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提交 85c3bfc1 编写于 作者: T tensor-tang
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Merge remote-tracking branch 'ups/develop' into fix

上级 6602db5b d07d9535
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Showing with 2545 addition and 644 deletion
CMakeLists.txt
浏览文件 @ 85c3bfc1
...@@ -61,6 +61,7 @@ option(EIGEN_USE_THREADS "Compile with multi-threaded Eigen" OFF) ...@@ -61,6 +61,7 @@ option(EIGEN_USE_THREADS "Compile with multi-threaded Eigen" OFF)
option(WITH_ARM_FP16 "Use half precision support on armv8.2-a cpu" OFF) option(WITH_ARM_FP16 "Use half precision support on armv8.2-a cpu" OFF)
option(WITH_FAST_BUNDLE_TEST "Bundle tests that can be run in a single process together to reduce launch overhead" OFF) option(WITH_FAST_BUNDLE_TEST "Bundle tests that can be run in a single process together to reduce launch overhead" OFF)
option(WITH_CONTRIB "Compile the third-party contributation" OFF) option(WITH_CONTRIB "Compile the third-party contributation" OFF)
option(WITH_ANAKIN "Compile with Anakin library" OFF)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE}) option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
# CMAKE_BUILD_TYPE # CMAKE_BUILD_TYPE
...@@ -193,7 +194,10 @@ set(EXTERNAL_LIBS ...@@ -193,7 +194,10 @@ set(EXTERNAL_LIBS
if(WITH_GPU) if(WITH_GPU)
include(cuda) include(cuda)
include(tensorrt) include(tensorrt)
endif(WITH_GPU) include(external/anakin)
else()
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when GPU is set." FORCE)
endif()
if(WITH_AMD_GPU) if(WITH_AMD_GPU)
find_package(HIP) find_package(HIP)
......
cmake/external/anakin.cmake 0 → 100644
浏览文件 @ 85c3bfc1
if (NOT WITH_ANAKIN)
return()
endif()
set(ANAKIN_INSTALL_DIR "${THIRD_PARTY_PATH}/install/anakin" CACHE PATH
"Anakin install path." FORCE)
set(ANAKIN_INCLUDE "${ANAKIN_INSTALL_DIR}" CACHE STRING "root of Anakin header files")
set(ANAKIN_LIBRARY "${ANAKIN_INSTALL_DIR}" CACHE STRING "path of Anakin library")
set(ANAKIN_COMPILE_EXTRA_FLAGS -Wno-error=unused-variable -Wno-error=format-extra-args -Wno-error=comment -Wno-error=format -Wno-error=switch -Wno-error=return-type -Wno-error=non-virtual-dtor -Wno-reorder -Wno-error=cpp)
set(ANAKIN_LIBRARY_URL "https://github.com/pangge/Anakin/releases/download/3.0/anakin_release_simple.tar.gz")
# A helper function used in Anakin, currently, to use it, one need to recursively include
# nearly all the header files.
function(fetch_include_recursively root_dir)
if (IS_DIRECTORY ${root_dir})
include_directories(${root_dir})
endif()
file(GLOB ALL_SUB RELATIVE ${root_dir} ${root_dir}/*)
foreach(sub ${ALL_SUB})
if (IS_DIRECTORY ${root_dir}/${sub})
fetch_include_recursively(${root_dir}/${sub})
endif()
endforeach()
endfunction()
# download library
message(STATUS "Download Anakin library from ${ANAKIN_LIBRARY_URL}")
execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
execute_process(COMMAND bash -c "rm -rf ${ANAKIN_INSTALL_DIR}/*")
execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; wget -q ${ANAKIN_LIBRARY_URL}")
execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; tar xzf anakin_release_simple.tar.gz")
if (WITH_ANAKIN)
message(STATUS "Anakin for inference is enabled")
message(STATUS "Anakin is set INCLUDE:${ANAKIN_INCLUDE} LIBRARY:${ANAKIN_LIBRARY}")
fetch_include_recursively(${ANAKIN_INCLUDE})
link_directories(${ANAKIN_LIBRARY})
endif()
doc/fluid/api/gen_doc.sh
浏览文件 @ 85c3bfc1
#!/bin/bash #!/bin/bash
python gen_doc.py layers --submodules control_flow device io nn ops tensor > layers.rst python gen_doc.py layers --submodules control_flow device io nn ops tensor detection learning_rate_scheduler > layers.rst
for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer
do do
......
doc/fluid/api/io.rst
浏览文件 @ 85c3bfc1
...@@ -59,21 +59,3 @@ get_inference_program ...@@ -59,21 +59,3 @@ get_inference_program
.. autofunction:: paddle.fluid.io.get_inference_program .. autofunction:: paddle.fluid.io.get_inference_program
:noindex: :noindex:
save_checkpoint
---------------
.. autofunction:: paddle.fluid.io.save_checkpoint
:noindex:
load_checkpoint
---------------
.. autofunction:: paddle.fluid.io.load_checkpoint
:noindex:
clean_checkpoint
----------------
.. autofunction:: paddle.fluid.io.clean_checkpoint
:noindex:
doc/fluid/api/layers.rst
浏览文件 @ 85c3bfc1
...@@ -181,12 +181,6 @@ Print ...@@ -181,12 +181,6 @@ Print
.. autofunction:: paddle.fluid.layers.Print .. autofunction:: paddle.fluid.layers.Print
:noindex: :noindex:
is_empty
--------
.. autofunction:: paddle.fluid.layers.is_empty
:noindex:
device device
====== ======
...@@ -261,19 +255,6 @@ double_buffer ...@@ -261,19 +255,6 @@ double_buffer
.. autofunction:: paddle.fluid.layers.double_buffer .. autofunction:: paddle.fluid.layers.double_buffer
:noindex: :noindex:
random_data_generator
---------------------
.. autofunction:: paddle.fluid.layers.random_data_generator
:noindex:
Preprocessor
------------
.. autoclass:: paddle.fluid.layers.Preprocessor
:members:
:noindex:
nn nn
== ==
...@@ -613,30 +594,6 @@ roi_pool ...@@ -613,30 +594,6 @@ roi_pool
.. autofunction:: paddle.fluid.layers.roi_pool .. autofunction:: paddle.fluid.layers.roi_pool
:noindex: :noindex:
dice_loss
---------
.. autofunction:: paddle.fluid.layers.dice_loss
:noindex:
resize_bilinear
---------------
.. autofunction:: paddle.fluid.layers.resize_bilinear
:noindex:
gather
------
.. autofunction:: paddle.fluid.layers.gather
:noindex:
random_crop
-----------
.. autofunction:: paddle.fluid.layers.random_crop
:noindex:
ops ops
=== ===
...@@ -784,12 +741,6 @@ sum ...@@ -784,12 +741,6 @@ sum
.. autofunction:: paddle.fluid.layers.sum .. autofunction:: paddle.fluid.layers.sum
:noindex: :noindex:
shape
-----
.. autofunction:: paddle.fluid.layers.shape
:noindex:
sigmoid sigmoid
------- -------
...@@ -1039,3 +990,93 @@ zeros ...@@ -1039,3 +990,93 @@ zeros
.. autofunction:: paddle.fluid.layers.zeros .. autofunction:: paddle.fluid.layers.zeros
:noindex: :noindex:
detection
=========
multi_box_head
--------------
.. autofunction:: paddle.fluid.layers.multi_box_head
:noindex:
bipartite_match
---------------
.. autofunction:: paddle.fluid.layers.bipartite_match
:noindex:
target_assign
-------------
.. autofunction:: paddle.fluid.layers.target_assign
:noindex:
detection_output
----------------
.. autofunction:: paddle.fluid.layers.detection_output
:noindex:
ssd_loss
--------
.. autofunction:: paddle.fluid.layers.ssd_loss
:noindex:
detection_map
-------------
.. autofunction:: paddle.fluid.layers.detection_map
:noindex:
iou_similarity
--------------
.. autofunction:: paddle.fluid.layers.iou_similarity
:noindex:
box_coder
---------
.. autofunction:: paddle.fluid.layers.box_coder
:noindex:
learning_rate_scheduler
=======================
exponential_decay
-----------------
.. autofunction:: paddle.fluid.layers.exponential_decay
:noindex:
natural_exp_decay
-----------------
.. autofunction:: paddle.fluid.layers.natural_exp_decay
:noindex:
inverse_time_decay
------------------
.. autofunction:: paddle.fluid.layers.inverse_time_decay
:noindex:
polynomial_decay
----------------
.. autofunction:: paddle.fluid.layers.polynomial_decay
:noindex:
piecewise_decay
---------------
.. autofunction:: paddle.fluid.layers.piecewise_decay
:noindex:
noam_decay
----------
.. autofunction:: paddle.fluid.layers.noam_decay
:noindex:
doc/fluid/api/optimizer.rst
浏览文件 @ 85c3bfc1
...@@ -89,13 +89,6 @@ DecayedAdagradOptimizer ...@@ -89,13 +89,6 @@ DecayedAdagradOptimizer
:members: :members:
:noindex: :noindex:
RMSPropOptimizer
----------------
.. autoclass:: paddle.fluid.optimizer.RMSPropOptimizer
:members:
:noindex:
Adadelta Adadelta
-------- --------
......
doc/fluid/api/profiler.rst
浏览文件 @ 85c3bfc1
...@@ -23,15 +23,3 @@ profiler ...@@ -23,15 +23,3 @@ profiler
.. autofunction:: paddle.fluid.profiler.profiler .. autofunction:: paddle.fluid.profiler.profiler
:noindex: :noindex:
start_profiler
--------------
.. autofunction:: paddle.fluid.profiler.start_profiler
:noindex:
stop_profiler
-------------
.. autofunction:: paddle.fluid.profiler.stop_profiler
:noindex:
doc/survey/dynamic_graph.md
浏览文件 @ 85c3bfc1
...@@ -171,7 +171,7 @@ Pytorch chooses immediate evaluation. It avoids ever materializing a "forward gr ...@@ -171,7 +171,7 @@ Pytorch chooses immediate evaluation. It avoids ever materializing a "forward gr
## What can fluid learn from them? ## What can fluid learn from them?
TBD Please refer to `paddle/contrib/dynamic/`.
# Appendix # Appendix
......
doc/v2/api/config/evaluators.rst
浏览文件 @ 85c3bfc1
...@@ -101,7 +101,7 @@ value_printer ...@@ -101,7 +101,7 @@ value_printer
:noindex: :noindex:
Detection Detection
===== ==========
detection_map detection_map
------------- -------------
......
doc/v2/api/config/layer.rst
浏览文件 @ 85c3bfc1
...@@ -11,7 +11,7 @@ Data layer ...@@ -11,7 +11,7 @@ Data layer
data data
---- ----
.. autoclass:: paddle.v2.layer.data .. autofunction:: paddle.v2.layer.data
:noindex: :noindex:
Fully Connected Layers Fully Connected Layers
...@@ -21,12 +21,12 @@ Fully Connected Layers ...@@ -21,12 +21,12 @@ Fully Connected Layers
fc fc
-- --
.. autoclass:: paddle.v2.layer.fc .. autofunction:: paddle.v2.layer.fc
:noindex: :noindex:
selective_fc selective_fc
------------ ------------
.. autoclass:: paddle.v2.layer.selective_fc .. autofunction:: paddle.v2.layer.selective_fc
:noindex: :noindex:
Conv Layers Conv Layers
...@@ -34,34 +34,34 @@ Conv Layers ...@@ -34,34 +34,34 @@ Conv Layers
conv_operator conv_operator
------------- -------------
.. autoclass:: paddle.v2.layer.conv_operator .. autofunction:: paddle.v2.layer.conv_operator
:noindex: :noindex:
conv_projection conv_projection
--------------- ---------------
.. autoclass:: paddle.v2.layer.conv_projection .. autofunction:: paddle.v2.layer.conv_projection
:noindex: :noindex:
conv_shift conv_shift
---------- ----------
.. autoclass:: paddle.v2.layer.conv_shift .. autofunction:: paddle.v2.layer.conv_shift
:noindex: :noindex:
img_conv img_conv
-------- --------
.. autoclass:: paddle.v2.layer.img_conv .. autofunction:: paddle.v2.layer.img_conv
:noindex: :noindex:
.. _api_v2.layer_context_projection: .. _api_v2.layer_context_projection:
context_projection context_projection
------------------ ------------------
.. autoclass:: paddle.v2.layer.context_projection .. autofunction:: paddle.v2.layer.context_projection
:noindex: :noindex:
row_conv row_conv
-------- --------
.. autoclass:: paddle.v2.layer.row_conv .. autofunction:: paddle.v2.layer.row_conv
:noindex: :noindex:
Image Pooling Layer Image Pooling Layer
...@@ -69,27 +69,27 @@ Image Pooling Layer ...@@ -69,27 +69,27 @@ Image Pooling Layer
img_pool img_pool
-------- --------
.. autoclass:: paddle.v2.layer.img_pool .. autofunction:: paddle.v2.layer.img_pool
:noindex: :noindex:
spp spp
--- ---
.. autoclass:: paddle.v2.layer.spp .. autofunction:: paddle.v2.layer.spp
:noindex: :noindex:
maxout maxout
------ ------
.. autoclass:: paddle.v2.layer.maxout .. autofunction:: paddle.v2.layer.maxout
:noindex: :noindex:
roi_pool roi_pool
-------- --------
.. autoclass:: paddle.v2.layer.roi_pool .. autofunction:: paddle.v2.layer.roi_pool
:noindex: :noindex:
pad pad
---- ----
.. autoclass:: paddle.v2.layer.pad .. autofunction:: paddle.v2.layer.pad
:noindex: :noindex:
Norm Layer Norm Layer
...@@ -97,27 +97,27 @@ Norm Layer ...@@ -97,27 +97,27 @@ Norm Layer
img_cmrnorm img_cmrnorm
----------- -----------
.. autoclass:: paddle.v2.layer.img_cmrnorm .. autofunction:: paddle.v2.layer.img_cmrnorm
:noindex: :noindex:
batch_norm batch_norm
---------- ----------
.. autoclass:: paddle.v2.layer.batch_norm .. autofunction:: paddle.v2.layer.batch_norm
:noindex: :noindex:
sum_to_one_norm sum_to_one_norm
--------------- ---------------
.. autoclass:: paddle.v2.layer.sum_to_one_norm .. autofunction:: paddle.v2.layer.sum_to_one_norm
:noindex: :noindex:
cross_channel_norm cross_channel_norm
------------------ ------------------
.. autoclass:: paddle.v2.layer.cross_channel_norm .. autofunction:: paddle.v2.layer.cross_channel_norm
:noindex: :noindex:
row_l2_norm row_l2_norm
----------- -----------
.. autoclass:: paddle.v2.layer.row_l2_norm .. autofunction:: paddle.v2.layer.row_l2_norm
:noindex: :noindex:
Recurrent Layers Recurrent Layers
...@@ -125,22 +125,22 @@ Recurrent Layers ...@@ -125,22 +125,22 @@ Recurrent Layers
recurrent recurrent
--------- ---------
.. autoclass:: paddle.v2.layer.recurrent .. autofunction:: paddle.v2.layer.recurrent
:noindex: :noindex:
lstmemory lstmemory
--------- ---------
.. autoclass:: paddle.v2.layer.lstmemory .. autofunction:: paddle.v2.layer.lstmemory
:noindex: :noindex:
grumemory grumemory
--------- ---------
.. autoclass:: paddle.v2.layer.grumemory .. autofunction:: paddle.v2.layer.grumemory
:noindex: :noindex:
gated_unit gated_unit
----------- -----------
.. autoclass:: paddle.v2.layer.gated_unit .. autofunction:: paddle.v2.layer.gated_unit
:noindex: :noindex:
Recurrent Layer Group Recurrent Layer Group
...@@ -148,32 +148,32 @@ Recurrent Layer Group ...@@ -148,32 +148,32 @@ Recurrent Layer Group
memory memory
------ ------
.. autoclass:: paddle.v2.layer.memory .. autofunction:: paddle.v2.layer.memory
:noindex: :noindex:
recurrent_group recurrent_group
--------------- ---------------
.. autoclass:: paddle.v2.layer.recurrent_group .. autofunction:: paddle.v2.layer.recurrent_group
:noindex: :noindex:
lstm_step lstm_step
--------- ---------
.. autoclass:: paddle.v2.layer.lstm_step .. autofunction:: paddle.v2.layer.lstm_step
:noindex: :noindex:
gru_step gru_step
-------- --------
.. autoclass:: paddle.v2.layer.gru_step .. autofunction:: paddle.v2.layer.gru_step
:noindex: :noindex:
beam_search beam_search
------------ ------------
.. autoclass:: paddle.v2.layer.beam_search .. autofunction:: paddle.v2.layer.beam_search
:noindex: :noindex:
get_output get_output
---------- ----------
.. autoclass:: paddle.v2.layer.get_output .. autofunction:: paddle.v2.layer.get_output
:noindex: :noindex:
Mixed Layer Mixed Layer
...@@ -183,54 +183,54 @@ Mixed Layer ...@@ -183,54 +183,54 @@ Mixed Layer
mixed mixed
----- -----
.. autoclass:: paddle.v2.layer.mixed .. autofunction:: paddle.v2.layer.mixed
:noindex: :noindex:
.. _api_v2.layer_embedding: .. _api_v2.layer_embedding:
embedding embedding
--------- ---------
.. autoclass:: paddle.v2.layer.embedding .. autofunction:: paddle.v2.layer.embedding
:noindex: :noindex:
scaling_projection scaling_projection
------------------ ------------------
.. autoclass:: paddle.v2.layer.scaling_projection .. autofunction:: paddle.v2.layer.scaling_projection
:noindex: :noindex:
dotmul_projection dotmul_projection
----------------- -----------------
.. autoclass:: paddle.v2.layer.dotmul_projection .. autofunction:: paddle.v2.layer.dotmul_projection
:noindex: :noindex:
dotmul_operator dotmul_operator
--------------- ---------------
.. autoclass:: paddle.v2.layer.dotmul_operator .. autofunction:: paddle.v2.layer.dotmul_operator
:noindex: :noindex:
full_matrix_projection full_matrix_projection
---------------------- ----------------------
.. autoclass:: paddle.v2.layer.full_matrix_projection .. autofunction:: paddle.v2.layer.full_matrix_projection
:noindex: :noindex:
identity_projection identity_projection
------------------- -------------------
.. autoclass:: paddle.v2.layer.identity_projection .. autofunction:: paddle.v2.layer.identity_projection
:noindex: :noindex:
slice_projection slice_projection
------------------- -------------------
.. autoclass:: paddle.v2.layer.slice_projection .. autofunction:: paddle.v2.layer.slice_projection
:noindex: :noindex:
table_projection table_projection
---------------- ----------------
.. autoclass:: paddle.v2.layer.table_projection .. autofunction:: paddle.v2.layer.table_projection
:noindex: :noindex:
trans_full_matrix_projection trans_full_matrix_projection
---------------------------- ----------------------------
.. autoclass:: paddle.v2.layer.trans_full_matrix_projection .. autofunction:: paddle.v2.layer.trans_full_matrix_projection
:noindex: :noindex:
Aggregate Layers Aggregate Layers
...@@ -245,51 +245,46 @@ AggregateLevel ...@@ -245,51 +245,46 @@ AggregateLevel
pooling pooling
------- -------
.. autoclass:: paddle.v2.layer.pooling .. autofunction:: paddle.v2.layer.pooling
:noindex: :noindex:
.. _api_v2.layer_last_seq: .. _api_v2.layer_last_seq:
last_seq last_seq
-------- --------
.. autoclass:: paddle.v2.layer.last_seq .. autofunction:: paddle.v2.layer.last_seq
:noindex: :noindex:
.. _api_v2.layer_first_seq: .. _api_v2.layer_first_seq:
first_seq first_seq
--------- ---------
.. autoclass:: paddle.v2.layer.first_seq .. autofunction:: paddle.v2.layer.first_seq
:noindex: :noindex:
sub_seq sub_seq
--------- ---------
.. autoclass:: paddle.v2.layer.sub_seq .. autofunction:: paddle.v2.layer.sub_seq
:noindex: :noindex:
concat concat
------ ------
.. autoclass:: paddle.v2.layer.concat .. autofunction:: paddle.v2.layer.concat
:noindex: :noindex:
seq_concat seq_concat
---------- ----------
.. autoclass:: paddle.v2.layer.seq_concat .. autofunction:: paddle.v2.layer.seq_concat
:noindex: :noindex:
seq_slice seq_slice
--------- ---------
.. autoclass:: paddle.v2.layer.seq_slice .. autofunction:: paddle.v2.layer.seq_slice
:noindex:
kmax_sequence_score
-------------------
.. autoclass:: paddle.v2.layer.kmax_sequence_score
:noindex: :noindex:
sub_nested_seq sub_nested_seq
-------------- --------------
.. autoclass:: paddle.v2.layer.sub_nested_seq .. autofunction:: paddle.v2.layer.sub_nested_seq
:noindex: :noindex:
Reshaping Layers Reshaping Layers
...@@ -297,7 +292,7 @@ Reshaping Layers ...@@ -297,7 +292,7 @@ Reshaping Layers
block_expand block_expand
------------ ------------
.. autoclass:: paddle.v2.layer.block_expand .. autofunction:: paddle.v2.layer.block_expand
:noindex: :noindex:
.. _api_v2.layer_expand: .. _api_v2.layer_expand:
...@@ -309,22 +304,22 @@ ExpandLevel ...@@ -309,22 +304,22 @@ ExpandLevel
expand expand
------ ------
.. autoclass:: paddle.v2.layer.expand .. autofunction:: paddle.v2.layer.expand
:noindex: :noindex:
repeat repeat
------ ------
.. autoclass:: paddle.v2.layer.repeat .. autofunction:: paddle.v2.layer.repeat
:noindex: :noindex:
rotate rotate
------ ------
.. autoclass:: paddle.v2.layer.rotate .. autofunction:: paddle.v2.layer.rotate
:noindex: :noindex:
seq_reshape seq_reshape
----------- -----------
.. autoclass:: paddle.v2.layer.seq_reshape .. autofunction:: paddle.v2.layer.seq_reshape
:noindex: :noindex:
Math Layers Math Layers
...@@ -332,94 +327,94 @@ Math Layers ...@@ -332,94 +327,94 @@ Math Layers
addto addto
----- -----
.. autoclass:: paddle.v2.layer.addto .. autofunction:: paddle.v2.layer.addto
:noindex: :noindex:
linear_comb linear_comb
----------- -----------
.. autoclass:: paddle.v2.layer.linear_comb .. autofunction:: paddle.v2.layer.linear_comb
:noindex: :noindex:
interpolation interpolation
------------- -------------
.. autoclass:: paddle.v2.layer.interpolation .. autofunction:: paddle.v2.layer.interpolation
:noindex: :noindex:
bilinear_interp bilinear_interp
--------------- ---------------
.. autoclass:: paddle.v2.layer.bilinear_interp .. autofunction:: paddle.v2.layer.bilinear_interp
:noindex: :noindex:
dropout dropout
-------- --------
.. autoclass:: paddle.v2.layer.dropout .. autofunction:: paddle.v2.layer.dropout
:noindex: :noindex:
dot_prod dot_prod
--------- ---------
.. autoclass:: paddle.v2.layer.dot_prod .. autofunction:: paddle.v2.layer.dot_prod
:noindex: :noindex:
out_prod out_prod
-------- --------
.. autoclass:: paddle.v2.layer.out_prod .. autofunction:: paddle.v2.layer.out_prod
:noindex: :noindex:
power power
----- -----
.. autoclass:: paddle.v2.layer.power .. autofunction:: paddle.v2.layer.power
:noindex: :noindex:
scaling scaling
------- -------
.. autoclass:: paddle.v2.layer.scaling .. autofunction:: paddle.v2.layer.scaling
:noindex: :noindex:
clip clip
---- ----
.. autoclass:: paddle.v2.layer.clip .. autofunction:: paddle.v2.layer.clip
:noindex: :noindex:
resize resize
------ ------
.. autoclass:: paddle.v2.layer.resize .. autofunction:: paddle.v2.layer.resize
:noindex: :noindex:
slope_intercept slope_intercept
--------------- ---------------
.. autoclass:: paddle.v2.layer.slope_intercept .. autofunction:: paddle.v2.layer.slope_intercept
:noindex: :noindex:
tensor tensor
------ ------
.. autoclass:: paddle.v2.layer.tensor .. autofunction:: paddle.v2.layer.tensor
:noindex: :noindex:
.. _api_v2.layer_cos_sim: .. _api_v2.layer_cos_sim:
cos_sim cos_sim
------- -------
.. autoclass:: paddle.v2.layer.cos_sim .. autofunction:: paddle.v2.layer.cos_sim
:noindex: :noindex:
l2_distance l2_distance
----------- -----------
.. autoclass:: paddle.v2.layer.l2_distance .. autofunction:: paddle.v2.layer.l2_distance
:noindex: :noindex:
trans trans
----- -----
.. autoclass:: paddle.v2.layer.trans .. autofunction:: paddle.v2.layer.trans
:noindex: :noindex:
scale_shift scale_shift
----------- -----------
.. autoclass:: paddle.v2.layer.scale_shift .. autofunction:: paddle.v2.layer.scale_shift
:noindex: :noindex:
factorization_machine factorization_machine
--------------------- ---------------------
.. autoclass:: paddle.v2.layer.factorization_machine .. autofunction:: paddle.v2.layer.factorization_machine
:noindex: :noindex:
Sampling Layers Sampling Layers
...@@ -427,17 +422,17 @@ Sampling Layers ...@@ -427,17 +422,17 @@ Sampling Layers
maxid maxid
----- -----
.. autoclass:: paddle.v2.layer.max_id .. autofunction:: paddle.v2.layer.max_id
:noindex: :noindex:
sampling_id sampling_id
----------- -----------
.. autoclass:: paddle.v2.layer.sampling_id .. autofunction:: paddle.v2.layer.sampling_id
:noindex: :noindex:
multiplex multiplex
--------- ---------
.. autoclass:: paddle.v2.layer.multiplex .. autofunction:: paddle.v2.layer.multiplex
:noindex: :noindex:
.. _api_v2.layer_costs: .. _api_v2.layer_costs:
...@@ -447,97 +442,97 @@ Cost Layers ...@@ -447,97 +442,97 @@ Cost Layers
cross_entropy_cost cross_entropy_cost
------------------ ------------------
.. autoclass:: paddle.v2.layer.cross_entropy_cost .. autofunction:: paddle.v2.layer.cross_entropy_cost
:noindex: :noindex:
cross_entropy_with_selfnorm_cost cross_entropy_with_selfnorm_cost
-------------------------------- --------------------------------
.. autoclass:: paddle.v2.layer.cross_entropy_with_selfnorm_cost .. autofunction:: paddle.v2.layer.cross_entropy_with_selfnorm_cost
:noindex: :noindex:
multi_binary_label_cross_entropy_cost multi_binary_label_cross_entropy_cost
------------------------------------- -------------------------------------
.. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost .. autofunction:: paddle.v2.layer.multi_binary_label_cross_entropy_cost
:noindex: :noindex:
classification_cost classification_cost
------------------- -------------------
.. autoclass:: paddle.v2.layer.classification_cost .. autofunction:: paddle.v2.layer.classification_cost
:noindex: :noindex:
huber_regression_cost huber_regression_cost
------------------------- -------------------------
.. autoclass:: paddle.v2.layer.huber_regression_cost .. autofunction:: paddle.v2.layer.huber_regression_cost
:noindex: :noindex:
huber_classification_cost huber_classification_cost
------------------------- -------------------------
.. autoclass:: paddle.v2.layer.huber_classification_cost .. autofunction:: paddle.v2.layer.huber_classification_cost
:noindex: :noindex:
lambda_cost lambda_cost
----------- -----------
.. autoclass:: paddle.v2.layer.lambda_cost .. autofunction:: paddle.v2.layer.lambda_cost
:noindex: :noindex:
square_error_cost square_error_cost
----------------- -----------------
.. autoclass:: paddle.v2.layer.square_error_cost .. autofunction:: paddle.v2.layer.square_error_cost
:noindex: :noindex:
rank_cost rank_cost
--------- ---------
.. autoclass:: paddle.v2.layer.rank_cost .. autofunction:: paddle.v2.layer.rank_cost
:noindex: :noindex:
sum_cost sum_cost
--------- ---------
.. autoclass:: paddle.v2.layer.sum_cost .. autofunction:: paddle.v2.layer.sum_cost
:noindex: :noindex:
crf crf
--- ---
.. autoclass:: paddle.v2.layer.crf .. autofunction:: paddle.v2.layer.crf
:noindex: :noindex:
crf_decoding crf_decoding
------------ ------------
.. autoclass:: paddle.v2.layer.crf_decoding .. autofunction:: paddle.v2.layer.crf_decoding
:noindex: :noindex:
ctc ctc
--- ---
.. autoclass:: paddle.v2.layer.ctc .. autofunction:: paddle.v2.layer.ctc
:noindex: :noindex:
warp_ctc warp_ctc
-------- --------
.. autoclass:: paddle.v2.layer.warp_ctc .. autofunction:: paddle.v2.layer.warp_ctc
:noindex: :noindex:
nce nce
--- ---
.. autoclass:: paddle.v2.layer.nce .. autofunction:: paddle.v2.layer.nce
:noindex: :noindex:
hsigmoid hsigmoid
--------- ---------
.. autoclass:: paddle.v2.layer.hsigmoid .. autofunction:: paddle.v2.layer.hsigmoid
:noindex: :noindex:
smooth_l1_cost smooth_l1_cost
-------------- --------------
.. autoclass:: paddle.v2.layer.smooth_l1_cost .. autofunction:: paddle.v2.layer.smooth_l1_cost
:noindex: :noindex:
multibox_loss multibox_loss
-------------- --------------
.. autoclass:: paddle.v2.layer.multibox_loss .. autofunction:: paddle.v2.layer.multibox_loss
:noindex: :noindex:
detection_output detection_output
---------------- ----------------
.. autoclass:: paddle.v2.layer.detection_output .. autofunction:: paddle.v2.layer.detection_output
:noindex: :noindex:
Check Layer Check Layer
...@@ -545,7 +540,7 @@ Check Layer ...@@ -545,7 +540,7 @@ Check Layer
eos eos
--- ---
.. autoclass:: paddle.v2.layer.eos .. autofunction:: paddle.v2.layer.eos
:noindex: :noindex:
Activation Activation
...@@ -553,5 +548,5 @@ Activation ...@@ -553,5 +548,5 @@ Activation
prelu prelu
-------- --------
.. autoclass:: paddle.v2.layer.prelu .. autofunction:: paddle.v2.layer.prelu
:noindex: :noindex:
doc/v2/api/index_en.rst
浏览文件 @ 85c3bfc1
...@@ -8,4 +8,3 @@ API ...@@ -8,4 +8,3 @@ API
model_configs.rst model_configs.rst
data.rst data.rst
run_logic.rst run_logic.rst
fluid/index.rst
doc/v2/build_and_install/pip_install_cn.rst
浏览文件 @ 85c3bfc1
...@@ -60,6 +60,7 @@ paddlepaddle-gpu==0.11.0 使用CUDA 7.5和cuDNN 5编译的0.11.0版 ...@@ -60,6 +60,7 @@ paddlepaddle-gpu==0.11.0 使用CUDA 7.5和cuDNN 5编译的0.11.0版
"cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_" "cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__" "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__" "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda9.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
.. _pip_dependency: .. _pip_dependency:
......
doc/v2/build_and_install/pip_install_en.rst
浏览文件 @ 85c3bfc1
...@@ -63,6 +63,7 @@ If the links below shows up the login form, just click "Log in as guest" to star ...@@ -63,6 +63,7 @@ If the links below shows up the login form, just click "Log in as guest" to star
"cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`__" "cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__" "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__" "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda9.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
.. _pip_dependency: .. _pip_dependency:
......
paddle/contrib/CMakeLists.txt
浏览文件 @ 85c3bfc1
...@@ -14,3 +14,4 @@ ...@@ -14,3 +14,4 @@
# #
add_subdirectory(inference) add_subdirectory(inference)
add_subdirectory(tape)
paddle/contrib/inference/CMakeLists.txt
浏览文件 @ 85c3bfc1
...@@ -17,48 +17,9 @@ if(APPLE) ...@@ -17,48 +17,9 @@ if(APPLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
endif(APPLE) endif(APPLE)
set(ANAKIN_INCLUDE "" CACHE STRING "root of Anakin header files")
set(ANAKIN_LIBRARY "" CACHE STRING "path of Anakin library")
set(inference_deps paddle_inference_api paddle_fluid_api) set(inference_deps paddle_inference_api paddle_fluid_api)
# if anakin is set enable anakin api implementation
if(ANAKIN_INCLUDE AND ANAKIN_LIBRARY)
set(ANAKIN_FOUND ON)
else()
set(ANAKIN_FOUND OFF)
endif()
function(fetch_include_recursively root_dir)
if (IS_DIRECTORY ${root_dir})
include_directories(${root_dir})
endif()
file(GLOB ALL_SUB RELATIVE ${root_dir} ${root_dir}/*)
foreach(sub ${ALL_SUB})
if (IS_DIRECTORY ${root_dir}/${sub})
fetch_include_recursively(${root_dir}/${sub})
endif()
endforeach()
endfunction()
if (ANAKIN_FOUND)
# Anakin's code style doesn't follow google c style.
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=unused-variable -Wno-error=format-extra-args -Wno-error=comment -Wno-error=format -Wno-error=switch -Wno-error=return-type -Wno-error=non-virtual-dtor -Wno-reorder -Wno-error=cpp")
message(STATUS "Anakin for inference is enabled")
message(STATUS "Anakin is set INCLUDE:${ANAKIN_INCLUDE} LIBRARY:${ANAKIN_LIBRARY}")
fetch_include_recursively(${ANAKIN_INCLUDE})
link_directories(${ANAKIN_LIBRARY})
nv_library(inference_anakin_api SHARED SRCS paddle_inference_api.cc paddle_inference_api_anakin_engine.cc)
target_link_libraries(inference_anakin_api anakin anakin_saber_common)
list(APPEND inference_deps inference_anakin_api)
endif()
function(inference_api_test TARGET_NAME) function(inference_api_test TARGET_NAME)
if (WITH_TESTING) if (WITH_TESTING)
set(options "") set(options "")
...@@ -79,7 +40,7 @@ function(inference_api_test TARGET_NAME) ...@@ -79,7 +40,7 @@ function(inference_api_test TARGET_NAME)
endfunction(inference_api_test) endfunction(inference_api_test)
cc_library(paddle_inference_api cc_library(paddle_inference_api
SRCS paddle_inference_api.cc paddle_inference_api_impl.cc SRCS paddle_inference_api.cc paddle_inference_api_impl.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB}) DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
cc_test(test_paddle_inference_api cc_test(test_paddle_inference_api
...@@ -89,9 +50,17 @@ cc_test(test_paddle_inference_api ...@@ -89,9 +50,17 @@ cc_test(test_paddle_inference_api
inference_api_test(test_paddle_inference_api_impl inference_api_test(test_paddle_inference_api_impl
ARGS test_word2vec test_image_classification) ARGS test_word2vec test_image_classification)
if (ANAKIN_FOUND) if (WITH_ANAKIN)
# Due to Anakin do not have official library releases and the versions of protobuf and cuda do not match Paddle's,
# so anakin library will not be merged to our official inference library. To use anakin prediction API, one need to
# compile the libinference_anakin_api.a and compile with anakin.so.
nv_library(inference_anakin_api SHARED SRCS paddle_inference_api.cc paddle_inference_api_anakin_engine.cc)
target_compile_options(inference_anakin_api BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
target_link_libraries(inference_anakin_api anakin anakin_saber_common)
cc_test(inference_anakin_test SRCS paddle_inference_api_anakin_engine_tester.cc cc_test(inference_anakin_test SRCS paddle_inference_api_anakin_engine_tester.cc
DEPS ${inference_deps}) ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api)
target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endif() endif()
if(WITH_TESTING) if(WITH_TESTING)
......
paddle/contrib/inference/paddle_inference_api_anakin_engine.cc
浏览文件 @ 85c3bfc1
...@@ -12,9 +12,8 @@ ...@@ -12,9 +12,8 @@
// See the License for the specific language governing permissions and // See the License for the specific language governing permissions and
// limitations under the License. // limitations under the License.
#include <cuda.h>
#include "paddle/contrib/inference/paddle_inference_api_anakin_engine.h" #include "paddle/contrib/inference/paddle_inference_api_anakin_engine.h"
#include <cuda.h>
namespace paddle { namespace paddle {
......
paddle/contrib/inference/paddle_inference_api_anakin_engine.h
浏览文件 @ 85c3bfc1
...@@ -19,10 +19,9 @@ limitations under the License. */ ...@@ -19,10 +19,9 @@ limitations under the License. */
#pragma once #pragma once
// NOTE This header file do not have namespace.
//#include <test/framework/net/paddle_api.h>
#include "paddle/contrib/inference/paddle_inference_api.h" #include "paddle/contrib/inference/paddle_inference_api.h"
// from anakin
#include "framework/core/net/net.h" #include "framework/core/net/net.h"
#include "saber/saber_types.h" #include "saber/saber_types.h"
......
paddle/contrib/inference/paddle_inference_api_anakin_engine_tester.cc
浏览文件 @ 85c3bfc1
...@@ -12,17 +12,19 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,17 +12,19 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include <gflags/gflags.h>
#include <glog/logging.h> #include <glog/logging.h>
#include <gtest/gtest.h> #include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "paddle/contrib/inference/paddle_inference_api.h" #include "paddle/contrib/inference/paddle_inference_api.h"
DEFINE_string(model, "", "Directory of the inference model.");
namespace paddle { namespace paddle {
AnakinConfig GetConfig() { AnakinConfig GetConfig() {
AnakinConfig config; AnakinConfig config;
config.model_file = "./mobilenet_v2.anakin.bin"; config.model_file = FLAGS_model;
config.device = 0; config.device = 0;
config.max_batch_size = 1; config.max_batch_size = 1;
return config; return config;
......
paddle/contrib/tape/CMakeLists.txt 0 → 100644
浏览文件 @ 85c3bfc1
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
if(APPLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
endif(APPLE)
cc_library(tape_variable SRCS variable.cc DEPS ${FLUID_CORE_MODULES})
cc_library(tape SRCS tape.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB} tape_variable)
cc_test(test_tape
SRCS test_tape.cc
DEPS tape tape_variable)
paddle/contrib/tape/README.md 0 → 100644
浏览文件 @ 85c3bfc1
# Dynamic Graph on Fluid
PaddlePaddle Fluid is targeting the autodiff without tape, which, however, is very
challenging and we are still way from there. DyNet and PyTorch provide a good design
idea, the *tape*, that significantly eases the challenge. Also, DyNet provides
a C++ API that is as convenient as Python but with higher efficiency and could
conveniently integrate with industrial/production systems. This package, `tape`,
combines the good of
1. tape from PyTorch and DyNet
2. C++ API and core from DyNet
3. rich set of operators from PaddlePaddle
## Overview
We can implement Dynet-like Tape(See this [survey](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/survey/dynamic_graph.md))
by wrapping Paddle Fluid's `Operator` and `Variable`.
The user API is straight forward since
1. it is imperative. And it uses host language's control flow logic.
1. it avoids extra concepts such as `Scope` and `Executor`.
All of these benefits come at the cost of just adding one line `reset_global_tape`
at every iteration.
## Code Structure
In short, the `Tape` contains a vector of `OpHandle`s. And an `OpHandle` contains its
`type`, the pointers to the `Variable`s, and necessary attributes.
```c++
class Variable {
public:
VriableHandle Grad(); // returns its gradient variable
private:
framework::VarDesc desc_; // compile time infershape, necessary for lazy execution
framework::Variable var_; // run time variable, holds data memory
};
using VariableHandle = shared_ptr<Variable>;
struct OpHandle {
string type_;
map<string, vector<VariableHandle>> inputs_;
map<string, vector<VariableHandle>> outputs_;
AttributeMap attrs_;
};
class Tape {
public:
void AddOp(OpHandle); // add op
void Forward(); // execute the tape_
void Backward(); // execute the backward of the tape_
private:
vector<OpHandle> tape_;
};
```
We uses `Function` to indicate layers. It takes care of parameter
initialization and `AddOp` to the Tape when it is called.
```c++
class Linear {
public:
Linear(int in_dim, int out_dim, const std::string &act)
: w_(new Variable("LinearWeight")),
b_(new Variable("LinearBias")),
act_(act) {
Tape init_tape;
std::string initializer = "fill_constant";
framework::AttributeMap attrs;
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{in_dim, out_dim};
attrs["value"] = 1.0f;
init_tape.AddOp(initializer, {}, {{"Out", {w_}}}, attrs);
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{out_dim};
attrs["value"] = 1.0f;
init_tape.AddOp(initializer, {}, {{"Out", {b_}}}, attrs);
init_tape.Forward();
}
VariableHandle operator()(VariableHandle input) {
VariableHandle pre_bias(new Variable("linear"));
get_global_tape().AddOp("mul",
{{"X", {input}}, {"Y", {w_}}},
{{"Out", {pre_bias}}},
{{"x_num_col_dims", 1}, {"y_num_col_dims", 1}});
VariableHandle pre_act(new Variable("linear"));
get_global_tape().AddOp("elementwise_add",
{{"X", {pre_bias}}, {"Y", {b_}}},
{{"Out", {pre_act}}},
{{"axis", 1}});
VariableHandle post_act(new Variable("linear"));
get_global_tape().AddOp(act_,
{{"X", {pre_act}}},
{{"Out", {post_act}}},
{});
return post_act;
}
std::vector<VariableHandle> Params() { return {w_, b_}; }
private:
VariableHandle w_;
VariableHandle b_;
std::string act_;
};
```
## User API
```c++
// Model function
paddle::tape::Linear linear1(3, 3, "relu"); // init weight and bias
paddle::tape::Linear linear2(3, 3, "relu"); // init weight and bias
paddle::tape::Mean mean;
// Optimizer
paddle::tape::SGD sgd(0.001);
// Data Feeder
paddle::tape::Fill data_feeder(...);
VariableHandle input(new paddle::tape::Variable("input"));
VariableHandle label(new paddle::tape::Variable("label"));
for (int i = 0; i < 2; ++i) {
reset_global_tape();
data_feeder(input, label);
auto loss = softmax(linear2(linear1(input)), label); // compile time InferShape & InferVarType
LOG(INFO) << loss.value(); // Run forward up to loss
// Run backward, store gradient of w at w->Grad()
get_global_tape.Backward(loss);
// Update w
sgd(linear1.Params());
sgd(linear2.Params());
}
```
<details>
<summary></summary>
digraph G {
subgraph cluster_0 {
node [shape=record,style=filled];
style=filled;
color=lightgrey;
linear1 [label="{type: mul | {input | {<before_mul1>X: before_mul1 |<weight1> Y: weight1}} | {output |<before_bias1> Out: before_bias1}}"];
elementwise_add1 [label="{type: elementwise_add | {input | {<before_bias1>X: before_bias1 |<bias1> Y: bias1}} | {output |<before_act1> Out: before_act1}}"];
relu1 [label="{type: relu | {input | {<before_act1>X: before_act1 }} | {output |<after_act1> Out: after_act1}}"];
linear1 -> elementwise_add1->relu1;
label = "forward tape";
}
linear1:before_mul1->before_mul1
linear1:weight1->weight1
linear1:before_bias1->before_bias1
elementwise_add1:bias1->bias1
elementwise_add1:before_bias1->before_bias1
elementwise_add1:before_act1->before_act1
relu1:before_act1->before_act1
relu1:after_act1->after_act1
subgraph cluster_1 {
node [shape=record,style=filled];
style=filled;
color=lightgrey;
linear1_grad [label="{type: mul_grad | {input | {<before_mul1>X: before_mul1 |<weight1> Y: weight1|<before_bias1_grad> Out_grad: before_bias1_grad}} | {output |{<before_mul1_grad>X_grad: before_mul1_grad |<weight1_grad> Y_grad: weight1_grad}}}"];
elementwise_add1_grad [label="{type: elementwise_add_grad | {input | <before_act1_grad> Out_grad: before_act1_grad} | {output |{<before_bias1_grad>X_grad: before_bias1_grad |<bias1_grad> Y_grad: bias1_grad}}}"];
relu1_grad [label="{type: relu_grad | {input |<after_act1_grad> Out_grad: after_act1_grad} | {ouput | {<before_act1_grad>X_grad: before_act1_grad }}}"];
linear1_grad -> elementwise_add1_grad ->relu1_grad [dir=back];
label = "backward tape";
}
relu1_grad:after_act1_grad->after_act1_grad
relu1_grad:before_act1_grad->before_act1_grad
elementwise_add1_grad:before_act1_grad->before_act1_grad
elementwise_add1_grad:before_bias1_grad->before_bias1_grad
elementwise_add1_grad:bias1_grad->bias1_grad
linear1_grad:before_mul1->before_mul1
linear1_grad:weight1->weight1
linear1_grad:before_bias1_grad->before_bias1_grad
linear1_grad:before_mul1_grad->before_mul1_grad
linear1_grad:weight1_grad->weight1_grad
subgraph cluster_2 {
node [shape=record];
label = "Linear1";
weight1
bias1
}
weight1 -> weight1_grad [ label="Grad()", style="dashed" ];
bias1 -> bias1_grad [ label="Grad()", style="dashed"];
}
</details>
![Image](https://github.com/tonyyang-svail/Paddle/blob/cpp_tap/paddle/contrib/tape/computation_graph.png)
## Code Reuse
We want to stay close to Paddle Fluid as much as possible.
### Reuse All Operators
As all Ops are registered at `OpInfoMap`, the effort of adding a new `Function`
is about 10 lines of code, similar to expose an operator to Python.
### Reuse Compile Time InferShape and InferVarType
Note that all the symbolic information is stored at `tape::Varaible::desc_`, instead
of `ProgramDesc.block.vars`, we create a temporary `BlockDesc` to do `InferShape` and
`InferVarType` every time we `AddOp` to the tape.
### Reuse Operator::Run
We use smart pointer, instead of `Scope`, to manage memory. So we create a temporary
`Scope` for every `Operator::Run()`.
## Possible Feature
### Release Memory on Backward
We can release memory aggressively. During backward, we can delete the OpHandle once
we have finished its backward. Since all the variable is managed by smart pointer, the
memory is automatically released when its `ref_count` goes to 0.
### Kernel Fusion
As a symbolic representation of the Tape is constructed first before the actual
execution, it would be possible to perform graph optimization. One use case is kernel
fusion.
paddle/contrib/tape/function.h 0 → 100644
浏览文件 @ 85c3bfc1
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include "paddle/contrib/tape/tape.h"
#include "paddle/contrib/tape/variable.h"
#include "paddle/fluid/framework/type_defs.h"
namespace paddle {
namespace tape {
class Function {};
class Fill {
public:
Fill(const std::string &initializer, const framework::AttributeMap &attrs)
: initializer_(initializer), attrs_(attrs) {}
void operator()(VariableHandle var) {
get_global_tape().AddOp(initializer_, {}, {{"Out", {var}}}, attrs_);
}
private:
const std::string initializer_;
const framework::AttributeMap attrs_;
};
class Mean {
public:
VariableHandle operator()(VariableHandle var) {
VariableHandle out(new Variable("mean"));
get_global_tape().AddOp("mean", {{"X", {var}}}, {{"Out", {out}}}, {});
return out;
}
};
class Linear {
public:
Linear(int in_dim, int out_dim, const std::string &act)
: w_(new Variable("LinearWeight")),
b_(new Variable("LinearBias")),
act_(act) {
Tape init_tape;
std::string initializer = "fill_constant";
framework::AttributeMap attrs;
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{in_dim, out_dim};
attrs["value"] = 1.0f;
init_tape.AddOp(initializer, {}, {{"Out", {w_}}}, attrs);
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{out_dim};
attrs["value"] = 1.0f;
init_tape.AddOp(initializer, {}, {{"Out", {b_}}}, attrs);
init_tape.Forward();
}
VariableHandle operator()(VariableHandle input) {
VariableHandle pre_bias(new Variable("linear"));
get_global_tape().AddOp("mul",
{{"X", {input}}, {"Y", {w_}}},
{{"Out", {pre_bias}}},
{{"x_num_col_dims", 1}, {"y_num_col_dims", 1}});
VariableHandle pre_act(new Variable("linear"));
get_global_tape().AddOp("elementwise_add",
{{"X", {pre_bias}}, {"Y", {b_}}},
{{"Out", {pre_act}}},
{{"axis", 1}});
VariableHandle post_act(new Variable("linear"));
get_global_tape().AddOp(
act_, {{"X", {pre_act}}}, {{"Out", {post_act}}}, {});
return post_act;
}
std::vector<VariableHandle> Params() { return {w_, b_}; }
private:
VariableHandle w_;
VariableHandle b_;
std::string act_;
};
class SGD {
public:
SGD(float learning_rate) : learning_rate_(new Variable("sgd")) {
Tape init_tape;
std::string initializer = "fill_constant";
framework::AttributeMap attrs;
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{1};
attrs["value"] = learning_rate;
init_tape.AddOp(initializer, {}, {{"Out", {learning_rate_}}}, attrs);
init_tape.Forward();
}
void operator()(VariableHandle input) {
PADDLE_ENFORCE(get_global_tape().HasBeenBackwarded(),
"optimization must happen after the backward");
Tape temp_tape;
temp_tape.AddOp("sgd",
{{"Param", {input}},
{"LearningRate", {learning_rate_}},
{"Grad", {input->Grad()}}},
{{"ParamOut", {input}}},
{});
temp_tape.Forward();
}
private:
VariableHandle learning_rate_;
};
}
}
paddle/contrib/tape/tape.cc 0 → 100644
浏览文件 @ 85c3bfc1
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/contrib/tape/tape.h"
#include <list>
#include <map>
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/dim.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/pybind/pybind.h"
namespace paddle {
namespace tape {
// borrowed from
// https://stackoverflow.com/questions/874134/find-if-string-ends-with-another-string-in-c
inline bool ends_with(std::string const &value, std::string const &ending) {
if (ending.size() > value.size()) return false;
return std::equal(ending.rbegin(), ending.rend(), value.rbegin());
}
std::ostream &operator<<(std::ostream &os, const framework::VarDesc &var_desc) {
os << var_desc.Name();
os << "[" << var_desc.GetType() << "]";
os << "[" << var_desc.GetDataType() << "]";
os << "{";
for (auto &i : var_desc.GetShape()) {
os << i << ",";
}
os << "}";
return os;
}
std::string to_string(const std::string &type,
const VariableHandleMap &in_vars,
const VariableHandleMap &out_vars,
const framework::AttributeMap &attrs) {
std::stringstream ss;
ss << type << " ";
for (auto &param_name : in_vars) {
for (auto &var : param_name.second) {
ss << param_name.first << ":(" << var->Desc() << ") ";
}
}
for (auto &param_name : out_vars) {
for (auto &var : param_name.second) {
ss << param_name.first << ":(" << var->Desc() << ") ";
}
}
return ss.str();
}
framework::OpDesc CreateOpDesc(const std::string &type,
const VariableHandleMap &in_vars,
const VariableHandleMap &out_vars,
const framework::AttributeMap &attrs) {
framework::VariableNameMap inputs;
for (auto &param_name : in_vars) {
for (auto &var : param_name.second) {
inputs[param_name.first].emplace_back(var->Name());
}
}
framework::VariableNameMap outputs;
for (auto &param_name : out_vars) {
for (auto &var : param_name.second) {
outputs[param_name.first].emplace_back(var->Name());
}
}
return framework::OpDesc(type, inputs, outputs, attrs);
}
void InferShapeAndVarType(const std::string &type,
const VariableHandleMap &in_vars,
VariableHandleMap *out_vars,
const framework::AttributeMap &attrs) {
framework::OpDesc op_desc = CreateOpDesc(type, in_vars, *out_vars, attrs);
// Create a temporary block for compile-time
framework::ProgramDesc program_desc;
framework::BlockDesc *block_desc = program_desc.MutableBlock(0);
PADDLE_ENFORCE(block_desc);
for (auto &param_name : in_vars) {
for (auto &var : param_name.second) {
*block_desc->Var(var->Name())->Proto() = *var->MutableDesc()->Proto();
}
}
for (auto &param_name : *out_vars) {
for (auto &var : param_name.second) {
*block_desc->Var(var->Name())->Proto() = *var->MutableDesc()->Proto();
}
}
LOG(INFO) << "- " << to_string(type, in_vars, *out_vars, attrs);
op_desc.InferShape(*block_desc);
op_desc.InferVarType(block_desc);
for (auto &param_name : *out_vars) {
for (auto &var : param_name.second) {
*var->MutableDesc()->Proto() = *block_desc->Var(var->Name())->Proto();
}
}
LOG(INFO) << "+ " << to_string(type, in_vars, *out_vars, attrs);
}
void Tape::AddOp(const std::string &type,
const VariableHandleMap &in_vars,
VariableHandleMap out_vars,
const framework::AttributeMap &attrs) {
InferShapeAndVarType(type, in_vars, &out_vars, attrs);
tape_.emplace_back(type, in_vars, out_vars, attrs);
}
// Temporary Scope for Operator::Run()
class ScopeWrapper : public framework::Scope {
public:
ScopeWrapper(const VariableHandleMap &in_vars,
const VariableHandleMap &out_vars) {
for (auto &v : in_vars) {
for (auto &vv : v.second) {
if (!vars_.count(vv->Name())) {
vars_[vv->Name()].reset(vv->Var());
}
}
}
for (auto &v : out_vars) {
for (auto &vv : v.second) {
if (!vars_.count(vv->Name())) {
vars_[vv->Name()].reset(vv->Var());
}
}
}
}
~ScopeWrapper() {
for (auto &pair : vars_) {
pair.second.release();
}
}
};
void Tape::Forward() {
LOG(INFO) << "Starting forward -------------------------";
PADDLE_ENFORCE(!has_been_backwarded_);
while (current_position_ < tape_.size()) {
OpHandle &op = tape_[current_position_];
// Create Output Tensor, this is only necessary for OpWithKernel
for (auto &param2var : op.outputs_) {
for (auto &var : param2var.second) {
var->InitializeVariable();
}
}
framework::OpDesc op_desc =
CreateOpDesc(op.type_, op.inputs_, op.outputs_, op.attrs_);
ScopeWrapper scope(op.inputs_, op.outputs_);
framework::OpRegistry::CreateOp(op_desc)->Run(scope, platform::CPUPlace());
current_position_++;
}
LOG(INFO) << "Finishing forward -------------------------";
}
void Tape::Backward(VariableHandle target) {
PADDLE_ENFORCE(!has_been_backwarded_);
Forward();
// TODO(tonyyang-svail): check output of last op is target
backward_tape_.reset(new Tape());
framework::AttributeMap attrs;
// FIXME(tonyyang-svail): Need to infer_data_type
attrs["dtype"] = framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{1};
attrs["value"] = 1.0f;
backward_tape_->AddOp(
"fill_constant", {}, {{"Out", {target->Grad()}}}, attrs);
for (auto it = tape_.rbegin(); it != tape_.rend(); ++it) {
framework::OpDesc op_desc =
CreateOpDesc(it->type_, it->inputs_, it->outputs_, it->attrs_);
std::unordered_map<std::string, std::string> grad_to_var;
std::vector<std::unique_ptr<framework::OpDesc>> grad_op_descs =
framework::OpInfoMap::Instance()
.Get(op_desc.Type())
.GradOpMaker()(op_desc, {}, &grad_to_var, {});
for (auto &op_desc : grad_op_descs) {
std::unordered_map<std::string, VariableHandle> name2var;
for (auto &param2vars : it->inputs_) {
for (auto &a : param2vars.second) {
name2var[a->Name()] = a;
}
}
for (auto &param2vars : it->outputs_) {
for (auto &a : param2vars.second) {
name2var[a->Name()] = a;
}
}
VariableHandleMap in_vars;
VariableHandleMap out_vars;
std::map<const framework::VariableNameMap *, VariableHandleMap *>
loop_over{{&op_desc->Inputs(), &in_vars},
{&op_desc->Outputs(), &out_vars}};
for (auto &each : loop_over) {
auto &vmp = *each.first;
auto &vhm = *each.second;
for (auto &p2a : vmp) {
for (auto &argu : p2a.second) {
if (name2var.count(argu)) {
vhm[p2a.first].push_back(name2var[argu]);
} else {
PADDLE_ENFORCE(ends_with(argu, framework::kGradVarSuffix),
argu.c_str());
std::string name = argu.substr(
0, argu.size() - std::strlen(framework::kGradVarSuffix));
PADDLE_ENFORCE(name2var.count(name), name.c_str());
vhm[p2a.first].push_back(name2var[name]->Grad());
}
}
}
}
backward_tape_->AddOp(
op_desc->Type(), in_vars, out_vars, op_desc->GetAttrMap());
}
// TODO(tonyyang-svail): how to fill empty grad?
// TODO(tonyyang-svail): Sum var grad is necessary
}
backward_tape_->Forward();
has_been_backwarded_ = true;
}
Tape &get_global_tape() {
static Tape T;
return T;
}
void reset_global_tape() { get_global_tape() = Tape(); }
}
}
paddle/contrib/tape/tape.h 0 → 100644
浏览文件 @ 85c3bfc1
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <map>
#include <memory>
#include <string>
#include <vector>
#include "paddle/contrib/tape/variable.h"
namespace paddle {
namespace tape {
using VariableHandleMap = std::map<std::string, std::vector<VariableHandle>>;
struct OpHandle {
OpHandle(const std::string &type,
const VariableHandleMap &in_vars,
const VariableHandleMap &out_vars,
const framework::AttributeMap &attrs)
: type_(type), inputs_(in_vars), outputs_(out_vars), attrs_(attrs) {}
std::string type_;
VariableHandleMap inputs_;
VariableHandleMap outputs_;
framework::AttributeMap attrs_;
};
class Tape {
public:
void AddOp(const std::string &type,
const VariableHandleMap &in_vars,
VariableHandleMap out_vars,
const framework::AttributeMap &attrs);
void Forward();
void Backward(VariableHandle target);
bool HasBeenBackwarded() { return has_been_backwarded_; }
private:
bool has_been_backwarded_ = false;
size_t current_position_ = 0;
std::vector<OpHandle> tape_;
std::shared_ptr<Tape> backward_tape_;
};
Tape &get_global_tape();
void reset_global_tape();
}
}
paddle/contrib/tape/test_tape.cc 0 → 100644
浏览文件 @ 85c3bfc1
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "gtest/gtest.h"
#include "paddle/contrib/tape/function.h"
using namespace paddle::tape;
TEST(Tape, TestMLP) {
LOG(INFO) << "TestMLP";
Linear linear1(3, 3, "relu");
Linear linear2(3, 3, "relu");
Mean mean;
SGD sgd(0.001);
std::string initializer = "fill_constant";
paddle::framework::AttributeMap attrs;
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{3, 3};
attrs["value"] = 1.0f;
Fill filler(initializer, attrs);
for (int i = 0; i < 2; ++i) {
reset_global_tape();
VariableHandle input(new Variable("input"));
filler(input);
auto loss = mean(linear2(linear1(input)));
get_global_tape().Backward(loss);
for (auto w : linear1.Params()) {
sgd(w);
}
for (auto w : linear2.Params()) {
sgd(w);
}
}
}
int main(int argc, char** argv) {
std::vector<paddle::platform::Place> places;
places.emplace_back(paddle::platform::CPUPlace());
paddle::platform::DeviceContextPool::Init(places);
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
paddle/contrib/tape/variable.cc 0 → 100644
浏览文件 @ 85c3bfc1
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/contrib/tape/variable.h"
namespace paddle {
namespace tape {
void Variable::InitializeVariable() {
LOG(INFO) << "Initialzing " << desc_.Name() << " as " << desc_.GetType();
framework::proto::VarType::Type var_type = desc_.GetType();
if (var_type == framework::proto::VarType::LOD_TENSOR) {
var_.GetMutable<framework::LoDTensor>();
} else if (var_type == framework::proto::VarType::SELECTED_ROWS) {
var_.GetMutable<framework::SelectedRows>();
} else {
PADDLE_THROW("Variable type %d is not in [LOD_TENSOR, SELECTED_ROWS]",
var_type);
}
}
}
}
paddle/contrib/tape/variable.h 0 → 100644
浏览文件 @ 85c3bfc1
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <memory>
#include "paddle/fluid/framework/operator.h" // framework::kGradVarSuffix
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/variable.h"
namespace paddle {
namespace tape {
class Variable;
using VariableHandle = std::shared_ptr<Variable>;
/*
* Combination of
* framework::VarDesc desc_;
* framework::Variable var_;
*/
class Variable {
public:
Variable(const std::string pre_fix)
: desc_(pre_fix + std::to_string(count())) {}
Variable(const std::string pre_fix, bool is_grad)
: desc_(pre_fix + (is_grad ? framework::kGradVarSuffix
: std::to_string(count()))) {}
~Variable() { LOG(INFO) << "Deleting " << Name(); }
// Instantiate LoDTensor/SelectedRow
void InitializeVariable();
VariableHandle Grad() {
if (grad_.expired()) {
VariableHandle new_grad(new Variable(desc_.Name(), true));
grad_ = new_grad;
return new_grad;
} else {
return VariableHandle(grad_);
}
}
// Stochastic Gradient Descent with Momentum
// VariableHandle Momentum ();
// void init(const std::string& initializer,
// const framework::AttributeMap& attrs);
// void value() {};
const framework::VarDesc& Desc() const { return desc_; }
framework::VarDesc* MutableDesc() { return &desc_; }
// TODO(tonyyang-svail): No need to expose name
std::string Name() const { return desc_.Name(); }
framework::Variable* Var() { return &var_; }
private:
int count() {
static int counter = 0;
return counter++;
}
framework::VarDesc desc_;
framework::Variable var_;
std::weak_ptr<Variable> grad_;
};
}
}
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 85c3bfc1
...@@ -84,7 +84,7 @@ cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor) ...@@ -84,7 +84,7 @@ cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog) cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog)
if(WITH_DISTRIBUTE) if(WITH_DISTRIBUTE)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr) cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method sendrecvop_grpc cares grpc++_unsecure grpc_unsecure gpr)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor") set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else() else()
......
paddle/fluid/framework/executor.cc
浏览文件 @ 85c3bfc1
...@@ -330,8 +330,12 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, ...@@ -330,8 +330,12 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
} }
for (auto& op : ctx->ops_) { for (auto& op : ctx->ops_) {
VLOG(3) << place_ << " " << op->DebugStringEx(local_scope); VLOG(4) << place_ << " " << op->DebugStringEx(local_scope);
op->Run(*local_scope, place_); op->Run(*local_scope, place_);
// NOTE! Please do not delete this line, it's usefull because the debug
// string before and after op.run are different, after run the output
// will have right shape which is usefull for debug.
VLOG(3) << place_ << " " << op->DebugStringEx(local_scope);
if (FLAGS_benchmark) { if (FLAGS_benchmark) {
VLOG(2) << "Memory used after operator " + op->Type() + " running: " VLOG(2) << "Memory used after operator " + op->Type() + " running: "
......
paddle/fluid/framework/operator.cc
浏览文件 @ 85c3bfc1
...@@ -69,6 +69,19 @@ static DDim GetDims(const Scope& scope, const std::string& name, ...@@ -69,6 +69,19 @@ static DDim GetDims(const Scope& scope, const std::string& name,
} }
} }
static int GetRowSize(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
if (var == nullptr) {
return -1;
}
if (var->IsType<SelectedRows>()) {
return var->Get<SelectedRows>().rows().size();
}
return -1;
}
static LoD GetLoD(const Scope& scope, const std::string& name) { static LoD GetLoD(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name); Variable* var = scope.FindVar(name);
auto default_lod = LoD({{}}); auto default_lod = LoD({{}});
...@@ -85,6 +98,7 @@ static LoD GetLoD(const Scope& scope, const std::string& name) { ...@@ -85,6 +98,7 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
} }
void OperatorBase::Run(const Scope& scope, const platform::Place& place) { void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
VLOG(10) << "- " << DebugStringEx(&scope);
if (platform::is_gpu_place(place)) { if (platform::is_gpu_place(place)) {
#ifndef PADDLE_WITH_CUDA #ifndef PADDLE_WITH_CUDA
PADDLE_THROW("Cannot run operator on place %s", place); PADDLE_THROW("Cannot run operator on place %s", place);
...@@ -94,6 +108,7 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) { ...@@ -94,6 +108,7 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
#endif #endif
} }
RunImpl(scope, place); RunImpl(scope, place);
VLOG(10) << "+ " << DebugStringEx(&scope);
} }
bool OperatorBase::HasInputs(const std::string& name) const { bool OperatorBase::HasInputs(const std::string& name) const {
...@@ -153,6 +168,10 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const { ...@@ -153,6 +168,10 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
for (size_t i = 0; i < input.second.size(); ++i) { for (size_t i = 0; i < input.second.size(); ++i) {
ss << input.second[i]; ss << input.second[i];
if (scope) { if (scope) {
int row_size = GetRowSize(*scope, input.second[i]);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
}
ss << "[" << GetDims(*scope, input.second[i], true) << "]"; ss << "[" << GetDims(*scope, input.second[i], true) << "]";
ss << "(" << GetLoD(*scope, input.second[i]) << ")"; ss << "(" << GetLoD(*scope, input.second[i]) << ")";
} }
...@@ -173,6 +192,10 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const { ...@@ -173,6 +192,10 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
for (size_t i = 0; i < output.second.size(); ++i) { for (size_t i = 0; i < output.second.size(); ++i) {
ss << output.second[i]; ss << output.second[i];
if (scope) { if (scope) {
int row_size = GetRowSize(*scope, output.second[i]);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
}
ss << "[" << GetDims(*scope, output.second[i], true) << "]"; ss << "[" << GetDims(*scope, output.second[i], true) << "]";
ss << "(" << GetLoD(*scope, output.second[i]) << ")"; ss << "(" << GetLoD(*scope, output.second[i]) << ")";
} }
......
paddle/fluid/framework/reader.h
浏览文件 @ 85c3bfc1
...@@ -35,14 +35,15 @@ class ReaderBase { ...@@ -35,14 +35,15 @@ class ReaderBase {
class DecoratedReader : public ReaderBase { class DecoratedReader : public ReaderBase {
public: public:
explicit DecoratedReader(ReaderBase* reader) : ReaderBase(), reader_(reader) { explicit DecoratedReader(const std::shared_ptr<ReaderBase>& reader)
: ReaderBase(), reader_(reader) {
PADDLE_ENFORCE_NOT_NULL(reader_); PADDLE_ENFORCE_NOT_NULL(reader_);
} }
void ReInit() override { reader_->ReInit(); } void ReInit() override { reader_->ReInit(); }
protected: protected:
ReaderBase* reader_; std::shared_ptr<ReaderBase> reader_;
}; };
class FileReader : public ReaderBase { class FileReader : public ReaderBase {
...@@ -64,7 +65,7 @@ class ReaderHolder { ...@@ -64,7 +65,7 @@ class ReaderHolder {
public: public:
void Reset(ReaderBase* reader) { reader_.reset(reader); } void Reset(ReaderBase* reader) { reader_.reset(reader); }
ReaderBase* Get() const { return reader_.get(); } std::shared_ptr<ReaderBase> Get() const { return reader_; }
void ReadNext(std::vector<LoDTensor>* out) { void ReadNext(std::vector<LoDTensor>* out) {
PADDLE_ENFORCE_NOT_NULL(reader_); PADDLE_ENFORCE_NOT_NULL(reader_);
...@@ -76,7 +77,7 @@ class ReaderHolder { ...@@ -76,7 +77,7 @@ class ReaderHolder {
} }
private: private:
std::unique_ptr<ReaderBase> reader_; std::shared_ptr<ReaderBase> reader_;
}; };
} // namespace framework } // namespace framework
......
paddle/fluid/framework/scope.h
浏览文件 @ 85c3bfc1
...@@ -81,6 +81,9 @@ class Scope { ...@@ -81,6 +81,9 @@ class Scope {
// Rename variable to a new name and return the new name // Rename variable to a new name and return the new name
std::string Rename(const std::string& origin_name) const; std::string Rename(const std::string& origin_name) const;
protected:
mutable std::unordered_map<std::string, std::unique_ptr<Variable>> vars_;
private: private:
// Call Scope::NewScope for a sub-scope. // Call Scope::NewScope for a sub-scope.
explicit Scope(Scope const* parent) : parent_(parent) {} explicit Scope(Scope const* parent) : parent_(parent) {}
...@@ -93,8 +96,6 @@ class Scope { ...@@ -93,8 +96,6 @@ class Scope {
// Caller doesn't own the returned Variable. // Caller doesn't own the returned Variable.
Variable* FindVarLocally(const std::string& name) const; Variable* FindVarLocally(const std::string& name) const;
mutable std::unordered_map<std::string, std::unique_ptr<Variable>> vars_;
// Scope in `kids_` are owned by this class. // Scope in `kids_` are owned by this class.
mutable std::list<Scope*> kids_; mutable std::list<Scope*> kids_;
Scope const* parent_{nullptr}; Scope const* parent_{nullptr};
......
paddle/fluid/operators/batch_norm_mkldnn_op.cc
浏览文件 @ 85c3bfc1
...@@ -19,10 +19,17 @@ limitations under the License. */ ...@@ -19,10 +19,17 @@ limitations under the License. */
namespace paddle { namespace paddle {
namespace operators { namespace operators {
using Tensor = framework::Tensor; using batch_norm_bwd = mkldnn::batch_normalization_backward;
using batch_norm_fwd = mkldnn::batch_normalization_forward;
using framework::DataLayout;
using framework::Tensor;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
using mkldnn::stream;
using paddle::platform::MKLDNNDeviceContext; using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::MKLDNNMemDesc; using paddle::platform::MKLDNNMemDesc;
using mkldnn::memory; using platform::to_void_cast;
template <typename T> template <typename T>
using EigenArrayMap = using EigenArrayMap =
...@@ -64,21 +71,12 @@ void run_batch_norm_op(Args &&... args) { ...@@ -64,21 +71,12 @@ void run_batch_norm_op(Args &&... args) {
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
} }
template <typename T>
inline void *cast_const_to_void(const T *t) {
return static_cast<void *>(const_cast<T *>(t));
}
} // namespace } // namespace
template <typename T> template <typename T>
class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> { class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public: public:
void Compute(const framework::ExecutionContext &ctx) const override { void Compute(const framework::ExecutionContext &ctx) const override {
auto data_layout_str = ctx.Attr<std::string>("data_layout");
auto data_layout = framework::StringToDataLayout(data_layout_str);
PADDLE_ENFORCE(data_layout == framework::DataLayout::kNCHW,
"MKLDNN batch normalization handles only NCHW data layout");
const float epsilon = ctx.Attr<float>("epsilon"); const float epsilon = ctx.Attr<float>("epsilon");
const float momentum = ctx.Attr<float>("momentum"); const float momentum = ctx.Attr<float>("momentum");
const bool is_test = ctx.Attr<bool>("is_test"); const bool is_test = ctx.Attr<bool>("is_test");
...@@ -99,41 +97,53 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -99,41 +97,53 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const auto *scale = ctx.Input<Tensor>("Scale"); const auto *scale = ctx.Input<Tensor>("Scale");
const auto *shift = ctx.Input<Tensor>("Bias"); const auto *shift = ctx.Input<Tensor>("Bias");
y->mutable_data<T>(ctx.GetPlace()); PADDLE_ENFORCE(x->layout() == DataLayout::kMKLDNN &&
mean_out->mutable_data<T>(ctx.GetPlace()); x->format() != memory::format::format_undef,
variance_out->mutable_data<T>(ctx.GetPlace()); "Wrong layout/format set for Input x tensor");
const T *x_data = x->data<T>();
const T *mean_data = mean->data<T>();
const T *variance_data = variance->data<T>();
T *y_data = y->mutable_data<T>(ctx.GetPlace());
T *mean_out_data = mean_out->mutable_data<T>(ctx.GetPlace());
T *variance_out_data = variance_out->mutable_data<T>(ctx.GetPlace());
T *batch_mean_data = nullptr;
T *batch_variance_data = nullptr;
if (!is_test) { if (!is_test) {
batch_mean->mutable_data<T>(ctx.GetPlace()); batch_mean_data = batch_mean->mutable_data<T>(ctx.GetPlace());
batch_variance->mutable_data<T>(ctx.GetPlace()); batch_variance_data = batch_variance->mutable_data<T>(ctx.GetPlace());
} }
auto propagation = is_test == true ? mkldnn::prop_kind::forward_scoring auto propagation = is_test == true ? mkldnn::prop_kind::forward_scoring
: mkldnn::prop_kind::forward_training; : mkldnn::prop_kind::forward_training;
auto dims = paddle::framework::vectorize2int(x->dims()); auto src_tz = paddle::framework::vectorize2int(x->dims());
auto scale_tz = paddle::framework::vectorize2int(scale->dims());
auto src_md = PADDLE_ENFORCE(scale_tz.size() == 1, "Dims of scale tensor is NOT 1");
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw); const unsigned int ic = scale_tz[0];
auto dst_md =
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
auto src_pd = mkldnn::memory::primitive_desc{src_md, mkldnn_engine};
auto dst_pd = mkldnn::memory::primitive_desc{dst_md, mkldnn_engine};
auto src = mkldnn::memory{src_pd, cast_const_to_void(x->data<T>())};
auto dst = mkldnn::memory{dst_pd, y->data<T>()};
unsigned flags = mkldnn::use_scale_shift; unsigned flags = mkldnn::use_scale_shift;
if (is_test) flags |= mkldnn::use_global_stats; if (is_test) flags |= mkldnn::use_global_stats;
// create mkldnn memory from input x tensor
auto src_memory =
memory({{{src_tz}, memory::data_type::f32, x->format()}, mkldnn_engine},
to_void_cast(x_data));
// create primitive descriptor for batch norm forward
using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>; using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>;
auto batch_norm_fwd_desc = auto batch_norm_fwd_desc = bn_fwd_types::op_desc{
bn_fwd_types::op_desc{propagation, src_md, epsilon, flags}; propagation, src_memory.get_primitive_desc().desc(), epsilon, flags};
auto batch_norm_fwd_pd = std::shared_ptr<batch_norm_fwd::primitive_desc> batch_norm_fwd_pd =
bn_fwd_types::op_prim{batch_norm_fwd_desc, mkldnn_engine}; std::shared_ptr<batch_norm_fwd::primitive_desc>(
new batch_norm_fwd::primitive_desc(batch_norm_fwd_desc,
mkldnn_engine));
const unsigned int ic = dims[1]; // Save the pd to be used in backward pass
const std::string key = ctx.op().Output("SavedMean");
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
dev_ctx.SetBlob(key_batch_norm_fwd_pd, batch_norm_fwd_pd);
// MKLDNN requires a single piece of memory for scale and shift/bias data // MKLDNN requires a single piece of memory for scale and shift/bias data
const size_t scaleshift_size = 2 * ic; const size_t scaleshift_size = 2 * ic;
...@@ -143,73 +153,58 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -143,73 +153,58 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
copy_to_weights(scale->data<T>(), scale->data<T>() + ic, shift->data<T>(), copy_to_weights(scale->data<T>(), scale->data<T>() + ic, shift->data<T>(),
shift->data<T>() + ic, &scaleshift_data); shift->data<T>() + ic, &scaleshift_data);
auto scaleshift_memory = mkldnn::memory{ // crate mkldnn memory for weights(scale/shift)
batch_norm_fwd_pd.weights_primitive_desc(), scaleshift_data.data()}; auto scaleshift_memory = memory(batch_norm_fwd_pd->weights_primitive_desc(),
scaleshift_data.data());
if (is_test) { // create mkldnn memory for output y tensor
auto mean_memory = mkldnn::memory{batch_norm_fwd_pd.mean_primitive_desc(), auto dst_memory = memory(batch_norm_fwd_pd->dst_primitive_desc(), y_data);
cast_const_to_void(mean->data<T>())};
if (is_test) {
// create mkldnn memory for stats (as input)
auto mean_memory = memory(batch_norm_fwd_pd->mean_primitive_desc(),
to_void_cast(mean_data));
auto variance_memory = auto variance_memory =
mkldnn::memory{batch_norm_fwd_pd.variance_primitive_desc(), memory(batch_norm_fwd_pd->variance_primitive_desc(),
cast_const_to_void(variance->data<T>())}; to_void_cast(variance_data));
run_batch_norm_op<typename bn_fwd_types::op_type>( run_batch_norm_op<typename bn_fwd_types::op_type>(
batch_norm_fwd_pd, src, (const mkldnn::primitive::at &)mean_memory, *batch_norm_fwd_pd, src_memory,
(const mkldnn::primitive::at &)mean_memory,
(const mkldnn::primitive::at &)variance_memory, scaleshift_memory, (const mkldnn::primitive::at &)variance_memory, scaleshift_memory,
dst); dst_memory);
} else { } else {
// create mkldnn memory for stats (as output)
auto mean_memory = auto mean_memory =
mkldnn::memory{batch_norm_fwd_pd.mean_primitive_desc(), memory(batch_norm_fwd_pd->mean_primitive_desc(), batch_mean_data);
cast_const_to_void(batch_mean->data<T>())}; auto variance_memory = memory(
batch_norm_fwd_pd->variance_primitive_desc(), batch_variance_data);
auto variance_memory =
mkldnn::memory{batch_norm_fwd_pd.variance_primitive_desc(),
cast_const_to_void(batch_variance->data<T>())};
run_batch_norm_op<bn_fwd_types::op_type>(batch_norm_fwd_pd, src, run_batch_norm_op<bn_fwd_types::op_type>(*batch_norm_fwd_pd, src_memory,
scaleshift_memory, dst, scaleshift_memory, dst_memory,
mean_memory, variance_memory); mean_memory, variance_memory);
} }
if (!is_test) { if (!is_test) {
const unsigned int in = dims[0]; // mkldnn only compute stats for current batch
const unsigned int sample_size = x->numel() / in / ic; // so we need compute momentum stats via Eigen lib
EigenVectorArrayMap<T> batch_mean_e(batch_mean_data, ic);
// saved_xx is use just in this batch of data EigenVectorArrayMap<T> batch_variance_e(batch_variance_data, ic);
EigenVectorArrayMap<T> saved_mean_e( ConstEigenVectorArrayMap<T> mean_e(mean_data, ic);
batch_mean->mutable_data<T>(ctx.GetPlace()), ic); ConstEigenVectorArrayMap<T> variance_e{variance_data, ic};
EigenVectorArrayMap<T> saved_variance_e(
batch_variance->mutable_data<T>(ctx.GetPlace()), ic); EigenVectorArrayMap<T> running_mean_e(mean_out_data, ic);
saved_mean_e.setZero(); EigenVectorArrayMap<T> running_variance_e(variance_out_data, ic);
saved_variance_e.setZero();
const unsigned int x_arr_size = in * ic;
ConstEigenArrayMap<T> x_arr(x->data<T>(), sample_size, x_arr_size);
for (unsigned int nc = 0; nc < x_arr_size; ++nc) {
saved_mean_e(nc % ic) += x_arr.col(nc).sum();
}
saved_mean_e /= in * sample_size;
for (unsigned int nc = 0; nc < x_arr_size; ++nc) {
saved_variance_e(nc % ic) +=
(x_arr.col(nc) - saved_mean_e(nc % ic)).matrix().squaredNorm();
}
saved_variance_e /= in * sample_size;
ConstEigenVectorArrayMap<T> mean_arr{mean->data<T>(), ic};
ConstEigenVectorArrayMap<T> variance_arr{variance->data<T>(), ic};
EigenVectorArrayMap<T> running_mean_arr(
mean_out->mutable_data<T>(ctx.GetPlace()), ic);
EigenVectorArrayMap<T> running_var_arr(
variance_out->mutable_data<T>(ctx.GetPlace()), ic);
auto one_minus_momentum = 1. - momentum; auto one_minus_momentum = 1. - momentum;
running_mean_arr = running_mean_e = mean_e * momentum + batch_mean_e * one_minus_momentum;
mean_arr * momentum + saved_mean_e * one_minus_momentum; running_variance_e =
running_var_arr = variance_e * momentum + batch_variance_e * one_minus_momentum;
variance_arr * momentum + saved_variance_e * one_minus_momentum;
} }
y->set_layout(DataLayout::kMKLDNN);
y->set_format(
(memory::format)dst_memory.get_primitive_desc().desc().data.format);
} }
}; };
...@@ -217,11 +212,6 @@ template <typename T> ...@@ -217,11 +212,6 @@ template <typename T>
class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> { class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
public: public:
void Compute(const paddle::framework::ExecutionContext &ctx) const override { void Compute(const paddle::framework::ExecutionContext &ctx) const override {
auto data_layout_str = ctx.Attr<std::string>("data_layout");
auto data_layout = framework::StringToDataLayout(data_layout_str);
PADDLE_ENFORCE(data_layout == framework::DataLayout::kNCHW,
"MKLDNN batch normalization handles only NCHW data layout");
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>(); auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
auto mkldnn_engine = dev_ctx.GetEngine(); auto mkldnn_engine = dev_ctx.GetEngine();
...@@ -238,88 +228,132 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> { ...@@ -238,88 +228,132 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
auto *diff_scale = ctx.Output<Tensor>(framework::GradVarName("Scale")); auto *diff_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto *diff_shift = ctx.Output<Tensor>(framework::GradVarName("Bias")); auto *diff_shift = ctx.Output<Tensor>(framework::GradVarName("Bias"));
diff_x->mutable_data<T>(ctx.GetPlace()); PADDLE_ENFORCE(diff_y->layout() == DataLayout::kMKLDNN &&
diff_scale->mutable_data<T>(ctx.GetPlace()); diff_y->format() != memory::format::format_undef,
diff_shift->mutable_data<T>(ctx.GetPlace()); "Wrong layout/format set for Input diff_y tensor");
const T *x_data = x->data<T>();
const T *diff_y_data = diff_y->data<T>();
const T *batch_mean_data = batch_mean->data<T>();
const T *batch_variance_data = batch_variance->data<T>();
const T *scale_data = scale->data<T>();
const T *shift_data = shift->data<T>();
T *diff_x_data = diff_x->mutable_data<T>(ctx.GetPlace());
T *diff_scale_data = diff_scale->mutable_data<T>(ctx.GetPlace());
T *diff_shift_data = diff_shift->mutable_data<T>(ctx.GetPlace());
auto src_tz = paddle::framework::vectorize2int(x->dims());
auto diff_src_tz = src_tz;
auto dst_tz = src_tz;
auto diff_dst_tz = dst_tz;
auto scale_tz = paddle::framework::vectorize2int(scale->dims());
PADDLE_ENFORCE(scale_tz.size() == 1, "Dims of scale tensor is NOT 1");
const unsigned int ic = scale_tz[0];
// Retrieve bn_fwd_pd from device context
const std::string key = ctx.op().Input("SavedMean");
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
auto batch_norm_fwd_pd =
std::static_pointer_cast<batch_norm_fwd::primitive_desc>(
dev_ctx.GetBlob(key_batch_norm_fwd_pd));
PADDLE_ENFORCE(batch_norm_fwd_pd != nullptr,
"Fail to find batch_norm_fwd_pd in device context");
auto dims = paddle::framework::vectorize2int(x->dims()); using bn_bwd_types = bn_type_traits<mkldnn::batch_normalization_backward>;
unsigned flags = mkldnn::use_scale_shift | !mkldnn::use_global_stats;
auto src_md = // create mkldnn memory from input diff_y tensor
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw); auto user_diff_dst_memory =
auto dst_md = memory({{{diff_dst_tz}, memory::data_type::f32, diff_y->format()},
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw); mkldnn_engine},
auto diff_src_md = to_void_cast(diff_y_data));
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
auto diff_dst_md =
MKLDNNMemDesc(dims, memory::data_type::f32, memory::format::nchw);
using bn_bwd_types = bn_type_traits<mkldnn::batch_normalization_backward>; // create mkldnn memory from input x tensor
using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>; auto src_memory =
memory({{{src_tz}, memory::data_type::f32, x->format()}, mkldnn_engine},
to_void_cast(x_data));
auto batch_norm_fwd_desc = bn_fwd_types::op_desc{ // for diff_dst, try to use same format as dst in forward pass
mkldnn::prop_kind::forward_training, src_md, epsilon, flags}; auto diff_dst_pd = batch_norm_fwd_pd.get()->dst_primitive_desc();
auto batch_norm_fwd_pd = auto diff_dst_md = diff_dst_pd.desc();
bn_fwd_types::op_prim{batch_norm_fwd_desc, mkldnn_engine};
// create primitive descriptor for batch norm backward
unsigned flags = mkldnn::use_scale_shift;
auto batch_norm_bwd_desc = bn_bwd_types::op_desc{ auto batch_norm_bwd_desc = bn_bwd_types::op_desc{
mkldnn::prop_kind::backward, diff_dst_md, dst_md, epsilon, flags}; mkldnn::prop_kind::backward, diff_dst_md,
src_memory.get_primitive_desc().desc(), epsilon, flags};
auto batch_norm_bwd_pd = bn_bwd_types::op_prim{ auto batch_norm_bwd_pd = bn_bwd_types::op_prim{
batch_norm_bwd_desc, mkldnn_engine, batch_norm_fwd_pd}; batch_norm_bwd_desc, mkldnn_engine, *batch_norm_fwd_pd};
auto src = mkldnn::memory{{src_md, mkldnn_engine}, // reorder user_diff_dst if it's not in preferred format
cast_const_to_void(x->data<T>())}; auto diff_dst_memory = user_diff_dst_memory;
primitive reorder_diff_dst;
auto mean = mkldnn::memory{batch_norm_bwd_pd.mean_primitive_desc(), bool is_diff_dst_reordered = false;
cast_const_to_void(batch_mean->data<T>())}; if (diff_dst_pd != user_diff_dst_memory.get_primitive_desc()) {
diff_dst_memory = memory(diff_dst_pd);
auto variance = reorder_diff_dst = reorder(user_diff_dst_memory, diff_dst_memory);
mkldnn::memory{batch_norm_bwd_pd.variance_primitive_desc(), is_diff_dst_reordered = true;
cast_const_to_void(batch_variance->data<T>())}; }
auto diff_dst = mkldnn::memory{{diff_dst_md, mkldnn_engine},
cast_const_to_void(diff_y->data<T>())};
const unsigned int ic = dims[1]; // create mkldnn memory for input tensors (src/mean/variance)
auto mean_memory = memory(batch_norm_bwd_pd.mean_primitive_desc(),
to_void_cast(batch_mean_data));
auto variance_memory = memory(batch_norm_bwd_pd.variance_primitive_desc(),
to_void_cast(batch_variance_data));
// MKLDNN requires a single piece of memory for scale and shift/bias data
const size_t scaleshift_size = 2 * ic; const size_t scaleshift_size = 2 * ic;
std::vector<T> scaleshift_data; std::vector<T> scaleshift_data;
scaleshift_data.reserve(scaleshift_size); scaleshift_data.reserve(scaleshift_size);
copy_to_weights(scale->data<T>(), scale->data<T>() + ic, shift->data<T>(), copy_to_weights(scale_data, scale_data + ic, shift_data, shift_data + ic,
shift->data<T>() + ic, &scaleshift_data); &scaleshift_data);
auto scaleshift_memory = mkldnn::memory{ // create mkldnn memory for input tensors (scale/shift)
batch_norm_bwd_pd.weights_primitive_desc(), scaleshift_data.data()}; auto scaleshift_memory = memory(batch_norm_bwd_pd.weights_primitive_desc(),
scaleshift_data.data());
// create mkldnn memory for output diff weights (combined scale/shift)
std::vector<T> diff_scaleshift_data; std::vector<T> diff_scaleshift_data;
diff_scaleshift_data.reserve(scaleshift_size); diff_scaleshift_data.reserve(scaleshift_size);
copy_to_weights(diff_scale->data<T>(), diff_scale->data<T>() + ic,
diff_shift->data<T>(), diff_shift->data<T>() + ic,
&diff_scaleshift_data);
auto diff_scaleshift_memory = auto diff_scaleshift_memory =
mkldnn::memory{batch_norm_bwd_pd.diff_weights_primitive_desc(), memory(batch_norm_bwd_pd.diff_weights_primitive_desc(),
diff_scaleshift_data.data()}; diff_scaleshift_data.data());
auto diff_src = mkldnn::memory{{diff_src_md, mkldnn_engine}, // here assume diff_src is in the same format of src
static_cast<void *>(diff_x->data<T>())}; auto diff_src_memory = memory(src_memory.get_primitive_desc(), diff_x_data);
run_batch_norm_op<bn_bwd_types::op_type>( // finally create batch_norm backward primitive
batch_norm_bwd_pd, src, mean, variance, diff_dst, scaleshift_memory, auto batch_norm_bwd_prim =
diff_src, diff_scaleshift_memory); batch_norm_bwd(batch_norm_bwd_pd, src_memory, mean_memory,
variance_memory, diff_dst_memory, scaleshift_memory,
diff_src_memory, diff_scaleshift_memory);
// execute optional reorder and batch_norm backward primitive
std::vector<primitive> pipeline;
if (is_diff_dst_reordered) pipeline.push_back(reorder_diff_dst);
pipeline.push_back(batch_norm_bwd_prim);
stream(stream::kind::eager).submit(pipeline).wait();
// copy back diff sacle/shift to output tensors (diff scale/shift)
diff_scaleshift_data.resize(scaleshift_size);
auto it = std::begin(diff_scaleshift_data); auto it = std::begin(diff_scaleshift_data);
std::copy(it, std::next(it, ic), diff_scale->data<T>()); std::copy(it, std::next(it, ic), diff_scale_data);
std::copy(std::next(it, ic), std::end(diff_scaleshift_data), std::copy(std::next(it, ic), std::end(diff_scaleshift_data),
diff_shift->data<T>()); diff_shift_data);
// set layout/format of output tensors
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format((memory::format)diff_src_memory.get_primitive_desc()
.desc()
.data.format);
} }
}; };
} // namespace operators } // namespace operators
} // namespace paddle } // namespace paddle
namespace ops = paddle::operators; namespace ops = paddle::operators;
REGISTER_OP_KERNEL(batch_norm, MKLDNN, paddle::platform::CPUPlace, REGISTER_OP_KERNEL(batch_norm, MKLDNN, ::paddle::platform::CPUPlace,
ops::BatchNormMKLDNNOpKernel<float>); ops::BatchNormMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL(batch_norm_grad, MKLDNN, paddle::platform::CPUPlace, REGISTER_OP_KERNEL(batch_norm_grad, MKLDNN, ::paddle::platform::CPUPlace,
ops::BatchNormMKLDNNGradOpKernel<float>); ops::BatchNormMKLDNNGradOpKernel<float>);
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 85c3bfc1
...@@ -110,19 +110,19 @@ class BatchNormOp : public framework::OperatorWithKernel { ...@@ -110,19 +110,19 @@ class BatchNormOp : public framework::OperatorWithKernel {
ctx.Input<Tensor>("Variance")->type()), ctx.Input<Tensor>("Variance")->type()),
"Variance input should be of float type"); "Variance input should be of float type");
framework::LibraryType library_{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::LibraryType library = framework::LibraryType::kPlain;
framework::DataLayout layout = framework::DataLayout::kAnyLayout; framework::DataLayout layout = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN #ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain && if (library == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) { platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN; library = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN; layout = framework::DataLayout::kMKLDNN;
} }
#endif #endif
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout, return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
library_); library);
} }
}; };
...@@ -370,19 +370,21 @@ class BatchNormGradOp : public framework::OperatorWithKernel { ...@@ -370,19 +370,21 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
PADDLE_THROW("can't find Y@GRAD"); PADDLE_THROW("can't find Y@GRAD");
} }
framework::LibraryType library_{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout_ = framework::DataLayout::kAnyLayout; framework::LibraryType library = framework::LibraryType::kPlain;
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN #ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain && if (library == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) { platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN; library = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN; layout = framework::DataLayout::kMKLDNN;
} }
#endif #endif
return framework::OpKernelType( return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(), framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
layout_, library_); layout, library);
} }
}; };
......
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @ 85c3bfc1
...@@ -18,6 +18,17 @@ ...@@ -18,6 +18,17 @@
namespace paddle { namespace paddle {
namespace operators { namespace operators {
using conv_bwd_data = mkldnn::convolution_backward_data;
using conv_bwd_weights = mkldnn::convolution_backward_weights;
using conv_fwd = mkldnn::convolution_forward;
using framework::DataLayout;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
using mkldnn::stream;
using platform::to_void_cast;
using platform::GetMKLDNNFormat;
template <typename T> template <typename T>
class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> { class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public: public:
...@@ -25,6 +36,10 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -25,6 +36,10 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace."); "It must use CPUPlace.");
// Get unique name for index
const std::string key = ctx.op().Output("Output");
const std::string key_conv_pd = key + "@conv_pd";
auto& dev_ctx = auto& dev_ctx =
ctx.template device_context<paddle::platform::MKLDNNDeviceContext>(); ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine(); const auto& mkldnn_engine = dev_ctx.GetEngine();
...@@ -33,10 +48,12 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -33,10 +48,12 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto* filter = ctx.Input<Tensor>("Filter"); auto* filter = ctx.Input<Tensor>("Filter");
auto* output = ctx.Output<Tensor>("Output"); auto* output = ctx.Output<Tensor>("Output");
// Get an unique name from "argument" name of "Output" variable PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN &&
// This name will be used as key when saving info into device context input->format() != memory::format::format_undef,
const std::string key = ctx.op().Output("Output"); "Wrong layout/format set for Input tensor");
const std::string key_conv_pd = key + "@conv_pd"; PADDLE_ENFORCE(filter->layout() == DataLayout::kMKLDNN &&
filter->format() != memory::format::format_undef,
"Wrong layout/format set for Filter tensor");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides"); std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings"); std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
...@@ -63,60 +80,86 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -63,60 +80,86 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
paddle::framework::vectorize2int(filter->dims()); paddle::framework::vectorize2int(filter->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims()); std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
// TODO(pzelazko-intel): support more formats // create mkldnn memory from input tensors (data/weights)
auto src_md = platform::MKLDNNMemDesc( auto user_src_memory = memory(
src_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); {{{src_tz}, memory::data_type::f32, input->format()}, mkldnn_engine},
auto weights_md = to_void_cast(input_data));
platform::MKLDNNMemDesc(weights_tz, mkldnn::memory::data_type::f32, auto user_weights_memory =
mkldnn::memory::format::oihw); memory({{{weights_tz}, memory::data_type::f32, filter->format()},
auto dst_md = platform::MKLDNNMemDesc( mkldnn_engine},
dst_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); to_void_cast(filter_data));
auto src_memory = /* create memory descriptor for convolution without specified format
mkldnn::memory({src_md, mkldnn_engine}, * ('any') which lets a primitive (convolution in this case) choose
reinterpret_cast<void*>(const_cast<T*>(input_data))); * the memory format preferred for best performance
auto weights_memory = */
mkldnn::memory({weights_md, mkldnn_engine}, auto src_md = platform::MKLDNNMemDesc(src_tz, memory::data_type::f32,
reinterpret_cast<void*>(const_cast<T*>(filter_data))); memory::format::any);
auto dst_memory = mkldnn::memory({dst_md, mkldnn_engine}, output_data); auto weights_md = platform::MKLDNNMemDesc(
weights_tz, memory::data_type::f32, memory::format::any);
std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd = auto dst_md = platform::MKLDNNMemDesc(dst_tz, memory::data_type::f32,
ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides, paddings, memory::format::any);
mkldnn_engine);
// create a conv primitive descriptor and save it for usage in backward
// save conv_pd into global device context to be referred in backward path std::shared_ptr<conv_fwd::primitive_desc> conv_pd = ConvFwdPrimitiveDesc(
dev_ctx.SetBlob(key_conv_pd, conv_pd); src_md, weights_md, dst_md, strides, paddings, mkldnn_engine);
// create reorder primitive if the input format is not the preferred one
auto src_memory = user_src_memory;
primitive reorder_src;
bool is_src_reordered = false;
if (memory::primitive_desc(conv_pd->src_primitive_desc()) !=
user_src_memory.get_primitive_desc()) {
src_memory = memory(conv_pd->src_primitive_desc());
reorder_src = reorder(user_src_memory, src_memory);
is_src_reordered = true;
}
auto weights_memory = user_weights_memory;
primitive reorder_weights;
bool is_weights_reordered = false;
if (memory::primitive_desc(conv_pd->weights_primitive_desc()) !=
user_weights_memory.get_primitive_desc()) {
weights_memory = memory(conv_pd->weights_primitive_desc());
reorder_weights = reorder(user_weights_memory, weights_memory);
is_weights_reordered = true;
}
// create memory primitive for conv dst
auto dst_memory = memory(conv_pd->dst_primitive_desc(), output_data);
// create convolution op primitive // create convolution op primitive
auto conv_prim = mkldnn::convolution_forward(*conv_pd, src_memory, auto conv_prim = conv_fwd(*conv_pd, src_memory, weights_memory, dst_memory);
weights_memory, dst_memory);
// push primitive to stream and wait until it's executed // push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{conv_prim}; std::vector<primitive> pipeline;
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); if (is_src_reordered) pipeline.push_back(reorder_src);
if (is_weights_reordered) pipeline.push_back(reorder_weights);
pipeline.push_back(conv_prim);
stream(stream::kind::eager).submit(pipeline).wait();
// Save conv_pd/src_memory/weights_memory for backward pass
dev_ctx.SetBlob(key_conv_pd, conv_pd);
output->set_layout(DataLayout::kMKLDNN);
output->set_format(GetMKLDNNFormat(dst_memory));
} }
private: private:
std::unique_ptr<mkldnn::convolution_forward::primitive_desc> std::unique_ptr<conv_fwd::primitive_desc> ConvFwdPrimitiveDesc(
ConvFwdPrimitiveDesc(const mkldnn::memory::desc& src, const memory::desc& src, const memory::desc& weights,
const mkldnn::memory::desc& weights, const memory::desc& dst, const std::vector<int>& strides,
const mkldnn::memory::desc& dst, const std::vector<int>& paddings, const mkldnn::engine& engine) const {
const std::vector<int>& strides, memory::dims stride_dims = {strides[0], strides[1]};
const std::vector<int>& paddings, memory::dims padding_dims = {paddings[0], paddings[1]};
const mkldnn::engine& engine) const {
mkldnn::memory::dims stride_dims = {strides[0], strides[1]}; auto conv_desc =
mkldnn::memory::dims padding_dims = {paddings[0], paddings[1]}; conv_fwd::desc(mkldnn::prop_kind::forward, mkldnn::convolution_direct,
src, weights, dst, stride_dims, padding_dims,
auto conv_desc = mkldnn::convolution_forward::desc( padding_dims, mkldnn::padding_kind::zero);
mkldnn::prop_kind::forward, mkldnn::convolution_direct, src, weights,
dst, stride_dims, padding_dims, padding_dims, auto p_conv_pd = new conv_fwd::primitive_desc(conv_desc, engine);
mkldnn::padding_kind::zero);
return std::unique_ptr<conv_fwd::primitive_desc>(p_conv_pd);
auto p_conv_pd =
new mkldnn::convolution_forward::primitive_desc(conv_desc, engine);
return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
p_conv_pd);
} }
}; };
...@@ -139,6 +182,19 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> { ...@@ -139,6 +182,19 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input")); Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
Tensor* filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter")); Tensor* filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN &&
input->format() != memory::format::format_undef,
"Wrong layout/format set for Input tensor");
PADDLE_ENFORCE(filter->layout() == DataLayout::kMKLDNN &&
filter->format() != memory::format::format_undef,
"Wrong layout/format set for Filter tensor");
PADDLE_ENFORCE(output->layout() == DataLayout::kMKLDNN &&
output->format() != memory::format::format_undef,
"Wrong layout/format set for Output tensor");
PADDLE_ENFORCE(output_grad->layout() == DataLayout::kMKLDNN &&
output_grad->format() != memory::format::format_undef,
"Wrong layout/format set for output_grad tensor");
if (!input_grad && !filter_grad) return; if (!input_grad && !filter_grad) return;
// Get an unique name from "argument" name of "Output" variable // Get an unique name from "argument" name of "Output" variable
...@@ -167,108 +223,147 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> { ...@@ -167,108 +223,147 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
paddle::framework::vectorize2int(filter->dims()); paddle::framework::vectorize2int(filter->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims()); std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
// TODO(pzelazko-intel): support more formats // create mkldnn memory from input tensors (input/weights/output_grad)
auto src_md = platform::MKLDNNMemDesc( auto user_src_memory = memory(
src_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); {{{src_tz}, memory::data_type::f32, input->format()}, mkldnn_engine},
auto diff_src_md = platform::MKLDNNMemDesc( to_void_cast(input_data));
src_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); auto user_weights_memory =
auto weights_md = memory({{{weights_tz}, memory::data_type::f32, filter->format()},
platform::MKLDNNMemDesc(weights_tz, mkldnn::memory::data_type::f32, mkldnn_engine},
mkldnn::memory::format::oihw); to_void_cast(filter_data));
auto diff_weights_md = auto user_diff_dst_memory =
platform::MKLDNNMemDesc(weights_tz, mkldnn::memory::data_type::f32, memory({{{dst_tz}, memory::data_type::f32, output_grad->format()},
mkldnn::memory::format::oihw); mkldnn_engine},
auto diff_dst_md = platform::MKLDNNMemDesc( to_void_cast(output_grad_data));
dst_tz, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
/* create memory descriptor for conv backward without specified format
// create memory * ('any') which lets a primitive (conv backward in this case) choose
auto diff_dst_memory = mkldnn::memory( * the memory format preferred for best performance
{diff_weights_md, mkldnn_engine}, */
reinterpret_cast<void*>(const_cast<T*>(output_grad_data))); auto src_md = platform::MKLDNNMemDesc(src_tz, memory::data_type::f32,
memory::format::any);
auto diff_src_md = platform::MKLDNNMemDesc(src_tz, memory::data_type::f32,
memory::format::any);
auto weights_md = platform::MKLDNNMemDesc(
weights_tz, memory::data_type::f32, memory::format::any);
auto diff_weights_md = platform::MKLDNNMemDesc(
weights_tz, memory::data_type::f32, memory::format::any);
auto diff_dst_md = platform::MKLDNNMemDesc(dst_tz, memory::data_type::f32,
memory::format::any);
// Retrieve conv_pd from device context // Retrieve conv_pd from device context
auto conv_pd = auto conv_pd = std::static_pointer_cast<conv_fwd::primitive_desc>(
std::static_pointer_cast<mkldnn::convolution_forward::primitive_desc>( dev_ctx.GetBlob(key_conv_pd));
dev_ctx.GetBlob(key_conv_pd));
PADDLE_ENFORCE(conv_pd != nullptr, PADDLE_ENFORCE(conv_pd != nullptr,
"Fail to find conv_pd in device context"); "Fail to find conv_pd in device context");
// create backward conv primitive for weights // create backward conv primitive for weights
if (filter_grad) { if (filter_grad) {
// create primitive descriptor // create backward convolution primitive descriptor
mkldnn::convolution_backward_weights::primitive_desc conv_bwd_weights_pd = auto conv_bwd_weights_desc = conv_bwd_weights::desc(
ConvBwdWeightsPrimitiveDesc(src_md, diff_weights_md, diff_dst_md, mkldnn::convolution_direct, src_md, diff_weights_md, diff_dst_md,
strides, paddings, *conv_pd, strides, paddings, paddings, mkldnn::padding_kind::zero);
mkldnn_engine); auto conv_bwd_weights_pd = conv_bwd_weights::primitive_desc(
conv_bwd_weights_desc, mkldnn_engine, *conv_pd);
// create memory
// create reorder primitive if the input format is not the preferred one
auto src_memory = user_src_memory;
primitive reorder_src;
bool is_src_reordered = false;
if (memory::primitive_desc(conv_bwd_weights_pd.src_primitive_desc()) !=
user_src_memory.get_primitive_desc()) {
src_memory = memory(conv_bwd_weights_pd.src_primitive_desc());
reorder_src = reorder(user_src_memory, src_memory);
is_src_reordered = true;
}
auto diff_dst_memory_4filter = user_diff_dst_memory;
primitive reorder_diff_dst_4filter;
bool is_diff_dst_reordered_4filter = false;
if (memory::primitive_desc(
conv_bwd_weights_pd.diff_dst_primitive_desc()) !=
user_diff_dst_memory.get_primitive_desc()) {
diff_dst_memory_4filter =
memory(conv_bwd_weights_pd.diff_dst_primitive_desc());
reorder_diff_dst_4filter =
reorder(user_diff_dst_memory, diff_dst_memory_4filter);
is_diff_dst_reordered_4filter = true;
}
// create mkldnn memory for output (i.e. diff weights)
auto diff_weights_memory = auto diff_weights_memory =
mkldnn::memory({diff_weights_md, mkldnn_engine}, memory(conv_bwd_weights_pd.diff_weights_primitive_desc(),
reinterpret_cast<void*>(filter_grad_data)); reinterpret_cast<void*>(filter_grad_data));
auto src_memory =
mkldnn::memory({src_md, mkldnn_engine},
reinterpret_cast<void*>(const_cast<T*>(input_data)));
// create backward conv primitive for weights // create backward conv primitive for weights
auto conv_bwd_weights_prim = mkldnn::convolution_backward_weights( auto conv_bwd_weights_prim =
conv_bwd_weights_pd, src_memory, diff_dst_memory, conv_bwd_weights(conv_bwd_weights_pd, src_memory,
diff_weights_memory); diff_dst_memory_4filter, diff_weights_memory);
// push primitive and execute it // push primitive and execute it
std::vector<mkldnn::primitive> pipeline{conv_bwd_weights_prim}; std::vector<primitive> pipeline;
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); if (is_src_reordered) pipeline.push_back(reorder_src);
if (is_diff_dst_reordered_4filter)
pipeline.push_back(reorder_diff_dst_4filter);
pipeline.push_back(conv_bwd_weights_prim);
stream(stream::kind::eager).submit(pipeline).wait();
filter_grad->set_layout(DataLayout::kMKLDNN);
filter_grad->set_format(GetMKLDNNFormat(diff_weights_memory));
} }
if (input_grad) { if (input_grad) {
// create primitive descriptor // create backward convolution primitive descriptor
mkldnn::convolution_backward_data::primitive_desc conv_bwd_data_pd = auto conv_bwd_data_desc = conv_bwd_data::desc(
ConvBwdDataPrimitiveDesc(diff_src_md, weights_md, diff_dst_md, mkldnn::convolution_direct, diff_src_md, weights_md, diff_dst_md,
strides, paddings, *conv_pd, mkldnn_engine); strides, paddings, paddings, mkldnn::padding_kind::zero);
auto conv_bwd_data_pd = conv_bwd_data::primitive_desc(
// create memory conv_bwd_data_desc, mkldnn_engine, *conv_pd);
auto diff_src_memory = mkldnn::memory(
{diff_src_md, mkldnn_engine}, // create reorder primitive if the input format is not the preferred one
reinterpret_cast<void*>(const_cast<T*>(input_grad_data))); auto weights_memory = user_weights_memory;
auto weights_memory = primitive reorder_weights;
mkldnn::memory({weights_md, mkldnn_engine}, bool is_weights_reordered = false;
reinterpret_cast<void*>(const_cast<T*>(filter_data))); if (memory::primitive_desc(conv_bwd_data_pd.weights_primitive_desc()) !=
user_weights_memory.get_primitive_desc()) {
weights_memory = memory(conv_bwd_data_pd.weights_primitive_desc());
reorder_weights = reorder(user_weights_memory, weights_memory);
is_weights_reordered = true;
}
auto diff_dst_memory_4data = user_diff_dst_memory;
primitive reorder_diff_dst_4data;
bool is_diff_dst_reordered_4data = false;
if (memory::primitive_desc(conv_bwd_data_pd.diff_dst_primitive_desc()) !=
user_diff_dst_memory.get_primitive_desc()) {
diff_dst_memory_4data =
memory(conv_bwd_data_pd.diff_dst_primitive_desc());
reorder_diff_dst_4data =
reorder(user_diff_dst_memory, diff_dst_memory_4data);
is_diff_dst_reordered_4data = true;
}
// create mkldnn memory for output (i.e. diff src)
auto diff_src_memory = memory(conv_bwd_data_pd.diff_src_primitive_desc(),
reinterpret_cast<void*>(input_grad_data));
// create backward conv primitive for data // create backward conv primitive for data
auto conv_bwd_data_prim = mkldnn::convolution_backward_data( auto conv_bwd_data_prim =
conv_bwd_data_pd, diff_dst_memory, weights_memory, diff_src_memory); conv_bwd_data(conv_bwd_data_pd, diff_dst_memory_4data, weights_memory,
diff_src_memory);
// push primitive to stream and wait until it's executed // push primitive and execute it
std::vector<mkldnn::primitive> pipeline{conv_bwd_data_prim}; std::vector<primitive> pipeline;
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); if (is_weights_reordered) pipeline.push_back(reorder_weights);
if (is_diff_dst_reordered_4data)
pipeline.push_back(reorder_diff_dst_4data);
pipeline.push_back(conv_bwd_data_prim);
stream(stream::kind::eager).submit(pipeline).wait();
input_grad->set_layout(DataLayout::kMKLDNN);
input_grad->set_format(GetMKLDNNFormat(diff_src_memory));
} }
} // Compute() } // Compute()
private:
mkldnn::convolution_backward_weights::primitive_desc
ConvBwdWeightsPrimitiveDesc(
const mkldnn::memory::desc& src, const mkldnn::memory::desc& diff_weights,
const mkldnn::memory::desc& diff_dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::convolution_forward::primitive_desc& conv_pd,
const mkldnn::engine& engine) const {
auto conv_bwd_weights_desc = mkldnn::convolution_backward_weights::desc(
mkldnn::convolution_direct, src, diff_weights, diff_dst, strides,
paddings, paddings, mkldnn::padding_kind::zero);
return mkldnn::convolution_backward_weights::primitive_desc(
conv_bwd_weights_desc, engine, conv_pd);
}
mkldnn::convolution_backward_data::primitive_desc ConvBwdDataPrimitiveDesc(
const mkldnn::memory::desc& diff_src, const mkldnn::memory::desc& weights,
const mkldnn::memory::desc& diff_dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::convolution_forward::primitive_desc& conv_pd,
const mkldnn::engine& engine) const {
auto conv_bwd_data_desc = mkldnn::convolution_backward_data::desc(
mkldnn::convolution_direct, diff_src, weights, diff_dst, strides,
paddings, paddings, mkldnn::padding_kind::zero);
return mkldnn::convolution_backward_data::primitive_desc(conv_bwd_data_desc,
engine, conv_pd);
}
}; };
} // namespace operators } // namespace operators
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ 85c3bfc1
...@@ -75,9 +75,8 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const { ...@@ -75,9 +75,8 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType ConvOp::GetExpectedKernelType( framework::OpKernelType ConvOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const { const framework::ExecutionContext& ctx) const {
framework::LibraryType library{framework::LibraryType::kPlain}; framework::LibraryType library{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout = framework::StringToDataLayout(data_format); framework::DataLayout layout = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA #ifdef PADDLE_WITH_CUDA
......
paddle/fluid/operators/gen_nccl_id_op.cc
浏览文件 @ 85c3bfc1
...@@ -67,6 +67,10 @@ class GenNCCLIdOp : public framework::OperatorBase { ...@@ -67,6 +67,10 @@ class GenNCCLIdOp : public framework::OperatorBase {
client->AsyncSendVar(ep, dev_ctx, *scope, NCCL_ID_VARNAME); client->AsyncSendVar(ep, dev_ctx, *scope, NCCL_ID_VARNAME);
} }
client->Wait(); client->Wait();
for (auto& ep : endpoint_list) {
client->AsyncSendBatchBarrier(ep);
}
client->Wait();
VLOG(3) << "sending completed..."; VLOG(3) << "sending completed...";
} }
......
paddle/fluid/operators/mean_iou_op.cc 0 → 100644
浏览文件 @ 85c3bfc1
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/mean_iou_op.h"
namespace paddle {
namespace operators {
class MeanIoUOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Predictions"),
"Input (Predictions) of MeanIoU op should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input (labels) of MeanIoU op should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("OutMeanIou"),
"Output (OutMeanIou) of MeanIoU op should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("OutWrong"),
"Output (OutWrong) of MeanIoU op should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("OutCorrect"),
"Output (OutWrong) of MeanIoU op should not be null.");
int64_t num_classes =
static_cast<int64_t>(ctx->Attrs().Get<int>("num_classes"));
ctx->SetOutputDim("OutMeanIou", {1});
ctx->SetOutputDim("OutWrong", {num_classes});
ctx->SetOutputDim("OutCorrect", {num_classes});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Predictions")->type()),
ctx.GetPlace());
}
};
class MeanIoUOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Predictions",
"(Tensor), A Tensor of prediction results for semantic labels"
" with type int32 or int64. The rank should be greater than 1.");
AddInput(
"Labels",
"(Tensor), A Tensor of ground truth labels with type int32 or int64."
"Its shape should be the same as Input(Predictions).");
AddInput("InWrongs",
"(vector<Tensor>), A list of Tensor with shape "
"[num_classes]. They are used to collect wrong number among "
"batches. Empty list is also valid here.")
.AsDuplicable()
.AsDispensable();
AddInput(
"InCorrects",
"(vector<Tensor>), A list of Tensor with shape "
"[num_classes]. They are used to collect correct number among batches. "
"Empty list is also valid here.")
.AsDuplicable()
.AsDispensable();
AddInput("InMeanIou",
"(vector<Tensor>), A list of Tensor that Output(mean_iou) should "
"be added to. Empty list is also valid here.")
.AsDuplicable()
.AsDispensable();
AddOutput("OutMeanIou",
"(vector<Tensor>), A Tensor representing the"
" mean intersection-over-union with shape [1].");
AddOutput("OutWrong", "(Tensor), A Tensor with shape [num_classes]. ");
AddOutput("OutCorrect", "(Tensor), A Tensor with shape [num_classes]. ");
AddAttr<int>("num_classes", "(int), The possible number of labels.");
AddComment(R"DOC(
mean-IOU Operator.
Mean Intersection-Over-Union is a common evaluation metric for
semantic image segmentation, which first computes the IOU for each
semantic class and then computes the average over classes.
IOU is defined as follows:
IOU = true_positive / (true_positive + false_positive + false_negative).
It is based on pixel level area while "IOU Similarity Operator"
is based on area of rectangle.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(mean_iou, ops::MeanIoUOp, ops::MeanIoUOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(mean_iou, ops::MeanIoUKernel<int>,
ops::MeanIoUKernel<int32_t>,
ops::MeanIoUKernel<int64_t>);
paddle/fluid/operators/mean_iou_op.cu 0 → 100644
浏览文件 @ 85c3bfc1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/mean_iou_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/gpu_info.h"
namespace paddle {
namespace operators {
using platform::PADDLE_CUDA_NUM_THREADS;
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
template <typename T>
__global__ void CountCUDAKernel(const int num_classes, const int count,
const T* predictions, const T* labels,
int* wrong, int* correct) {
extern __shared__ int blcok_cache[];
int* wrong_c = blcok_cache;
int* correct_c = blcok_cache + num_classes;
// init cache
for (int i = threadIdx.x; i < num_classes * 2; i += blockDim.x) {
blcok_cache[i] = 0;
}
__syncthreads();
T pred;
T label;
CUDA_1D_KERNEL_LOOP(i, count) {
pred = predictions[i];
label = labels[i];
if (pred == label) {
atomicAdd(correct_c + pred, 1);
} else {
atomicAdd(wrong_c + pred, 1);
atomicAdd(wrong_c + label, 1);
}
}
__syncthreads();
for (int i = threadIdx.x; i < num_classes; i += blockDim.x) {
atomicAdd(wrong + i, wrong_c[i]);
atomicAdd(correct + i, correct_c[i]);
}
}
__global__ void ComputeIoUCUDAKernel(const int num_classes, int* wrong,
int* correct, float* ious, float* iou) {
__shared__ int valid_count_c;
if (threadIdx.x == 0) {
valid_count_c = 0;
}
__syncthreads();
CUDA_1D_KERNEL_LOOP(i, num_classes) {
int wrong_n = wrong[i];
int correct_n = correct[i];
int denominator = wrong_n + correct_n;
if (denominator > 0) {
atomicAdd(&valid_count_c, 1);
ious[i] = static_cast<float>(correct_n) / denominator;
} else {
ious[i] = 0;
}
}
__syncthreads();
if (threadIdx.x == 0) {
float iou_sum = 0;
for (int i = 0; i < num_classes; ++i) {
iou_sum += ious[i];
}
iou[0] += iou_sum / valid_count_c;
}
}
template <typename T>
class MeanIoUCUDAOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& place = *ctx.template device_context<platform::CUDADeviceContext>()
.eigen_device();
// get input and output tensor
auto* predictions = ctx.Input<Tensor>("Predictions");
auto* labels = ctx.Input<Tensor>("Labels");
auto* out_mean_iou = ctx.Output<Tensor>("OutMeanIou");
auto* out_wrong = ctx.Output<Tensor>("OutWrong");
auto* out_correct = ctx.Output<Tensor>("OutCorrect");
int num_classes = static_cast<int>(ctx.Attr<int>("num_classes"));
// Get data ptr
const T* predictions_data = predictions->data<T>();
const T* labels_data = labels->data<T>();
int* out_wrong_data = out_wrong->mutable_data<int>(ctx.GetPlace());
int* out_correct_data = out_correct->mutable_data<int>(ctx.GetPlace());
float* out_mean_iou_data =
out_mean_iou->mutable_data<float>(ctx.GetPlace());
// Get Eigen tensor
auto out_mean_iou_t = EigenTensor<float, 1>::From(*out_mean_iou);
auto out_wrong_t = EigenTensor<int, 1>::From(*out_wrong);
auto out_correct_t = EigenTensor<int, 1>::From(*out_correct);
// Temporary tensor
Tensor ious;
float* ious_data = ious.mutable_data<float>(
{static_cast<int64_t>(num_classes)}, ctx.GetPlace());
auto ious_t = EigenTensor<float, 1>::From(ious);
// Init out_wrong, out_correct and out_mean_iou
out_wrong_t.device(place) = out_wrong_t.constant(0);
out_correct_t.device(place) = out_correct_t.constant(0);
out_mean_iou_t.device(place) = out_mean_iou_t.constant(0.0f);
// collect pre wrong, correct and mean_iou
auto in_mean_ious = ctx.MultiInput<Tensor>("InMeanIou");
for (int i = 0; i < in_mean_ious.size(); ++i) {
out_mean_iou_t.device(place) +=
EigenTensor<float, 1>::From(*in_mean_ious[i]);
}
auto in_wrongs = ctx.MultiInput<Tensor>("InWrongs");
for (int i = 0; i < in_wrongs.size(); ++i) {
out_wrong_t.device(place) += EigenTensor<int, 1>::From(*in_wrongs[i]);
}
auto in_corrects = ctx.MultiInput<Tensor>("InCorrects");
for (int i = 0; i < in_corrects.size(); ++i) {
out_correct_t.device(place) += EigenTensor<int, 1>::From(*in_corrects[i]);
}
// compute
auto stream = ctx.cuda_device_context().stream();
int block = PADDLE_CUDA_NUM_THREADS;
int grid = (predictions->numel() + block - 1) / block;
int cache_size = (num_classes * 2 + 1) * sizeof(int);
CountCUDAKernel<T><<<grid, block, cache_size, stream>>>(
num_classes, predictions->numel(), predictions_data, labels_data,
out_wrong_data, out_correct_data);
ctx.device_context().Wait();
ComputeIoUCUDAKernel<<<1, block, 0, stream>>>(num_classes, out_wrong_data,
out_correct_data, ious_data,
out_mean_iou_data);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(mean_iou, ops::MeanIoUCUDAOpKernel<int>,
ops::MeanIoUCUDAOpKernel<int64_t>,
ops::MeanIoUCUDAOpKernel<int32_t>);
paddle/fluid/operators/mean_iou_op.h 0 → 100644
浏览文件 @ 85c3bfc1
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename T>
class MeanIoUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& place = *ctx.template device_context<platform::CPUDeviceContext>()
.eigen_device();
// get input and output tensor
auto* predictions = ctx.Input<Tensor>("Predictions");
auto* labels = ctx.Input<Tensor>("Labels");
auto* out_mean_iou = ctx.Output<Tensor>("OutMeanIou");
auto* out_wrong = ctx.Output<Tensor>("OutWrong");
auto* out_correct = ctx.Output<Tensor>("OutCorrect");
int num_classes = static_cast<int>(ctx.Attr<int>("num_classes"));
// get data ptr
const T* predictions_data = predictions->data<T>();
const T* labels_data = labels->data<T>();
float* out_mean_iou_data =
out_mean_iou->mutable_data<float>(ctx.GetPlace());
int* out_wrong_data = out_wrong->mutable_data<int>(ctx.GetPlace());
int* out_correct_data = out_correct->mutable_data<int>(ctx.GetPlace());
// get eigen tensor
auto out_mean_iou_t = EigenTensor<float, 1>::From(*out_mean_iou);
auto out_wrong_t = EigenTensor<int, 1>::From(*out_wrong);
auto out_correct_t = EigenTensor<int, 1>::From(*out_correct);
// Tmp tensor
Tensor denominator;
Tensor valid_count;
Tensor iou_sum;
// get data ptr of tmp tensor
int* denominator_data = denominator.mutable_data<int>(
{static_cast<int64_t>(num_classes)}, ctx.GetPlace());
int* valid_count_data = valid_count.mutable_data<int>({1}, ctx.GetPlace());
float* iou_sum_data = iou_sum.mutable_data<float>({1}, ctx.GetPlace());
// get eigen tensor of tmp tensor
auto denominator_t = EigenTensor<int, 1>::From(denominator);
auto valid_count_t = EigenTensor<int, 1>::From(valid_count);
auto iou_sum_t = EigenTensor<float, 1>::From(iou_sum);
// init out_wrong, out_correct and out_mean_iou
out_wrong_t = out_wrong_t.constant(0);
out_correct_t = out_correct_t.constant(0);
out_mean_iou_t = out_mean_iou_t.constant(0);
// collect pre wrong, correct and mean_iou
auto in_mean_ious = ctx.MultiInput<Tensor>("InMeanIou");
for (size_t i = 0; i < in_mean_ious.size(); ++i) {
out_mean_iou_t.device(place) +=
EigenTensor<float, 1>::From(*in_mean_ious[i]);
}
auto in_wrongs = ctx.MultiInput<Tensor>("InWrongs");
for (size_t i = 0; i < in_wrongs.size(); ++i) {
out_wrong_t.device(place) += EigenTensor<int, 1>::From(*in_wrongs[i]);
}
auto in_corrects = ctx.MultiInput<Tensor>("InCorrects");
for (size_t i = 0; i < in_corrects.size(); ++i) {
out_correct_t.device(place) += EigenTensor<int, 1>::From(*in_corrects[i]);
}
// compute
for (int64_t i = 0; i < predictions->numel(); ++i) {
if (predictions_data[i] == labels_data[i]) {
out_correct_data[predictions_data[i]] += 1;
} else {
out_wrong_data[labels_data[i]] += 1;
out_wrong_data[predictions_data[i]] += 1;
}
}
denominator_t = out_wrong_t + out_correct_t;
valid_count_t =
(denominator_t > denominator_t.constant(0.0f)).cast<int>().sum();
for (int i = 0; i < num_classes; ++i) {
if (denominator_data[i] == 0) {
denominator_data[i] = 1;
}
}
iou_sum_t =
(out_correct_t.cast<float>() / denominator_t.cast<float>()).sum();
out_mean_iou_data[0] += (iou_sum_data[0] / valid_count_data[0]);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/merge_ids_op.cc 0 → 100644
浏览文件 @ 85c3bfc1
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/merge_ids_op.h"
namespace paddle {
namespace operators {
class MergeIdsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Ids", "(LoDTensor) the input ids with shape{batch_num, 1}");
AddInput(
"X",
"(LoDTensors) multi input tensor with shape{batch_num, N}, N is the "
"size of embedding table")
.AsDuplicable();
AddOutput("Out", "(LoDTensor) The merged outputs of the input tensors.");
AddComment(R"DOC(
Merge multi LoDTensor's into one according to Ids's shard num.
split_ids_op -> prefetch_op -> merge_ids_op
merge_ids_op should be used after split_ids_op and prefetch_op, split_ids_op
will split input Ids into multiple tensors according to Id's shard number.
prefetch_op will send them to parameter server to prefetch embedding value
back. During split, the order of ids is disordered. In merge_ids_op we use
the original Ids to restore the order of the fetched embedding value and
also pass the lod information to the merged output.
Example:
Ids = [1,2,3,4,5,6] # 3 shared
split_ids_op ->
Id0 = [3, 6] # id % 3 == 0
Id1 = [1, 4] # id % 3 == 1
Id2 = [2, 5] # id % 3 == 2
prefetch_op ->
X0 = [[0.3 0.3] # 3
[0.6 0.6]] # 6
X1 = [[0.1 0.1] # 1
[0.4 0.4]] # 4
X2 = [[0.2 0.2] # 2
[0.5 0.5]] # 5
merge_ids_op ->
Out = [[0.1 0.1] # 1
[0.2 0.2] # 2
[0.3 0.3] # 3
[0.4 0.4] # 4
[0.5 0.5] # 5
[0.6 0.6]] # 6
)DOC");
}
};
class MergeIdsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Ids"), "MergeIdsOp must has input Ids.");
PADDLE_ENFORCE(ctx->HasInputs("X"), "MergeIdsOp must has input X.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "MergeIdsOp must has output Out.");
auto ids_var_type = ctx->GetInputsVarType("Ids").front();
auto ids_dims = ctx->GetInputDim("Ids");
if (ids_var_type == framework::proto::VarType::LOD_TENSOR) {
PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
PADDLE_ENFORCE_EQ(ids_dims[1], 1);
}
auto x_var_type = ctx->GetInputsVarType("X");
for (auto &var_type : x_var_type) {
PADDLE_ENFORCE_EQ(var_type, framework::proto::VarType::LOD_TENSOR,
"input X only support lod tensors");
}
ctx->ShareLoD("Ids", "Out");
}
private:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(
ctx.MultiInput<framework::Tensor>("X").front()->type()),
ctx.GetPlace());
}
};
class MergeIdsOpInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
auto *input_var = block->Var(op_desc.Input("Ids")[0]);
for (auto &out_var : op_desc.Output("Out")) {
block->Var(out_var)->SetType(input_var->GetType());
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(merge_ids, ops::MergeIdsOp, ops::MergeIdsOpMaker,
ops::MergeIdsOpInferVarType);
REGISTER_OP_CPU_KERNEL(
merge_ids, ops::MergeIdsOpKernel<paddle::platform::CPUPlace, float>);
paddle/fluid/operators/merge_ids_op.h 0 → 100644
浏览文件 @ 85c3bfc1
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class MergeIdsOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
if (!platform::is_cpu_place(place)) {
PADDLE_THROW("MergeIds do not support GPU kernel");
}
VLOG(3) << "run in MergeIdsOpKernel";
const auto *ids_var = ctx.InputVar("Ids");
PADDLE_ENFORCE(ids_var->IsType<framework::LoDTensor>(),
"only support to merge Ids of LoDTensor");
const auto &ids_tensor = ids_var->Get<framework::LoDTensor>();
const auto &ids_dims = ids_tensor.dims();
const int64_t *ids = ids_tensor.data<int64_t>();
auto x_tensors = ctx.MultiInput<framework::LoDTensor>("X");
auto *out = ctx.Output<framework::LoDTensor>("Out");
int batch_size = 0;
int embedding_size = 0;
for (auto &input : x_tensors) {
if (framework::product(input->dims()) != 0) {
if (embedding_size == 0) {
embedding_size = input->dims()[1];
}
PADDLE_ENFORCE_EQ(embedding_size, input->dims()[1],
"embedding size of all input should be the same");
batch_size += input->dims()[0];
}
}
PADDLE_ENFORCE_EQ(
batch_size, ids_dims[0],
"the batch size of ids and merged embedding value should be the same");
const size_t shard_num = x_tensors.size();
if (shard_num == 1) {
VLOG(3) << "only one shard, we can copy the data directly";
TensorCopy(*x_tensors[0], place, out);
} else {
std::vector<int> in_indexs(shard_num, 0);
auto *out_data = out->mutable_data<T>(
framework::make_ddim({batch_size, embedding_size}), place);
// copy data from ins[shard_num] to out.
for (int i = 0; i < ids_dims[0]; ++i) {
int64_t id = ids[i];
size_t shard_id = static_cast<size_t>(id) % shard_num;
int index = in_indexs[shard_id];
memcpy(out_data + embedding_size * i,
x_tensors[shard_id]->data<T>() + index * embedding_size,
sizeof(T) * embedding_size);
in_indexs[shard_id] += 1;
}
for (size_t i = 0; i < shard_num; ++i) {
PADDLE_ENFORCE_EQ(in_indexs[i], x_tensors[i]->dims()[0],
"after merge, all data in x_tensor should be used");
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/reader/create_batch_reader_op.cc
浏览文件 @ 85c3bfc1
...@@ -20,7 +20,7 @@ namespace reader { ...@@ -20,7 +20,7 @@ namespace reader {
class BatchReader : public framework::DecoratedReader { class BatchReader : public framework::DecoratedReader {
public: public:
BatchReader(ReaderBase* reader, int batch_size) BatchReader(const std::shared_ptr<ReaderBase>& reader, int batch_size)
: DecoratedReader(reader), batch_size_(batch_size) { : DecoratedReader(reader), batch_size_(batch_size) {
buffer_.reserve(batch_size_); buffer_.reserve(batch_size_);
} }
......
paddle/fluid/operators/reader/create_custom_reader_op.cc
浏览文件 @ 85c3bfc1
...@@ -22,7 +22,8 @@ namespace reader { ...@@ -22,7 +22,8 @@ namespace reader {
class CustomReader : public framework::DecoratedReader { class CustomReader : public framework::DecoratedReader {
public: public:
CustomReader(ReaderBase* reader, const framework::BlockDesc& sub_block, CustomReader(const std::shared_ptr<ReaderBase>& reader,
const framework::BlockDesc& sub_block,
const std::vector<std::string>& source_var_names, const std::vector<std::string>& source_var_names,
const std::vector<std::string>& sink_var_names) const std::vector<std::string>& sink_var_names)
: DecoratedReader(reader), : DecoratedReader(reader),
......
paddle/fluid/operators/reader/create_double_buffer_reader_op.cc
浏览文件 @ 85c3bfc1
...@@ -34,7 +34,8 @@ static constexpr size_t kChannelSize = 1; // kCacheSize - 2 ...@@ -34,7 +34,8 @@ static constexpr size_t kChannelSize = 1; // kCacheSize - 2
class DoubleBufferReader : public framework::DecoratedReader { class DoubleBufferReader : public framework::DecoratedReader {
public: public:
explicit DoubleBufferReader( explicit DoubleBufferReader(
ReaderBase* reader, platform::Place target_place = platform::CPUPlace()) const std::shared_ptr<ReaderBase>& reader,
platform::Place target_place = platform::CPUPlace())
: DecoratedReader(reader), place_(target_place) { : DecoratedReader(reader), place_(target_place) {
cpu_tensor_cache_.resize(kCacheSize); cpu_tensor_cache_.resize(kCacheSize);
gpu_tensor_cache_.resize(kCacheSize); gpu_tensor_cache_.resize(kCacheSize);
......
paddle/fluid/operators/reader/create_multi_pass_reader_op.cc
浏览文件 @ 85c3bfc1
...@@ -21,7 +21,7 @@ namespace reader { ...@@ -21,7 +21,7 @@ namespace reader {
class MultiPassReader : public framework::DecoratedReader { class MultiPassReader : public framework::DecoratedReader {
public: public:
MultiPassReader(ReaderBase* reader, int pass_num) MultiPassReader(const std::shared_ptr<ReaderBase>& reader, int pass_num)
: DecoratedReader(reader), pass_num_(pass_num), pass_count_(0) {} : DecoratedReader(reader), pass_num_(pass_num), pass_count_(0) {}
void ReadNext(std::vector<framework::LoDTensor>* out) override { void ReadNext(std::vector<framework::LoDTensor>* out) override {
......
paddle/fluid/operators/reader/create_shuffle_reader_op.cc
浏览文件 @ 85c3bfc1
...@@ -23,7 +23,8 @@ namespace reader { ...@@ -23,7 +23,8 @@ namespace reader {
class ShuffleReader : public framework::DecoratedReader { class ShuffleReader : public framework::DecoratedReader {
public: public:
ShuffleReader(ReaderBase* reader, size_t buffer_size, size_t seed = 0) ShuffleReader(const std::shared_ptr<ReaderBase>& reader, size_t buffer_size,
size_t seed = 0)
: DecoratedReader(reader), buffer_size_(buffer_size), seed_(seed) { : DecoratedReader(reader), buffer_size_(buffer_size), seed_(seed) {
VLOG(10) << "Create shuffle reader of " << reader_; VLOG(10) << "Create shuffle reader of " << reader_;
if (seed_ == 0) { if (seed_ == 0) {
......
paddle/fluid/operators/reader/create_threaded_reader_op.cc
浏览文件 @ 85c3bfc1
...@@ -21,7 +21,8 @@ namespace reader { ...@@ -21,7 +21,8 @@ namespace reader {
class ThreadedReader : public framework::DecoratedReader { class ThreadedReader : public framework::DecoratedReader {
public: public:
explicit ThreadedReader(ReaderBase* reader) : DecoratedReader(reader) {} explicit ThreadedReader(const std::shared_ptr<ReaderBase>& reader)
: DecoratedReader(reader) {}
void ReadNext(std::vector<framework::LoDTensor>* out) override { void ReadNext(std::vector<framework::LoDTensor>* out) override {
std::lock_guard<std::mutex> lock(mutex_); std::lock_guard<std::mutex> lock(mutex_);
......
paddle/fluid/platform/cpu_info.cc
浏览文件 @ 85c3bfc1
...@@ -21,12 +21,17 @@ limitations under the License. */ ...@@ -21,12 +21,17 @@ limitations under the License. */
#include <unistd.h> #include <unistd.h>
#endif #endif
#include <algorithm>
#include "gflags/gflags.h" #include "gflags/gflags.h"
DEFINE_double(fraction_of_cpu_memory_to_use, 1, DEFINE_double(fraction_of_cpu_memory_to_use, 1,
"Default use 100% of CPU memory for PaddlePaddle," "Default use 100% of CPU memory for PaddlePaddle,"
"reserve the rest for page tables, etc"); "reserve the rest for page tables, etc");
DEFINE_uint64(
initial_cpu_memory_in_mb, 500,
"Default initial 500MB of CPU memory for PaddlePaddle, in MD unit.");
DEFINE_double( DEFINE_double(
fraction_of_cuda_pinned_memory_to_use, 0.5, fraction_of_cuda_pinned_memory_to_use, 0.5,
"Default use 50% of CPU memory as the pinned_memory for PaddlePaddle," "Default use 50% of CPU memory as the pinned_memory for PaddlePaddle,"
...@@ -54,7 +59,10 @@ inline size_t CpuTotalPhysicalMemory() { ...@@ -54,7 +59,10 @@ inline size_t CpuTotalPhysicalMemory() {
size_t CpuMaxAllocSize() { size_t CpuMaxAllocSize() {
// For distributed systems, it requires configuring and limiting // For distributed systems, it requires configuring and limiting
// the fraction of memory to use. // the fraction of memory to use.
return FLAGS_fraction_of_cpu_memory_to_use * CpuTotalPhysicalMemory(); return std::min(
static_cast<size_t>(FLAGS_fraction_of_cpu_memory_to_use *
CpuTotalPhysicalMemory()),
static_cast<size_t>(FLAGS_initial_cpu_memory_in_mb * 1 << 20));
} }
size_t CpuMinChunkSize() { size_t CpuMinChunkSize() {
......
paddle/fluid/platform/device_tracer.cc
浏览文件 @ 85c3bfc1
...@@ -322,7 +322,6 @@ class DeviceTracerImpl : public DeviceTracer { ...@@ -322,7 +322,6 @@ class DeviceTracerImpl : public DeviceTracer {
DisableActivity(); DisableActivity();
dynload::cuptiUnsubscribe(subscriber_); dynload::cuptiUnsubscribe(subscriber_);
CUPTI_CALL(dynload::cuptiGetTimestamp(&end_ns_)); CUPTI_CALL(dynload::cuptiGetTimestamp(&end_ns_));
PADDLE_ENFORCE(dynload::cuptiFinalize());
enabled_ = false; enabled_ = false;
} }
......
paddle/fluid/platform/dynload/cupti.h
浏览文件 @ 85c3bfc1
...@@ -72,7 +72,6 @@ extern void *cupti_dso_handle; ...@@ -72,7 +72,6 @@ extern void *cupti_dso_handle;
__macro(cuptiGetResultString); \ __macro(cuptiGetResultString); \
__macro(cuptiActivityGetNumDroppedRecords); \ __macro(cuptiActivityGetNumDroppedRecords); \
__macro(cuptiActivityFlushAll); \ __macro(cuptiActivityFlushAll); \
__macro(cuptiFinalize); \
__macro(cuptiSubscribe); \ __macro(cuptiSubscribe); \
__macro(cuptiUnsubscribe); \ __macro(cuptiUnsubscribe); \
__macro(cuptiEnableCallback); \ __macro(cuptiEnableCallback); \
......
paddle/scripts/paddle_build.sh
浏览文件 @ 85c3bfc1
...@@ -132,7 +132,8 @@ EOF ...@@ -132,7 +132,8 @@ EOF
-DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake \ -DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake \
-DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF} \ -DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF} \
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON \ -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
-DWITH_CONTRIB=${WITH_CONTRIB:-ON} -DWITH_CONTRIB=${WITH_CONTRIB:-ON} \
-DWITH_ANAKIN=ON
} }
function abort(){ function abort(){
......
python/paddle/batch.py
浏览文件 @ 85c3bfc1
...@@ -15,7 +15,7 @@ ...@@ -15,7 +15,7 @@
__all__ = ['batch'] __all__ = ['batch']
def batch(reader, batch_size, drop_last=False): def batch(reader, batch_size, drop_last=True):
""" """
Create a batched reader. Create a batched reader.
......
python/paddle/fluid/framework.py
浏览文件 @ 85c3bfc1
...@@ -382,7 +382,7 @@ class Operator(object): ...@@ -382,7 +382,7 @@ class Operator(object):
'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv', 'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
'listen_and_serv', 'parallel_do', 'save_combine', 'load_combine', 'listen_and_serv', 'parallel_do', 'save_combine', 'load_combine',
'ncclInit', 'channel_create', 'channel_close', 'channel_send', 'ncclInit', 'channel_create', 'channel_close', 'channel_send',
'channel_recv', 'select' 'channel_recv', 'select', 'gen_nccl_id'
} }
def __init__(self, def __init__(self,
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 85c3bfc1
...@@ -25,68 +25,20 @@ import utils ...@@ -25,68 +25,20 @@ import utils
import random import random
__all__ = [ __all__ = [
'fc', 'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
'embedding', 'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
'dynamic_lstm', 'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d',
'dynamic_lstmp', 'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'batch_norm',
'dynamic_gru', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand', 'lstm_unit',
'gru_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', 'reduce_prod',
'linear_chain_crf', 'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
'crf_decoding', 'ctc_greedy_decoder', 'edit_distance', 'l2_normalize', 'matmul', 'topk',
'cos_sim', 'warpctc', 'sequence_reshape', 'transpose', 'im2sequence', 'nce',
'cross_entropy', 'beam_search', 'row_conv', 'multiplex', 'layer_norm',
'square_error_cost', 'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
'chunk_eval', 'autoincreased_step_counter', 'reshape', 'lod_reset', 'lrn', 'pad',
'sequence_conv', 'label_smooth', 'roi_pool', 'dice_loss', 'image_resize',
'conv2d', 'image_resize_short', 'resize_bilinear', 'gather', 'random_crop', 'mean_iou'
'sequence_pool',
'sequence_softmax',
'softmax',
'pool2d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'sequence_expand',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'reduce_prod',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'topk',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'lod_reset',
'lrn',
'pad',
'label_smooth',
'roi_pool',
'dice_loss',
'image_resize',
'image_resize_short',
'resize_bilinear',
'gather',
'random_crop',
] ]
...@@ -261,9 +213,10 @@ def embedding(input, ...@@ -261,9 +213,10 @@ def embedding(input,
return tmp return tmp
# TODO(qijun): expose H0 and C0
def dynamic_lstm(input, def dynamic_lstm(input,
size, size,
h_0=None,
c_0=None,
param_attr=None, param_attr=None,
bias_attr=None, bias_attr=None,
use_peepholes=True, use_peepholes=True,
...@@ -324,6 +277,13 @@ def dynamic_lstm(input, ...@@ -324,6 +277,13 @@ def dynamic_lstm(input,
(T X 4D), where T is the total time steps in this (T X 4D), where T is the total time steps in this
mini-batch, D is the hidden size. mini-batch, D is the hidden size.
size(int): 4 * hidden size. size(int): 4 * hidden size.
h_0(Variable): The initial hidden state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size and D is the hidden size.
c_0(Variable): The initial cell state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size. `h_0` and `c_0` can be NULL but only at the same time.
param_attr(ParamAttr|None): The parameter attribute for the learnable param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weights. hidden-hidden weights.
...@@ -387,12 +347,20 @@ def dynamic_lstm(input, ...@@ -387,12 +347,20 @@ def dynamic_lstm(input,
cell = helper.create_tmp_variable(dtype) cell = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype) batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype) batch_cell_pre_act = helper.create_tmp_variable(dtype)
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
batch_size = input.shape[0]
if h_0:
assert h_0.shape == (batch_size, size), \
'The shape of h0 should be (batch_size, %d)' % size
inputs['H0'] = h_0
if c_0:
assert c_0.shape == (batch_size, size), \
'The shape of c0 should be (batch_size, %d)' % size
inputs['C0'] = c_0
helper.append_op( helper.append_op(
type='lstm', type='lstm',
inputs={'Input': input, inputs=inputs,
'Weight': weight,
'Bias': bias},
outputs={ outputs={
'Hidden': hidden, 'Hidden': hidden,
'Cell': cell, 'Cell': cell,
...@@ -677,11 +645,13 @@ def dynamic_gru(input, ...@@ -677,11 +645,13 @@ def dynamic_gru(input,
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype) attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
bias = helper.create_parameter( bias = helper.create_parameter(
attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True) attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
batch_size = input.shape[0]
inputs = {'Input': input, 'Weight': weight, 'Bias': bias} inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
if h_0 != None: if h_0 != None:
assert h_0.shape == ( assert h_0.shape == (
size, size), 'The shape of h0 should be(%d, %d)' % (size, size) batch_size, size
inputs['h0'] = h_0 ), 'The shape of h0 should be(batch_size, %d)' % size
inputs['H0'] = h_0
hidden = helper.create_tmp_variable(dtype) hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype) batch_gate = helper.create_tmp_variable(dtype)
...@@ -4213,6 +4183,7 @@ def gather(input, index): ...@@ -4213,6 +4183,7 @@ def gather(input, index):
output (Variable): The output is a tensor with the same rank as input. output (Variable): The output is a tensor with the same rank as input.
Examples: Examples:
.. code-block:: python .. code-block:: python
output = fluid.layers.gather(x, index) output = fluid.layers.gather(x, index)
...@@ -4277,3 +4248,53 @@ def random_crop(x, shape, seed=None): ...@@ -4277,3 +4248,53 @@ def random_crop(x, shape, seed=None):
"SeedOut": seed_out}, "SeedOut": seed_out},
attrs={"shape": shape}) attrs={"shape": shape})
return out return out
def mean_iou(input, label, num_classes):
"""
Mean Intersection-Over-Union is a common evaluation metric for
semantic image segmentation, which first computes the IOU for each
semantic class and then computes the average over classes.
IOU is defined as follows:
.. math::
IOU = true_positive / (true_positive + false_positive + false_negative).
The predictions are accumulated in a confusion matrix and mean-IOU
is then calculated from it.
Args:
input (Variable): A Tensor of prediction results for semantic labels with type int32 or int64.
label (Variable): A Tensor of ground truth labels with type int32 or int64.
Its shape should be the same as input.
Returns:
mean_iou (Variable): A Tensor representing the mean intersection-over-union with shape [1].
out_wrong(Variable): A Tensor with shape [num_classes]. The wrong numbers of each class.
out_correct(Variable): A Tensor with shape [num_classes]. The correct numbers of each class.
Examples:
.. code-block:: python
iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes)
"""
helper = LayerHelper('mean_iou', **locals())
dtype = helper.input_dtype()
out_mean_iou = helper.create_tmp_variable(dtype='float32')
out_wrong = helper.create_tmp_variable(dtype='int32')
out_correct = helper.create_tmp_variable(dtype='int32')
helper.append_op(
type="mean_iou",
inputs={"predictions": input,
"labels": label},
outputs={
"out_mean_iou": out_mean_iou,
"out_wrong": out_wrong,
"out_correct": out_correct
},
attrs={"num_classes": num_classes})
return out_mean_iou, out_wrong, out_correct
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py
浏览文件 @ 85c3bfc1
...@@ -96,10 +96,11 @@ def train(use_cuda, train_program, params_dirname): ...@@ -96,10 +96,11 @@ def train(use_cuda, train_program, params_dirname):
train_reader = paddle.batch( train_reader = paddle.batch(
paddle.reader.shuffle( paddle.reader.shuffle(
cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10), cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
batch_size=BATCH_SIZE) batch_size=BATCH_SIZE,
drop_last=False)
test_reader = paddle.batch( test_reader = paddle.batch(
paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE) paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE, drop_last=False)
def event_handler(event): def event_handler(event):
if isinstance(event, fluid.EndStepEvent): if isinstance(event, fluid.EndStepEvent):
......
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py
浏览文件 @ 85c3bfc1
...@@ -73,10 +73,11 @@ def train(use_cuda, train_program, params_dirname): ...@@ -73,10 +73,11 @@ def train(use_cuda, train_program, params_dirname):
train_reader = paddle.batch( train_reader = paddle.batch(
paddle.reader.shuffle( paddle.reader.shuffle(
cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10), cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
batch_size=BATCH_SIZE) batch_size=BATCH_SIZE,
drop_last=False)
test_reader = paddle.batch( test_reader = paddle.batch(
paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE) paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE, drop_last=False)
def event_handler(event): def event_handler(event):
if isinstance(event, fluid.EndStepEvent): if isinstance(event, fluid.EndStepEvent):
......
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py
浏览文件 @ 85c3bfc1
...@@ -87,7 +87,9 @@ def train(use_cuda, train_program, params_dirname): ...@@ -87,7 +87,9 @@ def train(use_cuda, train_program, params_dirname):
def event_handler(event): def event_handler(event):
if isinstance(event, fluid.EndEpochEvent): if isinstance(event, fluid.EndEpochEvent):
test_reader = paddle.batch( test_reader = paddle.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE) paddle.dataset.imdb.test(word_dict),
batch_size=BATCH_SIZE,
drop_last=False)
avg_cost, acc = trainer.test( avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label']) reader=test_reader, feed_order=['words', 'label'])
...@@ -113,7 +115,8 @@ def train(use_cuda, train_program, params_dirname): ...@@ -113,7 +115,8 @@ def train(use_cuda, train_program, params_dirname):
train_reader = paddle.batch( train_reader = paddle.batch(
paddle.reader.shuffle( paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000), paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE) batch_size=BATCH_SIZE,
drop_last=False)
trainer.train( trainer.train(
num_epochs=1, num_epochs=1,
......
python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
浏览文件 @ 85c3bfc1
...@@ -56,7 +56,7 @@ BATCH_SIZE = 200 ...@@ -56,7 +56,7 @@ BATCH_SIZE = 200
# fix the order of training data # fix the order of training data
train_reader = paddle.batch( train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE) paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE, drop_last=False)
# train_reader = paddle.batch( # train_reader = paddle.batch(
# paddle.reader.shuffle( # paddle.reader.shuffle(
......
python/paddle/fluid/tests/unittests/test_mean_iou.py 0 → 100644
浏览文件 @ 85c3bfc1
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import division
import unittest
import numpy as np
from op_test import OpTest
def compute_mean_iou(predictions, labels, num_classes, in_wrongs, in_corrects,
in_mean_ious):
assert predictions.shape == labels.shape
predictions = predictions.flatten()
labels = labels.flatten()
out_wrong = np.zeros([num_classes]).astype("int32")
for _, wrong in in_wrongs:
out_wrong += wrong
out_correct = np.zeros([num_classes]).astype("int32")
for _, correct in in_corrects:
out_correct += correct
for pred, label in zip(predictions, labels):
if pred == label:
out_correct[pred] += 1
else:
out_wrong[pred] += 1
out_wrong[label] += 1
denominator = out_wrong + out_correct
valid_count = (denominator != 0).sum()
denominator = np.where(denominator > 0, denominator,
np.ones(denominator.shape))
mean_iou = (out_correct / denominator).sum() / valid_count
for _, in_mean_iou in in_mean_ious:
mean_iou += in_mean_iou
return mean_iou, out_wrong, out_correct
class TestMeanIOUOp(OpTest):
def setUp(self):
self.config()
self.op_type = "mean_iou"
predictions = np.random.randint(0, self.num_classes,
self.image_size).astype("int32")
labels = np.random.randint(0, self.num_classes,
self.image_size).astype("int32")
in_wrongs = []
for i in range(self.in_wrong_num):
in_wrongs.append(("in_wrong_%d" % i, np.random.randint(
0, 10, [self.num_classes]).astype("int32")))
in_corrects = []
for i in range(self.in_correct_num):
in_corrects.append(("in_correct_%d" % i, np.random.randint(
0, 10, [self.num_classes]).astype("int32")))
in_mean_ious = []
for i in range(self.in_mean_iou_num):
in_mean_ious.append(("in_mean_iou_%d" % i, np.random.uniform(
0, 1, [1]).astype("float32")))
self.inputs = {
'Predictions': predictions,
'Labels': labels,
'InWrongs': in_wrongs,
'InCorrects': in_corrects,
'InMeanIou': in_mean_ious
}
self.attrs = {'num_classes': long(self.num_classes)}
mean_iou, out_wrong, out_correct = compute_mean_iou(
predictions, labels, self.num_classes, in_wrongs, in_corrects,
in_mean_ious)
self.outputs = {
'OutMeanIou': mean_iou,
'OutWrong': out_wrong,
'OutCorrect': out_correct
}
def config(self):
self.num_classes = 10
self.image_size = [128, 128]
self.in_wrong_num = 0
self.in_correct_num = 0
self.in_mean_iou_num = 0
def test_check_output(self):
self.check_output()
class TestCase1(TestMeanIOUOp):
def config(self):
self.num_classes = 5
self.image_size = [100, 128]
self.in_wrong_num = 2
self.in_correct_num = 2
self.in_mean_iou_num = 2
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_merge_ids_op.py 0 → 100644
浏览文件 @ 85c3bfc1
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from op_test import OpTest
class TestMergeIdsOp(OpTest):
def setUp(self):
self.op_type = "merge_ids"
ids = np.array([[0], [2], [2], [3], [5], [5], [6]]).astype('int64')
x0 = np.array([[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]]).astype('float32')
x1 = np.array([]).astype('float32')
x2 = np.array([[0.4, 0.5], [0.4, 0.5], [0.5, 0.6],
[0.5, 0.6]]).astype('float32')
out = np.array([[0.1, 0.2], [0.4, 0.5], [0.4, 0.5], [0.2, 0.3],
[0.5, 0.6], [0.5, 0.6], [0.3, 0.4]]).astype('float32')
self.inputs = {'Ids': ids, "X": [('x0', x0), ('x1', x1), ('x2', x2)]}
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 85c3bfc1
...@@ -629,7 +629,7 @@ class DistributeTranspiler: ...@@ -629,7 +629,7 @@ class DistributeTranspiler:
if op.type == LOOKUP_TABLE_TYPE: if op.type == LOOKUP_TABLE_TYPE:
continue_search_lookup_table_op = True continue_search_lookup_table_op = True
op_index = list(all_ops).index(op) lookup_table_op_index = list(all_ops).index(op)
ids_name = op.input("Ids") ids_name = op.input("Ids")
out_name = op.output("Out") out_name = op.output("Out")
...@@ -649,7 +649,7 @@ class DistributeTranspiler: ...@@ -649,7 +649,7 @@ class DistributeTranspiler:
# insert split_ids_op # insert split_ids_op
program.global_block().insert_op( program.global_block().insert_op(
index=op_index, index=lookup_table_op_index,
type="split_ids", type="split_ids",
inputs={ inputs={
'Ids': [ 'Ids': [
...@@ -661,7 +661,7 @@ class DistributeTranspiler: ...@@ -661,7 +661,7 @@ class DistributeTranspiler:
# insert prefetch_op # insert prefetch_op
program.global_block().insert_op( program.global_block().insert_op(
index=op_index + 1, index=lookup_table_op_index + 1,
type="prefetch", type="prefetch",
inputs={'X': prefetch_input_vars}, inputs={'X': prefetch_input_vars},
outputs={"Out": prefetch_output_vars}, outputs={"Out": prefetch_output_vars},
...@@ -672,16 +672,21 @@ class DistributeTranspiler: ...@@ -672,16 +672,21 @@ class DistributeTranspiler:
# insert concat_op # insert concat_op
program.global_block().insert_op( program.global_block().insert_op(
index=op_index + 2, index=lookup_table_op_index + 2,
type="concat", type="merge_ids",
inputs={'X': prefetch_output_vars}, inputs={
'Ids': [
program.global_block().vars[varname]
for varname in ids_name
],
'X': prefetch_output_vars
},
outputs={ outputs={
"Out": [ "Out": [
program.global_block().vars[varname] program.global_block().vars[varname]
for varname in out_name for varname in out_name
] ]
}, })
attrs={"axis": 0})
# delete lookup_table_op # delete lookup_table_op
delete_ops(program.global_block(), [op]) delete_ops(program.global_block(), [op])
......
python/paddle/trainer_config_helpers/attrs.py
浏览文件 @ 85c3bfc1
...@@ -240,14 +240,15 @@ class ExtraLayerAttribute(object): ...@@ -240,14 +240,15 @@ class ExtraLayerAttribute(object):
:type error_clipping_threshold: float :type error_clipping_threshold: float
:param drop_rate: Dropout rate. Dropout will create a mask on layer output. :param drop_rate: Dropout rate. Dropout will create a mask on layer output.
The dropout rate is the zero rate of this mask. The The dropout rate is the zero rate of this mask. The
details of what dropout is please refer to `here details of what dropout is please refer to `JMLRdropout
<https://www.cs.toronto.edu/~hinton/absps/ <https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
JMLRdropout.pdf>`_. >`_.
:type drop_rate: float :type drop_rate: float
:param device: device ID of layer. device=-1, use CPU. device>=0, use GPU. :param device: device ID of layer. device=-1, use CPU. device>=0, use GPU.
The details allocation in parallel_nn please refer to `here The details allocation in parallel_nn please refer to `use_case
<http://www.paddlepaddle.org/doc/ui/cmd_argument/ <https://github.com/PaddlePaddle/Paddle/blob/develop/doc/v2
use_case.html#case-2-specify-layers-in-different-devices>`_. /howto/cmd_parameter/use_case_en.md#case-2-specify-layers-in
-different-devices>`_.
:type device: int :type device: int
""" """
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ 85c3bfc1
...@@ -2556,7 +2556,7 @@ def img_conv_layer(input, ...@@ -2556,7 +2556,7 @@ def img_conv_layer(input,
the output will be obtained by concatenating the two results. the output will be obtained by concatenating the two results.
The details of grouped convolution, please refer to: The details of grouped convolution, please refer to:
`ImageNet Classification with Deep Convolutional Neural Networks `ImageNet Classification With Deep Convolutional Neural Networks
<http://www.cs.toronto.edu/~kriz/imagenet_classification_with_deep_convolutional.pdf>`_ <http://www.cs.toronto.edu/~kriz/imagenet_classification_with_deep_convolutional.pdf>`_
The example usage is: The example usage is:
...@@ -5678,8 +5678,8 @@ def warp_ctc_layer(input, ...@@ -5678,8 +5678,8 @@ def warp_ctc_layer(input,
<https://github.com/baidu-research/warp-ctc>`_ library, which is used in <https://github.com/baidu-research/warp-ctc>`_ library, which is used in
`Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin `Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin
<https://arxiv.org/pdf/1512.02595v1.pdf>`_, to compute Connectionist Temporal <https://arxiv.org/pdf/1512.02595v1.pdf>`_, to compute Connectionist Temporal
Classification (CTC) loss. Besides, another `warp-ctc Classification (CTC) loss. Besides, another `warp-ctc repository
<https://github.com/gangliao/warp-ctc>`_ repository, which is forked from <https://github.com/gangliao/warp-ctc>`_ , which is forked from
the official one, is maintained to enable more compiling options. During the the official one, is maintained to enable more compiling options. During the
building process, PaddlePaddle will clone the source codes, build and building process, PaddlePaddle will clone the source codes, build and
install it to :code:`third_party/install/warpctc` directory. install it to :code:`third_party/install/warpctc` directory.
......
python/paddle/v2/minibatch.py
浏览文件 @ 85c3bfc1
...@@ -15,7 +15,7 @@ ...@@ -15,7 +15,7 @@
__all__ = ['batch'] __all__ = ['batch']
def batch(reader, batch_size, drop_last=False): def batch(reader, batch_size, drop_last=True):
""" """
Create a batched reader. Create a batched reader.
......
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