From: @peilin-wang Reviewed-by: @tom__chen,@robingrosman Signed-off-by: @robingrosmantags/v1.2.0-rc1
| @@ -1,5 +1,5 @@ | |||||
| /** | /** | ||||
| * Copyright 2019 Huawei Technologies Co., Ltd | |||||
| * Copyright 2021 Huawei Technologies Co., Ltd | |||||
| * | * | ||||
| * Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
| * you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
| @@ -13,6 +13,7 @@ | |||||
| * See the License for the specific language governing permissions and | * See the License for the specific language governing permissions and | ||||
| * limitations under the License. | * limitations under the License. | ||||
| */ | */ | ||||
| #include <cstdint> | |||||
| #include "backend/kernel_compiler/gpu/arrays/one_hot_gpu_kernel.h" | #include "backend/kernel_compiler/gpu/arrays/one_hot_gpu_kernel.h" | ||||
| @@ -32,5 +33,19 @@ MS_REG_GPU_KERNEL_TWO(OneHot, | |||||
| .AddInputAttr(kNumberTypeFloat16) | .AddInputAttr(kNumberTypeFloat16) | ||||
| .AddOutputAttr(kNumberTypeFloat16), | .AddOutputAttr(kNumberTypeFloat16), | ||||
| OneHotGpuFwdKernel, half, int) | OneHotGpuFwdKernel, half, int) | ||||
| MS_REG_GPU_KERNEL_TWO(OneHot, | |||||
| KernelAttr() | |||||
| .AddInputAttr(kNumberTypeInt64) | |||||
| .AddInputAttr(kNumberTypeFloat32) | |||||
| .AddInputAttr(kNumberTypeFloat32) | |||||
| .AddOutputAttr(kNumberTypeFloat32), | |||||
| OneHotGpuFwdKernel, float, int64_t) | |||||
| MS_REG_GPU_KERNEL_TWO(OneHot, | |||||
| KernelAttr() | |||||
| .AddInputAttr(kNumberTypeInt64) | |||||
| .AddInputAttr(kNumberTypeFloat16) | |||||
| .AddInputAttr(kNumberTypeFloat16) | |||||
| .AddOutputAttr(kNumberTypeFloat16), | |||||
| OneHotGpuFwdKernel, half, int64_t) | |||||
| } // namespace kernel | } // namespace kernel | ||||
| } // namespace mindspore | } // namespace mindspore | ||||
| @@ -49,3 +49,9 @@ template void OneHot<float, int>(const int *indices, size_t depth, const float * | |||||
| size_t left_dim_size, size_t right_dim_size, float *output, cudaStream_t cuda_stream); | size_t left_dim_size, size_t right_dim_size, float *output, cudaStream_t cuda_stream); | ||||
| template void OneHot<half, int>(const int *indices, size_t depth, const half *on_value, const half *off_value, | template void OneHot<half, int>(const int *indices, size_t depth, const half *on_value, const half *off_value, | ||||
| size_t left_dim_size, size_t right_dim_size, half *output, cudaStream_t cuda_stream); | size_t left_dim_size, size_t right_dim_size, half *output, cudaStream_t cuda_stream); | ||||
| template void OneHot<float, int64_t>(const int64_t *indices, size_t depth, const float *on_value, | |||||
| const float *off_value, size_t left_dim_size, size_t right_dim_size, float *output, | |||||
| cudaStream_t cuda_stream); | |||||
| template void OneHot<half, int64_t>(const int64_t *indices, size_t depth, const half *on_value, const half *off_value, | |||||
| size_t left_dim_size, size_t right_dim_size, half *output, | |||||
| cudaStream_t cuda_stream); | |||||
| @@ -2985,7 +2985,7 @@ class OneHot(PrimitiveWithInfer): | |||||
| Inputs: | Inputs: | ||||
| - **indices** (Tensor) - A tensor of indices. Tensor of shape :math:`(X_0, \ldots, X_n)`. | - **indices** (Tensor) - A tensor of indices. Tensor of shape :math:`(X_0, \ldots, X_n)`. | ||||
| Data type must be int32. | |||||
| Data type must be int32 or int64. | |||||
| - **depth** (int) - A scalar defining the depth of the one hot dimension. | - **depth** (int) - A scalar defining the depth of the one hot dimension. | ||||
| - **on_value** (Tensor) - A value to fill in output when `indices[j] = i`. With data type of float16 or float32. | - **on_value** (Tensor) - A value to fill in output when `indices[j] = i`. With data type of float16 or float32. | ||||
| - **off_value** (Tensor) - A value to fill in output when `indices[j] != i`. | - **off_value** (Tensor) - A value to fill in output when `indices[j] != i`. | ||||
| @@ -3015,7 +3015,7 @@ class OneHot(PrimitiveWithInfer): | |||||
| def __infer__(self, indices, depth, on_value, off_value): | def __infer__(self, indices, depth, on_value, off_value): | ||||
| # check type | # check type | ||||
| validator.check_tensor_dtype_valid("indices", indices['dtype'], (mstype.int32,), self.name) | |||||
| validator.check_tensor_dtype_valid("indices", indices['dtype'], (mstype.int32, mstype.int64), self.name) | |||||
| validator.check_type_name("depth", depth['dtype'], mstype.int_type, self.name) | validator.check_type_name("depth", depth['dtype'], mstype.int_type, self.name) | ||||
| args = {"on_value": on_value['dtype'], "off_value": off_value['dtype']} | args = {"on_value": on_value['dtype'], "off_value": off_value['dtype']} | ||||
| validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name) | validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name) | ||||
| @@ -1,4 +1,4 @@ | |||||
| # Copyright 2019 Huawei Technologies Co., Ltd | |||||
| # Copyright 2021 Huawei Technologies Co., Ltd | |||||
| # | # | ||||
| # Licensed under the Apache License, Version 2.0 (the "License"); | # Licensed under the Apache License, Version 2.0 (the "License"); | ||||
| # you may not use this file except in compliance with the License. | # you may not use this file except in compliance with the License. | ||||
| @@ -44,16 +44,13 @@ class NetOneHot(nn.Cell): | |||||
| self.one_hot_3(indices3), self.one_hot_4(indices4)) | self.one_hot_3(indices3), self.one_hot_4(indices4)) | ||||
| @pytest.mark.level0 | |||||
| @pytest.mark.platform_x86_gpu_training | |||||
| @pytest.mark.env_onecard | |||||
| def test_one_hot(): | |||||
| one_hot = NetOneHot() | |||||
| indices1 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(np.int32)) | |||||
| indices2 = Tensor(np.array([1, 2, 3]).astype(np.int32)) | |||||
| indices3 = Tensor(np.array([[0, 1], [1, 0]]).astype(np.int32)) | |||||
| indices4 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(np.int32)) | |||||
| output = one_hot(indices1, indices2, indices3, indices4) | |||||
| def one_hot(nptype): | |||||
| one_hot_net = NetOneHot() | |||||
| indices1 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(nptype)) | |||||
| indices2 = Tensor(np.array([1, 2, 3]).astype(nptype)) | |||||
| indices3 = Tensor(np.array([[0, 1], [1, 0]]).astype(nptype)) | |||||
| indices4 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(nptype)) | |||||
| output = one_hot_net(indices1, indices2, indices3, indices4) | |||||
| expect_0 = np.array([ | expect_0 = np.array([ | ||||
| [[2., 3., 3., 3., 3., 3.], [3., 2., 3., 3., 3., 3.]], | [[2., 3., 3., 3., 3., 3.], [3., 2., 3., 3., 3., 3.]], | ||||
| [[3., 3., 3., 3., 2., 3.], [3., 3., 3., 3., 3., 2.]], | [[3., 3., 3., 3., 2., 3.], [3., 3., 3., 3., 3., 2.]], | ||||
| @@ -80,3 +77,15 @@ def test_one_hot(): | |||||
| assert (output[1].asnumpy() == expect_1).all() | assert (output[1].asnumpy() == expect_1).all() | ||||
| assert (output[2].asnumpy() == expect_2).all() | assert (output[2].asnumpy() == expect_2).all() | ||||
| assert (output[3].asnumpy() == expect_3).all() | assert (output[3].asnumpy() == expect_3).all() | ||||
| @pytest.mark.level0 | |||||
| @pytest.mark.platform_x86_gpu_training | |||||
| @pytest.mark.env_onecard | |||||
| def test_one_hot_int32(): | |||||
| one_hot(np.int32) | |||||
| @pytest.mark.level0 | |||||
| @pytest.mark.platform_x86_gpu_training | |||||
| @pytest.mark.env_onecard | |||||
| def test_one_hot_int64(): | |||||
| one_hot(np.int64) | |||||