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398 lines
16 KiB
398 lines
16 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef GE_OP_CANDIDATE_SAMPLING_OPS_H_
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#define GE_OP_CANDIDATE_SAMPLING_OPS_H_
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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*@brief Generates labels for candidate sampling with \n
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a learned unigram distribution.
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*@par Inputs:
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*Input "true_classes" is a 2D matrix. \n
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*true_classes: A "batch_size * num_true" matrix, in which each row contains \n
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the IDs of the "num_true" "target_classes" in the corresponding original label.
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*@par Attributes:
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*@li num_true: Number of true labels per context.
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*@li num_sampled: Number of candidates to randomly sample.
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*@li unique: If "unique" is true, samples with rejection, \n
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so that all sampled candidates in a batch are unique.
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*This requires some approximation to estimate the post-rejection \n
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sampling probabilities.
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*@li range_max: The sampler will sample integers from the interval \n
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[0, range_max).
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*@li seed: If either "seed" or "seed2" are set to be non-zero.
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*@li seed2: A second seed to avoid seed collision.
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*@par Outputs:
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*@li sampled_candidates: A vector of length "num_sampled", in which each \n
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element is the ID of a sampled candidate.
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*@li true_expected_count: A "batch_size * num_true" matrix, representing \n
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the number of times each candidate is expected to occur in a batch of sampled \n
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candidates. If "unique" is true, then this is a probability.
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*@li sampled_expected_count: A vector of length "num_sampled", \n
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for each sampled candidate.
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*representing the number of times the candidate is expected to occur \n
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in a batch of sampled candidates.
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* If "unique" is true, then this is a probability. \n
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*@attention Constraints: \n
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*ThreadUnsafeUnigramCandidateSampler runs on the Ascend AI CPU, \n
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which delivers poor performance.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator ThreadUnsafeUnigramCandidateSampler.
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*/
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REG_OP(ThreadUnsafeUnigramCandidateSampler)
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.INPUT(true_classes, TensorType({ DT_INT64 }))
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.OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
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.OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
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.OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
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.REQUIRED_ATTR(num_true, Int)
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.REQUIRED_ATTR(num_sampled, Int)
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.REQUIRED_ATTR(unique, Bool)
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.REQUIRED_ATTR(range_max, Int)
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.ATTR(seed, Int, 0)
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.ATTR(seed2, Int, 0)
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.OP_END_FACTORY_REG(ThreadUnsafeUnigramCandidateSampler)
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/**
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*@brief Generates labels for candidate sampling with a learned \n
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unigram distribution.
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*@par Inputs:
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*true_classes: A "batch_size * num_true" matrix, in which each row contains \n
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the IDs of the "num_true" "target_classes" in the corresponding original label.
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*Input "true_classes" is a 2D matrix.
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*@par Attributes:
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*@li num_true: Number of true labels per context.
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*@li num_sampled: Number of candidates to randomly sample.
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*@li unique: If "unique" is true, samples with rejection, \n
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so that all sampled candidates in a batch are unique.
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*This requires some approximation to estimate the post-rejection \n
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sampling probabilities.
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*@li range_max: The sampler will sample integers from the interval \n
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[0, range_max).
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*@li seed: If either "seed" or "seed2" are set to be non-zero.
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*@li seed2: A second seed to avoid seed collision.
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*@par Outputs:
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*@li sampled_candidates: A vector of length "num_sampled", \n
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in which each element is the ID of a sampled candidate.
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*@li true_expected_count: A "batch_size * num_true" matrix, representing the \n
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number of times each candidate is expected to occur \n
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in a batch of sampled candidates.
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*If "unique" is true, then this is a probability.
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*@li sampled_expected_count: A vector of length "num_sampled", for each \n
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sampled candidate representing the number of times.
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* the candidate is expected to occur in a batch of sampled candidates. \n
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*If "unique" is true, then this is a probability.
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*@attention Constraints: \n
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*UniformCandidateSampler runs on the Ascend AI CPU, \n
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which delivers poor performance.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator UniformCandidateSampler.
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*/
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REG_OP(UniformCandidateSampler)
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.INPUT(true_classes, TensorType({ DT_INT64 }))
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.OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
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.OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
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.OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
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.REQUIRED_ATTR(num_true, Int)
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.REQUIRED_ATTR(num_sampled, Int)
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.REQUIRED_ATTR(unique, Bool)
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.REQUIRED_ATTR(range_max, Int)
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.ATTR(seed, Int, 0)
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.ATTR(seed2, Int, 0)
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.OP_END_FACTORY_REG(UniformCandidateSampler)
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/**
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*@brief Generates labels for candidate sampling with a learned \n
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unigram distribution.
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*@par Inputs:
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*true_classes: A "batch_size * num_true" matrix, in which each row contains \n
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the IDs of the "num_true" "target_classes" in the corresponding original label.
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* Input "true_classes" is a 2D matrix.
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*@par Attributes:
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*@li num_true: Number of true labels per context.
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*@li num_sampled: Number of candidates to randomly sample.
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*@li unique: If "unique" is true, samples with rejection, \n
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so that all sampled candidates in a batch are unique. This requires \n
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some approximation to estimate the post-rejection sampling probabilities.
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*@li range_max: The sampler will sample integers from the interval [0, range_max).
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*@li vocab_file: Each valid line in this file (which should have a \n
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CSV-like format) corresponds to a valid word ID. \n
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*IDs are in sequential order, starting from num_reserved_ids.
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*@li distortion: The distortion is used to skew the unigram probability \n
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distribution. Each weight is first raised to the distortion's power before \n
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adding to the internal unigram distribution.
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*@li num_reserved_ids: Optionally some reserved IDs can be added in the range \n
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[0, ..., num_reserved_ids) by the users. \n
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* One use case is that a special unknown word token is used as ID 0.
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*@li num_shards: A sampler can be used to sample from a subset of the \n
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original range. in order to speed up the whole computation through parallelism.
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*@li shard: A sampler can be used to sample from a subset of the original \n
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range in order to speed up the whole computation through parallelism.
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*@li unigrams: A list of unigram counts or probabilities, one per ID in \n
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sequential order.
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*@li seed: If either "seed" or "seed2" are set to be non-zero.
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*@li seed2: A second seed to avoid seed collision.
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*@par Outputs:
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*@li sampled_candidates: A vector of length "num_sampled", in which each \n
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element is the ID of a sampled candidate.
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*@li true_expected_count: A "batch_size * num_true" matrix, representing the \n
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number of times each candidate is expected to occur in a batch of sampled \n
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candidates. If "unique" is true, then this is a probability.
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*@li sampled_expected_count: A vector of length "num_sampled", \n
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for each sampled candidate representing the number of times the candidate is \n
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expected to occur in a batch of sampled candidates. \n
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If "unique" is true, then this is a probability.
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*@attention Constraints: \n
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* FixedUnigramCandidateSampler runs on the Ascend AI CPU, \n
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which delivers poor performance.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator FixedUnigramCandidateSampler.
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*/
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REG_OP(FixedUnigramCandidateSampler)
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.INPUT(true_classes, TensorType({ DT_INT64 }))
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.OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
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.OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
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.OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
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.ATTR(num_true, Int, 0)
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.ATTR(num_sampled, Int, 0)
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.ATTR(unique, Bool, false)
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.ATTR(range_max, Int, 0)
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.ATTR(vocab_file, String, "")
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.ATTR(distortion, Float, 1.0)
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.ATTR(num_reserved_ids, Int, 0)
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.ATTR(num_shards, Int, 1)
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.ATTR(shard, Int, 0)
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.REQUIRED_ATTR(unigrams, ListFloat)
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.ATTR(seed, Int, 0)
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.ATTR(seed2, Int, 0)
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.OP_END_FACTORY_REG(FixedUnigramCandidateSampler)
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/**
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*@brief Generates labels for candidate sampling with a learned \n
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unigram distribution.
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*@par Inputs:
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*true_classes: A "batch_size * num_true" matrix, in which each row contains \n
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the IDs of the "num_true" "target_classes" in the corresponding original label.
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* Input "true_classes" is a 2D matrix.
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*@par Attributes:
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*@li num_true: Number of true labels per context.
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*@li num_sampled: Number of candidates to randomly sample.
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*@li unique: If "unique" is true, samples with rejection, \n
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so that all sampled candidates in a batch are unique. \n
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*This requires some approximation to estimate the post-rejection \n
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sampling probabilities.
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*@li range_max: The sampler will sample integers from the interval \n
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[0, range_max).
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*@li seed: If either "seed" or "seed2" are set to be non-zero.
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*@li seed2: A second seed to avoid seed collision.
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*@par Outputs:
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*@li sampled_candidates: A vector of length "num_sampled", in which each \n
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element is the ID of a sampled candidate.
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*@li true_expected_count: A "batch_size * num_true" matrix, representing \n
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the number of times each candidate is expected to occur in a batch of sampled candidates. \n
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*If "unique" is true, then this is a probability.
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*@li sampled_expected_count: A vector of length "num_sampled", for each \n
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sampled candidate representing the number of times the candidate is expected \n
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to occur in a batch of sampled candidates. \n
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*If "unique" is true, then this is a probability.
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*@attention Constraints: \n
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*LearnedUnigramCandidateSampler runs on the Ascend AI CPU, which delivers \n
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poor performance.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator LearnedUnigramCandidateSampler.
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*/
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REG_OP(LearnedUnigramCandidateSampler)
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.INPUT(true_classes, TensorType({ DT_INT64 }))
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.OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
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.OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
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.OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
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.REQUIRED_ATTR(num_true, Int)
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.REQUIRED_ATTR(num_sampled, Int)
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.REQUIRED_ATTR(unique, Bool)
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.REQUIRED_ATTR(range_max, Int)
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.ATTR(seed, Int, 0)
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.ATTR(seed2, Int, 0)
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.OP_END_FACTORY_REG(LearnedUnigramCandidateSampler)
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/**
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*@brief Generates labels for candidate sampling with a log-uniform \n
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distribution.
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*@par Inputs:
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*true_classes: A "batch_size * num_true" matrix, in which each row contains \n
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the IDs of the "num_true" "target_classes" in the corresponding original label. \n
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* Input "true_classes" is a 2D matrix.
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*@par Attributes:
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*@li num_true: Number of true labels per context.
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*@li num_sampled: Number of candidates to randomly sample.
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*@li unique: If "unique" is true, samples with rejection, so that all \n
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sampled candidates in a batch are unique. This requires some approximation \n
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to estimate the post-rejection sampling probabilities.
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*@li range_max: The sampler will sample integers from the interval \n
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[0, range_max).
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*@li seed: If either "seed" or "seed2" are set to be non-zero.
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*@li seed2: A second seed to avoid seed collision.
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*@par Outputs:
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*@li sampled_candidates: A vector of length "num_sampled", in which each \n
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element is the ID of a sampled candidate.
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*@li true_expected_count: A "batch_size * num_true" matrix, representing \n
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the number of times each candidate is expected to occur in a batch of sampled \n
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candidates. If "unique" is true, then this is a probability.
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*@li sampled_expected_count: A vector of length "num_sampled", for each \n
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sampled candidate representing the number of times the candidate is expected \n
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to occur in a batch of sampled candidates. \n
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*If "unique" is true, then this is a probability.
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*@attention Constraints: \n
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*LogUniformCandidateSampler runs on the Ascend AI CPU, which delivers \n
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poor performance.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator LogUniformCandidateSampler.
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*/
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REG_OP(LogUniformCandidateSampler)
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.INPUT(true_classes, TensorType({ DT_INT64 }))
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.OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
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.OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
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.OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
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.REQUIRED_ATTR(num_true, Int)
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.REQUIRED_ATTR(num_sampled, Int)
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.REQUIRED_ATTR(unique, Bool)
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.REQUIRED_ATTR(range_max, Int)
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.ATTR(seed, Int, 0)
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.ATTR(seed2, Int, 0)
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.OP_END_FACTORY_REG(LogUniformCandidateSampler)
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/**
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*@brief Generates labels for candidate sampling with a learned \n
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unigram distribution.
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*@par Inputs:
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*true_classes: A "batch_size * num_true" matrix, in which each row contains \n
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the IDs of the "num_true" "target_classes" in the corresponding original label. \n
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* Input "true_classes" is a 2D matrix.
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*@par Attributes:
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*@li num_true: Number of true labels per context.
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*@li num_sampled: Number of candidates to randomly sample.
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*@li unique: If "unique" is true, samples with rejection, \n
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so that all sampled candidates in a batch are unique. This requires some \n
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approximation to estimate the post-rejection sampling probabilities.
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*@li seed: If either "seed" or "seed2" are set to be non-zero.
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*@li seed2: A second seed to avoid seed collision.
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*@par Outputs:
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*@li sampled_candidates: A vector of length "num_sampled", \n
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in which each element is the ID of a sampled candidate.
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*@li true_expected_count: A "batch_size * num_true" matrix, representing the \n
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number of times each candidate is expected to occur in a batch of sampled candidates. \n
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*If "unique" is true, then this is a probability.
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*@li sampled_expected_count: A vector of length "num_sampled", for each \n
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sampled candidate representing the number of times the candidate is expected \n
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to occur in a batch of sampled candidates. If "unique" is true, then this is a probability.
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*@attention Constraints: \n
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*AllCandidateSampler runs on the Ascend AI CPU, which delivers poor performance. \n
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator AllCandidateSampler.
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*/
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REG_OP(AllCandidateSampler)
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.INPUT(true_classes, TensorType({ DT_INT64 }))
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.OUTPUT(sampled_candidates, TensorType({ DT_INT64 }))
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.OUTPUT(true_expected_count, TensorType({ DT_FLOAT }))
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.OUTPUT(sampled_expected_count, TensorType({ DT_FLOAT }))
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.REQUIRED_ATTR(num_true, Int)
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.REQUIRED_ATTR(num_sampled, Int)
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.REQUIRED_ATTR(unique, Bool)
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.ATTR(seed, Int, 0)
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.ATTR(seed2, Int, 0)
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.OP_END_FACTORY_REG(AllCandidateSampler)
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/**
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*@brief Computes the "ids" of the positions in "sampled_candidates" that \n
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match "true_labels".
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*@par Inputs:
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* @li Input "true_classes" is a 2D matrix. \n
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* @li true_classes: The "true_classes" output of UnpackSparseLabels. \n
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* @li sampled_candidates: The "sampled_candidates" output of CandidateSampler. \n
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*@par Attributes:
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*@li num_true: Number of true labels per context.
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*@li seed: If either "seed" or "seed2" are set to be non-zero.
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*@li seed2: A second seed to avoid seed collision.
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*@par Outputs:
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* @li indices: A vector of indices corresponding to rows of "true_candidates".
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* @li ids: A vector of IDs of positions in "sampled_candidates" that match a \n
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"true_label" for the row with the corresponding index in indices.
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* @li weights: A vector of the same length as "indices" and "ids", in which \n
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each element is -FLOAT_MAX.
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*@attention Constraints: \n
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*ComputeAccidentalHits runs on the Ascend AI CPU, which delivers poor performance. \n
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator ComputeAccidentalHits.
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*/
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REG_OP(ComputeAccidentalHits)
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.INPUT(true_classes, TensorType({ DT_INT64 }))
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.INPUT(sampled_candidates, TensorType({ DT_INT64 }))
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.OUTPUT(indices, TensorType({ DT_INT32 }))
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.OUTPUT(ids, TensorType({ DT_INT64 }))
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.OUTPUT(weights, TensorType({ DT_FLOAT }))
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.REQUIRED_ATTR(num_true, Int)
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.ATTR(seed, Int, 0)
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.ATTR(seed2, Int, 0)
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.OP_END_FACTORY_REG(ComputeAccidentalHits)
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} // namespace ge
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#endif // GE_OP_CANDIDATE_SAMPLING_OPS_H_
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