103 lines
2.7 KiB
Python
103 lines
2.7 KiB
Python
import torch
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import json
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import argparse
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from astrai.config.param_config import ModelParameter
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from astrai.inference.generator import BatchGenerator, GenerationRequest
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from astrai.inference.core import disable_random_init
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def processor(
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model_dir: str,
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input_json_file: str,
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output_json_file: str,
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batch_size: int,
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temperature: float,
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top_k: int,
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top_p: float,
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question_key: str,
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response_key: str,
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):
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with disable_random_init():
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param = ModelParameter.load(model_dir)
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param.to(device="cuda", dtype=torch.bfloat16)
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generator = BatchGenerator(param)
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with open(input_json_file, "r", encoding="utf-8") as f:
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input_data = [json.loads(line) for line in f]
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queries = [item[question_key] for item in input_data]
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request = GenerationRequest(
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query=queries,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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max_len=param.config.max_len,
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history=None,
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system_prompt=None,
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)
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responses = generator.generate(request)
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with open(output_json_file, "w", encoding="utf-8") as f:
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for query, response in zip(queries, responses):
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output_item = {question_key: query, response_key: response}
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f.write(json.dumps(output_item, ensure_ascii=False) + "\n")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run generate with a Khaosz model.")
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parser.add_argument(
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"--model_dir", type=str, required=True, help="Path to the model directory."
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)
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parser.add_argument(
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"--input_json_file",
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type=str,
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required=True,
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help="Path to the input JSONL file.",
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)
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parser.add_argument(
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"--output_json_file",
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type=str,
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required=True,
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help="Path to the output JSONL file.",
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)
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parser.add_argument(
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"--question_key",
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type=str,
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default="question",
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help="Key for the question in the input JSON.",
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)
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parser.add_argument(
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"--response_key",
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type=str,
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default="response",
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help="Key for the response in the output JSON.",
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)
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parser.add_argument(
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"--temperature",
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type=float,
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default=0.60,
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help="Temperature for generating responses.",
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)
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parser.add_argument(
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"--top_k", type=int, default=30, help="Top-k value for generating responses."
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)
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parser.add_argument(
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"--top_p",
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type=float,
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default=0.95,
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help="Top-p value for generating responses.",
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)
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parser.add_argument(
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"--batch_size", type=int, default=1, help="Batch size for generating responses."
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)
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args = parser.parse_args()
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with torch.inference_mode():
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processor(**vars(args))
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