You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
76 lines
2.8 KiB
76 lines
2.8 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# 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 numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore.common.parameter import Parameter
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops.operations import _grad_ops as G
|
|
|
|
|
|
class Conv2dBpropInputInplace(nn.Cell):
|
|
def __init__(self, w1, w2):
|
|
super(Conv2dBpropInputInplace, self).__init__()
|
|
self.conv2d_1 = P.Conv2DBackpropInput(out_channel=256, kernel_size=1)
|
|
self.w1 = Parameter(initializer(w1, w1.shape), name='w1')
|
|
self.conv2d_2 = P.Conv2DBackpropInput(out_channel=256, kernel_size=1)
|
|
self.w2 = Parameter(initializer(w2, w2.shape), name='w2')
|
|
self.add = P.Add()
|
|
self.maxpool = P.MaxPool(kernel_size=3, strides=2, pad_mode='SAME')
|
|
self.maxpool_grad = G.MaxPoolGrad(kernel_size=3, strides=2, pad_mode='SAME')
|
|
self.shape = (32, 64, 56, 56)
|
|
|
|
def construct(self, x1, x2, x3):
|
|
dx1 = self.conv2d_1(x1, self.w1, self.shape)
|
|
dx2 = self.conv2d_2(x2, self.w2, self.shape)
|
|
|
|
dx = self.add(dx1, dx2)
|
|
y = self.maxpool(x3)
|
|
y = self.maxpool_grad(x3, y, dx)
|
|
return y
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_inplace_fusion1():
|
|
|
|
np.random.seed(42)
|
|
w1_np = np.random.randn(64, 64, 1, 1)
|
|
w2_np = np.random.randn(256, 64, 1, 1)
|
|
x1_np = np.random.randn(32, 64, 56, 56)
|
|
x2_np = np.random.randn(32, 256, 56, 56)
|
|
x3_np = np.random.randn(32, 64, 112, 112)
|
|
|
|
w1 = Tensor(w1_np.astype(np.float32))
|
|
w2 = Tensor(w2_np.astype(np.float32))
|
|
x1 = Tensor(x1_np.astype(np.float32))
|
|
x2 = Tensor(x2_np.astype(np.float32))
|
|
x3 = Tensor(x3_np.astype(np.float32))
|
|
|
|
net = Conv2dBpropInputInplace(w1, w2)
|
|
context.set_context(device_target='GPU', mode=context.GRAPH_MODE)
|
|
fusion_output = net(x1, x2, x3)
|
|
|
|
context.set_context(device_target='GPU', mode=context.PYNATIVE_MODE)
|
|
no_fusion_output = net(x1, x2, x3)
|
|
|
|
assert np.allclose(fusion_output.asnumpy(), no_fusion_output.asnumpy(), atol=2e-5)
|