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6ad3ca7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | import torch
from torch.utils import benchmark
from transformers import AutoTokenizer, AutoModelForCausalLM, Mxfp4Config
# ============ CONFIGURATION ============
MODEL_ID = "openai/gpt-oss-20b"
MAX_NEW_TOKENS = 256
BENCHMARK_RUNS = 5
SOURCE_FILES = [
"/fsx/aritra/git-repos/transformers/src/transformers/models/gpt_oss/modeling_gpt_oss.py", # Fixed: missing comma
"/fsx/aritra/git-repos/transformers/src/transformers/models/gpt_oss/modular_gpt_oss.py",
]
# ============ MODEL LOADING ============
def load_model(model_id: str, use_attn_kernels: bool):
"""Load model with optional attention kernel optimization."""
quantization_config = Mxfp4Config(dequantize=True)
kwargs = {
"dtype": "auto",
"device_map": "cuda:0",
"use_kernels": False,
"quantization_config": quantization_config,
}
if use_attn_kernels:
kwargs["attn_implementation"] = "kernels-community/vllm-flash-attn3"
return AutoModelForCausalLM.from_pretrained(model_id, **kwargs).eval()
def unload_model(model):
"""Move model to CPU and free GPU memory."""
model.to("cpu")
del model
torch.cuda.empty_cache()
# ============ GENERATION ============
def generate(model, model_inputs: dict, max_new_tokens: int):
"""Run inference without sampling."""
with torch.inference_mode():
model.generate(
**model_inputs,
do_sample=False,
temperature=None,
max_new_tokens=max_new_tokens,
eos_token_id=-1,
disable_compile=True,
)
# ============ DATA PREPARATION ============
def load_prompts(filepaths: list[str]) -> list[str]:
"""Read source files and create summarization prompts."""
prompts = []
for filepath in filepaths:
with open(filepath, "r") as f:
prompts.append(f"{f.read()}\nSummarize this for me.")
return prompts
def tokenize_prompts(tokenizer, prompts: list[str]) -> list[tuple[dict, int]]:
"""Tokenize prompts and return inputs with their prefill sizes."""
tokenizer.padding_side = "left"
tokenized = []
for prompt in prompts:
message = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
message, # Fixed: was `m`
add_generation_prompt=True,
tokenize=False,
reasoning_effort="low",
)
inputs = tokenizer(text, return_tensors="pt", padding=True)
prefill_size = inputs.input_ids.size(1) # Fixed: was `input` and `.size[1]`
tokenized.append((inputs, prefill_size))
return tokenized
# ============ BENCHMARKING ============
def run_benchmarks(model, tokenized_inputs: list[tuple], use_attn_kernels: bool) -> list:
"""Run timing benchmarks for each input."""
results = []
for inputs, prefill_size in tokenized_inputs:
timer = benchmark.Timer(
stmt="generate(model, model_inputs, max_new_tokens)",
setup="from __main__ import generate",
globals={
"model": model,
"model_inputs": inputs.to(model.device),
"max_new_tokens": MAX_NEW_TOKENS,
},
num_threads=torch.get_num_threads(),
label=f"Time to generate {MAX_NEW_TOKENS} tokens",
sub_label=f"prefill_size={prefill_size}",
description=f"attn_kernels={use_attn_kernels}",
)
results.append(timer.timeit(BENCHMARK_RUNS))
return results
# ============ MAIN ============
def main():
prompts = load_prompts(SOURCE_FILES)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
tokenized_inputs = tokenize_prompts(tokenizer, prompts)
all_results = []
for use_attn_kernels in [True, False]:
print(f"\nBenchmarking with attn_kernels={use_attn_kernels}...")
model = load_model(MODEL_ID, use_attn_kernels)
results = run_benchmarks(model, tokenized_inputs, use_attn_kernels)
all_results.extend(results)
unload_model(model)
benchmark.Compare(all_results).print()
if __name__ == "__main__":
main()
# [------------------ Time to generate 256 tokens -------------------]
# | attn_kernels=True | attn_kernels=False
# 12 threads: --------------------------------------------------------
# prefill_size=7353 | 8.3 | 10.2
# prefill_size=4225 | 8.3 | 9.0
# Times are in seconds (s). |