wehub-resource-sync
79 lines
2.4 KiB
C++
79 lines
2.4 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 <tuple>
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#include <vector>
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#include "paddle/phi/api/include/tensor.h"
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#include "paddle/phi/common/int_array.h"
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#include "paddle/phi/common/place.h"
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#include "paddle/phi/common/scalar.h"
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#include "paddle/utils/optional.h"
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namespace paddle {
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namespace experimental {
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// NOTE: Separate forward and backward(grad) api impl
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// NOTE: The api_impl in this file are arranged in alphabetic order.
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////////////////// Forward api impls //////////////////////
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Tensor add_n_impl(const std::vector<Tensor>& x);
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Tensor copy_to_impl(const Tensor& x, Place place, bool blocking);
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std::tuple<Tensor, Tensor> fused_gemm_epilogue_impl(
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const Tensor& x,
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const Tensor& y,
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const Tensor& bias,
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bool trans_x,
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bool trans_y,
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const std::string& activation);
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std::tuple<Tensor, Tensor, Tensor, std::vector<Tensor>> cudnn_lstm_grad_impl(
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const Tensor& x,
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const Tensor& init_h,
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const Tensor& init_c,
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const paddle::optional<std::vector<Tensor>>& weight_list,
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const paddle::optional<Tensor>& sequence_length,
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const Tensor& out,
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const Tensor& reserve,
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const Tensor& state_out,
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const Tensor& out_grad,
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const Tensor& last_h_grad,
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const Tensor& last_c_grad,
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float dropout_prob,
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bool is_bidirec,
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int hidden_size,
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int num_layers,
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bool is_test,
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int seed);
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////////////////// Backward(grad) api impls //////////////////////
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void imag_grad_impl(const Tensor& out_grad, Tensor* x_grad);
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void embedding_grad_impl(const Tensor& x,
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const Tensor& weight,
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const Tensor& out_grad,
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int64_t padding_idx,
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bool sparse,
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Tensor* weight_grad);
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void real_grad_impl(const Tensor& out_grad, Tensor* x_grad);
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} // namespace experimental
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} // namespace paddle
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