101 lines
4.4 KiB
Python
101 lines
4.4 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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"VPTQ (Vector Post-Training Quantization) integration file"
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import torch.nn as nn
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from accelerate import init_empty_weights
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from vptq import VQuantLinear
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def replace_with_vptq_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 VPTQ 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 (`VptqConfig`):
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The quantization config object that contains the quantization parameters.
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modules_to_not_convert (`list[`str`]`, *optional*, defaults to `["lm_head"]`):
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Names of the modules to not convert in `VQuantLinear`. In practice we keep the `lm_head` in full precision
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for numerical stability reasons.
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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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modules_to_not_convert = ["lm_head"] if not modules_to_not_convert else 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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layer_name = ".".join(current_key_name)
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shared_layer_config = quantization_config.shared_layer_config
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config_for_layers = quantization_config.config_for_layers
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if (
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isinstance(module, nn.Linear)
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and layer_name not in modules_to_not_convert
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and ((layer_name in config_for_layers) or (current_key_name[-1] in shared_layer_config))
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):
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layer_params = config_for_layers.get(layer_name, None) or shared_layer_config.get(
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current_key_name[-1], None
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)
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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] = VQuantLinear(
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in_features,
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out_features,
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vector_lens=layer_params["vector_lens"],
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num_centroids=layer_params["num_centroids"],
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num_res_centroids=layer_params["num_res_centroids"],
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group_num=layer_params["group_num"],
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group_size=layer_params["group_size"],
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outlier_size=layer_params["outlier_size"],
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indices_as_float=layer_params["indices_as_float"],
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enable_norm=layer_params["enable_norm"],
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enable_perm=layer_params["enable_perm"],
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is_indice_packed=True,
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enable_proxy_error=False,
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bias=module.bias is not None,
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)
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has_been_replaced = True
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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_vptq_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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