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import os; os.environ["CUDA_VISIBLE_DEVICES"]="0" |
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import torch |
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from torch.utils import benchmark |
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from transformers import AutoTokenizer, AutoModelForCausalLM, Mxfp4Config |
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def load_model(in_mxfp4): |
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model_id = "openai/gpt-oss-20b" |
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if not in_mxfp4: |
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quantization_config = Mxfp4Config(dequantize=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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dtype="auto", |
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device_map="cuda:0", |
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use_kernels=True, |
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quantization_config=quantization_config, |
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).eval() |
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else: |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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dtype="auto", |
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device_map="cuda:0", |
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).eval() |
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return model |
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def generate(model, model_inputs, max_new_tokens): |
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with torch.inference_mode(): |
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model.generate( |
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**model_inputs, |
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do_sample=False, |
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temperature=None, |
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max_new_tokens=max_new_tokens, |
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eos_token_id=-1, |
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disable_compile=True, |
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) |
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if __name__ == "__main__": |
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results = [] |
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max_new_tokens = 256 |
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batch_size = 256 |
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base_prompts = [ |
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"What is Tensor Parallelism?", |
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"Explain machine learning fundamentals.", |
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"How do neural networks work?", |
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"What are the benefits of distributed computing?", |
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"Describe the attention mechanism in transformers.", |
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"What is gradient descent?", |
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"How does backpropagation work?", |
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"Explain the concept of overfitting.", |
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] |
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for in_mxfp4 in [True, False]: |
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model = load_model(in_mxfp4) |
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for batch_size in [32, 64, 128, 256]: |
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messages = [ |
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[{"role": "system", "content": base_prompts[i % len(base_prompts)]}] for i in range(batch_size) |
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] |
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tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b") |
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texts = [tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=False, reasoning_effort="low") for m in messages] |
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inputs = tokenizer( |
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texts, |
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return_tensors="pt", |
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padding=True, |
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padding_side="left", |
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).to("cuda:0") |
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label = "time taken to generate" |
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results.append( |
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benchmark.Timer( |
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stmt="generate(model, model_inputs, max_new_tokens)", |
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setup='from __main__ import generate', |
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globals={"model": model, "model_inputs": inputs, "max_new_tokens": max_new_tokens}, |
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num_threads=torch.get_num_threads(), |
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label=label, |
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sub_label=f"num tokens: {max_new_tokens} batch size: {batch_size}", |
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description=f"in mxfp4: {in_mxfp4}" |
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).timeit(5) |
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) |
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inputs.to("cpu") |
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del inputs |
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model.to("cpu") |
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del model |
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compare = benchmark.Compare(results) |
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compare.print() |
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