wehub-resource-sync
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
"""
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This file specifies how MLC's Gemma3 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 .gemma3_model import Gemma3Config, Gemma3ForCausalLM
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def huggingface(model_config: Gemma3Config, quantization: Quantization) -> ExternMapping:
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"""Create HF weight mapping for Gemma3."""
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model = Gemma3ForCausalLM(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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mlc_prefix = "language_model."
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if model_config.is_text_model:
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hf_prefix = ""
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else:
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hf_prefix = "language_model."
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def name_transform(name: str) -> str:
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if name.startswith(mlc_prefix):
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name = name[len(mlc_prefix) :]
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return f"{hf_prefix}{name}"
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def num_layers(config: object) -> int:
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return config.text_config.num_hidden_layers
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base_loader = make_standard_hf_loader(
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model_cls=Gemma3ForCausalLM,
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include_qkv=False,
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include_gate_up=True,
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gate_up_target_name="gate_up_proj",
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num_layers_getter=num_layers,
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layer_prefix=f"{mlc_prefix}model.layers",
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name_transform=name_transform,
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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[mlc_prefix + name]
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mapping.add_mapping(
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mlc_prefix + name,
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[name_transform(mlc_prefix + 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.text_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(f"model.layers.{i}.self_attn.k_norm.weight")
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add_one(f"model.layers.{i}.self_attn.q_norm.weight")
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add_one("model.norm.weight")
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return mapping
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