wehub-resource-sync
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
"""
|
|
This file specifies how MLC's Gemma parameter maps from other formats, for example HuggingFace
|
|
PyTorch, HuggingFace safetensors.
|
|
"""
|
|
|
|
import functools
|
|
|
|
from mlc_llm.loader import ExternMapping
|
|
from mlc_llm.loader.standard_loader import make_standard_hf_loader
|
|
from mlc_llm.quantization import Quantization
|
|
|
|
from .gemma_model import GemmaConfig, GemmaForCausalLM
|
|
|
|
|
|
def huggingface(model_config: GemmaConfig, quantization: Quantization) -> ExternMapping:
|
|
"""Create HF weight mapping for Gemma."""
|
|
model = GemmaForCausalLM(model_config)
|
|
if quantization is not None:
|
|
model.to(quantization.model_dtype)
|
|
_, _named_params, _ = model.export_tvm(
|
|
spec=model.get_default_spec(),
|
|
allow_extern=True,
|
|
)
|
|
named_parameters = dict(_named_params)
|
|
base_loader = make_standard_hf_loader(
|
|
model_cls=GemmaForCausalLM,
|
|
)
|
|
mapping = base_loader(model_config, quantization)
|
|
|
|
def add_one(name: str) -> None:
|
|
mlc_param = named_parameters[name]
|
|
mapping.add_mapping(
|
|
name,
|
|
[name],
|
|
functools.partial(
|
|
lambda x, dtype: (x + 1).astype(dtype),
|
|
dtype=mlc_param.dtype,
|
|
),
|
|
)
|
|
|
|
for i in range(model_config.num_hidden_layers):
|
|
add_one(f"model.layers.{i}.input_layernorm.weight")
|
|
add_one(f"model.layers.{i}.post_attention_layernorm.weight")
|
|
|
|
add_one("model.norm.weight")
|
|
return mapping
|