122 lines
5.4 KiB
Python
122 lines
5.4 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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"SpQR (Sparse-Quantized Representation) integration file"
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from ..utils import is_accelerate_available, is_spqr_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_spqr_linear(
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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 SpQR 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 successful 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 (`SpQRConfig`):
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The quantization config object that contains the quantization parameters.
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modules_to_not_convert (`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 modules_to_not_convert is None:
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modules_to_not_convert = []
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if is_accelerate_available():
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from accelerate import init_empty_weights
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if is_spqr_available():
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from spqr_quant 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 `modules_to_not_convert`
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if ".".join(current_key_name) + ".weight" not in modules_to_not_convert:
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with init_empty_weights():
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tensor_name = ".".join(current_key_name)
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shapes = quantization_config.shapes
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shapes_keys = shapes.keys()
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shapes_valid = (
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f"{tensor_name}.dense_weights.shape" in shapes_keys
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and f"{tensor_name}.row_offsets.shape" in shapes_keys
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and f"{tensor_name}.col_vals.shape" in shapes_keys
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and f"{tensor_name}.in_perm.shape" in shapes_keys
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)
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if not shapes_valid:
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raise ValueError(
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f"The SpQR quantization config does not contain the shape "
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f"configuration for {tensor_name}. This indicates that the "
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f"configuration is either invalid or corrupted."
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)
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dense_weights_shape = shapes[f"{tensor_name}.dense_weights.shape"]
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row_offsets_shape = shapes[f"{tensor_name}.row_offsets.shape"]
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col_vals_shape = shapes[f"{tensor_name}.col_vals.shape"]
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in_perm_shape = shapes[f"{tensor_name}.in_perm.shape"]
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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.create_placehodler(
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rows=out_features,
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cols=in_features,
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bits=quantization_config.bits,
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beta1=quantization_config.beta1,
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beta2=quantization_config.beta2,
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dense_weights_shape=dense_weights_shape,
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row_offsets_shape=row_offsets_shape,
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col_vals_shape=col_vals_shape,
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in_perm_shape=in_perm_shape,
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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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else:
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pass
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if len(list(module.children())) > 0:
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_, has_been_replaced = replace_with_spqr_linear(
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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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