* support inplace in eager_final state

* little change

* little bug

* add add yaml

* add elementwise add yaml; test=develop

* add norm

* update

* add some yaml config; test=develop

* fix bug; test=develop

* fix compare error; test=develop

* revert erger_gen.py

* update; test=deveop

* remove usless code; test=deveop

* fix bug; test=develop

* fix test error; test=develop

* remove int_type; test=develop

* fix type error; test=develop

* format; test=develop

* remove type register; test=develop

* polish code; test=develop

* fix ci error; test=develop

* move one hot to phi; test=develop

* fix bugs; test=develop

* fix bugs; test=develop

* add infer meta; test=develop

* fix bugs; test=develop

* resolve confilct

* resolve confilct

* fix bug;

* fix error; test=develop

* update; test=develop

* polish code; test=develop

* add one api in eager mode; test=develop

* add one hot test; test=develop

* remove use less code; test=develop

* fix bug; test=develop

* polish code; test=develop

* polish code; test=develop

* fix fold python examples, test=develop

* fix size type, test=develop

* fix python example, test=develop

* fix fold shape check

* fix fold dygraph mode, test=develop

* fix maxunpool2d example, test=document_fix

* fix maxunpool2d example, test=document_fix

* fix 'invalid escape sequence'

* fix assert error

* fix 'is with a literal'

* fix typo

* change dtype of pooling mask to 'int32' for Paddle2ONNX

* empty commit to rerun ci

* fix format

* optimize conv1d forward

* add conv opt

* Optimize memory copy

* delete share data with

* set num_filters=512

* add nlc optimize

* Optimize num_filter=512 data on A100 and V100

* Fix the workspace_size size setting of filter

* 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