wehub-resource-sync
531 lines
25 KiB
C++
531 lines
25 KiB
C++
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "paddle/phi/common/int_array.h"
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#include "paddle/phi/core/meta_tensor.h"
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namespace phi {
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// Common InferMeta Functions for ternary operators, The format like:
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//
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// 1. void [FunctionDesc|OpName]InferMeta(const MetaTensor& x,
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// const MetaTensor& y,
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// const MetaTensor& z,
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// ...,
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// MetaTensor* out) {}
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//
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// NOTE: The name "InferShape" may be not appropriate. "InferMeta" may be good.
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// Because functions in this file not only can infer shape, but also need
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// infer lod or other useful data.
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//
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// The InferMeta Functions in this file are arranged in alphabetic order.
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PADDLE_API void AccuracyInferMeta(const MetaTensor& out,
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const MetaTensor& indice,
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const MetaTensor& label,
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MetaTensor* accuracy,
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MetaTensor* correct,
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MetaTensor* total,
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MetaConfig config = MetaConfig());
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PADDLE_API void AddmmInferMeta(const MetaTensor& input,
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const MetaTensor& x,
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const MetaTensor& y,
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float beta,
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float alpha,
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MetaTensor* out);
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PADDLE_API void BaddbmmInferMeta(const MetaTensor& input,
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const MetaTensor& x,
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const MetaTensor& y,
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float beta,
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float alpha,
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DataType out_dtype,
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MetaTensor* out);
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PADDLE_API void AffineChannelInferMeta(const MetaTensor& x,
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const MetaTensor& scale,
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const MetaTensor& bias,
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const std::string& data_layout,
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MetaTensor* out,
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MetaConfig config = MetaConfig());
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PADDLE_API void ArangeTensorInferMeta(const MetaTensor& start,
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const MetaTensor& end,
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const MetaTensor& step,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void RangeTensorInferMeta(const MetaTensor& start,
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const MetaTensor& end,
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const MetaTensor& step,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void ArangeTensorInferMetaLegacy(const MetaTensor& start,
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const MetaTensor& end,
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const MetaTensor& step,
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MetaTensor* out);
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PADDLE_API void RangeTensorInferMetaLegacy(const MetaTensor& start,
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const MetaTensor& end,
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const MetaTensor& step,
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MetaTensor* out);
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PADDLE_API void AssignPosInferMeta(const MetaTensor& x,
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const MetaTensor& cum_count,
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const MetaTensor& eff_num_len,
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MetaTensor* out);
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PADDLE_API void BatchFCInferMeta(const MetaTensor& input,
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const MetaTensor& w,
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const MetaTensor& bias,
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MetaTensor* out);
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PADDLE_API void BoxCoderInferMeta(const MetaTensor& prior_box,
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const MetaTensor& prior_box_var,
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const MetaTensor& target_box,
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const std::string& code_type,
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bool box_normalized,
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int axis,
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const std::vector<float>& variance,
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MetaTensor* output_box,
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MetaConfig config = MetaConfig());
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PADDLE_API void CrossEntropyWithSoftmaxBwdWithDowncastInferMeta(
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const MetaTensor& label,
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const MetaTensor& softmax,
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const MetaTensor& loss_grad,
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MetaTensor* logits_grad);
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PADDLE_API void CollectFpnProposalsInferMeta(
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const std::vector<const MetaTensor*>& multi_level_rois,
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const std::vector<const MetaTensor*>& multi_level_scores,
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const paddle::optional<std::vector<const MetaTensor*>>&
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multi_level_rois_num,
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int post_nms_topn,
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MetaTensor* fpn_rois,
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MetaTensor* rois_num,
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MetaConfig config = MetaConfig());
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PADDLE_API void CSoftmaxWithMultiLabelCrossEntropyInferMeta(
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const MetaTensor& logits,
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const MetaTensor& label,
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const MetaTensor& smooth_weight,
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int64_t ignore_index,
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bool sum_multi_label_loss,
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int rank,
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int nranks,
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MetaTensor* softmax,
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MetaTensor* loss,
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MetaConfig config = MetaConfig());
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PADDLE_API void DistributedPushSparseInferMeta(
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const std::vector<const MetaTensor*>& ids,
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const std::vector<const MetaTensor*>& shows,
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const std::vector<const MetaTensor*>& clicks,
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int table_id,
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int size,
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bool is_distributed,
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const std::string& push_sparse_version,
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int64_t padding_idx,
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DataType dtype,
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bool is_test,
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bool use_cvm_op,
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std::vector<MetaTensor*> output);
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PADDLE_API void DpsgdInferMeta(const MetaTensor& param,
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const MetaTensor& grad,
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const MetaTensor& learning_rate,
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float clip,
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float batch_size,
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float sigma,
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int size,
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MetaTensor* param_out);
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PADDLE_API void FakeQuantizeRangeAbsMaxInferMeta(const MetaTensor& x,
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const MetaTensor& in_scale,
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const MetaTensor& iter,
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int window_size,
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int bit_length,
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bool is_test,
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int round_type,
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MetaTensor* out,
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MetaTensor* out_scale,
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MetaTensor* out_scales);
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PADDLE_API void FlashAttnInferMeta(const MetaTensor& q,
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const MetaTensor& k,
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const MetaTensor& v,
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MetaTensor* out,
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MetaTensor* softmax,
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MetaTensor* softmax_lse,
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MetaTensor* seed_offset);
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PADDLE_API void FlashAttnQKVPackedInferMeta(const MetaTensor& qkv,
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MetaTensor* out,
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MetaTensor* softmax,
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MetaTensor* softmax_lse,
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MetaTensor* seed_offset);
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PADDLE_API void CalcReducedAttnScoresInferMeta(const MetaTensor& q,
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const MetaTensor& k,
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const MetaTensor& softmax_lse,
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MetaTensor* reduced_scores);
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PADDLE_API void FlashAttnV3InferMeta(const MetaTensor& q,
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const MetaTensor& k,
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const MetaTensor& v,
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MetaTensor* out,
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MetaTensor* softmax_lse);
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PADDLE_API void FastLayerNormInfermeta(const MetaTensor& x,
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const MetaTensor& scale,
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const MetaTensor& bias,
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float epsilon,
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MetaTensor* y,
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MetaTensor* mean,
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MetaTensor* invvar);
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PADDLE_API void FlashAttnV3VarlenInferMeta(const MetaTensor& q,
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const MetaTensor& k,
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const MetaTensor& v,
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MetaTensor* out,
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MetaTensor* softmax_lse);
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PADDLE_API void FlashMaskV2InferMeta(const MetaTensor& q,
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const MetaTensor& k,
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const MetaTensor& v,
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MetaTensor* out,
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MetaTensor* softmax_lse);
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PADDLE_API void InstanceNormInferMeta(const MetaTensor& x,
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const MetaTensor& scale,
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const MetaTensor& bias,
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float epsilon,
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MetaTensor* y,
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MetaTensor* saved_mean,
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MetaTensor* saved_variance,
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MetaConfig config = MetaConfig());
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PADDLE_API void FasterTokenizerInferMeta(const MetaTensor& vocab,
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const MetaTensor& text,
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const MetaTensor& text_pair,
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bool do_lower_case,
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bool is_split_into_words,
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int max_seq_len,
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bool pad_to_max_seq_len,
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MetaTensor* input_ids,
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MetaTensor* segment_ids,
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MetaConfig config = MetaConfig());
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PADDLE_API void GlobalGatherInferMeta(const MetaTensor& x,
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const MetaTensor& local_count,
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const MetaTensor& global_count,
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MetaTensor* out);
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PADDLE_API void GlobalScatterInferMeta(const MetaTensor& x,
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const MetaTensor& local_count,
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const MetaTensor& global_count,
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MetaTensor* out);
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PADDLE_API void AddGroupNormSiluInferMeta(const MetaTensor& x,
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const MetaTensor& residual,
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const MetaTensor& scale,
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const MetaTensor& bias,
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double epsilon,
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int groups,
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const std::string& data_layout,
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const std::string& activation,
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MetaTensor* y,
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MetaTensor* residual_out,
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MetaTensor* mean,
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MetaTensor* variance);
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PADDLE_API void GroupNormInferMeta(const MetaTensor& x,
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const MetaTensor& scale,
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const MetaTensor& bias,
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double epsilon,
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int groups,
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const std::string& data_layout,
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MetaTensor* y,
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MetaTensor* mean,
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MetaTensor* variance,
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MetaConfig config = MetaConfig());
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PADDLE_API void LayerNormInferMeta(const MetaTensor& x,
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const MetaTensor& scale,
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const MetaTensor& bias,
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double epsilon,
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int begin_norm_axis,
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MetaTensor* out,
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MetaTensor* mean,
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MetaTensor* variance,
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MetaConfig config = MetaConfig());
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PADDLE_API void LayerNormGradInferMeta(const MetaTensor& x,
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const MetaTensor& y,
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const MetaTensor& z,
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MetaTensor* dx,
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MetaTensor* dy,
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MetaTensor* dz);
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PADDLE_API void LerpInferMeta(const MetaTensor& x,
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const MetaTensor& y,
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const MetaTensor& weight,
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MetaTensor* out);
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PADDLE_API void LinearV2InferMeta(const MetaTensor& input,
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const MetaTensor& weight,
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const MetaTensor& bias,
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const bool transpose_weight,
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MetaTensor* out,
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MetaConfig config = MetaConfig());
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PADDLE_API void LinspaceRawInferMeta(const MetaTensor& start,
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const MetaTensor& stop,
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const MetaTensor& number,
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MetaTensor* out);
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PADDLE_API void LinspaceInferMeta(const MetaTensor& start,
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const MetaTensor& stop,
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const MetaTensor& number,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void MatchMatrixTensorInferMeta(const MetaTensor& x,
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const MetaTensor& y,
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const MetaTensor& w,
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int dim_t,
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MetaTensor* out,
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MetaTensor* tmp,
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MetaConfig config = MetaConfig());
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PADDLE_API void MatrixRankAtolRtolInferMeta(const MetaTensor& x,
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const MetaTensor& atol,
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const MetaTensor& rtol,
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bool hermitian,
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MetaTensor* out);
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PADDLE_API void MoeCombineInferMeta(const MetaTensor& x,
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const MetaTensor& combine_weights,
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const MetaTensor& scatter_index,
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MetaTensor* y);
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PADDLE_API void MoeCombineNoWeightInferMeta(const MetaTensor& x,
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const MetaTensor& combine_weights,
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const MetaTensor& scatter_index,
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float epsilon,
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MetaTensor* y);
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PADDLE_API void MoeGateDispatchPartialNoSoftmaxTopKInferMeta(
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const MetaTensor& x,
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const MetaTensor& combine_weights,
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const MetaTensor& expert_id,
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int64_t k,
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int64_t capacity,
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int64_t num_experts,
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bool use_pad,
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int64_t expert_start_index,
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int64_t expert_end_index,
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bool reverse_token_drop,
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MetaTensor* y,
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MetaTensor* combine_weights_out,
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MetaTensor* scatter_index,
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MetaTensor* scatter_index_rev,
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MetaTensor* expert_offset,
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MetaTensor* expert_nums_local);
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PADDLE_API void MoeGateDispatchPermuteInferMeta(const MetaTensor& x,
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const MetaTensor& gate_logits,
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const MetaTensor& corr_bias,
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int64_t k,
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int64_t capacity,
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int64_t world_size,
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MetaTensor* y,
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MetaTensor* combine_weights,
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MetaTensor* scatter_index,
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MetaTensor* expert_offset,
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MetaTensor* expert_id);
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PADDLE_API void MoeGateDispatchAndQuantInferMeta(const MetaTensor& x,
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const MetaTensor& gate_logits,
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const MetaTensor& corr_bias,
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const int64_t k,
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const int64_t capacity,
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const bool use_pad,
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const bool use_pow2_scale,
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MetaTensor* fp8_out,
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MetaTensor* scale,
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MetaTensor* combine_weights,
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MetaTensor* scatter_index,
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MetaTensor* expert_offset,
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MetaTensor* expert_id);
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PADDLE_API void MovingAverageAbsMaxScaleInferMeta(const MetaTensor& x,
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const MetaTensor& in_accum,
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const MetaTensor& in_state,
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MetaTensor* out,
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MetaTensor* out_scale,
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MetaTensor* out_state,
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MetaTensor* out_accum);
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PADDLE_API void MultiClassNMSInferMeta(const MetaTensor& bboxes,
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const MetaTensor& scores,
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const MetaTensor& rois_num,
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float score_threshold,
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int nms_top_k,
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int keep_top_k,
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float nms_threshold,
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bool normalized,
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float nms_eta,
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int background_label,
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MetaTensor* out,
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MetaTensor* index,
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MetaTensor* nms_rois_num,
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MetaConfig config = MetaConfig());
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PADDLE_API void NllLossRawInferMeta(const MetaTensor& input,
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const MetaTensor& label,
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const MetaTensor& weight,
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int64_t ignore_index,
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const std::string& reduction,
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MetaTensor* out,
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MetaTensor* total_weight,
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MetaConfig config = MetaConfig());
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PADDLE_API void PushGpupsSparseInferMeta(
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const std::vector<const MetaTensor*>& ids,
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const std::vector<const MetaTensor*>& out,
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const std::vector<int>& size,
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bool is_sparse,
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bool is_distributed,
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std::vector<MetaTensor*> out_grad);
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PADDLE_API void PutAlongAxisInferMeta(const MetaTensor& x,
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const MetaTensor& index,
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const MetaTensor& value,
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int axis,
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const std::string& reduce,
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MetaTensor* out);
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PADDLE_API void RandomRoutingInferMeta(const MetaTensor& prob,
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const MetaTensor& topk_value,
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const MetaTensor& topk_idx,
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MetaTensor* out);
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PADDLE_API void RankAttentionInferMeta(const MetaTensor& x,
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const MetaTensor& rank_offset,
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const MetaTensor& rank_param,
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int max_rank,
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int max_size,
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MetaTensor* input_help,
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MetaTensor* out,
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MetaTensor* ins_rank);
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PADDLE_API void RoiAlignInferMeta(const MetaTensor& x,
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const MetaTensor& boxes,
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const MetaTensor& boxes_num,
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int pooled_height,
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int pooled_width,
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float spatial_scale,
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int sampling_ratio,
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bool aligned,
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MetaTensor* out,
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MetaConfig config = MetaConfig());
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PADDLE_API void RoiPoolInferMeta(const MetaTensor& x,
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const MetaTensor& boxes,
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const MetaTensor& boxes_num,
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int pooled_height,
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int pooled_width,
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float spatial_scale,
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MetaTensor* out,
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MetaTensor* arg_max);
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PADDLE_API void ScatterInferMeta(const MetaTensor& x,
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const MetaTensor& index,
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const MetaTensor& updates,
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bool overwrite,
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MetaTensor* out);
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PADDLE_API void ScatterNdAddInferMeta(const MetaTensor& x,
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const MetaTensor& index,
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const MetaTensor& updates,
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MetaTensor* out);
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PADDLE_API void SendURecvInferMeta(const MetaTensor& x,
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const MetaTensor& src_index,
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const MetaTensor& dst_index,
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const std::string& reduce_op,
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const IntArray& out_size,
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MetaTensor* out,
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MetaTensor* dst_count);
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PADDLE_API void SequenceConvInferMeta(const MetaTensor& x,
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const MetaTensor& padding_data,
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const MetaTensor& filter,
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int context_length,
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bool padding_trainable,
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int context_start,
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int context_stride,
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MetaTensor* out);
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PADDLE_API void SpectralNormInferMeta(const MetaTensor& weight,
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const MetaTensor& u,
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const MetaTensor& v,
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int dim,
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int power_iters,
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float eps,
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MetaTensor* out,
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MetaConfig config = MetaConfig());
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PADDLE_API void ViterbiDecodeInferMeta(const MetaTensor& input,
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const MetaTensor& transition,
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const MetaTensor& length,
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bool include_bos_eos_tag,
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MetaTensor* scores,
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MetaTensor* path,
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MetaConfig config = MetaConfig());
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PADDLE_API void QuantLinearInferMeta(const MetaTensor& x,
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const MetaTensor& w,
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const MetaTensor& bias,
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int in_num_col_dims,
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const std::string& activation_type,
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bool padding_weights,
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float scale_in,
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const std::vector<float>& scale_weights,
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int quant_round_type,
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float quant_max_bound,
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float quant_min_bound,
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MetaTensor* y);
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PADDLE_API void TdmSamplerInferMeta(
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const MetaTensor& x,
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const MetaTensor& travel,
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const MetaTensor& layer,
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bool output_positive,
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const std::vector<int>& neg_samples_num_list,
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const std::vector<int>& layer_offset,
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int seed,
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int dtype,
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MetaTensor* out,
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MetaTensor* labels,
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MetaTensor* mask,
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MetaConfig config = MetaConfig());
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} // namespace phi
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