From: @peilin-wang Reviewed-by: @robingrosman,@tom__chen Signed-off-by: @robingrosmantags/v1.2.0-rc1
| @@ -1,5 +1,5 @@ | |||
| /** | |||
| * Copyright 2020 Huawei Technologies Co., Ltd | |||
| * Copyright 2020-2021 Huawei Technologies Co., Ltd | |||
| * | |||
| * Licensed under the Apache License, Version 2.0 (the "License"); | |||
| * you may not use this file except in compliance with the License. | |||
| @@ -18,17 +18,26 @@ | |||
| namespace mindspore { | |||
| namespace kernel { | |||
| MS_REG_GPU_KERNEL_ONE( | |||
| Split, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16), | |||
| SplitGpuFwdKernel, half) | |||
| MS_REG_GPU_KERNEL_ONE( | |||
| Split, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), | |||
| SplitGpuFwdKernel, float) | |||
| MS_REG_GPU_KERNEL_ONE( | |||
| Split, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64), | |||
| SplitGpuFwdKernel, double) | |||
| MS_REG_GPU_KERNEL_ONE(Split, | |||
| KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), | |||
| SplitGpuFwdKernel, int) | |||
| MS_REG_GPU_KERNEL_ONE( | |||
| Split, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16), | |||
| SplitGpuFwdKernel, half) | |||
| MS_REG_GPU_KERNEL_ONE( | |||
| Split, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32), | |||
| SplitGpuFwdKernel, uint32_t) | |||
| MS_REG_GPU_KERNEL_ONE(Split, | |||
| KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), | |||
| SplitGpuFwdKernel, int64_t) | |||
| MS_REG_GPU_KERNEL_ONE(Split, | |||
| KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool), | |||
| SplitGpuFwdKernel, bool) | |||
| } // namespace kernel | |||
| } // namespace mindspore | |||
| @@ -39,15 +39,24 @@ void SplitKernel(const size_t size, const int axis_step, const int all_size_befo | |||
| return; | |||
| } | |||
| template void SplitKernel(const size_t size, const int axis_step, const int all_size_before_axis, | |||
| const int all_size_axis, const half* input, half** outputs, | |||
| cudaStream_t cuda_stream); | |||
| template void SplitKernel(const size_t size, const int axis_step, const int all_size_before_axis, | |||
| const int all_size_axis, const float* input, float** outputs, | |||
| cudaStream_t cuda_stream); | |||
| template void SplitKernel(const size_t size, const int axis_step, const int all_size_before_axis, | |||
| const int all_size_axis, const int* input, int** outputs, | |||
| const int all_size_axis, const double* input, double** outputs, | |||
| cudaStream_t cuda_stream); | |||
| template void SplitKernel(const size_t size, const int axis_step, const int all_size_before_axis, | |||
| const int all_size_axis, const half* input, half** outputs, | |||
| const int all_size_axis, const int* input, int** outputs, | |||
| cudaStream_t cuda_stream); | |||
| template void SplitKernel(const size_t size, const int axis_step, const int all_size_before_axis, | |||
| const int all_size_axis, const uint32_t* input, uint32_t** outputs, | |||
| cudaStream_t cuda_stream); | |||
| template void SplitKernel(const size_t size, const int axis_step, const int all_size_before_axis, | |||
| const int all_size_axis, const int64_t* input, int64_t** outputs, | |||
| cudaStream_t cuda_stream); | |||
| template void SplitKernel(const size_t size, const int axis_step, const int all_size_before_axis, | |||
| const int all_size_axis, const bool* input, bool** outputs, | |||
| cudaStream_t cuda_stream); | |||
| @@ -1,4 +1,4 @@ | |||
| # Copyright 2020 Huawei Technologies Co., Ltd | |||
| # Copyright 2020-2021 Huawei Technologies Co., Ltd | |||
| # | |||
| # Licensed under the Apache License, Version 2.0 (the "License"); | |||
| # you may not use this file except in compliance with the License. | |||
| @@ -46,13 +46,10 @@ class NetDynamic(nn.Cell): | |||
| context.set_context(mode=context.GRAPH_MODE, device_target="GPU") | |||
| @pytest.mark.level0 | |||
| @pytest.mark.platform_x86_gpu_training | |||
| @pytest.mark.env_onecard | |||
| def test_split(): | |||
| def split_basic(nptype): | |||
| x = np.array([[[1, -1, 1], [2, -2, 2]], | |||
| [[3, -3, 3], [4, -4, 4]], | |||
| [[5, -5, 5], [6, -6, 6]]]).astype(np.float32) | |||
| [[5, -5, 5], [6, -6, 6]]]).astype(nptype) | |||
| split_op = Net(0, 3) | |||
| outputs = split_op(Tensor(x)) | |||
| @@ -60,6 +57,55 @@ def test_split(): | |||
| assert (out.asnumpy() == x[i]).all() | |||
| @pytest.mark.level0 | |||
| @pytest.mark.platform_x86_gpu_training | |||
| @pytest.mark.env_onecard | |||
| def test_split_basic_float16(): | |||
| split_basic(np.float16) | |||
| @pytest.mark.level0 | |||
| @pytest.mark.platform_x86_gpu_training | |||
| @pytest.mark.env_onecard | |||
| def test_split_basic_float32(): | |||
| split_basic(np.float32) | |||
| @pytest.mark.level0 | |||
| @pytest.mark.platform_x86_gpu_training | |||
| @pytest.mark.env_onecard | |||
| def test_split_basic_float64(): | |||
| split_basic(np.float64) | |||
| @pytest.mark.level0 | |||
| @pytest.mark.platform_x86_gpu_training | |||
| @pytest.mark.env_onecard | |||
| def test_split_basic_int32(): | |||
| split_basic(np.int32) | |||
| @pytest.mark.level0 | |||
| @pytest.mark.platform_x86_gpu_training | |||
| @pytest.mark.env_onecard | |||
| def test_split_basic_uint32(): | |||
| split_basic(np.uint32) | |||
| @pytest.mark.level0 | |||
| @pytest.mark.platform_x86_gpu_training | |||
| @pytest.mark.env_onecard | |||
| def test_split_basic_int64(): | |||
| split_basic(np.int64) | |||
| @pytest.mark.level0 | |||
| @pytest.mark.platform_x86_gpu_training | |||
| @pytest.mark.env_onecard | |||
| def test_split_basic_bool(): | |||
| split_basic(np.bool) | |||
| @pytest.mark.level0 | |||
| @pytest.mark.platform_x86_gpu_training | |||
| @pytest.mark.env_onecard | |||