mlc-ai--mlc-llm
wehub-resource-sync
120 行
3.5 KiB
Python
120 行
3.5 KiB
Python
"""
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This file specifies how MLC's Llama 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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import numpy as np
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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 .llama4_model import Llama4Config, Llama4ForCausalLM
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def huggingface(model_config: Llama4Config, 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 : Llama4Config
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The configuration of the Llama 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 = Llama4ForCausalLM(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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for i in range(model_config.text_config.num_hidden_layers):
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# Add shared expert weights
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mlp = f"model.layers.{i}.feed_forward.shared_expert"
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mlc_name = f"{mlp}.gate_up_proj.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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[
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f"language_model.{mlp}.gate_proj.weight",
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f"language_model.{mlp}.up_proj.weight",
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],
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functools.partial(
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lambda gate, up, dtype: np.concatenate([gate, up], axis=0).astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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# Add router weights
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mlp = f"model.layers.{i}.feed_forward"
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mlc_name = f"{mlp}.router.router.weight"
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hf_name = f"language_model.{mlp}.router.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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[
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hf_name,
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],
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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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# Add experts weights
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mlp = f"model.layers.{i}.feed_forward"
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hf_name = f"language_model.{mlp}.experts.gate_up_proj"
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mlc_name = f"{mlp}.experts.gate_up_proj"
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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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[
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hf_name,
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],
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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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mlp = f"model.layers.{i}.feed_forward"
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mlc_name = f"{mlp}.experts.down_proj"
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hf_name = f"language_model.{mlp}.experts.down_proj"
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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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[
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hf_name,
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],
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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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[f"language_model.{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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