val_file = key_file[:-4] + 'vals' - with gfile.GFile(val_file, 'rb') as fin: - tmp_vals = np.frombuffer( - fin.read(), dtype=np.float32).reshape([-1, sok_var._dimension]) - all_vals.append(tmp_vals.take(tmp_ids, axis=0)) - logging.info('tmp_vals.shape=%s %s %s' % (str( - tmp_vals.shape), str(tmp_ids.shape), str(all_vals[-1].shape))) - - all_keys = np.concatenate(all_keys, axis=0) - all_vals = np.concatenate(all_vals, axis=0) - - shuffle_ids = np.array(range(len(all_keys))) - np.random.shuffle(shuffle_ids) - all_keys = all_keys.take(shuffle_ids, axis=0) - all_vals = all_vals.take(shuffle_ids, axis=0) - return all_keys, all_vals - - file_name = ops.get_default_graph().get_tensor_by_name( - self.saver_def.filename_tensor_name) - if load_embed_lib is not None: - keys, vals = load_embed_lib.load_kv_embed( - task_index=hvd.rank(), - task_num=hvd.size(), - embed_dim=sok_var._dimension, - var_name='embed-' + sok_var.name.replace('/', '__'), - ckpt_path=file_name) - else: - logging.warning('libload_embed.so not loaded, will use python script_ops') - keys, vals = script_ops.py_func(_load_key_vals, [file_name, sok_var.name], - (dtypes.int64, dtypes.float32)) - with ops.control_dependencies([sok_var._initializer_op]): - return dynamic_variable_ops.dummy_var_assign(sok_var.handle, keys, vals) - - def build(self): - if self._is_built: - return - super(SaverV2, self).build() - if self.saver_def.restore_op_name and len(self._sok_vars) > 0: - # load data from the model - restore_ops = [] - for sok_var in self._sok_vars: - restore_ops.append(self._load_sok_embedding(sok_var)) - old_restore_op = ops.get_default_graph().get_operation_by_name( - self.saver_def.restore_op_name) - restore_ops.append(old_restore_op) - restore_op_n = control_flow_ops.group(restore_ops) - self.saver_def.restore_op_name = restore_op_n.name - # TODO - # if self.saver_def.save_tensor_name: - # save_sok_ops = [] - # for sok_var in self._sok_vars: - # save_sok_ops. diff --git a/easy_rec/python/model/easy_rec_estimator.py b/easy_rec/python/model/easy_rec_estimator.py index 2877cc33c..ef22560c2 100644 --- a/easy_rec/python/model/easy_rec_estimator.py +++ b/easy_rec/python/model/easy_rec_estimator.py @@ -29,7 +29,6 @@ from easy_rec.python.compat.early_stopping import stop_if_no_decrease_hook from easy_rec.python.compat.early_stopping import stop_if_no_increase_hook from easy_rec.python.compat.ops import GraphKeys -from easy_rec.python.compat.saver import SaverV2 from easy_rec.python.input.input import Input from easy_rec.python.layers.utils import _tensor_to_tensorinfo from easy_rec.python.protos.pipeline_pb2 import EasyRecConfig @@ -40,6 +39,8 @@ from easy_rec.python.utils import pai_util from easy_rec.python.utils.multi_optimizer import MultiOptimizer +from easy_rec.python.compat.embedding_parallel_saver import EmbeddingParallelSaver # NOQA + try: import horovod.tensorflow as hvd except Exception: @@ -315,6 +316,11 @@ def _train_model_fn(self, features, labels, run_config): else: all_train_vars = tf.trainable_variables() + embedding_parallel = self.train_config.train_distribute in ( + DistributionStrategy.SokStrategy, + DistributionStrategy.EmbeddingParallelStrategy) + if embedding_parallel: + logging.info('embedding_parallel is enabled') train_op = optimizers.optimize_loss( loss=loss, global_step=tf.train.get_global_step(), @@ -328,7 +334,8 @@ def _train_model_fn(self, features, labels, run_config): not_apply_grad_after_first_step=run_config.is_chief and self._pipeline_config.data_config.chief_redundant, name='', # Preventing scope prefix on all variables. - incr_save=(self.incr_save_config is not None)) + incr_save=(self.incr_save_config is not None), + embedding_parallel=embedding_parallel) # online evaluation metric_update_op_dict = None @@ -411,16 +418,16 @@ def _train_model_fn(self, features, labels, run_config): tf.initializers.variables(incompatiable_shape_restore)) sok_vars = [] - other_vars = [] for tmp_var in var_list: - if 'Dynamic' in str(type(tmp_var)): + if sok is not None and isinstance(tmp_var, sok.DynamicVariable): sok_vars.append(tmp_var) - else: - other_vars.append(tmp_var) - # var_list = other_vars + + saver_cls = saver.Saver + if embedding_parallel: + saver_cls = EmbeddingParallelSaver scaffold = tf.train.Scaffold( - saver=SaverV2( + saver=saver_cls( var_list=var_list, sharded=True, max_to_keep=self.train_config.keep_checkpoint_max, @@ -482,10 +489,17 @@ def _eval_model_fn(self, features, labels, run_config): ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS)) + embedding_parallel = self.train_config.train_distribute in ( + DistributionStrategy.SokStrategy, + DistributionStrategy.EmbeddingParallelStrategy) + saver_cls = saver.Saver + if embedding_parallel: + saver_cls = EmbeddingParallelSaver + metric_variables = ops.get_collection(ops.GraphKeys.METRIC_VARIABLES) model_ready_for_local_init_op = tf.variables_initializer(metric_variables) scaffold = tf.train.Scaffold( - saver=SaverV2( + saver=saver_cls( var_list=var_list, sharded=True, save_relative_paths=True), ready_for_local_init_op=model_ready_for_local_init_op) end = time.time() @@ -630,8 +644,17 @@ def _export_model_fn(self, features, labels, run_config, params): var_list = ( ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS)) + + embedding_parallel = self.train_config.train_distribute in ( + DistributionStrategy.SokStrategy, + DistributionStrategy.EmbeddingParallelStrategy) + + saver_cls = saver.Saver + if embedding_parallel: + saver_cls = EmbeddingParallelSaver + scaffold = tf.train.Scaffold( - saver=SaverV2( + saver=saver_cls( var_list=var_list, sharded=True, save_relative_paths=True)) return tf.estimator.EstimatorSpec( diff --git a/easy_rec/python/model/ppnet_v3m.py b/easy_rec/python/model/ppnet_v3m.py new file mode 100644 index 000000000..d7939050b --- /dev/null +++ b/easy_rec/python/model/ppnet_v3m.py @@ -0,0 +1,94 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import json +import logging + +import tensorflow as tf +from tensorflow.python.framework import ops + +from easy_rec.python.core import metrics +# from easy_rec.python.core.easyrec_metrics import metrics_tf +from easy_rec.python.model.keep_model.model_ps_mmoe import CustomizedModel +from easy_rec.python.model.rank_model import RankModel +from easy_rec.python.protos.ppnet_pb2 import PPNetV3 as PPNetConfig + +if tf.__version__ >= '2.0': + tf = tf.compat.v1 + + +class PPNetV3M(RankModel): + + def __init__(self, + model_config, + feature_configs, + features, + labels=None, + is_training=False): + super(PPNetV3M, self).__init__(model_config, feature_configs, features, + labels, is_training) + assert self._model_config.WhichOneof('model') == 'ppnet', \ + 'invalid model config: %s' % self._model_config.WhichOneof('model') + self._model_config = self._model_config.ppnet + assert isinstance(self._model_config, PPNetConfig) + + self._features, _ = self._input_layer(self._feature_dict, 'all') + + with open(self._model_config.model_conf, 'r') as fin: + self._model_conf = json.load(fin) + indim = self._features.get_shape()[1] + logging.info('ppnetv3m indim = %d' % indim) + self._keras_model = CustomizedModel(self._model_conf, indim) + + def build_predict_graph(self): + # self._add_to_prediction_dict(tower_outputs) + output_list = self._keras_model(self._features, self._is_training) + trainable_variables = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES) + for var in self._keras_model.trainable_variables: + if var not in trainable_variables: + ops.add_to_collection(ops.GraphKeys.TRAINABLE_VARIABLES, var) + + update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS) + for var in self._keras_model.updates: + if var not in update_ops: + ops.add_to_collection(ops.GraphKeys.UPDATE_OPS, var) + + for lbl_id in range(len(self._model_conf['label'])): + lbl_info = self._model_conf['label'][lbl_id] + lbl_name = lbl_info.get('input_name') + output = output_list[lbl_id] + self._prediction_dict[lbl_name] = output + # return self._prediction_dict + + def build_metric_graph(self, eval_config): + metric_dict = {} + for lbl_id in range(len(self._model_conf['label'])): + lbl_info = self._model_conf['label'][lbl_id] + lbl_name = lbl_info.get('input_name') + output = self._prediction_dict.get(lbl_name) + # with ops.device('/cpu:0'): + # metric_dict['auc_' + lbl_name] = metrics_tf.auc( + # self._labels[lbl_name], output, num_thresholds=20000) + metric_dict['auc_' + lbl_name] = metrics.fast_auc( + self._labels[lbl_name], + output, + num_thresholds=1e5, + name='auc_' + lbl_name) + return metric_dict + + def build_loss_graph(self): + for lbl_id in range(len(self._model_conf['label'])): + lbl_info = self._model_conf['label'][lbl_id] + lbl_name = lbl_info.get('input_name') + lbl_weight = lbl_info.get('weight') + logging.info('target_name=%s target_weight=%.3f' % (lbl_name, lbl_weight)) + output = self._prediction_dict.get(lbl_name) + tf.summary.scalar('label/%s' % lbl_name, + tf.reduce_mean(tf.to_float(self._labels[lbl_name]))) + tf.summary.scalar('predict/%s' % lbl_name, tf.reduce_mean(output)) + loss_obj = tf.keras.losses.BinaryCrossentropy( + reduction='sum_over_batch_size')(self._labels[lbl_name], output) + self._loss_dict[lbl_name] = loss_obj * lbl_weight + return self._loss_dict + + def get_outputs(self): + return [x['input_name'] for x in self._model_conf['label']] diff --git a/easy_rec/python/protos/easy_rec_model.proto b/easy_rec/python/protos/easy_rec_model.proto index 6cfa48908..1a3333d84 100644 --- a/easy_rec/python/protos/easy_rec_model.proto +++ b/easy_rec/python/protos/easy_rec_model.proto @@ -93,6 +93,8 @@ message EasyRecModel { PLE ple = 305; RocketLaunching rocket_launching = 401; + + PPNetV3 ppnet = 402; } repeated SeqAttGroupConfig seq_att_groups = 7; // implemented in easy_rec/python/model/easy_rec_estimator diff --git a/easy_rec/python/protos/ppnet.proto b/easy_rec/python/protos/ppnet.proto new file mode 100644 index 000000000..1ecaecc8e --- /dev/null +++ b/easy_rec/python/protos/ppnet.proto @@ -0,0 +1,7 @@ +syntax = "proto2"; +package protos; + + +message PPNetV3 { + required string model_conf = 1; +} diff --git a/easy_rec/python/protos/train.proto b/easy_rec/python/protos/train.proto index 83cf09814..0df241fe4 100644 --- a/easy_rec/python/protos/train.proto +++ b/easy_rec/python/protos/train.proto @@ -22,6 +22,7 @@ enum DistributionStrategy { // use horovod strategy HorovodStrategy = 6; SokStrategy = 7; + EmbeddingParallelStrategy = 8; } message IncrementSaveConfig { diff --git a/easy_rec/python/test/train_eval_test.py b/easy_rec/python/test/train_eval_test.py index 859e2442c..d2a056027 100644 --- a/easy_rec/python/test/train_eval_test.py +++ b/easy_rec/python/test/train_eval_test.py @@ -29,6 +29,11 @@ except Exception: hvd = None +try: + from sparse_operation_kit import experiment as sok +except Exception: + sok = None + if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -1179,6 +1184,15 @@ def test_horovod(self): use_hvd=True) self.assertTrue(self._success) + @unittest.skipIf(hvd is None or sok is None, + 'horovod and sok is not installed') + def test_sok(self): + self._success = test_utils.test_distributed_train_eval( + 'samples/model_config/multi_tower_on_taobao_sok.config', + self._test_dir, + use_hvd=True) + self.assertTrue(self._success) + def test_pdn(self): self._success = test_utils.test_single_train_eval( 'samples/model_config/pdn_on_taobao.config', self._test_dir) diff --git a/easy_rec/python/utils/estimator_utils.py b/easy_rec/python/utils/estimator_utils.py index ff35b5da7..573c25361 100644 --- a/easy_rec/python/utils/estimator_utils.py +++ b/easy_rec/python/utils/estimator_utils.py @@ -477,12 +477,12 @@ def __init__(self, self._sparse_timer = None def after_create_session(self, session, coord): - if not is_chief(): - if has_sok(): - global_step = session.run(self._global_step_tensor) - self._save_sok(session, global_step) - self._timer.update_last_triggered_step(global_step) - return + # if not is_chief(): + # if has_sok(): + # global_step = session.run(self._global_step_tensor) + # self._save_sok(session, global_step) + # self._timer.update_last_triggered_step(global_step) + # return global_step = session.run(self._global_step_tensor) if self._write_graph: @@ -642,16 +642,16 @@ def after_run(self, run_context, run_values): # return # return - if not is_chief(): - if has_sok(): - stale_global_step = run_values.results - global_step = stale_global_step + self._steps_per_run - if self._timer.should_trigger_for_step(global_step): - logging.info('worker[%d] global_step=%d, save sok dynamic vars' % - (self._task_idx, global_step)) - self._save_sok(run_context.session, global_step) - self._timer.update_last_triggered_step(global_step) - return + # if not is_chief(): + # if has_sok(): + # stale_global_step = run_values.results + # global_step = stale_global_step + self._steps_per_run + # if self._timer.should_trigger_for_step(global_step): + # logging.info('worker[%d] global_step=%d, save sok dynamic vars' % + # (self._task_idx, global_step)) + # self._save_sok(run_context.session, global_step) + # self._timer.update_last_triggered_step(global_step) + # return super(CheckpointSaverHook, self).after_run(run_context, run_values) stale_global_step = run_values.results @@ -718,7 +718,7 @@ def _save(self, session, step): write_meta_graph=self._write_graph) # save sok - self._save_sok(session, step) + # self._save_sok(session, step) self._summary_writer.add_session_log( tf.SessionLog( @@ -736,10 +736,10 @@ def _save(self, session, step): def end(self, session): global_step = session.run(self._global_step_tensor) - if not is_chief(): - if has_sok(): - self._save_sok(session, global_step) - return + # if not is_chief(): + # if has_sok(): + # self._save_sok(session, global_step) + # return super(CheckpointSaverHook, self).end(session) if self._dense_timer is not None and \ diff --git a/taobao_train_data_8192 b/taobao_train_data_8192 deleted file mode 100644 index 850f4725d..000000000 --- a/taobao_train_data_8192 +++ /dev/null @@ -1,8192 +0,0 @@ -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 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-0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 -0,0,430548_1007,539227,4281,389957,44110,73804,122017,0,5,2,5,,2,0,,4589,93068,22 From b15eec99e036e41b219a186cf9706d2d19f29bab Mon Sep 17 00:00:00 2001 From: chengmengli06 Date: Sat, 9 Dec 2023 23:36:40 +0800 Subject: [PATCH 43/67] add embedding parallel support --- .../python/compat/embedding_parallel_saver.py | 7 +++-- easy_rec/python/main.py | 26 +++++++++--------- easy_rec/python/ops/2.12/libload_embed.so | Bin 60360 -> 60360 bytes 3 files changed, 17 insertions(+), 16 deletions(-) diff --git a/easy_rec/python/compat/embedding_parallel_saver.py b/easy_rec/python/compat/embedding_parallel_saver.py index d4ac05886..274f8c6fa 100644 --- a/easy_rec/python/compat/embedding_parallel_saver.py +++ b/easy_rec/python/compat/embedding_parallel_saver.py @@ -180,9 +180,8 @@ def _save_key_vals(indices, values, filename, var_name): var_name = var_name.decode('utf-8').replace('/', '__') filename = filename.decode('utf-8') sok_dir = filename + '-embedding/' - if gfile.Exists(sok_dir): - gfile.DeleteRecursively(sok_dir) - gfile.MakeDirs(sok_dir) + if not gfile.Exists(sok_dir): + gfile.MakeDirs(sok_dir) task_id = hvd.rank() key_file = filename + '-embedding/embed-' + var_name + '-part-%d.key' % task_id with gfile.GFile(key_file, 'wb') as fout: @@ -199,6 +198,8 @@ def _save_key_vals(indices, values, filename, var_name): if embed_id >= hvd.size(): gfile.DeleteRecursively(key_file) val_file = key_file[:-4] + '.val' + if gfile.Exists(val_file): + gfile.DeleteRecursively(val_file) return np.asarray([key_file, val_file], order='C', dtype=np.object) diff --git a/easy_rec/python/main.py b/easy_rec/python/main.py index 6bab5a320..855f9025e 100644 --- a/easy_rec/python/main.py +++ b/easy_rec/python/main.py @@ -814,19 +814,19 @@ def export(export_dir, checkpoint_path=ckpt_path, strip_default_attrs=True) - sok_embed_path = ckpt_path + '-sok/' - if gfile.Exists(sok_embed_path): - if isinstance(final_export_dir, bytes): - final_export_dir = final_export_dir.decode('utf-8') - export_sok_embed_dir = os.path.join(final_export_dir, - 'variables/variables-sok/') - gfile.MkDir(export_sok_embed_dir) - for tmp_file in gfile.Glob(sok_embed_path + '*'): - if (tmp_file.endswith('.keys') or tmp_file.endswith('.vals')) \ - and 'DynamicSlot' not in tmp_file: - _, tmp_name = os.path.split(tmp_file) - dst_file = os.path.join(export_sok_embed_dir, tmp_name) - gfile.Copy(tmp_file, dst_file) + # part_embed_path = ckpt_path + '-embedding/' + # if gfile.Exists(part_embed_path): + # if isinstance(final_export_dir, bytes): + # final_export_dir = final_export_dir.decode('utf-8') + # export_embed_dir = os.path.join(final_export_dir, + # 'variables/variables-embedding/') + # gfile.MkDir(export_embed_dir) + # for tmp_file in gfile.Glob(part_embed_path + '*'): + # if (tmp_file.endswith('.keys') or tmp_file.endswith('.vals') + # or tmp_file.endswith('.bin')): + # _, tmp_name = os.path.split(tmp_file) + # dst_file = os.path.join(export_embed_dir, tmp_name) + # gfile.Copy(tmp_file, dst_file) # add export ts as version info saved_model = saved_model_pb2.SavedModel() diff --git a/easy_rec/python/ops/2.12/libload_embed.so b/easy_rec/python/ops/2.12/libload_embed.so index 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