wehub-resource-sync
226 lines
9.0 KiB
Python
226 lines
9.0 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020-present the HuggingFace Inc. team.
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# Copyright 2020 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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import types
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from dataclasses import dataclass, field
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from typing import List, Optional
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import paddle
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from ..utils.log import logger
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from .training_args import TrainingArguments
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__all__ = [
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"CompressionArguments",
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]
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@dataclass
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class CompressionArguments(TrainingArguments):
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"""
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CompressionArguments is the subset of the arguments we use in our example
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scripts **which relate to the training loop itself**.
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Using [`PdArgumentParser`] we can turn this class into
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[argparse](https://docs.python.org/3/library/argparse#module-argparse)
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arguments that can be specified on the command line.
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"""
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do_compress: bool = field(default=False, metadata={"help": "Whether to run compression after training."})
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input_dtype: Optional[str] = field(
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default="int64",
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metadata={"help": "The data type of input tensor, it could be int32 or int64. Defaults to int64."},
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)
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# prune embeddings
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prune_embeddings: bool = field(default=False, metadata={"help": "Whether to prune embeddings before finetuning."})
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onnx_format: Optional[bool] = field(
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default=True,
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metadata={"help": "Whether to export onnx format quantized model, and it defaults to True."},
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)
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strategy: Optional[str] = field(
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default="dynabert+ptq",
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metadata={
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"help": "Compression strategy. It supports 'dynabert+qat+embeddings',"
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"'dynabert+qat', 'dynabert+ptq', 'dynabert+embeddings', 'dynabert', 'ptq' and 'qat' now."
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},
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)
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# dynabert
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width_mult_list: Optional[List[str]] = field(
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default=None,
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metadata={"help": ("List of width multiplicator for pruning using DynaBERT strategy.")},
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)
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logging_steps: int = field(default=100, metadata={"help": "Log every X updates steps."})
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save_steps: int = field(default=100, metadata={"help": "Save checkpoint every X updates steps."})
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warmup_ratio: float = field(
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default=0.1, metadata={"help": "Linear warmup over warmup_ratio fraction of total steps."}
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)
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# quant
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weight_quantize_type: Optional[str] = field(
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default="channel_wise_abs_max",
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metadata={
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"help": "Quantization type for weights. Supports 'abs_max' and 'channel_wise_abs_max'. "
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"This param only specifies the fake ops in saving quantized model, and "
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"we save the scale obtained by post training quantization in fake ops. "
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"Compared to 'abs_max' the model accuracy is usually higher when it is "
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"'channel_wise_abs_max'."
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},
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)
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activation_quantize_type: Optional[str] = field(
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default=None,
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metadata={
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"help": "Support 'abs_max', 'range_abs_max' and 'moving_average_abs_max'. "
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"In strategy 'ptq', it defaults to 'range_abs_max' and in strategy "
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"'qat', it defaults to 'moving_average_abs_max'."
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},
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)
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# ptq:
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algo_list: Optional[List[str]] = field(
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default=None,
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metadata={
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"help": "Algorithm list for Post-Quantization, and it supports 'hist', 'KL', "
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"'mse', 'avg', 'abs_max' and 'emd'.'KL' uses KL-divergenc method to get "
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"the KL threshold for quantized activations and get the abs_max value "
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"forquantized weights. 'abs_max' gets the abs max value for activations "
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"and weights. 'min_max' gets the min and max value for quantized "
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"activations and weights. 'avg' gets the average value among the max "
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"values for activations. 'hist' gets the value of 'hist_percent' "
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"quantile as the threshold. 'mse' gets the value which makes the "
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"quantization mse loss minimal."
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},
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)
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batch_num_list: Optional[List[int]] = field(
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default=None,
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metadata={
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"help": "List of batch_num. 'batch_num' is the number of batchs for sampling. "
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"the number of calibrate data is batch_size * batch_nums. "
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"If batch_nums is None, use all data provided by data loader as calibrate data."
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},
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)
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batch_size_list: Optional[List[int]] = field(
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default=None,
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metadata={"help": "List of batch_size. 'batch_size' is the batch of data loader."},
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)
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round_type: Optional[str] = field(
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default="round",
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metadata={
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"help": "The method of converting the quantized weights value float->int. "
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"Currently supports ['round', 'adaround'] methods. Default is `round`, "
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"which is rounding nearest to the integer. 'adaround' is refer to "
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"https://arxiv.org/abs/2004.10568."
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},
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)
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bias_correction: Optional[bool] = field(
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default=False,
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metadata={
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"help": "If set to True, use the bias correction method of "
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"https://arxiv.org/abs/1810.05723. Default is False."
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},
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)
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input_infer_model_path: Optional[str] = field(
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default=None,
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metadata={
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"help": "If you have only inference model, quantization is also supported."
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" The format is `dirname/file_prefix` or `file_prefix`. Default "
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"is None."
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},
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)
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# qat
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use_pact: Optional[bool] = field(
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default=True,
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metadata={
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"help": "Whether to use PACT(Parameterized Clipping Activation for Quantized Neural Networks) "
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"method in quantization aware training."
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},
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)
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moving_rate: Optional[float] = field(
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default=0.9,
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metadata={"help": "The decay coefficient of moving average. Defaults to 0.9."},
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)
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def print_config(self, args=None, key=""):
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"""
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Prints all config values.
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"""
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compression_arg_name = [
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"strategy",
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"width_mult_list",
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"batch_num_list",
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"bias_correction",
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"round_type",
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"algo_list",
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"batch_size_list",
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"weight_quantize_type",
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"activation_quantize_type",
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"input_infer_model_path",
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"activation_preprocess_type",
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"weight_preprocess_type",
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"moving_rate",
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"use_pact",
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"onnx_format",
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"prune_embeddings",
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"input_dtype",
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]
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default_arg_dict = {
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"width_mult_list": ["3/4"],
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"batch_size_list": [4, 8, 16],
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"algo_list": ["mse", "KL"],
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"batch_num_list": [1],
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}
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logger.info("=" * 60)
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if args is None:
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args = self
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key = "Compression"
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logger.info("{:^40}".format("{} Configuration Arguments".format(key)))
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if key == "Compression":
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logger.info(
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"Compression Suggestions: `Strategy` supports 'dynabert+qat+embeddings', "
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"'dynabert+qat', 'dynabert+ptq', 'dynabert+embeddings', "
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"'dynabert' and 'ptq'. `input_dtype`, `prune_embeddings`, "
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"and `onnx_format` are common needed. `width_mult_list` is needed in "
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"`dynabert`, and `algo_list`, `batch_num_list`, `batch_size_list`,"
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" `round_type`, `bias_correction`, `weight_quantize_type`, "
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"`input_infer_model_path` are needed in 'ptq'. `activation_preprocess_type'`, "
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"'weight_preprocess_type', 'moving_rate', 'weight_quantize_type', "
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"and 'activation_quantize_type' are needed in 'qat'."
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)
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logger.info("{:30}:{}".format("paddle commit id", paddle.version.commit))
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for arg in dir(args):
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if key == "Compression" and arg not in compression_arg_name:
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continue
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if arg[:2] != "__": # don't print double underscore methods
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v = getattr(args, arg)
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if v is None and arg in default_arg_dict:
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v = default_arg_dict[arg]
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setattr(args, arg, v)
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elif v is None and arg == "activation_quantize_type":
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if key == "Compression" and "ptq" in args.strategy:
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setattr(args, arg, "range_abs_max")
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elif key == "Compression" and "qat" in args.strategy:
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setattr(args, arg, "moving_average_abs_max")
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if not isinstance(v, types.MethodType):
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logger.info("{:30}:{}".format(arg, v))
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logger.info("")
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