forked from huawei/mindspore2022
124 lines
4.4 KiB
Python
124 lines
4.4 KiB
Python
# Copyright 2021 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 import Tensor
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops.operations import _inner_ops as inner
|
|
|
|
class MatMulNet(nn.Cell):
|
|
def __init__(self):
|
|
super(MatMulNet, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
|
|
def construct(self, x, y):
|
|
return self.matmul(x, y)
|
|
|
|
|
|
class MatMul_d(nn.Cell):
|
|
def __init__(self):
|
|
super(MatMul_d, self).__init__()
|
|
self.test_dynamic = inner.GpuConvertToDynamicShape()
|
|
self.matmul = P.MatMul()
|
|
|
|
def construct(self, x, y):
|
|
x = self.test_dynamic(x)
|
|
y = self.test_dynamic(y)
|
|
return self.matmul(x, y)
|
|
|
|
|
|
class MatMulComposite(nn.Cell):
|
|
def __init__(self):
|
|
super(MatMulComposite, self).__init__()
|
|
self.matmul = C.matmul
|
|
|
|
def construct(self, x, y):
|
|
return self.matmul(x, y)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_MatMul_dynamic():
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
net = MatMul_d()
|
|
|
|
x1 = np.arange(2).reshape(1, 2).astype(np.float32)
|
|
y1 = np.arange(4).reshape(2, 2).astype(np.float32)
|
|
output1 = net(Tensor(x1), Tensor(y1))
|
|
expect1 = np.matmul(x1, y1)
|
|
np.testing.assert_array_almost_equal(output1.asnumpy(), expect1)
|
|
|
|
x2 = np.arange(102).reshape(34, 3).astype(np.float32)
|
|
y2 = np.arange(18).reshape(3, 6).astype(np.float32)
|
|
output2 = net(Tensor(x2), Tensor(y2))
|
|
expect2 = np.matmul(x2, y2)
|
|
np.testing.assert_array_almost_equal(output2.asnumpy(), expect2)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_matmul_float64():
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
net = MatMulNet()
|
|
|
|
x = np.arange(102).reshape(34, 3).astype(np.float64)
|
|
y = np.arange(18).reshape(3, 6).astype(np.float64)
|
|
output = net(Tensor(x), Tensor(y))
|
|
expect = np.matmul(x, y)
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expect)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_matmul_composite():
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
net = MatMulComposite()
|
|
|
|
scalars = [np.random.randn(1).astype(np.float32), np.random.randn(1).astype(np.float32),
|
|
np.random.randn(1, 1).astype(np.float32),
|
|
np.random.randn(1, 1, 1).astype(np.float32)]
|
|
for x in scalars:
|
|
for y in scalars:
|
|
output = net(Tensor(x), Tensor(y))
|
|
expect = np.matmul(x, y)
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expect, decimal=4)
|
|
|
|
broadcastables = [
|
|
np.random.randn(3).astype(np.float32), np.random.randn(3).astype(np.float32),
|
|
np.random.randn(6).astype(np.float32), np.random.randn(6, 4).astype(np.float32),
|
|
np.random.randn(5, 2).astype(np.float32), np.random.randn(2).astype(np.float32),
|
|
np.random.randn(2, 9).astype(np.float32), np.random.randn(9, 8).astype(np.float32),
|
|
np.random.randn(6).astype(np.float32), np.random.randn(2, 6, 5).astype(np.float32),
|
|
np.random.randn(9, 2, 7).astype(np.float32), np.random.randn(7).astype(np.float32),
|
|
np.random.randn(5, 2, 4).astype(np.float32), np.random.randn(6, 1, 4, 9).astype(np.float32),
|
|
np.random.randn(7, 1, 5, 3, 2).astype(np.float32), np.random.randn(8, 1, 6, 1, 2, 9).astype(np.float32)
|
|
]
|
|
for i in range(8):
|
|
x = broadcastables[2*i]
|
|
y = broadcastables[2*i + 1]
|
|
output = net(Tensor(x), Tensor(y))
|
|
expect = np.matmul(x, y)
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expect, decimal=4)
|