wehub-resource-sync
95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
"""
|
|
This file specifies how MLC's InternLM2 parameter maps from other formats, for example HuggingFace
|
|
PyTorch, HuggingFace safetensors.
|
|
"""
|
|
|
|
import functools
|
|
|
|
import numpy as np
|
|
|
|
from mlc_llm.loader import ExternMapping
|
|
from mlc_llm.quantization import Quantization
|
|
|
|
from .internlm2_model import InternLM2ForCausalLM
|
|
|
|
|
|
def huggingface(model_config: InternLM2ForCausalLM, quantization: Quantization) -> ExternMapping:
|
|
"""Returns a parameter mapping that maps from the names of MLC LLM parameters to
|
|
the names of HuggingFace PyTorch parameters.
|
|
|
|
Parameters
|
|
----------
|
|
model_config : InternLM2Config
|
|
The configuration of the InternLM2 model.
|
|
|
|
quantization : Quantization
|
|
The quantization configuration.
|
|
|
|
Returns
|
|
-------
|
|
param_map : ExternMapping
|
|
The parameter mapping from MLC to HuggingFace PyTorch.
|
|
"""
|
|
model = InternLM2ForCausalLM(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)
|
|
|
|
mapping = ExternMapping()
|
|
|
|
def _convert_wqkv_layout(wqkv, dtype):
|
|
config = model_config
|
|
kv_groups = config.num_attention_heads // config.num_key_value_heads
|
|
head_dim = config.hidden_size // config.num_attention_heads
|
|
wqkv = wqkv.reshape(-1, 2 + kv_groups, head_dim, wqkv.shape[-1])
|
|
wq, wk, wv = np.split(wqkv, [kv_groups, kv_groups + 1], axis=1)
|
|
wq = wq.reshape(-1, wq.shape[-1])
|
|
wk = wk.reshape(-1, wk.shape[-1])
|
|
wv = wv.reshape(-1, wv.shape[-1])
|
|
return np.concatenate([wq, wk, wv], axis=0).astype(dtype)
|
|
|
|
for i in range(model_config.num_hidden_layers):
|
|
# Add gates in MLP
|
|
mlp = f"model.layers.{i}.feed_forward"
|
|
mlc_name = f"{mlp}.gate_up_proj.weight"
|
|
mlc_param = named_parameters[mlc_name]
|
|
mapping.add_mapping(
|
|
mlc_name,
|
|
[
|
|
f"{mlp}.w1.weight",
|
|
f"{mlp}.w3.weight",
|
|
],
|
|
functools.partial(
|
|
lambda w1, w3, dtype: np.concatenate([w1, w3], axis=0).astype(dtype),
|
|
dtype=mlc_param.dtype,
|
|
),
|
|
)
|
|
|
|
mlc_name = f"model.layers.{i}.attention.wqkv.weight"
|
|
mlc_param = named_parameters[mlc_name]
|
|
mapping.add_mapping(
|
|
mlc_name,
|
|
[mlc_name],
|
|
functools.partial(
|
|
_convert_wqkv_layout,
|
|
dtype=mlc_param.dtype,
|
|
),
|
|
)
|
|
|
|
for mlc_name, mlc_param in named_parameters.items():
|
|
if mlc_name not in mapping.param_map:
|
|
mapping.add_mapping(
|
|
mlc_name,
|
|
[mlc_name],
|
|
functools.partial(
|
|
lambda x, dtype: x.astype(dtype),
|
|
dtype=mlc_param.dtype,
|
|
),
|
|
)
|
|
|
|
return mapping
|