forked from mindspore-Ecosystem/mindspore
521 lines
20 KiB
Python
521 lines
20 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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from packaging import version
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import numpy as np
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import pytest
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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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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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from mindspore.ops import functional as F
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from mindspore.run_check._check_version import GPUEnvChecker
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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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@pytest.mark.level1
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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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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="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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# 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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assert (output.asnumpy() == expect).all()
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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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assert (output.asnumpy() == expect).all()
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@pytest.mark.level1
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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.level1
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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.level1
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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.level1
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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.level1
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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_fp_16():
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"""
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Feature: test BatchMatMul op.
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Description: test BatchMatMul 4d input dtype float16.
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Expectation: the result match with expected result.
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"""
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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 BatchMatMulDynamic(nn.Cell):
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def __init__(self, transpose_a=False, transpose_b=False):
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super(BatchMatMulDynamic, 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.level1
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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 = BatchMatMulDynamic()
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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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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.level1
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@pytest.mark.platform_x86_gpu_training
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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="GPU")
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test_bmm_forward_tensor_api(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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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.level1
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@pytest.mark.platform_x86_gpu_training
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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="GPU")
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test_bmm_forward_functional_api(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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test_bmm_forward_functional_api(np.float32)
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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.input_x1 = inputs[0]
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self.input_x2 = inputs[1]
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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_gpu_training
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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="GPU")
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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_gpu_training
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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="GPU")
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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_gpu_training
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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="GPU")
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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_gpu_training
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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. It is only supported on CUDA_VERSION >= 11.
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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="GPU")
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env_checker = GPUEnvChecker()
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v = env_checker.get_cudart_version()
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env_version = version.parse(v)
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if env_version.major >= 11:
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input_list = []
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input_x1 = Tensor(np.ones(shape=[4, 1, 4]), ms.int8)
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input_x2 = Tensor(np.ones(shape=[4, 4, 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([[[4, 4, 4, 4]],
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[[4, 4, 4, 4]],
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[[4, 4, 4, 4]],
|
|
[[4, 4, 4, 4]]], np.int32)
|
|
assert (out.asnumpy() == expect).all()
|
|
assert str(out.dtype) == "Int32"
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@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="GPU")
|
|
env_checker = GPUEnvChecker()
|
|
v = env_checker.get_cudart_version()
|
|
env_version = version.parse(v)
|
|
if env_version.major >= 11:
|
|
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_gpu_training
|
|
@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="GPU")
|
|
env_checker = GPUEnvChecker()
|
|
v = env_checker.get_cudart_version()
|
|
env_version = version.parse(v)
|
|
if env_version.major >= 11:
|
|
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"
|