未验证 提交 52af0a60 编写于 作者: H hlygit66666 提交者: GitHub

Add Inplace addto pass and unittest. (#39433)

* add fuse_relu_depthwise_conv_pass unittest

* fix atol and rtol

* fix according to review

* Update test_dist_fuse_relu_depthwise_conv_pass.py

* add inplace_addto pass and unittest
上级 14049ae5
......@@ -80,3 +80,16 @@ class FuseOptimizerPass(CPPPassWrapper):
def _type(self):
return PassType.FUSION_OPT
@register_pass("inplace_addto_op")
class InplaceAddtoOpPass(CPPPassWrapper):
def __init__(self):
super(InplaceAddtoOpPass, self).__init__()
@property
def cpp_name(self):
return "inplace_addto_op_pass"
def _type(self):
return PassType.CALC_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 DemoNet(nn.Layer):
def __init__(self):
super(DemoNet, self).__init__()
self.conv1 = nn.Conv2D(3, 3, (3, 3), padding=1, data_format="NHWC")
self.conv2 = nn.Conv2D(3, 3, (3, 3), padding=1, data_format="NHWC")
self.conv3 = nn.Conv2D(3, 3, (3, 3), padding=1, data_format="NHWC")
self.relu = nn.ReLU()
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out) + self.conv3(out)
out = self.relu(out)
out = paddle.flatten(out, 1)
return out
class TestInplaceAddtoPass(DistPassTestBase):
def init(self):
self.atol = 0.0
self.rtol = 0.0
paddle.fluid.set_flags({"FLAGS_max_inplace_grad_add": 8})
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 = DemoNet()
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
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()
print(main_program)
return main_program, startup_program, [image], [loss], reader
def apply_passes(self, main_prog, startup_prog):
pass_manager = PassManager(
[new_pass("inplace_addto_op", {"use_cuda": True})])
pass_manager.apply([main_prog], [startup_prog])
print(pass_manager.names)
conv2d_grad_attr = []
for op in main_prog.global_block().ops:
if op.type == "conv2d_grad":
conv2d_grad_attr.append(op.desc.attr("use_addto"))
self.assertTrue(True in conv2d_grad_attr)
def test_inplace_addto(self):
self.check_main()
if __name__ == "__main__":
unittest.main()
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