* add more int id type support for embedding

* add ut

* add more ut

* fix ci error

* Rearranged Eager AutoCodeGen directory structure

* Removed USE_OP in Eager AutoCodeGen

* Enabled generation for Operators without Grad/Inputs/Outputs

* Resolved operators without input

* Fixed merge conflicts

* Enabled Eager AutoCodeGen for 10+ more operators

* Refactored Eager AutoCodeGen with more organized helper objects

* Enabled Eager AutoCodeGen for operators with multiple OpBases

* Adjusted Eager AutoCodeGen to Enable Passing Output Tensor as Input Argument

* Handled Dispensable Inputs/Outputs in Eager AutoCodeGen

* Adjusted function generation/call between Python-C API & Dygraph API

* Synchronized auto-generated Python-C API with Dygraph Forward Functions

* support more eager tensor api

* fix merge compile error

* fix compile error and fit develop code

* support pure CPU

* fix some logic error in eager_mode

* support _varbase_creator in eager mode

* Added safe_initialized interface to EagerTensor for use in processing dispensable inputs

* for eager mode

* refine

* support multiple constructor for eager tensor

* add place related code

* polish code

* specific randint with dtype of int64

* Support pure cpu test

* eager logic

* refine test in pure cpu

* eager logic

* eager logic

* eager logic, test=develop

* skip core.eager when in inference, test=develop

* refine, test=develop

* refine, test=develop

* call RetainGrad after run forward kernel, test=develop

* refine, test=develop

* support dygraph util, meta, guard test

* eager test case

* support inference test

* refine test and fix initializer failed

* modify eagertensor patch method

* add eagertensor.clear_grandint, test=develop

* refine, test=develop

* refine, test=develop

* refine, test=develop

* support create varbase and fix retain grad error

* call monkey_patch_varbase in _test_eager_guard, test=develop

* fix windows error

* split clear_gradient to clear_gradient and zero_grads, test=develop

* refine, test=develop

* refine, test=develop

* support test_imperative_basic test in eager mode

* remove additional log in variable.h

* remove additional log in variable.h

* remove additional code create in merge

* eager

* fix some eager logic, test=develop

* refine, test=develop

* refine, test=develop

* refine, test=develop

* patch_tensor_method_func, test=develop

* refine, test=develop

* eager test case, test=develop

* refine, test=develop

* eager, test=develop

* eager, test=develop

* eager optimizer, test=develop

* eager optimizer, test=develop

* eager test_imperative_optimizer_v2, test=develop

* eager, test=develop

* refine, test=develop

* refine, test=develop

* eager, test=develop

* add resize in share buffer to, test=develop

* eager, test=develop

* fix _share_buffer_to, test=develop

* refine, test=develop

* refine, test=develop

* support eager for dataloader,test=develop
Co-authored-by: Njim19930609 <jim19930609@gmail.com>
Co-authored-by: NJiabinYang <360788950@qq.com>

* support 5d for nearest

* update nearest3d unittest, test=develop

* fix approve ci, test=develop

* fix approve ci, test=develop

* add maxunpool3d op

* update doc for maxunpool3d op

* update doc for maxunpool3d op

* update doc for maxunpool3d op

* update sample code for maxunpool3d

* add maxunpool1d op

* update some code for maxunpool1d

* add mish operator and api

* remove redundant code and modify grad_atol of mish unittest

* modify mish code to be consistent with other activation implementation

* add fold opereators, test=develop

* add fold opereators, test=develop

* add fold opereators, test=develop

* update fold op error test, test=develop

* fix unitext, test=develop

* fix unitext, test=develop

* add new API/OP:paddle.poisson

* fix comment

* add new API: paddle.clone;Tensor.element_size;nn.utils.parameters_to_vector

* fix comment

Add key_padding_mask and attn_mask in sparse_attention Api

1.Key padding mask is a tensor with dimensions [batch_size, seq_len], and attention mask is a tensor with dimensions [seq_len, seq_len]. The data types of the two masks are consistent with Q, K, and V, which are float32 or float64. If the value in Mask is 0, it means that the position needs to be masked.

2.The changed files are mainly paddle/fluid/operators/sparse_attention_op.cu and python/paddle/fluid/tests/unittests/test_sparse_attention_op.py. sparse_attention has three parts: sddmm, softmax, and dsd. Adding the mask operation only needs to modify the softmax. It has no effect on the other two parts. In addition, in order to test the mask function, related tests has been added.

* add hinge_embedding_loss

* fix test_API

* test_API succeed

* add English doc

* fixed using of expired fluid api

* fix doc

* fix doc and rm python/paddle/fluid/layers/loss.py

* get raw python/paddle/fluid/layers/loss.py back

* fix Examples bug in English doc

* unique -> flatten

* fix api code

* fix English doc

* fix functional loss English doc

* fix Example doc

* .numpy() -> paddle.unique()

* fix unique

* fix label_item_set

* modified judgment equation

* Got a beautiful loss equation

* use paddle.to_tensor

* fix loss and add static check

* fix loss and add static check

* delta -> margin

* promote to v2

* alter

* add maxunpool2d in __all__

* fix MaxUnPool2D example

* support data_format='NHWC' for prelu channel mode

* fix dropout static when axis != None

* update dropout test

* add dropout test

* fix test

* Update test_dropout_op.py

* Update test_dropout_op.py

* fix testcase

* fix testcase

* Update test_dropout_op.py

* fix testcase

* fix testcase

* optimize perf

* add new test

* fix testcase

* add new API paddle.nn.initializer.Dirac

* fix doc

* add zeropad2d v0.1

* add zeropad2d v0.2

* add zeropad2d v0.3

* add zeropad2d v0.3

* add zeropad2d v0.3

* add zeropad2d v0.4

* add zeropad2d v0.5

* add zeropad2d v0.5 codestyle

* add zeropad2d v0.5 codestyle

* add zeropad2d v0.6 functional

* add zeropad2d v0.6 functional

* add zeropad2d v0.6 functional

* add new API paddle.nn.initializer.Orthogonal and calculate_gain

* fix comment

* fix comment

* Fix the slow running speed of kl_div when option reduction is set

* fix unittest coverage

* modify sparse_attention docs, test=develop

* add warning

* add warning ,test=document_fix

* fix bug:
1. atten: set the default value of attn_dropout_rate to None
2. ffn: add activation parameter

* Revert "Align CTC grad scale same with ESPNet (#34729)"

This reverts commit 10f9644c.

* ctc grad compute on gpu

功能：本PR的目标是提高attention模块的计算性能。
为了减少框架层对op的调度开销，本PR通过在C++层手动实现attention模块，对外提供attention 大op；
为了减少防存开销，本PR采取了两种优化方法：
（1）在q,k,v计算时通过共享输入X，将该处的gemm，transpose和bias add从三次调用减少为一次；
（2）使用kernel融合优化技术，在不同cuda kernel之间通过寄存器传输数据；

将 #35905 和 #35843 PR中新增的的python api接口移到incubate目录下。

这个PR只包含fused_feedforward前向的代码。

相关kernel实现：fused_dropout_act_bias, fused_residual_dropout_bias, fused_layernorm_residual_dropout_bias

fused_feedforward是一个融合算子，该算子对transformer模型的feed forward层的算子进行融合和封装，使得前端只呈现一个接口，通过融合减少部分访存和kernel launch的时间，以此提升性能。