1. when slice_item is a slice:
1) the start of __getitem__ should be std::max(start, 0) if slice
2) the start of __getitem__ should be std::min(end, dim)
2. when slice_item is an integer, it should be in [-dim_len, dim_len)
3. Fix error message to use accurate data
* Support storage of large parameters
* Reduce the complexity of the unittest
* Reduce the complexity of the unittest,commented out unittest for
* add unittest for static.save/load
* Increase the timeout threshold of 'test_static_save_load'
* Increase the timeout threshold of 'test_static_save_load'
* Increase the timeout threshold of 'test_static_save_load' and 'test_paddle_save_load'
* Increase the timeout threshold of 'test_static_save_load' and 'test_paddle_save_load'
* dot op support complex types
* matmul support complex types
* add test case
* matmul broadcast gradient support complex
* move conjFunctor to complex_functor.h
* reopen python coverage --include for test, test=develop
* if no .py file modified, not use coverage run, test=develop
* remove test code, test=develop
* add WITH_INCREMENTAL_COVERAGE, test=develop
* refine if else, test=develop
1. Type of index: int, slice(step must be 1).
2. Type of value:
(1) int32, int64, float32, bool;
(2) numpy.array(int32, int64, float32, bool);<Note: float64 is not supported>
(3) paddle.Tensor(int32, int64, float32, float64, bool);
* add conj op for complex types
* add conj for complex types
* add more test case
* add conj_op test
* modify conj api and impl
* add complex type for fill_constant_op xpu
* add setConstant for complex type
* remove complex conj test file
* user define grad for test_conj_op
* add test case for static mode of conj api
* modify conj doc
* change input args name to x
* remove useless codes
* conj support real types
* add conj test case for real number
* delete no need to calculate inputs in dygraph op_test
* delete no need to calculate inputs in dygraph op_test
* modify grad of mul for complex types
* fix the grads of inputs args order not match bug
Before this commit, test_slice use old api `dygraph_to_static_func` to use Dynamic-t-Static and use Executor explicitly，which is not recommended to users.
After fixed, use recommended API `paddle.jit.to_static` to replace `dygraph_to_static_func`, which won't trigger the random exception on coverage CI.
* add conj op for complex types
* add conj for complex types
* add more test case
* add conj_op test
* modify conj api and impl
* add complex type for fill_constant_op xpu
* add setConstant for complex type
* remove complex conj test file
* user define grad for test_conj_op
* add test case for static mode of conj api
* modify conj doc
* change input args name to x
* remove useless codes
* conj support real types
* add conj test case for real number
* add complex real op & api & unittest
* add imag op & api & unittest
* refactor op impl
* revert simplify writing due to complile failed
* polish details
* polish grad op code
* fix expand && concat/transpose to new api
* update xpu_header
* update activation op on kunlun
* update activation op on kunlun
* update activation op on kunlun
* update activation op on kunlun
* update activation op on kunlun
* add nearest_interp on kunlun
* update error message
* added UT should not exceed 15s
* fix error
* UT limit of 15s is the first to be executed
* fix error
* fix error with CI_SKIP_CPP_TEST
* modfied tiemout setting
* fix error
1. Fix error in _build_cond_stmt of for-range stmts.
2. Support that step value is negative in for-range stmts
3. Fix code because of the diff between Py2 and Py3
Fix 3 Windows Unittests
test_fuse_all_reduce_pass: Paddle cannot run multiple-GPU on Windows so set single visible GPU flag
test_feed_data_check_shape_type: Paddle cannot run multiple-GPU on Windows so set single visible GPU flag
test_tsm: Winodws GPU size is not enough so decrease batch size and data size.
* Fix a bug when running on an operating system without "bash."
* add execution condition
* for ci-coverage
* get cpu information to check the precision problem
* Update compilation environment for musl version
* update dependencies
* remove test code
check cpu info
remove test code
review
* update alpine and third_party denpendencies
* add newline for ci Code format
* add complex64 and complex128 type; add +-*/@ and slice opreator for complex types
* add test cases for complex elementwise, matmul and getitem unittest
* add test cases for complex types
* add test cases for complex matmul unittest
* kron, reshape, transpose support complex types
* sum and trace op support complex types
* add test case of sum and trace op
* fix the bug of imag part of complex not initialized
* format file
* format code style
* kron support type promotion; modify test cases
* basic impl of type promote
* add comment & another testcase
* fix complex bugs & support python op promote type
* fix failed unittests & polish code
* add unittest for coverage
* change to only promote complex type
* polish code details
* polish several comments
Usage scenarios：A function could have run successfully in static mode, you can use it to decorate a function in the following cases:
1. An unknown error occurs in the dynamic-to-static conversion process of the function;
2. In the internal implementation of the function, it has two branches: dynamic branch and static branch;
3. Users don't want to convert the function in the process of dynamic to static.
* add the weight decay func for the momentum op
* Add the multi_precision function in Momentum Optimizer.
* Make sure that the initial value of master weights are same with the fp16 weights.
* add static loss scaling.
* add the rescale_grad function in the pure fp16 training.
* use the original momentum updating method.
* Polish some codes, such as variable names.
* add docstring for apis.
* update the var creation details of _create_master_weight.
* not modify codes about imperative momentum updating.
* Fix the error of test_dist_sparse_tensor_load_momentum UT.
* add unit test for multi precision fp16 training.
* add more unit tests for CI.
* Use lower threshold values for allclose comparing in test_multi_precision_fp16_train UT.
* For CI Coverage Checking.
* add fp16 for layer_norm op
* revert layernorm api
* fix forward
* fix forward
* fix backward for layernorm with fp16
* fix unit test for layernorm with fp16
* fix with_mkldnn compile error for layernorm with fp16
* 1. revert to PADDLE_ENFORCE_NOT_NULL, 2. change static_cast<float> to static_cast<U>
* fix with_mkldnn compile error for layernorm with fp16
* fix with_mkldnn compile error for layernorm with fp16
Co-authored-by: zhiqiu <chenqiuliang@baidu.com>
This PR fixes several problems in dy2stat for Deoldify model in PaddleGan.
In model, software engineer wrote if x.shape == y.shape, the Tenser shape is a tuple in dygraph so the == returns True/False, but in static graph the == becomes element-wise comparison, which is a different behavior. In this PR we reduce the element-wise comparison result.
If software engineer write computations which uses parameters in hooks, the static graph can loss the parameter variable because we put param_guard at forward of a Layer. In this PR we made param_guard cover pre-hook and post-hook.
In PaddleGan, software engineer calculated some parameter values in __init__ by running some dygraph code. Those code also run during dy2stat. So some variables may be assign as a VarBase (Tensor) first and then Variable, which raised an error. We fixed the bug in this PR by handling the case.
TODO: We just added testcase for the 1. shape comparison. Should add test case for 2. and 3. But since we are chasing 2.0RC, I will do it in the near future PR
* add complex64 and complex128 type; add +-*/@ and slice opreator for complex types
* add test cases for complex elementwise, matmul and getitem unittest
* add test cases for complex types
* add test cases for complex matmul unittest
* The leaf tensor concept is exposed and the gradient accumulation of leaf tensor
* The leaf tensor concept is exposed and the gradient accumulation of leaf tensor
* fix coverage
* fix api doc
* fix CI unittest
* fix CI unittest
* fix unitest
* empty tensor does’t need inner_var_
* fix some error message
* Add a class TensorInplaceVersion to count the inplace version and put it in framework::Tensor instead of Allocation or Variable.
* Add a new attribute `_inplace_version` for VarBase.
* Raise exception if an inplace operation can result in incorrect gradient computation.
* Add a new interface _bump_inplace_version() for VarBase to bump the version whenever the Tensor is modified through an inplace operation.
* For api assign, call _bump_inplace_version() when it's an inplace operation inn dynamic mode.
* Use original var_wrapper if the inplace_version is not changed.
* Replace SnapshotVarWrapperList with SnapshotVarWrapper to optimize performane.
* Changed a variable name error
* Add comments
* Move member functions of TranslatedLayer out of function
* edit code according to review
* Edit input argument of '_run_static_graph'
* reset due to Segmentation fault
* rename variables when stitching graph
* modify code according CI
* Add comments to '__i_m_p_l__'
* remove blanks befor 'Get...'
* edit code according to review
* Add a comment to '_execution_method_creator'
* Edit a comment to '_execution_method_creator'
* Generate code coverage reports only for incremental files, test=develop
* Generate code coverage reports only for incremental files, test=develop
* Generate code coverage reports only for incremental files, test=develop
* test for diff python file, test=develop
* fix no python diff report, test=develop
* add cc test file, test=develop
* fix bug in generic.cmake, test=develop
* for debug no cc report, test=develp
* modify compire branch form test_pr to test, test=develop
* fix bug, test=develop
* test for h file changed, test=develop
* debug for redefinition of argument optimize error, test=develop
* close -o3 for test, test=develop
* remove -o3 for test, test=develop
* remove coverage option for nvcc, test=develop
* use CMAKE_CXX_FLAGS open coverage option when header file changed, test=develop
* reopen -o3, test=develop
* remove debug code, test=develop
* remove unused code, test=develop
test_mnist failed on CUDA11. We found that it is due to PaddleInference IR Optimization after debugging. We disable it in this PR and we will re-enable it after PaddleInference fixes it.
Zhou Wei
WeiXin
liym27
ceci3
Jiaqi Liu
chentianyu03
WangXi
lilong12
ShenLiang
cc
tangwei12
Chen Weihang
liuyuhui
XiaoguangHu
littletomatodonkey
Leo Chen
LielinJiang
guofei
QingshuChen
wawltor
wanghuancoder
Tao Luo
jakpiase
lidanqing
YUNSHEN XIE
Guo Sheng
TTerror
whs
xiaoting
Wojciech Uss
Huihuang Zheng
Jack Zhou
AshburnLee
lijianshe02
qingqing01
wangchaochaohu
Bai Yifan
GeminiCarrie
taixiurong
joanna.wozna.intel
huangxu96
arlesniak
Zhen Wang
joejiong
Wei Shengyu
Zhong Hui
procr
Pei Yang
LoveAn
Zhang Ting
Shang Zhizhou
chajchaj
gongweibao
Aurelius84
furnace
Jiawei Wang
wanghuancoder
hong19860320
123malin
Thunderbrook