* add input type and dtype check for accuracy_op
* add input type and dtype check for accuracy_op
* modify python error on accuracy_op,add test=develop
* modify details on accuracy_op, test=develop
* test float16, test=develop
* add warning, test=develop
* 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
* Delete PadFuntion, include padding.h instead. test=develop
* move function(IsSymmetricPadding) from conv_cudnn_op.cu/conv_transpose_cudnn_op.cu to padding.h, test=develop
* 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
* 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
* 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
* 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
* 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
* 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
* 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
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].
* fix conflicts
test=develop
* change mask_bias_reorder
test=develop
* add ComputeMask function to make code clear
test=develop
* change according to reviews
test=develop
* change according to reviews
test=develop
* 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
* update elementwise double grad to save gpu memory, test=develop
* update elementwise_mul/div_grad_grad to save memory, test=develop
* remove eval function in eigen statement to save memory, test=develop
* add unittest for elementwise_div_grad_grad without dout, test=develop
* add unittest for elementwise_add_grad_grad without ddx, test=develop
* add float16 cuda kernel for elementwise double grad op, 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
* 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
* 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
* Enhance fc_fuse_pass to enable fusing relu.
* Allow print the shapes of var_desc in graph.
test=develop
* Enhance fc_fuse_pass_tester.
* Remove the use of PADDLE_ENFORCE.
test=develop
* Correct the number of ops after fusing.
test=develop
* Fix a typo.
test=develop
* Set activation_type to null when there is no relu in fc.
test=develop
* Refine fc_fuse_pass's codes.
* Enable the set of shape for tensor.
* Refine repeated_fc_relu_pass and add unittest.
test=develop
* add kernel for unstack_op, test=develop
* add kernel for unstack_op, test=develop
* add kernel for unstack_op, test=develop
* adjust the code format, test=develop
* modify some comment, 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
* add extra error message hint in optimizer ops
* polish format & delete useless change, test=develop
* extract init judue from shape compare, test=develop
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