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added support for quantize on LLM module (#1080)
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@ -38,6 +38,9 @@ class LLM:
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However, if the `torch_dtype` in the config is `float32`, we will
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However, if the `torch_dtype` in the config is `float32`, we will
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use `float16` instead.
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use `float16` instead.
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seed: The seed to initialize the random number generator for sampling.
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seed: The seed to initialize the random number generator for sampling.
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quantization: The method used to quantize the model weights. Currently,
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we support "awq". If None, we assume the model weights are not
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quantized and use `dtype` to determine the data type of the weights.
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revision: The specific model version to use. It can be a branch name,
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revision: The specific model version to use. It can be a branch name,
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a tag name, or a commit id.
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a tag name, or a commit id.
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"""
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"""
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@ -51,6 +54,7 @@ class LLM:
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tensor_parallel_size: int = 1,
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tensor_parallel_size: int = 1,
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dtype: str = "auto",
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dtype: str = "auto",
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seed: int = 0,
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seed: int = 0,
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quantization: Optional[str] = None,
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**kwargs,
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**kwargs,
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) -> None:
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) -> None:
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if "disable_log_stats" not in kwargs:
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if "disable_log_stats" not in kwargs:
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@ -63,6 +67,7 @@ class LLM:
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tensor_parallel_size=tensor_parallel_size,
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tensor_parallel_size=tensor_parallel_size,
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dtype=dtype,
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dtype=dtype,
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seed=seed,
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seed=seed,
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quantization=quantization,
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**kwargs,
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**kwargs,
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)
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)
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self.llm_engine = LLMEngine.from_engine_args(engine_args)
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self.llm_engine = LLMEngine.from_engine_args(engine_args)
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