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
* Fix api docs in RNN, Transformer, layer_norm, WeightNormParamAttr.
test=develop
* Fix api doc for print in label_smooth.
test=develop
* Update api docs according to review comments.
Add name argument in RNN back.
test=develop
* 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.
GridGenerator model failed because the output shape of `linspace` is (-1). The reason is that C++ InferShape fixes the shape to (-1):
5da3d514eb/paddle/fluid/operators/linspace_op.cc (L49)
We cannot set the shape in C++ infer shape because this Tensor may not be initialized during compile time, but when input `num` of `linspace` is an integer, we know the shape at compiler time. This PR simply set the shape in Python and add GridGenerator as unittest.
* add reducer
* refine envent for memorycopy
* add concat&split for allreduce
* apply concat & split for fuse tensor
* fix nccl dep
* fix the untest, compile problem and ddp initialize problem
* fix untest for mac & add some comments & solve the repeated param in sublayers
* fix untest for windows & fix document
* add lars to fleet meta optimizer
* add lamb to proto
* add lamb to fleet meta optimizer
* fixed syntax bug
* fixed syntax bug
* fixed syntax error in lamb, add config setter of lamb in distributed_strategy
* trigger unitest to rerun
* add new unitest func for lamb
* revise unitest for lars and lamb
* revise dgc meta unitest
* revise lars document in distribute_strategy
* revise lars lamb document in distributed_strategy.py
* revise lars lamb document in distributed_strategy.py
* add weight decay exclude logic to lars
* restore optimzier.py
* restore optimizer.py as develop except lars
* add epsilon and exclude fn to distributed_sttrategy
* add lars epsilon
* revise unitest for fleet lars and lamb
* revise lars lamb unitest for CI coverage
* revise lars argument api
* revise lars argument api
* revise lars argument api
* revise api doc of lars
* fix op role
* add sharding save and add_sync_comm_for_test function
* add comm_analyse to utlis
* revise sharding_utils
* add sharding saving unittest
* revise sharding utils for unittest
* revise sharding en doc
* update sharding utils api
* add doc for sharding
* fixed bug in sharding var size count
* update varsize count in sharding
* fix sharding num_nccl_comm
* Revert "fix sharding num_nccl_comm"
This reverts commit d51587c15e9323acf226ddd36154275f0d1daf76.
* add static_only for static api
* addd static_only for class init
* remove static_only for default_main_program
* remove creater_parameter & startup_program
* remove failed apis
* revert py_func import
* remove global scope
* remove some api
* remove cuda pinned place
* Rename variables when use 'jit.load'
* Check whether the original graph contains the variable with the same name
* add comment
* rename output/input of op and edit unittest
* modify the code according to CI
* edit code according to CI
* edit code according to CI
* edit code according to CI
* edit code according to CI
* edit code according to CI
* edit code according to CI
* Impelement 2.0 API version Conv2d and Linear layer quantization in imperative mode.
* use cudnn softmax in static Lenet
* Modified ChannelwiseQAT Unittest for 2.0 API.
* For CI python coverage.
* fix some docs test=develop;test=document_fix
* add code example test=develop;test=document_fix
* fix code example test=develop;test=document_fix
* fix code example test=develop;test=document_fix
* fix code example test=develop;test=document_fix
1) The operands are executed sequentially according to the running logic of Python.
2) If the left hand operand is True(for convert_logical_or)/False(for convert_logical_and), the right hand operand should be executed.
* fix eng doc, test=develop
* add import deprecated for layers, test=develop
* add block line for doc generate, test=develop
* remove todo for create_variable, test=develop
* add blank line for doc generate, test=develop
* add blank line for doc generate, test=develop
* add lstm, simple rnn op kernel
* fix the test_lstm for the rnn op
* change func name
* fix forward postprocess bug
* add gru forward, backward code
* remove unittest.skipIf; use a big rnn op instead of combination op
* fix input doesn't have gradient bug
* add eigen lstm forward, backward
Co-authored-by: wawltor <fangzeyang0904@hotmail.com>
* Support dy2stat error message when call jit.save;
* Polish dy2stat error message:
(1) the original dygraph code is marked with (* user code *) ;
(2) "In user code:" -> "In transformed code:"
Tao Luo
wangchaochaohu
WangXi
Bai Yifan
QingshuChen
liym27
Huihuang Zheng
GeminiCarrie
Chen Weihang
taixiurong
Leo Chen
Wojciech Uss
lijianshe02
joanna.wozna.intel
huangxu96
arlesniak
Zhen Wang
Zhou Wei
joejiong
Wei Shengyu
Zhong Hui
ShenLiang
procr
Aurelius84
Pei Yang
lilong12
LoveAn
TTerror
Zhang Ting
Shang Zhizhou
chentianyu03
chajchaj
liuyuhui
Guo Sheng
gongweibao
tangwei12
YUNSHEN XIE
LielinJiang
furnace
mls1999725
Jiawei Wang
wanghuancoder
huangjun12
yaoxuefeng
hong19860320
123malin
Thunderbrook
WeiXin
Kaipeng Deng
LutaoChu
徐铭远
Chen Long
pangyoki
Jack Zhou
GaoWei8
guofei
Guanghua Yu
Noel
JZ-LIANG
Yanghello
ceci3
Shibo Tao
hutuxian
Yiqun Liu
qingqing01
tianshuo78520a
ysh329
Wilber
TeslaZhao