111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import time
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import datasets
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation import GenerationConfig
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MODEL_ID = "Qwen/Qwen3-4B-Instruct-2507"
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DISPLAYED_SAMPLES = 3
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if __name__ == "__main__":
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# Parse args
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parser = argparse.ArgumentParser()
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parser.add_argument("--num-blocks", "-n", type=int, default=None)
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parser.add_argument("--max-batch-tokens", "-b", type=int, default=None)
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parser.add_argument(
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"--attn", type=str, default="paged_attention|kernels-community/flash-attn", help="Attention implementation"
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)
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parser.add_argument("--samples", type=int, default=500)
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args = parser.parse_args()
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# Prepare model
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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attn_implementation=args.attn,
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dtype=torch.bfloat16,
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)
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model = model.cuda().eval()
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# Prepare tokenizer and dataset
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, padding_side="left")
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dataset = datasets.load_dataset("openai/gsm8k", "socratic", split="test")
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dataset = dataset.select(range(args.samples))
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tokenized_datasets = dataset.map(lambda x: tokenizer(x["question"]), batched=True)
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simple_batch_inputs = [item["input_ids"] for item in tokenized_datasets]
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# Prepare generation config
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generation_config = GenerationConfig(
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max_new_tokens=512,
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use_cuda_graph=False, # Not supported for simple version
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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do_sample=False,
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num_blocks=args.num_blocks,
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max_batch_tokens=args.max_batch_tokens,
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)
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# Warmup iterations
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_ = model.generate_batch(
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inputs=simple_batch_inputs[: min(5, args.samples)],
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generation_config=generation_config,
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slice_inputs=True,
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)
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# Actual batch generation
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print("--- Running CB Generation Example ---")
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start_time = time.time()
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batch_outputs = model.generate_batch(
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inputs=simple_batch_inputs,
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generation_config=generation_config,
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slice_inputs=True,
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)
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end_time = time.time()
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print("Done with batch generation.")
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# Decode outputs
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token_count = 0
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for i, request in enumerate(batch_outputs):
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input_text = tokenizer.decode(batch_outputs[request].prompt_ids, skip_special_tokens=True)
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# Try to decode the output
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try:
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output_text = tokenizer.decode(batch_outputs[request].generated_tokens, skip_special_tokens=True)
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token_count += len(batch_outputs[request].generated_tokens[1:])
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except Exception as e:
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print(f"Decoding failed for request {request}: {e}")
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continue
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# Display sample if asked
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if i < DISPLAYED_SAMPLES:
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print("-" * 20)
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print(f"{request} Input: {input_text}")
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if len(output_text) > 0:
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print(f"{request} Output: {output_text}")
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else:
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print(f"[WARN] {request} Output was empty!")
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# Compute stats and maybe print them
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gen_time = end_time - start_time
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tok_per_sec = token_count / gen_time
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print("-" * 20)
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print("--- Finished CB Generation Example ---\n")
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print(f"CB generation took: {gen_time:.2f} seconds for {token_count} tokens. {tok_per_sec:.2f}tok/s")
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