100 lines
4.4 KiB
Python
100 lines
4.4 KiB
Python
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# 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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"AQLM (Additive Quantization of Language Model) integration file"
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from ..utils import is_accelerate_available, is_aqlm_available, is_torch_available
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if is_torch_available():
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import torch.nn as nn
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def replace_with_aqlm_linear(
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model,
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quantization_config=None,
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linear_weights_not_to_quantize=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 AQLM quantized layers.
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`accelerate` is needed to use this method. Returns the converted model and a boolean that indicates if the
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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`):
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The quantization config object that contains the quantization parameters.
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linear_weights_not_to_quantize (`list[str]`, *optional*):
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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
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converted.
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current_key_name (`list`, *optional*):
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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*):
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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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if not is_aqlm_available():
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raise ValueError("AQLM is not available. Please install it with `pip install aqlm[cpu,gpu]`")
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if not is_accelerate_available():
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raise ValueError("AQLM requires Accelerate to be installed: `pip install accelerate`")
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if linear_weights_not_to_quantize is None:
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linear_weights_not_to_quantize = []
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from accelerate import init_empty_weights
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from aqlm import QuantizedLinear
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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 isinstance(module, nn.Linear):
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# Check if the current key is not in the `linear_weights_not_to_quantize`
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if ".".join(current_key_name) + ".weight" not in linear_weights_not_to_quantize:
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with init_empty_weights():
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in_features = module.in_features
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out_features = module.out_features
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model._modules[name] = QuantizedLinear(
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in_features,
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out_features,
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bias=module.bias is not None,
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in_group_size=quantization_config.in_group_size,
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out_group_size=quantization_config.out_group_size,
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num_codebooks=quantization_config.num_codebooks,
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nbits_per_codebook=quantization_config.nbits_per_codebook,
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)
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has_been_replaced = True
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# Store the module class in case we need to transpose the weight later
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model._modules[name].source_cls = type(module)
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# Force requires grad to False to avoid unexpected errors
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model._modules[name].requires_grad_(False)
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if len(list(module.children())) > 0:
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_, has_been_replaced = replace_with_aqlm_linear(
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module,
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quantization_config=quantization_config,
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linear_weights_not_to_quantize=linear_weights_not_to_quantize,
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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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