wehub-resource-sync
116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
"""
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This file specifies how MLC's Cohere 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.loader.standard_loader import make_standard_hf_loader
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from mlc_llm.quantization import Quantization, make_awq_quant
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from .cohere_model import CohereConfig, CohereForCausalLM
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awq_quant = make_awq_quant(CohereForCausalLM)
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def _cohere_name_transform(name: str) -> str:
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if "out_proj." in name:
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return name.replace("out_proj.", "o_proj.")
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return name
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huggingface = make_standard_hf_loader(
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model_cls=CohereForCausalLM,
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include_gate_up=False,
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name_transform=_cohere_name_transform,
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)
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# https://huggingface.co/alijawad07/aya-23-8B-AWQ-GEMM/tree/main
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def awq(model_config: CohereConfig, 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 AWQ parameters.
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Parameters
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----------
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model_config : CohereConfig
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The configuration of the Cohere 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 AWQ.
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"""
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model, _ = awq_quant(model_config, quantization)
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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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def _add(mlc_name, hf_name):
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mapping.add_mapping(
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mlc_name,
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[hf_name],
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functools.partial(
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lambda x, dtype: x.astype(dtype),
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dtype=named_parameters[mlc_name].dtype,
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),
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)
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for i in range(model_config.num_hidden_layers):
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# Add QKV in self attention
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attn = f"model.layers.{i}.self_attn"
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for quantize_suffix in ["qweight", "qzeros", "scales"]:
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mlc_name = f"{attn}.qkv_proj.{quantize_suffix}"
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assert mlc_name in named_parameters
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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.{quantize_suffix}",
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f"{attn}.k_proj.{quantize_suffix}",
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f"{attn}.v_proj.{quantize_suffix}",
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],
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functools.partial(
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lambda q, k, v, dtype: np.concatenate(
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[q, k, v],
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axis=1, # AWQ GEMM would transpose the weight
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).astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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_add(f"{attn}.out_proj.{quantize_suffix}", f"{attn}.o_proj.{quantize_suffix}")
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# Concat gate and up in MLP
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mlp = f"model.layers.{i}.mlp"
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for quantize_suffix in ["qweight", "qzeros", "scales"]:
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_add(f"{mlp}.up_proj.{quantize_suffix}", f"{mlp}.up_proj.{quantize_suffix}")
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_add(
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f"{mlp}.gate_proj.{quantize_suffix}",
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f"{mlp}.gate_proj.{quantize_suffix}",
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)
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_add(
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f"{mlp}.down_proj.{quantize_suffix}",
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f"{mlp}.down_proj.{quantize_suffix}",
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)
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# inv_freq is not used in the model
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# mapping.add_unused(f"{attn}.rotary_emb.inv_freq")
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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(lambda x, dtype: x.astype(dtype), dtype=mlc_param.dtype),
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)
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return mapping
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