95 lines
4.2 KiB
Python
95 lines
4.2 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ..utils import is_torch_available
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if is_torch_available():
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import torch
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def replace_with_quanto_layers(
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model,
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quantization_config=None,
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modules_to_not_convert=None,
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current_key_name=None,
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has_been_replaced=False,
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):
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"""
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Public method that recursively replaces the Linear layers of the given model with Quanto quantized layers.
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Returns the converted model and a boolean that indicates if the conversion has been successfull or not.
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Args:
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model (`torch.nn.Module`):
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The model to convert, can be any `torch.nn.Module` instance.
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quantization_config (`AqlmConfig`, defaults to `None`):
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The quantization config object that contains the quantization parameters.
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modules_to_not_convert (`list`, *optional*, defaults to `None`):
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A list of modules to not convert. If a module name is in the list (e.g. `lm_head`), it will not be
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converted.
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current_key_name (`list`, *optional*, defaults to `None`):
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A list that contains the current key name. This is used for recursion and should not be passed by the user.
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has_been_replaced (`bool`, *optional*, defaults to `None`):
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A boolean that indicates if the conversion has been successful or not. This is used for recursion and
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should not be passed by the user.
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"""
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from accelerate import init_empty_weights
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from quanto import QLayerNorm, QLinear, qfloat8, qint2, qint4, qint8
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w_mapping = {"float8": qfloat8, "int8": qint8, "int4": qint4, "int2": qint2}
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a_mapping = {None: None, "float8": qfloat8, "int8": qint8}
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if modules_to_not_convert is None:
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modules_to_not_convert = []
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for name, module in model.named_children():
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if current_key_name is None:
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current_key_name = []
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current_key_name.append(name)
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if not any(key in ".".join(current_key_name) for key in modules_to_not_convert):
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with init_empty_weights():
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if isinstance(module, torch.nn.Linear):
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model._modules[name] = QLinear(
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in_features=module.in_features,
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out_features=module.out_features,
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bias=module.bias is not None,
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dtype=module.weight.dtype,
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weights=w_mapping[quantization_config.weights],
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activations=a_mapping[quantization_config.activations],
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)
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model._modules[name].requires_grad_(False)
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has_been_replaced = True
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elif isinstance(module, torch.nn.LayerNorm):
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if quantization_config.activations is not None:
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model._modules[name] = QLayerNorm(
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module.normalized_shape,
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module.eps,
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module.elementwise_affine,
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module.bias is not None,
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activations=a_mapping[quantization_config.activations],
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)
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has_been_replaced = True
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if len(list(module.children())) > 0:
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_, has_been_replaced = replace_with_quanto_layers(
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module,
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quantization_config=quantization_config,
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modules_to_not_convert=modules_to_not_convert,
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current_key_name=current_key_name,
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has_been_replaced=has_been_replaced,
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)
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# Remove the last key for recursion
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current_key_name.pop(-1)
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return model, has_been_replaced
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