wehub-resource-sync
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
"""
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This file specifies how MLC's ChatGLM3 parameter maps from other formats, for example HuggingFace
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PyTorch, HuggingFace safetensors.
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"""
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import functools
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from mlc_llm.loader import ExternMapping
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from mlc_llm.quantization import Quantization
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from .chatglm3_model import ChatGLMForCausalLM, GLMConfig
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def huggingface(model_config: GLMConfig, quantization: Quantization) -> ExternMapping:
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"""Returns a parameter mapping that maps from the names of MLC LLM parameters to
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the names of HuggingFace PyTorch parameters.
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Parameters
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----------
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model_config : GLMConfig
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The configuration of the Baichuan model.
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quantization : Quantization
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The quantization configuration.
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Returns
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-------
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param_map : ExternMapping
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The parameter mapping from MLC to HuggingFace PyTorch.
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"""
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model = ChatGLMForCausalLM(model_config)
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if quantization is not None:
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model.to(quantization.model_dtype)
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_, _named_params, _ = model.export_tvm(
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spec=model.get_default_spec(),
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allow_extern=True,
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)
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named_parameters = dict(_named_params)
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mapping = ExternMapping()
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mlc_name = "transformer.embedding.weight"
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mlc_param = named_parameters[mlc_name]
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mapping.add_mapping(
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mlc_name,
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["transformer.embedding.word_embeddings.weight"],
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functools.partial(
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lambda x, dtype: x.astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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for mlc_name, mlc_param in named_parameters.items():
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if mlc_name not in mapping.param_map:
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mapping.add_mapping(
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mlc_name,
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[mlc_name],
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functools.partial(
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lambda x, dtype: x.astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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return mapping
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