* test=document_fix
Fix english doc api, invloves the op of greater_equal,greater_than,less_equal,not_equal,
rank,rsqrt,diag,linspace,reduce_all,reduce_any,sign,where,zeros_like,unique_with_counts.
* Fix some format problem in the op of sign and greather_than.
test=develop
test=document_fix
* Fix the example of zeros_like, and update api.spec
test=develop
test=document_fix
* test=develop, fix docker with paddle nccl problem
* test=develop, refine en_doc for Variable and Program
* test=document_fix, fix English doc for Variable and Program
* test=document_fix, refine astype code block style
* test=document_fix, add example code for Variable properties
* add api check in fc test=develop
* enforce shape error info of sum op test=develop
* fix spelling test=develop
* print x_dims info test=develop
* enhance shape error info test=develop
* polish minimize en doc
* polish adam optimizer en doc
* polish adamax optimizer en doc
* polish adagrad and decayed adagrad optimizer en doc
* polish model average en doc, test=develop, test=document_fix, test=document_preview
* self review and further polishing doc
* update API.spec, test=develop, test=document_fix
* update fluid.data api in examples, test=develop, test=document_fix
* update fluid.data inferface, test=develop, test=document_fix
* replace -1 by none, test=document_fix
* fix fluid.data code example, test=develop, test=document_preview, test=document_fix
* use None instead of -1 in shape, test=develop, test=document_preview, test=document_fix
* rm unused ckpt and sort ckpt
* use max op idx to sort, test=develop
* remove unsed code,test=develop
* add testcase, test_develop
* modify test case, test=develop
* test=develop
Add input type and dtype check for sign_op.
* test=develop
Fix the api text format in sign op.
* test=develop
Fix the api examples in sign op add update the api.spec.
* Update crf_decoding api & example
test=develop
* Update api spec
test=develop
* Fix linear chain crf api
test=develop
* Avoid sharing data pointer with input
test=develop
* Simplify the logic in linear_chain_crf_decoding
* Add unittest for crf_decoding when label & path both are set
test=develop
* Update API spec
test=develop
* Add unittest for layers && correct infer_shape in chunk_eval
test=develop
* test=develop, fix docker with paddle nccl problem
* test=develop, Add Variable api and refine dygraph related API
* test=develop, Add Variable api and refine dygraph related API
* test=develop, refine test for new api and error info
* test=develop, refine error info and test_layers
* test=develop, add API.spec
* test=devleop, fix to_string python2 and python3 compat error and refien doc
* test=devleop, add API spec
* test=devleop, update API spec
* test=devleop, update API spec
* test=develop, invoke ci
* test=develop, fix example code
* test=develop, update API spec
* test=develop, fix auto_prune_error_on_leaf
* test=develop, fix auto prune error on loss stop_gradient
* test=develop, remove useless error check
* test=develop, add more ut for sorted gradient
* fix the error message for reduce_mean and reduce_sum op test=develop
* fix typo test=develop
* fix according review advice test=develop
* fix the test test=develop
* fix test=develop
* fix the constant error message test=develop
* fix typo test=develop
* fix typo test=develop
* fix code style test=develop
* fix comment and bugs test=develop
* fix the bug test=develop
* fix and add unittest test=develop
* fix the typo test=develop
* add support for the fill_constant op test=develop
* add test for ci coverage test=develop
1.support asymmetric padding;
2.support padding algorithm:"SAME" and "VALID";
3.support channel_last: data_format NHWC and NDHWC;
4.change doc of python API and c++;
test=develop, test=document_preview
* How to write custom op needs to follow framework OP spec.
* Package fluid_framework.so and headers into whl.
* Add paddle.sysconfig.get_include() and paddle.sysconfig.get_lib() to get include dir and lib dir.
* Export some C-APIs to merge OpInfo between core.so and custom_op.so.
* Add unit testing.
* Update API.spec.
* test=develop, argument shape support tensor and tensor in list
* test=develop,Increasing the coverage of CI tests
* test=develop, modify the document and update API.spec
* test=develop, modify the doc and update API.spec
* test=develop, modify the doc and update API.spec
* test=develop, modify the interface of UniformInitializer
* test=develop, modify the interface of XavierInitializer and MSRAInitializer
* test=develop, modify based on review's comments
* test=develop, modify based on review's comments
* test=develop, modify based on review's comments
* fix pool2d pool3d:
1. support asymmetric padding;
2. support padding algorithm:"SAME" and "VALID";
3. support channel_last: data_format NHWC and NDHWC;
4. support inferring shape when input with negative dims in compile time;
5. change doc of python API and c++;
6. fix bug in cuda kernel when Attr(adaptive) is true.
test=develop,test=document_preview
* fix 'tensors' to 'Tensors'. test=develop,test=document_preview
* add test for converage ValueError.test=develop,test=document_preview
* resolve conflict in test_pool2d. test=develop
* Follow Wangzhen's comment in PR 18970, test=develop
* Review comments, test=develop
* Leave fake quantization around mul
test=develop
* Replace Fake with Real Quantized Mul
test=develop
* Fix bug in quantize placement pass
Nodes in the graph now have checked type instead of node name when they are to be marked for quantization test=develop
* test=develop, fix docker with paddle nccl problem
* test=develop, Add Variable api and refine dygraph related API
* test=develop, Add Variable api and refine dygraph related API
* test=develop, refine test for new api and error info
* test=develop, refine error info and test_layers
* test=develop, add API.spec
* test=devleop, fix to_string python2 and python3 compat error and refien doc
* test=devleop, add API spec
* test=devleop, update API spec
* test=devleop, update API spec
* test=develop, invoke ci
* test=develop, fix example code
* test=develop, update API spec
* test=develop, add compat test and fix inplace campat dict error
* impove error message when passing ndarray with object dtype
* imporve message format
* change assert to raise TypeError
* remind user how to locate the irregular data instead of printing
* add unittest for input array type check
* Fix conv2d+dequantize squash for residual fusion
test=develop
* Correct int8 input
test=develop
* Add if exclude or include padding in pool2d mkldnn
test=develop
The new "fluid.data" changes old "fluid.layers.data":
1. Add shape and dtype check.
2. Remove "append_batch_size" parameter. We won't offer this in the new data layer because other deep learning platforms don't have this kind of data layer pre-processing. It may confuse users.
3. Remove "stop gradient" parameter because the data layer doesn't do back-propagation
TODO：
Now data layer feeded by executor is checked, will we want to check the feed data of readers in the future?
* add kernel for fill_op, test=develop
* modify PADDLE_ENFORCE to PADDLE_ENFORCE_EQ, test=develop
* add op test for fill_op, test=develop
* REGISTER COP CUDA KERNEL, test=develop
* update test_fill_op.py, test=develop
* change FillConstantOpVarTypeInference to FillOpVarTypeInference, test=develop
* fix op test, test=develop
* add head file, test=develop
* add support of matmul with multiple head even different width and height
Original matmul with multiple head supports only the mat_a.width == mat_b.height,
in that case, mat_b will be horizontally split. In this patch, we extend the
support when mat_a.width != mat_b.height but mat_a.width/head_number == mat_b.height,
in this case, mab_b will be vertically split.
One example is A is [3, 8], B is [2, 16], head_number is 4. In this
case, A will be split as [3, 2], B will be (vertically) split as
[2, 4]. The final result will be 4 matrix of 4 matrix of [3,4], i.e. [3, 16]
test=develop
* add support of matmul with multiple head even different width and height
Original matmul with multiple head supports only the mat_a.width == mat_b.height,
in that case, mat_b will be horizontally split. In this patch, we extend the
support when mat_a.width != mat_b.height but mat_a.width/head_number == mat_b.height,
in this case, mab_b will be vertically split.
One example is A is [3, 8], B is [2, 16], head_number is 4. In this
case, A will be split as [3, 2], B will be (vertically) split as
[2, 4]. The final result will be 4 matrix of 4 matrix of [3,4], i.e. [3, 16]
test=develop
* refactor the code of matmul with multiple head even different width and height
test=develop
* Add support for new QAT models
test=develop
Co-Authored-By: Michał Gallus <michal.gallus@intel.com>
Co-Authored-By: Wojciech Uss <wojciech.uss@intel.com>
* fixed fps results
test=develop
* fix top5 accuracy drop problem
* updated for new QAT models
* skip quantizing average pooling - dirty but working
* add missing pass
* added missing conv+brelu fuse pass
* removed a call to non-existent pass
test=develop
* renamed pass
test=develop
* Adjust finding pooling scale to newest QAT models
* Remove unnecessary code from quantization_mkldnn_pass
* Copy Pooling input scale to output scale in QAT
* Refactor & remove unused code in QAT
* Incorporate fp32 FC into QAT
test=develop
* Enable graph drawing with debug flag
test=develop
* Add tests for QATv2
* Fix paths for QATv2 models
test=develop
* Add option to save transformed int8 qat model
test=develop
* Remove redundant lines from qat mkldnn pass
test=develop
* Delegate disablement of avg pooling to qat
test=develop
* fix CI bug, test=develop
* Follow Wangzhen's Review, test=develop
* Update API.spec
test=develop
* Name False in (is_unsigned, TensorScale) tuple
test=develop
* Remove constraint that last dimension is forced to be 1 by add
lookup_table_v2 test=develop
* modify into PADDLE_ENFORCE_CUDA_SUCCESS test=develop
* Revert "modify into PADDLE_ENFORCE_CUDA_SUCCESS test=develop"
This reverts commit 8a960bfc61e51aa27c3c529df8fb90b93ebd19f9.
* move api into fluid.embedding test=develop
* fix example code test=develop
* move one_hot into fluid.one_hot
* modify api.spec test=develop
* fix loss shape test=develop
1. Support customize eval function instead of eval program.
2. Fix loading checkpoint in quantization strategy.
3. Support saving eval model when saving a checkpoint.
4. Fix decoder of loading context in PaddleSlim.
5. Fix restoring from the checkpoint of uniform prune strategy.
6. Support saving eval model and infer model during training.
7. Add ‘unitest’ for saving eval model, saving infer model and uniform pruning restoring from the checkpoint.
8. Fix pruning of depthwise_conv_grad op by updating the groups.
* support change shuffle thread num
* support change train thread num
* fix receive shuffle data of each channel
* data norm stop gradient
* add check thread_tensor type and root_tensor type when merge metric
* remove sleep in shuffle, add config
* add config of pslib client to client communication
* fix xbox str
* add data norm op testcase
* add flush in trainer finalize
* make OpTest check grad inplace even if forward has no inplace, test=develop
* do not run PE when enable_inplace is False, test=develop
* add conv3d cuda kernel for float16 type, test=develop
* refactor OpTest for inplace, test=develop
* add comments, test=develop
* add recompute based checkpoints methods for large batch training
test=develop
* add append_backward_with_forward_recomputation
test=develop
* refine optimizer
test=develop
* update backward and optimizer
test=develop
* make Variable usable
test=develop
* add recompute code
* refine optimizer
test=develop
* refine addup _append_backward_ops_with_checkpoints_
1) for recompute part, just cache the grad_op_desc without appending to block
2) before appending grad_op_desc to backward part, addup_repetitive_vars, remove unused branch
test=develop
* make method private
* add recompute strategy into DistributedStrategy
test=develop
* checkpoint version3
test=develop
* remove some print information
test=develop
* remove unused sumop
test=develop
* try to fix recompute with graph building modules
* add input names to vars should be held
* add memory debug tool
* backup backward
* Fix bugs
* add backward desc for op not in any segments
* add exception info for sub_block
test=develop
* modify code style
test=develop
* modify code style
test=develop
* remove print functions
test=develop
* add API spec
test=develop
test=document_preview
* make Recompute a child class of Optimizer
test=develop
test=document_preview
* add API spec
test=develop
test=document_preview
* modify API spec
test=develop
test=document_preview
* add document for Recompute
test=develop
test=document_preview
* change API doc of Rcompute
test=develop
test=document_preview
* code cleaning
test=develop
test=document_preview
* modify API spec
* fix bugs when segments hold no element
* add testcase for Recompute Optimizer
test=develop
test=document_preview
* add test for apply_gradient, and code cleaning
test=develop
test=document_preview
* add test case for load function
* enable CI
test=develop
test=document
* add test case
test=develop
test=document_preview
* add sample code for 4 function of recompute optimizer
test=develop
test=document_preview
* move tree_conv to fluid.contrib.layers
test=develop
* update API.spec for tree_conv
test=develop
* update tree_conv api to increase unit coverage
test=develop
* refactor dygraph,test=develop
* fix failed unittest,test=develop
* polish code,test=develop
* check windows ci error,test=develop
try to fix windows ci error by np.allclose,test=develop
* polish vlog and profiler, test=develop
* try to fix preceding ops order,test=develop
* test transformer in windows ci, test=develop
* use python c-api to speed up tracer.trace,test=develop
* test=develop, fix docker with paddle nccl problem
* test=develop, add ut for debug string and gradient_accumulator
* test=develop, add tests for layer/gradient_accumulator/prepared_op
* test=develop, fix complie error for test_prepared_op
* test=develop, add more ut for dygraph
* test=develop, create API.spec for dygraph api change
* test=develop, refoctor name to make it easier to understand
* test=develop, refoctor name to make it easier to understand
* test=develop, fix multi-gpu failed problem , add Tracer tests, change PADDLEENFORCE to PADDLEENFORCE_EQ
* test=develop, fix ut failed on parallel se-resnext
* test=develop, change one more PADDLE_ENFORCE
* support auto prune in dygraph mode
* test=develop, support auto prune
* test=develop, merge develop conflict
* test=develop, fix test_layer and test_tracer ut
* test=develop, fix bug which may cause stop_gradient disabled with a list of backward inputs
modified interpolate_op to support tensor attribute
1. the parameter out_shape of image_resize、resize_nearest/bilinear/trilinear can be a list or a 1-D tensor variable. If a list, each element can be an integer or a tensor variable with shape: [1].
2. the parameter scale of above Ops can be a 1-D tensor variable.
modified document of image_resize, resize_nearest, resize_bilinear, resize_trilinear and add some code example.
add crop_tensor op. The main difference with crop is :
1. If the argument shape is a list, each element is an integer or a tensor variable with shape: [1]. This way is suitable for the case that the shape may be changed each iteration.
2. If the argument shape is a variable. Its rank must be 1. In crop op, the rank of shape must be the same as x
offsets can be a list, in which each element is an integer or a tensor variavle with shape: [1].
* Add fc_elementwise_layernorm_fuse pass and unittest.
* Add fused_fc_elementwise_layernorm op and its GPU kernel.
test=develop
* Apply fc_elementwise_layernorm_fuse_pass to GPU inference.
* Add the setting of attrs in the definition of binary_op.
test=develop
* Add comment.
* Implement the unittest.
test=develop
* Change the unittest name of layer_norm.
test=develop
* strided_slice op basic function test=develop
* test=develop rewrite and fix
* fix bug test=develop
* fix for the PADDLE_ENFORCE usage
* add some unit testw
* fix for the aip test and copright and fix test=develop
* fix API.spec test=develop
* fix API.spec test=develop
* add axis parameter test=develop
* fix for the build error test=develop
* fix python api test=develop
* fix the build test=develop
* fix build test=develop
* fix API spec test=develop
* test=develop add some comment and single op test
* fix API spece test=develop
* fix test=develop
* fix test=develop
* fix api test=develop
* fix api test=develop
* fix API.spec test=develop
* fix typo test=develop
* fix API.spec test=develop
* fix API typo test=develop
* fix doc and API.spec test=develop
improve pow op according to reviews:
1. Delete unnecessary judgement statements in PowGradOpDescMaker;
2. Improve test of test_api;
overload GetKernelTypeForVar
add stop_gradient=True when attr(factor) is tensor Variable, change examples in API pow.
test=develop,test=document_preview
add support parameter inference when argument shape is a list containing integer and tensor variable;
test=develop
fix reshape op according to reviews:
1. improve or message;
2. improve test of test_api.
test=develop,test=document_preview
fix reshape op: Add error message in nn.py, test=develop
add stop_gradient=True when attr(shape) is tensor Variable.
change examples in API reshape.
test=develop,test=document_preview
add support parameter inference when arguments starts or ends is a list containing integer and tensor variable;
test=develop,test=document_preview
improve slice op according to review(from hongyu). test=develop
fix slice op according to review: infer_flags, test=develop
fix slice op: improve overload operator __getitem__ to support attrs(starts and ends) are Variable.
test=develop,test=document_preview
fix test_slice_op: add TestSliceOp_decs_dim_6 to resolve conflict with test_slice_ngraph_op. test=develop
add stop_gradient=True when attr(starts) or attr(ends) is tensor Variable.
test=develop,test=document_preview
1. add tensor support for argument expand_times in expand op;
2. add support parameter inference when argument expand_times is a list containing integer and tensor variable;
improve expand op according to reviews:
1. add doc of ExpandTimes in expand_op.cc;
2. improve the test of test_api.
add stop_gradient=True when attr(expand_times) is tensor Variable, change code examples.
test=develop,test=document_preview
* refactor dygraph,test=develop
* fix failed unittest,test=develop
* polish code,test=develop
* check windows ci error,test=develop
try to fix windows ci error by np.allclose,test=develop
* polish vlog and profiler, test=develop
* try to fix preceding ops order,test=develop
* test transformer in windows ci, test=develop
* use python c-api to speed up tracer.trace,test=develop
* test=develop, fix docker with paddle nccl problem
* test=develop, add ut for debug string and gradient_accumulator
* test=develop, add tests for layer/gradient_accumulator/prepared_op
* test=develop, fix complie error for test_prepared_op
* test=develop, add more ut for dygraph
* test=develop, create API.spec for dygraph api change
* add transform_data to dygraph
* test=develop, refoctor name to make it easier to understand
* test=develop, refoctor name to make it easier to understand
* add test and change input to const ref for safety
* test=develop, fix multi-gpu failed problem , add Tracer tests, change PADDLEENFORCE to PADDLEENFORCE_EQ
* add ut for data transform
* refine ut for data_transform
* test=develop, fix ut failed on parallel se-resnext
* test=develop, change one more PADDLE_ENFORCE
* add test_tracer on multiple devices
* test=develop, change place to mutable for data transform
* test=develop, add transform data on same place test and remove useless log
* test=develop, Add to do for data layout and and ut for conv2d with no bias
* Implement the operator with sprase matrix multiply
* Update the URL of mklml library.
test=develop
* Disable MKLML implematation when using no-linux.
test=develop
* optimize bp with mkl sparse matrix
test=develop
* tmp add fused_emb_seq layer
* Add the support of padding_idx attribute.
test=develop
* add padding_idx support
test=develop
* implement grad refer lego
test=develop
TemporaryAllocator is a singleton used for allocating memory for Cudnn. Since it is a singleton, we can delete it for better performance in memory.
We replace TemporaryAllocator by CUDADeviceContextAllocator and CUDADeviceContextAllocation, which uses stream callback to delete the memory allocated for the stream to avoid singleton.
Also added data_feed_proto to operator to fix CI in CPU compilation
* Refine the codes related to fc op.
* Add GPU implementation for fc functor.
* Apply fc_fuse_pass in GPU inference.
test=develop
* Change the cmake for fc op.
* Change PADDLE_ENFORCE to PADDLE_ENFORCE_EQ.
* Add an attribute to set the activation type in fc_op.
* Enhance the unittest of fc_op.
test=develop
* Remove the declaration of FCOpGrad back to the header file.
test=develop
* Set default value for newly added arguments in test_fc_op.
test=develop
* Remove constraint that last dimension is forced to be 1 in huber_loss
test=develop
* add y[rank-1] == 1 when x_rank=y_rank test=develop
* modify into contain_unknown_dim test=develop
* refactor dygraph,test=develop
* fix failed unittest,test=develop
* polish code,test=develop
* check windows ci error,test=develop
try to fix windows ci error by np.allclose,test=develop
* polish vlog and profiler, test=develop
* try to fix preceding ops order,test=develop
* test transformer in windows ci, test=develop
* use python c-api to speed up tracer.trace,test=develop
* test=develop, fix docker with paddle nccl problem
* test=develop, add ut for debug string and gradient_accumulator
* test=develop, add tests for layer/gradient_accumulator/prepared_op
* test=develop, fix complie error for test_prepared_op
* test=develop, add more ut for dygraph
* test=develop, create API.spec for dygraph api change
* test=develop, refoctor name to make it easier to understand
* test=develop, refoctor name to make it easier to understand
* test=develop, fix multi-gpu failed problem , add Tracer tests, change PADDLEENFORCE to PADDLEENFORCE_EQ
* test=develop, fix ut failed on parallel se-resnext
* test=develop, change one more PADDLE_ENFORCE
* test=develop add a argument for softshrink python api
* test=develop fix doc format
test=develop fix doc format
* test=develop fix API.spec
test=develop fix API.spec
* test=develop
Fix the scatter op bug when use the add mode, and support the int64 data type of scatter_op Index(#18804).
* test=develop
Remove the PADDLE_ENFORCE and use PADDLE_ENFORCE_EQ
* test=develop
Remove the fix bug of scatter_add, and just add the support of int64 in scatter_add
* test=develop
Add the test case for scatter op, the test case just for index int64
* add to and detach for Variable in dygraph, test=develop
* add detach for Variable in dygraph, test=develop
* add detach for Variable in dygraph, test=develop
* add detach for Variable in dygraph, test=develop
* add detach for Variable in dygraph, test=develop
* add detach for Variable in dygraph, test=develop
* add exception check, test=develop
* Support looking up embeddings from BoxPS.
* Add a _pull_box_sparse op, for now this op is not exposed to users.
* Add a BoxHelper class, providing 'BeginPass', 'EndPass', 'FeedPass' functions and so on.
* Add 'BoxPSDataset' in python code.
* Add a compile options WITH_BOX_PS and a MACRO PADDLE_WITH_BOX_PS.
* Add UT.
* More concrete information pls refer to: https://github.com/PaddlePaddle/Paddle/pull/18982
- Refactor step 1
- Compilation fix
- Yet another compilation fix
- Even more compilation fix
- Lint fixes
test=develop
- Removed deprectaed PADDLE_ENFORCE occurance
test=develop
- Candidate fix to BN forward
- Lint fixes
test=develop
- Refactoring in data_layout_transform
- compilation fix
- Another comppilation fix
- Step further into darkness
- Yet another compilation fix
- Yet another compilation fix
- missing header
- compilation fix
- Added MKLDNN -> Paddle conversion in fetch op
test=develop
- Compilation fix
test=develop
- Lint
test=develop
- Mul fix
- Fix to MKLDNN MUL op and Elementwise MUL UT
test=develop
- Workaround for diffrent weights with groups representation Paddle vs
MKL-DNN.
test=develop
- Candidate fix for 5D convolution with groups
- Refactor of fix for conv3d and conv2d in fetch op
test=develop
- Compilation fix
- Still same compilation fix
- Compilation fix
- Compilation fix
- Reverted refactoring of fixes
- Adapted test_conv2d_int8_mkldnn so it exects data in NCHW format
not NHWC
test=develop
- minor fix in UT
test=develop
- Lint fixes
test=develop
* fix con2d transpose bias by create and init it in build_onee
* fix API spec
* test=develop, invoke ci
* fix bias_attr and act has no effect error on layer norm, conv2dTranpose, billinearTensorProduct, sequece_conv. fix original_mode not used error on GRUunit. fix sample_weight not set error on NCE. Add ut for all thoese layer
* test=develop, change success standard for conv2dTranspose
* test=develop, fix test_layers to invoke some error branch
* test=develop, fix sample code
* test=develop, fix BilinearTensorProduct failed in dygraph mode
* test=develop, fix test_layers segment fault error
* fix correctness of the communicator
* fix a bug in send thread when sending var context is empty, test=develop
* add lookup_table_prefetch_op and prefetch optimize, test=develop
* remove remote prefetch GPU supported
* word2vec force with CPU, test=develop
* test dist remote lookup table force with CPU, test=develop
* supports multiple NCCL communicators preserved in NCCLCommContext
test=develop
* add ut for c_comm_init_all operator and fix cuda resource release problem
test=develop
* support tensor input with padding for warpctc op
* merge with develop
* test=develop
* modified python API examples test=develop
* nn.py is modified for code coverage test=develop
* update documents info about warpctc op in API.spec test=develop
* add test_warpctc_with_padding in test_layers test=develop
* add warning log for cuda_version back to warpctc_op.cc
* modify API.spec for warpctc op test=develop
* modify API.spec
* update warpctc test to new CompiledProgram API test=develop
* modify code examples for warpctc op test=develop
* modify API.spec for warpctc op test=develop
* modify API.spec for warpctc op test=develop
* Implement the operator with sprase matrix multiply
* Update the URL of mklml library.
test=develop
* Disable MKLML implematation when using no-linux.
test=develop
* optimize bp with mkl sparse matrix
test=develop
* add pybind interface to get all inplace ops, test=develop
* enhance OpTest to check whether the consistency of operator when using and not using inplace, test=develop
* handle corner cases in op_test, test=develop
* support outputs without tensor holder_, like XShape in reshape_op, test=develop
* fix bug, some op has GradOpMaker, but actually no grad_op in OpInfoMap, test=develop
* use reshape_grad instead of reshape in FlattenGradOp, test=develop
* fix error debug dims info for variables like XShape, test=develop
* change computational order in sum_op to relieve computation difference using inplace, test=develop
* add inplace_atol to check group_norm, and skip inplace_grad for mkldnn, test=develop
* follow sneaxiy's comments, test=develop
* remove unused DefaultGradOpDescMaker in mkldnn op, test=develop
* increase test_parallel_executor_seresnext time limit
test=develop
* split test_parallel_executor_seresnext
test=develop
* temporally disable reduce_and_allreduce test because of the random failure.
test=develop
* split gpu and cpu
test=develop
add fl_listen_and_serv op for Federated_learning and fl_distribute_transpiler add this op to pserver program . This op just listen the endpoint and sum&scale.
* change the default value of summarize from -1 to 20 in Print op to improve ease of use, test=develop
* change the doc of API Print to make the document easier to understand, test=develop
* instag lod tensor impl
* First PR for instag
* First PR for instag
* Before adding Selection Rows.
* Change name from instag to filter_instag, add upgrade the impl of filter_instag
* Change name from instag to filter_instag, add upgrade the impl of filter_instag
* Fix yapf error in gradient_checker.py to pass Travis-CI
* Fix Filter Instag Grad test=develop
* Fix Filter Instag Grad test=develop
* 1) Fix API.spec, add filter_instag Op. 2) Add Vector Support for CUDA. test=develop
* Impl Loss_weight and empty output handler
* change Loss Weight datatype to Float32, and add Loss Weight as 2nd output
* 1) Support Tensor Input(without LOD) 2) Add Unit test
* Filter By Instag Final test=develop
* Update API.spec for filter_by_instag test=develop
* Update API.spec for filter_by_instag 2 test=develop
* Add Filter By Instag Coverage
* code format of test_layers.py
* code format test_layers.py test=develop
* Make API args more readable test=develop
* Make API args more readable and pass code format test=develop
* Filter By Instag Op, Rename Map to Index Map test=develop
* Filter By Instag Op, code format err in filter_by_instag_op.cc test=develop
* Filter by instag op: code format of cpp files test=develop
* Filter by instag Op: Api spec modification test=develop
* Filter by instag Op: Api spec doc id modification test=develop
* Filter by instag Op: Api spec and doc preview test=develop test=document_preview
* Filter By Instag Op, fix doc erro test=document_preview test=develop
* Filter By Instag Op, fix doc err and Api spec test=document_preview test=develop
* Filter By Instag Op, fix Api spec test=document_preview test=develop
* Filter By Instag Op, fix Paddle Encoforce deprecated warning test=document_preview test=develop
* Filter By Instag Op, fix Paddle Encoforce deprecated and code format warning test=document_preview test=develop
* add hard_swish activation op (new op)
test=develop
* remove redundancy files
* modify document content of HardSwish OP
* add API test in test_layers.py
* add dynamic_graph for test_hard_swish
* add a place field in DataFeed to denote which place it will feed data to.
* abstract the copy process in CopyToFeedTensor function
* add UT for float32 type and for CUDAPlace
* Add call stack info during runtime and compile time
test=develop
* Rename operator_call_stack
test=develop
* Add unit test
test=develop
* follow comment
test=develop
* add train demo for imdb text classification task
* make inference library release data_feed dataset dataset_factory data_feed_factory
* add String Data Generator
* new feature of train demo: save model params
* New feature of train demo: set training config using gflags
* change code style for CI
* add readme and dataset for imdb demo trainer
* fix warpctc.dll not found issue, test=develop
* revert the linux platform change, test=develop
* delete warpctc_lib_path.h.in, test=develop
* add SetPySitePackagePath function
* fix warpctc.dylib not found issue on Mac, test=develop
* improve the paddle lib path setting logic, test=develop
* fix mac ci issue caused by test_warpctc_op unittest, test=develop
* tweak code, test=develop
* open gc by default, test=develop
* fix test_train_recognize_digits and disable gc when ngraph is enabled, test=develop
* fix conditional_block op eager deletion bug, test=develop
* add some comments to reviewers, test=develop
* support filelist size < trainer num
* pull dense when stop, to make sure local dense params are same as pserver, so save paddle model will save dense model same as pserver
* enable QueueDataset train same filelist for serveral times
* test=develop
Add the op of unique_with_counts, the op is calc the unqiue input of data, and output the corresponding indices and count of data.
* test=develop
Check the input and dtype in the op of unique_with_counts
* test=develop
test=document_preview
update the API.spec for `unique_with_counts`, at the same time, optimize the python api in the op of `unique_with_count`
* test=develop
test=document_preview
Fix some python api problem in the op of `unique_with_counts`, and change the error messsage in this op.
* Fix some API problem in the op of `unique_with_counts`
test=develop
test=document_preview
* test=develop
test=document_preview
Fix the api sample of op `unique_with_counts`, and update api.spec
(1) set fleet_send_batch_num a default value according to trainer num， the previous 80000 is fixed，if trainer num is much less or larger than 100，global shuffle may have timeout error.
(2) fix load one table bug, add barrier
* support center loss
* change tensor copy api to high level api tensorcopy
* test=develop rewrite the center_loss cuda_kernel to make it faster
and add document of the center loss api,also update test function
* test=document_preview test=develop
update document of center loss
* test=document_preview test=develop
modify API.spec modify test code remove nouse const_cast
* extend matmul op to support multiple head multiplication
With the support of multiple head, the multiplication of two big matrixes is
split into multiplication of several (head_number) small matrixes. e.g. if
Mat A is [3, 24] and Mat B is [24, 4], when multiple A and B with head_number
as 4, Mat A will be split as 4 matrix of [3, 6] and Mat B will be 4 matrix of
[6, 4]. The result of final matrix will be 4 matrix of [3, 4], i.e. [3, 16].
The change includes 2 things:
1. save delta model and shrink table are control by the same parameter before, now add delete_after_unseen_days to control shrink table.
2. value in sparse table has no slot before, now add slot in sparse table, and add DownpureCtrAccessor to support the new meta.
test=develop
（1）support patch data （merge slots of instances of same line id, modify dense layer which
changes its size）
（2）add fleet load_one_table interface, support load from paddle model and load from pslib model
（3）fix push sparse bug which cause push sparse cost more time（about 10% in my testcase）
（4）when some slots are not in one of your network (join/update, etc.)，data feed、collect label info、push/pull sparse will skip these slots， instead of throw error.
（5）add more debug info in TrainFilesWithProfiler
The change includes 3 things:
1. Set CPU_NUM to 1 in the tests because the ParallelExecutor will print warning that CPU_NUM is not set and use default 1.
2. Old tests compare two RNNs, hand written simple RNN and same RNN built by Paddle, but initialized RNN weights in numpy random and Paddle random separately. Fixed it by setting weights and bias values.
3. Also set numpy random seed in the tests. Now the two RNNs diff can be smaller (rtol from 0.1, 0.2 to. 0.01) in the tests.
test=develop
Test PaddingRNN on V100 GPU device.
Test configuration: large model, padding mode (which is the mode using recurrentOp), one GPU.
GPU memory (MiB): 6414 (this PR) vs 6837 (without this PR)
Speed (steps/s): 10.28 (this PR) vs 9.89 (without this PR)
* feature/auto_growth_allocator, test=develop
* add unittest of AlignedAllocator, test=develop
* try to turn on auto_growth to test on CI, test=develop
* fix segmentation fault in mixed_vector.h, test=develop
* add unittests, test=develop
1. Since allreduce op has 4 reduce types, We split these four reduce types into four ops
2. We also refined the collective op code, e.g. we separated the collective op kernel into CPUKernel and CUDAKernel, and remove the device specified DeviceContext parameter in template as we already knew the target DeviceContext
3. We remove the newly added Collective op role to reduce the complexity of program and graph analysis
* fix prepare context redundant code problem, optimize executor by caching create_varaiables
test=develop
* supports collective training in executor
* make fetch_list runable with variables, add more unittest for use_program_cache
test=develop
* fix comment
test=develop
* use unique name for nccl_id
* supports output to stream in program_to_code
* insert sync_comm_stream before regularization; add skip_op_callstack capability in program_to_code
* set op role in collective training
* add collective op role
* remove orig file
* add build optimizer by strategy
* add collective strategy
* refine collective strategy
* add multi-process role maker
* refine strategy building factory so that we can easily plugin more strategy
* scale loss grad in collective sgd transpiler
* add support for distributed fc
* code format
* revert some features for dist fc
* add support for distributed fc training
* fix prepare context redundant code problem, optimize executor by caching create_varaiables
test=develop
* supports collective training in executor
* make fetch_list runable with variables, add more unittest for use_program_cache
test=develop
* use unique name for nccl_id
* supports output to stream in program_to_code
* insert sync_comm_stream before regularization; add skip_op_callstack capability in program_to_code
* set op role in collective training
* add collective op role
* fix comment
test=develop
* remove orig file
* add build optimizer by strategy
* add collective strategy
* refine collective strategy
* add multi-process role maker
* refine strategy building factory so that we can easily plugin more strategy
* scale loss grad in collective sgd transpiler
* add support for distributed fc
* code format
* revert some features for dist fc
* add support for distributed fc training
* test=develop
add collective op unittest standard
* test=develop
remove the test_collective directory
* test=develop
remove the test_collective directory
* remove slicegather test
* code format for reducescatter
* update attr of shard_index_op
* Modify macro nccl_helper
* remove test without distribute
* macro collective_helper
* marcro update
* test=develop
update support python3.5
* test=develop change gpu memory use to 0.1 when test
* test=develop
update ut equal func
* test=develop
set flags to 1.5
* test=develop fix pickle dumple py35
* test=develop
fix divide in slice and add sync_comm_stream
update atol and rtol to 1e-05
rm shard_index op and test
modify read input from file to read from memory
remove origin_program in framework and add i/o in c_sync_calc_stream
* test=develop update unittest sync operator I/O
1. fix the bug that out_put_var in SaveSelectedRows would be empty string
2. use merge_sparse_lookup_table to replace sum op for load_persistables_for_inference
3. fix the bug in _clone_var_in_block_ when the var is SELECTED_ROWS.
(1) use channel instead of vector/BlockingQueue in Dataset，to keep same with existing implementation, and make code more readable and flexible (dataset single output channel or multi output channel). one previous memory out of limit problem is cause by not release memory after training.
(2) add Record because MultiSlotType costs too much memory (80B)，fix memory out of limit problem.
(3) add Channel, Archive in paddle/fluid/framework
(4) change dataset from shared_ptr to unique_ptr in pybind
(5) move create/destroy readers from trainer to dataset
(6) move shuffle from datafeed to dataset. dataset holds memory, datafeed is only for load data and feed data to network.
(7) fix thread num bug of Dataset when filelist size < thread num
(8) support set_queue_num in InMemoryDataset
* test=develop, add add_multi_gpu_install_check
* test=develop, refine warning doc
* test=develop, refine warning doc
* test=develop, refine warning doc
* test=develop, support multi cpu
* test=develop, find right num of cuda device
* test=develop, find right num of cuda device
* test=develop, fix multigpu processing and fix type bug in dygraph
* test=develop, fix multigpu processing and fix type bug in dygraph
* Update backward.py:
- If there is no input grad var in all outputs of previous ops, do not append this op into graph.
- Only apply this stragety when double backward.
* Update some double backward op.
* Update sum_op to judge whether a tensor is empty by numel or IsInitialized().
* test=develop add target assign for retinanet
* test=develop
run ci
* test=developp
add test_layers
* test=develop
add APi.spec
* test=develop
alter round 1
* test=develop
alter rpn_target_assign_op.cc
* test=develop
alter test_rpn_target_assign_op.py
* test=develop
alter rpn_target_assign_op.cc
* test=develop
alter API.spec
* test=develop
alter paddle/fluid/operators/detection/rpn_target_assign_op.cc
* test=develop
alter rpn_target_assign_op.cc
* test=develop
alter python/paddle/fluid/layers/detection.py
* test=develop
alter paddle/fluid/API.spec
* Remove layers.detection_map API
* Since uers can use fluid.metrics.DetectionMAP to calculate mAP of current-batch and cumulative-batch. layers.detection_map only can calculate cur-batch mAP.
* test=develop
The scatter op has a calc bug when the indices has same index, the scatter op use overwrite mode to calculate the same index, fix this bug by using the accumulate mode to calculate the same index.At the same time, the gather op has the same bug when the op calc the grad. And we use the lib of open-blas and eigen to optimize the time cost in accumulate mode.
* test=develop
Fix some code format problem, and the same time add the test case in gather and scatter op
* Cherry-pick fix random Python3 CI failure.
In some tests, SWEs used "print('xxx').format('xxx')". The syntax
is only supported in Python2, not python3. However, since those
lines are related to data download, if the CI machines already have
the data, it passes CI tests. That causes random failure.
* Cherry-pick: disable CUDNN case of test_warpctc_op
Also temporary disable a unit test. The test will be fixed under high priority.
* add deformable psroi pooling
* test=develop
* test=develop
* test=develop
modify format
* fix bug
* test=develop run ci
* test=develop
add API.spec
* add test_layers.py
* run ci again
* test=develop
run ci again
* run ci again
* test=develop
run ci again
* test=develop
run ci again
* test=develop
run ci again
* add space between two lines
* test=develop
add space between two lines
* test=develop
add space between lines
* test=develop
modify comment in nn.py
* test=develop
add space between two lines
* test=develop
add space between two lines
* update API.spec
* run ci again
* test=develop
run ci again
* rerun ci
* test=develop
rerun ci
* change input shape
* run ci
* test=develop
run ci
* modify format of nn.py
* test=develop
* test=develop
* test=develop
update API.spec
* test=develop
fix API doc
* modify API comment
* modift API comment
* test=develop
update API.spec
* test=develop
modify comment
* test=develop
modift comment
* test=develop
modift comment
* test=develop
update API.spec
* test=develop
modify comment
* test=develop
add inference in nn.py
* test=develop
update API.spec
* test=develop
resolve confict
* test=develop
update API.spec
* add unfold op
test=develop
* fix divide bug in python3 when calculating output width and height
test=develop
* add name=None in python api, move redundant code into inline function
* try to trigger ci for this code
test=develop
* add 'UserDefinedRoleMakerNCCL' for collective mode.
* code style
* add the name UserDefinedRoleMakerNCCL to __all__
* rename to UserDefinedRoleMakerCollective
* rename to UserDefinedCollectiveRoleMaker
Add Pipeline Concurrency Train Mode:
- Cpp: pipeline_trainer & section_worker
- Python: PipelineOptimizer
- Add a new data_feed type: PrivateInstantDataFeed
- Add a test demo of pipeline trainer and the test model is gnn
- Do not support win32 now
* Enable seq_pool op to accept len 0 input
test=develop
* Update sequence_pool's api
test=develop
* Add more unittest cases for seq_pool op
test=develop
* Remove legacy comments
test=develop
* Don't use template in op maker
test=develop
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