wehub-resource-sync
124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. 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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import copy
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import paddle
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from paddle.static.quantization.quanter import (
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_quant_config_default,
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quant_aware,
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)
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from .meta_optimizer_base import MetaOptimizerBase
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class QATOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super().__init__(optimizer)
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self.inner_opt = optimizer
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# we do not allow meta optimizer to be inner optimizer currently
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self.meta_optimizers_white_list = [
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"AMPOptimizer",
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"LarsOptimizer",
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"LambOptimizer",
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"GraphExecutionOptimizer",
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"RecomputeOptimizer",
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"GradientMergeOptimizer",
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]
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self.meta_optimizers_black_list = []
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def _set_basic_info(
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self, loss, role_maker, user_defined_optimizer, user_defined_strategy
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):
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super()._set_basic_info(
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loss, role_maker, user_defined_optimizer, user_defined_strategy
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)
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def _can_apply(self):
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if not self.role_maker._is_collective:
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return False
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if self.user_defined_strategy.qat:
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return True
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return False
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def _disable_strategy(self, dist_strategy):
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dist_strategy.qat = False
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dist_strategy.qat_configs = {}
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def _enable_strategy(self, dist_strategy, context):
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dist_strategy.qat = True
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dist_strategy.qat_configs = {
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'channel_wise_abs_max': True,
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'weight_bits': 8,
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'activation_bits': 8,
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'not_quant_pattern': [],
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'algo': "",
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}
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def _gen_qat_config(self):
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# Align the config to auto_parallel quantization pass
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config = self.user_defined_strategy.qat_configs
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qat_config = copy.deepcopy(_quant_config_default)
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qat_config['quantize_op_types'] = [
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'conv2d',
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'depthwise_conv2d',
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'mul',
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'matmul',
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'matmul_v2',
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]
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qat_config['weight_quantize_type'] = (
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'channel_wise_abs_max'
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if config['channel_wise_abs_max']
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else 'abs_max'
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)
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qat_config['weight_bits'] = config['weight_bits']
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qat_config['activation_bits'] = config['activation_bits']
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qat_config['not_quant_pattern'] = list(config['not_quant_pattern'])
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return qat_config
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def _replace_program(self, main_program, refer_program):
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main_program._rebuild_from_desc(refer_program.desc)
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def minimize_impl(
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self, loss, startup_program=None, parameter_list=None, no_grad_set=None
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):
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optimize_ops, params_grads = self.inner_opt.minimize(
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loss,
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startup_program,
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parameter_list,
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no_grad_set,
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)
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device = paddle.device.get_device()
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place = paddle.set_device(device)
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qat_config = self._gen_qat_config()
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qat_program = quant_aware(
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loss.block.program, place, config=qat_config, return_program=True
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)
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self._replace_program(loss.block.program, qat_program)
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return optimize_ops, params_grads
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def qat_init(self, place, scope=None, test_program=None):
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if test_program is not None:
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qat_config = self._gen_qat_config()
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qat_program = quant_aware(
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test_program,
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place,
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scope=scope,
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config=qat_config,
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for_test=True,
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return_program=True,
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)
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self._replace_program(test_program, qat_program)
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