You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
341 lines
16 KiB
341 lines
16 KiB
/**
|
|
* 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_
|