未验证 提交 09104d02 编写于 作者: H hlygit66666 提交者: GitHub

Add FuseBatchNormActPass and unittest. (#39176)

* add fuse_relu_depthwise_conv_pass unittest

* fix atol and rtol

* fix according to review

* Add fuse_bn_act_pass unittest

* rm others

* add fuse_bn_act_pass
上级 4c61e141
......@@ -26,3 +26,16 @@ class FuseElementwiseAddActPass(CPPPassWrapper):
def _type(self):
return PassType.FUSION_OPT
@register_pass("fuse_bn_act")
class FuseBatchNormActPass(CPPPassWrapper):
def __init__(self):
super(FuseBatchNormActPass, self).__init__()
@property
def cpp_name(self):
return "fuse_bn_act_pass"
def _type(self):
return PassType.FUSION_OPT
# 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
# limitations under the License.
import os
import paddle
import paddle.distributed.fleet as fleet
import numpy as np
import paddle.nn as nn
from paddle.distributed.passes import new_pass, PassManager
import unittest
from dist_pass_test_base import DistPassTestBase
class BatchNormActNet(nn.Layer):
def __init__(self):
super(BatchNormActNet, self).__init__()
self.conv1 = nn.Conv2D(3, 8, (3, 3), data_format="NHWC")
self.bn1 = nn.BatchNorm2D(8, data_format="NHWC")
self.relu = nn.ReLU()
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = paddle.flatten(out, 1)
return out
class TestFuseBatchNormActPass(DistPassTestBase):
def init(self):
self.atol = 1e-4
self.rtol = 1e-4
def get_model(self, place, batch_size=32, image_shape=[224, 224, 3]):
image = paddle.static.data(
shape=[batch_size] + image_shape, dtype='float32', name='image')
model = BatchNormActNet()
pred_out = model(image)
loss = paddle.mean(pred_out)
optimizer = paddle.optimizer.Adam(learning_rate=1e-3)
dist_strategy = fleet.DistributedStrategy()
dist_strategy.fuse_all_reduce_ops = False
dist_strategy.without_graph_optimization = True
dist_strategy.amp = True
dist_strategy.amp_configs = {
"init_loss_scaling": 32768,
"use_dynamic_loss_scaling": True,
}
fleet.init(is_collective=True, strategy=dist_strategy)
optimizer = fleet.distributed_optimizer(optimizer)
optimizer.minimize(loss)
rank = paddle.distributed.get_rank()
def reader():
seed = int(os.environ.get("SEED", 0))
np.random.seed(seed + rank)
for _ in range(10):
image_np = np.random.random(size=image.shape).astype('float32')
yield image_np,
main_program = paddle.static.default_main_program()
startup_program = paddle.static.default_startup_program()
return main_program, startup_program, [image], [loss], reader
def apply_passes(self, main_prog, startup_prog):
pass_manager = PassManager([new_pass("fuse_bn_act")])
pass_manager.apply([main_prog], [startup_prog])
print(pass_manager.names)
op_type = []
for op in main_prog.global_block().ops:
op_type.append(op.type)
self.assertTrue("fused_batch_norm_act" in op_type)
self.assertTrue("fused_batch_norm_act_grad" in op_type)
def test_fuse_bn_act(self):
self.check_main()
if __name__ == "__main__":
unittest.main()
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