Skip to content

P
Paddle

sven357 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle

体验新版 GitCode，发现更多精彩内容 >>

提交 f4bdfbc6 编写于 作者: G guosheng
浏览文件
操作

Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into fix-beam_search-dev

上级 49ca424d 69827f30
展开全部 隐藏空白更改
内联 并排
Showing with 6613 addition and 2464 deletion
AUTHORS.md
浏览文件 @ f4bdfbc6
......@@ -22,6 +22,7 @@
| jczaja | Jacek Czaja |
| JiayiFeng | Jia-Yi Feng |
| kbinias | Krzysztof Binias |
| kexinzhao | Ke-Xin Zhao |
| kuke | Yi-Bing Liu |
| lcy-seso | Ying Cao |
| lipeng-unisound | Peng Li |
......
CMakeLists.txt
浏览文件 @ f4bdfbc6
......@@ -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_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_ANAKIN "Compile with Anakin library" OFF)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
# CMAKE_BUILD_TYPE
......@@ -193,7 +194,10 @@ set(EXTERNAL_LIBS
if(WITH_GPU)
include(cuda)
include(tensorrt)
endif(WITH_GPU)
include(external/anakin)
else()
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when GPU is set." FORCE)
endif()
if(WITH_AMD_GPU)
find_package(HIP)
......
benchmark/fluid/models/machine_translation.py
浏览文件 @ f4bdfbc6
......@@ -173,21 +173,6 @@ def seq_to_seq_net(embedding_dim, encoder_size, decoder_size, source_dict_dim,
return avg_cost, feeding_list
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
lod_t = core.LoDTensor()
lod_t.set(flattened_data, place)
lod_t.set_lod([lod])
return lod_t, lod[-1]
def lodtensor_to_ndarray(lod_tensor):
dims = lod_tensor.get_dims()
ndarray = np.zeros(shape=dims).astype('float32')
......
benchmark/fluid/models/stacked_dynamic_lstm.py
浏览文件 @ f4bdfbc6
......@@ -125,18 +125,3 @@ def get_model(args):
batch_size=args.batch_size)
return loss, inference_program, adam, train_reader, test_reader, batch_acc
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = numpy.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
cmake/external/anakin.cmake 0 → 100644
浏览文件 @ f4bdfbc6
if (NOT WITH_ANAKIN)
return()
endif()
set(ANAKIN_INSTALL_DIR "${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()
cmake/external/openblas.cmake
浏览文件 @ f4bdfbc6
......@@ -29,6 +29,8 @@ IF(NOT ${CBLAS_FOUND})
"${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
CACHE FILEPATH "openblas library." FORCE)
ADD_DEFINITIONS(-DPADDLE_USE_OPENBLAS)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
SET(OPENBLAS_COMMIT "v0.2.20")
......
cmake/inference_lib.cmake
浏览文件 @ f4bdfbc6
......@@ -39,7 +39,7 @@ function(copy TARGET)
message(FATAL_ERROR "${TARGET} source numbers are not equal to destination numbers")
endif()
math(EXPR len "${copy_lib_SRCS_len} - 1")
add_custom_target(${TARGET} DEPENDS ${copy_lib_DEPS})
foreach(index RANGE ${len})
list(GET copy_lib_SRCS ${index} src)
......@@ -155,6 +155,15 @@ copy(inference_lib DEPS paddle_fluid_shared paddle_fluid
DSTS ${dst_dir}/${module} ${dst_dir}/${module}
)
if(WITH_CONTRIB)
set(contrib_dst_dir "${FLUID_INSTALL_DIR}/contrib/inference")
copy(contrib_inference_lib DEPS paddle_inference_api
SRCS ${PADDLE_SOURCE_DIR}/paddle/contrib/inference/paddle_inference_api.h
${PADDLE_BINARY_DIR}/paddle/contrib/inference/libpaddle_inference_api.*
DSTS ${contrib_dst_dir} ${contrib_dst_dir}
)
endif()
set(module "platform")
copy(platform_lib DEPS profiler_py_proto
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/dynload/*.h ${src_dir}/${module}/details/*.h
......
doc/fluid/api/gen_doc.sh
浏览文件 @ f4bdfbc6
#!/bin/bash
python gen_doc.py layers --submodules control_flow device io nn ops tensor detection > layers.rst
python gen_doc.py layers --submodules control_flow device io nn ops tensor detection learning_rate_scheduler metric > layers.rst
for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer
do
......
doc/fluid/api/initializer.rst
浏览文件 @ f4bdfbc6
......@@ -33,6 +33,13 @@ Xavier
:members:
:noindex:
Bilinear
--------
.. autoclass:: paddle.fluid.initializer.Bilinear
:members:
:noindex:
force_init_on_cpu
-----------------
......@@ -73,3 +80,10 @@ XavierInitializer
:members:
:noindex:
BilinearInitializer
-------------------
.. autoclass:: paddle.fluid.initializer.BilinearInitializer
:members:
:noindex:
doc/fluid/api/io.rst
浏览文件 @ f4bdfbc6
......@@ -59,3 +59,39 @@ get_inference_program
.. autofunction:: paddle.fluid.io.get_inference_program
: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:
load_persist_vars_without_grad
------------------------------
.. autofunction:: paddle.fluid.io.load_persist_vars_without_grad
:noindex:
save_persist_vars_without_grad
------------------------------
.. autofunction:: paddle.fluid.io.save_persist_vars_without_grad
:noindex:
get_latest_checkpoint_serial
----------------------------
.. autofunction:: paddle.fluid.io.get_latest_checkpoint_serial
:noindex:
doc/fluid/api/layers.rst
浏览文件 @ f4bdfbc6
......@@ -181,6 +181,12 @@ Print
.. autofunction:: paddle.fluid.layers.Print
:noindex:
is_empty
--------
.. autofunction:: paddle.fluid.layers.is_empty
:noindex:
device
======
......@@ -219,6 +225,12 @@ Send
.. autofunction:: paddle.fluid.layers.Send
:noindex:
Recv
----
.. autofunction:: paddle.fluid.layers.Recv
:noindex:
open_recordio_file
------------------
......@@ -255,6 +267,25 @@ double_buffer
.. autofunction:: paddle.fluid.layers.double_buffer
:noindex:
random_data_generator
---------------------
.. autofunction:: paddle.fluid.layers.random_data_generator
:noindex:
Preprocessor
------------
.. autoclass:: paddle.fluid.layers.Preprocessor
:members:
:noindex:
load
----
.. autofunction:: paddle.fluid.layers.load
:noindex:
nn
==
......@@ -342,6 +373,12 @@ conv2d
.. autofunction:: paddle.fluid.layers.conv2d
:noindex:
conv3d
------
.. autofunction:: paddle.fluid.layers.conv3d
:noindex:
sequence_pool
-------------
......@@ -366,6 +403,12 @@ pool2d
.. autofunction:: paddle.fluid.layers.pool2d
:noindex:
pool3d
------
.. autofunction:: paddle.fluid.layers.pool3d
:noindex:
batch_norm
----------
......@@ -384,6 +427,12 @@ conv2d_transpose
.. autofunction:: paddle.fluid.layers.conv2d_transpose
:noindex:
conv3d_transpose
----------------
.. autofunction:: paddle.fluid.layers.conv3d_transpose
:noindex:
sequence_expand
---------------
......@@ -594,6 +643,48 @@ roi_pool
.. autofunction:: paddle.fluid.layers.roi_pool
:noindex:
dice_loss
---------
.. autofunction:: paddle.fluid.layers.dice_loss
:noindex:
image_resize
------------
.. autofunction:: paddle.fluid.layers.image_resize
:noindex:
image_resize_short
------------------
.. autofunction:: paddle.fluid.layers.image_resize_short
: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:
mean_iou
--------
.. autofunction:: paddle.fluid.layers.mean_iou
:noindex:
ops
===
......@@ -699,12 +790,6 @@ logical_not
.. autofunction:: paddle.fluid.layers.logical_not
:noindex:
uniform_random
--------------
.. autofunction:: paddle.fluid.layers.uniform_random
:noindex:
uniform_random_batch_size_like
------------------------------
......@@ -723,12 +808,6 @@ gaussian_random_batch_size_like
.. autofunction:: paddle.fluid.layers.gaussian_random_batch_size_like
:noindex:
cumsum
------
.. autofunction:: paddle.fluid.layers.cumsum
:noindex:
scatter
-------
......@@ -741,6 +820,30 @@ sum
.. autofunction:: paddle.fluid.layers.sum
:noindex:
slice
-----
.. autofunction:: paddle.fluid.layers.slice
:noindex:
polygon_box_transform
---------------------
.. autofunction:: paddle.fluid.layers.polygon_box_transform
:noindex:
shape
-----
.. autofunction:: paddle.fluid.layers.shape
:noindex:
maxout
------
.. autofunction:: paddle.fluid.layers.maxout
:noindex:
sigmoid
-------
......@@ -897,18 +1000,6 @@ stanh
.. autofunction:: paddle.fluid.layers.stanh
:noindex:
hard_shrink
-----------
.. autofunction:: paddle.fluid.layers.hard_shrink
:noindex:
thresholded_relu
----------------
.. autofunction:: paddle.fluid.layers.thresholded_relu
:noindex:
hard_sigmoid
------------
......@@ -921,6 +1012,30 @@ swish
.. autofunction:: paddle.fluid.layers.swish
:noindex:
uniform_random
--------------
.. autofunction:: paddle.fluid.layers.uniform_random
:noindex:
hard_shrink
-----------
.. autofunction:: paddle.fluid.layers.hard_shrink
:noindex:
cumsum
------
.. autofunction:: paddle.fluid.layers.cumsum
:noindex:
thresholded_relu
----------------
.. autofunction:: paddle.fluid.layers.thresholded_relu
:noindex:
tensor
======
......@@ -978,6 +1093,18 @@ fill_constant
.. autofunction:: paddle.fluid.layers.fill_constant
:noindex:
argmin
------
.. autofunction:: paddle.fluid.layers.argmin
:noindex:
argmax
------
.. autofunction:: paddle.fluid.layers.argmax
:noindex:
ones
----
......@@ -993,6 +1120,12 @@ zeros
detection
=========
prior_box
---------
.. autofunction:: paddle.fluid.layers.prior_box
:noindex:
multi_box_head
--------------
......@@ -1041,3 +1174,57 @@ 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:
metric
======
accuracy
--------
.. autofunction:: paddle.fluid.layers.accuracy
:noindex:
auc
---
.. autofunction:: paddle.fluid.layers.auc
:noindex:
doc/fluid/api/optimizer.rst
浏览文件 @ f4bdfbc6
......@@ -89,6 +89,13 @@ DecayedAdagradOptimizer
:members:
:noindex:
RMSPropOptimizer
----------------
.. autoclass:: paddle.fluid.optimizer.RMSPropOptimizer
:members:
:noindex:
Adadelta
--------
......
doc/fluid/api/profiler.rst
浏览文件 @ f4bdfbc6
......@@ -23,3 +23,15 @@ profiler
.. autofunction:: paddle.fluid.profiler.profiler
: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 @ f4bdfbc6
......@@ -171,7 +171,7 @@ Pytorch chooses immediate evaluation. It avoids ever materializing a "forward gr
## What can fluid learn from them?
TBD
Please refer to `paddle/contrib/dynamic/`.
# Appendix
......
doc/v2/api/config/evaluators.rst
浏览文件 @ f4bdfbc6
......@@ -101,7 +101,7 @@ value_printer
:noindex:
Detection
=====
==========
detection_map
-------------
......
doc/v2/api/config/layer.rst
浏览文件 @ f4bdfbc6
......@@ -11,7 +11,7 @@ Data layer
data
----
.. autoclass:: paddle.v2.layer.data
.. autofunction:: paddle.v2.layer.data
:noindex:
Fully Connected Layers
......@@ -21,12 +21,12 @@ Fully Connected Layers
fc
--
.. autoclass:: paddle.v2.layer.fc
.. autofunction:: paddle.v2.layer.fc
:noindex:
selective_fc
------------
.. autoclass:: paddle.v2.layer.selective_fc
.. autofunction:: paddle.v2.layer.selective_fc
:noindex:
Conv Layers
......@@ -34,34 +34,34 @@ Conv Layers
conv_operator
-------------
.. autoclass:: paddle.v2.layer.conv_operator
.. autofunction:: paddle.v2.layer.conv_operator
:noindex:
conv_projection
---------------
.. autoclass:: paddle.v2.layer.conv_projection
.. autofunction:: paddle.v2.layer.conv_projection
:noindex:
conv_shift
----------
.. autoclass:: paddle.v2.layer.conv_shift
.. autofunction:: paddle.v2.layer.conv_shift
:noindex:
img_conv
--------
.. autoclass:: paddle.v2.layer.img_conv
.. autofunction:: paddle.v2.layer.img_conv
:noindex:
.. _api_v2.layer_context_projection:
context_projection
------------------
.. autoclass:: paddle.v2.layer.context_projection
.. autofunction:: paddle.v2.layer.context_projection
:noindex:
row_conv
--------
.. autoclass:: paddle.v2.layer.row_conv
.. autofunction:: paddle.v2.layer.row_conv
:noindex:
Image Pooling Layer
......@@ -69,27 +69,27 @@ Image Pooling Layer
img_pool
--------
.. autoclass:: paddle.v2.layer.img_pool
.. autofunction:: paddle.v2.layer.img_pool
:noindex:
spp
---
.. autoclass:: paddle.v2.layer.spp
.. autofunction:: paddle.v2.layer.spp
:noindex:
maxout
------
.. autoclass:: paddle.v2.layer.maxout
.. autofunction:: paddle.v2.layer.maxout
:noindex:
roi_pool
--------
.. autoclass:: paddle.v2.layer.roi_pool
.. autofunction:: paddle.v2.layer.roi_pool
:noindex:
pad
----
.. autoclass:: paddle.v2.layer.pad
.. autofunction:: paddle.v2.layer.pad
:noindex:
Norm Layer
......@@ -97,27 +97,27 @@ Norm Layer
img_cmrnorm
-----------
.. autoclass:: paddle.v2.layer.img_cmrnorm
.. autofunction:: paddle.v2.layer.img_cmrnorm
:noindex:
batch_norm
----------
.. autoclass:: paddle.v2.layer.batch_norm
.. autofunction:: paddle.v2.layer.batch_norm
:noindex:
sum_to_one_norm
---------------
.. autoclass:: paddle.v2.layer.sum_to_one_norm
.. autofunction:: paddle.v2.layer.sum_to_one_norm
:noindex:
cross_channel_norm
------------------
.. autoclass:: paddle.v2.layer.cross_channel_norm
.. autofunction:: paddle.v2.layer.cross_channel_norm
:noindex:
row_l2_norm
-----------
.. autoclass:: paddle.v2.layer.row_l2_norm
.. autofunction:: paddle.v2.layer.row_l2_norm
:noindex:
Recurrent Layers
......@@ -125,22 +125,22 @@ Recurrent Layers
recurrent
---------
.. autoclass:: paddle.v2.layer.recurrent
.. autofunction:: paddle.v2.layer.recurrent
:noindex:
lstmemory
---------
.. autoclass:: paddle.v2.layer.lstmemory
.. autofunction:: paddle.v2.layer.lstmemory
:noindex:
grumemory
---------
.. autoclass:: paddle.v2.layer.grumemory
.. autofunction:: paddle.v2.layer.grumemory
:noindex:
gated_unit
-----------
.. autoclass:: paddle.v2.layer.gated_unit
.. autofunction:: paddle.v2.layer.gated_unit
:noindex:
Recurrent Layer Group
......@@ -148,32 +148,32 @@ Recurrent Layer Group
memory
------
.. autoclass:: paddle.v2.layer.memory
.. autofunction:: paddle.v2.layer.memory
:noindex:
recurrent_group
---------------
.. autoclass:: paddle.v2.layer.recurrent_group
.. autofunction:: paddle.v2.layer.recurrent_group
:noindex:
lstm_step
---------
.. autoclass:: paddle.v2.layer.lstm_step
.. autofunction:: paddle.v2.layer.lstm_step
:noindex:
gru_step
--------
.. autoclass:: paddle.v2.layer.gru_step
.. autofunction:: paddle.v2.layer.gru_step
:noindex:
beam_search
------------
.. autoclass:: paddle.v2.layer.beam_search
.. autofunction:: paddle.v2.layer.beam_search
:noindex:
get_output
----------
.. autoclass:: paddle.v2.layer.get_output
.. autofunction:: paddle.v2.layer.get_output
:noindex:
Mixed Layer
......@@ -183,54 +183,54 @@ Mixed Layer
mixed
-----
.. autoclass:: paddle.v2.layer.mixed
.. autofunction:: paddle.v2.layer.mixed
:noindex:
.. _api_v2.layer_embedding:
embedding
---------
.. autoclass:: paddle.v2.layer.embedding
.. autofunction:: paddle.v2.layer.embedding
:noindex:
scaling_projection
------------------
.. autoclass:: paddle.v2.layer.scaling_projection
.. autofunction:: paddle.v2.layer.scaling_projection
:noindex:
dotmul_projection
-----------------
.. autoclass:: paddle.v2.layer.dotmul_projection
.. autofunction:: paddle.v2.layer.dotmul_projection
:noindex:
dotmul_operator
---------------
.. autoclass:: paddle.v2.layer.dotmul_operator
.. autofunction:: paddle.v2.layer.dotmul_operator
:noindex:
full_matrix_projection
----------------------
.. autoclass:: paddle.v2.layer.full_matrix_projection
.. autofunction:: paddle.v2.layer.full_matrix_projection
:noindex:
identity_projection
-------------------
.. autoclass:: paddle.v2.layer.identity_projection
.. autofunction:: paddle.v2.layer.identity_projection
:noindex:
slice_projection
-------------------
.. autoclass:: paddle.v2.layer.slice_projection
.. autofunction:: paddle.v2.layer.slice_projection
:noindex:
table_projection
----------------
.. autoclass:: paddle.v2.layer.table_projection
.. autofunction:: paddle.v2.layer.table_projection
:noindex:
trans_full_matrix_projection
----------------------------
.. autoclass:: paddle.v2.layer.trans_full_matrix_projection
.. autofunction:: paddle.v2.layer.trans_full_matrix_projection
:noindex:
Aggregate Layers
......@@ -245,51 +245,46 @@ AggregateLevel
pooling
-------
.. autoclass:: paddle.v2.layer.pooling
.. autofunction:: paddle.v2.layer.pooling
:noindex:
.. _api_v2.layer_last_seq:
last_seq
--------
.. autoclass:: paddle.v2.layer.last_seq
.. autofunction:: paddle.v2.layer.last_seq
:noindex:
.. _api_v2.layer_first_seq:
first_seq
---------
.. autoclass:: paddle.v2.layer.first_seq
.. autofunction:: paddle.v2.layer.first_seq
:noindex:
sub_seq
---------
.. autoclass:: paddle.v2.layer.sub_seq
.. autofunction:: paddle.v2.layer.sub_seq
:noindex:
concat
------
.. autoclass:: paddle.v2.layer.concat
.. autofunction:: paddle.v2.layer.concat
:noindex:
seq_concat
----------
.. autoclass:: paddle.v2.layer.seq_concat
.. autofunction:: paddle.v2.layer.seq_concat
:noindex:
seq_slice
---------
.. autoclass:: paddle.v2.layer.seq_slice
:noindex:
kmax_sequence_score
-------------------
.. autoclass:: paddle.v2.layer.kmax_sequence_score
.. autofunction:: paddle.v2.layer.seq_slice
:noindex:
sub_nested_seq
--------------
.. autoclass:: paddle.v2.layer.sub_nested_seq
.. autofunction:: paddle.v2.layer.sub_nested_seq
:noindex:
Reshaping Layers
......@@ -297,7 +292,7 @@ Reshaping Layers
block_expand
------------
.. autoclass:: paddle.v2.layer.block_expand
.. autofunction:: paddle.v2.layer.block_expand
:noindex:
.. _api_v2.layer_expand:
......@@ -309,22 +304,22 @@ ExpandLevel
expand
------
.. autoclass:: paddle.v2.layer.expand
.. autofunction:: paddle.v2.layer.expand
:noindex:
repeat
------
.. autoclass:: paddle.v2.layer.repeat
.. autofunction:: paddle.v2.layer.repeat
:noindex:
rotate
------
.. autoclass:: paddle.v2.layer.rotate
.. autofunction:: paddle.v2.layer.rotate
:noindex:
seq_reshape
-----------
.. autoclass:: paddle.v2.layer.seq_reshape
.. autofunction:: paddle.v2.layer.seq_reshape
:noindex:
Math Layers
......@@ -332,94 +327,94 @@ Math Layers
addto
-----
.. autoclass:: paddle.v2.layer.addto
.. autofunction:: paddle.v2.layer.addto
:noindex:
linear_comb
-----------
.. autoclass:: paddle.v2.layer.linear_comb
.. autofunction:: paddle.v2.layer.linear_comb
:noindex:
interpolation
-------------
.. autoclass:: paddle.v2.layer.interpolation
.. autofunction:: paddle.v2.layer.interpolation
:noindex:
bilinear_interp
---------------
.. autoclass:: paddle.v2.layer.bilinear_interp
.. autofunction:: paddle.v2.layer.bilinear_interp
:noindex:
dropout
--------
.. autoclass:: paddle.v2.layer.dropout
.. autofunction:: paddle.v2.layer.dropout
:noindex:
dot_prod
---------
.. autoclass:: paddle.v2.layer.dot_prod
.. autofunction:: paddle.v2.layer.dot_prod
:noindex:
out_prod
--------
.. autoclass:: paddle.v2.layer.out_prod
.. autofunction:: paddle.v2.layer.out_prod
:noindex:
power
-----
.. autoclass:: paddle.v2.layer.power
.. autofunction:: paddle.v2.layer.power
:noindex:
scaling
-------
.. autoclass:: paddle.v2.layer.scaling
.. autofunction:: paddle.v2.layer.scaling
:noindex:
clip
----
.. autoclass:: paddle.v2.layer.clip
.. autofunction:: paddle.v2.layer.clip
:noindex:
resize
------
.. autoclass:: paddle.v2.layer.resize
.. autofunction:: paddle.v2.layer.resize
:noindex:
slope_intercept
---------------
.. autoclass:: paddle.v2.layer.slope_intercept
.. autofunction:: paddle.v2.layer.slope_intercept
:noindex:
tensor
------
.. autoclass:: paddle.v2.layer.tensor
.. autofunction:: paddle.v2.layer.tensor
:noindex:
.. _api_v2.layer_cos_sim:
cos_sim
-------
.. autoclass:: paddle.v2.layer.cos_sim
.. autofunction:: paddle.v2.layer.cos_sim
:noindex:
l2_distance
-----------
.. autoclass:: paddle.v2.layer.l2_distance
.. autofunction:: paddle.v2.layer.l2_distance
:noindex:
trans
-----
.. autoclass:: paddle.v2.layer.trans
.. autofunction:: paddle.v2.layer.trans
:noindex:
scale_shift
-----------
.. autoclass:: paddle.v2.layer.scale_shift
.. autofunction:: paddle.v2.layer.scale_shift
:noindex:
factorization_machine
---------------------
.. autoclass:: paddle.v2.layer.factorization_machine
.. autofunction:: paddle.v2.layer.factorization_machine
:noindex:
Sampling Layers
......@@ -427,17 +422,17 @@ Sampling Layers
maxid
-----
.. autoclass:: paddle.v2.layer.max_id
.. autofunction:: paddle.v2.layer.max_id
:noindex:
sampling_id
-----------
.. autoclass:: paddle.v2.layer.sampling_id
.. autofunction:: paddle.v2.layer.sampling_id
:noindex:
multiplex
---------
.. autoclass:: paddle.v2.layer.multiplex
.. autofunction:: paddle.v2.layer.multiplex
:noindex:
.. _api_v2.layer_costs:
......@@ -447,97 +442,97 @@ Cost Layers
cross_entropy_cost
------------------
.. autoclass:: paddle.v2.layer.cross_entropy_cost
.. autofunction:: paddle.v2.layer.cross_entropy_cost
:noindex:
cross_entropy_with_selfnorm_cost
--------------------------------
.. autoclass:: paddle.v2.layer.cross_entropy_with_selfnorm_cost
.. autofunction:: paddle.v2.layer.cross_entropy_with_selfnorm_cost
:noindex:
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:
classification_cost
-------------------
.. autoclass:: paddle.v2.layer.classification_cost
.. autofunction:: paddle.v2.layer.classification_cost
:noindex:
huber_regression_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_regression_cost
.. autofunction:: paddle.v2.layer.huber_regression_cost
:noindex:
huber_classification_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_classification_cost
.. autofunction:: paddle.v2.layer.huber_classification_cost
:noindex:
lambda_cost
-----------
.. autoclass:: paddle.v2.layer.lambda_cost
.. autofunction:: paddle.v2.layer.lambda_cost
:noindex:
square_error_cost
-----------------
.. autoclass:: paddle.v2.layer.square_error_cost
.. autofunction:: paddle.v2.layer.square_error_cost
:noindex:
rank_cost
---------
.. autoclass:: paddle.v2.layer.rank_cost
.. autofunction:: paddle.v2.layer.rank_cost
:noindex:
sum_cost
---------
.. autoclass:: paddle.v2.layer.sum_cost
.. autofunction:: paddle.v2.layer.sum_cost
:noindex:
crf
---
.. autoclass:: paddle.v2.layer.crf
.. autofunction:: paddle.v2.layer.crf
:noindex:
crf_decoding
------------
.. autoclass:: paddle.v2.layer.crf_decoding
.. autofunction:: paddle.v2.layer.crf_decoding
:noindex:
ctc
---
.. autoclass:: paddle.v2.layer.ctc
.. autofunction:: paddle.v2.layer.ctc
:noindex:
warp_ctc
--------
.. autoclass:: paddle.v2.layer.warp_ctc
.. autofunction:: paddle.v2.layer.warp_ctc
:noindex:
nce
---
.. autoclass:: paddle.v2.layer.nce
.. autofunction:: paddle.v2.layer.nce
:noindex:
hsigmoid
---------
.. autoclass:: paddle.v2.layer.hsigmoid
.. autofunction:: paddle.v2.layer.hsigmoid
:noindex:
smooth_l1_cost
--------------
.. autoclass:: paddle.v2.layer.smooth_l1_cost
.. autofunction:: paddle.v2.layer.smooth_l1_cost
:noindex:
multibox_loss
--------------
.. autoclass:: paddle.v2.layer.multibox_loss
.. autofunction:: paddle.v2.layer.multibox_loss
:noindex:
detection_output
----------------
.. autoclass:: paddle.v2.layer.detection_output
.. autofunction:: paddle.v2.layer.detection_output
:noindex:
Check Layer
......@@ -545,7 +540,7 @@ Check Layer
eos
---
.. autoclass:: paddle.v2.layer.eos
.. autofunction:: paddle.v2.layer.eos
:noindex:
Activation
......@@ -553,5 +548,5 @@ Activation
prelu
--------
.. autoclass:: paddle.v2.layer.prelu
.. autofunction:: paddle.v2.layer.prelu
:noindex:
doc/v2/api/index_en.rst
浏览文件 @ f4bdfbc6
......@@ -8,4 +8,3 @@ API
model_configs.rst
data.rst
run_logic.rst
fluid/index.rst
doc/v2/build_and_install/pip_install_cn.rst
浏览文件 @ f4bdfbc6
......@@ -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>`_"
"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>`__"
"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:
......
doc/v2/build_and_install/pip_install_en.rst
浏览文件 @ f4bdfbc6
......@@ -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>`__"
"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>`__"
"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:
......
doc/v2/dev/contribute_to_paddle_cn.md
浏览文件 @ f4bdfbc6
......@@ -104,7 +104,7 @@ no changes added to commit (use "git add" and/or "git commit -a")
➜ docker run -it -v $(pwd):/paddle paddle:latest-dev bash -c "cd /paddle/build && ctest"
```
关于构建和测试的更多信息，请参见[这篇文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
关于构建和测试的更多信息，请参见[使用Docker安装运行](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/v2/build_and_install/docker_install_cn.rst)。
## 提交（commit）
......
paddle/contrib/inference/CMakeLists.txt
浏览文件 @ f4bdfbc6
......@@ -17,48 +17,9 @@ if(APPLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
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)
# 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)
if (WITH_TESTING)
set(options "")
......@@ -79,7 +40,7 @@ function(inference_api_test TARGET_NAME)
endfunction(inference_api_test)
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})
cc_test(test_paddle_inference_api
......@@ -89,9 +50,17 @@ cc_test(test_paddle_inference_api
inference_api_test(test_paddle_inference_api_impl
ARGS test_word2vec test_image_classification)
if (ANAKIN_FOUND)
if (WITH_ANAKIN AND WITH_TESTING) # only needed in CI
# 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
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()
if(WITH_TESTING)
......
paddle/contrib/inference/paddle_inference_api_anakin_engine.cc
浏览文件 @ f4bdfbc6
......@@ -12,9 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <cuda.h>
#include "paddle/contrib/inference/paddle_inference_api_anakin_engine.h"
#include <cuda.h>
namespace paddle {
......
paddle/contrib/inference/paddle_inference_api_anakin_engine.h
浏览文件 @ f4bdfbc6
......@@ -19,10 +19,9 @@ limitations under the License. */
#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"
// from anakin
#include "framework/core/net/net.h"
#include "saber/saber_types.h"
......
paddle/contrib/inference/paddle_inference_api_anakin_engine_tester.cc
浏览文件 @ f4bdfbc6
......@@ -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
limitations under the License. */
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "paddle/contrib/inference/paddle_inference_api.h"
DEFINE_string(model, "", "Directory of the inference model.");
namespace paddle {
AnakinConfig GetConfig() {
AnakinConfig config;
config.model_file = "./mobilenet_v2.anakin.bin";
config.model_file = FLAGS_model;
config.device = 0;
config.max_batch_size = 1;
return config;
......
paddle/fluid/framework/executor.cc
浏览文件 @ f4bdfbc6
......@@ -330,8 +330,12 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
}
for (auto& op : ctx->ops_) {
VLOG(3) << place_ << " " << op->DebugStringEx(local_scope);
VLOG(4) << place_ << " " << op->DebugStringEx(local_scope);
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) {
VLOG(2) << "Memory used after operator " + op->Type() + " running: "
......@@ -402,6 +406,9 @@ void Executor::EnableMKLDNN(const ProgramDesc& program) {
}
}
}
#else
LOG(WARNING)
<< "'MKLDNN' is not supported, Please re-compile with WITH_MKLDNN option";
#endif
}
......
paddle/fluid/framework/init.cc
浏览文件 @ f4bdfbc6
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/fluid/framework/init.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/piece.h"
......@@ -113,6 +114,9 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
}
places.emplace_back(platform::CPUPlace());
platform::DeviceContextPool::Init(places);
#ifndef PADDLE_WITH_MKLDNN
operators::math::SetNumThreads(1);
#endif
}
void InitGLOG(const std::string &prog_name) {
......
paddle/fluid/framework/lod_tensor.cc
浏览文件 @ f4bdfbc6
......@@ -410,5 +410,38 @@ void LoDTensor::MergeLoDTensor(
}
}
LoD ConvertToLengthBasedLoD(const LoD &offset_lod) {
LoD length_lod;
length_lod.reserve(offset_lod.size());
for (size_t lvl = 0; lvl < offset_lod.size(); ++lvl) {
std::vector<size_t> level;
if (offset_lod[lvl].size() > 0) {
level.reserve(offset_lod[lvl].size() - 1);
}
for (size_t idx = 0; idx < offset_lod[lvl].size() - 1; ++idx) {
level.push_back(offset_lod[lvl][idx + 1] - offset_lod[lvl][idx]);
}
length_lod.push_back(level);
}
return length_lod;
}
LoD ConvertToOffsetBasedLoD(const LoD &length_lod) {
LoD offset_lod;
offset_lod.reserve(length_lod.size());
for (size_t lvl = 0; lvl < length_lod.size(); ++lvl) {
std::vector<size_t> level;
level.reserve(length_lod[lvl].size() + 1);
size_t tmp = 0;
level.push_back(tmp);
for (size_t idx = 0; idx < length_lod[lvl].size(); ++idx) {
tmp += length_lod[lvl][idx];
level.push_back(tmp);
}
offset_lod.push_back(level);
}
return offset_lod;
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/lod_tensor.h
浏览文件 @ f4bdfbc6
......@@ -226,5 +226,19 @@ extern void WriteToRecordIO(recordio::Writer* writer,
extern std::vector<LoDTensor> ReadFromRecordIO(
recordio::Scanner* scanner, const platform::DeviceContext& dev_ctx);
/*
* Convert between length-based LoD and offset-based LoD.
* The implementation of LoDTensor class use offset-based LoD.
* However, we want to expose the more user-friendly length-based
* LoD to the Python side instead.
*
* Example:
* If offset_lod = [[0, 2, 3],[0, 3, 5, 9]]
* then length_lod = [[2, 1], [3, 2, 4]]
*/
LoD ConvertToLengthBasedLoD(const LoD& offset_lod);
LoD ConvertToOffsetBasedLoD(const LoD& length_lod);
} // namespace framework
} // namespace paddle
paddle/fluid/framework/lod_tensor_test.cc
浏览文件 @ f4bdfbc6
......@@ -228,6 +228,38 @@ TEST(LoD, CheckAbsLoD) {
ASSERT_FALSE(CheckAbsLoD(abs_lod0));
}
TEST(LoD, ConvertToLengthBasedLoD) {
LoD offset_lod;
offset_lod.push_back(std::vector<size_t>({0, 2}));
offset_lod.push_back(std::vector<size_t>({0, 1, 3}));
offset_lod.push_back(std::vector<size_t>({0, 2, 4, 5}));
LoD length_lod = ConvertToLengthBasedLoD(offset_lod);
LoD expected;
expected.push_back(std::vector<size_t>({2}));
expected.push_back(std::vector<size_t>({1, 2}));
expected.push_back(std::vector<size_t>({2, 2, 1}));
EXPECT_EQ(length_lod, expected);
}
TEST(LoD, ConvertToOffsetBasedLoD) {
LoD length_lod;
length_lod.push_back(std::vector<size_t>({2}));
length_lod.push_back(std::vector<size_t>({1, 2}));
length_lod.push_back(std::vector<size_t>({2, 2, 1}));
LoD offset_lod = ConvertToOffsetBasedLoD(length_lod);
LoD expected;
expected.push_back(std::vector<size_t>({0, 2}));
expected.push_back(std::vector<size_t>({0, 1, 3}));
expected.push_back(std::vector<size_t>({0, 2, 4, 5}));
EXPECT_EQ(offset_lod, expected);
}
template <typename T>
static void TestRecordIO() {
LoDTensor tensor;
......
paddle/fluid/framework/operator.cc
浏览文件 @ f4bdfbc6
......@@ -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) {
Variable* var = scope.FindVar(name);
auto default_lod = LoD({{}});
......@@ -85,6 +98,7 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
}
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
VLOG(10) << "- " << DebugStringEx(&scope);
if (platform::is_gpu_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("Cannot run operator on place %s", place);
......@@ -94,6 +108,7 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
#endif
}
RunImpl(scope, place);
VLOG(10) << "+ " << DebugStringEx(&scope);
}
bool OperatorBase::HasInputs(const std::string& name) const {
......@@ -153,6 +168,10 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
for (size_t i = 0; i < input.second.size(); ++i) {
ss << input.second[i];
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 << "(" << GetLoD(*scope, input.second[i]) << ")";
}
......@@ -173,6 +192,10 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
for (size_t i = 0; i < output.second.size(); ++i) {
ss << output.second[i];
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 << "(" << GetLoD(*scope, output.second[i]) << ")";
}
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ f4bdfbc6
......@@ -145,9 +145,9 @@ void ParallelExecutor::BCastParamsToGPUs(
auto &dims = main_tensor.dims();
if (paddle::platform::is_gpu_place(main_tensor.place())) {
#ifdef PADDLE_WITH_CUDA
std::vector<void *> buffers;
size_t numel = main_tensor.numel();
ncclDataType_t data_type = platform::ToNCCLDataType(main_tensor.type());
platform::NCCLGroupGuard guard;
for (size_t i = 0; i < member_->places_.size(); ++i) {
auto place = member_->places_[i];
void *buffer;
......@@ -159,11 +159,21 @@ void ParallelExecutor::BCastParamsToGPUs(
t->Resize(dims);
buffer = t->mutable_data(place, main_tensor.type());
}
auto &nccl_ctx = member_->nccl_ctxs_->at(place);
platform::dynload::ncclBcast(buffer, numel, data_type, 0,
nccl_ctx.comm_, nccl_ctx.stream());
buffers.push_back(buffer);
}
member_->nccl_ctxs_->WaitAll();
PADDLE_ENFORCE_EQ(member_->places_.size(), buffers.size(),
"variables' buffer size to bcast NOT equal to places");
{
platform::NCCLGroupGuard guard;
for (size_t i = 0; i < member_->places_.size(); ++i) {
auto &nccl_ctx = member_->nccl_ctxs_->at(member_->places_[i]);
platform::dynload::ncclBcast(buffers[i], numel, data_type, 0,
nccl_ctx.comm_, nccl_ctx.stream());
}
member_->nccl_ctxs_->WaitAll();
}
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
......
paddle/fluid/framework/scope.cc
浏览文件 @ f4bdfbc6
......@@ -43,48 +43,29 @@ Scope& Scope::NewScope() const {
}
Variable* Scope::Var(const std::string& name) {
// acquire the lock when new var under this scope
std::unique_lock<std::mutex> lock(mutex_);
auto* v = FindVarLocally(name);
if (v != nullptr) return v;
v = new Variable();
vars_[name].reset(v);
VLOG(3) << "Create variable " << name;
v->name_ = &(vars_.find(name)->first);
return v;
return VarInternal(name);
}
Variable* Scope::Var(std::string* name) {
auto var_name = string::Sprintf("%p.%d", this, vars_.size());
std::unique_lock<std::mutex> lock(mutex_);
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
if (name != nullptr) {
*name = var_name;
*name = new_name;
}
return Var(var_name);
return VarInternal(new_name);
}
Variable* Scope::FindVar(const std::string& name) const {
// acquire the lock when find var
std::unique_lock<std::mutex> lock(mutex_);
return FindVarInternal(name);
}
Variable* Scope::FindVarInternal(const std::string& name) const {
auto var = FindVarLocally(name);
if (var != nullptr) {
return var;
}
return (parent_ == nullptr) ? nullptr : parent_->FindVarInternal(name);
}
const Scope* Scope::FindScope(const Variable* var) const {
for (auto& kv : vars_) {
if (kv.second.get() == var) {
return this;
}
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
std::unique_lock<std::mutex> lock(mutex_);
return FindScopeInternal(var);
}
void Scope::DropKids() {
std::unique_lock<std::mutex> lock(mutex_);
for (Scope* s : kids_) delete s;
......@@ -92,6 +73,7 @@ void Scope::DropKids() {
}
std::vector<std::string> Scope::LocalVarNames() const {
std::unique_lock<std::mutex> lock(mutex_);
std::vector<std::string> known_vars;
known_vars.reserve(this->vars_.size());
for (auto& p : vars_) {
......@@ -127,6 +109,39 @@ void Scope::EraseVars(const std::vector<std::string>& var_names) {
void Scope::Rename(const std::string& origin_name,
const std::string& new_name) const {
std::unique_lock<std::mutex> lock(mutex_);
RenameInternal(origin_name, new_name);
}
std::string Scope::Rename(const std::string& origin_name) const {
std::unique_lock<std::mutex> lock(mutex_);
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
RenameInternal(origin_name, new_name);
return new_name;
}
Variable* Scope::VarInternal(const std::string& name) {
auto* v = FindVarLocally(name);
if (v != nullptr) return v;
v = new Variable();
vars_[name].reset(v);
VLOG(3) << "Create variable " << name;
v->name_ = &(vars_.find(name)->first);
return v;
}
const Scope* Scope::FindScopeInternal(const Variable* var) const {
for (auto& kv : vars_) {
if (kv.second.get() == var) {
return this;
}
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
}
void Scope::RenameInternal(const std::string& origin_name,
const std::string& new_name) const {
auto origin_it = vars_.find(origin_name);
PADDLE_ENFORCE(origin_it != vars_.end(),
"Cannot find original variable with name %s", origin_name);
......@@ -137,10 +152,12 @@ void Scope::Rename(const std::string& origin_name,
vars_.erase(origin_it);
}
std::string Scope::Rename(const std::string& origin_name) const {
auto var_name = string::Sprintf("%p.%d", this, vars_.size());
Rename(origin_name, var_name);
return var_name;
Variable* Scope::FindVarInternal(const std::string& name) const {
auto var = FindVarLocally(name);
if (var != nullptr) {
return var;
}
return (parent_ == nullptr) ? nullptr : parent_->FindVar(name);
}
Variable* Scope::FindVarLocally(const std::string& name) const {
......
paddle/fluid/framework/scope.h
浏览文件 @ f4bdfbc6
......@@ -81,20 +81,29 @@ class Scope {
// Rename variable to a new name and return the new name
std::string Rename(const std::string& origin_name) const;
protected:
mutable std::unordered_map<std::string, std::unique_ptr<Variable>> vars_;
private:
// Call Scope::NewScope for a sub-scope.
explicit Scope(Scope const* parent) : parent_(parent) {}
// Called by Var.
Variable* VarInternal(const std::string& name);
// Called by FindScope.
const Scope* FindScopeInternal(const Variable* var) const;
// Called by Rename.
void RenameInternal(const std::string& origin_name,
const std::string& new_name) const;
// Called by FindVar recursively.
// Caller doesn't own the returned Variable.
Variable* FindVarInternal(const std::string& name) const;
// Called by FindVarInternal and Var.
// Caller doesn't own the returned Variable.
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.
mutable std::list<Scope*> kids_;
Scope const* parent_{nullptr};
......
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @ f4bdfbc6
set(FLUID_CORE_MODULES proto_desc memory lod_tensor executor init)
cc_library(analysis SRCS dot.cc node.cc data_flow_graph.cc graph_traits.cc subgraph_splitter.cc fluid_to_data_flow_graph_pass.cc
DEPS paddle_fluid)
cc_library(analysis SRCS pass_manager.cc dot.cc node.cc data_flow_graph.cc graph_traits.cc subgraph_splitter.cc
fluid_to_data_flow_graph_pass.cc
data_flow_graph_to_fluid_pass.cc
tensorrt_subgraph_pass.cc
dfg_graphviz_draw_pass.cc
DEPS framework_proto)
cc_test(test_node SRCS node_tester.cc DEPS analysis)
cc_test(test_dot SRCS dot_tester.cc DEPS analysis)
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
cc_test(test_data_flow_graph SRCS data_flow_graph_tester.cc DEPS analysis ${FLUID_CORE_MODULES} paddle_fluid
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
set_tests_properties(test_data_flow_graph PROPERTIES DEPENDS test_word2vec)
function (inference_analysis_test TARGET)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS)
cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cc_test(test_subgraph_splitter
SRCS subgraph_splitter_tester.cc
DEPS analysis paddle_fluid tensor
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
set_tests_properties(test_subgraph_splitter PROPERTIES DEPENDS test_word2vec)
cc_test(${TARGET}
SRCS "${analysis_test_SRCS}"
DEPS analysis
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model --fraction_of_gpu_memory_to_use=0.5)
set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
endfunction(inference_analysis_test)
cc_test(test_dfg_graphviz_draw_pass
SRCS dfg_graphviz_draw_pass_tester.cc
DEPS analysis
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
set_tests_properties(test_dfg_graphviz_draw_pass PROPERTIES DEPENDS test_word2vec)
inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)
inference_analysis_test(test_fluid_to_data_flow_graph_pass SRCS fluid_to_data_flow_graph_pass_tester.cc)
inference_analysis_test(test_subgraph_splitter SRCS subgraph_splitter_tester.cc)
inference_analysis_test(test_dfg_graphviz_draw_pass SRCS dfg_graphviz_draw_pass_tester.cc)
#inference_analysis_test(test_tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass_tester.cc)
inference_analysis_test(test_pass_manager SRCS pass_manager_tester.cc)
paddle/fluid/inference/analysis/argument.cc 0 → 100644
浏览文件 @ f4bdfbc6
// 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/inference/analysis/argument.h"
paddle/fluid/inference/analysis/argument.h 0 → 100644
浏览文件 @ f4bdfbc6
// 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.
/*
* This file defines the class Argument, which is the input and output of the
* analysis module. All the fields that needed either by Passes or PassManagers
* are contained in Argument.
*
* TODO(Superjomn) Find some way better to contain the fields when it grow too
* big.
*/
#pragma once
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
namespace paddle {
namespace inference {
namespace analysis {
/*
* The argument definition of both Pass and PassManagers.
*
* All the fields should be registered here for clearness.
*/
struct Argument {
// The graph that process by the Passes or PassManagers.
std::unique_ptr<DataFlowGraph> main_dfg;
// The original program desc.
std::unique_ptr<framework::proto::ProgramDesc> origin_program_desc;
};
#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
#define ANALYSIS_ARGUMENT_CHECK_FIELD(field__) \
if (UNLIKELY(!(field__))) { \
LOG(ERROR) << "field " << #field__ << " should be set."; \
return false; \
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/data_flow_graph.cc
浏览文件 @ f4bdfbc6
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/dot.h"
#include "paddle/fluid/inference/analysis/node.h"
namespace paddle {
namespace inference {
......@@ -57,19 +58,7 @@ std::string DataFlowGraph::DotString() const {
// Add nodes
for (size_t i = 0; i < nodes.size(); i++) {
const Node &node = nodes.Get(i);
switch (node.type()) {
case Node::Type::kValue:
dot.AddNode(node.repr(), node.dot_attrs());
break;
case Node::Type::kFunction:
dot.AddNode(node.repr(), node.dot_attrs());
break;
case Node::Type::kFunctionBlock:
dot.AddNode(node.repr(), node.dot_attrs());
break;
default:
PADDLE_THROW("unsupported Node type %d", static_cast<int>(node.type()));
}
dot.AddNode(node.repr(), node.dot_attrs());
}
// Add edges
......
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc 0 → 100644
浏览文件 @ f4bdfbc6
// 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/inference/analysis/data_flow_graph_to_fluid_pass.h"
#include "paddle/fluid/framework/proto_desc.h"
namespace paddle {
namespace inference {
namespace analysis {
bool DataFlowGraphToFluidPass::Initialize(Argument* argument) {
ANALYSIS_ARGUMENT_CHECK_FIELD(argument)
ANALYSIS_ARGUMENT_CHECK_FIELD(argument->origin_program_desc)
desc_ = argument->origin_program_desc.get();
// Here some logic from program_desc.cc and will not add new interfaces into
// framework::ProgramDesc class, use some UT to assure the correctness.
auto* block = desc_->mutable_blocks()->Add();
block->set_idx(framework::kRootBlockIndex);
block->set_parent_idx(framework::kNoneBlockIndex);
return true;
}
bool DataFlowGraphToFluidPass::Finalize() { return true; }
void DataFlowGraphToFluidPass::Run(DataFlowGraph* graph) {
auto traits = GraphTraits<DataFlowGraph>(graph);
for (auto it = traits.nodes().begin(); it != traits.nodes().end(); ++it) {
if (it->deleted()) continue;
switch (it->type()) {
case Node::Type::kFunction:
LOG(INFO) << "add function " << it->name();
AddFluidOp(&(*it));
break;
case Node::Type::kFunctionBlock:
AddEngineOp(&(*it));
break;
default:
continue;
}
}
}
void DataFlowGraphToFluidPass::AddFluidOp(Node* node) {
LOG(INFO) << "processing func " << node->name();
auto* ori_op = static_cast<framework::proto::OpDesc*>(node->pb_desc());
// currently only the main block is analyzed.
auto* main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
auto* op = main_block->add_ops();
LOG(INFO) << "to copy the op";
*op = *ori_op; // copy the attributes, by default, these will not be changed
// by analysis phrase.
// The inputs and outputs of the existing ops are not changed by tensorrt
// subgraph pass.
// NOTE It might be changed by other passes in the long run.
}
void DataFlowGraphToFluidPass::AddEngineOp(Node* node) {
// auto* ori_op = static_cast<framework::proto::OpDesc*>(node->extra_info());
// auto* main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
// auto* op = main_block->add_ops();
// TODO(Superjomn) Here need to expose some arguments for default setting.
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h 0 → 100644
浏览文件 @ f4bdfbc6
/* 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. */
/*
* This file implements the transformation from fluid ProgramDesc to data flow
* graph.
*/
#pragma once
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/pass.h"
namespace paddle {
namespace inference {
namespace analysis {
class DataFlowGraphToFluidPass final : public DataFlowGraphPass {
public:
DataFlowGraphToFluidPass() = default;
bool Initialize(Argument *argument) override;
bool Finalize() override;
void Run(DataFlowGraph *graph) override;
std::string repr() const override { return "DFG to fluid"; }
std::string description() const override {
return "Transform a DFG to a Fluid ProgramDesc";
}
Pass *CreatePrinterPass(std::ostream &os,
const std::string &banner) const override {
return nullptr;
}
protected:
// Add a Fluid Op into the ProgramDesc.
void AddFluidOp(Node *node);
// Add a EngineOp into the ProgramDesc.
void AddEngineOp(Node *node);
private:
framework::proto::ProgramDesc *desc_;
};
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass_tester.cc
浏览文件 @ f4bdfbc6
......@@ -27,13 +27,12 @@ namespace inference {
namespace analysis {
TEST_F(DFG_Tester, Test) {
framework::proto::ProgramDesc new_desc;
DataFlowGraph graph;
FluidToDataFlowGraphPass pass0;
DataFlowGraphToFluidPass pass1;
pass0.Initialize(desc);
pass1.Initialize(&new_desc);
ASSERT_TRUE(pass0.Initialize(&argument));
ASSERT_TRUE(pass1.Initialize(&argument));
pass0.Run(&graph);
pass1.Run(&graph);
......
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.cc 0 → 100644
浏览文件 @ f4bdfbc6
/* 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/inference/analysis/dfg_graphviz_draw_pass.h"
namespace paddle {
namespace inference {
namespace analysis {
void DFG_GraphvizDrawPass::Run(DataFlowGraph *graph) {
auto content = Draw(graph);
std::ofstream file(GenDotPath());
file.write(content.c_str(), content.size());
file.close();
LOG(INFO) << "draw dot to " << GenDotPath();
}
std::string DFG_GraphvizDrawPass::Draw(DataFlowGraph *graph) {
Dot dot;
// Add nodes
for (size_t i = 0; i < graph->nodes.size(); i++) {
const Node &node = graph->nodes.Get(i);
if (config_.display_deleted_node || !node.deleted()) {
dot.AddNode(node.repr(), node.dot_attrs());
}
}
// Add edges
for (size_t i = 0; i < graph->nodes.size(); i++) {
const Node &node = graph->nodes.Get(i);
if (!config_.display_deleted_node && node.deleted()) continue;
for (auto &in : node.inlinks) {
if (!config_.display_deleted_node && in->deleted()) continue;
for (auto &in : node.inlinks) {
dot.AddEdge(in->repr(), node.repr(), {});
}
}
}
return dot.Build();
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h
浏览文件 @ f4bdfbc6
......@@ -21,6 +21,7 @@ limitations under the License. */
#include <fstream>
#include <string>
#include "paddle/fluid/inference/analysis/dot.h"
#include "paddle/fluid/inference/analysis/pass.h"
namespace paddle {
......@@ -32,35 +33,39 @@ namespace analysis {
*/
class DFG_GraphvizDrawPass : public DataFlowGraphPass {
public:
DFG_GraphvizDrawPass(const std::string& dir, const std::string& id)
: dir_(dir), id_(id) {}
bool Initialize() override { return Pass::Initialize(); }
void Run(DataFlowGraph* graph) override {
auto content = Draw(graph);
std::ofstream file(GenDotPath());
file.write(content.c_str(), content.size());
file.close();
LOG(INFO) << "draw dot to " << GenDotPath();
}
struct Config {
Config(const std::string &dir, const std::string &id,
bool display_deleted_node = false)
: dir(dir), id(id), display_deleted_node(display_deleted_node) {}
// The directory to store the .dot or .png files.
const std::string dir;
// The identifier for this dot file.
const std::string id;
// Whether to display deleted nodes, default false.
const bool display_deleted_node;
};
DFG_GraphvizDrawPass(const Config &config) : config_(config) {}
bool Initialize(Argument *argument) override { return true; }
void Run(DataFlowGraph *graph) override;
bool Finalize() override { return Pass::Finalize(); }
Pass* CreatePrinterPass(std::ostream& os,
const std::string& banner) const override {
return nullptr;
std::string repr() const override { return "DFG graphviz drawer"; }
std::string description() const override {
return "Debug a DFG by draw with graphviz";
}
private:
// Path of the dot file to output.
std::string GenDotPath() const {
return dir_ + "/" + "graph_" + id_ + ".dot";
return config_.dir + "/" + "graph_" + config_.id + ".dot";
}
std::string Draw(DataFlowGraph* graph) { return graph->DotString(); }
std::string Draw(DataFlowGraph *graph);
std::string dir_;
std::string id_;
Config config_;
};
} // namespace analysis
......
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc
浏览文件 @ f4bdfbc6
......@@ -24,9 +24,10 @@ namespace inference {
namespace analysis {
TEST_F(DFG_Tester, dfg_graphviz_draw_pass_tester) {
auto dfg = ProgramDescToDFG(desc);
DFG_GraphvizDrawPass pass("./", "test");
pass.Initialize();
auto dfg = ProgramDescToDFG(*argument.origin_program_desc);
DFG_GraphvizDrawPass::Config config("./", "test");
DFG_GraphvizDrawPass pass(config);
pass.Initialize(&argument);
pass.Run(&dfg);
// test content
......@@ -38,7 +39,8 @@ TEST_F(DFG_Tester, dfg_graphviz_draw_pass_tester) {
while (std::getline(file, line)) {
no++;
}
ASSERT_EQ(no, 82);
// DFG is sensitive to ProgramDesc, be careful to change the existing models.
ASSERT_EQ(no, 112);
}
} // namespace analysis
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.cc
浏览文件 @ f4bdfbc6
......@@ -21,19 +21,23 @@ namespace paddle {
namespace inference {
namespace analysis {
FluidToDataFlowGraphPass::FluidToDataFlowGraphPass() {}
bool FluidToDataFlowGraphPass::Initialize() { return Pass::Initialize(); }
bool FluidToDataFlowGraphPass::Initialize(
const framework::proto::ProgramDesc &desc) {
desc_ = &desc;
bool FluidToDataFlowGraphPass::Initialize(Argument *argument) {
ANALYSIS_ARGUMENT_CHECK_FIELD(argument);
ANALYSIS_ARGUMENT_CHECK_FIELD(argument->origin_program_desc);
PADDLE_ENFORCE(argument);
if (!argument->main_dfg) {
LOG(INFO) << "Init DFG";
argument->main_dfg.reset(new DataFlowGraph);
}
desc_ = argument->origin_program_desc.get();
return true;
}
bool FluidToDataFlowGraphPass::Finalize() { return Pass::Finalize(); }
void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
PADDLE_ENFORCE(graph);
PADDLE_ENFORCE(desc_);
// insert vars
std::unordered_map<std::string, size_t> var2id;
auto &main_block = desc_->blocks(framework::kRootBlockIndex);
......@@ -41,7 +45,7 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
const auto &var = main_block.vars(i);
auto *v = graph->nodes.Create(Node::Type::kValue);
v->SetName(var.name());
v->SetExtraInfo(const_cast<void *>(static_cast<const void *>(&var)));
v->SetPbDesc(const_cast<void *>(static_cast<const void *>(&var)));
var2id[var.name()] = v->id();
}
for (int i = 0; i < main_block.ops_size(); i++) {
......@@ -51,7 +55,7 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
static_cast<Function *>(o)->SetFuncType(op.type());
// Link to the original protobuf message's memory, make it easier to
// generate from a data flow graph to fluid ProgramDesc.
o->SetExtraInfo(const_cast<void *>(static_cast<const void *>(&op)));
o->SetPbDesc(const_cast<void *>(static_cast<const void *>(&op)));
// set inputs and outputs
// TODO(Superjomn) make sure the InputNames is the real variable name.
for (int j = 0; j < op.inputs_size(); j++) {
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h
浏览文件 @ f4bdfbc6
......@@ -34,13 +34,18 @@ namespace analysis {
*/
class FluidToDataFlowGraphPass final : public DataFlowGraphPass {
public:
FluidToDataFlowGraphPass();
bool Initialize() override;
bool Initialize(const framework::proto::ProgramDesc &desc) override;
FluidToDataFlowGraphPass() = default;
bool Initialize(Argument *argument) override;
bool Finalize() override;
void Run(DataFlowGraph *graph) override;
std::string repr() const override { return "fluid-to-data-flow-graph"; }
std::string description() const override {
return "transform a fluid ProgramDesc to a data flow graph.";
}
Pass *CreatePrinterPass(std::ostream &os,
const std::string &banner) const override;
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass_tester.cc
浏览文件 @ f4bdfbc6
......@@ -23,11 +23,11 @@ namespace analysis {
TEST_F(DFG_Tester, Init) {
FluidToDataFlowGraphPass pass;
pass.Initialize();
pass.Initialize(desc);
pass.Initialize(&argument);
DataFlowGraph graph;
pass.Run(&graph);
ASSERT_GT(graph.nodes.size(), 0);
// Analysis is sensitive to ProgramDesc, careful to change the original model.
ASSERT_EQ(graph.nodes.size(), 37);
pass.Finalize();
LOG(INFO) << '\n' << graph.DotString();
}
......
paddle/fluid/inference/analysis/helper.h
浏览文件 @ f4bdfbc6
......@@ -62,6 +62,7 @@ struct DataTypeNamer {
SET_TYPE(int);
SET_TYPE(bool);
SET_TYPE(float);
SET_TYPE(void *);
}
std::unordered_map<decltype(typeid(int).hash_code()), // NOLINT
......
paddle/fluid/inference/analysis/node.cc
浏览文件 @ f4bdfbc6
......@@ -40,6 +40,9 @@ Node *NodeMap::Create(Node::Type type) {
case Node::Type::kValue:
nodes_.emplace_back(new Value);
break;
case Node::Type::kFunctionBlock:
nodes_.emplace_back(new FunctionBlock);
break;
default:
PADDLE_THROW("Not supported node type.");
}
......
paddle/fluid/inference/analysis/node.h
浏览文件 @ f4bdfbc6
......@@ -71,12 +71,17 @@ class Node {
// Get an additional attribute and convert it to T data type. NOTE this will
// silently create a new attribute if not exists.
Attr &attr(const std::string &name) { return attrs_[name]; }
Attr &attr(const std::string &name) const { return attrs_[name]; }
int id() const { return id_; }
bool deleted() const { return deleted_; }
// The Protobuf description is set/get with a void* to decouple Node interface
// from a specific kind of Protobuf message.
void SetPbDesc(void *pb) { attr("pb_desc").Pointer() = pb; }
void *pb_desc() const { return attr("pb_desc").Pointer(); }
void SetDeleted() { deleted_ = true; }
bool deleted() const { return deleted_; }
void SetName(const std::string &name) { name_ = name; }
const std::string &name() const { return name_; }
......@@ -84,29 +89,25 @@ class Node {
void SetType(Type type) { type_ = type; }
Type type() const { return type_; }
void *extra_info() const { return extra_info_; }
void SetExtraInfo(void *extra_info) { extra_info_ = extra_info; }
// Input links.
std::vector<Node *> inlinks;
// Output links.
std::vector<Node *> outlinks;
// A helper class to maintain the status from Pass.
// TODO(superjomn) add a checker here to ensure the T is primary.
struct Attr {
// NOTE T should be a primary type or a struct combined by several primary
// types.
// NOTE the STL containers should not use here.
// Some usages
// Attr attr;
// T data;
// attr.data.assign((char*)data, sizeof(data));
// Attr attr;
// attr.Bool() = true;
bool &Bool() { return As<bool>(); }
float &Float() { return As<float>(); }
int32_t &Int32() { return As<int32_t>(); }
int64_t &Int64() { return As<int64_t>(); }
void *&Pointer() { return As<void *>(); }
private:
template <typename T>
......@@ -130,6 +131,7 @@ class Node {
size_t type_hash_{std::numeric_limits<size_t>::max()};
};
// Type checks.
bool IsFunction() const { return type_ == Node::Type::kFunction; }
bool IsValue() const { return type_ == Node::Type::kValue; }
bool IsFunctionBlock() const { return type_ == Node::Type::kFunctionBlock; }
......@@ -148,9 +150,6 @@ class Node {
Type type_{Type::kNone};
// Mark this node is deleted by some pass.
bool deleted_{false};
void *extra_info_;
mutable std::unordered_map<std::string, Attr> attrs_;
};
......
paddle/fluid/inference/analysis/pass.h
浏览文件 @ f4bdfbc6
......@@ -19,6 +19,7 @@ limitations under the License. */
#include <string>
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/inference/analysis/argument.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/helper.h"
#include "paddle/fluid/inference/analysis/node.h"
......@@ -30,19 +31,24 @@ namespace analysis {
class Pass {
public:
Pass() = default;
virtual ~Pass() {}
virtual ~Pass() = default;
// Virtual method overridden by subclasses to do only necessary initialization
// before any pass is run.
virtual bool Initialize() { return false; }
// virtual bool Initialize() { return false; }
// There is some passes such as FlowToDataFlowGraphPass that needs a
// ProgramDesc. Here use the native ProgramDesc ProtoBuf message, so that it
// only couple with the proto file.
virtual bool Initialize(const framework::proto::ProgramDesc &desc) {
return false;
}
// virtual bool Initialize(const framework::proto::ProgramDesc &desc) { return
// false; }
// There are some Passes such as DataFlowGraphToFluidPass that will output a
// ProgramDesc.
virtual bool Initialize(framework::proto::ProgramDesc *desc) { return false; }
// virtual bool Initialize(framework::proto::ProgramDesc *desc) { return
// false; }
// Mutable Pass.
virtual bool Initialize(Argument *argument) { return false; }
// Readonly Pass.
virtual bool Initialize(const Argument &argument) { return false; }
// Virtual method overriden by subclasses to do any necessary clean up after
// all passes have run.
......@@ -50,7 +56,9 @@ class Pass {
// Get a Pass appropriate to print the Node this pass operates on.
virtual Pass *CreatePrinterPass(std::ostream &os,
const std::string &banner) const = 0;
const std::string &banner) const {
return nullptr;
}
// Run on a single Node.
virtual void Run(Node *x) { LOG(FATAL) << "not valid"; }
......@@ -60,6 +68,11 @@ class Pass {
virtual void Run(FunctionBlock *x) { LOG(FATAL) << "not valid"; }
// Run on a single DataFlowGraph.
virtual void Run(DataFlowGraph *x) { LOG(FATAL) << "not valid"; }
// Human-readable short representation.
virtual std::string repr() const = 0;
// Human-readable long description.
virtual std::string description() const = 0;
};
// NodePass process on any Node types.
......
paddle/fluid/inference/analysis/pass_manager.cc 0 → 100644
浏览文件 @ f4bdfbc6
/* 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/inference/analysis/pass_manager.h"
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
namespace paddle {
namespace inference {
namespace analysis {
void DfgPassManager::RunAll() {
PADDLE_ENFORCE(argument_);
for (auto& pass : data_) {
VLOG(4) << "Running pass [" << pass->repr() << "]";
pass->Run(argument_->main_dfg.get());
}
}
void NodePassManager::RunAll() {
PADDLE_ENFORCE(argument_);
PADDLE_ENFORCE(argument_->main_dfg.get());
auto trait =
GraphTraits<DataFlowGraph>(argument_->main_dfg.get()).nodes_in_DFS();
for (auto& node : trait) {
for (auto& pass : data_) {
pass->Run(&node);
}
}
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/pass_manager.h 0 → 100644
浏览文件 @ f4bdfbc6
/* 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. */
/*
* This file defines the logic of pass management. The analysis for inference is
* a pipeline of Passes, a PassManager is a agency that helps to manage the
* executation of the Passes.
*
* There are two modes of Passes, the first one is called NodePass and takes
* an Node as input and output; the second one is called DFGPass and takes a
* DFG(Data Flow Graph) as input and output. It is hard to put all the passes in
* the same pipeline, there are two kinds of PassManagers, both takes a DFG as
* input and output a DFG, but the Passes inside are different:
*
* 1. NodePassManager: the passes inside are all NodePasses, it can have
* different graph trivial algorithm, for example, DFS_NodePassManager will
* trigger the passes in depth first order;
* 2. DfgPassManager: the passes inside are all DfgPasses.
*/
#pragma once
#include <string>
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/pass.h"
namespace paddle {
namespace inference {
namespace analysis {
/*
* PassManager is the base class for all pass managers, a pass manager has
* several Pass-es registered, and execute them in the linear order.
*/
class PassManager : public OrderedRegistry<Pass> {
public:
PassManager() = default;
// Call all the passes' Initialize methods. The desc and data_flow_graph are
// globally shared, so pass them as the arguemnts for all the pass managers.
virtual bool Initialize(const Argument& argument) { return false; }
virtual bool Initialize(Argument* argument) {
argument_ = argument;
for (auto& pass : data_) {
LOG(INFO) << "Initializing pass " << pass->repr();
if (!pass->Initialize(argument)) {
LOG(ERROR) << "Failed to initialize pass [" << pass->repr() << "]";
return false;
}
}
return true;
}
// Call all the passes' Finalize methods.
virtual bool Finalize() {
for (auto& pass : data_) {
if (!pass->Finalize()) {
LOG(ERROR) << "Failed to finalize pass [" << pass->repr() << "]";
return false;
}
}
return true;
}
// Run all the passes.
virtual void RunAll() = 0;
// Short identifier.
virtual std::string repr() const = 0;
// Long description.
virtual std::string description() const = 0;
virtual ~PassManager() = default;
protected:
Argument* argument_{nullptr};
};
/*
* A pass manager that process a DFG.
*/
class DfgPassManager : public PassManager {
public:
DfgPassManager() = default;
void RunAll() override;
virtual ~DfgPassManager() = default;
};
/*
* A pass manager that process a Node each time.
*/
class NodePassManager : public PassManager {
public:
NodePassManager() = default;
void RunAll() override;
virtual ~NodePassManager() = default;
};
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/pass_manager_tester.cc 0 → 100644
浏览文件 @ f4bdfbc6
/* 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/inference/analysis/pass_manager.h"
#include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include <gtest/gtest.h>
namespace paddle {
namespace inference {
namespace analysis {
class TestDfgPassManager final : public DfgPassManager {
public:
TestDfgPassManager() = default;
virtual ~TestDfgPassManager() = default;
// Short identifier.
std::string repr() const override { return "test-pass-manager"; }
// Long description.
std::string description() const override { return "test doc"; }
};
class TestNodePassManager final : public NodePassManager {
public:
virtual ~TestNodePassManager() = default;
std::string repr() const override { return "test-node-pass-manager"; }
std::string description() const override { return "test doc"; }
};
class TestNodePass final : public NodePass {
public:
virtual ~TestNodePass() = default;
bool Initialize(Argument* argument) override { return true; }
void Run(Node* node) override {
LOG(INFO) << "- Processing node " << node->repr();
}
std::string repr() const override { return "test-node"; }
std::string description() const override { return "some doc"; }
};
TEST_F(DFG_Tester, DFG_pass_manager) {
TestDfgPassManager manager;
DFG_GraphvizDrawPass::Config config("./", "dfg.dot");
manager.Register("fluid-to-flow-graph", new FluidToDataFlowGraphPass);
manager.Register("graphviz", new DFG_GraphvizDrawPass(config));
manager.Register("dfg-to-fluid", new DataFlowGraphToFluidPass);
ASSERT_TRUE(manager.Initialize(&argument));
manager.RunAll();
}
TEST_F(DFG_Tester, Node_pass_manager) {
// Pre-process: initialize the DFG with the ProgramDesc first.
FluidToDataFlowGraphPass pass0;
pass0.Initialize(&argument);
pass0.Run(argument.main_dfg.get());
TestNodePassManager manager;
manager.Register("test-node-pass", new TestNodePass);
ASSERT_TRUE(manager.Initialize(&argument));
manager.RunAll();
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/subgraph_splitter_tester.cc
浏览文件 @ f4bdfbc6
......@@ -19,22 +19,23 @@ namespace paddle {
namespace inference {
namespace analysis {
SubGraphSplitter::NodeInsideSubgraphTeller teller = [](const Node* node) {
if (node->type() != Node::Type::kFunction) return false;
const auto* func = static_cast<const Function*>(node);
if (func->func_type() == "elementwise_add" || func->func_type() == "relu" ||
func->func_type() == "conv2d" || func->func_type() == "mul" ||
func->func_type() == "sigmoid" || func->func_type() == "softmax") {
LOG(INFO) << "sub-graph marked " << node->repr();
return true;
}
return false;
};
TEST_F(DFG_Tester, Split) {
auto desc = LoadProgramDesc();
auto dfg = ProgramDescToDFG(desc);
LOG(INFO) << "spliter\n" << dfg.DotString();
SubGraphSplitter::NodeInsideSubgraphTeller teller = [](const Node* node) {
if (node->type() != Node::Type::kFunction) return false;
const auto* func = static_cast<const Function*>(node);
if (func->func_type() == "elementwise_add" || func->func_type() == "relu" ||
func->func_type() == "conv2d" || func->func_type() == "mul" ||
func->func_type() == "sigmoid" || func->func_type() == "softmax") {
LOG(INFO) << "sub-graph marked " << node->repr();
return true;
}
return false;
};
ASSERT_GT(dfg.nodes.size(), 5UL);
auto subgraphs = SubGraphSplitter(&dfg, teller)();
......@@ -62,6 +63,28 @@ TEST_F(DFG_Tester, Split) {
ASSERT_EQ(subgraphs.back().size(), 6UL);
}
TEST_F(DFG_Tester, Fuse) {
auto desc = LoadProgramDesc();
auto dfg = ProgramDescToDFG(desc);
size_t count0 = dfg.nodes.size();
SubGraphFuse fuse(&dfg, teller);
fuse();
int count1 = 0;
for (auto& node : dfg.nodes.nodes()) {
if (node->deleted()) {
LOG(INFO) << "deleted " << node->repr();
}
count1 += node->deleted();
}
// At least one nodes should be deleted.
ASSERT_EQ(dfg.nodes.size(), count0 + 1); // added a new FunctionBlock
ASSERT_EQ(6UL, count1);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc 0 → 100644
浏览文件 @ f4bdfbc6
// 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/inference/analysis/tensorrt_subgraph_pass.h"
#include "paddle/fluid/inference/analysis/subgraph_splitter.h"
namespace paddle {
namespace inference {
namespace analysis {
TensorRTSubGraphPass::TensorRTSubGraphPass(
const TensorRTSubGraphPass::NodeInsideSubgraphTeller &teller)
: node_inside_subgraph_teller_(teller) {}
void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
SubGraphFuse(graph, node_inside_subgraph_teller_);
}
} // analysis
} // inference
} // paddle
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h 0 → 100644
浏览文件 @ f4bdfbc6
/* 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 "paddle/fluid/inference/analysis/node.h"
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/subgraph_splitter.h"
namespace paddle {
namespace inference {
namespace analysis {
/*
* Parse the graph and replace TensorRT supported nodes with SubGraphNode
*/
class TensorRTSubGraphPass : public DataFlowGraphPass {
public:
// Tell whether to transform a sub-graph into TensorRT.
using NodeInsideSubgraphTeller = SubGraphFuse::NodeInsideSubgraphTeller;
TensorRTSubGraphPass(const NodeInsideSubgraphTeller& teller);
bool Initialize(Argument* argument) override { return true; }
// This class get a sub-graph as input and determine whether to transform this
// sub-graph into TensorRT.
void Run(DataFlowGraph* graph) override;
private:
NodeInsideSubgraphTeller node_inside_subgraph_teller_;
};
} // namespace analysis
} // namespace inference
} // paddle
paddle/fluid/inference/analysis/tensorrt_subgraph_pass_tester.cc 0 → 100644
浏览文件 @ f4bdfbc6
/* 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/inference/analysis/tensorrt_subgraph_pass.h"
#include <gflags/gflags.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
namespace paddle {
namespace inference {
namespace analysis {
DEFINE_string(model_dir, "", "inference test model dir");
TEST(TensorRTSubGraph, single_pass) {
auto desc = LoadProgramDesc();
auto dfg = ProgramDescToDFG(desc);
SubGraphSplitter::NodeInsideSubgraphTeller teller = [](const Node* node) {
if (node->type() != Node::Type::kFunction) return false;
const auto* func = static_cast<const Function*>(node);
if (func->func_type() == "elementwise_add" || func->func_type() == "relu" ||
func->func_type() == "conv2d" || func->func_type() == "mul" ||
func->func_type() == "sigmoid" || func->func_type() == "softmax") {
LOG(INFO) << "sub-graph marked " << node->repr();
return true;
}
return false;
};
DFG_GraphvizDrawPass::Config config{"./", "test"};
DFG_GraphvizDrawPass dfg_pass(config);
dfg_pass.Initialize();
DFG_GraphvizDrawPass dfg_pass1(config);
dfg_pass1.Initialize();
dfg_pass.Run(&dfg);
TensorRTSubGraphPass trt_pass(std::move(teller));
trt_pass.Initialize();
trt_pass.Run(&dfg);
dfg_pass1.Run(&dfg);
// Check the TRT op's block desc
for (auto node : dfg.nodes.nodes()) {
if (node->IsFunctionBlock()) {
}
}
}
TEST(TensorRTSubGraph, pass_manager) {}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/ut_helper.h
浏览文件 @ f4bdfbc6
......@@ -15,33 +15,46 @@ limitations under the License. */
#pragma once
#include <gflags/gflags.h>
#include <gtest/gtest.h>
#include <fstream>
#include <string>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/io.h"
namespace paddle {
namespace inference {
// Read ProgramDesc from a __model__ file, defined in io.cc
extern void ReadBinaryFile(const std::string& filename, std::string* contents);
namespace analysis {
DEFINE_string(inference_model_dir, "", "inference test model dir");
static framework::proto::ProgramDesc LoadProgramDesc(
const std::string& model_dir = FLAGS_inference_model_dir) {
paddle::platform::CPUPlace place;
paddle::framework::Executor executor(place);
paddle::framework::Scope scope;
auto program = Load(&executor, &scope, model_dir);
return *program->Proto();
std::string msg;
std::string net_file = FLAGS_inference_model_dir + "/__model__";
std::ifstream fin(net_file, std::ios::in | std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s", net_file);
fin.seekg(0, std::ios::end);
msg.resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(msg.at(0)), msg.size());
fin.close();
framework::proto::ProgramDesc program_desc;
program_desc.ParseFromString(msg);
return program_desc;
}
static DataFlowGraph ProgramDescToDFG(
const framework::proto::ProgramDesc& desc) {
DataFlowGraph graph;
FluidToDataFlowGraphPass pass;
pass.Initialize(desc);
Argument argument;
argument.origin_program_desc.reset(new framework::proto::ProgramDesc(desc));
pass.Initialize(&argument);
pass.Run(&graph);
pass.Finalize();
return graph;
......@@ -49,9 +62,12 @@ static DataFlowGraph ProgramDescToDFG(
class DFG_Tester : public ::testing::Test {
protected:
void SetUp() override { desc = LoadProgramDesc(FLAGS_inference_model_dir); }
void SetUp() override {
auto desc = LoadProgramDesc(FLAGS_inference_model_dir);
argument.origin_program_desc.reset(new framework::proto::ProgramDesc(desc));
}
framework::proto::ProgramDesc desc;
Argument argument;
};
} // namespace analysis
......
paddle/fluid/inference/io.cc
浏览文件 @ f4bdfbc6
......@@ -20,16 +20,20 @@ limitations under the License. */
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/pybind/pybind.h"
DEFINE_string(devices, "", "The devices to be used which is joined by comma.");
DEFINE_bool(init_p2p, false, "Whether to init p2p.");
DEFINE_int32(math_num_threads, 1,
"Number of threads used to run math functions.");
namespace paddle {
namespace inference {
void Init(const std::vector<std::string> argv) {
framework::InitGflags(argv);
operators::math::SetNumThreads(FLAGS_math_num_threads);
// init devices
std::vector<int> devices;
std::string token;
......
paddle/fluid/inference/tensorrt/convert/op_converter.h
浏览文件 @ f4bdfbc6
......@@ -64,7 +64,8 @@ class OpConverter {
(*it)(op, scope, test_mode);
}
// convert fluid block to tensorrt network
// Convert a fluid block to tensorrt network, NOTE it just convert operators,
// the INetwork's inputs and outputs should specified in some other modules.
void ConvertBlock(const framework::proto::BlockDesc& block,
const std::unordered_set<std::string>& parameters,
const framework::Scope& scope, TensorRTEngine* engine) {
......
paddle/fluid/inference/tensorrt/engine.h
浏览文件 @ f4bdfbc6
......@@ -51,11 +51,12 @@ class TensorRTEngine : public EngineBase {
nvinfer1::Weights w_;
};
TensorRTEngine(int max_batch, int max_workspace, cudaStream_t* stream,
TensorRTEngine(int max_batch, int max_workspace,
cudaStream_t* stream = nullptr,
nvinfer1::ILogger& logger = NaiveLogger::Global())
: max_batch_(max_batch),
max_workspace_(max_workspace),
stream_(stream),
stream_(stream ? stream : &default_stream_),
logger_(logger) {}
virtual ~TensorRTEngine();
......@@ -121,6 +122,8 @@ class TensorRTEngine : public EngineBase {
// the max memory size the engine uses
int max_workspace_;
cudaStream_t* stream_;
// If stream_ is not set from outside, hold its own stream.
cudaStream_t default_stream_;
nvinfer1::ILogger& logger_;
std::vector<Buffer> buffers_;
......@@ -165,20 +168,31 @@ class TensorRTEngine : public EngineBase {
*/
class TRT_EngineManager {
public:
TensorRTEngine* Create(int max_batch, int max_workspace,
cudaStream_t* stream) {
engines_.emplace_back(new TensorRTEngine(max_batch, max_workspace, stream));
return engines_.back().get();
bool HasEngine(const std::string& name) const {
return engines_.count(name) != 0;
}
// Get an engine called `name`.
TensorRTEngine* Get(const std::string& name) const {
return engines_.at(name).get();
}
// Create or get an engine called `name`
TensorRTEngine* Create(int max_batch, int max_workspace, cudaStream_t* stream,
const std::string& name) {
auto* p = new TensorRTEngine(max_batch, max_workspace, stream);
engines_[name].reset(p);
return p;
}
void DeleteALl() {
for (auto& ptr : engines_) {
ptr.reset(nullptr);
for (auto& item : engines_) {
item.second.reset(nullptr);
}
}
private:
std::vector<std::unique_ptr<TensorRTEngine>> engines_;
std::unordered_map<std::string, std::unique_ptr<TensorRTEngine>> engines_;
};
} // namespace tensorrt
......
paddle/fluid/inference/tests/book/test_inference_nlp.cc
浏览文件 @ f4bdfbc6
......@@ -29,6 +29,7 @@ DEFINE_string(data_file, "", "File of input index data.");
DEFINE_int32(repeat, 100, "Running the inference program repeat times");
DEFINE_bool(prepare_vars, true, "Prepare variables before executor");
DEFINE_int32(num_threads, 1, "Number of threads should be used");
DECLARE_bool(use_mkldnn);
inline double GetCurrentMs() {
struct timeval time;
......@@ -103,9 +104,9 @@ void ThreadRunInfer(
const int tid, paddle::framework::Scope* scope,
const std::vector<std::vector<const paddle::framework::LoDTensor*>>& jobs) {
// maybe framework:ProgramDesc is not thread-safe
paddle::platform::CPUPlace place;
paddle::framework::Executor executor(place);
auto& sub_scope = scope->NewScope();
auto place = paddle::platform::CPUPlace();
auto executor = paddle::framework::Executor(place);
auto inference_program =
paddle::inference::Load(&executor, scope, FLAGS_model_path);
......@@ -182,8 +183,8 @@ TEST(inference, nlp) {
stop_ms = GetCurrentMs();
} else {
// 1. Define place, executor, scope
auto place = paddle::platform::CPUPlace();
auto executor = paddle::framework::Executor(place);
paddle::platform::CPUPlace place;
paddle::framework::Executor executor(place);
// 2. Initialize the inference_program and load parameters
std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
......
paddle/fluid/memory/detail/system_allocator.cc
浏览文件 @ f4bdfbc6
......@@ -43,14 +43,16 @@ void* CPUAllocator::Alloc(size_t* index, size_t size) {
*index = 0; // unlock memory
void* p;
void* p = nullptr;
#ifdef PADDLE_WITH_MKLDNN
// refer to https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp
// memory alignment
PADDLE_ENFORCE_EQ(posix_memalign(&p, 4096ul, size), 0);
PADDLE_ENFORCE_EQ(posix_memalign(&p, 4096ul, size), 0, "Alloc %ld error!",
size);
#else
PADDLE_ENFORCE_EQ(posix_memalign(&p, 32ul, size), 0);
PADDLE_ENFORCE_EQ(posix_memalign(&p, 32ul, size), 0, "Alloc %ld error!",
size);
#endif
PADDLE_ENFORCE(p, "Fail to allocate CPU memory: size = %d .", size);
......
paddle/fluid/operators/activation_mkldnn_op.cc
浏览文件 @ f4bdfbc6
......@@ -12,16 +12,20 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "mkldnn.hpp"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/mkldnn_activation_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
using framework::DataLayout;
using framework::Tensor;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::stream;
using platform::GetMKLDNNFormat;
using platform::MKLDNNDeviceContext;
using platform::to_void_cast;
namespace {
std::string gethash(const mkldnn::memory::dims &operand_dims,
......@@ -35,188 +39,260 @@ std::string gethash(const mkldnn::memory::dims &operand_dims,
};
return dim2str(operand_dims) + std::to_string(algorithm);
}
} // namespace
template <typename Functor>
class MKLDNNActivationKernel
: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const auto *x = ctx.Input<Tensor>("X");
PADDLE_ENFORCE(x->layout() == DataLayout::kMKLDNN &&
x->format() != memory::format::format_undef,
"Wrong layout/format set for Input x tensor");
Functor functor;
auto attrs = functor.GetAttrs();
for (auto &attr : attrs) {
*attr.second = ctx.Attr<float>(attr.first);
}
functor(ctx);
}
};
template <typename T, typename ExecContext>
void eltwise_forward(const ExecContext &ctx, mkldnn::algorithm algorithm,
const T alpha = 0, const T beta = 0) {
template <typename Functor>
class MKLDNNActivationGradKernel
: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
PADDLE_ENFORCE(diff_y->layout() == DataLayout::kMKLDNN &&
diff_y->format() != memory::format::format_undef,
"Wrong layout/format set for Input OutGrad tensor");
Functor functor;
auto attrs = functor.GetAttrs();
for (auto &attr : attrs) {
*attr.second = ctx.Attr<float>(attr.first);
}
functor(ctx);
}
};
template <typename T>
void eltwise_forward(const framework::ExecutionContext &ctx,
mkldnn::algorithm algorithm, const T alpha = 0,
const T beta = 0) {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto &mkldnn_engine = dev_ctx.GetEngine();
// get buffers
const auto *src = ctx.template Input<Tensor>("X");
const auto *src_data = src->template data<T>();
const auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Out");
auto *dst = ctx.template Output<Tensor>("Out");
T *dst_data = dst->template mutable_data<T>(ctx.GetPlace());
const T *x_data = x->data<T>();
T *y_data = y->mutable_data<T>(ctx.GetPlace());
// get memory dim
PADDLE_ENFORCE(src->dims().size() == 2 || src->dims().size() == 4,
PADDLE_ENFORCE(x->dims().size() == 2 || x->dims().size() == 4,
"Input dim must be with 2 or 4");
std::vector<int> src_tz = framework::vectorize2int(src->dims());
std::vector<int> src_tz = framework::vectorize2int(x->dims());
auto src_format =
src_tz.size() == 2 ? mkldnn::memory::format::nc : x->format();
const std::string key = gethash(src_tz, algorithm);
const std::string key_src_data =
key + ctx.op().Output("Out") + "@eltwise_fwd_src_data";
const std::string key_src_mem = key + "@eltwise_fwd_src_mem";
const std::string key_dst_mem = key + "@eltwise_fwd_dst_mem";
const std::string key_fwd = key + "@eltwise_fwd";
const std::string key_src_layout =
key + ctx.op().Output("Out") + "@eltwise_fwd_src_layout";
const std::string key_with_layout = key + std::to_string(src_format);
const std::string key_src_mem = key_with_layout + "@eltwise_fwd_src_mem";
const std::string key_dst_mem = key_with_layout + "@eltwise_fwd_dst_mem";
const std::string key_fwd = key_with_layout + "@eltwise_fwd";
const std::string key_fwd_pd = key_with_layout + "@eltwise_fwd_pd";
// save input data and layout to be referred in backward path
auto p_src_data = std::make_shared<const T *>(x_data);
dev_ctx.SetBlob(key_src_data, p_src_data);
auto p_src_layout = std::make_shared<memory::format>(src_format);
dev_ctx.SetBlob(key_src_layout, p_src_layout);
auto p_fwd = std::static_pointer_cast<mkldnn::eltwise_forward>(
dev_ctx.GetBlob(key_fwd));
// save input data to be referred in backward path
auto p_src_data = std::make_shared<const T *>(src_data);
dev_ctx.SetBlob(key_src_data, p_src_data);
std::shared_ptr<memory> dst_memory;
if (p_fwd == nullptr) {
// create memory description
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// create memory primitives
auto p_src_mem = std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(src_data)));
dev_ctx.SetBlob(key_src_mem, p_src_mem);
auto p_dst_mem = std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(dst_data)));
dev_ctx.SetBlob(key_dst_mem, p_dst_mem);
auto fwd_desc = mkldnn::eltwise_forward::desc(
mkldnn::prop_kind::forward_training, algorithm, data_md, alpha, beta);
auto p_fwd_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
fwd_desc, mkldnn_engine);
const std::string key_fwd_pd = key + "eltwise_fwd_pd";
dev_ctx.SetBlob(key_fwd_pd, p_fwd_pd);
p_fwd = std::make_shared<mkldnn::eltwise_forward>(
*p_fwd_pd, *(p_src_mem.get()), *(p_dst_mem.get()));
// create mkldnn memory for input X
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<T>(), src_format);
auto src_memory = std::shared_ptr<memory>(
new memory({src_md, mkldnn_engine}, to_void_cast(x_data)));
// save src_memory to be referred in backward path
dev_ctx.SetBlob(key_src_mem, src_memory);
// create primitive descriptor for activation forward and save it
auto forward_desc = mkldnn::eltwise_forward::desc(
mkldnn::prop_kind::forward_training, algorithm,
src_memory->get_primitive_desc().desc(), alpha, beta);
auto forward_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
forward_desc, mkldnn_engine);
// save prim desc into global device context to be referred in backward path
dev_ctx.SetBlob(key_fwd_pd, forward_pd);
// create mkldnn memory for output y
dst_memory =
std::make_shared<memory>(forward_pd->dst_primitive_desc(), y_data);
dev_ctx.SetBlob(key_dst_mem, dst_memory);
// create activation primitive
p_fwd = std::make_shared<mkldnn::eltwise_forward>(*forward_pd, *src_memory,
*dst_memory);
dev_ctx.SetBlob(key_fwd, p_fwd);
} else {
// primitives already exist
auto p_src_mem =
auto src_memory =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
PADDLE_ENFORCE(p_src_mem != nullptr,
"Fail to find eltwise p_src_mem in device context.");
auto p_dst_mem =
PADDLE_ENFORCE(src_memory != nullptr,
"Fail to find eltwise src_memory in device context.");
dst_memory =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_dst_mem));
PADDLE_ENFORCE(p_dst_mem != nullptr,
"Fail to find eltwise p_src_mem in device context.");
PADDLE_ENFORCE(dst_memory != nullptr,
"Fail to find eltwise dst_memory in device context.");
p_src_mem->set_data_handle(platform::to_void_reinterpret_cast(src_data));
p_dst_mem->set_data_handle(dst_data);
src_memory->set_data_handle(platform::to_void_cast(x_data));
dst_memory->set_data_handle(y_data);
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline = {*(p_fwd.get())};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
std::vector<primitive> pipeline;
pipeline.push_back(*p_fwd);
stream(stream::kind::eager).submit(pipeline).wait();
y->set_layout(DataLayout::kMKLDNN);
y->set_format(GetMKLDNNFormat(*dst_memory));
}
template <typename T, typename ExecContext>
void eltwise_grad(const ExecContext &ctx, mkldnn::algorithm algorithm,
const T alpha = 0, const T beta = 0) {
template <typename T>
void eltwise_grad(const framework::ExecutionContext &ctx,
mkldnn::algorithm algorithm, const T alpha = 0,
const T beta = 0) {
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto &mkldnn_engine = dev_ctx.GetEngine();
// get buffers
const auto *out = ctx.template Input<Tensor>("Out");
auto *dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));
const auto *diff_dst = dout->template data<T>();
const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto *diff_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *dx =
ctx.template Output<framework::Tensor>(framework::GradVarName("X"));
const T *diff_src = dx->template mutable_data<T>(ctx.GetPlace());
const T *diff_y_data = diff_y->data<T>();
T *diff_x_data = diff_x->mutable_data<T>(ctx.GetPlace());
// get memory dim
std::vector<int> src_tz = framework::vectorize2int(out->dims());
std::vector<int> diff_dst_tz = framework::vectorize2int(diff_y->dims());
const std::string key = gethash(src_tz, algorithm);
const std::string key_diff_src_mem = key + "@eltwise_diff_src_mem";
const std::string key_diff_dst_mem = key + "@eltwise_diff_dst_mem";
const std::string key_grad = key + "@eltwise_grad";
auto diff_y_format =
diff_dst_tz.size() == 2 ? mkldnn::memory::format::nc : diff_y->format();
const std::string key = gethash(diff_dst_tz, algorithm);
const std::string key_src_data =
key + ctx.op().Input("Out") + "@eltwise_fwd_src_data";
const std::string key_src_layout =
key + ctx.op().Input("Out") + "@eltwise_fwd_src_layout";
const auto p_src_layout =
std::static_pointer_cast<memory::format>(dev_ctx.GetBlob(key_src_layout));
const std::string key_src_mem =
key + std::to_string(*p_src_layout) + "@eltwise_fwd_src_mem";
const std::string key_fwd_pd =
key + std::to_string(*p_src_layout) + "@eltwise_fwd_pd";
const std::string key_with_layouts =
key + std::to_string(*p_src_layout) + "-" + std::to_string(diff_y_format);
const std::string key_diff_src_mem =
key_with_layouts + "@eltwise_diff_src_mem";
const std::string key_diff_dst_mem =
key_with_layouts + "@eltwise_diff_dst_mem";
const std::string key_grad = key_with_layouts + "@eltwise_grad";
const auto p_src_data =
std::static_pointer_cast<T *>(dev_ctx.GetBlob(key_src_data));
const std::string key_src_mem = key + "@eltwise_fwd_src_mem";
auto p_src_mem =
auto src_memory =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
p_src_mem->set_data_handle(*p_src_data.get());
PADDLE_ENFORCE(src_memory != nullptr,
"Fail to find src_memory in device context");
src_memory->set_data_handle(*p_src_data.get());
std::shared_ptr<memory> diff_src_memory;
auto p_grad = std::static_pointer_cast<mkldnn::eltwise_forward::primitive>(
auto p_grad = std::static_pointer_cast<mkldnn::eltwise_backward>(
dev_ctx.GetBlob(key_grad));
if (p_grad == nullptr) {
// create memory description
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// create memory primitives
std::shared_ptr<void> p_diff_src_mem =
std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(diff_src)));
dev_ctx.SetBlob(key_diff_src_mem, p_diff_src_mem);
std::shared_ptr<void> p_diff_dst_mem =
std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(diff_dst)));
dev_ctx.SetBlob(key_diff_dst_mem, p_diff_dst_mem);
auto bwd_desc = mkldnn::eltwise_backward::desc(algorithm, data_md, data_md,
alpha, beta);
const std::string key_fwd_pd = key + "eltwise_fwd_pd";
auto *p_fwd_pd = static_cast<mkldnn::eltwise_forward::primitive_desc *>(
dev_ctx.GetBlob(key_fwd_pd).get());
auto eltwise_bwd_prim_desc = mkldnn::eltwise_backward::primitive_desc(
bwd_desc, mkldnn_engine, *p_fwd_pd);
// create mkldnn memory for input diff_y
auto diff_dst_md = platform::MKLDNNMemDesc(
diff_dst_tz, platform::MKLDNNGetDataType<T>(), diff_y_format);
auto diff_dst_memory = std::shared_ptr<memory>(
new memory({diff_dst_md, mkldnn_engine}, to_void_cast(diff_y_data)));
dev_ctx.SetBlob(key_diff_dst_mem, diff_dst_memory);
// retrieve eltwise primitive desc from device context
auto forward_pd =
std::static_pointer_cast<mkldnn::eltwise_forward::primitive_desc>(
dev_ctx.GetBlob(key_fwd_pd));
PADDLE_ENFORCE(forward_pd != nullptr,
"Fail to find eltwise_fwd_pd in device context");
// ceate primitive descriptor for activation backward
auto backward_desc = mkldnn::eltwise_backward::desc(
algorithm, diff_dst_memory->get_primitive_desc().desc(),
src_memory->get_primitive_desc().desc(), alpha, beta);
auto backward_pd = mkldnn::eltwise_backward::primitive_desc(
backward_desc, mkldnn_engine, *forward_pd);
// create mkldnn memory for output diff_src
diff_src_memory = std::make_shared<memory>(
backward_pd.diff_src_primitive_desc(), diff_x_data);
dev_ctx.SetBlob(key_diff_src_mem, diff_src_memory);
// create activation backward primitive
p_grad = std::make_shared<mkldnn::eltwise_backward>(
eltwise_bwd_prim_desc, *static_cast<mkldnn::memory *>(p_src_mem.get()),
*(static_cast<mkldnn::memory *>(p_diff_dst_mem.get())),
*(static_cast<mkldnn::memory *>(p_diff_src_mem.get())));
backward_pd, *src_memory, *diff_dst_memory, *diff_src_memory);
dev_ctx.SetBlob(key_grad, p_grad);
} else {
// primitives already exist
auto p_diff_src_mem = std::static_pointer_cast<mkldnn::memory>(
diff_src_memory = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(key_diff_src_mem));
auto p_diff_dst_mem = std::static_pointer_cast<mkldnn::memory>(
auto diff_dst_memory = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(key_diff_dst_mem));
p_diff_src_mem->set_data_handle(
platform::to_void_reinterpret_cast(diff_src));
p_diff_dst_mem->set_data_handle(
platform::to_void_reinterpret_cast(diff_dst));
diff_src_memory->set_data_handle(
platform::to_void_reinterpret_cast(diff_x_data));
diff_dst_memory->set_data_handle(
platform::to_void_reinterpret_cast(diff_y_data));
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline = {*(p_grad.get())};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
std::vector<primitive> pipeline;
pipeline.push_back(*p_grad);
stream(stream::kind::eager).submit(pipeline).wait();
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format(GetMKLDNNFormat(*diff_src_memory));
}
} // anonymous namespace
template <typename T, mkldnn::algorithm algorithm>
struct MKLDNNActivationFunc : public BaseActivationFunctor<T> {
template <typename ExecContext>
void operator()(const ExecContext &ctx) const {
void operator()(const framework::ExecutionContext &ctx) const {
eltwise_forward<T>(ctx, algorithm);
}
};
template <typename T, mkldnn::algorithm algorithm>
struct MKLDNNActivationGradFunc : public BaseActivationFunctor<T> {
template <typename ExecContext>
void operator()(const ExecContext &ctx) const {
void operator()(const framework::ExecutionContext &ctx) const {
eltwise_grad<T>(ctx, algorithm);
}
};
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ f4bdfbc6
......@@ -19,6 +19,8 @@ limitations under the License. */
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
#define REGISTER_ACTIVATION_OP_MAKER(OP_NAME, OP_COMMENT) \
class OP_NAME##OpMaker \
: public ::paddle::framework::OpProtoAndCheckerMaker { \
......@@ -29,7 +31,7 @@ namespace operators {
AddAttr<bool>("use_mkldnn", \
"(bool, default false) Only used in mkldnn kernel") \
.SetDefault(false); \
AddComment(OP_COMMENT); \
AddComment(#OP_COMMENT); \
} \
}
......@@ -58,7 +60,6 @@ framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel& oper,
const std::string& name) {
framework::LibraryType library{framework::LibraryType::kPlain};
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN
auto it = oper.Attrs().find("use_mkldnn");
......@@ -82,6 +83,7 @@ class ActivationOp : public framework::OperatorWithKernel {
ctx->ShareLoD("X", /*->*/ "Out");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetKernelType(ctx, *this, "X");
......@@ -96,6 +98,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetKernelType(ctx, *this, "Out");
......@@ -112,7 +115,7 @@ $$out = \frac{1}{1 + e^{-x}}$$
__attribute__((unused)) constexpr char LogSigmoidDoc[] = R"DOC(
Logsigmoid Activation Operator
$$out = \log \frac{1}{1 + e^{-x}}$$
$$out = \\log \\frac{1}{1 + e^{-x}}$$
)DOC";
......@@ -133,7 +136,7 @@ $out = \max(x, 0)$
__attribute__((unused)) constexpr char TanhDoc[] = R"DOC(
Tanh Activation Operator.
$$out = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
$$out = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC";
......@@ -196,7 +199,7 @@ $out = [x]$
__attribute__((unused)) constexpr char ReciprocalDoc[] = R"DOC(
Reciprocal Activation Operator.
$$out = \frac{1}{x}$$
$$out = \\frac{1}{x}$$
)DOC";
......@@ -252,15 +255,14 @@ class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "Output of Softshrink operator");
AddAttr<float>("lambda", "non-negative offset").SetDefault(0.5f);
AddComment(R"DOC(
Softshrink Activation Operator.
:strong:`Softshrink Activation Operator`
$$
out = \begin{cases}
x - \lambda, \text{if } x > \lambda \\
x + \lambda, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
$$
.. math::
out = \begin{cases}
x - \lambda, \text{if } x > \lambda \\
x + \lambda, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
)DOC");
}
......@@ -271,18 +273,18 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "Input of HardShrink operator");
AddOutput("Out", "Output of HardShrink operator");
AddAttr<float>("threshold", "The value of threshold for HardShrink")
AddAttr<float>("threshold",
"The value of threshold for HardShrink. [default: 0.5]")
.SetDefault(0.5f);
AddComment(R"DOC(
HardShrink Activation Operator.
:strong:`HardShrink activation operator`
$$
out = \begin{cases}
x, \text{if } x > \lambda \\
x, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
$$
.. math::
out = \begin{cases}
x, \text{if } x > \lambda \\
x, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
)DOC");
}
......@@ -394,18 +396,18 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "Input of ThresholdedRelu operator");
AddOutput("Out", "Output of ThresholdedRelu operator");
AddAttr<float>("threshold", "The threshold location of activation")
AddAttr<float>("threshold",
"The threshold location of activation. [default 1.0].")
.SetDefault(1.0f);
AddComment(R"DOC(
ThresholdedRelu Activation Operator.
:strong:`ThresholdedRelu activation operator`
$$
out = \begin{cases}
x, \text{if } x > threshold \\
0, \text{otherwise}
\end{cases}
$$
.. math::
out = \begin{cases}
x, \text{if } x > threshold \\
0, \text{otherwise}
\end{cases}
)DOC");
}
};
......@@ -444,7 +446,7 @@ class SwishOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Swish Activation Operator.
$$out = \frac{x}{1 + e^{- \beta x}}$$
$$out = \\frac{x}{1 + e^{- \beta x}}$$
)DOC");
}
......
paddle/fluid/operators/chunk_eval_op.cc
浏览文件 @ f4bdfbc6
......@@ -91,32 +91,31 @@ class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker {
"(int64_t). The number of chunks both in Inference and Label on the "
"given mini-batch.");
AddAttr<int>("num_chunk_types",
"(int). The number of chunk type. See below for details.");
AddAttr<std::string>(
"chunk_scheme",
"(string, default IOB). The labeling scheme indicating "
"how to encode the chunks. Must be IOB, IOE, IOBES or plain. See below "
"for details.")
"The number of chunk type. See the description for details.");
AddAttr<std::string>("chunk_scheme",
"The labeling scheme indicating "
"how to encode the chunks. Must be IOB, IOE, IOBES or "
"plain. See the description"
"for details.")
.SetDefault("IOB");
AddAttr<std::vector<int>>("excluded_chunk_types",
"(list<int>) A list including chunk type ids "
"A list including chunk type ids "
"indicating chunk types that are not counted. "
"See below for details.")
"See the description for details.")
.SetDefault(std::vector<int>{});
AddComment(R"DOC(
For some basics of chunking, please refer to
‘Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>’.
'Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>'.
CheckEvalOp computes the precision, recall, and F1-score of chunk detection,
ChunkEvalOp computes the precision, recall, and F1-score of chunk detection,
and supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.
Here is a NER example of labeling for these tagging schemes:
Li Ming works at Agricultural Bank of China in Beijing.
IO: I-PER I-PER O O I-ORG I-ORG I-ORG I-ORG O I-LOC
IOB: B-PER I-PER O O B-ORG I-ORG I-ORG I-ORG O B-LOC
IOE: I-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O E-LOC
IOBES: B-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O S-LOC
Li Ming works at Agricultural Bank of China in Beijing.
IO I-PER I-PER O O I-ORG I-ORG I-ORG I-ORG O I-LOC
IOB B-PER I-PER O O B-ORG I-ORG I-ORG I-ORG O B-LOC
IOE I-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O E-LOC
IOBES B-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O S-LOC
There are three chunk types(named entity types) including PER(person), ORG(organization)
and LOC(LOCATION), and we can see that the labels have the form <tag type>-<chunk type>.
......@@ -124,31 +123,31 @@ and LOC(LOCATION), and we can see that the labels have the form <tag type>-<chun
Since the calculations actually use label ids rather than labels, extra attention
should be paid when mapping labels to ids to make CheckEvalOp work. The key point
is that the listed equations are satisfied by ids.
tag_type = label % num_tag_type
chunk_type = label / num_tag_type
tag_type = label % num_tag_type
chunk_type = label / num_tag_type
where `num_tag_type` is the num of tag types in the tagging scheme, `num_chunk_type`
is the num of chunk types, and `tag_type` get its value from the following table.
Scheme Begin Inside End Single
plain 0 - - -
IOB 0 1 - -
IOE - 0 1 -
IOBES 0 1 2 3
Scheme Begin Inside End Single
plain 0 - - -
IOB 0 1 - -
IOE - 0 1 -
IOBES 0 1 2 3
Still use NER as example, assuming the tagging scheme is IOB while chunk types are ORG,
PER and LOC. To satisfy the above equations, the label map can be like this:
B-ORG 0
I-ORG 1
B-PER 2
I-PER 3
B-LOC 4
I-LOC 5
O 6
B-ORG 0
I-ORG 1
B-PER 2
I-PER 3
B-LOC 4
I-LOC 5
O 6
It’s not hard to verify the equations noting that the num of chunk types
It's not hard to verify the equations noting that the num of chunk types
is 3 and the num of tag types in IOB scheme is 2. For example, the label
id of I-LOC is 5, the tag type id of I-LOC is 1, and the chunk type id of
I-LOC is 2, which consistent with the results from the equations.
......
paddle/fluid/operators/clip_by_norm_op.cc
浏览文件 @ f4bdfbc6
......@@ -54,10 +54,19 @@ be linearly scaled to make the L2 norm of $Out$ equal to $max\_norm$, as
shown in the following formula:
$$
Out = \frac{max\_norm * X}{norm(X)},
Out = \\frac{max\\_norm * X}{norm(X)},
$$
where $norm(X)$ represents the L2 norm of $X$.
Examples:
.. code-block:: python
data = fluid.layer.data(
name='data', shape=[2, 4, 6], dtype='float32')
reshaped = fluid.layers.clip_by_norm(
x=data, max_norm=0.5)
)DOC");
}
};
......
paddle/fluid/operators/compare_op.cc
浏览文件 @ f4bdfbc6
......@@ -23,30 +23,26 @@ class CompareOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
OpComment comment;
AddInput("X",
string::Sprintf("(LoDTensor) the left hand operand of %s operator",
comment.type));
AddInput("Y", string::Sprintf(
"(LoDTensor) the right hand operand of %s operator",
comment.type));
AddInput("X", string::Sprintf("the left hand operand of %s operator",
comment.type));
AddInput("Y", string::Sprintf("the right hand operand of %s operator",
comment.type));
AddAttr<bool>("force_cpu",
"(bool, default false) Force fill output variable to cpu "
"Force fill output variable to cpu "
"memory. Otherwise, fill output variable to the running "
"device")
.SetDefault(false);
AddOutput("Out", string::Sprintf(
"(LoDTensor) n-dim bool tensor. Each element is %s",
comment.equation));
AddComment(string::Sprintf(R"DOC(%s Operator
"device [default true].")
.SetDefault(true);
AddOutput("Out", string::Sprintf("n-dim bool tensor. Each element is %s",
comment.equation));
AddComment(string::Sprintf(R"DOC(
It operates element-wise on X and Y, and returns the Out. Each of them is a
N-dim tensor. X and Y could be any type. The each element of the Out tensor is
calculated by %s
calculated by $%s$
)DOC",
comment.type, comment.equation));
AddAttr<int>("axis",
"(int, default -1). The start dimension index "
"for broadcasting Y onto X.")
comment.equation));
AddAttr<int>(
"axis",
"The start dimension index for broadcasting Y onto X. [default -1]")
.SetDefault(-1)
.EqualGreaterThan(-1);
}
......
paddle/fluid/operators/concat_op.cc
浏览文件 @ f4bdfbc6
......@@ -107,7 +107,13 @@ REGISTER_OPERATOR(concat, ops::ConcatOp, ops::ConcatOpMaker,
false> /* set false to disable empty grad */);
REGISTER_OPERATOR(concat_grad, ops::ConcatOpGrad);
REGISTER_OP_CPU_KERNEL(
concat, ops::ConcatKernel<paddle::platform::CPUDeviceContext, float>);
concat, ops::ConcatKernel<paddle::platform::CPUDeviceContext, double>,
ops::ConcatKernel<paddle::platform::CPUDeviceContext, float>,
ops::ConcatKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::ConcatKernel<paddle::platform::CPUDeviceContext, int>);
REGISTER_OP_CPU_KERNEL(
concat_grad,
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, float>);
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, int>);
paddle/fluid/operators/concat_op.cu.cc
浏览文件 @ f4bdfbc6
......@@ -15,7 +15,13 @@ limitations under the License. */
#include "paddle/fluid/operators/concat_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
concat, ops::ConcatKernel<paddle::platform::CUDADeviceContext, float>);
concat, ops::ConcatKernel<paddle::platform::CUDADeviceContext, double>,
ops::ConcatKernel<paddle::platform::CUDADeviceContext, float>,
ops::ConcatKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::ConcatKernel<paddle::platform::CUDADeviceContext, int>);
REGISTER_OP_CUDA_KERNEL(
concat_grad,
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, float>);
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, int>);
paddle/fluid/operators/concat_op.h
浏览文件 @ f4bdfbc6
......@@ -60,34 +60,45 @@ template <typename DeviceContext, typename T>
class ConcatGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* in = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto out_var_names = ctx.Outputs(framework::GradVarName("X"));
auto outs = ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
// get output tensor that the name is not kEmptyVarName
std::vector<framework::Tensor*> outputs;
for (size_t j = 0; j < outs.size(); ++j) {
if (out_var_names[j] != framework::kEmptyVarName) {
outs[j]->mutable_data<T>(ctx.GetPlace());
outputs.push_back(outs[j]);
} else {
outputs.push_back(nullptr);
}
}
// Sometimes direct copies will be faster, this maybe need deeply analysis.
if (axis == 0 && outs.size() < 10) {
size_t input_offset = 0;
auto in_stride = framework::stride_numel(in->dims());
const auto in_stride = framework::stride_numel(out_grad->dims());
for (auto& out : outs) {
out->mutable_data<T>(ctx.GetPlace());
auto out_stride = framework::stride_numel(out->dims());
StridedNumelCopyWithAxis<T>(ctx.device_context(), axis, out->data<T>(),
out_stride, in->data<T>() + input_offset,
in_stride, out_stride[axis]);
for (size_t i = 0; i < outs.size(); ++i) {
auto out_stride = framework::stride_numel(ins[i]->dims());
auto* out = outputs[i];
if (out != nullptr) {
StridedNumelCopyWithAxis<T>(
ctx.device_context(), axis, out->data<T>(), out_stride,
out_grad->data<T>() + input_offset, in_stride, out_stride[axis]);
}
input_offset += out_stride[axis];
}
} else {
std::vector<framework::Tensor> outputs(outs.size());
for (size_t j = 0; j < outs.size(); ++j) {
outs[j]->mutable_data<T>(ctx.GetPlace());
outputs[j] = *outs[j];
}
auto& dev_ctx = ctx.template device_context<DeviceContext>();
paddle::operators::math::ConcatGradFunctor<DeviceContext, T>
concat_grad_functor;
concat_grad_functor(dev_ctx, *in, static_cast<int>(axis), &outputs);
concat_grad_functor(dev_ctx, *out_grad, ins, static_cast<int>(axis),
&outputs);
}
}
};
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ f4bdfbc6
......@@ -75,9 +75,8 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType ConvOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
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
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
......
paddle/fluid/operators/conv_transpose_op.cc
浏览文件 @ f4bdfbc6
......@@ -156,7 +156,7 @@ Parameters(strides, paddings) are two elements. These two elements represent hei
and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
For an example:
Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{in}, C_{out}, H_f, W_f)$
......
paddle/fluid/operators/cos_sim_op.cc
浏览文件 @ f4bdfbc6
......@@ -76,9 +76,9 @@ class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
.AsIntermediate();
AddComment(R"DOC(
Cosine Similarity Operator.
**Cosine Similarity Operator**
$Out = X^T * Y / (\sqrt{X^T * X} * \sqrt{Y^T * Y})$
$Out = \frac{X^T * Y}{(\sqrt{X^T * X} * \sqrt{Y^T * Y})}$
The input X and Y must have the same shape, except that the 1st dimension
of input Y could be just 1 (different from input X), which will be
......
paddle/fluid/operators/crf_decoding_op.cc
浏览文件 @ f4bdfbc6
......@@ -53,21 +53,18 @@ sequence of observed tags.
The output of this operator changes according to whether Input(Label) is given:
1. Input(Label) is given:
This happens in training. This operator is used to co-work with the chunk_eval
operator.
When Input(Label) is given, the crf_decoding operator returns a row vector
with shape [N x 1] whose values are fixed to be 0, indicating an incorrect
prediction, or 1 indicating a tag is correctly predicted. Such an output is the
input to chunk_eval operator.
This happens in training. This operator is used to co-work with the chunk_eval
operator.
When Input(Label) is given, the crf_decoding operator returns a row vector
with shape [N x 1] whose values are fixed to be 0, indicating an incorrect
prediction, or 1 indicating a tag is correctly predicted. Such an output is the
input to chunk_eval operator.
2. Input(Label) is not given:
This is the standard decoding process.
This is the standard decoding process.
The crf_decoding operator returns a row vector with shape [N x 1] whose values
range from 0 to maximum tag number - 1. Each element indicates an index of a
range from 0 to maximum tag number - 1, Each element indicates an index of a
predicted tag.
)DOC");
}
......
paddle/fluid/operators/crop_op.h
浏览文件 @ f4bdfbc6
......@@ -52,7 +52,7 @@ static std::vector<int> GetOffsets(const framework::ExecutionContext& ctx) {
} else {
res = ctx.Attr<std::vector<int>>("offsets");
PADDLE_ENFORCE_EQ(
rank, res.size(),
rank, static_cast<int>(res.size()),
"Offsets size should be equal to dimension size of input tensor.");
}
return res;
......
paddle/fluid/operators/cumsum_op.cc
浏览文件 @ f4bdfbc6
......@@ -30,19 +30,19 @@ class CumOp : public framework::OperatorWithKernel {
class CumsumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "Input of Cumsum operator");
AddOutput("Out", "Output of Cumsum operator");
AddInput("X", "Input of cumsum operator");
AddOutput("Out", "Output of cumsum operator");
AddAttr<int>("axis",
"(int, default -1). The dimenstion to accumulate along. "
"-1 means the last dimenstion")
"The dimenstion to accumulate along. -1 means the last "
"dimenstion [default -1].")
.SetDefault(-1)
.EqualGreaterThan(-1);
AddAttr<bool>("exclusive",
"bool, default false). Whether to perform exclusive cumsum")
"Whether to perform exclusive cumsum. [default false].")
.SetDefault(false);
AddAttr<bool>("reverse",
"bool, default false). If true, the cumsum is performed in "
"the reversed direction")
"If true, the cumsum is performed in the reversed direction. "
"[default false].")
.SetDefault(false);
AddComment(R"DOC(
The cumulative sum of the elements along a given axis.
......
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @ f4bdfbc6
......@@ -245,7 +245,7 @@ void GRPCClient::Proceed() {
if (c->status_.ok()) {
c->Process();
} else {
LOG(ERROR) << "var: " << c->var_h_.String()
LOG(FATAL) << "var: " << c->var_h_.String()
<< " grpc error:" << c->status_.error_message();
}
delete c;
......
paddle/fluid/operators/detail/grpc_server.cc
浏览文件 @ f4bdfbc6
......@@ -169,7 +169,8 @@ class RequestPrefetch final : public RequestBase {
auto scope = request_->GetMutableLocalScope();
auto invar = scope->FindVar(in_var_name);
framework::Variable* outvar = scope->FindVar(out_var_name);
// out var must be created in local scope!
framework::Variable* outvar = scope->Var(out_var_name);
request_handler_->Handle(in_var_name, scope, invar, &outvar, out_var_name);
......
paddle/fluid/operators/detection/box_coder_op.cc
浏览文件 @ f4bdfbc6
......@@ -106,23 +106,36 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
"and M represents the number of deocded boxes.");
AddComment(R"DOC(
Bounding Box Coder Operator.
Bounding Box Coder.
Encode/Decode the target bounding box with the priorbox information.
The Encoding schema described below:
ox = (tx - px) / pw / pxv
oy = (ty - py) / ph / pyv
ow = log(abs(tw / pw)) / pwv
oh = log(abs(th / ph)) / phv
ox = (tx - px) / pw / pxv
oy = (ty - py) / ph / pyv
ow = log(abs(tw / pw)) / pwv
oh = log(abs(th / ph)) / phv
The Decoding schema described below:
ox = (pw * pxv * tx * + px) - tw / 2
oy = (ph * pyv * ty * + py) - th / 2
ow = exp(pwv * tw) * pw + tw / 2
oh = exp(phv * th) * ph + th / 2
where tx, ty, tw, th denote the target box's center coordinates, width and
height respectively. Similarly, px, py, pw, ph denote the priorbox's(anchor)
center coordinates, width and height. pxv, pyv, pwv, phv denote the variance
of the priorbox and ox, oy, ow, oh denote the encoded/decoded coordinates,
width and height.
ox = (pw * pxv * tx * + px) - tw / 2
oy = (ph * pyv * ty * + py) - th / 2
ow = exp(pwv * tw) * pw + tw / 2
oh = exp(phv * th) * ph + th / 2
where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates, width
and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote the
priorbox's (anchor) center coordinates, width and height. `pxv`, `pyv`, `pwv`,
`phv` denote the variance of the priorbox and `ox`, `oy`, `ow`, `oh` denote the
encoded/decoded coordinates, width and height.
)DOC");
}
};
......
paddle/fluid/operators/detection/iou_similarity_op.cc
浏览文件 @ f4bdfbc6
......@@ -68,15 +68,16 @@ class IOUSimilarityOpMaker : public framework::OpProtoAndCheckerMaker {
"representing pairwise iou scores.");
AddComment(R"DOC(
IOU Similarity Operator.
**IOU Similarity Operator**
Computes intersection-over-union (IOU) between two box lists.
Box list 'X' should be a LoDTensor and 'Y' is a common Tensor,
boxes in 'Y' are shared by all instance of the batched inputs of X.
Given two boxes A and B, the calculation of IOU is as follows:
Box list 'X' should be a LoDTensor and 'Y' is a common Tensor,
boxes in 'Y' are shared by all instance of the batched inputs of X.
Given two boxes A and B, the calculation of IOU is as follows:
$$
IOU(A, B) =
\frac{area(A\cap B)}{area(A)+area(B)-area(A\cap B)}
\\frac{area(A\\cap B)}{area(A)+area(B)-area(A\\cap B)}
$$
)DOC");
......
paddle/fluid/operators/detection/polygon_box_transform_op.cc
浏览文件 @ f4bdfbc6
......@@ -83,11 +83,13 @@ class PolygonBoxTransformOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
PolygonBoxTransform Operator.
PolygonBoxTransform Operator is used to transform the coordinate shift to the real coordinate.
The input is the final geometry output in detection network.
We use 2*n numbers to denote the coordinate shift from n corner vertices of
the polygon_box to the pixel location. As each distance offset contains two numbers (xi, yi),
the geometry output contains 2*n channels.
PolygonBoxTransform Operator is used to transform the coordinate shift to the real coordinate.
)DOC");
}
};
......
paddle/fluid/operators/elementwise_mul_op.cc
浏览文件 @ f4bdfbc6
......@@ -15,7 +15,7 @@ limitations under the License. */
#include "paddle/fluid/operators/elementwise_mul_op.h"
#include "paddle/fluid/operators/elementwise_op.h"
namespace ops = paddle::operators;
REGISTER_ELEMWISE_OP(elementwise_mul, "Mul", "Out = X \\odot\\ Y");
REGISTER_ELEMWISE_OP(elementwise_mul, "Mul", "Out = X \\\\odot Y");
REGISTER_OP_CPU_KERNEL(
elementwise_mul,
ops::ElementwiseMulKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/gaussian_random_batch_size_like_op.cc
浏览文件 @ f4bdfbc6
......@@ -36,11 +36,12 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
void Apply() override {
AddAttr<float>("mean",
"(float, default 0.0) "
"mean of random tensor.")
"The mean (or center) of the gaussian distribution.")
.SetDefault(.0f);
AddAttr<float>("std",
"(float, default 1.0) "
"std of random tensor.")
"The standard deviation (std, or spread) of the "
"gaussian distribution.")
.SetDefault(1.0f);
AddAttr<int>("seed",
"(int, default 0) "
......@@ -55,9 +56,11 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
.SetDefault(framework::proto::VarType::FP32);
AddComment(R"DOC(
GaussianRandom Operator.
Used to initialize tensors with gaussian random generator.
The defalut mean of the distribution is 0. and defalut standard
deviation (std) of the distribution is 1.. Uers can set mean and std
by input arguments.
)DOC");
}
};
......
paddle/fluid/operators/gen_nccl_id_op.cc
浏览文件 @ f4bdfbc6
......@@ -67,6 +67,10 @@ class GenNCCLIdOp : public framework::OperatorBase {
client->AsyncSendVar(ep, dev_ctx, *scope, NCCL_ID_VARNAME);
}
client->Wait();
for (auto& ep : endpoint_list) {
client->AsyncSendBatchBarrier(ep);
}
client->Wait();
VLOG(3) << "sending completed...";
}
......
paddle/fluid/operators/get_places_op.cc
浏览文件 @ f4bdfbc6
......@@ -85,7 +85,7 @@ class GetPlacesOpProtoMaker : public framework::OpProtoAndCheckerMaker {
.InEnum({"CUDA", "CPU", "AUTO"})
.SetDefault("AUTO");
AddComment(R"DOC(
Returns a list of places based on flags. The list will be used for parallel
Returns a list of places based on arguments. The list will be used for parallel
execution.
)DOC");
}
......
paddle/fluid/operators/layer_norm_op.cc
浏览文件 @ f4bdfbc6
......@@ -62,36 +62,33 @@ class LayerNormOp : public framework::OperatorWithKernel {
class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(LoDTensor) The input tensor.");
AddInput("X", "The input tensor.");
AddInput("Scale",
"(Tensor, optional) Scale is a 1-dimensional tensor of size "
"(optional) Scale is a 1-dimensional tensor of size "
"H(`begin_norm_axis` splits the tensor(`X`) to a matrix [N,H])."
"It is applied to the output.")
.AsDispensable();
AddInput("Bias",
"(Tensor, optional) Bias is a 1-dimensional tensor of size "
"(optional) Bias is a 1-dimensional tensor of size "
"H(`begin_norm_axis` splits the tensor(`X`) to a matrix [N,H])."
"It is applied to the output.")
.AsDispensable();
AddOutput("Y", "(LoDTensor) Result after normalization.");
AddOutput("Mean", "(Tensor) Mean of the current mini batch.")
.AsIntermediate();
AddOutput("Variance", "(Tensor) Variance of the current mini batch.")
AddOutput("Y", "Result after normalization.");
AddOutput("Mean", "Mean of the current mini batch.").AsIntermediate();
AddOutput("Variance", "Variance of the current mini batch.")
.AsIntermediate();
AddAttr<float>("epsilon",
"(float, default 1e-5) Constant for "
"numerical stability")
"Constant for numerical stability [default 1e-5].")
.SetDefault(1e-5)
.AddCustomChecker([](const float &epsilon) {
PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 0.001f,
"'epsilon' should be between 0.0 and 0.001.");
});
AddAttr<int>("begin_norm_axis",
"(int default:1), the "
"axis of `begin_norm_axis ... Rank(X) - 1` will be "
"the axis of `begin_norm_axis ... Rank(X) - 1` will be "
"normalized. `begin_norm_axis` splits the tensor(`X`) to a "
"matrix [N,H].")
"matrix [N,H]. [default 1].")
.SetDefault(1)
.AddCustomChecker([](const int &begin_norm_axis) {
PADDLE_ENFORCE_GT(begin_norm_axis, 0,
......@@ -99,10 +96,14 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
});
AddComment(R"DOC(
Layer Normalization.
Layer Norm has been implemented as discussed in the paper:
https://arxiv.org/abs/1607.06450
...
Assume feature vectors exist on dimensions
:attr:`begin_norm_axis ... rank(input)` and calculate the moment statistics
along these dimensions for each feature vector :math:`a` with size
:math:`H`, then normalize each feature vector using the corresponding
statistics. After that, apply learnable gain and bias on the normalized
tensor to scale and shift if :attr:`scale` and :attr:`shift` are set.
Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_
)DOC");
}
};
......
paddle/fluid/operators/linear_chain_crf_op.cc
浏览文件 @ f4bdfbc6
......@@ -84,6 +84,7 @@ CRF. Please refer to http://www.cs.columbia.edu/~mcollins/fb.pdf and
http://cseweb.ucsd.edu/~elkan/250Bwinter2012/loglinearCRFs.pdf for details.
Equation:
1. Denote Input(Emission) to this operator as $x$ here.
2. The first D values of Input(Transition) to this operator are for starting
weights, denoted as $a$ here.
......@@ -106,6 +107,7 @@ Finally, the linear chain CRF operator outputs the logarithm of the conditional
likelihood of each training sample in a mini-batch.
NOTE:
1. The feature function for a CRF is made up of the emission features and the
transition features. The emission feature weights are NOT computed in
this operator. They MUST be computed first before this operator is called.
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ f4bdfbc6
......@@ -348,7 +348,8 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
};
void SignalHandler::StopAndExit(int signal_num) {
VLOG(3) << "Catch interrupt signal: " << signal_num << ", program will exit";
// Do not use VLOG here for the device for printing maybe already released.
// exit will release interal allocated resoureces.
exit(0);
}
......
paddle/fluid/operators/lstm_op.cc
浏览文件 @ f4bdfbc6
......@@ -184,34 +184,32 @@ Long-Short Term Memory (LSTM) Operator.
The defalut implementation is diagonal/peephole connection
(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:
$$
i_t = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i) \\
$$ i_t = \\sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i) $$
f_t = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f) \\
$$ f_t = \\sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f) $$
\tilde{c_t} = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c) \\
$$ \\tilde{c_t} = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c) $$
o_t = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o) \\
$$ o_t = \\sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o) $$
c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t} \\
$$ c_t = f_t \\odot c_{t-1} + i_t \\odot \\tilde{c_t} $$
h_t = o_t \odot act_h(c_t)
$$
$$ h_t = o_t \\odot act_h(c_t) $$
where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
of weights from the input gate to the input), $W_{ic}, W_{fc}, W_{oc}$
are diagonal weight matrices for peephole connections. In our implementation,
we use vectors to reprenset these diagonal weight matrices. The b terms
denote bias vectors ($b_i$ is the input gate bias vector), $\sigma$
is the non-line activations, such as logistic sigmoid function, and
$i, f, o$ and $c$ are the input gate, forget gate, output gate,
and cell activation vectors, respectively, all of which have the same size as
the cell output activation vector $h$.
The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
are the cell input and cell output activation functions and `tanh` is usually
used for them. $\tilde{c_t}$ is also called candidate hidden state,
which is computed based on the current input and the previous hidden state.
- W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
of weights from the input gate to the input), $W_{ic}, W_{fc}, W_{oc}$
are diagonal weight matrices for peephole connections. In our implementation,
we use vectors to reprenset these diagonal weight matrices.
- The b terms denote bias vectors ($b_i$ is the input gate bias vector).
- $\sigma$ is the non-line activations, such as logistic sigmoid function.
- $i, f, o$ and $c$ are the input gate, forget gate, output gate,
and cell activation vectors, respectively, all of which have the same size as
the cell output activation vector $h$.
- The $\odot$ is the element-wise product of the vectors.
- $act_g$ and $act_h$ are the cell input and cell output activation functions
and `tanh` is usually used for them.
- $\tilde{c_t}$ is also called candidate hidden state,
which is computed based on the current input and the previous hidden state.
Set `use_peepholes` False to disable peephole connection. The formula
is omitted here, please refer to the paper
......
paddle/fluid/operators/math/blas.h
浏览文件 @ f4bdfbc6
......@@ -20,13 +20,16 @@
#ifdef PADDLE_WITH_MKLML
#include <mkl_cblas.h>
#include <mkl_lapacke.h>
#include <mkl_service.h>
#include <mkl_vml_functions.h>
#endif
#ifdef PADDLE_USE_OPENBLAS
#include <cblas.h>
#ifdef LAPACK_FOUND
#include <lapacke.h>
#endif
#endif
#ifndef LAPACK_FOUND
extern "C" {
......@@ -46,6 +49,18 @@ namespace paddle {
namespace operators {
namespace math {
static void SetNumThreads(int num_threads) {
#ifdef PADDLE_USE_OPENBLAS
int real_num_threads = num_threads > 1 ? num_threads : 1;
openblas_set_num_threads(real_num_threads);
#elif defined(PADDLE_WITH_MKLML)
int real_num_threads = num_threads > 1 ? num_threads : 1;
mkl_set_num_threads(real_num_threads);
#else
PADDLE_ENFORCE(false, "To be implemented.");
#endif
}
/**
* Matrix Descriptor of a memory buffer.
*
......
paddle/fluid/operators/math/concat.cc
浏览文件 @ f4bdfbc6
......@@ -70,35 +70,40 @@ template <typename T>
class ConcatGradFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, const int axis,
std::vector<framework::Tensor>* outputs) {
const framework::Tensor& input,
const std::vector<const framework::Tensor*>& ref_inputs,
const int axis, std::vector<framework::Tensor*>* outputs) {
// TODO(zcd): Add input data validity checking
int num = outputs->size();
size_t num = outputs->size();
int input_rows = 1;
auto dim_0 = outputs->at(0).dims();
auto dim_0 = ref_inputs[0]->dims();
for (int i = 0; i < axis; ++i) {
input_rows *= dim_0[i];
}
int input_cols = 0;
std::vector<int64_t> output_cols(outputs->size());
for (int i = 0; i < num; ++i) {
int t_cols = outputs->at(i).numel() / input_rows;
for (size_t i = 0; i < num; ++i) {
int t_cols = ref_inputs[i]->numel() / input_rows;
input_cols += t_cols;
output_cols[i] = t_cols;
}
auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
// computation
for (int k = 0; k < input_rows; ++k) {
for (size_t k = 0; k < input_rows; ++k) {
const T* src_ptr = input.data<T>() + k * input_cols;
int col_idx = 0;
for (int j = 0; j < num; ++j) {
int col_len = output_cols[j];
T* dst_ptr = outputs->at(j).data<T>() + k * col_len;
memory::Copy(cpu_place, dst_ptr, cpu_place, src_ptr + col_idx,
sizeof(T) * col_len);
auto* out_tensor = outputs->at(j);
if (out_tensor != nullptr) {
T* dst_ptr = out_tensor->data<T>() + k * col_len;
memory::Copy(cpu_place, dst_ptr, cpu_place, src_ptr + col_idx,
sizeof(T) * col_len);
}
col_idx += col_len;
}
}
......
paddle/fluid/operators/math/concat.cu
浏览文件 @ f4bdfbc6
......@@ -102,10 +102,12 @@ __global__ void KernelConcatGrad(const T* input_data, const int in_row,
int local_col = tid_x - curr_offset;
int segment_width = curr_col_offset - curr_offset;
T* output_ptr = outputs_data[curr_segment];
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * segment_width + local_col] =
input_data[tid_y * in_col + tid_x];
if (output_ptr != nullptr) {
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * segment_width + local_col] =
input_data[tid_y * in_col + tid_x];
}
}
}
......@@ -118,10 +120,12 @@ __global__ void KernelConcatGrad(const T* input_data, const int in_row,
int split = tid_x / fixed_out_col;
int in_offset = tid_x - split * fixed_out_col;
T* output_ptr = outputs_data[split];
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * fixed_out_col + in_offset] =
input_data[tid_y * in_col + tid_x];
if (output_ptr != nullptr) {
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * fixed_out_col + in_offset] =
input_data[tid_y * in_col + tid_x];
}
}
}
......@@ -203,17 +207,18 @@ template <typename T>
class ConcatGradFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input, const int axis,
std::vector<framework::Tensor>* outputs) {
const framework::Tensor& input,
const std::vector<const framework::Tensor*>& ref_inputs,
const int axis, std::vector<framework::Tensor*>* outputs) {
// TODO(zcd): Add input data validity checking
int o_num = outputs->size();
int out_row = 1;
auto dim_0 = outputs->at(0).dims();
auto dim_0 = ref_inputs[0]->dims();
for (int i = 0; i < axis; ++i) {
out_row *= dim_0[i];
}
int out_col = outputs->at(0).numel() / out_row;
int out0_col = ref_inputs[0]->numel() / out_row;
int in_col = 0, in_row = out_row;
bool sameShape = true;
......@@ -223,13 +228,17 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
outputs_cols[0] = 0;
for (int i = 0; i < o_num; ++i) {
int t_col = outputs->at(i).numel() / out_row;
int t_col = outputs->at(i)->numel() / out_row;
if (sameShape) {
if (t_col != out_col) sameShape = false;
if (t_col != out0_col) sameShape = false;
}
in_col += t_col;
outputs_cols[i + 1] = in_col;
outputs_ptr[i] = outputs->at(i).data<T>();
if (outputs->at(i) != nullptr) {
outputs_ptr[i] = outputs->at(i)->data<T>();
} else {
outputs_ptr[i] = nullptr;
}
}
T** dev_out_gpu_data =
......@@ -255,7 +264,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
if (sameShape) {
KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
input.data<T>(), in_row, in_col, out_col, dev_out_gpu_data);
input.data<T>(), in_row, in_col, out0_col, dev_out_gpu_data);
} else {
const int* dev_outs_col_data = outputs_cols.CUDAData(context.GetPlace());
KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
......
paddle/fluid/operators/math/concat.h
浏览文件 @ f4bdfbc6
......@@ -57,7 +57,8 @@ template <typename DeviceContext, typename T>
class ConcatGradFunctor {
public:
void operator()(const DeviceContext& context, const framework::Tensor& input,
const int axis, std::vector<framework::Tensor>* outputs);
const std::vector<const framework::Tensor*>& ref_inputs,
const int axis, std::vector<framework::Tensor*>* outputs);
};
} // namespace math
......
paddle/fluid/operators/math/math_function.h
浏览文件 @ f4bdfbc6
......@@ -21,8 +21,10 @@ limitations under the License. */
#ifdef PADDLE_USE_OPENBLAS
#include <cblas.h>
#ifdef LAPACK_FOUND
#include <lapacke.h>
#endif
#endif
#ifndef LAPACK_FOUND
extern "C" {
......
paddle/fluid/operators/mean_iou_op.cc 0 → 100644
浏览文件 @ f4bdfbc6
/* 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 @ f4bdfbc6
此差异已折叠。
paddle/fluid/operators/mean_iou_op.h 0 → 100644
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/operators/mean_op.cc
浏览文件 @ f4bdfbc6
......@@ -33,12 +33,10 @@ class MeanOp : public framework::OperatorWithKernel {
class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op").Reuse("X");
AddInput("X", "(Tensor) The input of mean op");
AddOutput("Out", "(Tensor) The output of mean op").Reuse("X");
AddComment(R"DOC(
Mean Operator.
Out is a scalar which is the mean of all elements in X.
Mean Operator calculates the mean of all elements in X.
)DOC");
}
......
paddle/fluid/operators/merge_ids_op.cc 0 → 100644
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/operators/merge_ids_op.h 0 → 100644
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/operators/nce_op.cc
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/operators/pool_op.cc
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/operators/scale_op.cc
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/operators/shape_op.cc
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/operators/slice_op.cc
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/operators/split_op.cc
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/platform/nccl_helper.h
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/fluid/pybind/pybind.cc
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/math/MathFunctions.h
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/scripts/paddle_build.sh
浏览文件 @ f4bdfbc6
此差异已折叠。
paddle/testing/paddle_gtest_main.cc
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/batch.py
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/fluid/data_feeder.py
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/fluid/framework.py
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/fluid/initializer.py
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/fluid/layers/io.py
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/fluid/layers/nn.py
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/fluid/layers/ops.py
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/fluid/lod_tensor.py
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/fluid/optimizer.py
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/fluid/tests/unittests/test_mean_iou.py 0 → 100644
浏览文件 @ f4bdfbc6
此差异已折叠。
python/paddle/v2/minibatch.py
浏览文件 @ f4bdfbc6
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或