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83 lines
3.5 KiB
83 lines
3.5 KiB
# 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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from ..quantizers.quantizers_utils import should_convert_module
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from ..utils import is_torch_available, logging
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if is_torch_available():
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import torch
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import torch.nn as nn
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logger = logging.get_logger(__name__)
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def replace_with_vptq_linear(model, modules_to_not_convert: list[str] | None = None, quantization_config=None):
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"""
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Public method that replaces the Linear layers of the given model with SPQR quantized layers.
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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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modules_to_not_convert (`list[str]`, *optional*, defaults to `None`):
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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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quantization_config (`VptqConfig`):
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The quantization config object that contains the quantization parameters.
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"""
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from vptq import VQuantLinear
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has_been_replaced = False
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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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for module_name, module in model.named_modules():
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if not should_convert_module(module_name, modules_to_not_convert):
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continue
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with torch.device("meta"):
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if isinstance(module, nn.Linear):
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layer_params = config_for_layers.get(module_name, None) or shared_layer_config.get(
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module_name.rsplit(".")[1], None
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)
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new_module = VQuantLinear(
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module.in_features,
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module.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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# Force requires grad to False to avoid unexpected errors
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model._modules[module_name].requires_grad_(False)
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model.set_submodule(module_name, new_module)
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has_been_replaced = True
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if not has_been_replaced:
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logger.warning(
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"You are loading your model using eetq but no linear modules were found in your model."
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" Please double check your model architecture, or submit an issue on github if you think this is"
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" a bug."
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)
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return model
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