130 lines
5 KiB
Python
130 lines
5 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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"HQQ (Half-Quadratic Quantization) integration file"
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from ..utils import is_hqq_available, is_torch_available, logging
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if is_torch_available():
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import torch
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logger = logging.get_logger(__name__)
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# Name all modules inside the model
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def autoname_modules(model):
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for name, module in model.named_modules():
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module.name = name
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# Get the linear_tag from a module name. For example: model.layers.31.self_attn.k_proj -> self_attn.k_proj
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def name_to_linear_tag(name):
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return ".".join([n for n in name.split(".") if ((n not in ["model", "layers"]) and (not n.isnumeric()))])
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# Get all linear tags available
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def get_linear_tags(model):
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if is_hqq_available():
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from hqq.core.quantize import HQQLinear
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linear_tags = set()
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for name, module in model.named_modules():
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if isinstance(module, (torch.nn.Linear, HQQLinear)):
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linear_tags.add(name_to_linear_tag(name))
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return list(linear_tags)
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def _prepare_for_hqq_linear(model, patch_params, has_been_replaced, current_key_name=None):
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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, torch.nn.Linear):
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# Get linear tag
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linear_tag = name_to_linear_tag(module.name)
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# We put the module quant_config into the nn.Linear layer so we can access it later in quantizer_hqq.create_quantized_param()
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if linear_tag in patch_params:
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if patch_params[linear_tag] is not None:
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model._modules[name].quant_config = patch_params[linear_tag]
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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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has_been_replaced = True
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# Add these fake parameters to avoid loading fail
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for att in ["W_q", "meta"]:
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setattr(module, att, None)
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if len(list(module.children())) > 0:
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_, has_been_replaced = _prepare_for_hqq_linear(
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module,
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patch_params=patch_params,
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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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def prepare_for_hqq_linear(model, quantization_config=None, modules_to_not_convert=None, has_been_replaced=False):
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"""
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Prepares nn.Linear layers for HQQ quantization.
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Since each layer type can have separate quantization parameters, we need to do the following:
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1- tag each module with its name via autoname_modules()
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2- Extract linear_tags (e.g. ['self_attn.q_proj', ...])
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3- Map quantization parameters as a dictionary linear_tag -> quant_params as HQQLinear expects it, this is referred to as patch_params
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"""
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modules_to_not_convert = [] if modules_to_not_convert is None else modules_to_not_convert
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# Add name to module
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autoname_modules(model)
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# Get linear tags. This allows us to use different quant params to different layer types
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linear_tags = get_linear_tags(model)
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# Convert quantization_config to layer-wise config
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skip_modules = quantization_config.skip_modules
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quant_config = quantization_config.quant_config
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linear_tags = list(set(linear_tags) - set(skip_modules) - set(modules_to_not_convert))
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if any(key in linear_tags for key in quant_config.keys()):
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# If the user doesn't specify a key from get_linear_tags, the layer is not quantized via (key, None)
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patch_params = dict.fromkeys(linear_tags)
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patch_params.update(quant_config)
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else:
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# Same quant_config for all layers
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patch_params = dict.fromkeys(linear_tags, quant_config)
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model, has_been_replaced = _prepare_for_hqq_linear(
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model, patch_params=patch_params, has_been_replaced=has_been_replaced
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)
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# We store quantization config as linear_tag -> hqq quant config
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model.config.quantization_config = {
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"quant_config": quant_config,
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"quant_method": quantization_config.quant_method,
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"skip_modules": skip_modules,
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}
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if not has_been_replaced:
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logger.warning("No linear modules were found in your model for quantization.")
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return model
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