move P.UniformCandidateSampler to ops.random

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/uniform_candidate_sampler_gpu_kernel.cc → mindspore/ccsrc/backend/kernel_compiler/gpu/random/uniform_candidate_sampler_gpu_kernel.cc

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-#include "backend/kernel_compiler/gpu/nn/uniform_candidate_sampler_gpu_kernel.h"
+#include "backend/kernel_compiler/gpu/random/uniform_candidate_sampler_gpu_kernel.h"
 
 namespace mindspore {
 namespace kernel {

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/uniform_candidate_sampler_gpu_kernel.h → mindspore/ccsrc/backend/kernel_compiler/gpu/random/uniform_candidate_sampler_gpu_kernel.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_UNIFORM_CANDIDATE_SAMPLER_GPU_KERNEL_H_
-#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_UNIFORM_CANDIDATE_SAMPLER_GPU_KERNEL_H_
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_RANDOM_UNIFORM_CANDIDATE_SAMPLER_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_RANDOM_UNIFORM_CANDIDATE_SAMPLER_GPU_KERNEL_H_
 
 #include <cmath>
 #include <set>
@@ -160,4 +160,4 @@ class UniformCandidateSamplerGpuKernel : public GpuKernel {
 };
 }  // namespace kernel
 }  // namespace mindspore
-#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_UNIFORM_CANDIDATE_SAMPLER_GPU_KERNEL_H_
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_RANDOM_UNIFORM_CANDIDATE_SAMPLER_GPU_KERNEL_H_

mindspore/ops/operations/__init__.py

@@ -57,7 +57,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
                        Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps, Tan)
 
 from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, Poisson, UniformInt, UniformReal,
-                         RandomCategorical, StandardLaplace, Multinomial)
+                         RandomCategorical, StandardLaplace, Multinomial, UniformCandidateSampler)
 from .nn_ops import (LSTM, SGD, Adam, FusedSparseAdam, FusedSparseLazyAdam, AdamNoUpdateParam, ApplyMomentum, BatchNorm,
                      BiasAdd, Conv2D,
                      DepthwiseConv2dNative,
@@ -79,7 +79,7 @@ from .nn_ops import (LSTM, SGD, Adam, FusedSparseAdam, FusedSparseLazyAdam, Adam
                      FusedSparseFtrl, FusedSparseProximalAdagrad,
                      ApplyAdaMax, ApplyAdadelta, ApplyAdagrad, ApplyAdagradV2,
                      ApplyAddSign, ApplyPowerSign, ApplyGradientDescent, ApplyProximalGradientDescent,
-                     ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK, UniformCandidateSampler)
+                     ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK)
 from . import _quant_ops
 from ._quant_ops import *
 from .other_ops import (Assign, InplaceAssign, IOU, BoundingBoxDecode, BoundingBoxEncode,

mindspore/ops/operations/nn_ops.py

@@ -6201,61 +6201,3 @@ class LRN(PrimitiveWithInfer):
     def infer_shape(self, x_shape):
         validator.check_int(len(x_shape), 4, Rel.EQ, "x_shape", self.name)
         return x_shape
-
-
-class UniformCandidateSampler(PrimitiveWithInfer):
-    r"""
-    Uniform candidate sampler.
-
-    This function samples a set of classes(sampled_candidates) from [0, range_max-1] based on uniform distribution.
-    If unique=True, candidates are drawn without replacement, else unique=False with replacement.
-
-    Args:
-        num_true (int): The number of target classes in each training example.
-        num_sampled (int): The number of classes to randomly sample. The sampled_candidates will have a shape
-            of num_sampled. If unique=True, num_sampled must be less than or equal to range_max.
-        unique (bool): Whether all sampled classes in a batch are unique.
-        range_max (int): The number of possible classes.
-        seed (int): Random seed, must be non-negative. Default: 0.
-        remove_accidental_hits (bool): Whether accidental hit is removed. Default: False.
-
-    Inputs:
-        true_classes (int): A tensor. The target classes with a tensor shape of (batch_size, num_true).
-
-    Outputs:
-        A tuple of 3 tensors.
-
-        sampled_candidates: (int): The sampled_candidates is independent of the true classes. Shape: (num_sampled, ).
-        true_expected_count: (float): The expected counts under the sampling distribution of each of true_classes.
-            Shape: (batch_size, num_true).
-        sampled_expected_count: (float): The expected counts under the sampling distribution of each of
-            sampled_candidates. Shape: (num_sampled, ).
-
-    Examples:
-        >>> sampler = P.UniformCandidateSampler(1, 3, False, 4)
-        >>> output1, output2, output3 = sampler(Tensor(np.array([[1],[3],[4],[6],[3]], dtype=np.int32)))
-        [1, 1, 3], [[0.75], [0.75], [0.75], [0.75], [0.75]], [0.75, 0.75, 0.75]
-    """
-
-    @prim_attr_register
-    def __init__(self, num_true, num_sampled, unique, range_max, seed=0, remove_accidental_hits=False):
-        """Initialize UniformCandidateSampler"""
-        validator.check_value_type("num_true", num_true, [int], self.name)
-        validator.check_value_type("num_sampled", num_sampled, [int], self.name)
-        validator.check_value_type("unique", unique, [bool], self.name)
-        validator.check_value_type("range_max", range_max, [int], self.name)
-        validator.check_value_type("seed", seed, [int], self.name)
-        validator.check_value_type("remove_accidental_hits", remove_accidental_hits, [bool], self.name)
-        validator.check("value of num_sampled", num_sampled, '', 0, Rel.GT, self.name)
-        self.num_true = num_true
-        if unique:
-            validator.check('value of num_sampled', num_sampled, "value of range_max", range_max, Rel.LE, self.name)
-        validator.check("value of seed", seed, '', 0, Rel.GE, self.name)
-        self.num_sampled = num_sampled
-
-    def infer_dtype(self, true_classes_type):
-        return (true_classes_type, mstype.float32, mstype.float32)
-
-    def infer_shape(self, true_classes_shape):
-        validator.check("true_class[1]", true_classes_shape[1], "num_true", self.num_true, Rel.EQ, self.name)
-        return ([self.num_sampled], true_classes_shape, [self.num_sampled])

mindspore/ops/operations/random_ops.py

@@ -500,3 +500,61 @@ class Multinomial(PrimitiveWithInfer):
             "dtype": mstype.int32,
             "value": None}
         return out
+
+class UniformCandidateSampler(PrimitiveWithInfer):
+    r"""
+    Uniform candidate sampler.
+
+    This function samples a set of classes(sampled_candidates) from [0, range_max-1] based on uniform distribution.
+    If unique=True, candidates are drawn without replacement, else unique=False with replacement.
+
+    Args:
+        num_true (int): The number of target classes in each training example.
+        num_sampled (int): The number of classes to randomly sample. The sampled_candidates will have a shape
+            of num_sampled. If unique=True, num_sampled must be less than or equal to range_max.
+        unique (bool): Whether all sampled classes in a batch are unique.
+        range_max (int): The number of possible classes, must be non-negative.
+        seed (int): Random seed, must be non-negative. Default: 0.
+        remove_accidental_hits (bool): Whether accidental hit is removed. Default: False.
+
+    Inputs:
+        - **true_classes** (Tensor) - A Tensor. The target classes with a Tensor shape of (batch_size, num_true).
+
+    Outputs:
+        - **sampled_candidates** (Tensor) - The sampled_candidates is independent of the true classes.
+            Shape: (num_sampled, ).
+        - **true_expected_count** (Tensor) - The expected counts under the sampling distribution of each
+            of true_classes. Shape: (batch_size, num_true).
+        - **sampled_expected_count** (Tensor) - The expected counts under the sampling distribution of
+            each of sampled_candidates. Shape: (num_sampled, ).
+
+    Examples:
+        >>> sampler = P.UniformCandidateSampler(1, 3, False, 4)
+        >>> output1, output2, output3 = sampler(Tensor(np.array([[1],[3],[4],[6],[3]], dtype=np.int32)))
+        >>> print(output1, output2, output3)
+        [1, 1, 3], [[0.75], [0.75], [0.75], [0.75], [0.75]], [0.75, 0.75, 0.75]
+    """
+
+    @prim_attr_register
+    def __init__(self, num_true, num_sampled, unique, range_max, seed=0, remove_accidental_hits=False):
+        """Initialize UniformCandidateSampler"""
+        Validator.check_value_type("num_true", num_true, [int], self.name)
+        Validator.check_value_type("num_sampled", num_sampled, [int], self.name)
+        Validator.check_value_type("unique", unique, [bool], self.name)
+        Validator.check_value_type("range_max", range_max, [int], self.name)
+        Validator.check_value_type("seed", seed, [int], self.name)
+        Validator.check_value_type("remove_accidental_hits", remove_accidental_hits, [bool], self.name)
+        Validator.check("value of num_sampled", num_sampled, '', 0, Rel.GT, self.name)
+        Validator.check("value of range_max", range_max, '', 0, Rel.GT, self.name)
+        self.num_true = num_true
+        if unique:
+            Validator.check('value of num_sampled', num_sampled, "value of range_max", range_max, Rel.LE, self.name)
+        Validator.check("value of seed", seed, '', 0, Rel.GE, self.name)
+        self.num_sampled = num_sampled
+
+    def infer_dtype(self, true_classes_type):
+        return (true_classes_type, mstype.float32, mstype.float32)
+
+    def infer_shape(self, true_classes_shape):
+        Validator.check("true_class.shape[1]", true_classes_shape[1], "num_true", self.num_true, Rel.EQ, self.name)
+        return ([self.num_sampled], true_classes_shape, [self.num_sampled])