You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
75 lines
3.2 KiB
75 lines
3.2 KiB
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"SpQR (Sparse-Quantized Representation) integration file"
|
|
|
|
from ..quantizers.quantizers_utils import should_convert_module
|
|
from ..utils import is_spqr_available, is_torch_available, logging
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
def replace_with_spqr_linear(model, modules_to_not_convert: list[str] | None = None, quantization_config=None):
|
|
"""
|
|
Public method that replaces the Linear layers of the given model with SPQR quantized layers.
|
|
|
|
Args:
|
|
model (`torch.nn.Module`):
|
|
The model to convert, can be any `torch.nn.Module` instance.
|
|
modules_to_not_convert (`list[str]`, *optional*, defaults to `None`):
|
|
A list of nn.Linear weights to not convert. If a parameter path is in the list (e.g. `lm_head.weight`), the corresponding module will not be
|
|
converted.
|
|
quantization_config (`SpQRConfig`):
|
|
The quantization config object that contains the quantization parameters.
|
|
"""
|
|
if is_spqr_available():
|
|
from spqr_quant import QuantizedLinear
|
|
|
|
has_been_replaced = False
|
|
# we need this to correctly materialize the weights during quantization
|
|
for module_name, module in model.named_modules():
|
|
if not should_convert_module(module_name, modules_to_not_convert):
|
|
continue
|
|
with torch.device("meta"):
|
|
if isinstance(module, nn.Linear):
|
|
shapes = quantization_config.shapes
|
|
|
|
new_module = QuantizedLinear.create_placehodler(
|
|
rows=module.out_features,
|
|
cols=module.in_features,
|
|
bits=quantization_config.bits,
|
|
beta1=quantization_config.beta1,
|
|
beta2=quantization_config.beta2,
|
|
dense_weights_shape=shapes[f"{module_name}.dense_weights.shape"],
|
|
row_offsets_shape=shapes[f"{module_name}.row_offsets.shape"],
|
|
col_vals_shape=shapes[f"{module_name}.col_vals.shape"],
|
|
in_perm_shape=shapes[f"{module_name}.in_perm.shape"],
|
|
)
|
|
# Force requires grad to False to avoid unexpected errors
|
|
model._modules[module_name].requires_grad_(False)
|
|
model.set_submodule(module_name, new_module)
|
|
has_been_replaced = True
|
|
if not has_been_replaced:
|
|
logger.warning(
|
|
"You are loading your model using eetq but no linear modules were found in your model."
|
|
" Please double check your model architecture, or submit an issue on github if you think this is"
|
|
" a bug."
|
|
)
|
|
|
|
return model
|