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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_CANDIDATE_SAMPLING_OPS_H_
- #define GE_OP_CANDIDATE_SAMPLING_OPS_H_
-
- #include "graph/operator_reg.h"
-
- namespace ge {
-
- /**
- *@brief Generates labels for candidate sampling with a learned unigram distribution.
-
- *@par Inputs:
- *The input true_classes must be two-dimensional matrices. Inputs include: \n
- *true_classes:A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
-
- *@par Attributes:
- *@li num_true:Number of true labels per context.
- *@li num_sampled:Number of candidates to randomly sample.
- *@li unique:If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
- *@li range_max:The sampler will sample integers from the interval [0, range_max).
- *@li seed:If either seed or seed2 are set to be non-zero.
- *@li seed2:An second seed to avoid seed collision.
-
- *@par Outputs:
- *sampled_candidates:A vector of length num_sampled, in which each element is the ID of a sampled candidate.
- *true_expected_count:A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
- *sampled_expected_count:A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
-
- *@attention Constraints: \n
- *-The implementation for ThreadUnsafeUnigramCandidateSampler on Ascend uses AI CPU, with bad performance. \n
-
- *@par Quantization supported or not
- *Not supported
- *@par Quantized inference supported or not
- *Supported
- *@par L2 convergence supported or not
- *@par Multiple batches supported or not
- */
-
- REG_OP(ThreadUnsafeUnigramCandidateSampler)
- .INPUT(true_classes, TensorType({ DT_INT64 }))
- .OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
- .OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
- .OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
- .REQUIRED_ATTR(num_true, Int)
- .REQUIRED_ATTR(num_sampled, Int)
- .REQUIRED_ATTR(unique, Bool)
- .REQUIRED_ATTR(range_max, Int)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(ThreadUnsafeUnigramCandidateSampler)
-
- /**
- *@brief Generates labels for candidate sampling with a learned unigram distribution.
-
- *@par Inputs:
- *The input true_classes must be two-dimensional matrices. Inputs include: \n
- *true_classes:A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
-
- *@par Attributes:
- *@li num_true:Number of true labels per context.
- *@li num_sampled:Number of candidates to randomly sample.
- *@li unique:If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
- *@li range_max:The sampler will sample integers from the interval [0, range_max).
- *@li seed:If either seed or seed2 are set to be non-zero.
- *@li seed2:An second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates:A vector of length num_sampled, in which each element is the ID of a sampled candidate.
- *@li true_expected_count:A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
- *@li sampled_expected_count:A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
-
- *@attention Constraints: \n
- *-The implementation for UniformCandidateSampler on Ascend uses AI CPU, with bad performance. \n
-
- *@par Quantization supported or not
- *Not supported
- *@par Quantized inference supported or not
- *Supported
- *@par L2 convergence supported or not
- *@par Multiple batches supported or not
- */
-
- REG_OP(UniformCandidateSampler)
- .INPUT(true_classes, TensorType({ DT_INT64 }))
- .OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
- .OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
- .OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
- .REQUIRED_ATTR(num_true, Int)
- .REQUIRED_ATTR(num_sampled, Int)
- .REQUIRED_ATTR(unique, Bool)
- .REQUIRED_ATTR(range_max, Int)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(UniformCandidateSampler)
-
- /**
- *@brief Generates labels for candidate sampling with a learned unigram distribution.
-
- *@par Inputs:
- *The input true_classes can be two-dimensional matrices. Inputs include: \n
- *true_classes:A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
-
- *@par Attributes:
- *@li num_true:Number of true labels per context.
- *@li num_sampled:Number of candidates to randomly sample.
- *@li unique:If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
- *@li range_max:The sampler will sample integers from the interval [0, range_max).
- *@li vocab_file:Each valid line in this file (which should have a CSV-like format) corresponds to a valid word ID. IDs are in sequential order, starting from num_reserved_ids.
- *@li distortion:The distortion is used to skew the unigram probability distribution. Each weight is first raised to the distortion's power before adding to the internal unigram distribution.
- *@li num_reserved_ids:Optionally some reserved IDs can be added in the range [0, ..., num_reserved_ids) by the users. One use case is that a special unknown word token is used as ID 0.
- *@li num_shards:A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism.
- *@li shard:A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism.
- *@li unigrams:A list of unigram counts or probabilities, one per ID in sequential order.
- *@li seed:If either seed or seed2 are set to be non-zero.
- *@li seed2:An second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates:A vector of length num_sampled, in which each element is the ID of a sampled candidate.
- *@li true_expected_count:A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
- *@li sampled_expected_count:A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
-
- *@attention Constraints: \n
- *-The implementation for FixedUnigramCandidateSampler on Ascend uses AI CPU, with bad performance. \n
-
- *@par Quantization supported or not
- *Not supported
- *@par Quantized inference supported or not
- *Supported
- *@par L2 convergence supported or not
- *@par Multiple batches supported or not
- */
-
- REG_OP(FixedUnigramCandidateSampler)
- .INPUT(true_classes, TensorType({ DT_INT64 }))
- .OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
- .OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
- .OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
- .ATTR(num_true, Int, 0)
- .ATTR(num_sampled, Int, 0)
- .ATTR(unique, Bool, false)
- .ATTR(range_max, Int, 0)
- .ATTR(vocab_file, String, "")
- .ATTR(distortion, Float, 1.0)
- .ATTR(num_reserved_ids, Int, 0)
- .ATTR(num_shards, Int, 1)
- .ATTR(shard, Int, 0)
- .REQUIRED_ATTR(unigrams, ListFloat)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(FixedUnigramCandidateSampler)
-
- /**
- *@brief Generates labels for candidate sampling with a learned unigram distribution.
-
- *@par Inputs:
- *The input true_classes can be two-dimensional matrices. Inputs include: \n
- *true_classes:A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
-
- *@par Attributes:
- *@li num_true:Number of true labels per context.
- *@li num_sampled:Number of candidates to randomly sample.
- *@li unique:If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
- *@li range_max:The sampler will sample integers from the interval [0, range_max).
- *@li seed:If either seed or seed2 are set to be non-zero.
- *@li seed2:An second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates:A vector of length num_sampled, in which each element is the ID of a sampled candidate.
- *@li true_expected_count:A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
- *@li sampled_expected_count:A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
-
- *@attention Constraints: \n
- *-The implementation for LearnedUnigramCandidateSampler on Ascend uses AI CPU, with bad performance. \n
-
- *@par Quantization supported or not
- *Not supported
- *@par Quantized inference supported or not
- *Supported
- *@par L2 convergence supported or not
- *@par Multiple batches supported or not
- */
-
- REG_OP(LearnedUnigramCandidateSampler)
- .INPUT(true_classes, TensorType({ DT_INT64 }))
- .OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
- .OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
- .OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
- .REQUIRED_ATTR(num_true, Int)
- .REQUIRED_ATTR(num_sampled, Int)
- .REQUIRED_ATTR(unique, Bool)
- .REQUIRED_ATTR(range_max, Int)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(LearnedUnigramCandidateSampler)
-
- /**
- *@brief Generates labels for candidate sampling with a log-uniform distribution.
-
- *@par Inputs:
- *The input true_classes can be two-dimensional matrices. Inputs include: \n
- *true_classes:A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
-
- *@par Attributes:
- *@li num_true:Number of true labels per context.
- *@li num_sampled:Number of candidates to randomly sample.
- *@li unique:If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
- *@li range_max:The sampler will sample integers from the interval [0, range_max).
- *@li seed:If either seed or seed2 are set to be non-zero.
- *@li seed2:An second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates:A vector of length num_sampled, in which each element is the ID of a sampled candidate.
- *@li true_expected_count:A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
- *@li sampled_expected_count:A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
-
- *@attention Constraints:\n
- *-The implementation for LogUniformCandidateSampler on Ascend uses AI CPU, with bad performance.\n
-
- *@par Quantization supported or not
- *Not supported
- *@par Quantized inference supported or not
- *Supported
- *@par L2 convergence supported or not
- *@par Multiple batches supported or not
- */
-
- REG_OP(LogUniformCandidateSampler)
- .INPUT(true_classes, TensorType({ DT_INT64 }))
- .OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
- .OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
- .OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
- .REQUIRED_ATTR(num_true, Int)
- .REQUIRED_ATTR(num_sampled, Int)
- .REQUIRED_ATTR(unique, Bool)
- .REQUIRED_ATTR(range_max, Int)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(LogUniformCandidateSampler)
-
- /**
- *@brief Generates labels for candidate sampling with a learned unigram distribution.
-
- *@par Inputs:
- *The input true_classes can be two-dimensional matrices. Inputs include: \n
- *true_classes:A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
-
- *@par Attributes:
- *@li num_true:Number of true labels per context.
- *@li num_sampled:Number of candidates to randomly sample.
- *@li unique:If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
- *@li seed:If either seed or seed2 are set to be non-zero.
- *@li seed2:An second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates:A vector of length num_sampled, in which each element is the ID of a sampled candidate.
- *@li true_expected_count:A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
- *@li sampled_expected_count:A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.
-
- *@attention Constraints:\n
- *-The implementation for AllCandidateSampler on Ascend uses AI CPU, with bad performance.\n
-
- *@par Quantization supported or not
- *Not supported
- *@par Quantized inference supported or not
- *Supported
- *@par L2 convergence supported or not
- *@par Multiple batches supported or not
- */
-
- REG_OP(AllCandidateSampler)
- .INPUT(true_classes, TensorType({ DT_INT64 }))
- .OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
- .OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
- .OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
- .REQUIRED_ATTR(num_true, Int)
- .REQUIRED_ATTR(num_sampled, Int)
- .REQUIRED_ATTR(unique, Bool)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(AllCandidateSampler)
-
- /**
- *@brief Computes the ids of the positions in sampled_candidates that match true_labels.
-
- *@par Inputs:
- * @li The input true_classes can be two-dimensional matrices. Inputs include: \n
- * @li true_classes:The true_classes output of UnpackSparseLabels. \n
- * @li sampled_candidates:The sampled_candidates output of CandidateSampler. \n
-
- *@par Attributes:
- *@li num_true:Number of true labels per context.
- *@li seed:If either seed or seed2 are set to be non-zero.
- *@li seed2:An second seed to avoid seed collision.
-
- *@par Outputs:
- * @li indices:A vector of indices corresponding to rows of true_candidates.
- * @li ids:A vector of IDs of positions in sampled_candidates that match a true_label for the row with the corresponding index in indices.
- * @li weights:A vector of the same length as indices and ids, in which each element is -FLOAT_MAX.
-
- *@attention Constraints:\n
- *-The implementation for ComputeAccidentalHits on Ascend uses AI CPU, with bad performance.\n
-
- *@par Quantization supported or not
- *Not supported
- *@par Quantized inference supported or not
- *Supported
- *@par L2 convergence supported or not
- *@par Multiple batches supported or not
- */
-
- REG_OP(ComputeAccidentalHits)
- .INPUT(true_classes, TensorType({ DT_INT64 }))
- .INPUT(sampled_candidates, TensorType({ DT_INT64 }))
- .OUTPUT(indices, TensorType({ DT_INT32 }))
- .OUTPUT(ids, TensorType({ DT_INT64 }))
- .OUTPUT(weights, TensorType({ DT_FLOAT }))
- .REQUIRED_ATTR(num_true, Int)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(ComputeAccidentalHits)
-
- } // namespace ge
-
- #endif // GE_OP_CANDIDATE_SAMPLING_OPS_H_
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