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https://github.com/huggingface/search-and-learn" + ] + }, + { + "cell_type": "code", + "source": [ + "%cd search-and-learn\n", + "!pip install -e '.[dev]'" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "kT3jH_d_XcEb", + "outputId": "8a21921b-f513-4ee3-f31e-ea0ba9ea610f" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/search-and-learn\n", + "Obtaining file:///content/search-and-learn\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n", + " Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from search-and-learn==0.1.0) (1.2.1)\n", + "Collecting pebble (from 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directory: /root/.cache/pip/wheels/84/ff/26/d3cfbd971e96c5aa3737ecfced81628830d7359b55fbb8ca3b\n", + "Successfully built search-and-learn antlr4-python3-runtime word2number\n", + "Installing collected packages: word2number, pyairports, nvidia-ml-py, antlr4-python3-runtime, xxhash, uvloop, uvicorn, triton, ruff, python-dotenv, pycountry, pillow, pebble, partial-json-parser, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, msgspec, lark, isort, interegular, httptools, gguf, fsspec, diskcache, dill, watchfiles, tiktoken, starlette, nvidia-cusolver-cu12, nvidia-cudnn-cu12, multiprocess, latex2sympy2, torch, prometheus-fastapi-instrumentator, lm-format-enforcer, fastapi, xformers, torchvision, ray, mistral-common, search-and-learn, datasets, outlines, vllm\n", + " Attempting uninstall: pillow\n", + " Found existing installation: pillow 11.0.0\n", + " Uninstalling pillow-11.0.0:\n", + " Successfully uninstalled pillow-11.0.0\n", + " Attempting uninstall: nvidia-nccl-cu12\n", + " Found existing installation: nvidia-nccl-cu12 2.23.4\n", + " Uninstalling nvidia-nccl-cu12-2.23.4:\n", + " Successfully uninstalled nvidia-nccl-cu12-2.23.4\n", + " Attempting uninstall: nvidia-cusparse-cu12\n", + " Found existing installation: nvidia-cusparse-cu12 12.5.4.2\n", + " Uninstalling nvidia-cusparse-cu12-12.5.4.2:\n", + " Successfully uninstalled nvidia-cusparse-cu12-12.5.4.2\n", + " Attempting uninstall: nvidia-curand-cu12\n", + " Found existing installation: nvidia-curand-cu12 10.3.7.77\n", + " Uninstalling nvidia-curand-cu12-10.3.7.77:\n", + " Successfully uninstalled nvidia-curand-cu12-10.3.7.77\n", + " Attempting uninstall: nvidia-cufft-cu12\n", + " Found existing installation: nvidia-cufft-cu12 11.3.0.4\n", + " Uninstalling nvidia-cufft-cu12-11.3.0.4:\n", + " Successfully uninstalled nvidia-cufft-cu12-11.3.0.4\n", + " Attempting uninstall: nvidia-cuda-runtime-cu12\n", + " Found existing installation: nvidia-cuda-runtime-cu12 12.6.77\n", + " Uninstalling nvidia-cuda-runtime-cu12-12.6.77:\n", + " Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.77\n", + " Attempting uninstall: nvidia-cuda-cupti-cu12\n", + " Found existing installation: nvidia-cuda-cupti-cu12 12.6.80\n", + " Uninstalling nvidia-cuda-cupti-cu12-12.6.80:\n", + " Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.80\n", + " Attempting uninstall: nvidia-cublas-cu12\n", + " Found existing installation: nvidia-cublas-cu12 12.6.4.1\n", + " Uninstalling nvidia-cublas-cu12-12.6.4.1:\n", + " Successfully uninstalled nvidia-cublas-cu12-12.6.4.1\n", + " Attempting uninstall: fsspec\n", + " Found existing installation: fsspec 2024.10.0\n", + " Uninstalling fsspec-2024.10.0:\n", + " Successfully uninstalled fsspec-2024.10.0\n", + " Attempting uninstall: nvidia-cusolver-cu12\n", + " Found existing installation: nvidia-cusolver-cu12 11.7.1.2\n", + " Uninstalling nvidia-cusolver-cu12-11.7.1.2:\n", + " Successfully uninstalled nvidia-cusolver-cu12-11.7.1.2\n", + " Attempting uninstall: nvidia-cudnn-cu12\n", + " Found existing installation: nvidia-cudnn-cu12 9.6.0.74\n", + " Uninstalling nvidia-cudnn-cu12-9.6.0.74:\n", + " Successfully uninstalled nvidia-cudnn-cu12-9.6.0.74\n", + " Attempting uninstall: torch\n", + " Found existing installation: torch 2.5.1+cu121\n", + " Uninstalling torch-2.5.1+cu121:\n", + " Successfully uninstalled torch-2.5.1+cu121\n", + " Attempting uninstall: torchvision\n", + " Found existing installation: torchvision 0.20.1+cu121\n", + " Uninstalling torchvision-0.20.1+cu121:\n", + " Successfully uninstalled torchvision-0.20.1+cu121\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n", + "torchaudio 2.5.1+cu121 requires torch==2.5.1, but you have torch 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed antlr4-python3-runtime-4.7.2 datasets-3.2.0 dill-0.3.8 diskcache-5.6.3 fastapi-0.115.6 fsspec-2024.9.0 gguf-0.10.0 httptools-0.6.4 interegular-0.3.3 isort-5.13.2 lark-1.2.2 latex2sympy2-1.9.1 lm-format-enforcer-0.10.6 mistral-common-1.5.1 msgspec-0.18.6 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-ml-py-12.560.30 nvidia-nccl-cu12-2.20.5 nvidia-nvtx-cu12-12.1.105 outlines-0.0.46 partial-json-parser-0.2.1.1.post4 pebble-5.1.0 pillow-10.4.0 prometheus-fastapi-instrumentator-7.0.0 pyairports-2.1.1 pycountry-24.6.1 python-dotenv-1.0.1 ray-2.40.0 ruff-0.8.4 search-and-learn-0.1.0 starlette-0.41.3 tiktoken-0.7.0 torch-2.4.0 torchvision-0.19.0 triton-3.0.0 uvicorn-0.34.0 uvloop-0.21.0 vllm-0.6.3 watchfiles-1.0.3 word2number-1.1 xformers-0.0.27.post2 xxhash-3.5.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "PIL" + ] + }, + "id": "fc544a7f07cc4018a39944c8e9e446f0" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "!huggingface-cli login" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pnEaTlFYZF_H", + "outputId": "9c8dfeeb-192f-4b62-ccb4-cfeb1ea6184b" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", + " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", + " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", + " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", + " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", + "\n", + " To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n", + "Enter your token (input will not be visible): \n", + "Add token as git credential? (Y/n) n\n", + "Token is valid (permission: write).\n", + "The token `Cookbook` has been saved to /root/.cache/huggingface/stored_tokens\n", + "Your token has been saved to /root/.cache/huggingface/token\n", + "Login successful.\n", + "The current active token is: `Cookbook`\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#%cd search-and-learn\n", + "#!python scripts/test_time_compute.py recipes/Llama-3.2-1B-Instruct/best_of_n.yaml" + ], + "metadata": { + "id": "4zycuGk1XeW9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#%cd search-and-learn\n", + "#!python scripts/test_time_compute.py recipes/Llama-3.2-1B-Instruct/dvts.yaml" + ], + "metadata": { + "id": "uYwH8s11jDzu" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Make a recipe for building an LLM system with scaling test-time compute:\n", + "\n", + "* Use the same code as in the recipes but with the smallest approach, thinking about an LLM chat bot system instead of a benchmark\n", + "* LLM class where a user talks and the LLM response after thinking for a while.\n", + "* Add the \"thinking for x seconds message\"\n", + "* Final function that only generates the final output based on the input, without the rest of the intermediate messages.\n", + "* Can this be combined with agents/VLMs...?\n", + "* Space in HF\n", + "\n" + ], + "metadata": { + "id": "dSvyCIKqmUKh" + } + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from vllm import LLM\n", + "\n", + "from sal.config import Config\n", + "from sal.models.reward_models import RLHFFlow\n", + "from sal.search import beam_search, best_of_n, dvts\n", + "from sal.utils.data import get_dataset, save_dataset\n", + "from sal.utils.score import score\n", + "\n", + "APPROACHES = {\n", + " \"beam_search\": beam_search,\n", + " \"dvts\": dvts,\n", + " \"best_of_n\": best_of_n,\n", + "}\n", + "\n", + "config = Config\n", + "print(Config.approach)\n", + "print(config)\n", + "\n", + "approach_fn = APPROACHES['best_of_n']\n", + "\n", + "model_path=\"meta-llama/Llama-3.2-1B-Instruct\"\n", + "prm_path=\"RLHFlow/Llama3.1-8B-PRM-Deepseek-Data\"\n", + "gpu_memory_utilization = (0.5)\n", + "seed=42\n", + "\n", + "llm = LLM(\n", + " model=model_path,\n", + " gpu_memory_utilization=gpu_memory_utilization,\n", + " enable_prefix_caching=True,\n", + " seed=seed,\n", + ")\n", + "\n", + "prm = RLHFFlow(prm_path)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "659992713175425a9854cf533b34a22c", + "1c784900895041c2973396cb56770f2e", + "876a169dc31f45e0ad918016d9d95864", + "eadfb78a5dc44541a0c7b20ff6f846c7", + "1dbc9cf912614106a06846b7c3fb3dc8", + "80c91b50844041dcb54d2ecb01041905", + "38439a05d28b44698768ce09dad77a94", + "275f3698bdb5433fa983fa3d2c8ccb27", + "9b86c58d06644edaa4c29c78984f68be", + "8ceed10779b3450b9dc7fd5e77267504", + 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vllm.version import __version__ as VLLM_VERSION\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "best_of_n\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/877 [00:00 32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False.\n", + "INFO 12-24 16:16:27 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=512.\n", + "INFO 12-24 16:16:27 llm_engine.py:237] Initializing an LLM engine (vdev) with config: model='meta-llama/Llama-3.2-1B-Instruct', speculative_config=None, tokenizer='meta-llama/Llama-3.2-1B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=42, served_model_name=meta-llama/Llama-3.2-1B-Instruct, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=True, use_cached_outputs=False, mm_processor_kwargs=None)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/54.5k [00:00 0$ and $0 \\\\le \\\\theta < 2 \\\\pi.$',\n", + " 'solution': 'We have that $r = \\\\sqrt{0^2 + 3^2} = 3.$ Also, if we draw the line connecting the origin and $(0,3),$ this line makes an angle of $\\\\frac{\\\\pi}{2}$ with the positive $x$-axis.\\n\\n[asy]\\nunitsize(0.8 cm);\\n\\ndraw((-0.5,0)--(3.5,0));\\ndraw((0,-0.5)--(0,3.5));\\ndraw(arc((0,0),3,0,90),red,Arrow(6));\\n\\ndot((0,3), red);\\nlabel(\"$(0,3)$\", (0,3), W);\\ndot((3,0), red);\\n[/asy]\\n\\nTherefore, the polar coordinates are $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}.$',\n", + " 'answer': '\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)',\n", + " 'subject': 'Precalculus',\n", + " 'level': 2,\n", + " 'unique_id': 'test/precalculus/807.json'}" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#dataset = get_dataset(config)\n", + "'''\n", + "dataset = dataset.map(\n", + " #approach_fn,\n", + " best_of_n,\n", + " batched=True,\n", + " batch_size=search_batch_size,\n", + " fn_kwargs={\"config\": config, \"llm\": llm, \"prm\": prm},\n", + " desc=\"Running search\",\n", + " load_from_cache_file=False,\n", + ")\n", + "'''" + ], + "metadata": { + "id": "ieEe6l4U_jTW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "batch = {\n", + " \"problem\": [dataset[0][\"problem\"]],\n", + " #\"solution\": [dataset[0][\"solution\"]],\n", + " #\"answer\": [dataset[0][\"answer\"]],\n", + " #\"subject\": [dataset[0][\"subject\"]],\n", + " #\"level\": [dataset[0][\"level\"]],\n", + " #\"unique_id\": [dataset[0][\"unique_id\"]],\n", + "}" + ], + "metadata": { + "id": "JWymy27mW_HD" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result = best_of_n(x=batch, config=config, llm=llm, prm=prm)" + ], + "metadata": { + "id": "C6s6GS16QZLV" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result = Dataset.from_dict(result)" + ], + "metadata": { + "id": "VtEPnhnQbF9W" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result = score(result, config)" + ], + "metadata": { + "id": "29t49WKAAOA7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "a83a29c772394574ae7cc17cfcb6cfd5", + "69e8374853bb485bb1dcb10b9f47e76c", + "8819647943eb4184b942a641cd5e18d4", + 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"outputId": "ac18901d-7061-4490-d924-b73c798e8dab" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/1 [00:00assistant<|end_header_id|>\\n\\n## Step 1: Recall the conversion formulas\\nTo convert from rectangular (Cartesian) coordinates $(x,y)$ to polar coordinates $(r,\\\\theta)$, we use the following formulas:\\n$r = \\\\sqrt{x^2 + y^2}$ and $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{y}{x}\\\\right)$.\\n\\n## Step 2: Substitute the values\\nGiven the point $(0,3)$ in rectangular coordinates, we can substitute $x = 0$ and $y = 3$ into the formulas:\\n$r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n\\n## Step 3: Calculate $\\\\theta$\\nNow, we can calculate $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{3}{0}\\\\right)$. However, since division by zero is undefined, and the point $(0,3)$ is on the positive y-axis, the angle $\\\\theta$ will be $\\\\frac{\\\\pi}{2}$ radians.\\n\\n## Step 4: Write the polar coordinates\\nThe polar coordinates of $(0,3)$ are $(r, \\\\theta) = \\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)}$'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "source": [ + "formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", + "formatted_output" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 122 + }, + "id": "flbIu6-rDapM", + "outputId": "b013904c-71ac-4bb2-c759-207f374583f1" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'## Step 1: Recall the conversion formulas\\nTo convert from rectangular (Cartesian) coordinates $(x,y)$ to polar coordinates $(r,\\\\theta)$, we use the following formulas:\\n$r = \\\\sqrt{x^2 + y^2}$ and $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{y}{x}\\\\right)$.\\n\\n## Step 2: Substitute the values\\nGiven the point $(0,3)$ in rectangular coordinates, we can substitute $x = 0$ and $y = 3$ into the formulas:\\n$r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n\\n## Step 3: Calculate $\\\\theta$\\nNow, we can calculate $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{3}{0}\\\\right)$. However, since division by zero is undefined, and the point $(0,3)$ is on the positive y-axis, the angle $\\\\theta$ will be $\\\\frac{\\\\pi}{2}$ radians.\\n\\n## Step 4: Write the polar coordinates\\nThe polar coordinates of $(0,3)$ are $(r, \\\\theta) = \\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)}$'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import display, Markdown, Latex\n", + "display(Markdown(formatted_output))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "P4En0qJRD0cl", + "outputId": "6f689dca-6391-4dc7-d59d-84328ac7d62e" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/markdown": "## Step 1: Recall the conversion formulas\nTo convert from rectangular (Cartesian) coordinates $(x,y)$ to polar coordinates $(r,\\theta)$, we use the following formulas:\n$r = \\sqrt{x^2 + y^2}$ and $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$.\n\n## Step 2: Substitute the values\nGiven the point $(0,3)$ in rectangular coordinates, we can substitute $x = 0$ and $y = 3$ into the formulas:\n$r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n\n## Step 3: Calculate $\\theta$\nNow, we can calculate $\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right)$. However, since division by zero is undefined, and the point $(0,3)$ is on the positive y-axis, the angle $\\theta$ will be $\\frac{\\pi}{2}$ radians.\n\n## Step 4: Write the polar coordinates\nThe polar coordinates of $(0,3)$ are $(r, \\theta) = \\left(3, \\frac{\\pi}{2}\\right)$.\n\nThe final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import gc\n", + "from vllm.distributed.parallel_state import destroy_model_parallel, destroy_distributed_environment\n", + "\n", + "destroy_model_parallel()\n", + "destroy_distributed_environment()\n", + "del llm.llm_engine.model_executor\n", + "del llm\n", + "gc.collect()\n", + "torch.cuda.empty_cache()\n", + "print(\"Successfully delete the llm pipeline and free the GPU memory!\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "M8DDhSZ3G2IS", + "outputId": "9f1448dc-5318-4a32-cdf5-350282f9d5e0" + }, + "execution_count": 67, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Processed prompts: 28%|██▊ | 1457/5152 [12:25<31:30, 1.95it/s, est. speed input: 6101.02 toks/s, output: 3865.57 toks/s]\n", + "Processed prompts: 3%|▎ | 108/3727 [06:38<3:42:45, 3.69s/it, est. speed input: 5268.77 toks/s, output: 3819.28 toks/s]\n", + "Processed prompts: 2%|▏ | 94/4067 [06:24<4:31:04, 4.09s/it, est. speed input: 6506.02 toks/s, output: 5001.06 toks/s]\n", + "Processed prompts: 0%| | 0/4106 [01:56 Date: Fri, 27 Dec 2024 17:40:01 +0100 Subject: [PATCH 02/10] Updated notebook --- notebooks/en/search_and_learn.ipynb | 12681 ++++++++++++++++++++------ 1 file changed, 9888 insertions(+), 2793 deletions(-) diff --git a/notebooks/en/search_and_learn.ipynb b/notebooks/en/search_and_learn.ipynb index bf7328fb..6c7185fb 100644 --- a/notebooks/en/search_and_learn.ipynb +++ b/notebooks/en/search_and_learn.ipynb @@ -17,29 +17,25 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "659992713175425a9854cf533b34a22c": { + "60796f0e38e04bd0bbf01d0f47b6c193": { "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", + "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", + "_view_name": "VBoxView", "box_style": "", - "children": [ - "IPY_MODEL_1c784900895041c2973396cb56770f2e", - "IPY_MODEL_876a169dc31f45e0ad918016d9d95864", - "IPY_MODEL_eadfb78a5dc44541a0c7b20ff6f846c7" - ], - "layout": "IPY_MODEL_1dbc9cf912614106a06846b7c3fb3dc8" + "children": [], + "layout": "IPY_MODEL_7ceb6890ce394d219558d73c035b0132" } }, - "1c784900895041c2973396cb56770f2e": { + "a35db2d634b44fa18b156bf1219bbbc6": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -54,37 +50,80 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_80c91b50844041dcb54d2ecb01041905", + "layout": "IPY_MODEL_14cfc9eaed814d81a22c0d07872c48cf", "placeholder": "​", - "style": "IPY_MODEL_38439a05d28b44698768ce09dad77a94", - "value": "config.json: 100%" + "style": "IPY_MODEL_107474000a2f44b88e468b76cdb629a2", + "value": "

Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
" } }, - "876a169dc31f45e0ad918016d9d95864": { + "39c02b227859468f937eaf0dfeb30397": { "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", + "model_name": "PasswordModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "PasswordModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", + "_view_name": "PasswordView", + "continuous_update": true, + "description": "Token:", "description_tooltip": null, - "layout": "IPY_MODEL_275f3698bdb5433fa983fa3d2c8ccb27", - "max": 877, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_9b86c58d06644edaa4c29c78984f68be", - "value": 877 + "disabled": false, + "layout": "IPY_MODEL_5b07a2985c884134acf6663bd11b68e5", + "placeholder": "​", + "style": "IPY_MODEL_dc8d18e4d81e4b038eb1b5b6e08d7468", + "value": "" + } + }, + "6097a7d7386b4fb7b2626b127f7c9fd9": { + "model_module": "@jupyter-widgets/controls", + "model_name": "CheckboxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "CheckboxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "CheckboxView", + "description": "Add token as git credential?", + "description_tooltip": null, + "disabled": false, + "indent": true, + "layout": "IPY_MODEL_bb8d455434774af48a2bbf67b60f7c4c", + "style": "IPY_MODEL_1f4355b6ddf04ca1ad979107db86a028", + "value": true + } + }, + "91bd247295b2403780cc9085933596e8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ButtonModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ButtonModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ButtonView", + "button_style": "", + "description": "Login", + "disabled": false, + "icon": "", + "layout": "IPY_MODEL_84e6c09df2674da98d929c259285cc50", + "style": "IPY_MODEL_f186c6187e0f4c12a134495046472f78", + "tooltip": "" } }, - "eadfb78a5dc44541a0c7b20ff6f846c7": { + "9521965237ac46eabb8853ad15871406": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -99,13 +138,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_8ceed10779b3450b9dc7fd5e77267504", + "layout": "IPY_MODEL_b8e03810026942e39772aff1535734ef", "placeholder": "​", - "style": "IPY_MODEL_76ab2cc65ca547b1b2c999184a55ac21", - "value": " 877/877 [00:00<00:00, 60.1kB/s]" + "style": "IPY_MODEL_d54843bdabe443bf92d0602ee5e5aba7", + "value": "\nPro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. 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"description_tooltip": null, - "layout": "IPY_MODEL_2e3ecc23852f47778b126fc7490d354f", + "layout": "IPY_MODEL_d9f5493279a44ddd8ae58fb087a728b9", "placeholder": "​", - "style": "IPY_MODEL_2ce1bcea92c24260bdd9921b610003f5", - "value": " 1/1 [00:00<00:00, 47.00 examples/s]" + "style": "IPY_MODEL_e95e4ece327b49728597179e4f4ed407", + "value": " 1/1 [00:00<00:00, 48.04 examples/s]" } }, - "4aca3e1004a74bb1bcb88200861ca523": { + "d54aaffc11204632aedd67b35e3a1fca": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -8707,7 +8626,7 @@ "width": null } }, - "c243ca521ee242c792cbf0ab318fe993": { + "87fe479a1be642e587179317368c8f86": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -8759,7 +8678,7 @@ "width": null } }, - "92b14b1ff9ea4456af7b33aa8f562095": { + "845328d8fd354e71839e56a5bcf09326": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", 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"DescriptionStyleModel", "model_module_version": "1.5.0", @@ -18142,133 +18061,6973 @@ "_view_name": "StyleView", "description_width": "" } - } - } - } - }, - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "t0YDC2_7XTm8", - "outputId": "4541bf88-dcc5-4008-8a44-5a5fce3112c5" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Cloning into 'search-and-learn'...\n", - "remote: Enumerating objects: 235, done.\u001b[K\n", - "remote: Counting objects: 100% (75/75), done.\u001b[K\n", - "remote: Compressing objects: 100% (32/32), done.\u001b[K\n", - "remote: Total 235 (delta 48), reused 43 (delta 43), pack-reused 160 (from 1)\u001b[K\n", - "Receiving objects: 100% (235/235), 87.99 KiB | 1.69 MiB/s, done.\n", - "Resolving deltas: 100% (107/107), done.\n" - ] - } - ], - "source": [ - "!git clone https://github.com/huggingface/search-and-learn" - ] - }, - { - "cell_type": "code", - "source": [ - "%cd search-and-learn\n", - "!pip install -e '.[dev]'" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "939a762ce5aa4fa083ec1d0792dbafad": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_abe53af7eafa4c67b6a8862ec3f8c882", + "IPY_MODEL_2b807499a2204a6ba437426ccdbef68f", + "IPY_MODEL_98e4f25a763b4182a736fa26ffe189c7" + ], + "layout": "IPY_MODEL_39e1654ca0054ef48032924cf2a5a407" + } }, - "id": "kT3jH_d_XcEb", - "outputId": "8a21921b-f513-4ee3-f31e-ea0ba9ea610f" - }, - "execution_count": 2, - "outputs": [ - { - 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done.\u001b[K\n", + "remote: Compressing objects: 100% (32/32), done.\u001b[K\n", + "remote: Total 235 (delta 48), reused 43 (delta 43), pack-reused 160 (from 1)\u001b[K\n", + "Receiving objects: 100% (235/235), 87.99 KiB | 1.69 MiB/s, done.\n", + "Resolving deltas: 100% (107/107), done.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/huggingface/search-and-learn" + ] + }, + { + "cell_type": "code", + "source": [ + "%cd search-and-learn\n", + "!pip install -e '.[dev]'" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "kT3jH_d_XcEb", + "outputId": "014f82f0-277b-40a1-8182-15c3ae8d6b52" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/search-and-learn\n", + "Obtaining file:///content/search-and-learn\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Checking if build backend supports build_editable ... 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filename=word2number-1.1-py3-none-any.whl size=5568 sha256=542daaf075839b4bbe4549cb1deec637458d61813d16eaef33575e55dced07fd\n", + " Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5568 sha256=83b558080d5472d3cff0671462a27963cedbd8ad5e0ca4a42c9c4c57fcf2f199\n", " Stored in directory: /root/.cache/pip/wheels/84/ff/26/d3cfbd971e96c5aa3737ecfced81628830d7359b55fbb8ca3b\n", "Successfully built search-and-learn antlr4-python3-runtime word2number\n", "Installing collected packages: word2number, pyairports, nvidia-ml-py, antlr4-python3-runtime, xxhash, uvloop, uvicorn, triton, ruff, python-dotenv, pycountry, pillow, pebble, partial-json-parser, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, msgspec, lark, isort, interegular, httptools, gguf, fsspec, diskcache, dill, watchfiles, tiktoken, starlette, nvidia-cusolver-cu12, nvidia-cudnn-cu12, multiprocess, latex2sympy2, torch, prometheus-fastapi-instrumentator, lm-format-enforcer, fastapi, xformers, torchvision, ray, mistral-common, search-and-learn, datasets, outlines, vllm\n", @@ -18548,418 +25307,1084 @@ { "output_type": "display_data", "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "PIL" - ] - }, - "id": "fc544a7f07cc4018a39944c8e9e446f0" + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "PIL" + ] + }, + "id": "f7ea7d6dfd36400faf02a9173ea5b956" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17, + "referenced_widgets": [ + "60796f0e38e04bd0bbf01d0f47b6c193", + "a35db2d634b44fa18b156bf1219bbbc6", + "39c02b227859468f937eaf0dfeb30397", + "6097a7d7386b4fb7b2626b127f7c9fd9", + "91bd247295b2403780cc9085933596e8", + "9521965237ac46eabb8853ad15871406", + "7ceb6890ce394d219558d73c035b0132", + "14cfc9eaed814d81a22c0d07872c48cf", + "107474000a2f44b88e468b76cdb629a2", + "5b07a2985c884134acf6663bd11b68e5", + "dc8d18e4d81e4b038eb1b5b6e08d7468", + "bb8d455434774af48a2bbf67b60f7c4c", + "1f4355b6ddf04ca1ad979107db86a028", + "84e6c09df2674da98d929c259285cc50", + "f186c6187e0f4c12a134495046472f78", + "b8e03810026942e39772aff1535734ef", + "d54843bdabe443bf92d0602ee5e5aba7", + "de3783a5692541008a6999405296dee0", + "6e4fc4f5913646c28cc0386a85b5117a", + "33f91a3eaf3a40dbb28469150fa84f48" + ] + }, + "id": "pnEaTlFYZF_H", + "outputId": "9e7229f5-ed14-403b-bb15-d1596f2b187f" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/877 [00:00 32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False.\n", + "INFO 12-27 16:16:03 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=512.\n", + "INFO 12-27 16:16:03 llm_engine.py:237] Initializing an LLM engine (vdev) with config: model='meta-llama/Llama-3.2-1B-Instruct', speculative_config=None, tokenizer='meta-llama/Llama-3.2-1B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=42, served_model_name=meta-llama/Llama-3.2-1B-Instruct, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=True, use_cached_outputs=False, mm_processor_kwargs=None)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/54.5k [00:00 0$ and $0 \\le \\theta < 2 \\pi.$'\n", + "\n", + "batch = {\n", + " \"problem\": [question],\n", + "}" ], "metadata": { - "id": "4zycuGk1XeW9" + "id": "83puLxhzsOM0" }, - "execution_count": null, + "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ - "#%cd search-and-learn\n", - "#!python scripts/test_time_compute.py recipes/Llama-3.2-1B-Instruct/dvts.yaml" + "from sal.search import beam_search, best_of_n, dvts\n", + "from sal.utils.score import score\n", + "\n", + "config.n=32\n", + "config.sort_completed= True\n", + "config.filter_duplicates= True\n", + "\n", + "result = best_of_n(x=batch, config=config, llm=llm, prm=prm)" ], "metadata": { - "id": "uYwH8s11jDzu" + "id": "C6s6GS16QZLV" }, - "execution_count": null, + "execution_count": 4, "outputs": [] }, - { - "cell_type": "markdown", - "source": [ - "Make a recipe for building an LLM system with scaling test-time compute:\n", - "\n", - "* Use the same code as in the recipes but with the smallest approach, thinking about an LLM chat bot system instead of a benchmark\n", - "* LLM class where a user talks and the LLM response after thinking for a while.\n", - "* Add the \"thinking for x seconds message\"\n", - "* Final function that only generates the final output based on the input, without the rest of the intermediate messages.\n", - "* Can this be combined with agents/VLMs...?\n", - "* Space in HF\n", - "\n" - ], - "metadata": { - "id": "dSvyCIKqmUKh" - } - }, { "cell_type": "code", "source": [ - "import torch\n", - "from vllm import LLM\n", - "\n", - "from sal.config import Config\n", - "from sal.models.reward_models import RLHFFlow\n", - "from sal.search import beam_search, best_of_n, dvts\n", - "from sal.utils.data import get_dataset, save_dataset\n", - "from sal.utils.score import score\n", - "\n", - "APPROACHES = {\n", - " \"beam_search\": beam_search,\n", - " \"dvts\": dvts,\n", - " \"best_of_n\": best_of_n,\n", - "}\n", - "\n", - "config = Config\n", - "print(Config.approach)\n", - "print(config)\n", - "\n", - "approach_fn = APPROACHES['best_of_n']\n", - "\n", - "model_path=\"meta-llama/Llama-3.2-1B-Instruct\"\n", - "prm_path=\"RLHFlow/Llama3.1-8B-PRM-Deepseek-Data\"\n", - "gpu_memory_utilization = (0.5)\n", - "seed=42\n", + "from datasets import Dataset\n", "\n", - "llm = LLM(\n", - " model=model_path,\n", - " gpu_memory_utilization=gpu_memory_utilization,\n", - " enable_prefix_caching=True,\n", - " seed=seed,\n", - ")\n", - "\n", - "prm = RLHFFlow(prm_path)" + "result = Dataset.from_dict(result)" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "659992713175425a9854cf533b34a22c", - "1c784900895041c2973396cb56770f2e", - "876a169dc31f45e0ad918016d9d95864", - "eadfb78a5dc44541a0c7b20ff6f846c7", - "1dbc9cf912614106a06846b7c3fb3dc8", - "80c91b50844041dcb54d2ecb01041905", - "38439a05d28b44698768ce09dad77a94", - "275f3698bdb5433fa983fa3d2c8ccb27", - "9b86c58d06644edaa4c29c78984f68be", - "8ceed10779b3450b9dc7fd5e77267504", - "76ab2cc65ca547b1b2c999184a55ac21", - "5074653c90224bb2bd026a40fee62a61", - "e71f0678f7ec4fae9a52f7db3f230d49", - "4d8fa578f4b6480d97ae6e603830aec9", - "dd1e5cd692d14c7983b0313ef5ef24f2", - "f477acee32384252a25014f06b26b349", - "4dab5de42d2f449cb784351aecf9cdb8", - "5a6d9b5acce34b309c658e532aad10f7", - "436c38d8fef7478c978a50eb3d6a0193", - "6d675d812c8a4edab3916a081cfe3a79", - "2280ceed995245f2ac70cbd65b64182b", - 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authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result = score(result, config)" + ], + "metadata": { + "id": "29t49WKAAOA7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "b97e442a53b04bbea9041ecab8d8e5ab", + "a4b0386cbc534f5b8bf9919724ab7992", + "e64979d6f88a48a9b5b88c1e3206dbac", + "bd85430005a542e5a6b2ffd661959041", + "7cbf982d5b5e4bd7bdf86b736dcf3d4a", + "e20c0be4fc154398909135c903035a46", + "f9f250d49c3c4b988f8e47260e2699a9", + "fbefd9b432524b32b80c8fa659a99b7c", + "6c9694dc8ced493f8c4b011f93b6185a", + 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Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False.\n", - "INFO 12-24 16:16:27 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=512.\n", - "INFO 12-24 16:16:27 llm_engine.py:237] Initializing an LLM engine (vdev) with config: model='meta-llama/Llama-3.2-1B-Instruct', speculative_config=None, tokenizer='meta-llama/Llama-3.2-1B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=42, served_model_name=meta-llama/Llama-3.2-1B-Instruct, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=True, use_cached_outputs=False, mm_processor_kwargs=None)\n" + "\rComputing majority & weighted predictions: 0%| | 0/6 [00:00 0$ and $0 \\\\le \\\\theta < 2 \\\\pi.$',\n", - " 'solution': 'We have that $r = \\\\sqrt{0^2 + 3^2} = 3.$ Also, if we draw the line connecting the origin and $(0,3),$ this line makes an angle of $\\\\frac{\\\\pi}{2}$ with the positive $x$-axis.\\n\\n[asy]\\nunitsize(0.8 cm);\\n\\ndraw((-0.5,0)--(3.5,0));\\ndraw((0,-0.5)--(0,3.5));\\ndraw(arc((0,0),3,0,90),red,Arrow(6));\\n\\ndot((0,3), red);\\nlabel(\"$(0,3)$\", (0,3), W);\\ndot((3,0), red);\\n[/asy]\\n\\nTherefore, the polar coordinates are $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}.$',\n", - " 'answer': '\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)',\n", - " 'subject': 'Precalculus',\n", - " 'level': 2,\n", - " 'unique_id': 'test/precalculus/807.json'}" - ] + "Compute majority pred 16: 0%| | 0/1 [00:00assistant<|end_header_id|>\\n\\n## Step 1: Recall the formulas for converting rectangular coordinates to polar coordinates.\\nThe conversion formulas are: $r = \\\\sqrt{x^2 + y^2}$ and $heta = \\\\arctan{\\\\left( \\\\frac{y}{x} \\\\right)}$.\\n\\n## Step 2: Plug in the values of $x$ and $y$ into the formula for $r$.\\n$r = \\\\sqrt{(0)^2 + (3)^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n\\n## Step 3: Plug in the values of $x$ and $y$ into the formula for $heta$.\\n$heta = \\\\arctan{\\\\left( \\\\frac{(3)}{(0)} \\\\right)} = \\\\arctan{(0)} = \\\\frac{\\\\pi}{2}$\\n\\n## Step 4: Write the answer in the required format.\\nTherefore, the polar coordinates are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "df180024dd904644aba1e7b90c8aebe8" + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" } }, - "metadata": {} + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", + "formatted_output" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104 }, + "id": "flbIu6-rDapM", + "outputId": "d0e71736-6a0c-46f8-ae21-d0e85d884fbc" + }, + "execution_count": 8, + "outputs": [ { - "output_type": "display_data", + "output_type": "execute_result", "data": { "text/plain": [ - "Compute weighted pred 2: 0%| | 0/1 [00:00" ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "f34d7d9f3d1e41088f8ce5a5cd51006a" - } + "text/markdown": "## Step 1: Recall the formulas for converting rectangular coordinates to polar coordinates.\nThe conversion formulas are: $r = \\sqrt{x^2 + y^2}$ and $heta = \\arctan{\\left( \\frac{y}{x} \\right)}$.\n\n## Step 2: Plug in the values of $x$ and $y$ into the formula for $r$.\n$r = \\sqrt{(0)^2 + (3)^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n\n## Step 3: Plug in the values of $x$ and $y$ into the formula for $heta$.\n$heta = \\arctan{\\left( \\frac{(3)}{(0)} \\right)} = \\arctan{(0)} = \\frac{\\pi}{2}$\n\n## Step 4: Write the answer in the required format.\nTherefore, the polar coordinates are $\\left( 3, \\frac{\\pi}{2} \\right)$.\n\nThe final answer is: $\\boxed{\\left( 3, \\frac{\\pi}{2} \\right)}$" }, "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import time\n", + "\n", + "def generate_with_search_and_learn(question, config, llm, prm, method='best_of_n'):\n", + " batch = {\"problem\": [question]}\n", + "\n", + " start_time = time.time()\n", + " if method == 'best_of_n':\n", + " result = best_of_n(x=batch, config=config, llm=llm, prm=prm)\n", + " elif method == 'beam_search':\n", + " result = beam_search(examples=batch, config=config, llm=llm, prm=prm)\n", + " elif method == 'dvts':\n", + " result = dvts(examples=batch, config=config, llm=llm, prm=prm)\n", + "\n", + " elapsed_time = time.time() - start_time\n", + " print(f\"\\nFinished in {elapsed_time:.2f} seconds\\n\")\n", + "\n", + " result = Dataset.from_dict(result)\n", + " result = score(result, config)\n", + " formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", + " return formatted_output\n", + "\n", + "\n", + "question = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n", + "#question = 'What\\'s the capital of Spain?'\n", + "\n", + "config.n=8\n", + "config.sort_completed= True\n", + "config.filter_duplicates= True\n", + "# dvts specific\n", + "config.n_beams = config.n // config.beam_width\n", + "\n", + "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='beam_search')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 845, + "referenced_widgets": [ + "108ccf83145342d9b2cefda0c02fde1b", + "949bc74652014fafb461d169cf506c63", + "5306d8d395544e83bf5acae7f95adf85", + "3f506b89055e4c5699d46dc3fb6444e2", + "14c9a213150b45f7959a4474e7c13346", + "d5af7f8d911b4fc69233db43afaf7902", + "8a78105b742d499389616606c4d78cb0", + "c371e6c8e01146e4bf405c3ea2afeecf", + "418e0db249334a4e94d7d654613335a4", + "6bfc4da2cfe24380962de6e3e77ad0f4", + "4ed34145a8f04a89bc90749b954d925f", + 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"5c7d4398d5764c9b8feb5d868a39634c", + "400da98d85c54a1783785446879ff339", + "acbdd8df37684dfa8c7615b2c01e29c6", + "c9e2e1c0079a429392e647ca64f2f0a1", + "8ed483c24cce471fa8673e54ae9d3ed8", + "c7c005a22b8348178d447dcac529da34", + "71c65546390f47b88fe7e38f26cf2efa", + "a48b8c41df3f48f587efef51e83a8e90", + "7e26f678326f49abab3179f0f440da26", + "52f2134d25e04a2db9ab6d3132b85e6d", + "0edfc61df92c4fb09a9626a277163636", + "e9f7606a664042b6b0af171b461b2adc" + ] + }, + "id": "YpswbcVi37KR", + "outputId": "e680ba19-82b2-4ca7-89f3-4417734fc5b1" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Beam search iterations: 48%|████▊ | 19/40 [00:19<00:21, 1.02s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Finished in 19.36 seconds\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] }, { "output_type": "display_data", "data": { "text/plain": [ - "Compute naive pred 2: 0%| | 0/1 [00:00assistant<|end_header_id|>\\n\\n## Step 1: Recall the conversion formulas\\nTo convert from rectangular (Cartesian) coordinates $(x,y)$ to polar coordinates $(r,\\\\theta)$, we use the following formulas:\\n$r = \\\\sqrt{x^2 + y^2}$ and $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{y}{x}\\\\right)$.\\n\\n## Step 2: Substitute the values\\nGiven the point $(0,3)$ in rectangular coordinates, we can substitute $x = 0$ and $y = 3$ into the formulas:\\n$r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n\\n## Step 3: Calculate $\\\\theta$\\nNow, we can calculate $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{3}{0}\\\\right)$. However, since division by zero is undefined, and the point $(0,3)$ is on the positive y-axis, the angle $\\\\theta$ will be $\\\\frac{\\\\pi}{2}$ radians.\\n\\n## Step 4: Write the polar coordinates\\nThe polar coordinates of $(0,3)$ are $(r, \\\\theta) = \\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)}$'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 22 - } - ] - }, - { - "cell_type": "code", - "source": [ - "formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", - "formatted_output" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 122 - }, - "id": "flbIu6-rDapM", - "outputId": "b013904c-71ac-4bb2-c759-207f374583f1" - }, - "execution_count": 23, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'## Step 1: Recall the conversion formulas\\nTo convert from rectangular (Cartesian) coordinates $(x,y)$ to polar coordinates $(r,\\\\theta)$, we use the following formulas:\\n$r = \\\\sqrt{x^2 + y^2}$ and $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{y}{x}\\\\right)$.\\n\\n## Step 2: Substitute the values\\nGiven the point $(0,3)$ in rectangular coordinates, we can substitute $x = 0$ and $y = 3$ into the formulas:\\n$r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n\\n## Step 3: Calculate $\\\\theta$\\nNow, we can calculate $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{3}{0}\\\\right)$. However, since division by zero is undefined, and the point $(0,3)$ is on the positive y-axis, the angle $\\\\theta$ will be $\\\\frac{\\\\pi}{2}$ radians.\\n\\n## Step 4: Write the polar coordinates\\nThe polar coordinates of $(0,3)$ are $(r, \\\\theta) = \\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)}$'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 23 - } - ] - }, - { - "cell_type": "code", - "source": [ - "from IPython.display import display, Markdown, Latex\n", "display(Markdown(formatted_output))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 363 + "height": 320 }, - "id": "P4En0qJRD0cl", - "outputId": "6f689dca-6391-4dc7-d59d-84328ac7d62e" + "id": "uzKfFoKG9ejC", + "outputId": "e99f2ff7-ecf4-4acc-e72a-bc3b75261ca7" }, - "execution_count": 24, + "execution_count": 18, "outputs": [ { "output_type": "display_data", @@ -20387,111 +27582,11 @@ "text/plain": [ "" ], - "text/markdown": "## Step 1: Recall the conversion formulas\nTo convert from rectangular (Cartesian) coordinates $(x,y)$ to polar coordinates $(r,\\theta)$, we use the following formulas:\n$r = \\sqrt{x^2 + y^2}$ and $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$.\n\n## Step 2: Substitute the values\nGiven the point $(0,3)$ in rectangular coordinates, we can substitute $x = 0$ and $y = 3$ into the formulas:\n$r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n\n## Step 3: Calculate $\\theta$\nNow, we can calculate $\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right)$. However, since division by zero is undefined, and the point $(0,3)$ is on the positive y-axis, the angle $\\theta$ will be $\\frac{\\pi}{2}$ radians.\n\n## Step 4: Write the polar coordinates\nThe polar coordinates of $(0,3)$ are $(r, \\theta) = \\left(3, \\frac{\\pi}{2}\\right)$.\n\nThe final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" + "text/markdown": "To convert the point $(0,3)$ from rectangular coordinates to polar coordinates, we use the following formulas:\n\n$r = \\sqrt{x^2 + y^2}$\n\n$heta = \\tan^{-1} \\left(\\frac{y}{x}\\right)$\n\nFirst, we calculate $r$:\n\n$r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n\nThen, we calculate $heta$:\n\n$heta = \\tan^{-1} \\left(\\frac{3}{0}\\right)$\n\nSince $\\frac{3}{0}$ is undefined, we recognize that the point $(0,3)$ lies on the positive $y$-axis. Therefore, $heta = \\frac{\\pi}{2}$ since $\\tan^{-1}(0) = 0$.\n\nThe polar coordinates of the point $(0,3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$.\n\nTherefore, the final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" }, "metadata": {} } ] - }, - { - "cell_type": "code", - "source": [ - "import gc\n", - "from vllm.distributed.parallel_state import destroy_model_parallel, destroy_distributed_environment\n", - "\n", - "destroy_model_parallel()\n", - "destroy_distributed_environment()\n", - "del llm.llm_engine.model_executor\n", - "del llm\n", - "gc.collect()\n", - "torch.cuda.empty_cache()\n", - "print(\"Successfully delete the llm pipeline and free the GPU memory!\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "M8DDhSZ3G2IS", - "outputId": "9f1448dc-5318-4a32-cdf5-350282f9d5e0" - }, - "execution_count": 67, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Processed prompts: 28%|██▊ | 1457/5152 [12:25<31:30, 1.95it/s, est. speed input: 6101.02 toks/s, output: 3865.57 toks/s]\n", - "Processed prompts: 3%|▎ | 108/3727 [06:38<3:42:45, 3.69s/it, est. speed input: 5268.77 toks/s, output: 3819.28 toks/s]\n", - "Processed prompts: 2%|▏ | 94/4067 [06:24<4:31:04, 4.09s/it, est. speed input: 6506.02 toks/s, output: 5001.06 toks/s]\n", - "Processed prompts: 0%| | 0/4106 [01:56 Date: Mon, 30 Dec 2024 17:09:27 +0100 Subject: [PATCH 03/10] Completed notebook --- notebooks/en/search_and_learn.ipynb | 34537 +++++++++++++++++++++++--- 1 file changed, 30943 insertions(+), 3594 deletions(-) diff --git a/notebooks/en/search_and_learn.ipynb b/notebooks/en/search_and_learn.ipynb index 6c7185fb..a8d69222 100644 --- a/notebooks/en/search_and_learn.ipynb +++ b/notebooks/en/search_and_learn.ipynb @@ -17,7 +17,7 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "60796f0e38e04bd0bbf01d0f47b6c193": { + "e917741ae21e4943880cee24489b4551": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -32,10 +32,10 @@ "_view_name": "VBoxView", "box_style": "", "children": [], - "layout": "IPY_MODEL_7ceb6890ce394d219558d73c035b0132" + "layout": "IPY_MODEL_45783fb1f5d344e69c173a82213a7eda" } }, - "a35db2d634b44fa18b156bf1219bbbc6": { + "53c62adc2c7346beb95f524c11b28f4d": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -50,13 +50,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_14cfc9eaed814d81a22c0d07872c48cf", + "layout": "IPY_MODEL_2537715f9d1c435ab78cccfa69909282", "placeholder": "​", - "style": "IPY_MODEL_107474000a2f44b88e468b76cdb629a2", + "style": "IPY_MODEL_63643e708fe74718b7ed6d9e6c8cf29b", "value": "

Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
" } }, - "39c02b227859468f937eaf0dfeb30397": { + "1317f8c4c8014102af169acb05b8b80c": { "model_module": "@jupyter-widgets/controls", "model_name": "PasswordModel", "model_module_version": "1.5.0", @@ -73,13 +73,13 @@ "description": "Token:", "description_tooltip": null, "disabled": false, - "layout": "IPY_MODEL_5b07a2985c884134acf6663bd11b68e5", + "layout": "IPY_MODEL_f2c26a25593143a39307c6cba355e701", "placeholder": "​", - "style": "IPY_MODEL_dc8d18e4d81e4b038eb1b5b6e08d7468", + "style": "IPY_MODEL_c5f91f3971344ac19cd5d333d51380b6", "value": "" } }, - "6097a7d7386b4fb7b2626b127f7c9fd9": { + "d579833c056642e9b5dccd01067900ef": { "model_module": "@jupyter-widgets/controls", "model_name": "CheckboxModel", "model_module_version": "1.5.0", @@ -96,12 +96,12 @@ "description_tooltip": null, "disabled": false, "indent": true, - "layout": "IPY_MODEL_bb8d455434774af48a2bbf67b60f7c4c", - "style": "IPY_MODEL_1f4355b6ddf04ca1ad979107db86a028", + "layout": "IPY_MODEL_a6e64dd55f2c411893fcdcc44d189da3", + "style": "IPY_MODEL_1bc17f6074d44eb1b67cac268134343e", "value": true } }, - "91bd247295b2403780cc9085933596e8": { + "e0b6b66dd6d24f39a7b733c09fb20a3d": { "model_module": "@jupyter-widgets/controls", "model_name": "ButtonModel", "model_module_version": "1.5.0", @@ -118,12 +118,12 @@ "description": "Login", "disabled": false, "icon": "", - "layout": "IPY_MODEL_84e6c09df2674da98d929c259285cc50", - "style": "IPY_MODEL_f186c6187e0f4c12a134495046472f78", + "layout": "IPY_MODEL_e3736af22c694530a1abc7ca964604f4", + "style": "IPY_MODEL_8f34192e6a194e6589850991ebd42a5b", "tooltip": "" } }, - "9521965237ac46eabb8853ad15871406": { + "300334e6fbef4e0a80b5be760a52c904": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -138,13 +138,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_b8e03810026942e39772aff1535734ef", + "layout": "IPY_MODEL_b63fc2983be348e8970838809b1f5489", "placeholder": "​", - "style": "IPY_MODEL_d54843bdabe443bf92d0602ee5e5aba7", + "style": "IPY_MODEL_c1b137362fc6442a8040e6eede964dd8", "value": "\nPro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks.
" } }, - "7ceb6890ce394d219558d73c035b0132": { + "45783fb1f5d344e69c173a82213a7eda": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -196,7 +196,7 @@ "width": "50%" } }, - "14cfc9eaed814d81a22c0d07872c48cf": { + "2537715f9d1c435ab78cccfa69909282": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -248,7 +248,7 @@ "width": null } }, - "107474000a2f44b88e468b76cdb629a2": { + "63643e708fe74718b7ed6d9e6c8cf29b": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -263,7 +263,7 @@ "description_width": "" } }, - "5b07a2985c884134acf6663bd11b68e5": { + "f2c26a25593143a39307c6cba355e701": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -315,7 +315,7 @@ "width": null } }, - "dc8d18e4d81e4b038eb1b5b6e08d7468": { + "c5f91f3971344ac19cd5d333d51380b6": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -330,7 +330,7 @@ "description_width": "" } }, - "bb8d455434774af48a2bbf67b60f7c4c": { + "a6e64dd55f2c411893fcdcc44d189da3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -382,7 +382,7 @@ "width": null } }, - "1f4355b6ddf04ca1ad979107db86a028": { + "1bc17f6074d44eb1b67cac268134343e": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -397,7 +397,7 @@ "description_width": "" } }, - "84e6c09df2674da98d929c259285cc50": { + "e3736af22c694530a1abc7ca964604f4": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -449,7 +449,7 @@ "width": null } }, - "f186c6187e0f4c12a134495046472f78": { + "8f34192e6a194e6589850991ebd42a5b": { "model_module": "@jupyter-widgets/controls", "model_name": "ButtonStyleModel", "model_module_version": "1.5.0", @@ -465,7 +465,7 @@ "font_weight": "" } }, - "b8e03810026942e39772aff1535734ef": { + "b63fc2983be348e8970838809b1f5489": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -517,7 +517,7 @@ "width": null } }, - "d54843bdabe443bf92d0602ee5e5aba7": { + "c1b137362fc6442a8040e6eede964dd8": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -532,7 +532,7 @@ "description_width": "" } }, - "de3783a5692541008a6999405296dee0": { + "af9d9a7123884ccfa163669689fe52e7": { "model_module": "@jupyter-widgets/controls", "model_name": "LabelModel", "model_module_version": "1.5.0", @@ -547,13 +547,13 @@ "_view_name": "LabelView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_6e4fc4f5913646c28cc0386a85b5117a", + "layout": "IPY_MODEL_28afb981ce33437ebef71d40e19ec16e", "placeholder": "​", - "style": "IPY_MODEL_33f91a3eaf3a40dbb28469150fa84f48", + "style": "IPY_MODEL_ab3daf6fb18d464882250be793a3b086", "value": "Connecting..." } }, - "6e4fc4f5913646c28cc0386a85b5117a": { + "28afb981ce33437ebef71d40e19ec16e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -605,7 +605,7 @@ "width": null } }, - "33f91a3eaf3a40dbb28469150fa84f48": { + "ab3daf6fb18d464882250be793a3b086": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -620,7 +620,7 @@ "description_width": "" } }, - "e11380f0d85f476d9f2f5f16a59f64be": { + "628c1b21e27040aebb898a268564db9b": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -635,14 +635,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_a5fe95e4ce2842f6aa46207ba47a793f", - "IPY_MODEL_7f5f156103df4cc2928cbf9c4bbfb10e", - "IPY_MODEL_3a68043df84f4576afe9e6c65933803e" + "IPY_MODEL_33c02e21b0c54d92a51de1e54d1964f1", + "IPY_MODEL_66a9b8bd2ec14682b6f7b5a0c7726764", + "IPY_MODEL_c65440b657b543558fac9cd454eb2420" ], - "layout": "IPY_MODEL_f8aaffb996cd45f0bed5dd5efe3b5048" + "layout": "IPY_MODEL_f5c19d01f4fb444cbd1cd1480ff3c7e5" } }, - "a5fe95e4ce2842f6aa46207ba47a793f": { + "33c02e21b0c54d92a51de1e54d1964f1": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -657,13 +657,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_51188579a3914f6ca9ba24494dcef7a6", + "layout": "IPY_MODEL_6626cf35b6524c3aae5d792c5f50a97d", "placeholder": "​", - "style": "IPY_MODEL_b5c3ec31af54462782b3d590655dfa98", + "style": "IPY_MODEL_840ba16acc0648db80002c32e77b1c87", "value": "config.json: 100%" } }, - "7f5f156103df4cc2928cbf9c4bbfb10e": { + "66a9b8bd2ec14682b6f7b5a0c7726764": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -679,15 +679,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_40ee4e4f13bb4d25993fbe6b617fd3d3", + "layout": "IPY_MODEL_1dcea2ee01a84302a5f0e505dbe0e4f3", "max": 877, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_d672b51dad784c1ea6a070f2d85c2a02", + "style": "IPY_MODEL_62748249e6a247978988006485334dcd", "value": 877 } }, - "3a68043df84f4576afe9e6c65933803e": { + "c65440b657b543558fac9cd454eb2420": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -702,13 +702,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_6e5f35b60cc244e98ca2445be2967dc1", + "layout": "IPY_MODEL_d4d3abfa797843b0addcc1e9c78328df", "placeholder": "​", - "style": "IPY_MODEL_f0702dd4ef074cc084d1b12cc49b321c", - "value": " 877/877 [00:00<00:00, 77.2kB/s]" + "style": 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"placeholder": "​", - "style": "IPY_MODEL_2f9dd120805b4949b84cfe643a93dc70", + "style": "IPY_MODEL_e81ed878babe4a72bc7d179044797752", "value": "Subsample 8: 100%" } }, - "bf8e275d290445db8e1f3c3755ff192e": { + "9f9adbdc0efc406db227145e289cf826": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -23251,15 +23251,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_79b7d16ca9cf402da0d8ca616ab888b7", + "layout": "IPY_MODEL_2039c620f3d241d99179ee22d234ae45", "max": 1, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_da151ca538e1478b8eda48946a2dcc57", + "style": "IPY_MODEL_9d0e6d42ff904766be21ee663ea39c1d", "value": 1 } }, - "4f096df380a945a1a4c24f864f40f728": { + "0de1ca26c0b349fd9a178abb0220c612": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -23274,13 +23274,13 @@ "_view_name": "HTMLView", 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"id": "t0YDC2_7XTm8", - "outputId": "d3b5534e-0128-4843-ee51-ebcbcee1e6a8" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Cloning into 'search-and-learn'...\n", - "remote: Enumerating objects: 235, done.\u001b[K\n", - "remote: Counting objects: 100% (75/75), done.\u001b[K\n", - "remote: Compressing objects: 100% (32/32), done.\u001b[K\n", - "remote: Total 235 (delta 48), reused 43 (delta 43), pack-reused 160 (from 1)\u001b[K\n", - "Receiving objects: 100% (235/235), 87.99 KiB | 1.69 MiB/s, done.\n", - "Resolving deltas: 100% (107/107), done.\n" - ] - } - ], - "source": [ - "!git clone https://github.com/huggingface/search-and-learn" - ] - }, - { - "cell_type": "code", - "source": [ - "%cd search-and-learn\n", - "!pip install -e '.[dev]'" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "kT3jH_d_XcEb", - "outputId": "014f82f0-277b-40a1-8182-15c3ae8d6b52" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content/search-and-learn\n", - "Obtaining file:///content/search-and-learn\n", - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n", - " Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from search-and-learn==0.1.0) (1.2.1)\n", - "Collecting pebble (from search-and-learn==0.1.0)\n", - " Downloading Pebble-5.1.0-py3-none-any.whl.metadata (3.6 kB)\n", - "Collecting latex2sympy2==1.9.1 (from search-and-learn==0.1.0)\n", - " Downloading latex2sympy2-1.9.1-py3-none-any.whl.metadata (6.6 kB)\n", - "Collecting word2number (from search-and-learn==0.1.0)\n", - " Downloading word2number-1.1.zip (9.7 kB)\n", - " Preparing 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size=6722 sha256=f0058494f2517d378eee791d49ee34f11cf0f61c4ff12c72a6c447e8f1f3fb8e\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-sl4vsyqy/wheels/c8/0a/9d/dae572a3e5287d72b78bd42e1c9bc5d91117df64445c4b5d62\n", - " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.7.2-py3-none-any.whl size=140931 sha256=7683d171c835a4b20616d4f1423ff885debd113000db8271d275e4ba5b1402d2\n", - " Stored in directory: /root/.cache/pip/wheels/79/20/ec/30bf7dabc29319ccc0d0c96f910a640513a3c81faa960fed43\n", - " Building wheel for word2number (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5568 sha256=83b558080d5472d3cff0671462a27963cedbd8ad5e0ca4a42c9c4c57fcf2f199\n", - " Stored in directory: /root/.cache/pip/wheels/84/ff/26/d3cfbd971e96c5aa3737ecfced81628830d7359b55fbb8ca3b\n", - "Successfully built search-and-learn antlr4-python3-runtime word2number\n", - "Installing collected packages: word2number, pyairports, nvidia-ml-py, antlr4-python3-runtime, xxhash, uvloop, uvicorn, triton, ruff, python-dotenv, pycountry, pillow, pebble, partial-json-parser, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, msgspec, lark, isort, interegular, httptools, gguf, fsspec, diskcache, dill, watchfiles, tiktoken, starlette, nvidia-cusolver-cu12, nvidia-cudnn-cu12, multiprocess, latex2sympy2, torch, prometheus-fastapi-instrumentator, lm-format-enforcer, fastapi, xformers, torchvision, ray, mistral-common, search-and-learn, datasets, outlines, vllm\n", - " Attempting uninstall: pillow\n", - " Found existing installation: pillow 11.0.0\n", - " Uninstalling pillow-11.0.0:\n", - " Successfully uninstalled pillow-11.0.0\n", - " Attempting uninstall: nvidia-nccl-cu12\n", - " Found existing installation: nvidia-nccl-cu12 2.23.4\n", - " Uninstalling nvidia-nccl-cu12-2.23.4:\n", - " Successfully uninstalled nvidia-nccl-cu12-2.23.4\n", - " Attempting uninstall: nvidia-cusparse-cu12\n", - " Found existing installation: nvidia-cusparse-cu12 12.5.4.2\n", - " Uninstalling nvidia-cusparse-cu12-12.5.4.2:\n", - " Successfully uninstalled nvidia-cusparse-cu12-12.5.4.2\n", - " Attempting uninstall: nvidia-curand-cu12\n", - " Found existing installation: nvidia-curand-cu12 10.3.7.77\n", - " Uninstalling nvidia-curand-cu12-10.3.7.77:\n", - " Successfully uninstalled nvidia-curand-cu12-10.3.7.77\n", - " Attempting uninstall: nvidia-cufft-cu12\n", - " Found existing installation: nvidia-cufft-cu12 11.3.0.4\n", - " Uninstalling nvidia-cufft-cu12-11.3.0.4:\n", - " Successfully uninstalled nvidia-cufft-cu12-11.3.0.4\n", - " Attempting uninstall: nvidia-cuda-runtime-cu12\n", - " Found existing installation: nvidia-cuda-runtime-cu12 12.6.77\n", - " Uninstalling nvidia-cuda-runtime-cu12-12.6.77:\n", - " Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.77\n", - " Attempting uninstall: nvidia-cuda-cupti-cu12\n", - " Found existing installation: nvidia-cuda-cupti-cu12 12.6.80\n", - " Uninstalling nvidia-cuda-cupti-cu12-12.6.80:\n", - " Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.80\n", - " Attempting uninstall: nvidia-cublas-cu12\n", - " Found existing installation: nvidia-cublas-cu12 12.6.4.1\n", - " Uninstalling nvidia-cublas-cu12-12.6.4.1:\n", - " Successfully uninstalled nvidia-cublas-cu12-12.6.4.1\n", - " Attempting uninstall: fsspec\n", - " Found existing installation: fsspec 2024.10.0\n", - " Uninstalling fsspec-2024.10.0:\n", - " Successfully uninstalled fsspec-2024.10.0\n", - " Attempting uninstall: nvidia-cusolver-cu12\n", - " Found existing installation: nvidia-cusolver-cu12 11.7.1.2\n", - " Uninstalling nvidia-cusolver-cu12-11.7.1.2:\n", - " Successfully uninstalled nvidia-cusolver-cu12-11.7.1.2\n", - " Attempting uninstall: nvidia-cudnn-cu12\n", - " Found existing installation: nvidia-cudnn-cu12 9.6.0.74\n", - " Uninstalling nvidia-cudnn-cu12-9.6.0.74:\n", - " Successfully uninstalled nvidia-cudnn-cu12-9.6.0.74\n", - " Attempting uninstall: torch\n", - " Found existing installation: torch 2.5.1+cu121\n", - " Uninstalling torch-2.5.1+cu121:\n", - " Successfully uninstalled torch-2.5.1+cu121\n", - " Attempting uninstall: torchvision\n", - " Found existing installation: torchvision 0.20.1+cu121\n", - " Uninstalling torchvision-0.20.1+cu121:\n", - " Successfully uninstalled torchvision-0.20.1+cu121\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n", - "torchaudio 2.5.1+cu121 requires torch==2.5.1, but you have torch 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed antlr4-python3-runtime-4.7.2 datasets-3.2.0 dill-0.3.8 diskcache-5.6.3 fastapi-0.115.6 fsspec-2024.9.0 gguf-0.10.0 httptools-0.6.4 interegular-0.3.3 isort-5.13.2 lark-1.2.2 latex2sympy2-1.9.1 lm-format-enforcer-0.10.6 mistral-common-1.5.1 msgspec-0.18.6 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-ml-py-12.560.30 nvidia-nccl-cu12-2.20.5 nvidia-nvtx-cu12-12.1.105 outlines-0.0.46 partial-json-parser-0.2.1.1.post4 pebble-5.1.0 pillow-10.4.0 prometheus-fastapi-instrumentator-7.0.0 pyairports-2.1.1 pycountry-24.6.1 python-dotenv-1.0.1 ray-2.40.0 ruff-0.8.4 search-and-learn-0.1.0 starlette-0.41.3 tiktoken-0.7.0 torch-2.4.0 torchvision-0.19.0 triton-3.0.0 uvicorn-0.34.0 uvloop-0.21.0 vllm-0.6.3 watchfiles-1.0.3 word2number-1.1 xformers-0.0.27.post2 xxhash-3.5.0\n" - ] }, - { - "output_type": "display_data", - "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "PIL" - ] - }, - "id": "f7ea7d6dfd36400faf02a9173ea5b956" - } - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "from huggingface_hub import notebook_login\n", - "\n", - "notebook_login()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17, - "referenced_widgets": [ - "60796f0e38e04bd0bbf01d0f47b6c193", - "a35db2d634b44fa18b156bf1219bbbc6", - "39c02b227859468f937eaf0dfeb30397", - "6097a7d7386b4fb7b2626b127f7c9fd9", - "91bd247295b2403780cc9085933596e8", - "9521965237ac46eabb8853ad15871406", - "7ceb6890ce394d219558d73c035b0132", - "14cfc9eaed814d81a22c0d07872c48cf", - "107474000a2f44b88e468b76cdb629a2", - "5b07a2985c884134acf6663bd11b68e5", - "dc8d18e4d81e4b038eb1b5b6e08d7468", - "bb8d455434774af48a2bbf67b60f7c4c", - "1f4355b6ddf04ca1ad979107db86a028", - "84e6c09df2674da98d929c259285cc50", - "f186c6187e0f4c12a134495046472f78", - "b8e03810026942e39772aff1535734ef", - "d54843bdabe443bf92d0602ee5e5aba7", - "de3783a5692541008a6999405296dee0", - "6e4fc4f5913646c28cc0386a85b5117a", - "33f91a3eaf3a40dbb28469150fa84f48" - ] - }, - "id": "pnEaTlFYZF_H", - "outputId": "9e7229f5-ed14-403b-bb15-d1596f2b187f" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(HTML(value='
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(https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] + "b377b15fc4f84c9faca9d859080839d9": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f15fe0f64c31453ab5c5990cd5332af3", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_cdaf3b91708f4893bdfec8bf36250724", + "value": 1 + } }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "config.json: 0%| | 0.00/877 [00:00=4.47.0 in /usr/local/lib/python3.10/dist-packages (from search-and-learn==0.1.0) (4.47.1)\n", + "Collecting fastapi (from search-and-learn==0.1.0)\n", + " Downloading fastapi-0.115.6-py3-none-any.whl.metadata (27 kB)\n", + "Requirement already satisfied: sympy>=1.4 in /usr/local/lib/python3.10/dist-packages (from latex2sympy2==1.9.1->search-and-learn==0.1.0) (1.13.1)\n", + "Collecting antlr4-python3-runtime==4.7.2 (from latex2sympy2==1.9.1->search-and-learn==0.1.0)\n", + " Downloading antlr4-python3-runtime-4.7.2.tar.gz (112 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.3/112.3 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Collecting vllm==0.6.3 (from 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/tmp/pip-ephem-wheel-cache-y1ijzcks/wheels/c8/0a/9d/dae572a3e5287d72b78bd42e1c9bc5d91117df64445c4b5d62\n", + " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.7.2-py3-none-any.whl size=140931 sha256=059793a96b24d192ea9f998bde9273f6c0bc3463d442a7ee2e0e227efb7a129b\n", + " Stored in directory: /root/.cache/pip/wheels/79/20/ec/30bf7dabc29319ccc0d0c96f910a640513a3c81faa960fed43\n", + " Building wheel for word2number (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5568 sha256=409a0e1d285814f018ba1b93af1ac2324fadc82718551851c84201b206a92751\n", + " Stored in directory: /root/.cache/pip/wheels/84/ff/26/d3cfbd971e96c5aa3737ecfced81628830d7359b55fbb8ca3b\n", + "Successfully built search-and-learn antlr4-python3-runtime word2number\n", + "Installing collected packages: word2number, pyairports, nvidia-ml-py, antlr4-python3-runtime, xxhash, uvloop, uvicorn, triton, ruff, python-dotenv, pycountry, pillow, pebble, partial-json-parser, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, msgspec, lark, isort, interegular, httptools, gguf, fsspec, diskcache, dill, watchfiles, tiktoken, starlette, nvidia-cusolver-cu12, nvidia-cudnn-cu12, multiprocess, latex2sympy2, torch, prometheus-fastapi-instrumentator, lm-format-enforcer, fastapi, xformers, torchvision, ray, mistral-common, search-and-learn, datasets, outlines, vllm\n", + " Attempting uninstall: pillow\n", + " Found existing installation: pillow 11.0.0\n", + " Uninstalling pillow-11.0.0:\n", + " Successfully uninstalled pillow-11.0.0\n", + " Attempting uninstall: nvidia-nccl-cu12\n", + " Found existing installation: nvidia-nccl-cu12 2.23.4\n", + " Uninstalling nvidia-nccl-cu12-2.23.4:\n", + " Successfully uninstalled nvidia-nccl-cu12-2.23.4\n", + " Attempting uninstall: nvidia-cusparse-cu12\n", + " Found existing installation: nvidia-cusparse-cu12 12.5.4.2\n", + " Uninstalling nvidia-cusparse-cu12-12.5.4.2:\n", + " Successfully uninstalled nvidia-cusparse-cu12-12.5.4.2\n", + " Attempting uninstall: nvidia-curand-cu12\n", + " Found existing installation: nvidia-curand-cu12 10.3.7.77\n", + " Uninstalling nvidia-curand-cu12-10.3.7.77:\n", + " Successfully uninstalled nvidia-curand-cu12-10.3.7.77\n", + " Attempting uninstall: nvidia-cufft-cu12\n", + " Found existing installation: nvidia-cufft-cu12 11.3.0.4\n", + " Uninstalling nvidia-cufft-cu12-11.3.0.4:\n", + " Successfully uninstalled nvidia-cufft-cu12-11.3.0.4\n", + " Attempting uninstall: nvidia-cuda-runtime-cu12\n", + " Found existing installation: nvidia-cuda-runtime-cu12 12.6.77\n", + " Uninstalling nvidia-cuda-runtime-cu12-12.6.77:\n", + " Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.77\n", + " Attempting uninstall: nvidia-cuda-cupti-cu12\n", + " Found existing installation: nvidia-cuda-cupti-cu12 12.6.80\n", + " Uninstalling nvidia-cuda-cupti-cu12-12.6.80:\n", + " Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.80\n", + " Attempting uninstall: nvidia-cublas-cu12\n", + " Found existing installation: nvidia-cublas-cu12 12.6.4.1\n", + " Uninstalling nvidia-cublas-cu12-12.6.4.1:\n", + " Successfully uninstalled nvidia-cublas-cu12-12.6.4.1\n", + " Attempting uninstall: fsspec\n", + " Found existing installation: fsspec 2024.10.0\n", + " Uninstalling fsspec-2024.10.0:\n", + " Successfully uninstalled fsspec-2024.10.0\n", + " Attempting uninstall: nvidia-cusolver-cu12\n", + " Found existing installation: nvidia-cusolver-cu12 11.7.1.2\n", + " Uninstalling nvidia-cusolver-cu12-11.7.1.2:\n", + " Successfully uninstalled nvidia-cusolver-cu12-11.7.1.2\n", + " Attempting uninstall: nvidia-cudnn-cu12\n", + " Found existing installation: nvidia-cudnn-cu12 9.6.0.74\n", + " Uninstalling nvidia-cudnn-cu12-9.6.0.74:\n", + " Successfully uninstalled nvidia-cudnn-cu12-9.6.0.74\n", + " Attempting uninstall: torch\n", + " Found existing installation: torch 2.5.1+cu121\n", + " Uninstalling torch-2.5.1+cu121:\n", + " Successfully uninstalled torch-2.5.1+cu121\n", + " Attempting uninstall: torchvision\n", + " Found existing installation: torchvision 0.20.1+cu121\n", + " Uninstalling torchvision-0.20.1+cu121:\n", + " Successfully uninstalled torchvision-0.20.1+cu121\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n", + "torchaudio 2.5.1+cu121 requires torch==2.5.1, but you have torch 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed antlr4-python3-runtime-4.7.2 datasets-3.2.0 dill-0.3.8 diskcache-5.6.3 fastapi-0.115.6 fsspec-2024.9.0 gguf-0.10.0 httptools-0.6.4 interegular-0.3.3 isort-5.13.2 lark-1.2.2 latex2sympy2-1.9.1 lm-format-enforcer-0.10.6 mistral-common-1.5.1 msgspec-0.19.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-ml-py-12.560.30 nvidia-nccl-cu12-2.20.5 nvidia-nvtx-cu12-12.1.105 outlines-0.0.46 partial-json-parser-0.2.1.1.post4 pebble-5.1.0 pillow-10.4.0 prometheus-fastapi-instrumentator-7.0.0 pyairports-2.1.1 pycountry-24.6.1 python-dotenv-1.0.1 ray-2.40.0 ruff-0.8.4 search-and-learn-0.1.0 starlette-0.41.3 tiktoken-0.7.0 torch-2.4.0 torchvision-0.19.0 triton-3.0.0 uvicorn-0.34.0 uvloop-0.21.0 vllm-0.6.3 watchfiles-1.0.3 word2number-1.1 xformers-0.0.27.post2 xxhash-3.5.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "PIL" + ] + }, + "id": "7b888e521d464a01a0ee9a7408940e2a" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Log in to Hugging Face to access [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct), as it is a gated model! 🗝️ \n", + "If you haven't previously requested access, you'll need to submit a request before proceeding.\n" + ], + "metadata": { + "id": "VAQHu9T176zh" + } + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17, + "referenced_widgets": [ + "e917741ae21e4943880cee24489b4551", + "53c62adc2c7346beb95f524c11b28f4d", + "1317f8c4c8014102af169acb05b8b80c", + "d579833c056642e9b5dccd01067900ef", + "e0b6b66dd6d24f39a7b733c09fb20a3d", + "300334e6fbef4e0a80b5be760a52c904", + "45783fb1f5d344e69c173a82213a7eda", + "2537715f9d1c435ab78cccfa69909282", + "63643e708fe74718b7ed6d9e6c8cf29b", + "f2c26a25593143a39307c6cba355e701", + "c5f91f3971344ac19cd5d333d51380b6", + "a6e64dd55f2c411893fcdcc44d189da3", + "1bc17f6074d44eb1b67cac268134343e", + "e3736af22c694530a1abc7ca964604f4", + "8f34192e6a194e6589850991ebd42a5b", + "b63fc2983be348e8970838809b1f5489", + "c1b137362fc6442a8040e6eede964dd8", + "af9d9a7123884ccfa163669689fe52e7", + "28afb981ce33437ebef71d40e19ec16e", + "ab3daf6fb18d464882250be793a3b086" + ] + }, + "id": "pnEaTlFYZF_H", + "outputId": "cf356fab-7e71-4997-a10a-78ee2d7925c7" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False.\n", + "INFO 12-30 15:21:46 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=512.\n", + "INFO 12-30 15:21:46 llm_engine.py:237] Initializing an LLM engine (vdev) with config: model='meta-llama/Llama-3.2-1B-Instruct', speculative_config=None, tokenizer='meta-llama/Llama-3.2-1B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=42, served_model_name=meta-llama/Llama-3.2-1B-Instruct, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=True, use_cached_outputs=False, mm_processor_kwargs=None)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/54.5k [00:00 0$ and $0 \\le \\theta < 2 \\pi.$'\n", + "input_batch = {\"problem\": [question_text]}" + ], + "metadata": { + "id": "83puLxhzsOM0" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sal.config import Config\n", + "from sal.search import beam_search, best_of_n, dvts\n", + "\n", + "config = Config()\n", + "config.n=32 # Number of answers to generate during the search\n", + "\n", + "search_result = best_of_n(x=input_batch, config=config, llm=llm, prm=prm)" + ], + "metadata": { + "id": "C6s6GS16QZLV" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from datasets import Dataset\n", + "\n", + "search_dataset = Dataset.from_dict(search_result)" + ], + "metadata": { + "id": "VtEPnhnQbF9W" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sal.utils.score import score\n", + "\n", + "final_result = score(search_dataset, config)" + ], + "metadata": { + "id": "29t49WKAAOA7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "8347e2d9c6084defb92836697f1ee5c6", + "db67ef43ab864eac9b32e1159140b554", + "f1dd77c0b07044e4b161628e84fab059", + "0e0ab349010d4b319ea9a414ccd61bc8", + "73e6b699031e4835a293c013f129c08b", + "e5f89c42a9b14ceea06b49ba9789c6e5", + "0d1c8cf9d5da41188fa92418392ae6c3", + "9fd3cc59344f403ba0834883aca14590", + 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[00:00assistant<|end_header_id|>\\n\\n## Step 1: The relationship between rectangular and polar coordinates is given by:\\n\\\\[ r = \\\\sqrt{x^2 + y^2} \\\\] and \\\\[ heta = \\\\arctan{\\\\left(\\\\frac{y}{x}\\\\right)}. \\\\]\\n\\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\\n\\\\[ r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{9} = 3. \\\\]\\n\\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\\n\\\\[ heta = \\\\arctan{\\\\left(\\\\frac{3}{0}\\\\right)} = \\\\arctan{\\\\left( \\\\infty \\\\right)}. \\\\]\\n\\n## Step 4: Since $\\\\arctan{\\\\left( \\\\infty \\\\right)}$ is equal to $\\\\frac{\\\\pi}{2}$, we have:\\n\\\\[ heta = \\\\frac{\\\\pi}{2}. \\\\]\\n\\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right).$\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "formatted_output = final_result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", + "formatted_output" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "id": "flbIu6-rDapM", + "outputId": "08b76f0c-db41-4069-b559-5cd3aa832e25" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'## Step 1: The relationship between rectangular and polar coordinates is given by:\\n\\\\[ r = \\\\sqrt{x^2 + y^2} \\\\] and \\\\[ heta = \\\\arctan{\\\\left(\\\\frac{y}{x}\\\\right)}. \\\\]\\n\\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\\n\\\\[ r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{9} = 3. \\\\]\\n\\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\\n\\\\[ heta = \\\\arctan{\\\\left(\\\\frac{3}{0}\\\\right)} = \\\\arctan{\\\\left( \\\\infty \\\\right)}. \\\\]\\n\\n## Step 4: Since $\\\\arctan{\\\\left( \\\\infty \\\\right)}$ is equal to $\\\\frac{\\\\pi}{2}$, we have:\\n\\\\[ heta = \\\\frac{\\\\pi}{2}. \\\\]\\n\\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right).$\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import display, Markdown, Latex\n", + "\n", + "display(Markdown(formatted_output))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433 + }, + "id": "P4En0qJRD0cl", + "outputId": "6a190922-729d-489f-b330-ad1ee7873692" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/markdown": "## Step 1: The relationship between rectangular and polar coordinates is given by:\n\\[ r = \\sqrt{x^2 + y^2} \\] and \\[ heta = \\arctan{\\left(\\frac{y}{x}\\right)}. \\]\n\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\n\\[ r = \\sqrt{0^2 + 3^2} = \\sqrt{9} = 3. \\]\n\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\n\\[ heta = \\arctan{\\left(\\frac{3}{0}\\right)} = \\arctan{\\left( \\infty \\right)}. \\]\n\n## Step 4: Since $\\arctan{\\left( \\infty \\right)}$ is equal to $\\frac{\\pi}{2}$, we have:\n\\[ heta = \\frac{\\pi}{2}. \\]\n\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\left( 3, \\frac{\\pi}{2} \\right).$\n\nThe final answer is: $\\boxed{\\left( 3, \\frac{\\pi}{2} \\right)}$" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# 3. Assembling It All! 🧑‍🏭️\n", + "\n", + "Now, let's create a method that encapsulates the entire pipeline. This will allow us to easily reuse the process in future applications, making it efficient and modular.\n", + "\n", + "By combining the LLM, PRM, search strategy, and result display, we can simplify the workflow and ensure that it’s reusable for other tasks or questions.\n", + "\n", + "We simplify the workflow, ensuring that it’s reusable for different tasks or questions. Additionally, we’ll track the time spent on each method so that we can **understand the practical implications** of using each strategy and configuration.\n", + "\n", + "Here’s how we can structure the method:" + ], + "metadata": { + "id": "4uCpYzAw_4o9" + } + }, + { + "cell_type": "code", + "source": [ + "import time\n", + "\n", + "def generate_with_search_and_learn(question, config, llm, prm, method='best_of_n'):\n", + " \"\"\"\n", + " Generate an answer for a given question using the search-and-learn pipeline.\n", + "\n", + " Args:\n", + " - question (str): The input question to generate an answer for.\n", + " - config (Config): Configuration object containing parameters for search strategy.\n", + " - llm (LLM): Pretrained large language model used for generating answers.\n", + " - prm (RLHFFlow): Process reward model used for evaluating answers.\n", + " - method (str): Search strategy to use. Options are 'best_of_n', 'beam_search', 'dvts'. Default is 'best_of_n'.\n", + "\n", + " Returns:\n", + " - str: The formatted output after processing the question.\n", + " \"\"\"\n", + " batch = {\"problem\": [question]}\n", + "\n", + " start_time = time.time()\n", + " if method == 'best_of_n':\n", + " result = best_of_n(x=batch, config=config, llm=llm, prm=prm)\n", + " elif method == 'beam_search':\n", + " result = beam_search(examples=batch, config=config, llm=llm, prm=prm)\n", + " elif method == 'dvts':\n", + " result = dvts(examples=batch, config=config, llm=llm, prm=prm)\n", + "\n", + " elapsed_time = time.time() - start_time\n", + " print(f\"\\nFinished in {elapsed_time:.2f} seconds\\n\")\n", + "\n", + " result = Dataset.from_dict(result)\n", + " result = score(result, config)\n", + " formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", + " return formatted_output" + ], + "metadata": { + "id": "YpswbcVi37KR" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## ⏳ 3.1 Comparing Thinking Time for Each Strategy\n", + "\n", + "Let’s now run each method and track the **thinking time** for a given question.\n", + "\n", + "### 1. **Best of n**\n", + "\n", + "We’ll begin by using the `best_of_n` strategy. Here’s how to track the thinking time for this method:" + ], + "metadata": { + "id": "RWbOqkiKPVd2" + } + }, + { + "cell_type": "code", + "source": [ + "question = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n", + "\n", + "config.n=8\n", + "\n", + "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 827, + "referenced_widgets": [ + "07fcbf44dd91438bafeb110244fac6bb", + "a307d4986a9a489284333fccd05baea2", + "c9d12093f9df4c578fcfd4bdfa3752ad", + "cbd77563a91341a08556ce09a437509e", + "710be6f8e16f4c319818e44dc75d2109", + "ad710d0763e84f2ba806b2816a2d6164", + "43e234fbe88e429d9f1f1388b790fb24", + "df87c0d0f4174c9a9f82e83186676290", + "14d4fbbc24354308b1772be1da606d00", + "2409843f70af4f51944425a773995bdf", + "40bbe419b638489a9c7d19ebb5771543", + "9e3baabd96c542719070c9052c6c9910", + "a5d40b55ac6c4035a848563856a3f36a", + "b8ee35cd223e478088d6a54dda002f3b", + "6a7824ea88fb40df86d2e7a25a159075", + "31d2d679894d42f5b783b0cbb1d9e8cf", + 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Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False.\n", - "INFO 12-27 16:16:03 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=512.\n", - "INFO 12-27 16:16:03 llm_engine.py:237] Initializing an LLM engine (vdev) with config: model='meta-llama/Llama-3.2-1B-Instruct', speculative_config=None, tokenizer='meta-llama/Llama-3.2-1B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=42, served_model_name=meta-llama/Llama-3.2-1B-Instruct, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=True, use_cached_outputs=False, mm_processor_kwargs=None)\n" + "\rComputing majority & weighted predictions: 25%|██▌ | 1/4 [00:00<00:00, 8.57it/s]" ] }, { "output_type": "display_data", "data": { "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/54.5k [00:00" + ], + "text/markdown": "## Step 1: Recall the conversion formulas between rectangular and polar coordinates.\nThe conversion formulas are given as $r = \\sqrt{x^2 + y^2}$ for the radial coordinate and $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$ for the angular coordinate.\n\n## Step 2: Apply the conversion formulas to the given point (0,3).\nGiven the point $(0,3)$, we can substitute its rectangular coordinates into the formulas:\n- $r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n- $\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right)$\n\n## Step 3: Handle the division by zero explicitly.\nSince $\\tan^{-1}\\left(\\frac{3}{0}\\right)$ is undefined, we must recognize that the point (0,3) lies on the y-axis. Thus, its polar coordinates should be in the form $(r, \\frac{\\pi}{2})$.\n\n## Step 4: Conclude the polar coordinates of the point (0,3).\nTherefore, the polar coordinates of the point (0,3) are $\\left(3, \\frac{\\pi}{2}\\right)$.\n\nThe final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 2. **Beam Search**\n", + "\n", + "Now, let's try using the `beam_search` strategy." + ], + "metadata": { + "id": "7S9AwP5lQvUN" + } + }, + { + "cell_type": "code", + "source": [ + "config.n=8\n", + "# beam search specific\n", + "config.sort_completed=True\n", + "config.filter_duplicates=True\n", + "\n", + "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='beam_search')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 845, + "referenced_widgets": [ + "1783db79fcca4dfa8cf4464428913061", + 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"name": "stderr", + "text": [ + "Beam search iterations: 22%|██▎ | 9/40 [00:10<00:35, 1.13s/it]" + ] + }, { "output_type": "stream", "name": "stdout", "text": [ - "INFO 12-27 16:17:08 model_runner.py:1071] Loading model weights took 2.3185 GB\n", - "INFO 12-27 16:17:08 gpu_executor.py:122] # GPU blocks: 33069, # CPU blocks: 8192\n", - "INFO 12-27 16:17:08 gpu_executor.py:126] Maximum concurrency for 131072 tokens per request: 4.04x\n", - "INFO 12-27 16:17:11 model_runner.py:1402] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.\n", - "INFO 12-27 16:17:11 model_runner.py:1406] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.\n", - "INFO 12-27 16:17:32 model_runner.py:1530] Graph capturing finished in 22 secs.\n" + "\n", + "Finished in 10.19 seconds\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/55.4k [00:00 0$ and $0 \\le \\theta < 2 \\pi.$'\n", - "\n", - "batch = {\n", - " \"problem\": [question],\n", - "}" + "display(Markdown(formatted_output))" ], "metadata": { - "id": "83puLxhzsOM0" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 285 + }, + "id": "Hw6tQD_dMwXZ", + "outputId": "58667f02-710c-4ad3-887a-d7d31ddbb31a" }, - "execution_count": 3, - "outputs": [] + "execution_count": 26, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/markdown": "To convert the point $(0,3)$ from rectangular coordinates to polar coordinates, we need to find the radius $r$ and the angle $\\theta$.\n\nThe formula to find $r$ is:\n\n\\[ r = \\sqrt{x^2 + y^2} \\]\n\nIn this case, $x = 0$ and $y = 3$.\n\n\\[ r = \\sqrt{0^2 + 3^2} = \\sqrt{9} = 3 \\]\n\nNow, we need to find the angle $\\theta$.\n\nSince the point $(0,3)$ lies on the positive $y$-axis, the angle $\\theta$ is $\\frac{\\pi}{2}$ (or $90^\\circ$).\n\nSo, the polar coordinates are:\n\n\\[(r, \\theta) = \\left(3, \\frac{\\pi}{2}\\right)\\]\n\nTherefore, the polar coordinates of the point $(0,3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$." + }, + "metadata": {} + } + ] }, { - "cell_type": "code", + "cell_type": "markdown", "source": [ - "from sal.search import beam_search, best_of_n, dvts\n", - "from sal.utils.score import score\n", + "### 3. **Diverse Verifier Tree Search (DVTS)**\n", "\n", - "config.n=32\n", - "config.sort_completed= True\n", - "config.filter_duplicates= True\n", - "\n", - "result = best_of_n(x=batch, config=config, llm=llm, prm=prm)" + "Finally, let's try the `dvts` strategy." ], "metadata": { - "id": "C6s6GS16QZLV" - }, - "execution_count": 4, - "outputs": [] + "id": "GxBBUd7HQzhd" + } }, { "cell_type": "code", "source": [ - "from datasets import Dataset\n", + "config.n=8\n", + "# dvts specific\n", + "config.n_beams = config.n // config.beam_width\n", "\n", - "result = Dataset.from_dict(result)" - ], - "metadata": { - "id": "VtEPnhnQbF9W" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "result = score(result, config)" + "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='dvts')" ], "metadata": { - "id": "29t49WKAAOA7", "colab": { "base_uri": "https://localhost:8080/", - "height": 1000, + "height": 845, "referenced_widgets": [ - "b97e442a53b04bbea9041ecab8d8e5ab", - "a4b0386cbc534f5b8bf9919724ab7992", - "e64979d6f88a48a9b5b88c1e3206dbac", - "bd85430005a542e5a6b2ffd661959041", - "7cbf982d5b5e4bd7bdf86b736dcf3d4a", - 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"output_type": "stream", + "name": "stderr", + "text": [ + "Beam search iterations: 25%|██▌ | 10/40 [00:11<00:34, 1.16s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Finished in 11.56 seconds\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + }, { "output_type": "display_data", "data": { @@ -26363,7 +53647,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b97e442a53b04bbea9041ecab8d8e5ab" + "model_id": "01707152aef241589991e3209a20b76c" } }, "metadata": {} @@ -26372,7 +53656,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "\rComputing majority & weighted predictions: 0%| | 0/6 [00:00" ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "519a11d1dacc451aa90e85e0dadd9e25" - } + "text/markdown": "## Step 1: To convert the point $(0,3)$ from rectangular coordinates to polar coordinates, we need to find the radius $r$ and the angle $\\theta$.\n\nThe formula to convert rectangular coordinates $(x, y)$ to polar coordinates $(r, \\theta)$ is given by:\n\n$r = \\sqrt{x^2 + y^2}$\n\n$\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$\n\n## Step 2: Substitute the values of $x = 0$ and $y = 3$ into the formulas to find the radius $r$ and the angle $\\theta$.\n\n$r = \\sqrt{0^2 + 3^2} = \\sqrt{9} = 3$\n\n$\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right) = \\tan^{-1}(\\infty)$\n\n## Step 3: Since $\\tan^{-1}(\\infty)$ is equal to $\\frac{\\pi}{2}$, we can conclude that the polar coordinates are $(3, \\frac{\\pi}{2})$.\n\nTherefore, the polar coordinates of the point $(0,3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$.\n\nThe final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" }, "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "In the table below, we compare the three methods (`best_of_n`, `beam_search`, and `dvts`) with the same number of answers during the search process, considering the time spent thinking (in seconds).\n", + "\n", + "As seen from the results, the `best_of_n` method requires the least amount of thinking time, while the `dvts` method requires the most time.\n", + "\n", + "| **Method** | **Number of Answers During Search** | **Time Thinking (Seconds)** |\n", + "|-----------------|-------------------------------------|-----------------------------|\n", + "| **best_of_n** | 8 | 2.45 |\n", + "| **beam_search** | 8 | 10.19 |\n", + "| **dvts** | 8 | 11.56 |\n", + "\n", + "This comparison helps us understand the trade-offs between the different search strategies, especially when it comes to balancing time and solution quality." + ], + "metadata": { + "id": "Lu2cAxU_N01V" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 🙋 3.2 Testing the System with a Simple Question\n", + "\n", + "In this final example, we’ll test the system using a straightforward question to observe how it performs in simpler cases. This allows us to verify that the system works as expected even for basic queries.\n", + "\n", + "Let's try the following question:" + ], + "metadata": { + "id": "5PM9HHwBSYWk" + } + }, + { + "cell_type": "code", + "source": [ + "question = 'What\\'s the capital of Spain?'\n", + "\n", + "config.n=32\n", + "\n", + "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "2b834080393d4a0c82b406fde2e85731", + "3342bed22fb044b39089a2f356eeb793", + "469c237fe02f4b5b85c4721ef6d5c301", + "81894d4748ae422a95f3648a5840dfa6", + "c85ed0d8716746459f8e4646f0e46198", + "411707d5f2844a31a45785741d889080", + "45243902393f4fc4979743777962022c", + "cfa3b5955c244804b78fa95c27a89aef", + "b8ee2056430a4ad789ecd2cba55f5aa1", + "5d4df6c8f5014c2e82ffbe853cc47101", + "e98d1b74d1f64ce486af663e27679c8c", + 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answers 16: 0%| | 0/1 [00:00assistant<|end_header_id|>\\n\\n## Step 1: Recall the formulas for converting rectangular coordinates to polar coordinates.\\nThe conversion formulas are: $r = \\\\sqrt{x^2 + y^2}$ and $heta = \\\\arctan{\\\\left( \\\\frac{y}{x} \\\\right)}$.\\n\\n## Step 2: Plug in the values of $x$ and $y$ into the formula for $r$.\\n$r = \\\\sqrt{(0)^2 + (3)^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n\\n## Step 3: Plug in the values of $x$ and $y$ into the formula for $heta$.\\n$heta = \\\\arctan{\\\\left( \\\\frac{(3)}{(0)} \\\\right)} = \\\\arctan{(0)} = \\\\frac{\\\\pi}{2}$\\n\\n## Step 4: Write the answer in the required format.\\nTherefore, the polar coordinates are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 7 - } - ] - }, - { - "cell_type": "code", - "source": [ - "formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", - "formatted_output" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 104 - }, - "id": "flbIu6-rDapM", - "outputId": "d0e71736-6a0c-46f8-ae21-d0e85d884fbc" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'## Step 1: Recall the formulas for converting rectangular coordinates to polar coordinates.\\nThe conversion formulas are: $r = \\\\sqrt{x^2 + y^2}$ and $heta = \\\\arctan{\\\\left( \\\\frac{y}{x} \\\\right)}$.\\n\\n## Step 2: Plug in the values of $x$ and $y$ into the formula for $r$.\\n$r = \\\\sqrt{(0)^2 + (3)^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n\\n## Step 3: Plug in the values of $x$ and $y$ into the formula for $heta$.\\n$heta = \\\\arctan{\\\\left( \\\\frac{(3)}{(0)} \\\\right)} = \\\\arctan{(0)} = \\\\frac{\\\\pi}{2}$\\n\\n## Step 4: Write the answer in the required format.\\nTherefore, the polar coordinates are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 8 - } - ] - }, - { - "cell_type": "code", - "source": [ - "from IPython.display import display, Markdown, Latex\n", - "display(Markdown(formatted_output))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 379 - }, - "id": "P4En0qJRD0cl", - "outputId": "e191f867-11c1-4c3f-cc7a-5068fbf469ca" - }, - "execution_count": 9, - "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ - "" + "Compute majority pred 2: 0%| | 0/1 [00:00assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", - " return formatted_output\n", - "\n", - "\n", - "question = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n", - "#question = 'What\\'s the capital of Spain?'\n", - "\n", - "config.n=8\n", - "config.sort_completed= True\n", - "config.filter_duplicates= True\n", - "# dvts specific\n", - "config.n_beams = config.n // config.beam_width\n", - "\n", - "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='beam_search')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 845, - "referenced_widgets": [ - "108ccf83145342d9b2cefda0c02fde1b", - "949bc74652014fafb461d169cf506c63", - "5306d8d395544e83bf5acae7f95adf85", - "3f506b89055e4c5699d46dc3fb6444e2", - "14c9a213150b45f7959a4474e7c13346", - "d5af7f8d911b4fc69233db43afaf7902", - "8a78105b742d499389616606c4d78cb0", - "c371e6c8e01146e4bf405c3ea2afeecf", - "418e0db249334a4e94d7d654613335a4", - "6bfc4da2cfe24380962de6e3e77ad0f4", - "4ed34145a8f04a89bc90749b954d925f", - "939a762ce5aa4fa083ec1d0792dbafad", - 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Therefore, $heta = \\frac{\\pi}{2}$ since $\\tan^{-1}(0) = 0$.\n\nThe polar coordinates of the point $(0,3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$.\n\nTherefore, the final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" + "text/markdown": "The capital of Spain is Madrid." }, "metadata": {} } ] + }, + { + "cell_type": "markdown", + "source": [ + "Even though we set a larger number of candidate answers (`N`), the time spent thinking remains relatively small (1.21 seconds). This demonstrates the system’s ability to efficiently handle easier problems, spending less time on them, while leveraging its enhanced capabilities for more complex questions.\n", + "\n", + "🏆 **We now have a fully operational pipeline** that leverages test-time compute, enabling the system to \"think longer\" for more complicated queries, while also maintaining fast response times for straightforward questions.\n", + "\n", + "This approach ensures the system can scale its thinking time based on the task's complexity, offering an efficient and responsive solution for both simple and challenging problems.\n" + ], + "metadata": { + "id": "NgdeSegeANoT" + } + }, + { + "cell_type": "markdown", + "source": [ + "# 4. Continuing the Journey 🧑‍🎓️\n", + "\n", + "If you're eager to continue exploring, be sure to check out the original experimental [blog](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute) and all the references mentioned within it. These resources will deepen your understanding of test-time compute, its benefits, and its applications in LLMs.\n", + "\n", + "Happy learning and experimenting! 🚀" + ], + "metadata": { + "id": "92znAyJ0AOPY" + } } ] } \ No newline at end of file From f6fdb191c22114544224a99848deddaee0cedcca Mon Sep 17 00:00:00 2001 From: sergiopaniego Date: Mon, 30 Dec 2024 17:16:44 +0100 Subject: [PATCH 04/10] Updated notebook and removed outputs --- notebooks/en/search_and_learn.ipynb | 46509 +++++++++++++------------- 1 file changed, 22934 insertions(+), 23575 deletions(-) diff --git a/notebooks/en/search_and_learn.ipynb b/notebooks/en/search_and_learn.ipynb index a8d69222..cca55863 100644 --- a/notebooks/en/search_and_learn.ipynb +++ b/notebooks/en/search_and_learn.ipynb @@ -1,6123 +1,4450 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "machine_shape": "hm", - "gpuType": "A100" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "xctusmJ6BZ6_" + }, + "source": [ + "# Scaling Test-Time Compute for Longer Thinking in LLMs\n", + "\n", + "_Authored by: [Sergio Paniego](https://github.com/sergiopaniego)_" + ] }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + { + "cell_type": "markdown", + "metadata": { + "id": "1YgobuWDCRiK" + }, + "source": [ + "🚨 **WARNING**: This notebook is **resource-intensive** and requires substantial computational power. If you’re running this in **Colab**, it will utilize an **A100 GPU**.\n", + "\n", + "---\n", + "\n", + "## 🧠 Extending Inference Time for Instruct LLM Systems\n", + "\n", + "In this recipe, we'll guide you through extending the inference time for an **Instruct LLM system** using **test-time compute**, which enhances its ability to solve more challenging problems, such as **complex math problems**. This approach is inspired by the [**OpenAI o1-o3 models**](https://openai.com/index/learning-to-reason-with-llms/), which demonstrate how **longer reasoning time** during inference can improve model performance.\n", + "\n", + "### 🔍 Concept Behind the Approach\n", + "\n", + "This technique is based on insights from [this **blog post**](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute), which presents experiments validating the strategy with open models. The results show that the smaller **1B** and **3B Llama Instruct models** outperform their much larger **8B** and **70B** counterparts on the **challenging MATH-500 benchmark**, provided they are given enough **\"time to think\"**.\n", + "\n", + "![Instruct LLM Methodology](https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/methods-thumbnail.png)\n", + "\n", + "### 🚀 New Repository and Experimentation\n", + "\n", + "The blog introduces a [**new repository**](https://github.com/huggingface/search-and-learn) designed for running these experiments. In this recipe, however, we will focus on building a **small chatbot** that is capable of engaging in **longer reasoning** to tackle **harder problems** using small open models.\n", + "\n", + "---\n", + "\n", + "## 📚 Explore Further\n", + "\n", + "As stated in the blog: `Although we don’t know how o1 was trained, recent research from DeepMind shows that test-time compute can be scaled optimally through strategies like iterative self-refinement or using a reward model to perform search over the space of solutions`. Learn more from the paper: [**Scaling LLM Test-Time Compute Optimally Can Be More Effective Than Scaling Model Parameters**](https://arxiv.org/abs/2408.03314).\n" + ] }, - "language_info": { - "name": "python" + { + "cell_type": "markdown", + "metadata": { + "id": "twKCzVIg71Xa" + }, + "source": [ + "# 1. Install Dependencies\n", + "\n", + "Let’s start by installing the [search-and-learn](https://github.com/huggingface/search-and-learn) repository! 🚀 \n", + "This repo is designed to replicate the experimental results and is not a Python pip package. However, we can still use it to generate our system. To do so, we’ll need to install it from source with the following steps:" + ] }, - "accelerator": "GPU", - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "e917741ae21e4943880cee24489b4551": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [], - "layout": "IPY_MODEL_45783fb1f5d344e69c173a82213a7eda" - } - }, - "53c62adc2c7346beb95f524c11b28f4d": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2537715f9d1c435ab78cccfa69909282", - "placeholder": "​", - "style": "IPY_MODEL_63643e708fe74718b7ed6d9e6c8cf29b", - "value": "

Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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" - } + "id": "pnEaTlFYZF_H", + "outputId": "cf356fab-7e71-4997-a10a-78ee2d7925c7" + }, + "outputs": [], + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wX07zCTA8MWL" + }, + "source": [ + "# 2. Setup the Large Language Model (LLM) and the Process Reward Model (PRM) 💬\n", + "\n", + "As illustrated in the diagram, the system consists of an LLM that generates intermediate answers based on user input, a [PRM model](https://huggingface.co/papers/2211.14275) that evaluates and scores these answers, and a search strategy that uses the PRM feedback to guide the subsequent steps in the search process until reaching the final answer.\n", + "\n", + "Let’s begin by initializing each model. For the LLM, we’ll use the [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) model, and for the PRM, we’ll use the [RLHFlow/Llama3.1-8B-PRM-Deepseek-Data](https://huggingface.co/RLHFlow/Llama3.1-8B-PRM-Deepseek-Data) model.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IkJw0x7gDJEY" + }, + "source": [ + "![system](https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/system.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "628c1b21e27040aebb898a268564db9b", + "33c02e21b0c54d92a51de1e54d1964f1", + "66a9b8bd2ec14682b6f7b5a0c7726764", + "c65440b657b543558fac9cd454eb2420", + "f5c19d01f4fb444cbd1cd1480ff3c7e5", + "6626cf35b6524c3aae5d792c5f50a97d", + "840ba16acc0648db80002c32e77b1c87", + "1dcea2ee01a84302a5f0e505dbe0e4f3", + 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"outputId": "2261497c-0111-458c-f8b7-8ca1545b292d" + }, + "outputs": [], + "source": [ + "import torch\n", + "from vllm import LLM\n", + "from sal.models.reward_models import RLHFFlow\n", + "\n", + "model_path=\"meta-llama/Llama-3.2-1B-Instruct\"\n", + "prm_path=\"RLHFlow/Llama3.1-8B-PRM-Deepseek-Data\"\n", + "\n", + "llm = LLM(\n", + " model=model_path,\n", + " gpu_memory_utilization=0.5, # Utilize 50% of GPU memory\n", + " enable_prefix_caching=True, # Optimize repeated prefix computations\n", + " seed=42, # Set seed for reproducibility\n", + ")\n", + "\n", + "prm = RLHFFlow(prm_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xYtPn0_V_YRx" + }, + "source": [ + "## 2.1 Instantiate the Question, Search Method, and Call the Pipeline\n", + "\n", + "Now that we've set up the LLM and PRM, let's proceed by defining the question, selecting a search strategy to retrieve relevant information, and calling the pipeline to process the question through the models.\n", + "\n", + "1. **Instantiate the Question**: In this step, we define the input question that the system will answer, considering the given context.\n", + "\n", + "2. **Search Strategy**: The system currently supports the following search strategies: `best_of_n`, `beam_search`, and `dvts` (see diagram). For this example, we'll use `best_of_n`, but you can easily switch to any of the other strategies based on your needs. We need to define some configuration parameters for the configuration of the search strategy. You can check the full list [here](https://github.com/huggingface/search-and-learn/blob/main/src/sal/config.py).\n", + "\n", + "3. **Call the Pipeline**: With the question and search strategy in place, we’ll call the inference pipeline, processing the inputs through both the LLM and PRM to generate the final answer." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xSWINPerJrhm" + }, + "source": [ + "![](https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/search-strategies.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z69xD6i2L5a6" + }, + "source": [ + "Here’s how we can structure the process:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "83puLxhzsOM0" + }, + "outputs": [], + "source": [ + "question_text = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n", + "input_batch = {\"problem\": [question_text]}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "C6s6GS16QZLV" + }, + "outputs": [], + "source": [ + "from sal.config import Config\n", + "from sal.search import beam_search, best_of_n, dvts\n", + "\n", + "config = Config()\n", + "config.n=32 # Number of answers to generate during the search\n", + "\n", + "search_result = best_of_n(x=input_batch, config=config, llm=llm, prm=prm)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "VtEPnhnQbF9W" + }, + "outputs": [], + "source": [ + "from datasets import Dataset\n", + "\n", + "search_dataset = Dataset.from_dict(search_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + 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"justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + "id": "29t49WKAAOA7", + "outputId": "7caef5b4-e9fd-4984-9007-b88cab212ca8" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8347e2d9c6084defb92836697f1ee5c6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/1 [00:00assistant<|end_header_id|>\\n\\n## Step 1: The relationship between rectangular and polar coordinates is given by:\\n\\\\[ r = \\\\sqrt{x^2 + y^2} \\\\] and \\\\[ heta = \\\\arctan{\\\\left(\\\\frac{y}{x}\\\\right)}. \\\\]\\n\\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\\n\\\\[ r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{9} = 3. \\\\]\\n\\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\\n\\\\[ heta = \\\\arctan{\\\\left(\\\\frac{3}{0}\\\\right)} = \\\\arctan{\\\\left( \\\\infty \\\\right)}. \\\\]\\n\\n## Step 4: Since $\\\\arctan{\\\\left( \\\\infty \\\\right)}$ is equal to $\\\\frac{\\\\pi}{2}$, we have:\\n\\\\[ heta = \\\\frac{\\\\pi}{2}. \\\\]\\n\\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right).$\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_result['pred'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 }, - "2bdf3b567f344dc5a4474db8990e8803": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + "id": "flbIu6-rDapM", + "outputId": "08b76f0c-db41-4069-b559-5cd3aa832e25" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'## Step 1: The relationship between rectangular and polar coordinates is given by:\\n\\\\[ r = \\\\sqrt{x^2 + y^2} \\\\] and \\\\[ heta = \\\\arctan{\\\\left(\\\\frac{y}{x}\\\\right)}. \\\\]\\n\\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\\n\\\\[ r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{9} = 3. \\\\]\\n\\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\\n\\\\[ heta = \\\\arctan{\\\\left(\\\\frac{3}{0}\\\\right)} = \\\\arctan{\\\\left( \\\\infty \\\\right)}. \\\\]\\n\\n## Step 4: Since $\\\\arctan{\\\\left( \\\\infty \\\\right)}$ is equal to $\\\\frac{\\\\pi}{2}$, we have:\\n\\\\[ heta = \\\\frac{\\\\pi}{2}. \\\\]\\n\\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right).$\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "formatted_output = final_result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", + "formatted_output" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433 + }, + "id": "P4En0qJRD0cl", + "outputId": "6a190922-729d-489f-b330-ad1ee7873692" + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Step 1: The relationship between rectangular and polar coordinates is given by:\n", + "\\[ r = \\sqrt{x^2 + y^2} \\] and \\[ heta = \\arctan{\\left(\\frac{y}{x}\\right)}. \\]\n", + "\n", + "## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\n", + "\\[ r = \\sqrt{0^2 + 3^2} = \\sqrt{9} = 3. \\]\n", + "\n", + "## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\n", + "\\[ heta = \\arctan{\\left(\\frac{3}{0}\\right)} = \\arctan{\\left( \\infty \\right)}. \\]\n", + "\n", + "## Step 4: Since $\\arctan{\\left( \\infty \\right)}$ is equal to $\\frac{\\pi}{2}$, we have:\n", + "\\[ heta = \\frac{\\pi}{2}. \\]\n", + "\n", + "## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\left( 3, \\frac{\\pi}{2} \\right).$\n", + "\n", + "The final answer is: $\\boxed{\\left( 3, \\frac{\\pi}{2} \\right)}$" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Markdown, Latex\n", + "\n", + "display(Markdown(formatted_output))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4uCpYzAw_4o9" + }, + "source": [ + "# 3. Assembling It All! 🧑‍🏭️\n", + "\n", + "Now, let's create a method that encapsulates the entire pipeline. This will allow us to easily reuse the process in future applications, making it efficient and modular.\n", + "\n", + "By combining the LLM, PRM, search strategy, and result display, we can simplify the workflow and ensure that it’s reusable for other tasks or questions.\n", + "\n", + "We simplify the workflow, ensuring that it’s reusable for different tasks or questions. Additionally, we’ll track the time spent on each method so that we can **understand the practical implications** of using each strategy and configuration.\n", + "\n", + "Here’s how we can structure the method:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "YpswbcVi37KR" + }, + "outputs": [], + "source": [ + "import time\n", + "\n", + "def generate_with_search_and_learn(question, config, llm, prm, method='best_of_n'):\n", + " \"\"\"\n", + " Generate an answer for a given question using the search-and-learn pipeline.\n", + "\n", + " Args:\n", + " - question (str): The input question to generate an answer for.\n", + " - config (Config): Configuration object containing parameters for search strategy.\n", + " - llm (LLM): Pretrained large language model used for generating answers.\n", + " - prm (RLHFFlow): Process reward model used for evaluating answers.\n", + " - method (str): Search strategy to use. Options are 'best_of_n', 'beam_search', 'dvts'. Default is 'best_of_n'.\n", + "\n", + " Returns:\n", + " - str: The formatted output after processing the question.\n", + " \"\"\"\n", + " batch = {\"problem\": [question]}\n", + "\n", + " start_time = time.time()\n", + " if method == 'best_of_n':\n", + " result = best_of_n(x=batch, config=config, llm=llm, prm=prm)\n", + " elif method == 'beam_search':\n", + " result = beam_search(examples=batch, config=config, llm=llm, prm=prm)\n", + " elif method == 'dvts':\n", + " result = dvts(examples=batch, config=config, llm=llm, prm=prm)\n", + "\n", + " elapsed_time = time.time() - start_time\n", + " print(f\"\\nFinished in {elapsed_time:.2f} seconds\\n\")\n", + "\n", + " result = Dataset.from_dict(result)\n", + " result = score(result, config)\n", + " formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", + " return formatted_output" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RWbOqkiKPVd2" + }, + "source": [ + "## ⏳ 3.1 Comparing Thinking Time for Each Strategy\n", + "\n", + "Let’s now run each method and track the **thinking time** for a given question.\n", + "\n", + "### 1. **Best of n**\n", + "\n", + "We’ll begin by using the `best_of_n` strategy. Here’s how to track the thinking time for this method:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 827, + "referenced_widgets": [ + "07fcbf44dd91438bafeb110244fac6bb", + "a307d4986a9a489284333fccd05baea2", + "c9d12093f9df4c578fcfd4bdfa3752ad", + "cbd77563a91341a08556ce09a437509e", + "710be6f8e16f4c319818e44dc75d2109", + "ad710d0763e84f2ba806b2816a2d6164", + "43e234fbe88e429d9f1f1388b790fb24", + "df87c0d0f4174c9a9f82e83186676290", + "14d4fbbc24354308b1772be1da606d00", + "2409843f70af4f51944425a773995bdf", + "40bbe419b638489a9c7d19ebb5771543", + "9e3baabd96c542719070c9052c6c9910", + "a5d40b55ac6c4035a848563856a3f36a", + "b8ee35cd223e478088d6a54dda002f3b", + "6a7824ea88fb40df86d2e7a25a159075", + "31d2d679894d42f5b783b0cbb1d9e8cf", + "b9c063eedbeb47ae934a0dfb6f432f8f", + "5f99ec8d11044d938924a90ac17830e8", + "c310ece89c204a46950886d3a9932244", + "fff60d90b600454c83c0e7cdab576244", + 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"@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_161e782270844dc4a2fc0da508dfd680", - "IPY_MODEL_fa079be20564424e9f1ef2f550a1c8ab", - "IPY_MODEL_f848e064bd054faf8bc40ca00c96f2c9" - ], - "layout": "IPY_MODEL_1d903d6da3b645638d2221f21a704f77" - } + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "07fcbf44dd91438bafeb110244fac6bb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/1 [00:00 0$ and $0 \\le \\theta < 2 \\pi.$'\n", + "\n", + "config.n=8\n", + "\n", + "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 407 + }, + "id": "uzKfFoKG9ejC", + "outputId": "04508ede-2ee6-4cbb-8f6a-142e1aa3977f" + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Step 1: Recall the conversion formulas between rectangular and polar coordinates.\n", + "The conversion formulas are given as $r = \\sqrt{x^2 + y^2}$ for the radial coordinate and $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$ for the angular coordinate.\n", + "\n", + "## Step 2: Apply the conversion formulas to the given point (0,3).\n", + "Given the point $(0,3)$, we can substitute its rectangular coordinates into the formulas:\n", + "- $r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n", + "- $\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right)$\n", + "\n", + "## Step 3: Handle the division by zero explicitly.\n", + "Since $\\tan^{-1}\\left(\\frac{3}{0}\\right)$ is undefined, we must recognize that the point (0,3) lies on the y-axis. Thus, its polar coordinates should be in the form $(r, \\frac{\\pi}{2})$.\n", + "\n", + "## Step 4: Conclude the polar coordinates of the point (0,3).\n", + "Therefore, the polar coordinates of the point (0,3) are $\\left(3, \\frac{\\pi}{2}\\right)$.\n", + "\n", + "The final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" ], - "layout": "IPY_MODEL_64d5d98033f94e8691e443951473f460" - } + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(formatted_output))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7S9AwP5lQvUN" + }, + "source": [ + "### 2. **Beam Search**\n", + "\n", + "Now, let's try using the `beam_search` strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 845, + "referenced_widgets": [ + "1783db79fcca4dfa8cf4464428913061", + "8a7829fd003a4d9782603d0a78ab244a", + 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"cell_type": "markdown", + "metadata": { + "id": "GxBBUd7HQzhd" + }, + "source": [ + "### 3. **Diverse Verifier Tree Search (DVTS)**\n", + "\n", + "Finally, let's try the `dvts` strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 845, + "referenced_widgets": [ + "01707152aef241589991e3209a20b76c", + "ea733cd07f5c41db9b90af4a2ce6ffa7", + "b67ce02bb07340f3b7989a70ba8b1517", + "44324de58c0f4b34b54111c790e47221", + "f0873855d3774c9487561988cb0951e5", + "b3694daa6d51469d8ab75ee975a3362c", + "602af55805804d9fb9872204ecff8be9", + "df8b69aac0974d68a065711c0a15c2d0", + "980358f22b5a40a9bdb88c75f7532d50", + "3503db8563924b66b9f7d54471115310", + "adcd2313401b4b95aea8dfb0c709cbce", + "0cbab0dcab4e46dc92110b1017712c75", + "4b154f024acd411ba37a8f5c6eb0018a", + "c5daace427cb4ba6b8023fc14b728327", + "8a943954c15840cdba962f0ab539afbb", + "7cf3dea87f844dc89c3a5a22267f03eb", + 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"name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished in 11.56 seconds\n", + "\n" + ] }, - "94232bc7544144e59aac4f209d160ac4": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] }, - "7a95da1ad77d4ea1b9500eb8b835e94a": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - 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"01707152aef241589991e3209a20b76c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/1 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(formatted_output))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lu2cAxU_N01V" + }, + "source": [ + "In the table below, we compare the three methods (`best_of_n`, `beam_search`, and `dvts`) with the same number of answers during the search process, considering the time spent thinking (in seconds).\n", + "\n", + "As seen from the results, the `best_of_n` method requires the least amount of thinking time, while the `dvts` method requires the most time.\n", + "\n", + "| **Method** | **Number of Answers During Search** | **Time Thinking (Seconds)** |\n", + "|-----------------|-------------------------------------|-----------------------------|\n", + "| **best_of_n** | 8 | 2.45 |\n", + "| **beam_search** | 8 | 10.19 |\n", + "| **dvts** | 8 | 11.56 |\n", + "\n", + "This comparison helps us understand the trade-offs between the different search strategies, especially when it comes to balancing time and solution quality." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5PM9HHwBSYWk" + }, + "source": [ + "## 🙋 3.2 Testing the System with a Simple Question\n", + "\n", + "In this final example, we’ll test the system using a straightforward question to observe how it performs in simpler cases. This allows us to verify that the system works as expected even for basic queries.\n", + "\n", + "Let's try the following question:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "2b834080393d4a0c82b406fde2e85731", + "3342bed22fb044b39089a2f356eeb793", + "469c237fe02f4b5b85c4721ef6d5c301", + "81894d4748ae422a95f3648a5840dfa6", + "c85ed0d8716746459f8e4646f0e46198", + "411707d5f2844a31a45785741d889080", + "45243902393f4fc4979743777962022c", + "cfa3b5955c244804b78fa95c27a89aef", + "b8ee2056430a4ad789ecd2cba55f5aa1", + "5d4df6c8f5014c2e82ffbe853cc47101", + "e98d1b74d1f64ce486af663e27679c8c", + "07e49cde343544e9915b6555ab978a57", + "167d718b691845f499f1b9914958f9c7", + "c8cbd462594545aea2e3474989aa9f48", + "50112bcce28b4831907fefe7096e8847", + "862233869ea84a3693bf3070afdd6555", + "9dab64ca972040d19e95a308dc89fc55", + "83006608eca24d66a51fa647c266bbb3", + 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This demonstrates the system’s ability to efficiently handle easier problems, spending less time on them, while leveraging its enhanced capabilities for more complex questions.\n", + "\n", + "🏆 **We now have a fully operational pipeline** that leverages test-time compute, enabling the system to \"think longer\" for more complicated queries, while also maintaining fast response times for straightforward questions.\n", + "\n", + "This approach ensures the system can scale its thinking time based on the task's complexity, offering an efficient and responsive solution for both simple and challenging problems.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "92znAyJ0AOPY" + }, + "source": [ + "# 4. Continuing the Journey 🧑‍🎓️\n", + "\n", + "If you're eager to continue exploring, be sure to check out the original experimental [blog](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute) and all the references mentioned within it. 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If you’re running this in **Colab**, it will utilize an **A100 GPU**.\n", - "\n", - "---\n", - "\n", - "## 🧠 Extending Inference Time for Instruct LLM Systems\n", - "\n", - "In this recipe, we'll guide you through extending the inference time for an **Instruct LLM system** using **test-time compute**, which enhances its ability to solve more challenging problems, such as **complex math problems**. This approach is inspired by the [**OpenAI o1-o3 models**](https://openai.com/index/learning-to-reason-with-llms/), which demonstrate how **longer reasoning time** during inference can improve model performance.\n", - "\n", - "### 🔍 Concept Behind the Approach\n", - "\n", - "This technique is based on insights from [this **blog post**](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute), which presents experiments validating the strategy with open models. The results show that the smaller **1B** and **3B Llama Instruct models** outperform their much larger **8B** and **70B** counterparts on the **challenging MATH-500 benchmark**, provided they are given enough **\"time to think\"**.\n", - "\n", - "![Instruct LLM Methodology](https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/methods-thumbnail.png)\n", - "\n", - "### 🚀 New Repository and Experimentation\n", - "\n", - "The blog introduces a [**new repository**](https://github.com/huggingface/search-and-learn) designed for running these experiments. In this recipe, however, we will focus on building a **small chatbot** that is capable of engaging in **longer reasoning** to tackle **harder problems** using small open models.\n", - "\n", - "---\n", - "\n", - "## 📚 Explore Further\n", - "\n", - "- Learn more from the paper: [**Scaling LLM Test-Time Compute Optimally Can Be More Effective Than Scaling Model Parameters**](https://arxiv.org/abs/2408.03314).\n" - ], - "metadata": { - "id": "1YgobuWDCRiK" - } - }, - { - "cell_type": "markdown", - "source": [ - "# 1. Install Dependencies\n", - "\n", - "Let’s start by installing the [search-and-learn](https://github.com/huggingface/search-and-learn) repository! 🚀 \n", - "This repo is designed to replicate the experimental results and is not a Python pip package. However, we can still use it to generate our system. To do so, we’ll need to install it from source with the following steps:" - ], - "metadata": { - "id": "twKCzVIg71Xa" - } - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t0YDC2_7XTm8", - "outputId": "2375a29f-ede4-497a-a7f0-606ad9f221c5" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Cloning into 'search-and-learn'...\n", - "remote: Enumerating objects: 235, done.\u001b[K\n", - "remote: Counting objects: 100% (75/75), done.\u001b[K\n", - "remote: Compressing objects: 100% (32/32), done.\u001b[K\n", - "remote: Total 235 (delta 48), reused 43 (delta 43), pack-reused 160 (from 1)\u001b[K\n", - "Receiving objects: 100% (235/235), 87.99 KiB | 17.60 MiB/s, done.\n", - "Resolving deltas: 100% (107/107), done.\n" - ] - } - ], - "source": [ - "!git clone https://github.com/huggingface/search-and-learn" - ] - }, - { - "cell_type": "code", - "source": [ - "%cd search-and-learn\n", - "!pip install -e '.[dev]'" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "kT3jH_d_XcEb", - "outputId": "e5939fd5-97e5-4eac-939a-21f3be47f60e" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content/search-and-learn\n", - "Obtaining file:///content/search-and-learn\n", - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n", - " Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from search-and-learn==0.1.0) (1.2.1)\n", - "Collecting pebble (from search-and-learn==0.1.0)\n", - " Downloading Pebble-5.1.0-py3-none-any.whl.metadata (3.6 kB)\n", - "Collecting latex2sympy2==1.9.1 (from search-and-learn==0.1.0)\n", - 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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hBuilding wheels for collected packages: search-and-learn, antlr4-python3-runtime, word2number\n", - " Building editable for search-and-learn (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for search-and-learn: filename=search_and_learn-0.1.0-0.editable-py3-none-any.whl size=6722 sha256=5d092779fe518e3b1652891b5d322c2d90f5d17ace9ad6e98b1e3d8c453fbe7f\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-y1ijzcks/wheels/c8/0a/9d/dae572a3e5287d72b78bd42e1c9bc5d91117df64445c4b5d62\n", - " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.7.2-py3-none-any.whl size=140931 sha256=059793a96b24d192ea9f998bde9273f6c0bc3463d442a7ee2e0e227efb7a129b\n", - " Stored in directory: /root/.cache/pip/wheels/79/20/ec/30bf7dabc29319ccc0d0c96f910a640513a3c81faa960fed43\n", - " Building wheel for word2number (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5568 sha256=409a0e1d285814f018ba1b93af1ac2324fadc82718551851c84201b206a92751\n", - " Stored in directory: /root/.cache/pip/wheels/84/ff/26/d3cfbd971e96c5aa3737ecfced81628830d7359b55fbb8ca3b\n", - "Successfully built search-and-learn antlr4-python3-runtime word2number\n", - "Installing collected packages: word2number, pyairports, nvidia-ml-py, antlr4-python3-runtime, xxhash, uvloop, uvicorn, triton, ruff, python-dotenv, pycountry, pillow, pebble, partial-json-parser, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, msgspec, lark, isort, interegular, httptools, gguf, fsspec, diskcache, dill, watchfiles, tiktoken, starlette, nvidia-cusolver-cu12, nvidia-cudnn-cu12, multiprocess, latex2sympy2, torch, prometheus-fastapi-instrumentator, lm-format-enforcer, fastapi, xformers, torchvision, ray, mistral-common, search-and-learn, datasets, outlines, vllm\n", - " Attempting uninstall: pillow\n", - " Found existing installation: pillow 11.0.0\n", - " Uninstalling pillow-11.0.0:\n", - " Successfully uninstalled pillow-11.0.0\n", - " Attempting uninstall: nvidia-nccl-cu12\n", - " Found existing installation: nvidia-nccl-cu12 2.23.4\n", - " Uninstalling nvidia-nccl-cu12-2.23.4:\n", - " Successfully uninstalled nvidia-nccl-cu12-2.23.4\n", - " Attempting uninstall: nvidia-cusparse-cu12\n", - " Found existing installation: nvidia-cusparse-cu12 12.5.4.2\n", - " Uninstalling nvidia-cusparse-cu12-12.5.4.2:\n", - " Successfully uninstalled nvidia-cusparse-cu12-12.5.4.2\n", - " Attempting uninstall: nvidia-curand-cu12\n", - " Found existing installation: nvidia-curand-cu12 10.3.7.77\n", - " Uninstalling nvidia-curand-cu12-10.3.7.77:\n", - " Successfully uninstalled nvidia-curand-cu12-10.3.7.77\n", - " Attempting uninstall: nvidia-cufft-cu12\n", - " Found existing installation: nvidia-cufft-cu12 11.3.0.4\n", - " Uninstalling nvidia-cufft-cu12-11.3.0.4:\n", - " Successfully uninstalled nvidia-cufft-cu12-11.3.0.4\n", - " Attempting uninstall: nvidia-cuda-runtime-cu12\n", - " Found existing installation: nvidia-cuda-runtime-cu12 12.6.77\n", - " Uninstalling nvidia-cuda-runtime-cu12-12.6.77:\n", - " Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.77\n", - " Attempting uninstall: nvidia-cuda-cupti-cu12\n", - " Found existing installation: nvidia-cuda-cupti-cu12 12.6.80\n", - " Uninstalling nvidia-cuda-cupti-cu12-12.6.80:\n", - " Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.80\n", - " Attempting uninstall: nvidia-cublas-cu12\n", - " Found existing installation: nvidia-cublas-cu12 12.6.4.1\n", - " Uninstalling nvidia-cublas-cu12-12.6.4.1:\n", - " Successfully uninstalled nvidia-cublas-cu12-12.6.4.1\n", - " Attempting uninstall: fsspec\n", - " Found existing installation: fsspec 2024.10.0\n", - " Uninstalling fsspec-2024.10.0:\n", - " Successfully uninstalled fsspec-2024.10.0\n", - " Attempting uninstall: nvidia-cusolver-cu12\n", - " Found existing installation: nvidia-cusolver-cu12 11.7.1.2\n", - " Uninstalling nvidia-cusolver-cu12-11.7.1.2:\n", - " Successfully uninstalled nvidia-cusolver-cu12-11.7.1.2\n", - " Attempting uninstall: nvidia-cudnn-cu12\n", - " Found existing installation: nvidia-cudnn-cu12 9.6.0.74\n", - " Uninstalling nvidia-cudnn-cu12-9.6.0.74:\n", - " Successfully uninstalled nvidia-cudnn-cu12-9.6.0.74\n", - " Attempting uninstall: torch\n", - " Found existing installation: torch 2.5.1+cu121\n", - " Uninstalling torch-2.5.1+cu121:\n", - " Successfully uninstalled torch-2.5.1+cu121\n", - " Attempting uninstall: torchvision\n", - " Found existing installation: torchvision 0.20.1+cu121\n", - " Uninstalling torchvision-0.20.1+cu121:\n", - " Successfully uninstalled torchvision-0.20.1+cu121\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n", - "torchaudio 2.5.1+cu121 requires torch==2.5.1, but you have torch 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed antlr4-python3-runtime-4.7.2 datasets-3.2.0 dill-0.3.8 diskcache-5.6.3 fastapi-0.115.6 fsspec-2024.9.0 gguf-0.10.0 httptools-0.6.4 interegular-0.3.3 isort-5.13.2 lark-1.2.2 latex2sympy2-1.9.1 lm-format-enforcer-0.10.6 mistral-common-1.5.1 msgspec-0.19.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-ml-py-12.560.30 nvidia-nccl-cu12-2.20.5 nvidia-nvtx-cu12-12.1.105 outlines-0.0.46 partial-json-parser-0.2.1.1.post4 pebble-5.1.0 pillow-10.4.0 prometheus-fastapi-instrumentator-7.0.0 pyairports-2.1.1 pycountry-24.6.1 python-dotenv-1.0.1 ray-2.40.0 ruff-0.8.4 search-and-learn-0.1.0 starlette-0.41.3 tiktoken-0.7.0 torch-2.4.0 torchvision-0.19.0 triton-3.0.0 uvicorn-0.34.0 uvloop-0.21.0 vllm-0.6.3 watchfiles-1.0.3 word2number-1.1 xformers-0.0.27.post2 xxhash-3.5.0\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "PIL" - ] - }, - "id": "7b888e521d464a01a0ee9a7408940e2a" - } - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Log in to Hugging Face to access [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct), as it is a gated model! 🗝️ \n", - "If you haven't previously requested access, you'll need to submit a request before proceeding.\n" - ], - "metadata": { - "id": "VAQHu9T176zh" - } - }, - { - "cell_type": "code", - "source": [ - "from huggingface_hub import notebook_login\n", - "\n", - "notebook_login()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17, - "referenced_widgets": [ - "e917741ae21e4943880cee24489b4551", - "53c62adc2c7346beb95f524c11b28f4d", - "1317f8c4c8014102af169acb05b8b80c", - "d579833c056642e9b5dccd01067900ef", - "e0b6b66dd6d24f39a7b733c09fb20a3d", - "300334e6fbef4e0a80b5be760a52c904", - "45783fb1f5d344e69c173a82213a7eda", - "2537715f9d1c435ab78cccfa69909282", - "63643e708fe74718b7ed6d9e6c8cf29b", - "f2c26a25593143a39307c6cba355e701", - "c5f91f3971344ac19cd5d333d51380b6", - "a6e64dd55f2c411893fcdcc44d189da3", - "1bc17f6074d44eb1b67cac268134343e", - "e3736af22c694530a1abc7ca964604f4", - "8f34192e6a194e6589850991ebd42a5b", - "b63fc2983be348e8970838809b1f5489", - "c1b137362fc6442a8040e6eede964dd8", - "af9d9a7123884ccfa163669689fe52e7", - "28afb981ce33437ebef71d40e19ec16e", - "ab3daf6fb18d464882250be793a3b086" - ] - }, - "id": "pnEaTlFYZF_H", - "outputId": "cf356fab-7e71-4997-a10a-78ee2d7925c7" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(HTML(value='
32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False.\n", - "INFO 12-30 15:21:46 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=512.\n", - "INFO 12-30 15:21:46 llm_engine.py:237] Initializing an LLM engine (vdev) with config: model='meta-llama/Llama-3.2-1B-Instruct', speculative_config=None, tokenizer='meta-llama/Llama-3.2-1B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=42, served_model_name=meta-llama/Llama-3.2-1B-Instruct, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=True, use_cached_outputs=False, mm_processor_kwargs=None)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/54.5k [00:00 0$ and $0 \\le \\theta < 2 \\pi.$'\n", - "input_batch = {\"problem\": [question_text]}" - ], - "metadata": { - "id": "83puLxhzsOM0" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from sal.config import Config\n", - "from sal.search import beam_search, best_of_n, dvts\n", - "\n", - "config = Config()\n", - "config.n=32 # Number of answers to generate during the search\n", - "\n", - "search_result = best_of_n(x=input_batch, config=config, llm=llm, prm=prm)" - ], - "metadata": { - "id": "C6s6GS16QZLV" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from datasets import Dataset\n", - "\n", - "search_dataset = Dataset.from_dict(search_result)" - ], - "metadata": { - "id": "VtEPnhnQbF9W" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from sal.utils.score import score\n", - "\n", - "final_result = score(search_dataset, config)" - ], - "metadata": { - "id": "29t49WKAAOA7", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "8347e2d9c6084defb92836697f1ee5c6", - "db67ef43ab864eac9b32e1159140b554", - "f1dd77c0b07044e4b161628e84fab059", - "0e0ab349010d4b319ea9a414ccd61bc8", - "73e6b699031e4835a293c013f129c08b", - "e5f89c42a9b14ceea06b49ba9789c6e5", - "0d1c8cf9d5da41188fa92418392ae6c3", - "9fd3cc59344f403ba0834883aca14590", - "28600649893e47448bef2aaa138e41e7", - "9e7800aa855e451f9522c244b488b277", - "4fe172a542a64c6cb1daea147b385030", - "ffb883a74ad54da7a079cdfa3074c6d8", - "37094451ff964979ae5f2fb6d4371d9d", - "146278d2748e44d2be8b3504244fcc2f", - "9c35d141a821443ca25f0ed16364b6cf", - "e880475f86b84f0681264b8785910852", - "8f41aa38f9a14757963dc63c1cb27763", - "36c330a2a43c42ddb6c9897ba861def2", - "e4862238b9a74c04b78db456a5016afd", - "30031d2f7fa743379972da9bb4bfe96a", - 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"outputId": "7caef5b4-e9fd-4984-9007-b88cab212ca8" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map: 0%| | 0/1 [00:00assistant<|end_header_id|>\\n\\n## Step 1: The relationship between rectangular and polar coordinates is given by:\\n\\\\[ r = \\\\sqrt{x^2 + y^2} \\\\] and \\\\[ heta = \\\\arctan{\\\\left(\\\\frac{y}{x}\\\\right)}. \\\\]\\n\\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\\n\\\\[ r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{9} = 3. \\\\]\\n\\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\\n\\\\[ heta = \\\\arctan{\\\\left(\\\\frac{3}{0}\\\\right)} = \\\\arctan{\\\\left( \\\\infty \\\\right)}. \\\\]\\n\\n## Step 4: Since $\\\\arctan{\\\\left( \\\\infty \\\\right)}$ is equal to $\\\\frac{\\\\pi}{2}$, we have:\\n\\\\[ heta = \\\\frac{\\\\pi}{2}. \\\\]\\n\\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right).$\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 11 - } - ] - }, - { - "cell_type": "code", - "source": [ - "formatted_output = final_result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", - "formatted_output" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 105 + "eaa5d70352a34c6e8d04f2eb0f984c63": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - "id": "flbIu6-rDapM", - "outputId": "08b76f0c-db41-4069-b559-5cd3aa832e25" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'## Step 1: The relationship between rectangular and polar coordinates is given by:\\n\\\\[ r = \\\\sqrt{x^2 + y^2} \\\\] and \\\\[ heta = \\\\arctan{\\\\left(\\\\frac{y}{x}\\\\right)}. \\\\]\\n\\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\\n\\\\[ r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{9} = 3. \\\\]\\n\\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\\n\\\\[ heta = \\\\arctan{\\\\left(\\\\frac{3}{0}\\\\right)} = \\\\arctan{\\\\left( \\\\infty \\\\right)}. \\\\]\\n\\n## Step 4: Since $\\\\arctan{\\\\left( \\\\infty \\\\right)}$ is equal to $\\\\frac{\\\\pi}{2}$, we have:\\n\\\\[ heta = \\\\frac{\\\\pi}{2}. \\\\]\\n\\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right).$\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 8 - } - ] - }, - { - "cell_type": "code", - "source": [ - "from IPython.display import display, Markdown, Latex\n", - "\n", - "display(Markdown(formatted_output))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 433 + "eadc48537df245bbb7a58f7651030792": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - "id": "P4En0qJRD0cl", - "outputId": "6a190922-729d-489f-b330-ad1ee7873692" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/markdown": "## Step 1: The relationship between rectangular and polar coordinates is given by:\n\\[ r = \\sqrt{x^2 + y^2} \\] and \\[ heta = \\arctan{\\left(\\frac{y}{x}\\right)}. \\]\n\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\n\\[ r = \\sqrt{0^2 + 3^2} = \\sqrt{9} = 3. \\]\n\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\n\\[ heta = \\arctan{\\left(\\frac{3}{0}\\right)} = \\arctan{\\left( \\infty \\right)}. \\]\n\n## Step 4: Since $\\arctan{\\left( \\infty \\right)}$ is equal to $\\frac{\\pi}{2}$, we have:\n\\[ heta = \\frac{\\pi}{2}. \\]\n\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\left( 3, \\frac{\\pi}{2} \\right).$\n\nThe final answer is: $\\boxed{\\left( 3, \\frac{\\pi}{2} \\right)}$" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# 3. Assembling It All! 🧑‍🏭️\n", - "\n", - "Now, let's create a method that encapsulates the entire pipeline. This will allow us to easily reuse the process in future applications, making it efficient and modular.\n", - "\n", - "By combining the LLM, PRM, search strategy, and result display, we can simplify the workflow and ensure that it’s reusable for other tasks or questions.\n", - "\n", - "We simplify the workflow, ensuring that it’s reusable for different tasks or questions. Additionally, we’ll track the time spent on each method so that we can **understand the practical implications** of using each strategy and configuration.\n", - "\n", - "Here’s how we can structure the method:" - ], - "metadata": { - "id": "4uCpYzAw_4o9" - } - }, - { - "cell_type": "code", - "source": [ - "import time\n", - "\n", - "def generate_with_search_and_learn(question, config, llm, prm, method='best_of_n'):\n", - " \"\"\"\n", - " Generate an answer for a given question using the search-and-learn pipeline.\n", - "\n", - " Args:\n", - " - question (str): The input question to generate an answer for.\n", - " - config (Config): Configuration object containing parameters for search strategy.\n", - " - llm (LLM): Pretrained large language model used for generating answers.\n", - " - prm (RLHFFlow): Process reward model used for evaluating answers.\n", - " - method (str): Search strategy to use. Options are 'best_of_n', 'beam_search', 'dvts'. Default is 'best_of_n'.\n", - "\n", - " Returns:\n", - " - str: The formatted output after processing the question.\n", - " \"\"\"\n", - " batch = {\"problem\": [question]}\n", - "\n", - " start_time = time.time()\n", - " if method == 'best_of_n':\n", - " result = best_of_n(x=batch, config=config, llm=llm, prm=prm)\n", - " elif method == 'beam_search':\n", - " result = beam_search(examples=batch, config=config, llm=llm, prm=prm)\n", - " elif method == 'dvts':\n", - " result = dvts(examples=batch, config=config, llm=llm, prm=prm)\n", - "\n", - " elapsed_time = time.time() - start_time\n", - " print(f\"\\nFinished in {elapsed_time:.2f} seconds\\n\")\n", - "\n", - " result = Dataset.from_dict(result)\n", - " result = score(result, config)\n", - " formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", - " return formatted_output" - ], - "metadata": { - "id": "YpswbcVi37KR" - }, - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## ⏳ 3.1 Comparing Thinking Time for Each Strategy\n", - "\n", - "Let’s now run each method and track the **thinking time** for a given question.\n", - "\n", - "### 1. **Best of n**\n", - "\n", - "We’ll begin by using the `best_of_n` strategy. Here’s how to track the thinking time for this method:" - ], - "metadata": { - "id": "RWbOqkiKPVd2" - } - }, - { - "cell_type": "code", - "source": [ - "question = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n", - "\n", - "config.n=8\n", - "\n", - "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 827, - "referenced_widgets": [ - "07fcbf44dd91438bafeb110244fac6bb", - "a307d4986a9a489284333fccd05baea2", - "c9d12093f9df4c578fcfd4bdfa3752ad", - "cbd77563a91341a08556ce09a437509e", - "710be6f8e16f4c319818e44dc75d2109", - "ad710d0763e84f2ba806b2816a2d6164", - "43e234fbe88e429d9f1f1388b790fb24", - "df87c0d0f4174c9a9f82e83186676290", - "14d4fbbc24354308b1772be1da606d00", - "2409843f70af4f51944425a773995bdf", - "40bbe419b638489a9c7d19ebb5771543", - "9e3baabd96c542719070c9052c6c9910", - "a5d40b55ac6c4035a848563856a3f36a", - "b8ee35cd223e478088d6a54dda002f3b", - "6a7824ea88fb40df86d2e7a25a159075", - "31d2d679894d42f5b783b0cbb1d9e8cf", - "b9c063eedbeb47ae934a0dfb6f432f8f", - 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], - "text/markdown": "## Step 1: Recall the conversion formulas between rectangular and polar coordinates.\nThe conversion formulas are given as $r = \\sqrt{x^2 + y^2}$ for the radial coordinate and $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$ for the angular coordinate.\n\n## Step 2: Apply the conversion formulas to the given point (0,3).\nGiven the point $(0,3)$, we can substitute its rectangular coordinates into the formulas:\n- $r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n- $\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right)$\n\n## Step 3: Handle the division by zero explicitly.\nSince $\\tan^{-1}\\left(\\frac{3}{0}\\right)$ is undefined, we must recognize that the point (0,3) lies on the y-axis. Thus, its polar coordinates should be in the form $(r, \\frac{\\pi}{2})$.\n\n## Step 4: Conclude the polar coordinates of the point (0,3).\nTherefore, the polar coordinates of the point (0,3) are $\\left(3, \\frac{\\pi}{2}\\right)$.\n\nThe final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### 2. **Beam Search**\n", - "\n", - "Now, let's try using the `beam_search` strategy." - ], - "metadata": { - "id": "7S9AwP5lQvUN" - } - }, - { - "cell_type": "code", - "source": [ - "config.n=8\n", - "# beam search specific\n", - "config.sort_completed=True\n", - "config.filter_duplicates=True\n", - "\n", - "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='beam_search')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 845, - "referenced_widgets": [ - "1783db79fcca4dfa8cf4464428913061", - "8a7829fd003a4d9782603d0a78ab244a", - 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{ - "output_type": "stream", - "name": "stderr", - "text": [ - "\n" - ] + "effca1bc095a4b1d926a1f571b68a9f9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map: 0%| | 0/1 [00:00" - ], - "text/markdown": "To convert the point $(0,3)$ from rectangular coordinates to polar coordinates, we need to find the radius $r$ and the angle $\\theta$.\n\nThe formula to find $r$ is:\n\n\\[ r = \\sqrt{x^2 + y^2} \\]\n\nIn this case, $x = 0$ and $y = 3$.\n\n\\[ r = \\sqrt{0^2 + 3^2} = \\sqrt{9} = 3 \\]\n\nNow, we need to find the angle $\\theta$.\n\nSince the point $(0,3)$ lies on the positive $y$-axis, the angle $\\theta$ is $\\frac{\\pi}{2}$ (or $90^\\circ$).\n\nSo, the polar coordinates are:\n\n\\[(r, \\theta) = \\left(3, \\frac{\\pi}{2}\\right)\\]\n\nTherefore, the polar coordinates of the point $(0,3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$." - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### 3. **Diverse Verifier Tree Search (DVTS)**\n", - "\n", - "Finally, let's try the `dvts` strategy." - ], - "metadata": { - "id": "GxBBUd7HQzhd" - } - }, - { - "cell_type": "code", - "source": [ - "config.n=8\n", - "# dvts specific\n", - "config.n_beams = config.n // config.beam_width\n", - "\n", - "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='dvts')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 845, - "referenced_widgets": [ - "01707152aef241589991e3209a20b76c", - "ea733cd07f5c41db9b90af4a2ce6ffa7", - "b67ce02bb07340f3b7989a70ba8b1517", - "44324de58c0f4b34b54111c790e47221", - "f0873855d3774c9487561988cb0951e5", - "b3694daa6d51469d8ab75ee975a3362c", - "602af55805804d9fb9872204ecff8be9", - "df8b69aac0974d68a065711c0a15c2d0", - "980358f22b5a40a9bdb88c75f7532d50", - "3503db8563924b66b9f7d54471115310", - 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{ - "output_type": "display_data", - "data": { - "text/plain": [ - "Map: 0%| | 0/1 [00:00" + "f8c85a8b26f24bf89a7a3e48255820ce": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_fae14c0874bd4ca4923fc5f40ddf7212", + "IPY_MODEL_6550aeef3c5b431a89e952e2a353d570", + "IPY_MODEL_68d595e6b20748d58382de1a9257f8c7" ], - "text/markdown": "## Step 1: To convert the point $(0,3)$ from rectangular coordinates to polar coordinates, we need to find the radius $r$ and the angle $\\theta$.\n\nThe formula to convert rectangular coordinates $(x, y)$ to polar coordinates $(r, \\theta)$ is given by:\n\n$r = \\sqrt{x^2 + y^2}$\n\n$\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$\n\n## Step 2: Substitute the values of $x = 0$ and $y = 3$ into the formulas to find the radius $r$ and the angle $\\theta$.\n\n$r = \\sqrt{0^2 + 3^2} = \\sqrt{9} = 3$\n\n$\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right) = \\tan^{-1}(\\infty)$\n\n## Step 3: Since $\\tan^{-1}(\\infty)$ is equal to $\\frac{\\pi}{2}$, we can conclude that the polar coordinates are $(3, \\frac{\\pi}{2})$.\n\nTherefore, the polar coordinates of the point $(0,3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$.\n\nThe final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "In the table below, we compare the three methods (`best_of_n`, `beam_search`, and `dvts`) with the same number of answers during the search process, considering the time spent thinking (in seconds).\n", - "\n", - "As seen from the results, the `best_of_n` method requires the least amount of thinking time, while the `dvts` method requires the most time.\n", - "\n", - "| **Method** | **Number of Answers During Search** | **Time Thinking (Seconds)** |\n", - "|-----------------|-------------------------------------|-----------------------------|\n", - "| **best_of_n** | 8 | 2.45 |\n", - "| **beam_search** | 8 | 10.19 |\n", - "| **dvts** | 8 | 11.56 |\n", - "\n", - "This comparison helps us understand the trade-offs between the different search strategies, especially when it comes to balancing time and solution quality." - ], - "metadata": { - "id": "Lu2cAxU_N01V" - } - }, - { - "cell_type": "markdown", - "source": [ - "## 🙋 3.2 Testing the System with a Simple Question\n", - "\n", - "In this final example, we’ll test the system using a straightforward question to observe how it performs in simpler cases. This allows us to verify that the system works as expected even for basic queries.\n", - "\n", - "Let's try the following question:" - ], - "metadata": { - "id": "5PM9HHwBSYWk" - } - }, - { - "cell_type": "code", - "source": [ - "question = 'What\\'s the capital of Spain?'\n", - "\n", - "config.n=32\n", - "\n", - "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "2b834080393d4a0c82b406fde2e85731", - "3342bed22fb044b39089a2f356eeb793", - "469c237fe02f4b5b85c4721ef6d5c301", - "81894d4748ae422a95f3648a5840dfa6", - "c85ed0d8716746459f8e4646f0e46198", - "411707d5f2844a31a45785741d889080", - "45243902393f4fc4979743777962022c", - "cfa3b5955c244804b78fa95c27a89aef", - "b8ee2056430a4ad789ecd2cba55f5aa1", - "5d4df6c8f5014c2e82ffbe853cc47101", - "e98d1b74d1f64ce486af663e27679c8c", - "07e49cde343544e9915b6555ab978a57", - 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] - }, - { - "cell_type": "markdown", - "source": [ - "Even though we set a larger number of candidate answers (`N`), the time spent thinking remains relatively small (1.21 seconds). This demonstrates the system’s ability to efficiently handle easier problems, spending less time on them, while leveraging its enhanced capabilities for more complex questions.\n", - "\n", - "🏆 **We now have a fully operational pipeline** that leverages test-time compute, enabling the system to \"think longer\" for more complicated queries, while also maintaining fast response times for straightforward questions.\n", - "\n", - "This approach ensures the system can scale its thinking time based on the task's complexity, offering an efficient and responsive solution for both simple and challenging problems.\n" - ], - "metadata": { - "id": "NgdeSegeANoT" - } - }, - { - "cell_type": "markdown", - "source": [ - "# 4. Continuing the Journey 🧑‍🎓️\n", - "\n", - "If you're eager to continue exploring, be sure to check out the original experimental [blog](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute) and all the references mentioned within it. These resources will deepen your understanding of test-time compute, its benefits, and its applications in LLMs.\n", - "\n", - "Happy learning and experimenting! 🚀" - ], - "metadata": { - "id": "92znAyJ0AOPY" } } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} From bd8c2d8cebb8e9ab16e99666e838dad514e57345 Mon Sep 17 00:00:00 2001 From: sergiopaniego Date: Mon, 30 Dec 2024 17:17:59 +0100 Subject: [PATCH 05/10] Updated toctree and index --- notebooks/en/_toctree.yml | 2 ++ notebooks/en/index.md | 1 + 2 files changed, 3 insertions(+) diff --git a/notebooks/en/_toctree.yml b/notebooks/en/_toctree.yml index cc99286d..f2dbf88b 100644 --- a/notebooks/en/_toctree.yml +++ b/notebooks/en/_toctree.yml @@ -64,6 +64,8 @@ title: Enhancing RAG Reasoning with Knowledge Graphs - local: phoenix_observability_on_hf_spaces title: Phoenix Observability Dashboard on HF Spaces + - local: search_and_learn + title: Scaling Test-Time Compute for Longer Thinking in LLMs - title: Computer Vision Recipes isExpanded: false diff --git a/notebooks/en/index.md b/notebooks/en/index.md index 884789af..7952207b 100644 --- a/notebooks/en/index.md +++ b/notebooks/en/index.md @@ -14,6 +14,7 @@ Check out the recently added notebooks: - [Fine-tuning SmolVLM with TRL on a consumer GPU](fine_tuning_smol_vlm_sft_trl) - [Smol Multimodal RAG: Building with ColSmolVLM and SmolVLM on Colab's Free-Tier GPU](multimodal_rag_using_document_retrieval_and_smol_vlm) - [Fine-tuning SmolVLM using direct preference optimization (DPO) with TRL on a consumer GPU](fine_tuning_vlm_dpo_smolvlm_instruct) +- [Scaling Test-Time Compute for Longer Thinking in LLMs](search_and_learn) From ccf1a67a2868cb64bff9e46ba8c50da8a4e40447 Mon Sep 17 00:00:00 2001 From: sergiopaniego Date: Mon, 30 Dec 2024 17:23:02 +0100 Subject: [PATCH 06/10] Updated method to strategy --- notebooks/en/search_and_learn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/en/search_and_learn.ipynb b/notebooks/en/search_and_learn.ipynb index cca55863..721b59dd 100644 --- a/notebooks/en/search_and_learn.ipynb +++ b/notebooks/en/search_and_learn.ipynb @@ -407,7 +407,7 @@ "id": "xYtPn0_V_YRx" }, "source": [ - "## 2.1 Instantiate the Question, Search Method, and Call the Pipeline\n", + "## 2.1 Instantiate the Question, Search Strategy, and Call the Pipeline\n", "\n", "Now that we've set up the LLM and PRM, let's proceed by defining the question, selecting a search strategy to retrieve relevant information, and calling the pipeline to process the question through the models.\n", "\n", From 4c8076a464a905ce5cc5fb7ccd711311270aac26 Mon Sep 17 00:00:00 2001 From: sergiopaniego Date: Mon, 30 Dec 2024 17:31:56 +0100 Subject: [PATCH 07/10] Improved explanations --- notebooks/en/search_and_learn.ipynb | 42 +++++++++++++++++++++-------- 1 file changed, 31 insertions(+), 11 deletions(-) diff --git a/notebooks/en/search_and_learn.ipynb b/notebooks/en/search_and_learn.ipynb index 721b59dd..b5d7b117 100644 --- a/notebooks/en/search_and_learn.ipynb +++ b/notebooks/en/search_and_learn.ipynb @@ -433,7 +433,7 @@ "id": "z69xD6i2L5a6" }, "source": [ - "Here’s how we can structure the process:" + "The first step is to clearly define the question that the system will answer. This ensures that we have a precise task for the model to tackle." ] }, { @@ -448,6 +448,15 @@ "input_batch = {\"problem\": [question_text]}" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we define the configuration, including parameters like the number of candidate answers `(N)`, and choose the search strategy that will be used. The search strategy dictates how we explore the potential answers.\n", + "\n", + "With the question and configuration in place, we use the selected search strategy to generate multiple candidate answers. These candidates will be evaluated based on their relevance and quality." + ] + }, { "cell_type": "code", "execution_count": 4, @@ -466,16 +475,10 @@ ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "VtEPnhnQbF9W" - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "from datasets import Dataset\n", - "\n", - "search_dataset = Dataset.from_dict(search_result)" + "Finally, we evaluate the generated answers to select the best one. This scoring process helps us determine which answer best addresses the question." ] }, { @@ -1319,7 +1322,10 @@ ], "source": [ "from sal.utils.score import score\n", + "from datasets import Dataset\n", + "\n", "\n", + "search_dataset = Dataset.from_dict(search_result)\n", "final_result = score(search_dataset, config)" ] }, @@ -1366,6 +1372,13 @@ "final_result['pred'][0]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model’s output might include special tokens, such as `<|start_header_id|>` or `<|end_header_id|>`. To make the answer more readable, we can safely remove them before displaying it to the end user." + ] + }, { "cell_type": "code", "execution_count": 8, @@ -1397,6 +1410,13 @@ "formatted_output" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After removing any special tokens, we can display the final answer to the user. Since the answer is based on markdown, it can be rendered properly by displaying it as markdown." + ] + }, { "cell_type": "code", "execution_count": 9, @@ -1437,7 +1457,7 @@ } ], "source": [ - "from IPython.display import display, Markdown, Latex\n", + "from IPython.display import display, Markdown\n", "\n", "display(Markdown(formatted_output))" ] From 35acbeff58a91587e70ff15621dc486b8eba9f70 Mon Sep 17 00:00:00 2001 From: sergiopaniego Date: Mon, 30 Dec 2024 17:35:43 +0100 Subject: [PATCH 08/10] Impreved readability --- notebooks/en/search_and_learn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/en/search_and_learn.ipynb b/notebooks/en/search_and_learn.ipynb index b5d7b117..e446b982 100644 --- a/notebooks/en/search_and_learn.ipynb +++ b/notebooks/en/search_and_learn.ipynb @@ -452,7 +452,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we define the configuration, including parameters like the number of candidate answers `(N)`, and choose the search strategy that will be used. The search strategy dictates how we explore the potential answers.\n", + "Next, we define the configuration, including parameters like the number of candidate answers `(N)`, and choose the search strategy that will be used. The search strategy dictates how we explore the potential answers. In this case, we'll use `best_of_n`.\n", "\n", "With the question and configuration in place, we use the selected search strategy to generate multiple candidate answers. These candidates will be evaluated based on their relevance and quality." ] From 9d66b28a7a2366ac4326bae18eeab78117dcd7b6 Mon Sep 17 00:00:00 2001 From: sergiopaniego Date: Fri, 3 Jan 2025 14:45:31 +0100 Subject: [PATCH 09/10] Updated based on feedback --- notebooks/en/search_and_learn.ipynb | 51901 ++------------------------ 1 file changed, 2894 insertions(+), 49007 deletions(-) diff --git a/notebooks/en/search_and_learn.ipynb b/notebooks/en/search_and_learn.ipynb index e446b982..ca3e141a 100644 --- a/notebooks/en/search_and_learn.ipynb +++ b/notebooks/en/search_and_learn.ipynb @@ -14,32 +14,20 @@ { "cell_type": "markdown", "metadata": { - "id": "1YgobuWDCRiK" + "id": "JmgoppItAO7B" }, "source": [ "🚨 **WARNING**: This notebook is **resource-intensive** and requires substantial computational power. If you’re running this in **Colab**, it will utilize an **A100 GPU**.\n", "\n", "---\n", "\n", - "## 🧠 Extending Inference Time for Instruct LLM Systems\n", + "In this recipe, we'll guide you through extending the inference time for an **Instruct LLM system** using **test-time compute** to solve more challenging problems, such as **complex math problems**. This approach, inspired by [**OpenAI o1-o3 models**](https://openai.com/index/learning-to-reason-with-llms/), demonstrates that **longer reasoning time** during inference can enhance model performance.\n", "\n", - "In this recipe, we'll guide you through extending the inference time for an **Instruct LLM system** using **test-time compute**, which enhances its ability to solve more challenging problems, such as **complex math problems**. This approach is inspired by the [**OpenAI o1-o3 models**](https://openai.com/index/learning-to-reason-with-llms/), which demonstrate how **longer reasoning time** during inference can improve model performance.\n", + "This technique builds on experiments shared in [this **blog post**](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute), which show that smaller models, like the **1B** and **3B Llama Instruct models**, can outperform much larger ones on the **MATH-500 benchmark** when given enough **\"time to think\"**. Recent research from [DeepMind](https://arxiv.org/abs/2408.03314) suggests that **test-time compute** can be scaled optimally through strategies like iterative self-refinement or using a reward model.\n", "\n", - "### 🔍 Concept Behind the Approach\n", + "The blog introduces a [**new repository**](https://github.com/huggingface/search-and-learn) for running these experiments. In this recipe, we'll focus on building a **small chatbot** that engages in **longer reasoning** to tackle **harder problems** using small open models.\n", "\n", - "This technique is based on insights from [this **blog post**](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute), which presents experiments validating the strategy with open models. The results show that the smaller **1B** and **3B Llama Instruct models** outperform their much larger **8B** and **70B** counterparts on the **challenging MATH-500 benchmark**, provided they are given enough **\"time to think\"**.\n", - "\n", - "![Instruct LLM Methodology](https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/methods-thumbnail.png)\n", - "\n", - "### 🚀 New Repository and Experimentation\n", - "\n", - "The blog introduces a [**new repository**](https://github.com/huggingface/search-and-learn) designed for running these experiments. In this recipe, however, we will focus on building a **small chatbot** that is capable of engaging in **longer reasoning** to tackle **harder problems** using small open models.\n", - "\n", - "---\n", - "\n", - "## 📚 Explore Further\n", - "\n", - "As stated in the blog: `Although we don’t know how o1 was trained, recent research from DeepMind shows that test-time compute can be scaled optimally through strategies like iterative self-refinement or using a reward model to perform search over the space of solutions`. Learn more from the paper: [**Scaling LLM Test-Time Compute Optimally Can Be More Effective Than Scaling Model Parameters**](https://arxiv.org/abs/2408.03314).\n" + "![Instruct LLM Methodology](https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/methods-thumbnail.png)" ] }, { @@ -62,7 +50,7 @@ "base_uri": "https://localhost:8080/" }, "id": "t0YDC2_7XTm8", - "outputId": "2375a29f-ede4-497a-a7f0-606ad9f221c5" + "outputId": "d804071f-8a3e-4463-9721-d6100ae1d48b" }, "outputs": [], "source": [ @@ -78,7 +66,7 @@ "height": 1000 }, "id": "kT3jH_d_XcEb", - "outputId": "e5939fd5-97e5-4eac-939a-21f3be47f60e" + "outputId": "b50f463e-2c9d-4554-cd20-00887acc2114" }, "outputs": [], "source": [ @@ -104,30 +92,30 @@ "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ - "e917741ae21e4943880cee24489b4551", - "53c62adc2c7346beb95f524c11b28f4d", - "1317f8c4c8014102af169acb05b8b80c", - "d579833c056642e9b5dccd01067900ef", - "e0b6b66dd6d24f39a7b733c09fb20a3d", - 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}, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "yGpyzMNkAO7H" + }, "source": [ "Next, we define the configuration, including parameters like the number of candidate answers `(N)`, and choose the search strategy that will be used. The search strategy dictates how we explore the potential answers. In this case, we'll use `best_of_n`.\n", "\n", - "With the question and configuration in place, we use the selected search strategy to generate multiple candidate answers. These candidates will be evaluated based on their relevance and quality." + "With the question and configuration in place, we use the selected search strategy to generate multiple candidate answers. These candidates are evaluated based on their relevance and quality and the final answer is returned.\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 39, "metadata": { "id": "C6s6GS16QZLV" }, @@ -476,980 +466,512 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "lsLHD_6C_15p" + }, "source": [ - "Finally, we evaluate the generated answers to select the best one. This scoring process helps us determine which answer best addresses the question." + "## 2.2 Display the Final Result\n", + "\n", + "Once the pipeline has processed the question through the LLM and PRM, we can display the final result. This result will be the model's output after considering the intermediate answers and scoring them using the PRM.\n", + "\n", + "Here's how to display the final answer:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 40, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "8347e2d9c6084defb92836697f1ee5c6", - "db67ef43ab864eac9b32e1159140b554", - "f1dd77c0b07044e4b161628e84fab059", - "0e0ab349010d4b319ea9a414ccd61bc8", - "73e6b699031e4835a293c013f129c08b", - "e5f89c42a9b14ceea06b49ba9789c6e5", - "0d1c8cf9d5da41188fa92418392ae6c3", - "9fd3cc59344f403ba0834883aca14590", - "28600649893e47448bef2aaa138e41e7", - "9e7800aa855e451f9522c244b488b277", - "4fe172a542a64c6cb1daea147b385030", - "ffb883a74ad54da7a079cdfa3074c6d8", - "37094451ff964979ae5f2fb6d4371d9d", - "146278d2748e44d2be8b3504244fcc2f", - "9c35d141a821443ca25f0ed16364b6cf", - "e880475f86b84f0681264b8785910852", - "8f41aa38f9a14757963dc63c1cb27763", - 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"adb26b52e7e248d1ab0a989a22e61110", - "5bc5a4e43def4ff1963e0ba8cc4c02d0", - "9f09b6b844b64a2c8ee6eb4a05902e8c", - "0c82534f5e334b49b4f4bb56473161dd", - "d79165052b9d49d18642ad6e3b09eacc", - "d5d85119722d4109a6b50e81783bce9e", - "bf2dac3f72374ad1be468939b2e8d682", - "aec1285f8b7b48bdb911936cd7a88e63", - "06d01bb7482841ddab0b9b1d7ea510ef", - "b240d13ed36140b88d79e1f228ea180e", - "50df13c642c54e19a829b992acb1d1dc", - "cc87eebe3fe2496bbf7947f8bda66999", - "c1c7b7fa0ec04ae382498fc3840350ab", - "ccb55d41fd8f4cb6896a1669d0f6108e", - "ecf73eaaf2e444bb87cc8ade7d472c07", - "72c89ef7029044f58d6e866e81c2d9f4", - "d457f3a69e85454486d8acea07e47236", - "90c5e593e8a041189b80a4ba32cd2b26", - "44201f6d1ddb43aa91ba6f0c17097afc", - "fd7d45d332d240169a424271b75caa5e", - "95c6151d6c484d28bf9e433e86fe0a51", - "ff0e8da7401a4d618e7d6efbc2e9d34b", - "fdaba052766243548a8cd50854ab25bb", - "96b0949149094eb0a5a0bdbea3f3dba7", - "3559ffddf37a4e4b944be93d5852275f", - "0fbcacf1e02045fcab4c8c149997344c", - "e15a574dca144641aabd6066b9643c85" - ] + "height": 105 }, - "id": "29t49WKAAOA7", - "outputId": "7caef5b4-e9fd-4984-9007-b88cab212ca8" + "id": "v8medbURbgdI", + "outputId": "3620f3e6-a25d-4bec-f41c-c4f03a6ed770" }, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8347e2d9c6084defb92836697f1ee5c6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map: 0%| | 0/1 [00:00` or `<|end_header_id|>`. To make the answer more readable, we can safely remove them before displaying it to the end user." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 }, + "id": "flbIu6-rDapM", + "outputId": "fcb197d5-0f21-4953-8a21-869c92a1f957" + }, + "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e6614e6fc4804cd2a5ede3a47cf564f1", - "version_major": 2, - "version_minor": 0 + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" }, "text/plain": [ - "Compute majority pred 1: 0%| | 0/1 [00:00assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", + "formatted_output" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7ZuLZNirAO7J" + }, + "source": [ + "After removing any special tokens, we can display the final answer to the user. Since the answer is based on markdown, it can be rendered properly by displaying it as markdown." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 385 }, + "id": "P4En0qJRD0cl", + "outputId": "56400fea-e304-4f16-d255-909f42f636e0" + }, + "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f089f41bd8154447a6e1b79e7ba33cf9", - "version_major": 2, - "version_minor": 0 - }, + "text/markdown": [ + "## Step 1: Recall the conversion formulas\n", + "To convert from rectangular coordinates $(x, y)$ to polar coordinates $(r, \\theta)$, we use the following formulas:\n", + "- $r = \\sqrt{x^2 + y^2}$\n", + "- $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$\n", + "\n", + "## Step 2: Substitute the given values into the formulas\n", + "Given $(x, y) = (0, 3)$, we substitute these values into the formulas:\n", + "- $r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n", + "- $\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right)$. However, since division by zero is undefined, we recognize that the point $(0, 3)$ is on the positive y-axis, meaning $\\theta = \\frac{\\pi}{2}$.\n", + "\n", + "## Step 3: Combine the results for the polar coordinates\n", + "Therefore, the polar coordinates of the point $(0, 3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$.\n", + "\n", + "The final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" + ], "text/plain": [ - "Compute naive pred 1: 0%| | 0/1 [00:00" ] }, "metadata": {}, "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Markdown\n", + "\n", + "display(Markdown(formatted_output))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4uCpYzAw_4o9" + }, + "source": [ + "# 3. Assembling It All! 🧑‍🏭️\n", + "\n", + "Now, let's create a method that encapsulates the entire pipeline. This will allow us to easily reuse the process in future applications, making it efficient and modular.\n", + "\n", + "By combining the LLM, PRM, search strategy, and result display, we can simplify the workflow and ensure that it’s reusable for other tasks or questions.\n", + "\n", + "We simplify the workflow, ensuring that it’s reusable for different tasks or questions. Additionally, we’ll track the time spent on each method so that we can **understand the practical implications** of using each strategy and configuration.\n", + "\n", + "Here’s how we can structure the method:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "YpswbcVi37KR" + }, + "outputs": [], + "source": [ + "import time\n", + "\n", + "def generate_with_search_and_learn(question, config, llm, prm, method='best_of_n'):\n", + " \"\"\"\n", + " Generate an answer for a given question using the search-and-learn pipeline.\n", + "\n", + " Args:\n", + " - question (str): The input question to generate an answer for.\n", + " - config (Config): Configuration object containing parameters for search strategy.\n", + " - llm (LLM): Pretrained large language model used for generating answers.\n", + " - prm (RLHFFlow): Process reward model used for evaluating answers.\n", + " - method (str): Search strategy to use. Options are 'best_of_n', 'beam_search', 'dvts'. Default is 'best_of_n'.\n", + "\n", + " Returns:\n", + " - str: The formatted output after processing the question.\n", + " \"\"\"\n", + " batch = {\"problem\": [question]}\n", + "\n", + " start_time = time.time()\n", + " if method == 'best_of_n':\n", + " result = best_of_n(x=batch, config=config, llm=llm, prm=prm)\n", + " elif method == 'beam_search':\n", + " result = beam_search(examples=batch, config=config, llm=llm, prm=prm)\n", + " elif method == 'dvts':\n", + " result = dvts(examples=batch, config=config, llm=llm, prm=prm)\n", + "\n", + " elapsed_time = time.time() - start_time\n", + " print(f\"\\nFinished in {elapsed_time:.2f} seconds\\n\")\n", + "\n", + " tokenizer = llm.get_tokenizer()\n", + " total_tokens = 0\n", + " for completion in result['completions']:\n", + " for comp in completion:\n", + " output_tokens = tokenizer.encode(comp)\n", + " total_tokens += len(output_tokens)\n", + "\n", + " print(f\"Total tokens in all completions: {total_tokens}\")\n", + "\n", + " formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", + " return formatted_output" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RWbOqkiKPVd2" + }, + "source": [ + "## ⏳ 3.1 Comparing Thinking Time for Each Strategy\n", + "\n", + "Let’s compare the **thinking time** of three methods: `best_of_n`, `beam_search`, and `dvts`. Each method is evaluated using the same number of answers during the search process, measuring the time spent thinking in seconds and the number of generated tokens.\n", + "\n", + "In the results below, the `best_of_n` method shows the least thinking time, while the `dvts` method takes the most time. However, `best_of_n` generates more tokens due to its simpler search strategy.\n", + "\n", + "| **Method** | **Number of Answers During Search** | **Thinking Time (Seconds)** | **Generated Tokens** |\n", + "|------------------|-------------------------------------|-----------------------------|-----------------------|\n", + "| **best_of_n** | 8 | 3.54 | 3087 |\n", + "| **beam_search** | 8 | 10.06 | 2049 |\n", + "| **dvts** | 8 | 8.46 | 2544 |\n", + "\n", + "This comparison illustrates the trade-offs between the strategies, balancing time spent thinking and the complexity of the search process.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2ROJwROGX8q-" + }, + "source": [ + "### 1. **Best of n**\n", + "\n", + "We’ll begin by using the `best_of_n` strategy. Here’s how to track the thinking time for this method:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "c_fWKy5CCTLV", + "outputId": "8d77eea3-b23e-4eba-cfe3-5935fae1405d" + }, + "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\rComputing majority & weighted predictions: 17%|█▋ | 1/6 [00:00<00:00, 5.72it/s]" + "\n", + "Finished in 3.54 seconds\n", + "\n", + "Total tokens in all completions: 3087\n" ] + } + ], + "source": [ + "question = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n", + "\n", + "config.n=8\n", + "\n", + "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 428 }, + "id": "uzKfFoKG9ejC", + "outputId": "38326907-685e-4a9c-ca8b-32a7c40f1d3e" + }, + "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "db356e4d3a454231950dae1375dab165", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Subsample 2: 0%| | 0/1 [00:00" ] }, "metadata": {}, "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(formatted_output))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7S9AwP5lQvUN" + }, + "source": [ + "### 2. **Beam Search**\n", + "\n", + "Now, let's try using the `beam_search` strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "F7CH6KN8Izp9", + "outputId": "adef4782-3278-4994-9520-43e23ea047a6" + }, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\rComputing majority & weighted predictions: 33%|███▎ | 2/6 [00:00<00:00, 6.93it/s]" + "Beam search iterations: 20%|██ | 8/40 [00:10<00:40, 1.26s/it]" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ac927c994d234318af57f97fc5ff224a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Subsample 4: 0%| | 0/1 [00:00" ] }, "metadata": {}, "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(formatted_output))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GxBBUd7HQzhd" + }, + "source": [ + "### 3. **Diverse Verifier Tree Search (DVTS)**\n", + "\n", + "Finally, let's try the `dvts` strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "HzXW1g-dI5wN", + "outputId": "86979d67-7dfa-4346-9adb-c386a52af58c" + }, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\rComputing majority & weighted predictions: 67%|██████▋ | 4/6 [00:00<00:00, 6.92it/s]" + "Beam search iterations: 22%|██▎ | 9/40 [00:08<00:29, 1.06it/s]" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "575cb860ad9e435a8d57e157d858bacb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Subsample 16: 0%| | 0/1 [00:00assistant<|end_header_id|>\\n\\n## Step 1: The relationship between rectangular and polar coordinates is given by:\\n\\\\[ r = \\\\sqrt{x^2 + y^2} \\\\] and \\\\[ heta = \\\\arctan{\\\\left(\\\\frac{y}{x}\\\\right)}. \\\\]\\n\\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\\n\\\\[ r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{9} = 3. \\\\]\\n\\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\\n\\\\[ heta = \\\\arctan{\\\\left(\\\\frac{3}{0}\\\\right)} = \\\\arctan{\\\\left( \\\\infty \\\\right)}. \\\\]\\n\\n## Step 4: Since $\\\\arctan{\\\\left( \\\\infty \\\\right)}$ is equal to $\\\\frac{\\\\pi}{2}$, we have:\\n\\\\[ heta = \\\\frac{\\\\pi}{2}. \\\\]\\n\\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right).$\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_result['pred'][0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model’s output might include special tokens, such as `<|start_header_id|>` or `<|end_header_id|>`. To make the answer more readable, we can safely remove them before displaying it to the end user." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 105 - }, - "id": "flbIu6-rDapM", - "outputId": "08b76f0c-db41-4069-b559-5cd3aa832e25" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - }, - "text/plain": [ - "'## Step 1: The relationship between rectangular and polar coordinates is given by:\\n\\\\[ r = \\\\sqrt{x^2 + y^2} \\\\] and \\\\[ heta = \\\\arctan{\\\\left(\\\\frac{y}{x}\\\\right)}. \\\\]\\n\\n## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\\n\\\\[ r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{9} = 3. \\\\]\\n\\n## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\\n\\\\[ heta = \\\\arctan{\\\\left(\\\\frac{3}{0}\\\\right)} = \\\\arctan{\\\\left( \\\\infty \\\\right)}. \\\\]\\n\\n## Step 4: Since $\\\\arctan{\\\\left( \\\\infty \\\\right)}$ is equal to $\\\\frac{\\\\pi}{2}$, we have:\\n\\\\[ heta = \\\\frac{\\\\pi}{2}. \\\\]\\n\\n## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right).$\\n\\nThe final answer is: $\\\\boxed{\\\\left( 3, \\\\frac{\\\\pi}{2} \\\\right)}$'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "formatted_output = final_result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", - "formatted_output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After removing any special tokens, we can display the final answer to the user. Since the answer is based on markdown, it can be rendered properly by displaying it as markdown." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 433 - }, - "id": "P4En0qJRD0cl", - "outputId": "6a190922-729d-489f-b330-ad1ee7873692" - }, - "outputs": [ - { - "data": { - "text/markdown": [ - "## Step 1: The relationship between rectangular and polar coordinates is given by:\n", - "\\[ r = \\sqrt{x^2 + y^2} \\] and \\[ heta = \\arctan{\\left(\\frac{y}{x}\\right)}. \\]\n", - "\n", - "## Step 2: Given the point $(0, 3)$, we have $x = 0$ and $y = 3$. Plugging these into the formula for $r$, we get:\n", - "\\[ r = \\sqrt{0^2 + 3^2} = \\sqrt{9} = 3. \\]\n", - "\n", - "## Step 3: Plugging the values of $x = 0$ and $y = 3$ into the formula for $heta$, we get:\n", - "\\[ heta = \\arctan{\\left(\\frac{3}{0}\\right)} = \\arctan{\\left( \\infty \\right)}. \\]\n", - "\n", - "## Step 4: Since $\\arctan{\\left( \\infty \\right)}$ is equal to $\\frac{\\pi}{2}$, we have:\n", - "\\[ heta = \\frac{\\pi}{2}. \\]\n", - "\n", - "## Step 5: Therefore, the polar coordinates of the point $(0, 3)$ are $\\left( 3, \\frac{\\pi}{2} \\right).$\n", - "\n", - "The final answer is: $\\boxed{\\left( 3, \\frac{\\pi}{2} \\right)}$" - ], - "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1457,45891 +979,126 @@ } ], "source": [ - "from IPython.display import display, Markdown\n", - "\n", "display(Markdown(formatted_output))" ] }, { "cell_type": "markdown", "metadata": { - "id": "4uCpYzAw_4o9" - }, - "source": [ - "# 3. Assembling It All! 🧑‍🏭️\n", - "\n", - "Now, let's create a method that encapsulates the entire pipeline. This will allow us to easily reuse the process in future applications, making it efficient and modular.\n", - "\n", - "By combining the LLM, PRM, search strategy, and result display, we can simplify the workflow and ensure that it’s reusable for other tasks or questions.\n", - "\n", - "We simplify the workflow, ensuring that it’s reusable for different tasks or questions. Additionally, we’ll track the time spent on each method so that we can **understand the practical implications** of using each strategy and configuration.\n", - "\n", - "Here’s how we can structure the method:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "YpswbcVi37KR" - }, - "outputs": [], - "source": [ - "import time\n", - "\n", - "def generate_with_search_and_learn(question, config, llm, prm, method='best_of_n'):\n", - " \"\"\"\n", - " Generate an answer for a given question using the search-and-learn pipeline.\n", - "\n", - " Args:\n", - " - question (str): The input question to generate an answer for.\n", - " - config (Config): Configuration object containing parameters for search strategy.\n", - " - llm (LLM): Pretrained large language model used for generating answers.\n", - " - prm (RLHFFlow): Process reward model used for evaluating answers.\n", - " - method (str): Search strategy to use. Options are 'best_of_n', 'beam_search', 'dvts'. Default is 'best_of_n'.\n", - "\n", - " Returns:\n", - " - str: The formatted output after processing the question.\n", - " \"\"\"\n", - " batch = {\"problem\": [question]}\n", - "\n", - " start_time = time.time()\n", - " if method == 'best_of_n':\n", - " result = best_of_n(x=batch, config=config, llm=llm, prm=prm)\n", - " elif method == 'beam_search':\n", - " result = beam_search(examples=batch, config=config, llm=llm, prm=prm)\n", - " elif method == 'dvts':\n", - " result = dvts(examples=batch, config=config, llm=llm, prm=prm)\n", - "\n", - " elapsed_time = time.time() - start_time\n", - " print(f\"\\nFinished in {elapsed_time:.2f} seconds\\n\")\n", - "\n", - " result = Dataset.from_dict(result)\n", - " result = score(result, config)\n", - " formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n", - " return formatted_output" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RWbOqkiKPVd2" + "id": "5PM9HHwBSYWk" }, "source": [ - "## ⏳ 3.1 Comparing Thinking Time for Each Strategy\n", - "\n", - "Let’s now run each method and track the **thinking time** for a given question.\n", + "## 🙋 3.2 Testing the System with a Simple Question\n", "\n", - "### 1. **Best of n**\n", + "In this final example, we’ll test the system using a straightforward question to observe how it performs in simpler cases. This allows us to verify that the system works as expected even for basic queries.\n", "\n", - "We’ll begin by using the `best_of_n` strategy. Here’s how to track the thinking time for this method:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 827, - "referenced_widgets": [ - "07fcbf44dd91438bafeb110244fac6bb", - "a307d4986a9a489284333fccd05baea2", - "c9d12093f9df4c578fcfd4bdfa3752ad", - "cbd77563a91341a08556ce09a437509e", - "710be6f8e16f4c319818e44dc75d2109", - "ad710d0763e84f2ba806b2816a2d6164", - "43e234fbe88e429d9f1f1388b790fb24", - "df87c0d0f4174c9a9f82e83186676290", - "14d4fbbc24354308b1772be1da606d00", - "2409843f70af4f51944425a773995bdf", - "40bbe419b638489a9c7d19ebb5771543", - "9e3baabd96c542719070c9052c6c9910", - "a5d40b55ac6c4035a848563856a3f36a", - "b8ee35cd223e478088d6a54dda002f3b", - "6a7824ea88fb40df86d2e7a25a159075", - "31d2d679894d42f5b783b0cbb1d9e8cf", - "b9c063eedbeb47ae934a0dfb6f432f8f", - "5f99ec8d11044d938924a90ac17830e8", - "c310ece89c204a46950886d3a9932244", - "fff60d90b600454c83c0e7cdab576244", - "4c0749ad19da409db1b34ecc04af2571", - 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"\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "07fcbf44dd91438bafeb110244fac6bb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map: 0%| | 0/1 [00:00 0$ and $0 \\le \\theta < 2 \\pi.$'\n", - "\n", - "config.n=8\n", - "\n", - "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 407 - }, - "id": "uzKfFoKG9ejC", - "outputId": "04508ede-2ee6-4cbb-8f6a-142e1aa3977f" - }, - "outputs": [ - { - "data": { - "text/markdown": [ - "## Step 1: Recall the conversion formulas between rectangular and polar coordinates.\n", - "The conversion formulas are given as $r = \\sqrt{x^2 + y^2}$ for the radial coordinate and $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$ for the angular coordinate.\n", - "\n", - "## Step 2: Apply the conversion formulas to the given point (0,3).\n", - "Given the point $(0,3)$, we can substitute its rectangular coordinates into the formulas:\n", - "- $r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n", - "- $\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right)$\n", - "\n", - "## Step 3: Handle the division by zero explicitly.\n", - "Since $\\tan^{-1}\\left(\\frac{3}{0}\\right)$ is undefined, we must recognize that the point (0,3) lies on the y-axis. Thus, its polar coordinates should be in the form $(r, \\frac{\\pi}{2})$.\n", - "\n", - "## Step 4: Conclude the polar coordinates of the point (0,3).\n", - "Therefore, the polar coordinates of the point (0,3) are $\\left(3, \\frac{\\pi}{2}\\right)$.\n", - "\n", - "The final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(Markdown(formatted_output))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7S9AwP5lQvUN" - }, - "source": [ - "### 2. **Beam Search**\n", - "\n", - "Now, let's try using the `beam_search` strategy." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 845, - "referenced_widgets": [ - "1783db79fcca4dfa8cf4464428913061", - "8a7829fd003a4d9782603d0a78ab244a", - "b377b15fc4f84c9faca9d859080839d9", - "9d4b5f44dc2b4eb49b2a94056008bb61", - 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}, - "text/plain": [ - "Map: 0%| | 0/1 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(Markdown(formatted_output))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GxBBUd7HQzhd" - }, - "source": [ - "### 3. **Diverse Verifier Tree Search (DVTS)**\n", - "\n", - "Finally, let's try the `dvts` strategy." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 845, - "referenced_widgets": [ - "01707152aef241589991e3209a20b76c", - "ea733cd07f5c41db9b90af4a2ce6ffa7", - "b67ce02bb07340f3b7989a70ba8b1517", - "44324de58c0f4b34b54111c790e47221", - "f0873855d3774c9487561988cb0951e5", - "b3694daa6d51469d8ab75ee975a3362c", - "602af55805804d9fb9872204ecff8be9", - "df8b69aac0974d68a065711c0a15c2d0", - "980358f22b5a40a9bdb88c75f7532d50", - "3503db8563924b66b9f7d54471115310", - "adcd2313401b4b95aea8dfb0c709cbce", - "0cbab0dcab4e46dc92110b1017712c75", - 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"\n", - "As seen from the results, the `best_of_n` method requires the least amount of thinking time, while the `dvts` method requires the most time.\n", - "\n", - "| **Method** | **Number of Answers During Search** | **Time Thinking (Seconds)** |\n", - "|-----------------|-------------------------------------|-----------------------------|\n", - "| **best_of_n** | 8 | 2.45 |\n", - "| **beam_search** | 8 | 10.19 |\n", - "| **dvts** | 8 | 11.56 |\n", - "\n", - "This comparison helps us understand the trade-offs between the different search strategies, especially when it comes to balancing time and solution quality." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5PM9HHwBSYWk" - }, - "source": [ - "## 🙋 3.2 Testing the System with a Simple Question\n", - "\n", - "In this final example, we’ll test the system using a straightforward question to observe how it performs in simpler cases. This allows us to verify that the system works as expected even for basic queries.\n", - "\n", - "Let's try the following question:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "2b834080393d4a0c82b406fde2e85731", - "3342bed22fb044b39089a2f356eeb793", - "469c237fe02f4b5b85c4721ef6d5c301", - "81894d4748ae422a95f3648a5840dfa6", - "c85ed0d8716746459f8e4646f0e46198", - "411707d5f2844a31a45785741d889080", - "45243902393f4fc4979743777962022c", - "cfa3b5955c244804b78fa95c27a89aef", - "b8ee2056430a4ad789ecd2cba55f5aa1", - "5d4df6c8f5014c2e82ffbe853cc47101", - "e98d1b74d1f64ce486af663e27679c8c", - "07e49cde343544e9915b6555ab978a57", - "167d718b691845f499f1b9914958f9c7", - "c8cbd462594545aea2e3474989aa9f48", - "50112bcce28b4831907fefe7096e8847", - "862233869ea84a3693bf3070afdd6555", - "9dab64ca972040d19e95a308dc89fc55", - "83006608eca24d66a51fa647c266bbb3", - "cfd923146ef0416798b68712163106a9", - 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}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Finished in 1.21 seconds\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2b834080393d4a0c82b406fde2e85731", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map: 0%| | 0/1 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(Markdown(formatted_output))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NgdeSegeANoT" - }, - "source": [ - "Even though we set a larger number of candidate answers (`N`), the time spent thinking remains relatively small (1.21 seconds). 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} + "Let's try the following question:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - "e15a574dca144641aabd6066b9643c85": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } + "id": "bq9vM1uRM7A8", + "outputId": "65ef318d-2b89-4d46-b660-293195c2b8e1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished in 1.03 seconds\n", + "\n", + "Total tokens in all completions: 544\n" + ] + } + ], + "source": [ + "question = 'What\\'s the capital of Spain?'\n", + "\n", + "config.n=32\n", + "\n", + "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 46 }, - "e1b8ec0eb85e4555869e918a7947e800": { + "id": "ysfR0nPfM-Ub", + "outputId": "b474aeb6-6cb7-4f15-ba48-fa59022f31ef" + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "The capital of Spain is Madrid." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(formatted_output))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NgdeSegeANoT" + }, + "source": [ + "Even though we set a larger number of candidate answers (`N`), the time spent thinking remains relatively small (1.03 seconds and 544 generated tokens). This demonstrates the system’s ability to efficiently handle easier problems, spending less time on them, while leveraging its enhanced capabilities for more complex questions.\n", + "\n", + "🏆 **We now have a fully operational pipeline** that leverages test-time compute, enabling the system to \"think longer\" for more complicated queries, while also maintaining fast response times for straightforward questions.\n", + "\n", + "This approach ensures the system can scale its thinking time based on the task's complexity, offering an efficient and responsive solution for both simple and challenging problems.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "92znAyJ0AOPY" + }, + "source": [ + "# 4. Continuing the Journey and Resources 🧑‍🎓️\n", + "\n", + "If you're eager to continue exploring, be sure to check out the original experimental [blog](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute) and all the references mentioned within it. These resources will deepen your understanding of test-time compute, its benefits, and its applications in LLMs.\n", + "\n", + "\n", + "Happy learning and experimenting! 🚀" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "A100", + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "047c712bf83749b5865154b6c9b9b2cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -47393,7 +1150,7 @@ "width": null } }, - "e1b9a92dd0cf4b85a66e4f7c47200e91": { + "058cf74b83fd45abaa97527a87d426b8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -47445,80 +1202,62 @@ "width": null } }, - "e1f099c3ddb64670996ea48466dd166f": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - 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[Smol Multimodal RAG: Building with ColSmolVLM and SmolVLM on Colab's Free-Tier GPU](multimodal_rag_using_document_retrieval_and_smol_vlm) - [Fine-tuning SmolVLM with TRL on a consumer GPU](fine_tuning_smol_vlm_sft_trl) -- [Multimodal RAG with ColQwen2, Reranker, and Quantized VLMs on Consumer GPUs](multimodal_rag_using_document_retrieval_and_reranker_and_vlms) You can also check out the notebooks in the cookbook's [GitHub repo](https://github.com/huggingface/cookbook).