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- /**
- * Copyright 2019-2020 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.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
- #ifndef GE_OP_RANDOM_OPS_H_
- #define GE_OP_RANDOM_OPS_H_
-
- #include <vector>
-
- #include "graph/operator_reg.h"
-
- namespace ge {
-
- /**
- *@brief Draws samples from a multinomial distribution.
-
- *@par Inputs:
- *Inputs include: \n
- * @li logits: A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, \n
- int64, bfloat16, uint16, half, uint32, uint64. 2-D Tensor with shape [batch_size, num_classes].
- * @li num_samples: A Tensor of type int32. 0-D. Number of independent samples to draw for each row slice.
-
- *@par Attributes:
- *@li output_dtype: An optional type from: int32, int64. Defaults to int64.
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *y_indices: A Tensor of type output_dtype.
-
- *@attention Constraints:\n
- *-The implementation for Multinomial on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(Multinomial)
- .INPUT(logits, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(num_samples, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_INT32, DT_INT64}))
- .ATTR(dtype, Type, DT_INT64)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(Multinomial)
-
- /**
- *@brief Outputs random values from a normal distribution.
-
- *@par Inputs:
- *Inputs include: \n
- * @li shape: A Tensor. Must be one of the following types: int32, int64. \n
- The shape of the output tensor. Batches are indexed by the 0th dimension.
- * @li means: A Tensor. Must be one of the following types: half, bfloat16, float32, float64.
- * @li stdevs: A Tensor. Must have the same type as means.
- * @li min: A Tensor. Must have the same type as means. The minimum cutoff. May be -infinity.
- * @li max: A Tensor. Must have the same type as means.
-
- *@par Attributes:
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as means.
-
- *@attention Constraints:\n
- *-The implementation for ParameterizedTruncatedNormal on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(ParameterizedTruncatedNormal)
- .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
- .INPUT(means, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(stdevs, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(min, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(max, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(ParameterizedTruncatedNormal)
-
- /**
- *@brief Computes the derivative of a Gamma random sample w.r.t. alpha.
-
- *@par Inputs:
- *Inputs include: \n
- * @li alpha: A Tensor. Must be one of the following types: float32, float64.
- * @li sample: A Tensor. Must have the same type as alpha.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as alpha.
-
- *@attention Constraints:\n
- *-The implementation for RandomGammaGrad on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(RandomGammaGrad)
- .INPUT(alpha, TensorType({DT_FLOAT, DT_DOUBLE}))
- .INPUT(sample, TensorType({DT_FLOAT, DT_DOUBLE}))
- .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
- .OP_END_FACTORY_REG(RandomGammaGrad)
-
- /**
- *@brief Outputs random values from the Gamma distribution(s) described by alpha.
-
- *@par Inputs:
- *Inputs include: \n
- * @li shape: A Tensor. Must be one of the following types: int32, int64. 1-D integer tensor.
- * @li alpha: A Tensor. Must be one of the following types: half, float32, float64.
-
- *@par Attributes:
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as alpha.
-
- *@attention Constraints:\n
- *-The implementation for RandomGamma on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(RandomGamma)
- .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
- .INPUT(alpha, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(RandomGamma)
-
- /**
- *@brief Outputs random values from the Poisson distribution(s) described by rate.
-
- *@par Inputs:
- *Inputs include: \n
- * @li shape: A Tensor. Must be one of the following types: int32, int64. 1-D integer tensor.
- * @li rate: A Tensor. Must be one of the following types: half, float32, float64, int32, int64.
-
- *@par Attributes:
- *@li dtype: An optional type from: half, float32, float64, int32, int64. Defaults to int64.
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *y: A Tensor of type dtype.
-
- *@attention Constraints:\n
- *-The implementation for RandomPoisson on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(RandomPoisson)
- .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
- .INPUT(rate, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
- DT_INT32, DT_INT64}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
- DT_INT32, DT_INT64}))
- .ATTR(dtype, Type, DT_INT64)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(RandomPoisson)
-
- /**
- *@brief Randomly shuffles a tensor along its first dimension.
-
- *@par Inputs:
- *Inputs include: \n
- *x: A Tensor. The tensor to be shuffled.
-
- *@par Attributes:
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as x.
-
- *@attention Constraints:\n
- *-The implementation for RandomShuffle on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(RandomShuffle)
- .INPUT(x, TensorType({DT_INT64, DT_INT32, DT_UINT16, DT_INT16,
- DT_UINT8, DT_INT8, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64,
- DT_COMPLEX128, DT_BOOL, DT_STRING, DT_RESOURCE}))
- .OUTPUT(y, TensorType({DT_INT64, DT_INT32, DT_UINT16, DT_INT16,
- DT_UINT8, DT_INT8, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64,
- DT_COMPLEX128, DT_BOOL, DT_STRING, DT_RESOURCE}))
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(RandomShuffle)
-
- /**
- *@brief Outputs random values from a normal distribution.
-
- *@par Inputs:
- *Inputs include: \n
- *shape: A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
-
- *@par Attributes:
- *@li dtype: A type from: half, float16, float32, float64. The type of the output.
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *y: A Tensor of type dtype.
-
- *@attention Constraints:\n
- *-The implementation for RandomStandardNormal on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(RandomStandardNormal)
- .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .REQUIRED_ATTR(dtype, Type)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(RandomStandardNormal)
-
- /**
- *@brief Outputs random integers from a uniform distribution.
-
- *@par Inputs:
- *Inputs include: \n
- * @li shape: A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
- * @li min: A Tensor. Must be one of the following types: int32, int64. 0-D.
- * @li max: A Tensor. Must have the same type as minval. 0-D.
-
- *@par Attributes:
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as min.
-
- *@attention Constraints:\n
- *-The implementation for RandomUniformInt on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(RandomUniformInt)
- .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
- .INPUT(min, TensorType({DT_INT32, DT_INT64}))
- .INPUT(max, TensorType({DT_INT32, DT_INT64}))
- .OUTPUT(y, TensorType({DT_INT32, DT_INT64}))
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(RandomUniformInt)
-
- /**
- *@brief Outputs random values from a uniform distribution.
-
- *@par Inputs:
- *Inputs include: \n
- *shape: A Tensor. Must be one of the following types: int32, int64. The shape of the output tensor.
-
- *@par Attributes:
- *@li dtype: A type from: half, float16, float32, float64. The type of the output.
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *y: A Tensor of type dtype.
-
- *@attention Constraints:\n
- *-The implementation for RandomUniform on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(RandomUniform)
- .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .REQUIRED_ATTR(dtype, Type)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(RandomUniform)
-
- /**
- *@brief Outputs random values from a truncated normal distribution.
-
- *@par Inputs:
- *Inputs include: \n
- *shape: A Tensor. Must be one of the following types: int32, int64.
-
- *@par Attributes:
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *size: A Tensor of types: float16, float32, double.
-
- *@attention Constraints:\n
- *-The implementation for TruncatedNormal on Ascend uses AICPU, with bad performance.\n
-
- */
- REG_OP(TruncatedNormal)
- .INPUT(shape, TensorType({ DT_INT32, DT_INT64 }))
- .OUTPUT(y, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(TruncatedNormal)
-
- /**
- *@brief Generate random bit mask for dropout.
-
- *@par Inputs:
- include: \n
- *@li shape:The shape of the output tensor.
- *@li prob:0-D. Number of bit 1.
-
- *@par Attributes:
- *@li seed:If either seed or seed2 are set to be non-zero, the random number\n
- *generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
- *@li seed2:A second seed to avoid seed collision.
-
- *@par Outputs:
- *y:Output (1-D) random number using uint data format.
-
- *@attention Constraints:\n
- *The output is aligned with 128 bits
-
- *@see DropOutGenMask()
- */
- REG_OP(DropOutGenMask)
- .INPUT(shape, TensorType({ DT_INT32, DT_INT64 }))
- .INPUT(prob, TensorType({ DT_FLOAT16, DT_FLOAT }))
- .OUTPUT(y, TensorType({ DT_UINT8 }))
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(DropOutGenMask)
-
- /**
- *@brief Generates values in an interval.
-
- *@par Inputs:\n
- * Four ND inputs, including:
- *@li input_assist: A 1D Tensor of type float32.
- *@li input_start: A 1D Tensor of type float32, for the first entry in the range.
- *@li input_stop: A 1D Tensor of type float32, for the last entry in the range.
- *@li input_num: A 1D Tensor of type int32, for the common difference of the entries.
-
- *@par Outputs:\n
- *output_op: A 1D Tensor of type float32.
-
- *@attention Constraints:\n
- * "input_assist" is a sequence of "input_num" evenly-spaced values beginning at 0 with an common difference of 1.
- */
- REG_OP(LinSpaceD)
- .INPUT(assist, TensorType({DT_FLOAT}))
- .INPUT(start, TensorType({DT_FLOAT}))
- .INPUT(stop, TensorType({DT_FLOAT}))
- .INPUT(num, TensorType::IndexNumberType())
- .OUTPUT(output, TensorType({DT_FLOAT}))
- .OP_END_FACTORY_REG(LinSpaceD)
-
- /**
- *@brief Generates values in an interval.
-
- *@par Inputs:\n
- * Four ND inputs, including:
- *@li input_assist: A 1D Tensor of type float32.
- *@li input_start: A 1D Tensor of type float32, for the first entry in the range.
- *@li input_stop: A 1D Tensor of type float32, for the last entry in the range.
- *@li input_num: A 1D Tensor of type int32, for the common difference of the entries.
-
- *@par Outputs:\n
- *output_op: A 1D Tensor of type float32.
-
- *@attention Constraints:\n
- * "input_assist" is a sequence of "input_num" evenly-spaced values beginning at 0 with an common difference of 1.
-
- */
- REG_OP(LinSpace)
- .INPUT(start, TensorType({DT_FLOAT, DT_DOUBLE}))
- .INPUT(stop, TensorType({DT_FLOAT, DT_DOUBLE}))
- .INPUT(num, TensorType::IndexNumberType())
- .OUTPUT(output, TensorType({DT_FLOAT, DT_DOUBLE}))
- .OP_END_FACTORY_REG(LinSpace)
-
- REG_OP(Dropout)
- .INPUT(x, TensorType{DT_FLOAT})
- .OUTPUT(y, TensorType{DT_FLOAT})
- .ATTR(dropout_ratio, Float, 0.5)
- .ATTR(scale_train, Bool, true)
- .ATTR(alpha, Float, 1.0)
- .ATTR(beta, Float, 0.0)
- .OP_END_FACTORY_REG(Dropout)
-
- /**
- *@brief Shuffle index of no-zero element.
-
- *@par Inputs:
- include: \n
- *x:A tensor <= 5-D.
-
- *@par Attributes:
- *@li count:the count of output, if 0, out all no-zero elements.
- *@li seed:If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed.
- Otherwise, it is seeded by a random seed.
- *@li seed2:A second seed to avoid seed collision.
-
- *@par Outputs:
- *@li y:2-D tensor, no-zero element index.
- *@li mask:1-D, whether the corresponding index is valid.
-
- *@see RandomChoiceWithMask()
- */
- REG_OP(RandomChoiceWithMask)
- .INPUT(x, TensorType({DT_BOOL}))
- .OUTPUT(y, TensorType({DT_INT32}))
- .OUTPUT(mask, TensorType({DT_BOOL}))
- .ATTR(count, Int, 0)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(RandomChoiceWithMask)
-
- /**
- *@brief Permutes data in the channel dimension of the input
-
- *@par Inputs:
- *Inputs including: \n
- * @li x: A required Tensor. Must be one of the following types:
- float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64.
-
- *@par Attributes:
- *@li group: A required int32, specifying the number of groups to split the channel dimension into. Defaults to "1".
-
- *@par Outputs:
- *y: A required Tensor. Has same type and shape as "x". Must be one of the following types:
- float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64.
-
- *@attention Constraints:\n
- *@li "group" must be greater than 0 and must evenly divide the channel dimension size.
- *@li The format of input "x" must be NCHW.
- */
- REG_OP(ShuffleChannel)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT8, DT_UINT8, DT_INT16,
- DT_UINT16, DT_INT32, DT_UINT32,DT_INT64,DT_UINT64}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT8, DT_UINT8, DT_INT16,
- DT_UINT16, DT_INT32, DT_UINT32,DT_INT64,DT_UINT64}))
- .ATTR(group, Int, 1)
- .OP_END_FACTORY_REG(ShuffleChannel)
- } // namespace ge
-
- #endif // GE_OP_RANDOM_OPS_H_
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