* improve the mul_mkldnn_op line coverage
test=develop
* remove fp32 mul mkldnn kernel
test=develop
* locally refactoring
test=develop
* change according to reviews
test=develop
* Add TopK Op Grad CPU&GPU Kernel test=develop
* Add TopK Op Grad, modify grad op maker test=develop
* Add TopK Op Grad, modify grad op maker test=develop
* Add TopK Op Grad, modify PADDLE_ENFORCE test=develop
* Add TopK Op Grad, modify PADDLE_THROW test=develop
* Add TopK Op Grad, modify unittest test=develop
* fix ngraph top k op unittest test=develop
* update ops's unittest of elementwise_pow, elementwise_max, elementwise_min, scale and sqrt
1. update elementwise_pow, elementwise_max and scale's unitests with input data type (float32 -> float64)
2. fix bug that the elementwise_pow doesn't meet threshold requirements with tackling float64 data
3. remove sqrt from op_accuracy_white_list.py
4. update the unittests of elementwise_pow, elementwise_max and elementwise_min ops that their input data shape over 100
5. test=develop
* modify the writing style according suggestions
test=develop
* Add support for dynamic_decode(while) training. test=develop
* Fix assign_op and tensor_array_read_write_op after solving conflict. test=develop
* Fix test_rnn_decode_api.py. test=develop
* Refine docs for apis in rnn.py. test=develop
* Adjust outputs of dynamic_decode. test=develop
* Remove the force_cpu update in assign_op. test=develop
* Remove the force_cpu update in assign_op. test=develop
* Make RNNCell.get_initial_states support batch_dim_idx argument. test=develop
* Rename _create_array_outof_while as _create_array_out_of_while in rnn.py.
test=develop
* support slice double grad, test=develop
* merge two doublegradopmaker to one doublegradopmaker,test=develop
* change the shape of slice_OP's unittest, test=develop
Refine PaddleBox Framework, Main functions:
* Add MetricMsg util class, which can calculate metrics like AUC, bucket_error, COPC.
* Replace FeedPass with new interface: BeginFeedPass & EndFeedPass
* Refactor Pull/Push Sparse Function in box_wrapper.
* Use CUDA Kernel to copy keys and copy feasign between tensor and boxps struct.
* Cache copied keys in pull sparse in order to reuse it in push period.
* Add the first implememtation of fusion_group op #19621 (#3)
* Add the dynamic load of nvrtc, and support runtime compiling of CUDA kernel using nvrtc.
test=develop
* Call CUDA driver api to launch the kernel compiled by nvrtc.
test=develop
* Disable for mac and windows.
test=develop
* Refine the codes to support manually specified num_threads and workload_per_thread.
test=develop
* Refine the CUDA kernel to support large dims.
test=develop
* Add DeviceCodePool to manage all device codes.
* Add the first implementation fusion_group op.
* Add unit-test for fusion_group op.
* Add the check of result.
* Add the check of nvrtc in unit-test.
test=develop
* Add comment to explain the inputs, outputs and features of fusion_group op.
test=develop
* Disable fusion_group op for mac and windows.
test=develop
* Make the compiling of device code return status instead of hanging up.
test=develop
* Add the check of whether there is CUDA driver library, and do not core dump when failing to call the CUDA driver API.
* Unify fusion_group_op's input and output names.
test=develop
* Add the check of CUDA driver library in unittest.
test=develop
* Enable generating code for a given subgraph. #21126 (#4)
* Enable generating code for a given subgraph.
* Support sorting the subgraph.
* Remove the rearange of expressions because we use the sorted subgraph directly.
* Enable generating code for a subgraph which is composed of grad ops.
* Use expression information to check the accuracy in unittest.
* Separate load and store from computation expressions.
test=develop
* Improve the loading statements in generated codes.
test=develop
* Remove unused arguments from formal list.
test=develop
* Enable the detection of subgraph of grad ops.
* Generate code for detected subgraph in fusion_group_pass.
* Add an option in BuildStrategy to enable fusion_group_pass and add unittest.
test=develop
* Fix a bug when checking whether the shape of all inputs are the same.
* Add debug information.
* Remove subgraph_detector from inference/analysis to the common framework/ir directory. (#5)
test=develop
* Call subgraph_detector in fusion_group pass.
test=develop
* Disable fusion_group when WITH_GPU is OFF.
test=develop
* Refine all PADDLE_ENFORCE message.
test=develop
* Fix the case that some inputs are not defined in grad ops, and set op_role for fused op.
test=develop
* Follow review comments.
test=develop
* Add log in memory::Copy for debug purpose.
* Change to use context in DeviceContextPool directly in sequence_pooling_test, instead to new one.
* Change to use context in DeviceContextPool directly in sequence_padding_test, instead to new one.
test=develop
* Change the type of second_dim from size_t to int64_t.
test=develop
* Enable quantize to reorder to nchw as well
* Correct FC MKL-DNN input dim requirements to accept 3D
* Improve DNNL FC format, error and 3D input handling
test=develop
* Improve error checking in FC
test=develop
* Improve PADDLE_ENFORCE messages in fc-related files
* Remove data layout attribute from obligatory pass args
test=develop
* Fix message in fc_mkldnn_pass to be logically correct
test=develop
* Implement a common python unittest to test the ir passes.
test=develop
* Save the results in np.array and support to startup on CPU.
test=develop
* Fix the unittest.
test=develop
* Add check_program to check whether the optimized program is different from the origin one.
test=develop
* Remove the inferface all_ops.
test=develop
* Add exception test in pass_test.
test=develop
* add bn and relu fuse pass
* add op attr assert and dtype assert
* fix some inputs&&outputs bugs for the fused op and pattern.
* add the unittest for fuse_bn_act_pass. test=develop
* use normative enforce statements. test=develop
* add the cpu test. test=develop
* add the support of batch_size=1 for the bn with relu op. test=develop
* add the error type for paddle throws. test=develop
* add fused_batch_norm_act and fused_batch_norm_act_grad to op_has_unsed_vars_white_list. test=develop
* Polish the PADDLE_ENFORCE in fusion_group pass related codes.
test=develop
* Correct the unittest because of the change relu_grad's formula.
test=develop
* modify fc to linear in sample code, test=develop
* remove FC, test=develop
* remove warnings, test=develop
* drop fluid/imperative/README.md , test=develop
* change fc to linear, test=develop
* polish code style, test=develop
1. Add a new input named batch_roi_nums for prroi_pool_op. batch_roi_nums includes the number of roi for each image in batch when rois is Tensor. This information is saved in rois's lod when rois is LoDTensor.
2. add grad check to prroi_pool_op and solve unnormal X grad diff in CPU.
* support elu activation double grad,test=develop
* delete the code commit in .cc,test=develop
* fix relu test unpass, test=develop
* add elu double grad kernel and unit test
* add caculate dX in elu double grad functor, test=develop
* update the commit code,test=develop
* Add the dynamic load of nvrtc, and support runtime compiling of CUDA kernel using nvrtc.
test=develop
* Call CUDA driver api to launch the kernel compiled by nvrtc.
test=develop
* Disable for mac and windows.
test=develop
* Refine the codes to support manually specified num_threads and workload_per_thread.
test=develop
* Refine the CUDA kernel to support large dims.
test=develop
* Add DeviceCodePool to manage all device codes.
* Add the first implementation fusion_group op.
* Add unit-test for fusion_group op.
* Add the check of result.
* Add the check of nvrtc in unit-test.
test=develop
* Add comment to explain the inputs, outputs and features of fusion_group op.
test=develop
* Disable fusion_group op for mac and windows.
test=develop
* Make the compiling of device code return status instead of hanging up.
test=develop
* Add the check of whether there is CUDA driver library, and do not core dump when failing to call the CUDA driver API.
* Unify fusion_group_op's input and output names.
test=develop
* Add the check of CUDA driver library in unittest.
test=develop
* Refine the calling of PADDLE_ENFORCE.
test=develop
* optimize adam speed by removing _finish_update test=develop
* fix SparseAdamFunctor param list test=develop
* Remove scale_op in expect_list of adam_op test=develop
* fix test optimizer loss assert error test=develop
* fix test optimizer loss assert error test=develop
* modify PADDLE_ENFORCE usage test=develop
* fix op_type in lamb_op.cc test=develop
* fix errors ostream format bug test=develop
* add betaPowOut in ngraph op test=develop
* fix ngraph::op api for gcc8 test=develop
* clean code test=develop
* modify struct into class test=develop
* remove code of beta1Tensor in lamb_op test=develop
* fix elementwise_pow bug on integer, test=develop
* use llrint to support elementwise_pow_grad, test=develop
* add some tests, test=develop
* revert grad functor, test=develop
* fc-dequantize squash
test=develop
* change according to reviews
test=develop
* change PADDLE_ENFORCE
test=develop
* add second test when fc-dequant do not fuse
test=develop
* change all related PADDLE_ENFORCE
test=develop
* add fake init for the trainer, fix large memory hold in the trainer
* do not merge recv vars from a remote endpoint, test=develop
* add recv and save op, merge slice var in one op, save memory
* remove hsigmoid with pull sparse, test=develop
* update benchmark for int8v2, QAT1, QAT2 accuracy and performance
test=document_fix
* change according to reviews
test=develop test=document_fix
* improve some descriptions and some models
test=develop test=document_fix
* update models benchmark data
test=develop test=document_fix
* update int8v2 and qat2 performance
test=develop test=document_fix
Add tests to use dy/dx to make sure the gradient values calculated by the control flow backward is correct. Also fixed bugs detected by those tests.
Fix bugs:
1. Unlike sum_op, optimizer ops don't allow uninitialized input tensor. But in conditional_block_grad_op, since the conditional_block may not run, the output gradient tensor may be uninitialized, which will cause the optimizer op error. To fix it, we should let optimizer ops support uninitialized input like sum_op or assign the uninitialized gradient to 0 when the conditional_block_grad_op doesn't run. I found there are about 10+ optimizer ops. **To be simpler, I just assign output gradient of the conditional_block_grad_op to 0 in this PR**. But it can be further explored whether we can make optimizer ops like sum_op to support uninitialized input tensor because theoretically we can speed up without the assigning in conditional_block_grad_op.
2. Infer parameter shapes during append_backward. I didn't know that all our parameters are in global block. When op_desc is inferring shapes at the sub-block, it may not know the shape of gradients of parameters whose shape information is at global block. I fixed it by inferring shapes of gradients from forward var.
This PR also did some code clean up:
1. Print the var name when sgd_op catches shape error so that it is easier to debug
2. Fix a typo: dicta -> dict
* add file check_op_desc.py and add interface to get default value. test=develop
* add test for c++ coverage rate. test=develop
* Correct typo. test=develop
* test=develop, fix docker with paddle nccl problem
* don't expose numerous Tensor.set(), test=develop
* fix condition, test=develop
* fix float16 bug, test=develop
* feed should be Tensor or np.array, not Variable or number, test=develop
* use forcecast to copy numpy slice to new array, test=develop
* remove float16-uint16 hacking, test=develop
* add variable method to varbase and refactor to_variable to support return varbase
* support kwargs in varbase constructor
* add VarBase constructor to support default python args
* refine varbase initial method
* reset branch
* fix ut for change VarBase error info to PaddleEnforce
* cherry is parameter change before
* overload isinstance to replace too many change of is_variable
* rm useless files
* rm useless code merged by git
* test=develop, fix some ut failed error
* test=develop, fix test_graph_wrapper
* add some tests, test=develop
* refine __getitem__, test=develop
* add tests, test=develop
* fix err_msg, test=develop
* add data_type register CI test=develop
* add op add test case test=develop
* add test case for register op kernel test=develop
* fix shell script bug test=develop
* fix checkout branch test=develop
* remove test case code test=develop
* fix op_type.spec name test=develop
* fix default grad op maker ci bug, test=develop, test=document_fix
* remove some ops from paddle/fluid/op_use_default_grad_op_maker.spec, test=develop, test=document_fix
* fix the device supported of the op unique and unique_with_counts.
test=develop
test=document_fix
* Fix the precision of test in the op of unique and unique_with_counts.
test=develop
test=document_fix
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