* [Custom Op]Remove PADDLE_WITH_MKLDNN in custom_op  (#32903)

* [Dy2Stat]BugFix StaticAanlysis with gast.Subscript (#32969)

* BugFix StaticAanlysis with gast.Subscript

* remove codes

fix error log for reducer

fix doc

fix bug of utest

fix spawn

fix converage

* Add deprecated warning info.

* Add unittest for deprecated decorator.

* Add warning info for tensor.grad

cherry-pick #32561

Dy2stat failed when user writes return paddle.to_tensor(xxx), the reason is that visit_Expr doesn't work when the Expr is in return. Some other statements may trigger same bug. To fix it, we re-wrote a transformer to transform paddle.to_tensor to paddle.assign for all Call nodes.

* add relu6_ hardsigmoid_ leaky_relu_ Inplace APIs

* add softmax_with_cross_entropy_ Inplace API

* add clip_ scale_ add_ subtract_ Inplace APIs

* add wlist

* fix parameter of scale api

* add add_n_ Inplace API and remove log_ Inplace API

* fix elementwise_add_ and elementwise_sub_ broadcast problem

* elementwise inplace api give error message before run the op

* use broadcast_shape in elementwise inplace op

* add 8 inplace apis that is auto generated

* add unittest for all inplace apis

* add decorator for inplace apis in static mode

* fix windows blas fail of exp inplace api, change array_equal to allclose

* add flatten inplace api

* add flatten unittest

* fix flatten unittest

* add decorator

* fix grad.numpy in test_pylayer_op

* unsupport softmax_with_cross_entropy_

* add test_inplace_softmax_with_cross_entropy to static_mode_white_list

* delete __all__ in inplace_utils

* delete activation inplace function and add Tensor.inplace_func

* change paddle.inplace_ to Tensor.inplace_

* fix little problem

* add paddle in inplace_utils

* jit.save/load support function.

* delete unnittest test_jit_load_model_incomplete.

* edit code according to CI

* Modify the documentation.

* add note to doc.

* Modified params of some APIs to support tuple and list.

* fixed bug.

* Modified the return value of tensor.grad from numpy as tensor.

* Modify unittests.

* fixed bugs.

* Add warning info for x.grad

* fixed unittests which used x.grad

* fixed bug.

* Unset ReserveSpace for inference program.

* Support training from an inference program.

* add clip/check

* add amp & clip grad in dygraph

* add logging

* support dp & mp

* fix sublayer error with include_sublayers=False

* add ut

* refactor include_sublayers related api

* fix ut

* fix ut of transformer

* fix ut of transformer

* remove useless code

* change sublayer api

* polish code

* add test for include_self=True

* add layer.to api

* add layer.to api

* add layer.to api

* add the doc for Layer.to

* add input type checking

* modify assert and import bug

* format code style

* format code style

* make place support str type

* add SetGradVarBase method to set the gradient after conversion

* modify argument palce to device

* modify argument palce to device

* modify doc of layers.to API

* add xpuplace to device argument

* fix undefind var in For

* fix code style

* support DictCmp and zip grammar

* fix code style

As the title

* support save/load single tensor

* compatibility modification according to unnittest

* Some python2.7 don't have 'copyreg' modules

* Handle a syntax error.

* Dealing with compatibility problems on Mac.

* Dealing with compatibility problems on Mac.

* edit unittest to improve coverage.

* Modify the code according to the review comments

* Reduce redundant code.

* support for static graph loading dygraph state_dict

* edit code according to CI

* edit unittest

* edit unnittest

* delete redundant file

* edit code according to Comments

* edit english doc

* edit english doc

* edit English DOC.

* get/set_tensor->get/set_value; return_numpy=False

* get/set_tensor->get/set_value; return_numpy=False

* edit unnittest

* edit unnittest

* polish code.

* support control flow

* supoort sync_parameters_buffers

* fix the bug of sparse embedding

* add custom init grad for backward function

* add custom init grad for backward function

* handle when the grad_tensor is none

* handle when the grad_tensor is none

* fix the args type error on windows platform

* modify the args order and doc

* format code

* add grad_tensor to xpu

* modify the grad_tensor type check

* add paddle.backward api to support multi tensors gradient compute

* add paddle.backward api to support multi tensors gradient compute

* add paddle.atuograd module and backward api

* change tensor.backward func args

* modify tensor backward api

* remove create_graph intputs args

* add doc and examplex code for backward api

* when have the same tensor, throw error

* modify test Init func args

* modify the execute.Init func args in test files

* add paddle.autograd package in setup.py.in

* modify error msg, remove _run_backward method in class Tensor

* add test cases for backward api

* refactor and simplify hook design

* fix reducer add hook error

* add Tensor.register_hook basic impl

* refine prepare data impl

* revert prepare data change

* support register_hook for Tensor

* add hook test in model

* polish tests and doc example

* fix double grad test failed

* remove reduce hook func

* fix set empty error

* polish code by comments

* change reduce_hook to mutable_hook

* remove useless tmp_ins

* fix shape code format error

* fix shape code format error

* support minus-int idx to LayerList
* update layerlist test

Our old `loop_body` function may return single element when `loop_vars` just contains only 1 element, which can cause bug. The key point of this PR is forcing `loop_body` functions always return tuple.

* Fix bug with static_convert_var_shape

* replace dot with dash

* fix bug with jit.save

* refine code

Fix wrong code comment

Fix Read-Only Attribute as while_loop Output:

Usually, our convert_while_loop will be like:
```
    [a, b, c] = paddle.jit.dy2static.convert_while_loop(
            condition_name, body_name, [a, b, c])
```
where a, b, c are in loop_var_names.

However, if loop_var_names contains property such as foo.x, we cannot
assign the attribute as output of convert_while_loop because Python
property is a kind of read-only attribute. To handle the case, we replace
the attributes which are output of convert_while_loop with generated
variables, then if we know the attribute is not read-only at runtime, we
assign the attribute. The created statements are like:
```
    [a, b, __attribute_variable_1] = paddle.jit.dy2static.convert_while_loop(
            condition_name, body_name, [a, b, foo.x])
    if not isinstance(getattr(type(foo), x, None), property): foo.x = __attribute_variable_1
```

* fix eval_if_exist_else_none bug

* fix typo

* fix typo

* fix test_op_num unittest

* add error msg when dtypes of operator are not same

* add error msg when dtypes of operator are not same

* change error msg to warning msg when dtypes of operator are not same

* modify test case to fit for python2

**Problem**
In our old shape transformer logic, if user write:
```
s = tensor.shape
...
y = paddle.some_api(s)
```
Dy2stat will change it to
```
...
y = paddle.some_api(convert_var_shape(tensor))
```
However it will cause fatal bug if user changes the shape of `x` after assign. For example:
```
s = tensor.shape
...
tensor = paddle.some_change_shape_api(tensor)
...
y = paddle.some_api(s)
```
Then the Dy2stat will get wrong result because the code is translated into:
```
tensor = paddle.some_change_shape_api(tensor)
...
y = paddle.some_api(convert_var_shape(tensor)) # tensor shape has been changed, not origin `s` value
```

**Solution Logic**

It can not be solved in the old logic, so I refactoring tensor_shape_transformer logic. Now we will use `s` to store shape attribute and generate a var `s__STATIC_CONVERT_VAR_SHAPE_SUFFIX` to store static shape API `shape(tensor)`
```
s = tensor.shape
...
y = paddle.some_api(s)
```
Dy2stat will change it to
```
s = tensor.shape
s__STATIC_CONVERT_VAR_SHAPE_SUFFIX = shape(tensor)
...
y = paddle.some_api(choose_shape_attr_or_api(s, s__STATIC_CONVERT_VAR_SHAPE_SUFFIX ))
```
In this case, the code is consistent with origin dygraph meaning and it fixed the change after assign bug.

**Code Key Note**

To help reviewers, the key change of this PR is changing `self.name_to_var_shape` from "mapping name to shape node" to "mapping name to its STATIC_CONVERT_VAR_SHAPE_SUFFIX name", then if a variable name has the SUFFIX, we can choose to use attribute shape or shape api. Other changes go with the key change.

**Consideration**
The issue of this PR is that we store extra static `shape` API result, will it harms the speed of Dy2stat? In some cases it will, but we argue that the benefit would be greater than the cost.

1. The extra calling to static `shape` API will happen when coder assign among shape variables. Take the following dygraph code as an instance:
```
s1 = tensor.shape
s2 = s1
s3 = s2
...
```
Then we called extra static `shape` APIs again and again, however users seldom write code like this.

2. If the shape variable is used a lot, for example:
```
s = tensor.shape
y1 = paddle.some_api1(s)
y2 = paddle.some_api2(s)
y3 = paddle.some_api3(s)
```
Our old logic will create 3 shape APIs but now just 1. This is more common user code pattern. In fact, if reviewers take a look at the current unit test in this PR, you could see the op numbers decrease after this PR. So we argue that this PR can also improve speed in this code pattern.

As the title, when slice_node like 1:3 being passed to idx of convert_var_shape, it will cause syntax error because a function cannot take this as argument. This PR fixed it.

Dy2stat didn't support tuple as iteration variable in the past. This PR added there main cases:

       1). Non-enumerate case: for var1, var2 in var|var.numpy() will be re-written as:
          for FOR_ITER_TUPLE_PREFIX_x in var | var.numpy():
            var1 = FOR_ITER_TUPLE_PREFIX_x[0]
            var2 = FOR_ITER_TUPLE_PREFIX_x[1]
        2). Enumerate out tuple case: for t in enumerate(var|var.numpy) will be rewritten as:
          for FOR_ITER_TUPLE_INDEX_PREFIX_x, FOR_ITER_TUPLE_PREFIX_x in enumerate(var|var.numpy):
            t = (FOR_ITER_TUPLE_INDEX_PREFIX_x, FOR_ITER_TUPLE_PREFIX_x)
        3). Enumerate inner tuple case: for i, (var1, (var2, va3)) in enumerate(var|var.numpy()) will
        be re-written as:
          for i, FOR_ITER_TUPLE_PREFIX_x in var | var.numpy():
            var1 = FOR_ITER_TUPLE_PREFIX_x[0]
            var2 = FOR_ITER_TUPLE_PREFIX_x[1][0]
            var3 = FOR_ITER_TUPLE_PREFIX_x[1][1]