wehub-resource-sync
133 lines
4.8 KiB
Python
133 lines
4.8 KiB
Python
"""
|
|
This file specifies how MLC's Phi parameter maps from other formats, for example HuggingFace
|
|
PyTorch, HuggingFace safetensors.
|
|
"""
|
|
|
|
import functools
|
|
|
|
from mlc_llm.loader import ExternMapping
|
|
from mlc_llm.quantization import Quantization
|
|
|
|
from .phi3v_model import Phi3VConfig, Phi3VForCausalLM
|
|
|
|
|
|
def huggingface(model_config: Phi3VConfig, quantization: Quantization) -> ExternMapping:
|
|
"""Returns a parameter mapping that maps from the names of MLC LLM parameters to
|
|
the names of Phi-1/Phi-1.5 HuggingFace PyTorch parameters.
|
|
|
|
Parameters
|
|
----------
|
|
model_config : PhiConfig
|
|
The configuration of the Phi model.
|
|
|
|
quantization : Quantization
|
|
The quantization configuration.
|
|
|
|
Returns
|
|
-------
|
|
param_map : ExternMapping
|
|
The parameter mapping from MLC to HuggingFace PyTorch.
|
|
"""
|
|
model = Phi3VForCausalLM(model_config)
|
|
if quantization is not None:
|
|
model.to(quantization.model_dtype)
|
|
_, _named_params = model.export_tvm(spec=model.get_default_spec())
|
|
named_parameters = dict(_named_params)
|
|
|
|
mapping = ExternMapping()
|
|
|
|
def _add(mlc_name, hf_name=None):
|
|
if None is hf_name:
|
|
hf_name = mlc_name
|
|
|
|
mapping.add_mapping(
|
|
mlc_name,
|
|
[hf_name],
|
|
functools.partial(
|
|
lambda x, dtype: x.astype(dtype),
|
|
dtype=named_parameters[mlc_name].dtype,
|
|
),
|
|
)
|
|
|
|
def _add_vision(name):
|
|
_add(name, "model." + name)
|
|
|
|
_add("model.embd.weight", "model.embed_tokens.weight")
|
|
|
|
prefix = "model.h"
|
|
hf_prefix = "model.layers"
|
|
for i in range(model_config.num_hidden_layers):
|
|
_add(f"{prefix}.{i}.ln.weight", f"{hf_prefix}.{i}.input_layernorm.weight")
|
|
_add(
|
|
f"{prefix}.{i}.mlp.down_proj.weight",
|
|
f"{hf_prefix}.{i}.mlp.down_proj.weight",
|
|
)
|
|
_add(
|
|
f"{prefix}.{i}.mlp.gate_up_proj.weight",
|
|
f"{hf_prefix}.{i}.mlp.gate_up_proj.weight",
|
|
)
|
|
_add(
|
|
f"{prefix}.{i}.post_attention_layernorm.weight",
|
|
f"{hf_prefix}.{i}.post_attention_layernorm.weight",
|
|
)
|
|
_add(
|
|
f"{prefix}.{i}.mixer.out_proj.weight",
|
|
f"{hf_prefix}.{i}.self_attn.o_proj.weight",
|
|
)
|
|
_add(
|
|
f"{prefix}.{i}.mixer.qkv_proj.weight",
|
|
f"{hf_prefix}.{i}.self_attn.qkv_proj.weight",
|
|
)
|
|
|
|
prefix = "vision_embed_tokens.img_processor.vision_model.encoder.layers"
|
|
for i in range(model_config.vision_config.num_hidden_layers):
|
|
_add_vision(f"{prefix}.{i}.layer_norm1.bias")
|
|
_add_vision(f"{prefix}.{i}.layer_norm1.weight")
|
|
_add_vision(f"{prefix}.{i}.layer_norm2.bias")
|
|
_add_vision(f"{prefix}.{i}.layer_norm2.weight")
|
|
_add_vision(f"{prefix}.{i}.mlp.fc1.bias")
|
|
_add_vision(f"{prefix}.{i}.mlp.fc1.weight")
|
|
_add_vision(f"{prefix}.{i}.mlp.fc2.bias")
|
|
_add_vision(f"{prefix}.{i}.mlp.fc2.weight")
|
|
_add_vision(f"{prefix}.{i}.self_attn.k_proj.bias")
|
|
_add_vision(f"{prefix}.{i}.self_attn.k_proj.weight")
|
|
_add_vision(f"{prefix}.{i}.self_attn.out_proj.bias")
|
|
_add_vision(f"{prefix}.{i}.self_attn.out_proj.weight")
|
|
_add_vision(f"{prefix}.{i}.self_attn.q_proj.bias")
|
|
_add_vision(f"{prefix}.{i}.self_attn.q_proj.weight")
|
|
_add_vision(f"{prefix}.{i}.self_attn.v_proj.bias")
|
|
_add_vision(f"{prefix}.{i}.self_attn.v_proj.weight")
|
|
|
|
_add_vision("vision_embed_tokens.sub_GN")
|
|
_add_vision("vision_embed_tokens.glb_GN")
|
|
_add_vision("vision_embed_tokens.img_processor.vision_model.embeddings.class_embedding")
|
|
_add_vision("vision_embed_tokens.img_processor.vision_model.embeddings.patch_embedding.weight")
|
|
_add_vision(
|
|
"vision_embed_tokens.img_processor.vision_model.embeddings.position_embedding.weight"
|
|
)
|
|
_add_vision("vision_embed_tokens.img_processor.vision_model.post_layernorm.bias")
|
|
_add_vision("vision_embed_tokens.img_processor.vision_model.post_layernorm.weight")
|
|
_add_vision("vision_embed_tokens.img_processor.vision_model.pre_layrnorm.bias")
|
|
_add_vision("vision_embed_tokens.img_processor.vision_model.pre_layrnorm.weight")
|
|
|
|
prefix = "vision_embed_tokens.img_projection"
|
|
_add(f"{prefix}.linear_1.bias", f"model.{prefix}.0.bias")
|
|
_add(f"{prefix}.linear_1.weight", f"model.{prefix}.0.weight")
|
|
_add(f"{prefix}.linear_2.bias", f"model.{prefix}.2.bias")
|
|
_add(f"{prefix}.linear_2.weight", f"model.{prefix}.2.weight")
|
|
|
|
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,
|
|
),
|
|
)
|
|
|
|
mapping.add_unused("model.embed_tokens.weight")
|
|
|
|
return mapping
|