**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.

* fix dist fleet ctr ut

Change-Id: I59bf5123c7bd47bd0e8f1ca2a26295257597c0f5

* fix dist fleet ctr ut

Change-Id: Iafcdd172364be47fe67b753774ce09af050bcbce

* Update CMakeLists.txt

* update trt int8 calibrator to IEntropyCalibratorV2

* add delele opt_cache for trt_split_converter_test

* [2.0.1]Support New Custom OP on windows

* fix CI

* fix code style

* fix CI

* fix CI

* fix coverage

* fix CI

* fix CI

* [ROCM] update fluid imperative for rocm (part1), test=develop

* [ROCM] update reducer.cc after merge, test=develop

* update reducer cmake after merge, test=develop

* remove pe special profiler

* add profiler info

* add more dispatch marco

* add more dispatch marco

* add more tests

* revert unneeded change

* add timeout for test dispatch

* add float and complex test

* remove and marco

add squeeze_op/unsqueeze_op on kunlun;fix conv op and parallel executor;optimize lookup_table op (#31056)

* add squeeze_op/unsqueeze_op on kunlun; fix conv op and parallel executor on kunlun; optimize lookup_table op on kunlun

* update squeeze/unsqueeze op

* test=develop, save/load, shrink
Co-authored-by: NseiriosPlus <tangwei12@baidu.com>

* export paddle.static.normalize_program method. test=develop

* fix ut coverage.test=develop

* update paddle_fluid.so to paddle_inference.so

* change reviewer, test=document

Change-Id: I7592ee5c93bd580300ce39df885b603597b09026

* Update check_file_diff_approvals.sh

test=document_fix

* [static setitem] support the index step > 1. tensor_a[::3] = value

* [static setitem] support the index step < 0. Eg: tensor_a[::-3] = value

* [static setitem] support the index is Tensor. eg: tensor_a[tensor_3:0:-1] = value

* Add op version.

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.

* - added Reshape grad bf16

* - Added reshape grad bf16

* - cosmetics in py

* refine setup name usage

* fix unittest failed

* Modify relu native implementation

* fix GPU performance

* fix dataloader collate return list mix tensor and numpy array. test=develop

* add  parameter in roi_align op

* enable exhaustive_search for input_grad when dtype is float16

* enable exhaustive_search for forward algos

* add more unitest for ABI compatibility

* add more unittest

* refine warning style

* support compile multi custom ops in same time

* fix not import paddle in unittest

* fix typo

* add more unittest

* add comment for details

* Add conv transpose BF16

* Share function GetWeightsTz

* Adjust to review and fix op compatibility

* Add bias to unique handler name

* Remove errors related to paddle enforce

* Add conv2d_transpose to bf16 list and kernel refator

Refine fake_interface Error Message

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]