wehub-resource-sync
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
"""
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This file specifies how MLC's Gemma2 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.loader.standard_loader import make_standard_hf_loader
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from mlc_llm.quantization import Quantization
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from .gemma2_model import Gemma2Config, Gemma2ForCausalLM
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def huggingface(model_config: Gemma2Config, quantization: Quantization) -> ExternMapping:
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"""Create HF weight mapping for Gemma2."""
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model = Gemma2ForCausalLM(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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base_loader = make_standard_hf_loader(
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model_cls=Gemma2ForCausalLM,
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)
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mapping = base_loader(model_config, quantization)
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def add_one(name: str) -> None:
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mlc_param = named_parameters[name]
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mapping.add_mapping(
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name,
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[name],
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functools.partial(
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lambda x, dtype: (x + 1).astype(dtype),
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dtype=mlc_param.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_one(f"model.layers.{i}.input_layernorm.weight")
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add_one(f"model.layers.{i}.post_attention_layernorm.weight")
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add_one(f"model.layers.{i}.pre_feedforward_layernorm.weight")
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add_one(f"model.layers.{i}.post_feedforward_layernorm.weight")
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add_one("model.norm.weight")
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return mapping
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