forked from mindspore-Ecosystem/mindspore
669 lines
26 KiB
Python
669 lines
26 KiB
Python
# Copyright 2020-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore as ms
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import mindspore.ops as op
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from mindspore.nn import Cell
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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class BatchMatMulNet(nn.Cell):
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def __init__(self, transpose_a=False, transpose_b=False):
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super(BatchMatMulNet, self).__init__()
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self.batch_matmul = P.BatchMatMul(transpose_a, transpose_b)
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def construct(self, x, y):
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return self.batch_matmul(x, y)
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class BatchMatMulDynamicRank(nn.Cell):
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def __init__(self, transpose_a=False, transpose_b=False):
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super(BatchMatMulDynamicRank, self).__init__()
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self.op = P.BatchMatMul(transpose_a, transpose_b)
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self.reduce_sum = P.ReduceSum(keep_dims=False)
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def construct(self, x, y, dyn_reduce_axis):
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x = self.reduce_sum(x, dyn_reduce_axis)
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y = self.reduce_sum(y, dyn_reduce_axis)
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res = self.op(x, y)
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return res
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def judge_result_correct(result, expect):
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assert result.dtype == expect.dtype
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assert result.shape == expect.shape
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assert np.allclose(result, expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_4d_no_transpose_vec():
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x = np.arange(2 * 4 * 1 * 3).reshape((2, 4, 1, 3)).astype(np.float32)
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y = np.arange(2 * 4 * 3 * 4).reshape((2, 4, 3, 4)).astype(np.float32)
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input_x = Tensor(x, mstype.float32)
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input_y = Tensor(y, mstype.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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net = BatchMatMulNet()
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output = net(input_x, input_y)
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expect = np.array([[[[20, 23, 26, 29]],
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[[200, 212, 224, 236]],
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[[596, 617, 638, 659]],
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[[1208, 1238, 1268, 1298]]],
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[[[2036, 2075, 2114, 2153]],
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[[3080, 3128, 3176, 3224]],
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[[4340, 4397, 4454, 4511]],
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[[5816, 5882, 5948, 6014]]]], dtype=np.float32)
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judge_result_correct(output.asnumpy(), expect)
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# test dynamic_shape
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dyn_shape_net = BatchMatMulNet()
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input_x_dyn = Tensor(shape=[2, None, 1, 3], dtype=mstype.float32)
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input_y_dyn = Tensor(shape=[2, None, 3, 4], dtype=mstype.float32)
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dyn_shape_net.set_inputs(input_x_dyn, input_y_dyn)
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output = dyn_shape_net(input_x, input_y)
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judge_result_correct(output.asnumpy(), expect)
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# test dynamic_rank
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dyn_rank_net = BatchMatMulDynamicRank()
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input_x_dyn = Tensor(shape=[2, None, 1, 3, 1], dtype=mstype.float32)
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input_y_dyn = Tensor(shape=[2, None, 3, 4, 1], dtype=mstype.float32)
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dyn_reduce_axis = Tensor(shape=[None], dtype=mstype.int64)
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dyn_rank_net.set_inputs(input_x_dyn, input_y_dyn, dyn_reduce_axis)
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reduce_axis = np.array([-1], dtype=np.int64)
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output = dyn_rank_net(Tensor(np.expand_dims(x, -1)),
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Tensor(np.expand_dims(y, -1)), Tensor(reduce_axis))
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judge_result_correct(output.asnumpy(), expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_4d_no_transpose():
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input_x = Tensor(np.arange(2 * 3 * 2 * 3).reshape((2, 3, 2, 3)), mstype.float32)
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input_y = Tensor(np.arange(2 * 3 * 3 * 4).reshape((2, 3, 3, 4)), mstype.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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net = BatchMatMulNet()
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output = net(input_x, input_y)
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expect = np.array([[[[20., 23., 26., 29.],
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[56., 68., 80., 92.]],
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[[344., 365., 386., 407.],
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[488., 518., 548., 578.]],
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[[1100., 1139., 1178., 1217.],
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[1352., 1400., 1448., 1496.]]],
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[[[2288., 2345., 2402., 2459.],
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[2648., 2714., 2780., 2846.]],
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[[3908., 3983., 4058., 4133.],
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[4376., 4460., 4544., 4628.]],
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[[5960., 6053., 6146., 6239.],
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[6536., 6638., 6740., 6842.]]]], dtype=np.float32)
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judge_result_correct(output.asnumpy(), expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_4d_transpose_a():
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input_x = Tensor(np.arange(2 * 3 * 3 * 2).reshape((2, 3, 3, 2)), mstype.float32)
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input_y = Tensor(np.arange(2 * 3 * 3 * 4).reshape((2, 3, 3, 4)), mstype.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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net = BatchMatMulNet(transpose_a=True)
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output = net(input_x, input_y)
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expect = np.array([[[[40., 46., 52., 58.],
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[52., 61., 70., 79.]],
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[[400., 424., 448., 472.],
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[448., 475., 502., 529.]],
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[[1192., 1234., 1276., 1318.],
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[1276., 1321., 1366., 1411.]]],
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[[[2416., 2476., 2536., 2596.],
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[2536., 2599., 2662., 2725.]],
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[[4072., 4150., 4228., 4306.],
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[4228., 4309., 4390., 4471.]],
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[[6160., 6256., 6352., 6448.],
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[6352., 6451., 6550., 6649.]]]], dtype=np.float32)
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judge_result_correct(output.asnumpy(), expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_4d_transpose_b():
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input_x = Tensor(np.arange(2 * 3 * 2 * 3).reshape((2, 3, 2, 3)), mstype.float32)
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input_y = Tensor(np.arange(2 * 3 * 4 * 3).reshape((2, 3, 4, 3)), mstype.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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net = BatchMatMulNet(transpose_b=True)
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output = net(input_x, input_y)
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expect = np.array([[[[5.000e+00, 1.400e+01, 2.300e+01, 3.200e+01],
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[1.400e+01, 5.000e+01, 8.600e+01, 1.220e+02]],
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[[2.750e+02, 3.380e+02, 4.010e+02, 4.640e+02],
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[3.920e+02, 4.820e+02, 5.720e+02, 6.620e+02]],
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[[9.770e+02, 1.094e+03, 1.211e+03, 1.328e+03],
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[1.202e+03, 1.346e+03, 1.490e+03, 1.634e+03]]],
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[[[2.111e+03, 2.282e+03, 2.453e+03, 2.624e+03],
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[2.444e+03, 2.642e+03, 2.840e+03, 3.038e+03]],
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[[3.677e+03, 3.902e+03, 4.127e+03, 4.352e+03],
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[4.118e+03, 4.370e+03, 4.622e+03, 4.874e+03]],
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[[5.675e+03, 5.954e+03, 6.233e+03, 6.512e+03],
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[6.224e+03, 6.530e+03, 6.836e+03, 7.142e+03]]]], dtype=np.float32)
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judge_result_correct(output.asnumpy(), expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_4d_transpose_ab():
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input_x = Tensor(np.arange(2 * 3 * 3 * 2).reshape((2, 3, 3, 2)), mstype.float16)
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input_y = Tensor(np.arange(2 * 3 * 4 * 3).reshape((2, 3, 4, 3)), mstype.float16)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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net = BatchMatMulNet(transpose_a=True, transpose_b=True)
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output = net(input_x, input_y)
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expect = np.array([[[[10., 28., 46., 64.],
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[13., 40., 67., 94.]],
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[[316., 388., 460., 532.],
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[355., 436., 517., 598.]],
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[[1054., 1180., 1306., 1432.],
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[1129., 1264., 1399., 1534.]]],
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[[[2224., 2404., 2584., 2764.],
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[2335., 2524., 2713., 2902.]],
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[[3826., 4060., 4294., 4528.],
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[3973., 4216., 4459., 4702.]],
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[[5860., 6148., 6436., 6724.],
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[6043., 6340., 6637., 6934.]]]], np.float16)
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judge_result_correct(output.asnumpy(), expect)
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def test_bmm_forward_tensor_api(nptype):
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"""
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Feature: test bmm forward tensor api for given input dtype.
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Description: test inputs for given input dtype.
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Expectation: the result match with expected result.
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"""
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x = Tensor(np.ones(shape=[2, 4, 1, 3]).astype(nptype))
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y = Tensor(np.ones(shape=[2, 4, 3, 4]).astype(nptype))
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output = x.bmm(y)
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expected = 3 * np.ones(shape=[2, 4, 1, 4]).astype(nptype)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_bmm_forward_float32_tensor_api():
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"""
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Feature: test bmm forward tensor api.
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Description: test float32 inputs.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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test_bmm_forward_tensor_api(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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test_bmm_forward_tensor_api(np.float32)
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def test_bmm_forward_functional_api(nptype):
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"""
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Feature: test bmm forward functional api for given input dtype.
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Description: test inputs for given input dtype.
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Expectation: the result match with expected result.
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"""
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x = Tensor(np.ones(shape=[2, 4, 1, 3]).astype(nptype))
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y = Tensor(np.ones(shape=[2, 4, 3, 4]).astype(nptype))
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output = F.bmm(x, y)
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expected = 3 * np.ones(shape=[2, 4, 1, 4]).astype(nptype)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_bmm_forward_float32_functional_api():
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"""
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Feature: test bmm forward functional api.
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Description: test float32 inputs.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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test_bmm_forward_functional_api(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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test_bmm_forward_functional_api(np.float32)
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if __name__ == '__main__':
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test_bmm_forward_float32_tensor_api()
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test_bmm_forward_float32_functional_api()
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class BatchMatMul(Cell):
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def __init__(self):
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super().__init__()
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self.batchmatmul = op.matmul
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def construct(self, x1, x2):
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return self.batchmatmul(x1, x2)
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class BatchMatMulTestNet(Cell):
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def __init__(self, inputs=None):
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self.ms_type = inputs[0].dtype
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self.input_x1 = inputs[0]
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self.input_x1_np = inputs[0].asnumpy()
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self.input_x1_shape = inputs[0].shape
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self.input_x2 = inputs[1]
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self.input_x2_np = inputs[1].asnumpy()
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self.input_x2_shape = inputs[1].shape
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self.loss = 1e-3
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def forward_mindspore_impl(self):
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input_x1 = Tensor(self.input_x1)
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input_x2 = Tensor(self.input_x2)
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net = BatchMatMul()
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out = net(input_x1, input_x2)
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return out
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_batchmatmul_type_float16():
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"""
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Feature: test bmm with dtype float16.
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Description: test bmm with dtype float16.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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input_list = []
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input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.float16)
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input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.float16)
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input_list.append(input_x1)
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input_list.append(input_x2)
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fact = BatchMatMulTestNet(inputs=input_list)
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float16_out = fact.forward_mindspore_impl()
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expect = np.array([[[3., 3., 3., 3.]],
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[[3., 3., 3., 3.]],
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[[3., 3., 3., 3.]],
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[[3., 3., 3., 3.]]], np.float16)
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assert (float16_out.asnumpy() == expect).all()
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assert str(float16_out.dtype) == "Float16"
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_batchmatmul_type_float32():
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"""
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Feature: test bmm with dtype float32.
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Description: test bmm with dtype float32.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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input_list = []
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input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.float32)
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input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.float32)
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input_list.append(input_x1)
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input_list.append(input_x2)
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fact = BatchMatMulTestNet(inputs=input_list)
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float32_out = fact.forward_mindspore_impl()
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expect = np.array([[[3., 3., 3., 3.]],
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[[3., 3., 3., 3.]],
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[[3., 3., 3., 3.]],
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[[3., 3., 3., 3.]]], np.float32)
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assert (float32_out.asnumpy() == expect).all()
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assert str(float32_out.dtype) == "Float32"
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_batchmatmul_type_float64():
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"""
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Feature: test bmm with dtype float64.
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Description: test bmm with dtype float64.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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input_list = []
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input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.float64)
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input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.float64)
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input_list.append(input_x1)
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input_list.append(input_x2)
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fact = BatchMatMulTestNet(inputs=input_list)
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float64_out = fact.forward_mindspore_impl()
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expect = np.array([[[3., 3., 3., 3.]],
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[[3., 3., 3., 3.]],
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[[3., 3., 3., 3.]],
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[[3., 3., 3., 3.]]], np.float64)
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assert (float64_out.asnumpy() == expect).all()
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assert str(float64_out.dtype) == "Float64"
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_batchmatmul_type_int8():
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"""
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Feature: test bmm with dtype int8.
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Description: test bmm with dtype int8.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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input_list = []
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input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.int8)
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input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.int8)
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input_list.append(input_x1)
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input_list.append(input_x2)
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fact = BatchMatMulTestNet(inputs=input_list)
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out = fact.forward_mindspore_impl()
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expect = np.array([[[3, 3, 3, 3]],
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[[3, 3, 3, 3]],
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[[3, 3, 3, 3]],
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[[3, 3, 3, 3]]], np.int8)
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assert (out.asnumpy() == expect).all()
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assert str(out.dtype) == "Int32"
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_batchmatmul_type_int16():
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"""
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Feature: test bmm with dtype int16.
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Description: test bmm with dtype int16.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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input_list = []
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input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.int16)
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input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.int16)
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input_list.append(input_x1)
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input_list.append(input_x2)
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fact = BatchMatMulTestNet(inputs=input_list)
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int16_out = fact.forward_mindspore_impl()
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expect = np.array([[[3, 3, 3, 3]],
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[[3, 3, 3, 3]],
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[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]]], np.int16)
|
|
assert (int16_out.asnumpy() == expect).all()
|
|
assert str(int16_out.dtype) == "Int16"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_batchmatmul_type_int32():
|
|
"""
|
|
Feature: test bmm with dtype int32.
|
|
Description: test bmm with dtype int32.
|
|
Expectation: the result match with expected result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
input_list = []
|
|
input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.int32)
|
|
input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.int32)
|
|
input_list.append(input_x1)
|
|
input_list.append(input_x2)
|
|
fact = BatchMatMulTestNet(inputs=input_list)
|
|
int32_out = fact.forward_mindspore_impl()
|
|
expect = np.array([[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]]], np.int32)
|
|
assert (int32_out.asnumpy() == expect).all()
|
|
assert str(int32_out.dtype) == "Int32"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_batchmatmul_type_int64():
|
|
"""
|
|
Feature: test bmm with dtype int64.
|
|
Description: test bmm with dtype int64.
|
|
Expectation: the result match with expected result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
input_list = []
|
|
input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.int64)
|
|
input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.int64)
|
|
input_list.append(input_x1)
|
|
input_list.append(input_x2)
|
|
fact = BatchMatMulTestNet(inputs=input_list)
|
|
int64_out = fact.forward_mindspore_impl()
|
|
expect = np.array([[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]]], np.int64)
|
|
assert (int64_out.asnumpy() == expect).all()
|
|
assert str(int64_out.dtype) == "Int64"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_batchmatmul_type_uint8():
|
|
"""
|
|
Feature: test bmm with dtype uint8.
|
|
Description: test bmm with dtype uint8.
|
|
Expectation: the result match with expected result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
input_list = []
|
|
input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.uint8)
|
|
input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.uint8)
|
|
input_list.append(input_x1)
|
|
input_list.append(input_x2)
|
|
fact = BatchMatMulTestNet(inputs=input_list)
|
|
uint8_out = fact.forward_mindspore_impl()
|
|
expect = np.array([[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]]], np.int8)
|
|
assert (uint8_out.asnumpy() == expect).all()
|
|
assert str(uint8_out.dtype) == "UInt8"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_batchmatmul_type_uint16():
|
|
"""
|
|
Feature: test bmm with dtype uint16.
|
|
Description: test bmm with dtype uint16.
|
|
Expectation: the result match with expected result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
input_list = []
|
|
input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.uint16)
|
|
input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.uint16)
|
|
input_list.append(input_x1)
|
|
input_list.append(input_x2)
|
|
fact = BatchMatMulTestNet(inputs=input_list)
|
|
uint16_out = fact.forward_mindspore_impl()
|
|
expect = np.array([[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]]], np.int16)
|
|
assert (uint16_out.asnumpy() == expect).all()
|
|
assert str(uint16_out.dtype) == "UInt16"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_batchmatmul_type_uint32():
|
|
"""
|
|
Feature: test bmm with dtype uint32.
|
|
Description: test bmm with dtype uint32.
|
|
Expectation: the result match with expected result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
input_list = []
|
|
input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.uint32)
|
|
input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.uint32)
|
|
input_list.append(input_x1)
|
|
input_list.append(input_x2)
|
|
fact = BatchMatMulTestNet(inputs=input_list)
|
|
uint32_out = fact.forward_mindspore_impl()
|
|
expect = np.array([[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]]], np.uint32)
|
|
assert (uint32_out.asnumpy() == expect).all()
|
|
assert str(uint32_out.dtype) == "UInt32"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_batchmatmul_type_uint64():
|
|
"""
|
|
Feature: test bmm with dtype uint64.
|
|
Description: test bmm with dtype uint64.
|
|
Expectation: the result match with expected result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
input_list = []
|
|
input_x1 = Tensor(np.ones(shape=[4, 1, 3]), ms.uint64)
|
|
input_x2 = Tensor(np.ones(shape=[4, 3, 4]), ms.uint64)
|
|
input_list.append(input_x1)
|
|
input_list.append(input_x2)
|
|
fact = BatchMatMulTestNet(inputs=input_list)
|
|
uint64_out = fact.forward_mindspore_impl()
|
|
expect = np.array([[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]],
|
|
[[3, 3, 3, 3]]], np.uint64)
|
|
assert (uint64_out.asnumpy() == expect).all()
|
|
assert str(uint64_out.dtype) == "UInt64"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_batchmatmul_type_complex64():
|
|
"""
|
|
Feature: test bmm with dtype complex64.
|
|
Description: test bmm with dtype complex64.
|
|
Expectation: the result match with expected result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
input_list = []
|
|
input_x1 = Tensor(np.array([[[1 + 1j, 1 + 1j, 1 + 1j, 1 + 1j]],
|
|
[[1 + 1j, 1 + 1j, 1 + 1j, 1 + 1j]],
|
|
[[1 + 1j, 1 + 1j, 1 + 1j, 1 + 1j]],
|
|
[[1 + 1j, 1 + 1j, 1 + 1j, 1 + 1j]]]).astype(np.complex64))
|
|
input_x2 = Tensor(np.array([[[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j]],
|
|
[[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j]],
|
|
[[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j]],
|
|
[[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j]]]).astype(np.complex64))
|
|
input_list.append(input_x1)
|
|
input_list.append(input_x2)
|
|
fact = BatchMatMulTestNet(inputs=input_list)
|
|
out = fact.forward_mindspore_impl()
|
|
expect = np.array([[[8+0j, 8+0j, 8+0j, 8+0j]],
|
|
[[8+0j, 8+0j, 8+0j, 8+0j]],
|
|
[[8+0j, 8+0j, 8+0j, 8+0j]],
|
|
[[8+0j, 8+0j, 8+0j, 8+0j]]], np.complex64)
|
|
assert (out.asnumpy() == expect).all()
|
|
assert str(out.dtype) == "Complex64"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_batchmatmul_type_complex128():
|
|
"""
|
|
Feature: test bmm with dtype complex128.
|
|
Description: test bmm with dtype complex128.
|
|
Expectation: the result match with expected result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
input_list = []
|
|
input_x1 = Tensor(np.array([[[1 + 1j, 1 + 1j, 1 + 1j, 1 + 1j]],
|
|
[[1 + 1j, 1 + 1j, 1 + 1j, 1 + 1j]],
|
|
[[1 + 1j, 1 + 1j, 1 + 1j, 1 + 1j]],
|
|
[[1 + 1j, 1 + 1j, 1 + 1j, 1 + 1j]]]).astype(np.complex128))
|
|
input_x2 = Tensor(np.array([[[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j]],
|
|
[[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j]],
|
|
[[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j]],
|
|
[[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j],
|
|
[1 - 1j, 1 - 1j, 1 - 1j, 1 - 1j]]]).astype(np.complex128))
|
|
input_list.append(input_x1)
|
|
input_list.append(input_x2)
|
|
fact = BatchMatMulTestNet(inputs=input_list)
|
|
out = fact.forward_mindspore_impl()
|
|
expect = np.array([[[8+0j, 8+0j, 8+0j, 8+0j]],
|
|
[[8+0j, 8+0j, 8+0j, 8+0j]],
|
|
[[8+0j, 8+0j, 8+0j, 8+0j]],
|
|
[[8+0j, 8+0j, 8+0j, 8+0j]]], np.complex128)
|
|
assert (out.asnumpy() == expect).all()
|
|
assert str(out.dtype) == "Complex128"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_batchmatmul_default_transpose():
|
|
"""
|
|
Feature: test bmm with default transpose.
|
|
Description: test bmm with default transpose.
|
|
Expectation: the result match with expected result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
input_a = [[[1.73676595], [0.12718279]]]
|
|
input_b = [[[1.04756699, 0.40877772, -0.05073088]]]
|
|
net = BatchMatMulNet()
|
|
output = net(Tensor(input_a), Tensor(input_b))
|
|
expected = [[[1.8193787, 0.7099512, -0.08810767],
|
|
[0.1332325, 0.05198949, -0.0064521]]]
|
|
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|