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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 \n
- a learned unigram distribution.
-
- *@par Inputs:
- *Input "true_classes" is a 2D matrix. \n
- *true_classes: A "batch_size * num_true" matrix, in which each row contains \n
- 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, samples with rejection, \n
- so that all sampled candidates in a batch are unique.
- *This requires some approximation to estimate the post-rejection \n
- sampling probabilities.
- *@li range_max: The sampler will sample integers from the interval \n
- [0, range_max).
- *@li seed: If either "seed" or "seed2" are set to be non-zero.
- *@li seed2: A second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates: A vector of length "num_sampled", in which each \n
- element is the ID of a sampled candidate.
- *@li true_expected_count: A "batch_size * num_true" matrix, representing \n
- the number of times each candidate is expected to occur in a batch of sampled \n
- candidates. If "unique" is true, then this is a probability.
- *@li sampled_expected_count: A vector of length "num_sampled", \n
- for each sampled candidate.
- *representing the number of times the candidate is expected to occur \n
- in a batch of sampled candidates.
- * If "unique" is true, then this is a probability. \n
-
- *@attention Constraints: \n
- *ThreadUnsafeUnigramCandidateSampler runs on the Ascend AI CPU, \n
- which delivers poor performance.
- */
-
- 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 \n
- unigram distribution.
-
- *@par Inputs:
- *true_classes: A "batch_size * num_true" matrix, in which each row contains \n
- the IDs of the "num_true" "target_classes" in the corresponding original label.
- *Input "true_classes" is a 2D matrix.
-
- *@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, samples with rejection, \n
- so that all sampled candidates in a batch are unique.
- *This requires some approximation to estimate the post-rejection \n
- sampling probabilities.
- *@li range_max: The sampler will sample integers from the interval \n
- [0, range_max).
- *@li seed: If either "seed" or "seed2" are set to be non-zero.
- *@li seed2: A second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates: A vector of length "num_sampled", \n
- in which each element is the ID of a sampled candidate.
- *@li true_expected_count: A "batch_size * num_true" matrix, representing the \n
- number of times each candidate is expected to occur \n
- in a batch of sampled candidates.
- *If "unique" is true, then this is a probability.
- *@li sampled_expected_count: A vector of length "num_sampled", for each \n
- sampled candidate representing the number of times.
- * the candidate is expected to occur in a batch of sampled candidates. \n
- *If "unique" is true, then this is a probability.
-
- *@attention Constraints: \n
- *UniformCandidateSampler runs on the Ascend AI CPU, \n
- which delivers poor performance.
- */
-
- 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 \n
- unigram distribution.
-
- *@par Inputs:
- *true_classes: A "batch_size * num_true" matrix, in which each row contains \n
- the IDs of the "num_true" "target_classes" in the corresponding original label.
- * Input "true_classes" is a 2D matrix.
-
- *@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, samples with rejection, \n
- so that all sampled candidates in a batch are unique. This requires \n
- 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 \n
- CSV-like format) corresponds to a valid word ID. \n
- *IDs are in sequential order, starting from num_reserved_ids.
- *@li distortion: The distortion is used to skew the unigram probability \n
- distribution. Each weight is first raised to the distortion's power before \n
- adding to the internal unigram distribution.
- *@li num_reserved_ids: Optionally some reserved IDs can be added in the range \n
- [0, ..., num_reserved_ids) by the users. \n
- * 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 \n
- 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 \n
- range in order to speed up the whole computation through parallelism.
- *@li unigrams: A list of unigram counts or probabilities, one per ID in \n
- sequential order.
- *@li seed: If either "seed" or "seed2" are set to be non-zero.
- *@li seed2: A second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates: A vector of length "num_sampled", in which each \n
- element is the ID of a sampled candidate.
- *@li true_expected_count: A "batch_size * num_true" matrix, representing the \n
- number of times each candidate is expected to occur in a batch of sampled \n
- candidates. If "unique" is true, then this is a probability.
- *@li sampled_expected_count: A vector of length "num_sampled", \n
- for each sampled candidate representing the number of times the candidate is \n
- expected to occur in a batch of sampled candidates. \n
- If "unique" is true, then this is a probability.
-
- *@attention Constraints: \n
- * FixedUnigramCandidateSampler runs on the Ascend AI CPU, \n
- which delivers poor performance.
- */
-
- 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 \n
- unigram distribution.
-
- *@par Inputs:
- *true_classes: A "batch_size * num_true" matrix, in which each row contains \n
- the IDs of the "num_true" "target_classes" in the corresponding original label.
- * Input "true_classes" is a 2D matrix.
-
- *@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, samples with rejection, \n
- so that all sampled candidates in a batch are unique. \n
- *This requires some approximation to estimate the post-rejection \n
- sampling probabilities.
- *@li range_max: The sampler will sample integers from the interval \n
- [0, range_max).
- *@li seed: If either "seed" or "seed2" are set to be non-zero.
- *@li seed2: A second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates: A vector of length "num_sampled", in which each \n
- element is the ID of a sampled candidate.
- *@li true_expected_count: A "batch_size * num_true" matrix, representing \n
- the number of times each candidate is expected to occur in a batch of sampled candidates. \n
- *If "unique" is true, then this is a probability.
- *@li sampled_expected_count: A vector of length "num_sampled", for each \n
- sampled candidate representing the number of times the candidate is expected \n
- to occur in a batch of sampled candidates. \n
- *If "unique" is true, then this is a probability.
-
- *@attention Constraints: \n
- *LearnedUnigramCandidateSampler runs on the Ascend AI CPU, which delivers \n
- poor performance.
- */
-
- 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 \n
- distribution.
-
- *@par Inputs:
- *true_classes: A "batch_size * num_true" matrix, in which each row contains \n
- the IDs of the "num_true" "target_classes" in the corresponding original label. \n
- * Input "true_classes" is a 2D matrix.
-
- *@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, samples with rejection, so that all \n
- sampled candidates in a batch are unique. This requires some approximation \n
- to estimate the post-rejection sampling probabilities.
- *@li range_max: The sampler will sample integers from the interval \n
- [0, range_max).
- *@li seed: If either "seed" or "seed2" are set to be non-zero.
- *@li seed2: A second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates: A vector of length "num_sampled", in which each \n
- element is the ID of a sampled candidate.
- *@li true_expected_count: A "batch_size * num_true" matrix, representing \n
- the number of times each candidate is expected to occur in a batch of sampled \n
- candidates. If "unique" is true, then this is a probability.
- *@li sampled_expected_count: A vector of length "num_sampled", for each \n
- sampled candidate representing the number of times the candidate is expected \n
- to occur in a batch of sampled candidates. \n
- *If "unique" is true, then this is a probability.
-
- *@attention Constraints: \n
- *LogUniformCandidateSampler runs on the Ascend AI CPU, which delivers \n
- poor performance.
- */
-
- 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 \n
- unigram distribution.
-
- *@par Inputs:
- *true_classes: A "batch_size * num_true" matrix, in which each row contains \n
- the IDs of the "num_true" "target_classes" in the corresponding original label. \n
- * Input "true_classes" is a 2D matrix.
-
- *@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, samples with rejection, \n
- so that all sampled candidates in a batch are unique. This requires some \n
- approximation to estimate the post-rejection sampling probabilities.
- *@li seed: If either "seed" or "seed2" are set to be non-zero.
- *@li seed2: A second seed to avoid seed collision.
-
- *@par Outputs:
- *@li sampled_candidates: A vector of length "num_sampled", \n
- in which each element is the ID of a sampled candidate.
- *@li true_expected_count: A "batch_size * num_true" matrix, representing the \n
- number of times each candidate is expected to occur in a batch of sampled candidates. \n
- *If "unique" is true, then this is a probability.
- *@li sampled_expected_count: A vector of length "num_sampled", for each \n
- sampled candidate representing the number of times the candidate is expected \n
- to occur in a batch of sampled candidates. If "unique" is true, then this is a probability.
-
- *@attention Constraints: \n
- *AllCandidateSampler runs on the Ascend AI CPU, which delivers poor performance. \n
- */
-
- 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 \n
- match "true_labels".
-
- *@par Inputs:
- * @li Input "true_classes" is a 2D matrix. \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: A 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 \n
- "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 \n
- each element is -FLOAT_MAX.
-
- *@attention Constraints: \n
- *ComputeAccidentalHits runs on the Ascend AI CPU, which delivers poor performance. \n
- */
-
- 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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