chore: import upstream snapshot with attribution

python/paddle/distributed/fleet/meta_optimizers/qat_optimizer.py

@@ -0,0 +1,123 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+import copy
+
+import paddle
+from paddle.static.quantization.quanter import (
+    _quant_config_default,
+    quant_aware,
+)
+
+from .meta_optimizer_base import MetaOptimizerBase
+
+
+class QATOptimizer(MetaOptimizerBase):
+    def __init__(self, optimizer):
+        super().__init__(optimizer)
+        self.inner_opt = optimizer
+        # we do not allow meta optimizer to be inner optimizer currently
+        self.meta_optimizers_white_list = [
+            "AMPOptimizer",
+            "LarsOptimizer",
+            "LambOptimizer",
+            "GraphExecutionOptimizer",
+            "RecomputeOptimizer",
+            "GradientMergeOptimizer",
+        ]
+        self.meta_optimizers_black_list = []
+
+    def _set_basic_info(
+        self, loss, role_maker, user_defined_optimizer, user_defined_strategy
+    ):
+        super()._set_basic_info(
+            loss, role_maker, user_defined_optimizer, user_defined_strategy
+        )
+
+    def _can_apply(self):
+        if not self.role_maker._is_collective:
+            return False
+
+        if self.user_defined_strategy.qat:
+            return True
+
+        return False
+
+    def _disable_strategy(self, dist_strategy):
+        dist_strategy.qat = False
+        dist_strategy.qat_configs = {}
+
+    def _enable_strategy(self, dist_strategy, context):
+        dist_strategy.qat = True
+        dist_strategy.qat_configs = {
+            'channel_wise_abs_max': True,
+            'weight_bits': 8,
+            'activation_bits': 8,
+            'not_quant_pattern': [],
+            'algo': "",
+        }
+
+    def _gen_qat_config(self):
+        # Align the config to auto_parallel quantization pass
+        config = self.user_defined_strategy.qat_configs
+        qat_config = copy.deepcopy(_quant_config_default)
+        qat_config['quantize_op_types'] = [
+            'conv2d',
+            'depthwise_conv2d',
+            'mul',
+            'matmul',
+            'matmul_v2',
+        ]
+        qat_config['weight_quantize_type'] = (
+            'channel_wise_abs_max'
+            if config['channel_wise_abs_max']
+            else 'abs_max'
+        )
+        qat_config['weight_bits'] = config['weight_bits']
+        qat_config['activation_bits'] = config['activation_bits']
+        qat_config['not_quant_pattern'] = list(config['not_quant_pattern'])
+        return qat_config
+
+    def _replace_program(self, main_program, refer_program):
+        main_program._rebuild_from_desc(refer_program.desc)
+
+    def minimize_impl(
+        self, loss, startup_program=None, parameter_list=None, no_grad_set=None
+    ):
+        optimize_ops, params_grads = self.inner_opt.minimize(
+            loss,
+            startup_program,
+            parameter_list,
+            no_grad_set,
+        )
+        device = paddle.device.get_device()
+        place = paddle.set_device(device)
+        qat_config = self._gen_qat_config()
+        qat_program = quant_aware(
+            loss.block.program, place, config=qat_config, return_program=True
+        )
+        self._replace_program(loss.block.program, qat_program)
+        return optimize_ops, params_grads
+
+    def qat_init(self, place, scope=None, test_program=None):
+        if test_program is not None:
+            qat_config = self._gen_qat_config()
+            qat_program = quant_aware(
+                test_program,
+                place,
+                scope=scope,
+                config=qat_config,
+                for_test=True,
+                return_program=True,
+            )
+            self._replace_program(test_program, qat_program)