forked from mindspore-Ecosystem/mindspore
200 lines
7.1 KiB
Python
200 lines
7.1 KiB
Python
# Copyright 2020-2021 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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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.operations import _inner_ops as inner
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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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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_4d():
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input_x = Tensor(np.arange(2 * 4 * 1 * 3).reshape(2, 4, 1, 3), mstype.float32)
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input_y = Tensor(np.arange(2 * 4 * 3 * 4).reshape(2, 4, 3, 4), mstype.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = BatchMatMulNet()
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output = net(input_x, input_y)
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expect = [[[[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]]]]
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_4d_float64():
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input_x = Tensor(np.arange(2 * 4 * 1 * 3).reshape(2, 4, 1, 3), mstype.float64)
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input_y = Tensor(np.arange(2 * 4 * 3 * 4).reshape(2, 4, 3, 4), mstype.float64)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = BatchMatMulNet()
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output = net(input_x, input_y)
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expect = [[[[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]]]]
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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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 * 4 * 3 * 1).reshape(2, 4, 3, 1), mstype.float32)
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input_y = Tensor(np.arange(2 * 4 * 3 * 4).reshape(2, 4, 3, 4), mstype.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = BatchMatMulNet(transpose_a=True)
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output = net(input_x, input_y)
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expect = [[[[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]]]]
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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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 * 4 * 1 * 3).reshape(2, 4, 1, 3), mstype.float32)
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input_y = Tensor(np.arange(2 * 4 * 4 * 3).reshape(2, 4, 4, 3), mstype.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = BatchMatMulNet(transpose_b=True)
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output = net(input_x, input_y)
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expect = [[[[5, 14, 23, 32]],
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[[158, 194, 230, 266]],
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[[527, 590, 653, 716]],
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[[1112, 1202, 1292, 1382]]],
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[[[1913, 2030, 2147, 2264]],
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[[2930, 3074, 3218, 3362]],
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[[4163, 4334, 4505, 4676]],
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[[5612, 5810, 6008, 6206]]]]
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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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 * 4 * 3 * 1).reshape(2, 4, 3, 1), mstype.float32)
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input_y = Tensor(np.arange(2 * 4 * 4 * 3).reshape(2, 4, 4, 3), mstype.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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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 = [[[[5, 14, 23, 32]],
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[[158, 194, 230, 266]],
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[[527, 590, 653, 716]],
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[[1112, 1202, 1292, 1382]]],
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[[[1913, 2030, 2147, 2264]],
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[[2930, 3074, 3218, 3362]],
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[[4163, 4334, 4505, 4676]],
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[[5612, 5810, 6008, 6206]]]]
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_4D_fp16():
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input_x = Tensor(np.arange(2 * 4 * 1 * 3).reshape(2, 4, 1, 3), mstype.float16)
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input_y = Tensor(np.arange(2 * 4 * 3 * 4).reshape(2, 4, 3, 4), mstype.float16)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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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, 2076, 2114, 2152]],
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[[3080, 3128, 3176, 3224]],
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[[4340, 4396, 4456, 4510]],
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[[5816, 5880, 5948, 6016]]]]).astype(np.float16)
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assert (output.asnumpy() == expect).all()
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class BatchMatMul_d(nn.Cell):
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def __init__(self, transpose_a=False, transpose_b=False):
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super(BatchMatMul_d, self).__init__()
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self.batch_matmul = P.BatchMatMul(transpose_a, transpose_b)
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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def construct(self, x, y):
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x = self.test_dynamic(x)
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y = self.test_dynamic(y)
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return self.batch_matmul(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_batchmatmul_dynamic():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = BatchMatMul_d()
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x1 = np.arange(8).reshape(2, 2, 2).astype(np.float32)
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y1 = np.arange(28).reshape(2, 2, 7).astype(np.float32)
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output1 = net(Tensor(x1), Tensor(y1))
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expect1 = np.matmul(x1, y1)
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assert (output1.asnumpy() == expect1).all()
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x2 = np.arange(2 * 4 * 1 * 3).reshape(2, 4, 1, 3).astype(np.float32)
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y2 = np.arange(2 * 4 * 3 * 4).reshape(2, 4, 3, 4).astype(np.float32)
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output2 = net(Tensor(x2), Tensor(y2))
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expect2 = np.matmul(x2, y2)
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assert (output2.asnumpy() == expect2).all()
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