forked from mindspore-Ecosystem/mindspore
112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
# Copyright 2022 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
|
|
|
|
from mindspore import context, nn, set_seed
|
|
from mindspore.common.tensor import Tensor
|
|
import mindspore.common.dtype as mstype
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
context.set_context(device_target="Ascend")
|
|
set_seed(2)
|
|
|
|
|
|
class AdamApplyOneNet(nn.Cell):
|
|
def __init__(self):
|
|
super(AdamApplyOneNet, self).__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.real_div = P.RealDiv()
|
|
self.sqrt = P.Sqrt()
|
|
self.square = P.Square()
|
|
|
|
def construct(self, input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
|
|
square0 = self.square(input0)
|
|
mul1 = self.mul(mul1_x, input0)
|
|
mul0 = self.mul(mul0_x, input2)
|
|
mul2 = self.mul(mul2_x, input1)
|
|
mul3 = self.mul(mul3_x, square0)
|
|
add0 = self.add(mul0, mul1)
|
|
add1 = self.add(mul2, mul3)
|
|
sqrt0 = self.sqrt(add1)
|
|
add2 = self.add(add2_y, sqrt0)
|
|
true_div0 = self.real_div(add0, add2)
|
|
mul4 = self.mul(input4, true_div0)
|
|
sub0 = self.sub(input3, mul4)
|
|
return add1, add0, sub0
|
|
|
|
|
|
def adam_apply_one_np(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
|
|
square0 = input0 * input0
|
|
mul1 = mul1_x * input0
|
|
mul0 = mul0_x * input2
|
|
mul2 = mul2_x * input1
|
|
mul3 = mul3_x * square0
|
|
add0 = mul0 + mul1
|
|
add1 = mul2 + mul3
|
|
sqrt0 = np.sqrt(add1)
|
|
add2 = add2_y + sqrt0
|
|
true_div0 = np.true_divide(add0, add2)
|
|
mul4 = input4 * true_div0
|
|
sub0 = input3 - mul4
|
|
return add1, add0, sub0
|
|
|
|
|
|
def compute_func(ms_net, np_net, is_dyn=False):
|
|
if is_dyn:
|
|
inputs = Tensor(shape=[2, None], dtype=mstype.float32)
|
|
ms_net.set_inputs(inputs, inputs, inputs, inputs, inputs, inputs, inputs, inputs, inputs, inputs)
|
|
input0 = np.array([[0.1, 0.3, 3.6], [0.4, 0.5, 3.2]]).astype(np.float32)
|
|
out0, out1, out2 = ms_net(Tensor(input0), Tensor(input0), Tensor(input0), Tensor(input0), \
|
|
Tensor(input0), Tensor(input0), Tensor(input0), Tensor(input0), Tensor(input0), Tensor(input0))
|
|
np0, np1, np2 = np_net(input0, input0, input0, input0, input0, input0, input0, input0, input0, input0)
|
|
assert np.all(out0.asnumpy() == np0)
|
|
assert np.all(out1.asnumpy() == np1)
|
|
assert np.all(out2.asnumpy() == np2)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_adam_apply_one_dyn():
|
|
"""
|
|
Feature: Test Dynamic AdamApplyOne.
|
|
Description: The input shape is dynamic.
|
|
Expectation: Assert that results are consistent with numpy.
|
|
"""
|
|
ms_net = AdamApplyOneNet()
|
|
np_net = adam_apply_one_np
|
|
compute_func(ms_net, np_net, True)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_adam_apply_one():
|
|
"""
|
|
Feature: Test AdamApplyOne.
|
|
Description: The input shape is static.
|
|
Expectation: Assert that results are consistent with numpy.
|
|
"""
|
|
ms_net = AdamApplyOneNet()
|
|
np_net = adam_apply_one_np
|
|
compute_func(ms_net, np_net)
|