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https://github.com/QwenLM/Qwen.git
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84 lines
2.5 KiB
Python
84 lines
2.5 KiB
Python
# Copyright (c) Alibaba Cloud.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import argparse
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.trainer_utils import set_seed
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def _load_model_tokenizer(args):
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tokenizer = AutoTokenizer.from_pretrained(
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args.checkpoint_path, trust_remote_code=True,
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)
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print("load tokenizer")
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if args.cpu_only:
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device_map = "cpu"
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max_memory = None
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else:
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device_map = "auto"
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max_memory_str = f"{int(torch.cuda.mem_get_info()[0] / 1024 ** 3) - 2}GB"
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n_gpus = torch.cuda.device_count()
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max_memory = {i: max_memory_str for i in range(n_gpus)}
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model = AutoModelForCausalLM.from_pretrained(
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args.checkpoint_path,
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device_map=device_map,
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max_memory=max_memory,
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trust_remote_code=True,
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).eval()
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return model, tokenizer
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def demo_qwen_pretrain(args):
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model, tokenizer = _load_model_tokenizer(args)
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inputs = tokenizer(
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"蒙古国的首都是乌兰巴托（Ulaanbaatar）\n冰岛的首都是雷克雅未克（Reykjavik）\n埃塞俄比亚的首都是",
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return_tensors="pt",
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)
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inputs = inputs.to(model.device)
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pred = model.generate(inputs=inputs["input_ids"])
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print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
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def demo_qwen_chat(args):
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model, tokenizer = _load_model_tokenizer(args)
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queries = [
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"请问把大象关冰箱总共要几步？",
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"1+3=?",
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"请将下面这句话翻译为英文：在哪里跌倒就在哪里趴着",
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]
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history = None
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for turn_idx, query in enumerate(queries, start=1):
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response, history = model.chat(
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tokenizer,
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query,
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history=history,
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)
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print(f"===== Turn {turn_idx} ====")
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print("Query:", query, end="\n")
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print("Response:", response, end="\n")
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def main():
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parser = argparse.ArgumentParser(description="Test HF checkpoint.")
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parser.add_argument("-c", "--checkpoint-path", type=str, help="Checkpoint path")
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parser.add_argument("-s", "--seed", type=int, default=1234, help="Random seed")
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parser.add_argument("--cpu-only", action="store_true", help="Run demo with CPU only")
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args = parser.parse_args()
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set_seed(args.seed)
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if "chat" in args.checkpoint_path.lower():
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demo_qwen_chat(args)
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else:
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demo_qwen_pretrain(args)
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if __name__ == "__main__":
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main()
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