wehub-resource-sync
151 lines
5.1 KiB
Python
151 lines
5.1 KiB
Python
"""
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This file specifies how MLC's MiniCPM 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 .minicpm_model import MiniCPMConfig, MiniCPMForCausalLM
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def huggingface(model_config: MiniCPMConfig, 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 : MiniCPMConfig
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The configuration of the MiniCPM 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 = MiniCPMForCausalLM(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.num_hidden_layers):
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# map attention weight
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attn = f"model.layers.{i}.self_attn"
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for weight_type in ["weight"]:
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mlc_name = f"{attn}.wqkv_pack.{weight_type}"
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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"{attn}.q_proj.{weight_type}",
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f"{attn}.k_proj.{weight_type}",
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f"{attn}.v_proj.{weight_type}",
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],
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functools.partial(
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lambda q, k, v, dtype: np.concatenate([q, k, v], axis=0).astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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if model_config.num_experts == 0:
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for i in range(model_config.num_hidden_layers):
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# map mlp weight
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mlp = f"model.layers.{i}.mlp"
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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"{mlp}.gate_proj.weight",
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f"{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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else:
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for i in range(model_config.num_hidden_layers):
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# map mlp weight
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mlp = f"model.layers.{i}.mlp"
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mlc_mlp = f"model.layers.{i}.mlp"
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mlc_name = f"{mlc_mlp}.e1_e3.weight"
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mlc_param = named_parameters[mlc_name]
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def combine_expert_gate_up(*hf_params, dtype):
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stack = []
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for i in range(0, len(hf_params), 2):
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stack.append(np.concatenate([hf_params[i], hf_params[i + 1]], axis=0))
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return np.stack(stack, axis=0).astype(dtype)
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mapping.add_mapping(
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mlc_name,
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functools.reduce(
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lambda a, b: a + b,
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[
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[
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f"{mlp}.experts.{expert_id}.w1.weight",
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f"{mlp}.experts.{expert_id}.w3.weight",
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]
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for expert_id in range(model_config.num_experts)
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],
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),
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functools.partial(
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combine_expert_gate_up,
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dtype=mlc_param.dtype,
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),
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)
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mlc_name = f"{mlc_mlp}.e2.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"{mlp}.experts.{expert_id}.w2.weight"
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for expert_id in range(model_config.num_experts)
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],
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functools.partial(
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lambda *hf_params, dtype: np.stack(hf_params, axis=0).astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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mlc_name = f"{mlc_mlp}.gate.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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[f"{mlp}.gate.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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# Skip lm_head.weight if tie_word_embeddings is enabled
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if mlc_name == "lm_head.weight" and model_config.tie_word_embeddings:
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continue
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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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