initial commit

ci fix

fix ci

mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/one_hot_gpu_kernel.cc

@@ -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");
  * 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
  * limitations under the License.
  */
+#include <cstdint>
 
 #include "backend/kernel_compiler/gpu/arrays/one_hot_gpu_kernel.h"
 
@@ -32,5 +33,19 @@ MS_REG_GPU_KERNEL_TWO(OneHot,
                         .AddInputAttr(kNumberTypeFloat16)
                         .AddOutputAttr(kNumberTypeFloat16),
                       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 mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/one_hot_impl.cu

@@ -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);
 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);
+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);

mindspore/ops/operations/nn_ops.py

@@ -3030,7 +3030,7 @@ class OneHot(PrimitiveWithInfer):
 
     Inputs:
         - **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.
         - **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`.
@@ -3060,7 +3060,7 @@ class OneHot(PrimitiveWithInfer):
 
     def __infer__(self, indices, depth, on_value, off_value):
         # 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)
         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)

tests/st/ops/gpu/test_one_hot_op.py

@@ -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");
 # 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))
 
 
-@pytest.mark.level0
+def one_hot(nptype):
-@pytest.mark.platform_x86_gpu_training
+    one_hot_net = NetOneHot()
-@pytest.mark.env_onecard
+    indices1 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(nptype))
-def test_one_hot():
+    indices2 = Tensor(np.array([1, 2, 3]).astype(nptype))
-    one_hot = NetOneHot()
+    indices3 = Tensor(np.array([[0, 1], [1, 0]]).astype(nptype))
-    indices1 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(np.int32))
+    indices4 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(nptype))
-    indices2 = Tensor(np.array([1, 2, 3]).astype(np.int32))
+    output = one_hot_net(indices1, indices2, indices3, indices4)
-    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)
     expect_0 = np.array([
         [[2., 3., 3., 3., 3., 3.], [3., 2., 3., 3., 3., 3.]],
         [[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[2].asnumpy() == expect_2).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)