Adding Mamba blog

---------

Signed-off-by: Song <[email protected]>
Co-authored-by: Peter Jun Park <[email protected]>

blogs/artificial-intelligence/mamba/src/mamba_transformer_evaluation.py

@@ -0,0 +1,94 @@
+# Copyright (c) 2023, Tri Dao, Albert Gu.
+
+import argparse
+import time
+import torch
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
+
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+parser = argparse.ArgumentParser(description="Generation benchmarking")
+parser.add_argument("--model-name", type=str, default="state-spaces/mamba-130m")
+parser.add_argument("--prompt", type=str, default=None)
+parser.add_argument("--promptlen", type=int, default=100)
+parser.add_argument("--genlen", type=int, default=100)
+parser.add_argument("--temperature", type=float, default=1.0)
+parser.add_argument("--topk", type=int, default=1)
+parser.add_argument("--topp", type=float, default=1.0)
+parser.add_argument("--minp", type=float, default=0.0)
+parser.add_argument("--repetition-penalty", type=float, default=1.0)
+parser.add_argument("--batch", type=int, default=1)
+parser.add_argument("--attn_implementation", type=str, default="sdpa")
+args = parser.parse_args()
+
+repeats = 3
+device = "cuda"
+dtype = torch.float16
+torch.cuda.reset_peak_memory_stats()
+
+print(f"Loading model {args.model_name}")
+is_mamba = args.model_name.startswith("state-spaces/mamba")
+if is_mamba:
+    tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
+    model = MambaLMHeadModel.from_pretrained(args.model_name, device=device, dtype=dtype)
+else:
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
+    model = AutoModelForCausalLM.from_pretrained(args.model_name, device_map={"": device}, torch_dtype=dtype, attn_implementation=args.attn_implementation)
+
+    print(f"{args.model_name} model configuration attn_implementation:{args.attn_implementation}")
+model.eval()
+print(f"Number of parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
+
+torch.random.manual_seed(0)
+if args.prompt is None:
+    input_ids = torch.randint(1, 1000, (args.batch, args.promptlen), dtype=torch.long, device="cuda")
+    attn_mask = torch.ones_like(input_ids, dtype=torch.long, device="cuda")
+else:
+    tokens = tokenizer(args.prompt, return_tensors="pt")
+    input_ids = tokens.input_ids.to(device=device)
+    attn_mask = tokens.attention_mask.to(device=device)
+max_length = input_ids.shape[1] + args.genlen
+
+if is_mamba:
+    fn = lambda: model.generate(
+        input_ids=input_ids,
+        max_length=max_length,
+        cg=True,
+        return_dict_in_generate=True,
+        output_scores=True,
+        enable_timing=False,
+        temperature=args.temperature,
+        top_k=args.topk,
+        top_p=args.topp,
+        min_p=args.minp,
+        repetition_penalty=args.repetition_penalty,
+    )
+else:
+    fn = lambda: model.generate(
+        input_ids=input_ids,
+        attention_mask=attn_mask,
+        max_length=max_length,
+        return_dict_in_generate=True,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        temperature=args.temperature,
+        top_k=args.topk,
+        top_p=args.topp,
+        repetition_penalty=args.repetition_penalty,
+    )
+out = fn()
+if args.prompt is not None:
+    print(tokenizer.batch_decode(out.sequences.tolist()))
+
+torch.cuda.synchronize()
+start = time.time()
+for _ in range(repeats):
+    fn()
+torch.cuda.synchronize()
+memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0 * 1024.0)
+print(f"Prompt length: {len(input_ids[0])}, generation length: {len(out.sequences[0]) - len(input_ids[0])}")
+print(f"prompt processing + decoding time: {(time.time() - start) / repeats :.2f}s")
+print(f"memory used: {memory_used:.0f}GB")