Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
wmsofts
Paddle
提交
ac0e0f51
P
Paddle
项目概览
wmsofts
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
ac0e0f51
编写于
3月 14, 2019
作者:
S
sneaxiy
浏览文件
操作
浏览文件
下载
差异文件
merge develop
test=develop
上级
fc12f383
4ef6f738
变更
148
展开全部
隐藏空白更改
内联
并排
Showing
148 changed file
with
5634 addition
and
2676 deletion
+5634
-2676
benchmark/fluid/fluid_benchmark.py
benchmark/fluid/fluid_benchmark.py
+0
-1
paddle/fluid/API.spec
paddle/fluid/API.spec
+3
-0
paddle/fluid/framework/CMakeLists.txt
paddle/fluid/framework/CMakeLists.txt
+3
-3
paddle/fluid/framework/details/CMakeLists.txt
paddle/fluid/framework/details/CMakeLists.txt
+2
-1
paddle/fluid/framework/details/computation_op_handle.h
paddle/fluid/framework/details/computation_op_handle.h
+3
-0
paddle/fluid/framework/details/eager_deletion_op_handle.cc
paddle/fluid/framework/details/eager_deletion_op_handle.cc
+12
-2
paddle/fluid/framework/details/eager_deletion_pass.cc
paddle/fluid/framework/details/eager_deletion_pass.cc
+166
-5
paddle/fluid/framework/details/inplace_op_pass.cc
paddle/fluid/framework/details/inplace_op_pass.cc
+5
-0
paddle/fluid/framework/details/memory_optimize_pass.cc
paddle/fluid/framework/details/memory_optimize_pass.cc
+5
-0
paddle/fluid/framework/details/reference_count_pass.cc
paddle/fluid/framework/details/reference_count_pass.cc
+4
-9
paddle/fluid/framework/details/reference_count_pass_helper.cc
...le/fluid/framework/details/reference_count_pass_helper.cc
+14
-1
paddle/fluid/framework/details/reference_count_pass_helper.h
paddle/fluid/framework/details/reference_count_pass_helper.h
+8
-1
paddle/fluid/framework/details/while_op_eager_deletion_pass.cc
...e/fluid/framework/details/while_op_eager_deletion_pass.cc
+62
-0
paddle/fluid/framework/executor.cc
paddle/fluid/framework/executor.cc
+38
-19
paddle/fluid/framework/executor.h
paddle/fluid/framework/executor.h
+13
-4
paddle/fluid/framework/ir/graph.cc
paddle/fluid/framework/ir/graph.cc
+34
-1
paddle/fluid/framework/ir/graph.h
paddle/fluid/framework/ir/graph.h
+11
-1
paddle/fluid/framework/ir/node.h
paddle/fluid/framework/ir/node.h
+1
-0
paddle/fluid/framework/operator.cc
paddle/fluid/framework/operator.cc
+7
-5
paddle/fluid/framework/operator.h
paddle/fluid/framework/operator.h
+9
-0
paddle/fluid/framework/parallel_executor.cc
paddle/fluid/framework/parallel_executor.cc
+26
-11
paddle/fluid/framework/parallel_executor.h
paddle/fluid/framework/parallel_executor.h
+7
-2
paddle/fluid/framework/tensor_util.cc
paddle/fluid/framework/tensor_util.cc
+5
-0
paddle/fluid/imperative/layer.cc
paddle/fluid/imperative/layer.cc
+36
-29
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+90
-37
paddle/fluid/imperative/tracer.cc
paddle/fluid/imperative/tracer.cc
+149
-88
paddle/fluid/imperative/tracer.h
paddle/fluid/imperative/tracer.h
+5
-2
paddle/fluid/inference/api/details/zero_copy_tensor.cc
paddle/fluid/inference/api/details/zero_copy_tensor.cc
+5
-0
paddle/fluid/inference/api/helper.h
paddle/fluid/inference/api/helper.h
+11
-4
paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc
.../fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc
+17
-1
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
+13
-126
paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
...le/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
+13
-130
paddle/fluid/inference/tests/api/tester_helper.h
paddle/fluid/inference/tests/api/tester_helper.h
+154
-65
paddle/fluid/inference/tests/test.cmake
paddle/fluid/inference/tests/test.cmake
+5
-4
paddle/fluid/memory/CMakeLists.txt
paddle/fluid/memory/CMakeLists.txt
+1
-1
paddle/fluid/memory/allocation/CMakeLists.txt
paddle/fluid/memory/allocation/CMakeLists.txt
+1
-1
paddle/fluid/memory/allocation/legacy_allocator.cc
paddle/fluid/memory/allocation/legacy_allocator.cc
+9
-4
paddle/fluid/memory/memcpy.cc
paddle/fluid/memory/memcpy.cc
+20
-0
paddle/fluid/operators/activation_op.cc
paddle/fluid/operators/activation_op.cc
+47
-0
paddle/fluid/operators/activation_op.h
paddle/fluid/operators/activation_op.h
+100
-3
paddle/fluid/operators/controlflow/CMakeLists.txt
paddle/fluid/operators/controlflow/CMakeLists.txt
+1
-0
paddle/fluid/operators/controlflow/while_op.cc
paddle/fluid/operators/controlflow/while_op.cc
+1
-8
paddle/fluid/operators/controlflow/while_op_helper.cc
paddle/fluid/operators/controlflow/while_op_helper.cc
+291
-0
paddle/fluid/operators/controlflow/while_op_helper.h
paddle/fluid/operators/controlflow/while_op_helper.h
+43
-0
paddle/fluid/operators/crf_decoding_op.h
paddle/fluid/operators/crf_decoding_op.h
+3
-2
paddle/fluid/operators/detection/box_coder_op.h
paddle/fluid/operators/detection/box_coder_op.h
+52
-37
paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc
...operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc
+3
-2
paddle/fluid/operators/fake_dequantize_op.cc
paddle/fluid/operators/fake_dequantize_op.cc
+66
-0
paddle/fluid/operators/fake_dequantize_op.cu
paddle/fluid/operators/fake_dequantize_op.cu
+4
-0
paddle/fluid/operators/fake_dequantize_op.h
paddle/fluid/operators/fake_dequantize_op.h
+38
-0
paddle/fluid/operators/fake_quantize_op.cc
paddle/fluid/operators/fake_quantize_op.cc
+61
-0
paddle/fluid/operators/fake_quantize_op.cu
paddle/fluid/operators/fake_quantize_op.cu
+2
-0
paddle/fluid/operators/fake_quantize_op.h
paddle/fluid/operators/fake_quantize_op.h
+33
-0
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc
+2
-3
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h
+8
-4
paddle/fluid/operators/fused/fusion_gru_op.cc
paddle/fluid/operators/fused/fusion_gru_op.cc
+26
-23
paddle/fluid/operators/fused/fusion_lstm_op.cc
paddle/fluid/operators/fused/fusion_lstm_op.cc
+28
-26
paddle/fluid/operators/fused/fusion_repeated_fc_relu_op.cc
paddle/fluid/operators/fused/fusion_repeated_fc_relu_op.cc
+4
-2
paddle/fluid/operators/fused/fusion_seqpool_concat_op.cc
paddle/fluid/operators/fused/fusion_seqpool_concat_op.cc
+1
-1
paddle/fluid/operators/fused/fusion_squared_mat_sub_op.cc
paddle/fluid/operators/fused/fusion_squared_mat_sub_op.cc
+12
-8
paddle/fluid/operators/hash_op.cc
paddle/fluid/operators/hash_op.cc
+2
-3
paddle/fluid/operators/jit/CMakeLists.txt
paddle/fluid/operators/jit/CMakeLists.txt
+1
-1
paddle/fluid/operators/jit/benchmark.cc
paddle/fluid/operators/jit/benchmark.cc
+141
-148
paddle/fluid/operators/jit/gen/act.cc
paddle/fluid/operators/jit/gen/act.cc
+8
-7
paddle/fluid/operators/jit/gen/blas.cc
paddle/fluid/operators/jit/gen/blas.cc
+3
-2
paddle/fluid/operators/jit/gen/embseqpool.cc
paddle/fluid/operators/jit/gen/embseqpool.cc
+2
-1
paddle/fluid/operators/jit/gen/gru.cc
paddle/fluid/operators/jit/gen/gru.cc
+2
-1
paddle/fluid/operators/jit/gen/hopv.cc
paddle/fluid/operators/jit/gen/hopv.cc
+2
-1
paddle/fluid/operators/jit/gen/jitcode.h
paddle/fluid/operators/jit/gen/jitcode.h
+1
-1
paddle/fluid/operators/jit/gen/lstm.cc
paddle/fluid/operators/jit/gen/lstm.cc
+2
-1
paddle/fluid/operators/jit/gen/matmul.cc
paddle/fluid/operators/jit/gen/matmul.cc
+2
-2
paddle/fluid/operators/jit/gen/seqpool.cc
paddle/fluid/operators/jit/gen/seqpool.cc
+2
-1
paddle/fluid/operators/jit/gen/sgd.cc
paddle/fluid/operators/jit/gen/sgd.cc
+2
-1
paddle/fluid/operators/jit/gen/vbroadcast.cc
paddle/fluid/operators/jit/gen/vbroadcast.cc
+1
-1
paddle/fluid/operators/jit/gen_base.cc
paddle/fluid/operators/jit/gen_base.cc
+1
-1
paddle/fluid/operators/jit/gen_base.h
paddle/fluid/operators/jit/gen_base.h
+4
-3
paddle/fluid/operators/jit/helper.h
paddle/fluid/operators/jit/helper.h
+116
-49
paddle/fluid/operators/jit/kernel_base.h
paddle/fluid/operators/jit/kernel_base.h
+73
-25
paddle/fluid/operators/jit/kernel_key.cc
paddle/fluid/operators/jit/kernel_key.cc
+17
-41
paddle/fluid/operators/jit/kernel_key.h
paddle/fluid/operators/jit/kernel_key.h
+1
-1
paddle/fluid/operators/jit/kernel_pool.h
paddle/fluid/operators/jit/kernel_pool.h
+4
-3
paddle/fluid/operators/jit/more/intrinsic/crf_decoding.cc
paddle/fluid/operators/jit/more/intrinsic/crf_decoding.cc
+1
-1
paddle/fluid/operators/jit/more/intrinsic/crf_decoding.h
paddle/fluid/operators/jit/more/intrinsic/crf_decoding.h
+3
-3
paddle/fluid/operators/jit/more/intrinsic/layer_norm.cc
paddle/fluid/operators/jit/more/intrinsic/layer_norm.cc
+1
-1
paddle/fluid/operators/jit/more/intrinsic/layer_norm.h
paddle/fluid/operators/jit/more/intrinsic/layer_norm.h
+3
-2
paddle/fluid/operators/jit/more/mix/mix.cc
paddle/fluid/operators/jit/more/mix/mix.cc
+41
-43
paddle/fluid/operators/jit/more/mix/mix.h
paddle/fluid/operators/jit/more/mix/mix.h
+14
-14
paddle/fluid/operators/jit/more/mkl/mkl.cc
paddle/fluid/operators/jit/more/mkl/mkl.cc
+40
-39
paddle/fluid/operators/jit/more/mkl/mkl.h
paddle/fluid/operators/jit/more/mkl/mkl.h
+23
-26
paddle/fluid/operators/jit/refer/refer.cc
paddle/fluid/operators/jit/refer/refer.cc
+36
-44
paddle/fluid/operators/jit/refer/refer.h
paddle/fluid/operators/jit/refer/refer.h
+37
-43
paddle/fluid/operators/jit/registry.h
paddle/fluid/operators/jit/registry.h
+3
-2
paddle/fluid/operators/jit/test.cc
paddle/fluid/operators/jit/test.cc
+932
-682
paddle/fluid/operators/layer_norm_op.h
paddle/fluid/operators/layer_norm_op.h
+2
-2
paddle/fluid/operators/math/fc_compute.h
paddle/fluid/operators/math/fc_compute.h
+5
-6
paddle/fluid/operators/math/sequence_pooling.cc
paddle/fluid/operators/math/sequence_pooling.cc
+2
-2
paddle/fluid/operators/math/softmax_impl.h
paddle/fluid/operators/math/softmax_impl.h
+1
-2
paddle/fluid/operators/ngraph/ngraph_engine.cc
paddle/fluid/operators/ngraph/ngraph_engine.cc
+362
-242
paddle/fluid/operators/ngraph/ngraph_engine.h
paddle/fluid/operators/ngraph/ngraph_engine.h
+45
-16
paddle/fluid/operators/ngraph/ngraph_engine_op.cc
paddle/fluid/operators/ngraph/ngraph_engine_op.cc
+1
-0
paddle/fluid/operators/ngraph/ngraph_engine_op.h
paddle/fluid/operators/ngraph/ngraph_engine_op.h
+1
-3
paddle/fluid/operators/optimizers/sgd_op.h
paddle/fluid/operators/optimizers/sgd_op.h
+4
-2
paddle/fluid/operators/reader/buffered_reader.cc
paddle/fluid/operators/reader/buffered_reader.cc
+6
-1
paddle/fluid/operators/recurrent_op.cc
paddle/fluid/operators/recurrent_op.cc
+6
-2
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc
+2
-3
paddle/fluid/platform/device_tracer.cc
paddle/fluid/platform/device_tracer.cc
+108
-12
paddle/fluid/platform/device_tracer.h
paddle/fluid/platform/device_tracer.h
+32
-0
paddle/fluid/platform/event.h
paddle/fluid/platform/event.h
+33
-0
paddle/fluid/platform/profiler.cc
paddle/fluid/platform/profiler.cc
+190
-67
paddle/fluid/platform/profiler.h
paddle/fluid/platform/profiler.h
+76
-1
paddle/fluid/platform/profiler.proto
paddle/fluid/platform/profiler.proto
+17
-0
paddle/fluid/pybind/imperative.cc
paddle/fluid/pybind/imperative.cc
+12
-4
paddle/fluid/pybind/imperative.h
paddle/fluid/pybind/imperative.h
+3
-0
paddle/fluid/pybind/ir.cc
paddle/fluid/pybind/ir.cc
+6
-2
paddle/fluid/pybind/protobuf.cc
paddle/fluid/pybind/protobuf.cc
+1
-91
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+42
-45
paddle/fluid/pybind/pybind_boost_headers.h
paddle/fluid/pybind/pybind_boost_headers.h
+115
-0
paddle/fluid/pybind/recordio.cc
paddle/fluid/pybind/recordio.cc
+1
-1
paddle/fluid/recordio/scanner.cc
paddle/fluid/recordio/scanner.cc
+3
-1
paddle/scripts/paddle_build.sh
paddle/scripts/paddle_build.sh
+6
-2
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+9
-6
python/paddle/fluid/compiler.py
python/paddle/fluid/compiler.py
+15
-13
python/paddle/fluid/contrib/slim/tests/test_graph.py
python/paddle/fluid/contrib/slim/tests/test_graph.py
+72
-31
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
...paddle/fluid/contrib/slim/tests/test_quantization_pass.py
+10
-2
python/paddle/fluid/executor.py
python/paddle/fluid/executor.py
+1
-1
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+282
-217
python/paddle/fluid/imperative/layers.py
python/paddle/fluid/imperative/layers.py
+1
-1
python/paddle/fluid/imperative/nn.py
python/paddle/fluid/imperative/nn.py
+137
-2
python/paddle/fluid/imperative/tracer.py
python/paddle/fluid/imperative/tracer.py
+9
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+3
-2
python/paddle/fluid/layers/ops.py
python/paddle/fluid/layers/ops.py
+3
-0
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+5
-4
python/paddle/fluid/tests/unittests/op_test.py
python/paddle/fluid/tests/unittests/op_test.py
+112
-4
python/paddle/fluid/tests/unittests/test_accuracy_op.py
python/paddle/fluid/tests/unittests/test_accuracy_op.py
+2
-2
python/paddle/fluid/tests/unittests/test_activation_op.py
python/paddle/fluid/tests/unittests/test_activation_op.py
+54
-0
python/paddle/fluid/tests/unittests/test_eager_deletion_transformer.py
.../fluid/tests/unittests/test_eager_deletion_transformer.py
+1
-2
python/paddle/fluid/tests/unittests/test_eager_deletion_while_op.py
...dle/fluid/tests/unittests/test_eager_deletion_while_op.py
+153
-0
python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py
...n/paddle/fluid/tests/unittests/test_fake_dequantize_op.py
+74
-0
python/paddle/fluid/tests/unittests/test_fake_quantize_op.py
python/paddle/fluid/tests/unittests/test_fake_quantize_op.py
+24
-0
python/paddle/fluid/tests/unittests/test_gru_op.py
python/paddle/fluid/tests/unittests/test_gru_op.py
+1
-1
python/paddle/fluid/tests/unittests/test_imperative_basic.py
python/paddle/fluid/tests/unittests/test_imperative_basic.py
+1
-1
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
...on/paddle/fluid/tests/unittests/test_imperative_resnet.py
+2
-2
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+132
-2
python/paddle/fluid/tests/unittests/test_partial_eager_deletion_transformer.py
...ests/unittests/test_partial_eager_deletion_transformer.py
+25
-0
python/paddle/fluid/tests/unittests/test_random_crop_op.py
python/paddle/fluid/tests/unittests/test_random_crop_op.py
+1
-1
tools/diff_api.py
tools/diff_api.py
+1
-1
tools/print_signatures.py
tools/print_signatures.py
+3
-0
tools/timeline.py
tools/timeline.py
+105
-1
未找到文件。
benchmark/fluid/fluid_benchmark.py
浏览文件 @
ac0e0f51
...
...
@@ -179,7 +179,6 @@ def train_parallel(train_args, test_args, args, train_prog, test_prog,
else
:
build_strategy
.
reduce_strategy
=
fluid
.
BuildStrategy
(
).
ReduceStrategy
.
AllReduce
build_strategy
.
fuse_broadcast_op
=
args
.
fuse_broadcast_op
avg_loss
=
train_args
[
0
]
...
...
paddle/fluid/API.spec
浏览文件 @
ac0e0f51
...
...
@@ -302,6 +302,7 @@ paddle.fluid.layers.sigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=
paddle.fluid.layers.logsigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '81ccb7acafd06c7728e11581f5d342e3'))
paddle.fluid.layers.exp (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e6b3e769413d96aab4176f96db25984b'))
paddle.fluid.layers.tanh (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e9d586a0b5bd05f67ee78048f9d503b6'))
paddle.fluid.layers.atan (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3a46e0b5f9ce82348406478e610f14c9'))
paddle.fluid.layers.tanh_shrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1e521554b9fdda9061ec6d306f0709b7'))
paddle.fluid.layers.softshrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9eef31597bbafa2bd49691e072296e13'))
paddle.fluid.layers.sqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '072a8541e0f632366bba10f67cb0db27'))
...
...
@@ -309,6 +310,8 @@ paddle.fluid.layers.abs (ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.ceil (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c75d67dc5fe28f68e4cfffead4f698ad'))
paddle.fluid.layers.floor (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '647b16c5da5ef909649ae02abb434973'))
paddle.fluid.layers.cos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '485f2686bcc2fe37a4bd893769c8a3e2'))
paddle.fluid.layers.acos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '920a47734482276c069ba24c61c26b25'))
paddle.fluid.layers.asin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'cf4ee2c9b9d7293556f8c5173dfb5d2c'))
paddle.fluid.layers.sin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '01f1766aa76eff1df30147505b59f7c4'))
paddle.fluid.layers.round (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b47f5da13913d3e56bdb1e612a73f3f2'))
paddle.fluid.layers.reciprocal (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'cc6ac2f14f03c52aaa83a59bf83b8d26'))
...
...
paddle/fluid/framework/CMakeLists.txt
浏览文件 @
ac0e0f51
...
...
@@ -38,10 +38,10 @@ if(WITH_GPU)
nv_library
(
tensor SRCS tensor.cc .tensor_util.cu DEPS place memory data_type device_context
)
add_dependencies
(
tensor tensor_util
)
else
()
nv_library
(
tensor SRCS tensor.cc tensor_util.cu DEPS place memory data_type device_context
)
nv_library
(
tensor SRCS tensor.cc tensor_util.cu DEPS place memory data_type device_context
profiler
)
endif
(
WIN32
)
else
()
cc_library
(
tensor SRCS tensor.cc tensor_util.cc DEPS place memory data_type device_context
)
cc_library
(
tensor SRCS tensor.cc tensor_util.cc DEPS place memory data_type device_context
profiler
)
endif
()
cc_test
(
tensor_test SRCS tensor_test.cc DEPS tensor
)
...
...
@@ -174,7 +174,7 @@ else()
cc_test
(
test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor elementwise_add_op
)
endif
()
target_link_libraries
(
executor garbage_collector
)
target_link_libraries
(
executor garbage_collector
while_op_helper
)
cc_library
(
parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor parallel_ssa_graph_executor
...
...
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @
ac0e0f51
...
...
@@ -61,7 +61,8 @@ cc_library(inplace_op_pass SRCS inplace_op_pass.cc DEPS memory_optimize_pass op_
cc_library
(
modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper
)
cc_library
(
reference_count_pass_helper SRCS reference_count_pass_helper.cc DEPS garbage_collector computation_op_handle
)
cc_library
(
eager_deletion_op_handle SRCS eager_deletion_op_handle.cc DEPS lod_tensor selected_rows reference_count_pass_helper
)
cc_library
(
eager_deletion_pass SRCS eager_deletion_pass.cc DEPS computation_op_handle eager_deletion_op_handle graph graph_helper pass
)
cc_library
(
while_op_eager_deletion_pass SRCS while_op_eager_deletion_pass.cc DEPS while_op_helper graph_helper pass computation_op_handle
)
cc_library
(
eager_deletion_pass SRCS eager_deletion_pass.cc DEPS computation_op_handle eager_deletion_op_handle graph graph_helper pass while_op_eager_deletion_pass
)
cc_library
(
reference_count_pass SRCS reference_count_pass.cc DEPS computation_op_handle graph graph_helper pass op_graph_view reference_count_pass_helper
)
cc_library
(
sequential_execution_pass SRCS sequential_execution_pass.cc DEPS graph graph_helper pass
)
...
...
paddle/fluid/framework/details/computation_op_handle.h
浏览文件 @
ac0e0f51
...
...
@@ -14,6 +14,7 @@
#pragma once
#include <memory>
#include <string>
#include <vector>
...
...
@@ -31,6 +32,8 @@ class ComputationOpHandle : public OpHandleBase {
ComputationOpHandle
(
ir
::
Node
*
node
,
Scope
*
scope
,
platform
::
Place
place
,
size_t
scope_idx
);
OperatorBase
*
GetOp
()
{
return
op_
.
get
();
}
std
::
string
Name
()
const
override
;
const
Scope
*
GetScope
()
const
{
return
scope_
;
}
...
...
paddle/fluid/framework/details/eager_deletion_op_handle.cc
浏览文件 @
ac0e0f51
...
...
@@ -12,6 +12,10 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <memory>
#include <unordered_set>
#include <utility>
#include "paddle/fluid/framework/details/eager_deletion_op_handle.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/scope.h"
...
...
@@ -45,6 +49,7 @@ EagerDeletionOpHandle::EagerDeletionOpHandle(
}
}
#endif
PADDLE_ENFORCE
(
!
var_names_
.
empty
(),
"Var names cannot be empty"
);
}
EagerDeletionOpHandle
::~
EagerDeletionOpHandle
()
{
...
...
@@ -60,15 +65,20 @@ EagerDeletionOpHandle::~EagerDeletionOpHandle() {
std
::
string
EagerDeletionOpHandle
::
Name
()
const
{
return
"eager_deletion"
;
}
void
EagerDeletionOpHandle
::
RunImpl
()
{
auto
*
exec_scope
=
scope_
->
FindVar
(
kLocalExecScopeName
)
->
Get
<
Scope
*>
()
;
Scope
*
exec_scope
=
nullptr
;
std
::
deque
<
std
::
shared_ptr
<
memory
::
Allocation
>>
garbages
;
for
(
auto
&
name
:
var_names_
)
{
auto
it
=
ref_cnts_
->
find
(
name
);
//
Var not found, not r
eference count has not decreased to 0
//
R
eference count has not decreased to 0
if
(
it
==
ref_cnts_
->
end
()
||
it
->
second
.
fetch_sub
(
1
)
!=
1
)
{
continue
;
}
if
(
!
exec_scope
)
{
exec_scope
=
scope_
->
FindVar
(
kLocalExecScopeName
)
->
Get
<
Scope
*>
();
}
// Var not found
auto
*
var
=
exec_scope
->
FindVar
(
name
);
if
(
var
==
nullptr
)
{
continue
;
...
...
paddle/fluid/framework/details/eager_deletion_pass.cc
浏览文件 @
ac0e0f51
...
...
@@ -12,20 +12,173 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <algorithm>
#include <functional>
#include <queue>
#include <string>
#include <tuple>
#include <vector>
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/eager_deletion_op_handle.h"
#include "paddle/fluid/framework/details/eager_deletion_pass.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
DEFINE_double
(
memory_fraction_of_eager_deletion
,
1.0
,
"Fraction of eager deletion. If less than 1.0, all variables in "
"the program would be sorted according to its memory size, and "
"only the FLAGS_memory_fraction_of_eager_deletion of the largest "
"variables would be deleted."
);
namespace
paddle
{
namespace
framework
{
namespace
details
{
// op -> variables which can be deleted after op runs
using
OpToVarNameSetMap
=
std
::
unordered_map
<
ComputationOpHandle
*
,
std
::
unordered_set
<
std
::
string
>>
;
// Check whether the variable is LoDTensor based on static VarDesc info
static
bool
IsLoDTensor
(
VarDesc
*
var
)
{
return
var
->
Proto
()
->
type
().
type
()
==
proto
::
VarType
::
LOD_TENSOR
;
}
// Get memory size of LoDTensor
static
int64_t
GetMemorySize
(
const
std
::
unordered_map
<
std
::
string
,
std
::
vector
<
VarHandle
*>>
&
vars
,
const
std
::
string
&
var_name
)
{
auto
*
var_desc
=
TryGetLatestVarDesc
(
vars
.
at
(
var_name
));
PADDLE_ENFORCE_NOT_NULL
(
var_desc
);
PADDLE_ENFORCE
(
IsLoDTensor
(
var_desc
));
auto
dims
=
var_desc
->
GetShape
();
return
SizeOfType
(
var_desc
->
GetDataType
())
*
std
::
accumulate
(
dims
.
begin
(),
dims
.
end
(),
static_cast
<
int64_t
>
(
1
),
std
::
multiplies
<
int64_t
>
());
}
// Split all variables in the graph into LoDTensor and Non-LoDTensor (e.g.
// SelectedRows, LoDTensorArray)
// Since partial GC is based on static analysis of memory size of each variable
// So we should skip SelectedRows and LoDTensorArray here
static
void
SplitIntoLoDTensorAndNonLoDTensorVars
(
const
OpToVarNameSetMap
&
m
,
const
GraphVars
&
vars
,
OpToVarNameSetMap
*
lod_tensors
,
OpToVarNameSetMap
*
other_vars
)
{
lod_tensors
->
clear
();
other_vars
->
clear
();
for
(
auto
&
op_vars_pair
:
m
)
{
for
(
auto
&
var_name
:
op_vars_pair
.
second
)
{
auto
*
var_desc
=
TryGetLatestVarDesc
(
vars
[
op_vars_pair
.
first
->
GetScopeIdx
()].
at
(
var_name
));
if
(
IsLoDTensor
(
var_desc
))
{
(
*
lod_tensors
)[
op_vars_pair
.
first
].
insert
(
var_name
);
}
else
{
(
*
other_vars
)[
op_vars_pair
.
first
].
insert
(
var_name
);
}
}
}
}
struct
GCVarInfo
{
GCVarInfo
(
const
std
::
string
&
name
,
int64_t
memory_size
,
ComputationOpHandle
*
op
,
size_t
scope_idx
)
:
name_
(
name
),
memory_size_
(
memory_size
),
op_
(
op
),
scope_idx_
(
scope_idx
)
{}
std
::
string
name_
;
// variable name
int64_t
memory_size_
;
// memory size
ComputationOpHandle
*
op_
;
// op after which the variable could be deleted
size_t
scope_idx_
;
// scope index where the variable locates
int64_t
AbsMemorySize
()
const
{
return
std
::
abs
(
memory_size_
);
}
};
// Delete delete_lod_tensor_only is not used currently
static
OpToVarNameSetMap
ShrinkGCVars
(
const
OpToVarNameSetMap
&
m
,
const
GraphVars
&
vars
,
const
std
::
vector
<
platform
::
Place
>
&
places
,
double
fraction_of_memory_size
,
bool
delete_lod_tensor_only
=
false
)
{
// Do not perform gc when fraction_of_memory_size = 0
if
(
fraction_of_memory_size
<=
0.0
)
return
{};
/**
* Step 1: Split all variables into LoDTensor and Non-LoDTensor.
* We can only calculate memory size of LoDTensors
*/
OpToVarNameSetMap
lod_tensors
,
other_vars
;
SplitIntoLoDTensorAndNonLoDTensorVars
(
m
,
vars
,
&
lod_tensors
,
&
other_vars
);
// Perform complete gc when fraction_of_memory_size >= 1
if
(
fraction_of_memory_size
>=
1.0
)
{
return
delete_lod_tensor_only
?
lod_tensors
:
m
;
}
/**
* Step 2: build GCVarInfos, and calculate total memory sizes of each device
*/
// place -> variable info (name, memory size, place, scope_idx)
std
::
map
<
platform
::
Place
,
std
::
vector
<
GCVarInfo
>>
place_to_vars
;
// place -> total memory sizes
std
::
map
<
platform
::
Place
,
int64_t
>
place_to_size
;
for
(
auto
&
op_vars_pair
:
lod_tensors
)
{
auto
*
op
=
op_vars_pair
.
first
;
auto
&
var_names
=
op_vars_pair
.
second
;
auto
scope_idx
=
op
->
GetScopeIdx
();
auto
&
place
=
places
[
scope_idx
];
for
(
auto
&
var_name
:
var_names
)
{
auto
var_size
=
GetMemorySize
(
vars
[
scope_idx
],
var_name
);
GCVarInfo
var_info
(
var_name
,
var_size
,
op
,
scope_idx
);
place_to_size
[
place
]
+=
var_info
.
AbsMemorySize
();
place_to_vars
[
place
].
emplace_back
(
std
::
move
(
var_info
));
}
}
/**
* Step 3: sort GCVarInfos, and only delete the largest variables.
*/
OpToVarNameSetMap
partial_vars
;
for
(
auto
&
place_to_var_pair
:
place_to_vars
)
{
auto
&
place
=
place_to_var_pair
.
first
;
auto
&
gc_vars
=
place_to_var_pair
.
second
;
std
::
sort
(
gc_vars
.
begin
(),
gc_vars
.
end
(),
[](
const
GCVarInfo
&
var1
,
const
GCVarInfo
&
var2
)
{
return
var1
.
AbsMemorySize
()
>
var2
.
AbsMemorySize
();
});
int64_t
accumulated_size
=
0
;
int64_t
size_threshold
=
static_cast
<
int64_t
>
(
fraction_of_memory_size
*
place_to_size
[
place
]);
for
(
size_t
i
=
0
;
i
<
gc_vars
.
size
()
&&
accumulated_size
<
size_threshold
;
++
i
)
{
partial_vars
[
gc_vars
[
i
].
op_
].
insert
(
gc_vars
[
i
].
name_
);
accumulated_size
+=
gc_vars
[
i
].
AbsMemorySize
();
}
}
/**
* Step 4: Combine other vars (SelectedRows, LoDTensorArray)
*/
if
(
!
delete_lod_tensor_only
)
{
for
(
auto
&
op_vars_pair
:
other_vars
)
{
partial_vars
[
op_vars_pair
.
first
].
insert
(
op_vars_pair
.
second
.
begin
(),
op_vars_pair
.
second
.
end
());
}
}
return
partial_vars
;
}
class
EagerDeletionPass
:
public
ir
::
Pass
{
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
override
;
};
std
::
unique_ptr
<
ir
::
Graph
>
EagerDeletionPass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
auto
&
ref_cnts
=
...
...
@@ -43,9 +196,7 @@ std::unique_ptr<ir::Graph> EagerDeletionPass::ApplyImpl(
// a reverse map of last_live_ops
// i.e., last op --> variable names which can be deleted.
std
::
unordered_map
<
ComputationOpHandle
*
,
std
::
unordered_set
<
std
::
string
>>
op_vars_map
;
OpToVarNameSetMap
op_vars_map
;
for
(
auto
&
var_ops_map
:
last_live_ops
)
{
for
(
auto
&
var_ops_pair
:
var_ops_map
)
{
const
std
::
string
&
var_name
=
var_ops_pair
.
first
;
...
...
@@ -55,6 +206,9 @@ std::unique_ptr<ir::Graph> EagerDeletionPass::ApplyImpl(
}
}
op_vars_map
=
ShrinkGCVars
(
op_vars_map
,
vars
,
places
,
FLAGS_memory_fraction_of_eager_deletion
);
for
(
auto
&
pair
:
op_vars_map
)
{
auto
*
op
=
pair
.
first
;
auto
&
var_names
=
pair
.
second
;
...
...
@@ -85,8 +239,13 @@ std::unique_ptr<ir::Graph> EagerDeletionPass::ApplyImpl(
eager_deletion_op
->
AddOutput
(
dummy_leaf
);
}
VLOG
(
10
)
<<
"FLAGS_memory_fraction_of_eager_deletion = "
<<
FLAGS_memory_fraction_of_eager_deletion
;
VLOG
(
10
)
<<
"Create "
<<
op_vars_map
.
size
()
<<
" EagerDeletionOpHandle(s)"
;
return
graph
;
auto
while_op_eager_deletion_pass
=
ir
::
PassRegistry
::
Instance
().
Get
(
"while_op_eager_deletion_pass"
);
return
while_op_eager_deletion_pass
->
Apply
(
std
::
move
(
graph
));
}
}
// namespace details
...
...
@@ -99,3 +258,5 @@ REGISTER_PASS(eager_deletion_pass,
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kLastLiveOpsOfVars
)
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kAllPlaces
)
.
RequirePassAttr
(
paddle
::
framework
::
details
::
kGarbageCollector
);
USE_PASS
(
while_op_eager_deletion_pass
);
paddle/fluid/framework/details/inplace_op_pass.cc
浏览文件 @
ac0e0f51
...
...
@@ -16,6 +16,7 @@
#include <algorithm>
#include <deque>
#include <iterator>
#include <memory>
#include <stack>
#include <string>
#include <unordered_map>
...
...
@@ -263,6 +264,10 @@ void InplacePass::WithdrawModify(const NodeSwapQueue& nodes,
void
InplacePass
::
TryInplaceOpInputOutput
(
ir
::
Node
*
op
,
ir
::
Graph
*
graph
)
const
{
VLOG
(
4
)
<<
"Try to inplace op "
<<
op
->
Name
();
// FIXME(liuwei1031): Graph is not aware of the existence of BlockDescs and
// ProgramDescs.
// The operations related to BlockDesc or ProgramDesc should perform on Graph
// or Node directly!
PADDLE_ENFORCE
(
op
->
Op
()
!=
nullptr
&&
op
->
Op
()
->
Block
()
!=
nullptr
,
"op_desc is nullptr"
);
// some pre-requirments need to meet if the op want to inplaced.
...
...
paddle/fluid/framework/details/memory_optimize_pass.cc
浏览文件 @
ac0e0f51
...
...
@@ -24,6 +24,7 @@
#include <sstream>
#include <string>
#include <type_traits>
#include <unordered_set>
#include <vector>
#include "gflags/gflags.h"
#include "paddle/fluid/framework/data_type.h"
...
...
@@ -191,6 +192,10 @@ void MemoryOptimizePass::SubGraphOptimize(OpDesc* op_desc) const {
// immediately to make the subblock variable reuse strategy take
// effect. Because it is a single op in graph. No need to
// update the ir nodes.
// FIXME(liuwei1031): Graph is not aware of the existence of
// BlockDescs and ProgramDescs.
// The operations related to BlockDesc or ProgramDesc should perform
// on Graph or Node directly!
sub_op_desc
->
Rename
(
var
->
Name
(),
cache
->
Name
());
if
(
sub_op_desc
->
Block
()
!=
nullptr
&&
sub_op_desc
->
Block
()
->
HasVar
(
var
->
Name
()))
{
...
...
paddle/fluid/framework/details/reference_count_pass.cc
浏览文件 @
ac0e0f51
...
...
@@ -12,9 +12,13 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <memory>
#include <queue>
#include <string>
#include <type_traits>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/computation_op_handle.h"
...
...
@@ -189,15 +193,6 @@ ExtractComputationOpFromLastLivedVar(VarHandle *var, size_t scope_idx,
return
shrink_func
(
computation_op
);
}
static
VarDesc
*
TryGetLatestVarDesc
(
const
std
::
vector
<
VarHandle
*>
&
vars
)
{
VarDesc
*
var_desc
=
nullptr
;
std
::
find_if
(
vars
.
rbegin
(),
vars
.
rend
(),
[
&
](
VarHandle
*
var_handle
)
->
bool
{
var_desc
=
var_handle
->
Node
()
->
Var
();
return
var_desc
!=
nullptr
;
});
return
var_desc
;
}
std
::
unique_ptr
<
ir
::
Graph
>
ReferenceCountPass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
auto
&
ref_cnts
=
Get
<
std
::
vector
<
ReferenceCountMap
>>
(
kGlobalReferenceCount
);
...
...
paddle/fluid/framework/details/reference_count_pass_helper.cc
浏览文件 @
ac0e0f51
...
...
@@ -13,9 +13,22 @@
// limitations under the License.
#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
#include "paddle/fluid/framework/details/var_handle.h"
#include "paddle/fluid/framework/var_desc.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{}
// namespace details
namespace
details
{
VarDesc
*
TryGetLatestVarDesc
(
const
std
::
vector
<
VarHandle
*>
&
vars
)
{
VarDesc
*
var_desc
=
nullptr
;
std
::
find_if
(
vars
.
rbegin
(),
vars
.
rend
(),
[
&
](
VarHandle
*
var_handle
)
->
bool
{
var_desc
=
var_handle
->
Node
()
->
Var
();
return
var_desc
!=
nullptr
;
});
return
var_desc
;
}
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/reference_count_pass_helper.h
浏览文件 @
ac0e0f51
...
...
@@ -16,6 +16,7 @@
#include <atomic>
#include <map>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
...
...
@@ -25,6 +26,10 @@
namespace
paddle
{
namespace
framework
{
class
VarDesc
;
class
VarHandle
;
namespace
details
{
class
ComputationOpHandle
;
...
...
@@ -43,9 +48,11 @@ const char kGarbageCollector[] = "garbage_collector";
const
char
kAllPlaces
[]
=
"all_places"
;
using
LastLiveOpsOfVars
=
std
::
unordered_map
<
std
::
string
,
std
::
unordered_set
<
ComputationOpHandle
*>>
;
std
::
unordered_map
<
std
::
string
,
std
::
unordered_set
<
ComputationOpHandle
*>>
;
const
char
kLastLiveOpsOfVars
[]
=
"last_live_ops_of_var"
;
VarDesc
*
TryGetLatestVarDesc
(
const
std
::
vector
<
VarHandle
*>
&
vars
);
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/while_op_eager_deletion_pass.cc
0 → 100644
浏览文件 @
ac0e0f51
// Copyright (c) 2019 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/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/operators/controlflow/while_op_helper.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
class
WhileOpEagerDeletionPass
:
public
ir
::
Pass
{
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
override
{
auto
all_ops
=
ir
::
FilterByNodeWrapper
<
OpHandleBase
>
(
*
graph
);
// Find all while_op and while_grad_op
std
::
unordered_map
<
size_t
,
std
::
pair
<
std
::
vector
<
OperatorBase
*>
,
std
::
vector
<
OperatorBase
*>>>
target_ops
;
for
(
auto
*
op
:
all_ops
)
{
auto
compute_op
=
dynamic_cast
<
ComputationOpHandle
*>
(
op
);
if
(
compute_op
==
nullptr
)
continue
;
if
(
compute_op
->
Name
()
==
"while"
)
{
target_ops
[
compute_op
->
GetScopeIdx
()].
first
.
emplace_back
(
compute_op
->
GetOp
());
}
else
if
(
compute_op
->
Name
()
==
"while_grad"
)
{
target_ops
[
compute_op
->
GetScopeIdx
()].
second
.
emplace_back
(
compute_op
->
GetOp
());
}
}
for
(
auto
&
ops_pair
:
target_ops
)
{
auto
&
while_ops
=
ops_pair
.
second
.
first
;
auto
&
while_grad_ops
=
ops_pair
.
second
.
second
;
operators
::
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp
(
while_ops
,
while_grad_ops
);
}
return
graph
;
}
};
}
// namespace details
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
while_op_eager_deletion_pass
,
paddle
::
framework
::
details
::
WhileOpEagerDeletionPass
);
paddle/fluid/framework/executor.cc
浏览文件 @
ac0e0f51
...
...
@@ -14,6 +14,10 @@ limitations under the License. */
#include "paddle/fluid/framework/executor.h"
#include <deque>
#include <memory>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/lod_rank_table.h"
...
...
@@ -23,17 +27,18 @@ limitations under the License. */
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/framework/transfer_scope_cache.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/operators/controlflow/while_op_helper.h"
#include "paddle/fluid/operators/distributed/distributed.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
#ifdef PADDLE_WITH_NGRAPH
#include "paddle/fluid/operators/ngraph/ngraph_engine.h"
DEFINE_bool
(
use_ngraph
,
false
,
"Use NGRAPH to run"
);
#endif
DECLARE_bool
(
benchmark
);
DEFINE_bool
(
use_mkldnn
,
false
,
"Use MKLDNN to run"
);
DEFINE_bool
(
use_ngraph
,
false
,
"Use NGRAPH to run"
);
namespace
paddle
{
namespace
framework
{
...
...
@@ -75,11 +80,11 @@ static std::unordered_map<std::string, size_t> GetNonPersistableReferenceCounts(
ExecutorPrepareContext
::
ExecutorPrepareContext
(
const
framework
::
ProgramDesc
&
prog
,
size_t
block_id
,
const
std
::
vector
<
std
::
string
>&
skip_ref_cnt_vars
)
:
prog_
(
prog
),
block_id_
(
block_id
)
{
if
(
GetEagerDeletionThreshold
()
>=
0
)
{
global_ref_cnts_
=
GetNonPersistableReferenceCounts
(
prog
.
Block
(
block_id
),
skip_ref_cnt
_vars
);
const
std
::
vector
<
std
::
string
>&
keep_vars
,
bool
force_disable_gc
)
:
prog_
(
prog
),
block_id_
(
block_id
)
,
force_disable_gc_
(
force_disable_gc
)
{
if
(
GetEagerDeletionThreshold
()
>=
0
&&
!
force_disable_gc_
)
{
global_ref_cnts_
=
GetNonPersistableReferenceCounts
(
prog
.
Block
(
block_id
),
keep
_vars
);
}
}
...
...
@@ -184,13 +189,12 @@ void Executor::CreateVariables(const ProgramDesc& pdesc, Scope* scope,
}
void
Executor
::
Run
(
const
ProgramDesc
&
pdesc
,
Scope
*
scope
,
int
block_id
,
bool
create_local_scope
,
bool
create_vars
)
{
bool
create_local_scope
,
bool
create_vars
,
const
std
::
vector
<
std
::
string
>&
skip_ref_cnt_vars
,
bool
force_disable_gc
)
{
platform
::
RecordBlock
b
(
block_id
);
if
(
FLAGS_use_mkldnn
)
EnableMKLDNN
(
pdesc
);
#ifdef PADDLE_WITH_NGRAPH
if
(
FLAGS_use_ngraph
)
operators
::
NgraphEngine
::
EnableNgraph
(
pdesc
);
#endif
auto
ctx
=
Prepare
(
pdesc
,
block_id
);
auto
ctx
=
Prepare
(
pdesc
,
block_id
,
skip_ref_cnt_vars
,
force_disable_gc
);
RunPreparedContext
(
ctx
.
get
(),
scope
,
create_local_scope
,
create_vars
);
}
...
...
@@ -357,20 +361,27 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
std
::
unique_ptr
<
ExecutorPrepareContext
>
Executor
::
Prepare
(
const
ProgramDesc
&
program
,
int
block_id
,
const
std
::
vector
<
std
::
string
>&
skip_ref_cnt_vars
)
{
std
::
unique_ptr
<
ExecutorPrepareContext
>
ctx
(
new
ExecutorPrepareContext
(
program
,
block_id
,
skip_ref_cnt_vars
));
const
std
::
vector
<
std
::
string
>&
skip_ref_cnt_vars
,
bool
force_disable_gc
)
{
std
::
unique_ptr
<
ExecutorPrepareContext
>
ctx
(
new
ExecutorPrepareContext
(
program
,
block_id
,
skip_ref_cnt_vars
,
force_disable_gc
));
PADDLE_ENFORCE_LT
(
static_cast
<
size_t
>
(
block_id
),
program
.
Size
());
auto
&
block
=
program
.
Block
(
block_id
);
for
(
auto
&
op_desc
:
block
.
AllOps
())
{
ctx
->
ops_
.
push_back
(
OpRegistry
::
CreateOp
(
*
op_desc
));
}
#ifdef PADDLE_WITH_NGRAPH
if
(
FLAGS_use_ngraph
)
{
paddle
::
operators
::
NgraphEngine
::
FuseNgraphOps
(
ctx
->
prog_
.
Block
(
ctx
->
block_id_
),
&
ctx
->
ops_
);
}
#endif
return
ctx
;
}
std
::
vector
<
std
::
shared_ptr
<
ExecutorPrepareContext
>>
Executor
::
Prepare
(
const
ProgramDesc
&
program
,
const
std
::
vector
<
int
>&
block_ids
,
const
std
::
vector
<
std
::
vector
<
std
::
string
>>&
skip_ref_cnt_vars
)
{
const
std
::
vector
<
std
::
vector
<
std
::
string
>>&
skip_ref_cnt_vars
,
bool
force_disable_gc
)
{
PADDLE_ENFORCE
(
skip_ref_cnt_vars
.
empty
()
||
skip_ref_cnt_vars
.
size
()
==
block_ids
.
size
(),
"skip_ref_cnt_vars should be either empty or equals to block number %d"
,
...
...
@@ -380,9 +391,11 @@ std::vector<std::shared_ptr<ExecutorPrepareContext>> Executor::Prepare(
for
(
auto
&
bid
:
block_ids
)
{
ExecutorPrepareContext
*
ctx
;
if
(
skip_ref_cnt_vars
.
empty
())
{
ctx
=
new
ExecutorPrepareContext
(
program
,
bid
);
ctx
=
new
ExecutorPrepareContext
(
program
,
bid
,
std
::
vector
<
std
::
string
>
(),
force_disable_gc
);
}
else
{
ctx
=
new
ExecutorPrepareContext
(
program
,
bid
,
skip_ref_cnt_vars
[
idx
]);
ctx
=
new
ExecutorPrepareContext
(
program
,
bid
,
skip_ref_cnt_vars
[
idx
],
force_disable_gc
);
}
PADDLE_ENFORCE_LT
(
static_cast
<
size_t
>
(
bid
),
program
.
Size
());
auto
&
block
=
program
.
Block
(
bid
);
...
...
@@ -409,8 +422,9 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
int64_t
max_memory_size
=
GetEagerDeletionThreshold
();
std
::
unique_ptr
<
GarbageCollector
>
gc
;
// skip while_op and while_grad_op temporarily
if
(
max_memory_size
>=
0
&&
!
keep_kids
)
{
// FIXME(zjl): recurrent_op is rather complex, we would
// disable gc forcely in recurrent_op
if
(
!
ctx
->
force_disable_gc_
&&
max_memory_size
>=
0
)
{
ctx
->
ResetReferenceCount
();
#ifdef PADDLE_WITH_CUDA
if
(
platform
::
is_gpu_place
(
place_
))
{
...
...
@@ -428,6 +442,11 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
#ifdef PADDLE_WITH_CUDA
}
#endif
// If gc is enabled and block size > 1
if
(
gc
&&
ctx
->
prog_
.
Size
()
>
1
)
{
operators
::
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp
(
ctx
->
block_id_
,
ctx
->
ops_
);
}
}
for
(
auto
&
op
:
ctx
->
ops_
)
{
...
...
paddle/fluid/framework/executor.h
浏览文件 @
ac0e0f51
...
...
@@ -15,7 +15,9 @@ limitations under the License. */
#pragma once
#include <map>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/op_info.h"
...
...
@@ -30,7 +32,8 @@ namespace framework {
struct
ExecutorPrepareContext
{
ExecutorPrepareContext
(
const
framework
::
ProgramDesc
&
prog
,
size_t
block_id
,
const
std
::
vector
<
std
::
string
>&
skip_ref_cnt_vars
=
std
::
vector
<
std
::
string
>
());
std
::
vector
<
std
::
string
>
(),
bool
force_disable_gc
=
false
);
~
ExecutorPrepareContext
();
...
...
@@ -38,6 +41,7 @@ struct ExecutorPrepareContext {
const
framework
::
ProgramDesc
&
prog_
;
size_t
block_id_
;
bool
force_disable_gc_
;
std
::
vector
<
std
::
unique_ptr
<
OperatorBase
>>
ops_
;
std
::
unordered_map
<
std
::
string
,
size_t
>
global_ref_cnts_
;
...
...
@@ -66,7 +70,10 @@ class Executor {
* Scope
*/
void
Run
(
const
ProgramDesc
&
prog
,
Scope
*
scope
,
int
block_id
,
bool
create_local_scope
=
true
,
bool
create_vars
=
true
);
bool
create_local_scope
=
true
,
bool
create_vars
=
true
,
const
std
::
vector
<
std
::
string
>&
skip_ref_cnt_vars
=
std
::
vector
<
std
::
string
>
(),
bool
force_disable_gc
=
false
);
// This API is very slow.
void
Run
(
const
ProgramDesc
&
program
,
Scope
*
scope
,
...
...
@@ -79,12 +86,14 @@ class Executor {
static
std
::
unique_ptr
<
ExecutorPrepareContext
>
Prepare
(
const
ProgramDesc
&
program
,
int
block_id
,
const
std
::
vector
<
std
::
string
>&
skip_ref_cnt_vars
=
std
::
vector
<
std
::
string
>
());
std
::
vector
<
std
::
string
>
(),
bool
force_disable_gc
=
false
);
static
std
::
vector
<
std
::
shared_ptr
<
ExecutorPrepareContext
>>
Prepare
(
const
ProgramDesc
&
program
,
const
std
::
vector
<
int
>&
block_ids
,
const
std
::
vector
<
std
::
vector
<
std
::
string
>>&
skip_ref_cnt_vars
=
std
::
vector
<
std
::
vector
<
std
::
string
>>
());
std
::
vector
<
std
::
vector
<
std
::
string
>>
(),
bool
force_disable_gc
=
false
);
void
CreateVariables
(
const
ProgramDesc
&
pdesc
,
Scope
*
scope
,
int
block_id
);
...
...
paddle/fluid/framework/ir/graph.cc
浏览文件 @
ac0e0f51
...
...
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <unordered_
set
>
#include <unordered_
map
>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/op_proto_maker.h"
...
...
@@ -152,6 +152,39 @@ void Graph::ResolveHazard(
}
}
std
::
shared_ptr
<
Graph
>
Graph
::
Clone
()
{
auto
cloned_graph
=
std
::
make_shared
<
Graph
>
(
this
->
program_
);
cloned_graph
->
ReleaseNodes
();
cloned_graph
->
num_node_created_
=
0
;
std
::
unordered_map
<
ir
::
Node
*
,
ir
::
Node
*>
origin_to_cloned
;
for
(
auto
*
n
:
this
->
node_set_
)
{
ir
::
Node
*
cloned_node
=
nullptr
;
if
(
n
->
IsCtrlVar
())
{
cloned_node
=
cloned_graph
->
CreateControlDepVar
();
}
else
if
(
!
n
->
var_desc_
&&
!
n
->
op_desc_
)
{
// empty node
cloned_node
=
cloned_graph
->
CreateEmptyNode
(
n
->
Name
(),
n
->
NodeType
());
}
else
if
(
n
->
IsVar
())
{
cloned_node
=
cloned_graph
->
CreateVarNode
(
n
->
Var
());
}
else
if
(
n
->
IsOp
())
{
cloned_node
=
cloned_graph
->
CreateOpNode
(
n
->
Op
());
}
if
(
cloned_node
)
{
origin_to_cloned
[
n
]
=
cloned_node
;
}
else
{
PADDLE_THROW
(
"The cloned node's type is not supported!"
);
}
}
for
(
auto
*
n
:
this
->
node_set_
)
{
for
(
auto
it
=
n
->
inputs
.
begin
();
it
!=
n
->
inputs
.
end
();
it
++
)
{
origin_to_cloned
[
n
]
->
inputs
.
push_back
(
origin_to_cloned
[
*
it
]);
}
for
(
auto
it
=
n
->
outputs
.
begin
();
it
!=
n
->
outputs
.
end
();
it
++
)
{
origin_to_cloned
[
n
]
->
outputs
.
push_back
(
origin_to_cloned
[
*
it
]);
}
}
return
cloned_graph
;
}
bool
IsControlDepVar
(
const
ir
::
Node
&
var
)
{
return
var
.
Name
().
find
(
ir
::
Node
::
kControlDepVarName
)
!=
std
::
string
::
npos
;
}
...
...
paddle/fluid/framework/ir/graph.h
浏览文件 @
ac0e0f51
...
...
@@ -17,6 +17,7 @@ limitations under the License. */
#include <map>
#include <memory>
#include <string>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ir/node.h"
...
...
@@ -199,7 +200,12 @@ class Graph {
// WARN: After a series of passes, the current graph can be quite
// different from OriginProgram. Caller shouldn't assume much from
// the returned OriginProgram.
const
ProgramDesc
&
OriginProgram
()
const
{
return
program_
;
}
const
ProgramDesc
&
OriginProgram
()
const
{
LOG
(
WARNING
)
<<
"WARN: After a series of passes, the current graph can be "
"quite different from OriginProgram. So, please avoid "
"using the `OriginProgram()` method!"
;
return
program_
;
}
// This method takes ownership of `node`.
ir
::
Node
*
AddNode
(
ir
::
Node
*
node
)
{
...
...
@@ -212,6 +218,10 @@ class Graph {
void
ResolveHazard
(
const
std
::
map
<
std
::
string
,
std
::
vector
<
ir
::
Node
*>>
&
var_nodes
);
// Create a new and duplicated graph.
// WARN: The method only clones the graph structure, not its attributes.
std
::
shared_ptr
<
Graph
>
Clone
();
private:
std
::
map
<
std
::
string
,
std
::
vector
<
ir
::
Node
*>>
InitFromProgram
(
const
ProgramDesc
&
program
);
...
...
paddle/fluid/framework/ir/node.h
浏览文件 @
ac0e0f51
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include <typeindex>
#include <typeinfo>
...
...
paddle/fluid/framework/operator.cc
浏览文件 @
ac0e0f51
...
...
@@ -186,14 +186,14 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
VLOG
(
3
)
<<
place
<<
" "
<<
DebugStringEx
(
&
scope
);
}
catch
(
platform
::
EnforceNotMet
exception
)
{
if
(
Attrs
().
count
(
"sub_block"
)
!=
0
)
{
throw
;
throw
std
::
move
(
exception
)
;
}
auto
&
callstack
=
Attr
<
std
::
vector
<
std
::
string
>>
(
OpProtoAndCheckerMaker
::
OpCreationCallstackAttrName
());
if
(
callstack
.
empty
())
{
throw
;
throw
std
::
move
(
exception
)
;
}
std
::
ostringstream
sout
;
sout
<<
"Invoke operator "
<<
Type
()
<<
" error.
\n
"
;
...
...
@@ -204,7 +204,7 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
sout
<<
"C++ Callstacks:
\n
"
;
sout
<<
exception
.
err_str_
;
exception
.
err_str_
=
sout
.
str
();
throw
;
throw
std
::
move
(
exception
)
;
}
catch
(...)
{
std
::
rethrow_exception
(
std
::
current_exception
());
}
...
...
@@ -926,8 +926,10 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
dev_ctx
=
pool
.
Get
(
expected_kernel_key
.
place_
);
}
RuntimeInferShapeContext
infer_shape_ctx
(
*
this
,
exec_scope
,
ctx
);
this
->
InferShape
(
&
infer_shape_ctx
);
if
(
!
HasAttr
(
kAllKernelsMustComputeRuntimeShape
))
{
RuntimeInferShapeContext
infer_shape_ctx
(
*
this
,
exec_scope
,
ctx
);
this
->
InferShape
(
&
infer_shape_ctx
);
}
// TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
// not Scope. Imperative mode only pass inputs and get outputs.
kernel_iter
->
second
(
...
...
paddle/fluid/framework/operator.h
浏览文件 @
ac0e0f51
...
...
@@ -62,6 +62,15 @@ constexpr char kZeroVarSuffix[] = "@ZERO";
/// Variables with this suffix are the new Gradient.
constexpr
char
kNewGradSuffix
[]
=
"@NEWGRAD@"
;
/// If an Op has this attribute, all its kernels should calculate output
/// variable's shape in the corresponding Compute() function. And
/// OperatorWithKernel::RunImpl() would skip call this Op's InferShape()
/// function in its runtime for speedup.
/// TODO(luotao): Note that this temporal attribute would be deleted after all
/// ops contain it.
constexpr
char
kAllKernelsMustComputeRuntimeShape
[]
=
"@ALL_KERNELS_MUST_COMPUTE_RUNTIME_SHAPE@"
;
// define some kernel priority
/* Define multiple kernel type fallback order*/
extern
std
::
vector
<
std
::
tuple
<
platform
::
Place
,
LibraryType
>>
kKernelPriority
;
...
...
paddle/fluid/framework/parallel_executor.cc
浏览文件 @
ac0e0f51
...
...
@@ -181,13 +181,14 @@ std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
return
member_
->
local_scopes_
;
}
ParallelExecutor
::
ParallelExecutor
(
const
std
::
vector
<
platform
::
Place
>
&
places
,
const
std
::
unordered_set
<
std
::
string
>
&
bcast_vars
,
const
std
::
string
&
loss_var_name
,
Scope
*
scope
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
const
ExecutionStrategy
&
exec_strategy
,
const
BuildStrategy
&
build_strategy
,
ir
::
Graph
*
graph
)
ParallelExecutor
::
ParallelExecutor
(
const
std
::
vector
<
platform
::
Place
>
&
places
,
const
std
::
vector
<
std
::
string
>
&
bcast_vars
,
const
std
::
string
&
loss_var_name
,
Scope
*
scope
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
const
ExecutionStrategy
&
exec_strategy
,
const
BuildStrategy
&
build_strategy
,
ir
::
Graph
*
graph
)
:
member_
(
new
ParallelExecutorPrivate
(
places
))
{
member_
->
global_scope_
=
scope
;
member_
->
use_cuda_
=
exec_strategy
.
use_cuda_
;
...
...
@@ -254,9 +255,23 @@ ParallelExecutor::ParallelExecutor(
PADDLE_THROW
(
"Not compiled with CUDA"
);
#endif
}
if
(
member_
->
local_scopes_
.
size
()
!=
1
&&
local_scopes
.
empty
())
{
BCastParamsToDevices
(
bcast_vars
);
// broadcast parameters from the 0th device to others:
auto
need_broadcast
=
[
&
]()
->
bool
{
if
(
build_strategy
.
num_trainers_
>
1
)
{
// 1. num_tariners would be grater than 1 for nccl distributed training.
return
true
;
}
else
if
(
member_
->
local_scopes_
.
size
()
!=
1
&&
local_scopes
.
empty
())
{
// 2. Only one trainer process, but ParallelExecutor hold multiple
// devices.
return
true
;
}
return
false
;
};
if
(
need_broadcast
())
{
BCastParamsToDevices
(
bcast_vars
,
build_strategy
.
trainer_id_
);
}
// Startup Program has been run. All local scopes has correct parameters.
// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
...
...
@@ -338,7 +353,7 @@ ParallelExecutor::ParallelExecutor(
}
void
ParallelExecutor
::
BCastParamsToDevices
(
const
std
::
unordered_set
<
std
::
string
>
&
vars
)
const
{
const
std
::
vector
<
std
::
string
>
&
vars
,
int
trainer_id
)
const
{
// the initializing bcast, all vars would be bcast from device(0).
for
(
auto
&
var
:
vars
)
{
framework
::
Variable
*
main_var
=
member_
->
local_scopes_
[
0
]
->
FindVar
(
var
);
...
...
@@ -362,7 +377,7 @@ void ParallelExecutor::BCastParamsToDevices(
auto
place
=
member_
->
places_
[
i
];
void
*
buffer
;
if
(
i
==
0
)
{
if
(
i
==
0
&&
trainer_id
==
0
)
{
buffer
=
const_cast
<
void
*>
(
main_tensor
.
data
<
void
>
());
}
else
{
auto
local_scope
=
member_
->
local_scopes_
[
i
];
...
...
paddle/fluid/framework/parallel_executor.h
浏览文件 @
ac0e0f51
...
...
@@ -14,9 +14,11 @@ limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
...
...
@@ -45,7 +47,7 @@ class ParallelExecutor {
public:
explicit
ParallelExecutor
(
const
std
::
vector
<
platform
::
Place
>
&
places
,
const
std
::
unordered_set
<
std
::
string
>
&
bcast_vars
,
const
std
::
vector
<
std
::
string
>
&
bcast_vars
,
const
std
::
string
&
loss_var_name
,
Scope
*
scope
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
const
ExecutionStrategy
&
exec_strategy
,
...
...
@@ -70,7 +72,10 @@ class ParallelExecutor {
const
std
::
string
&
fetched_var_name
);
private:
void
BCastParamsToDevices
(
const
std
::
unordered_set
<
std
::
string
>
&
vars
)
const
;
// broadcast the parameters from the 0th device.
// trainer_id the trainer index in nccl distributed training.
void
BCastParamsToDevices
(
const
std
::
vector
<
std
::
string
>
&
vars
,
int
trainer_id
=
0
)
const
;
bool
EnableParallelGraphExecution
(
const
ir
::
Graph
&
graph
,
const
ExecutionStrategy
&
exec_strategy
,
const
BuildStrategy
&
build_strategy
)
const
;
...
...
paddle/fluid/framework/tensor_util.cc
浏览文件 @
ac0e0f51
...
...
@@ -18,6 +18,7 @@
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/platform/profiler.h"
namespace
paddle
{
namespace
framework
{
...
...
@@ -137,16 +138,19 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
#ifdef PADDLE_WITH_CUDA
else
if
(
platform
::
is_gpu_place
(
src_place
)
&&
// NOLINT
platform
::
is_cpu_place
(
dst_place
))
{
platform
::
RecordEvent
record_event
(
"TensorCopy:GPU->CPU"
);
auto
src_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
src_place
);
auto
dst_cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
dst_place
);
memory
::
Copy
(
dst_cpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
nullptr
);
}
else
if
(
platform
::
is_cpu_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
platform
::
RecordEvent
record_event
(
"TensorCopy:CPU->GPU"
);
auto
src_cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_cpu_place
,
src_ptr
,
size
,
nullptr
);
}
else
if
(
platform
::
is_gpu_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
platform
::
RecordEvent
record_event
(
"TensorCopy:GPU->GPU"
);
if
(
src_ptr
==
dst_ptr
&&
platform
::
is_same_place
(
src_place
,
dst_place
))
{
VLOG
(
3
)
<<
"Skip copy the same data from "
<<
src_place
<<
" to "
<<
dst_place
;
...
...
@@ -157,6 +161,7 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
nullptr
);
}
else
if
(
platform
::
is_cuda_pinned_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
platform
::
RecordEvent
record_event
(
"TensorCopy:CUDAPinned->GPU"
);
auto
src_pinned_place
=
boost
::
get
<
platform
::
CUDAPinnedPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_pinned_place
,
src_ptr
,
size
,
...
...
paddle/fluid/imperative/layer.cc
浏览文件 @
ac0e0f51
...
...
@@ -159,10 +159,9 @@ class Autograd {
for
(
auto
it
:
candidate
->
pre_ops_
)
{
for
(
OpBase
*
pre_op
:
it
.
second
)
{
if
(
!
pre_op
)
continue
;
VLOG
(
5
)
<<
"op dep "
<<
candidate
->
op_desc_
->
Type
()
<<
" trace id "
VLOG
(
5
)
<<
"op dep "
<<
candidate
->
Type
()
<<
" trace id "
<<
candidate
->
trace_id_
<<
" <---- "
<<
it
.
first
<<
" <---- "
<<
pre_op
->
op_desc_
->
Type
()
<<
" trace id "
<<
pre_op
->
trace_id_
;
<<
pre_op
->
Type
()
<<
" trace id "
<<
pre_op
->
trace_id_
;
if
(
visited
.
find
(
pre_op
)
==
visited
.
end
())
{
visited
.
insert
(
pre_op
);
queue
.
push_back
(
pre_op
);
...
...
@@ -180,10 +179,12 @@ std::unique_ptr<VarBase> VarBase::NewVarBase(const platform::Place& dst_place,
PADDLE_ENFORCE
(
var_
->
IsInitialized
(),
"Variable must be initialized when getting numpy tensor"
);
std
::
unique_ptr
<
VarBase
>
new_var
(
new
VarBase
());
// TODO(minqiyang): change this after move unique_name generator to CXX
const
framework
::
LoDTensor
&
self_tensor
=
var_
->
Get
<
framework
::
LoDTensor
>
();
std
::
unique_ptr
<
VarBase
>
new_var
(
new
VarBase
(
"Itmp"
,
self_tensor
.
type
(),
self_tensor
.
dims
(),
dst_place
,
true
,
false
));
framework
::
LoDTensor
*
tensor
=
new_var
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
();
tensor
->
Resize
(
var_
->
Get
<
framework
::
LoDTensor
>
().
dims
());
tensor
->
set_lod
(
var_
->
Get
<
framework
::
LoDTensor
>
().
lod
());
if
(
blocking
)
{
...
...
@@ -199,52 +200,62 @@ std::unique_ptr<VarBase> VarBase::NewVarBase(const platform::Place& dst_place,
}
if
(
platform
::
is_gpu_place
(
dst_place
))
{
VLOG
(
3
)
<<
"copy tensor "
<<
var_desc_
->
Name
()
<<
" from gpu"
;
VLOG
(
3
)
<<
"copy tensor "
<<
Name
()
<<
" from gpu"
;
}
return
new_var
;
}
framework
::
LoDTensor
&
VarBase
::
GradValue
()
{
VLOG
(
3
)
<<
"get var grad "
<<
var_desc_
->
Name
();
VLOG
(
3
)
<<
"get var grad "
<<
Name
();
PADDLE_ENFORCE_NOT_NULL
(
grads_
,
"Could not get grad value from no grad variable"
);
return
*
(
grads_
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
());
}
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
OpBase
::
ApplyGrad
()
{
if
(
grad_op_descs_
.
empty
()
&&
backward_id_
<=
0
)
{
VLOG
(
3
)
<<
"op with no grad: "
<<
op_desc_
->
Type
();
VLOG
(
3
)
<<
"op with no grad: "
<<
Type
();
return
{};
}
VLOG
(
3
)
<<
"apply op grad: "
<<
op_desc_
->
Type
();
std
::
vector
<
framework
::
VariableValueMap
>
grad_outputs
;
VLOG
(
3
)
<<
"apply op grad: "
<<
Type
();
std
::
vector
<
framework
::
VariableValueMap
>
tmp_
grad_outputs
;
if
(
backward_id_
>
0
)
{
VLOG
(
3
)
<<
"py_layer_grad"
;
grad_outputs
.
resize
(
1
);
grad_outputs
[
0
][
framework
::
GradVarName
(
PyLayer
::
kFwdOut
)]
=
tmp_
grad_outputs
.
resize
(
1
);
tmp_
grad_outputs
[
0
][
framework
::
GradVarName
(
PyLayer
::
kFwdOut
)]
=
PyLayer
::
ApplyGrad
(
backward_id_
,
grad_input_vars_
[
0
][
framework
::
GradVarName
(
PyLayer
::
kFwdInp
)]);
}
else
{
grad_outputs
.
resize
(
grad_op_descs_
.
size
());
for
(
size_t
k
=
0
;
k
<
grad_op_descs_
.
size
();
++
k
)
{
const
size_t
grad_op_count
=
grad_op_descs_
.
size
();
tmp_grad_outputs
.
resize
(
grad_op_count
);
for
(
size_t
k
=
0
;
k
<
grad_op_count
;
++
k
)
{
framework
::
OpDesc
*
grad_op_desc
=
grad_op_descs_
[
k
];
VLOG
(
3
)
<<
"op grad "
<<
grad_op_desc
->
Type
();
for
(
auto
it
:
grad_output_vars_
[
k
])
{
auto
&
outputs
=
grad_outputs
[
k
][
it
.
first
];
auto
&
grad_output_variable_map
=
grad_output_vars_
[
k
];
VLOG
(
3
)
<<
"apply grad op "
<<
grad_op_desc
->
Type
();
// Allocate tmp grad output variable
for
(
auto
it
:
grad_output_variable_map
)
{
auto
&
outputs
=
tmp_grad_outputs
[
k
][
it
.
first
];
outputs
.
reserve
(
it
.
second
.
size
());
for
(
size_t
i
=
0
;
i
<
it
.
second
.
size
();
++
i
)
{
// Allocate a new variable
Variable
*
tmp_var
=
new
framework
::
Variable
();
tmp_var
->
GetMutable
<
framework
::
LoDTensor
>
();
outputs
.
push
_back
(
tmp_var
);
outputs
.
emplace
_back
(
tmp_var
);
}
}
framework
::
RuntimeContext
ctx
(
grad_input_vars_
[
k
],
grad_outputs
[
k
]);
// Run grad op
framework
::
RuntimeContext
ctx
(
grad_input_vars_
[
k
],
tmp_grad_outputs
[
k
]);
// No need to do compile time infer shape here.
// grad_op_desc_->InferShape(*block_);
grad_op_desc
->
InferVarType
(
block_
);
//
grad_op_desc->InferVarType(block_);
std
::
unique_ptr
<
framework
::
OperatorBase
>
opbase
=
framework
::
OpRegistry
::
CreateOp
(
*
grad_op_desc
);
...
...
@@ -260,9 +271,10 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
}
}
// Add tmp grad outputs to original grad vars
for
(
size_t
k
=
0
;
k
<
grad_output_vars_
.
size
();
++
k
)
{
for
(
auto
it
:
grad_output_vars_
[
k
])
{
auto
&
outputs
=
grad_outputs
[
k
][
it
.
first
];
auto
&
outputs
=
tmp_
grad_outputs
[
k
][
it
.
first
];
auto
&
origin_outputs
=
it
.
second
;
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
origin_outputs
.
size
());
...
...
@@ -316,19 +328,14 @@ void PyLayer::RegisterFunc(int func_id, const py::object& py_func) {
int
PyLayer
::
NumFuncs
()
{
return
py_funcs_
.
size
();
}
std
::
vector
<
Var
Bas
e
*>
PyLayer
::
Apply
(
int
func_id
,
const
std
::
vector
<
VarBase
*>&
inputs
)
{
std
::
vector
<
Var
iabl
e
*>
PyLayer
::
Apply
(
int
func_id
,
const
std
::
vector
<
VarBase
*>&
inputs
)
{
std
::
vector
<
framework
::
Variable
*>
invars
;
for
(
const
VarBase
*
in
:
inputs
)
{
invars
.
push_back
(
in
->
var_
);
}
PADDLE_ENFORCE
(
py_funcs_
.
find
(
func_id
)
!=
py_funcs_
.
end
());
std
::
vector
<
Variable
*>
outvars
=
CallPythonFunc
(
py_funcs_
[
func_id
],
invars
);
std
::
vector
<
VarBase
*>
ret
;
for
(
Variable
*
v
:
outvars
)
{
ret
.
push_back
(
new
VarBase
(
v
,
new
VarBase
(
true
)));
}
return
ret
;
return
CallPythonFunc
(
py_funcs_
[
func_id
],
invars
);
}
std
::
vector
<
Variable
*>
PyLayer
::
ApplyGrad
(
...
...
paddle/fluid/imperative/layer.h
浏览文件 @
ac0e0f51
...
...
@@ -112,31 +112,53 @@ class OpBase;
*/
class
VarBase
{
public:
VarBase
()
:
VarBase
(
new
framework
::
Variable
(),
new
VarBase
(
true
))
{}
explicit
VarBase
(
bool
stop_gradient
)
:
VarBase
(
new
framework
::
Variable
(),
stop_gradient
?
nullptr
:
new
VarBase
(
true
),
stop_gradient
)
{}
VarBase
(
framework
::
Variable
*
var
,
VarBase
*
grad
)
:
VarBase
(
var
,
grad
,
false
)
{}
// Internal interface, create VarBase from exist variable
VarBase
(
const
std
::
string
&
name
,
framework
::
Variable
*
var
,
VarBase
*
grad
,
bool
stop_gradient
)
:
VarBase
(
name
,
var
->
Get
<
framework
::
LoDTensor
>
().
type
(),
var
->
Get
<
framework
::
LoDTensor
>
().
dims
(),
var
->
Get
<
framework
::
LoDTensor
>
().
place
(),
var
,
grad
,
stop_gradient
,
false
)
{}
// Python interface
VarBase
(
const
std
::
string
&
name
,
const
framework
::
proto
::
VarType
::
Type
dtype
,
const
std
::
vector
<
int64_t
>&
shape
,
const
platform
::
Place
&
place
,
bool
stop_gradient
,
bool
persistable
)
:
VarBase
(
name
,
dtype
,
framework
::
make_ddim
(
shape
),
place
,
stop_gradient
,
persistable
)
{}
// Internal interface, create VarBase from with ddim
VarBase
(
const
std
::
string
&
name
,
const
framework
::
proto
::
VarType
::
Type
dtype
,
const
framework
::
DDim
&
shape
,
const
platform
::
Place
&
place
,
bool
stop_gradient
,
bool
persistable
)
:
VarBase
(
name
,
dtype
,
shape
,
place
,
nullptr
,
nullptr
,
stop_gradient
,
persistable
)
{}
private:
VarBase
(
framework
::
Variable
*
var
,
VarBase
*
grad
,
bool
stop_gradient
)
:
name_
(),
var_desc_
(
nullptr
),
VarBase
(
const
std
::
string
&
name
,
framework
::
proto
::
VarType
::
Type
dtype
,
const
framework
::
DDim
&
shape
,
const
platform
::
Place
&
place
,
framework
::
Variable
*
var
,
VarBase
*
grad
,
bool
stop_gradient
,
bool
persistable
)
:
name_
(
name
),
dtype_
(
dtype
),
place_
(
place
),
var_
(
var
),
grads_
(
grad
),
block_
(
nullptr
),
persistable_
(
false
),
stop_gradient_
(
stop_gradient
),
persistable_
(
persistable
),
pre_op_
(
nullptr
),
pre_op_out_name_
(),
pre_op_out_idx_
(
-
1
)
{}
pre_op_out_idx_
(
-
1
)
{
if
(
!
var_
)
{
var_
=
new
framework
::
Variable
();
auto
tensor
=
var_
->
GetMutable
<
framework
::
LoDTensor
>
();
tensor
->
Resize
(
shape
);
tensor
->
mutable_data
(
place_
,
dtype_
);
}
}
public:
virtual
~
VarBase
()
{
// TODO(minqiyang): remove var desc from block desc
if
(
var_
)
{
delete
var_
;
var_
=
nullptr
;
...
...
@@ -151,14 +173,30 @@ class VarBase {
pre_op_out_idx_
=
-
1
;
}
inline
OpBase
*
PreOp
()
const
{
return
pre_op_
;
}
inline
int
PreOpOutIdx
()
const
{
return
pre_op_out_idx_
;
}
inline
void
SetName
(
const
std
::
string
&
name
)
{
name_
=
name
;
}
inline
std
::
string
Name
()
const
{
return
name_
;
}
inline
std
::
vector
<
int64_t
>
Shape
()
const
{
if
(
var_
->
IsInitialized
())
{
return
framework
::
vectorize
(
var_
->
Get
<
framework
::
LoDTensor
>
().
dims
());
}
else
{
return
{};
}
}
inline
framework
::
proto
::
VarType
::
Type
DType
()
const
{
return
dtype_
;
}
inline
void
SetStopGradient
(
bool
stop_gradient
)
{
stop_gradient_
=
stop_gradient
;
}
inline
bool
IsStopGradient
()
const
{
return
stop_gradient_
;
}
inline
void
SetPersistable
(
bool
persistable
)
{
persistable_
=
persistable
;
}
inline
bool
IsPersistable
()
const
{
return
persistable_
;
}
inline
OpBase
*
PreOp
()
const
{
return
pre_op_
;
}
inline
int
PreOpOutIdx
()
const
{
return
pre_op_out_idx_
;
}
void
RunBackward
();
inline
void
ResetPreOp
(
OpBase
*
op
)
{
...
...
@@ -180,7 +218,7 @@ class VarBase {
}
void
ClearGradient
()
{
VLOG
(
1
)
<<
"clear gradient of "
<<
var_desc_
->
Name
();
VLOG
(
1
)
<<
"clear gradient of "
<<
Name
();
if
(
grads_
&&
grads_
->
var_
&&
grads_
->
var_
->
IsInitialized
())
{
auto
grads_t
=
grads_
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
();
operators
::
math
::
set_constant
(
...
...
@@ -196,23 +234,20 @@ class VarBase {
const
bool
blocking
)
const
;
inline
std
::
string
GradName
()
const
{
PADDLE_ENFORCE
(
var_desc_
,
"Couldn't get gradient variable's name, please call backward() first"
);
return
string
::
Sprintf
(
"%s@IGrad"
,
var_desc_
->
Name
());
return
string
::
Sprintf
(
"%s@IGrad"
,
Name
());
}
std
::
string
name_
;
framework
::
VarDesc
*
var_desc_
;
framework
::
proto
::
VarType
::
Type
dtype_
;
platform
::
Place
place_
;
framework
::
Variable
*
var_
;
VarBase
*
grads_
;
framework
::
BlockDesc
*
block_
;
bool
persistable_
;
private:
bool
stop_gradient_
;
bool
persistable_
;
OpBase
*
pre_op_
;
std
::
string
pre_op_out_name_
;
int
pre_op_out_idx_
;
...
...
@@ -223,11 +258,11 @@ class VarBase {
*/
class
PYBIND11_HIDDEN
OpBase
{
public:
OpBase
()
:
op_desc_
(
nullptr
),
OpBase
(
const
std
::
string
&
type
)
:
type_
(
type
),
trace_id_
(
-
1
),
forward_id_
(
-
1
),
backward_id_
(
-
1
),
trace_id_
(
-
1
),
place_
(
platform
::
CPUPlace
()),
backward_hooks_
()
{}
...
...
@@ -249,13 +284,34 @@ class PYBIND11_HIDDEN OpBase {
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
ApplyGrad
();
inline
std
::
string
Type
()
const
{
return
type_
;
}
inline
std
::
string
GradOpType
(
size_t
index
)
const
{
PADDLE_ENFORCE_NOT_NULL
(
grad_op_descs_
[
index
]);
return
grad_op_descs_
[
index
]
->
Type
();
}
void
RegisterBackwardHooks
(
const
py
::
object
&
callable
);
void
InvokeBackwardHooks
();
// One of `op_desc_` or `forward_id_` is set, not both.
// For pure python PyLayer, use `forward_id_`, otherwise, use op_desc_.
framework
::
OpDesc
*
op_desc_
;
void
TrackPreOp
(
const
VarBase
*
inp_var
,
const
std
::
string
&
inp_name
)
{
if
(
inp_var
->
PreOp
()
&&
!
inp_var
->
IsStopGradient
())
{
VLOG
(
3
)
<<
"add pre op "
<<
inp_var
->
PreOp
()
->
Type
()
<<
" in slot "
<<
inp_name
;
pre_ops_
[
inp_name
].
push_back
(
inp_var
->
PreOp
());
pre_ops_out_idx_
[
inp_name
].
push_back
(
inp_var
->
PreOpOutIdx
());
}
else
{
VLOG
(
3
)
<<
"no pre op in slot "
<<
inp_name
<<
" input var stop_gradient: "
<<
inp_var
->
IsStopGradient
();
pre_ops_
[
inp_name
].
push_back
(
nullptr
);
// pre_ops_out_idx_[inp_name].push_back(-1);
}
}
std
::
string
type_
;
// One of `trace_id_` or `forward_id_` is set, not both.
// For pure python PyLayer, use `forward_id_`, otherwise, use trace_id_.
int
trace_id_
;
int
forward_id_
;
// When has backward, one of `grad_op_descs_` or `backward_id_` is set,
...
...
@@ -263,7 +319,6 @@ class PYBIND11_HIDDEN OpBase {
// Note: each fwd op corresponds to a vector of bwd ops.
std
::
vector
<
framework
::
OpDesc
*>
grad_op_descs_
;
int
backward_id_
;
int
trace_id_
;
platform
::
Place
place_
;
...
...
@@ -277,8 +332,6 @@ class PYBIND11_HIDDEN OpBase {
// Outputs to a vector of bwd ops.
std
::
vector
<
framework
::
VariableValueMap
>
grad_output_vars_
;
framework
::
BlockDesc
*
block_
;
std
::
vector
<
py
::
object
>
backward_hooks_
;
};
...
...
@@ -303,8 +356,8 @@ class PyLayer {
static
int
NumFuncs
();
static
std
::
vector
<
VarBase
*>
Apply
(
int
func_id
,
const
std
::
vector
<
VarBase
*>&
inputs
);
static
std
::
vector
<
framework
::
Variable
*>
Apply
(
int
func_id
,
const
std
::
vector
<
VarBase
*>&
inputs
);
static
std
::
vector
<
framework
::
Variable
*>
ApplyGrad
(
int
func_id
,
const
std
::
vector
<
framework
::
Variable
*>&
inputs
);
...
...
paddle/fluid/imperative/tracer.cc
浏览文件 @
ac0e0f51
...
...
@@ -56,15 +56,19 @@ void CreateGradOp(const framework::OpDesc& op_desc,
}
}
void
Init
Var
(
framework
::
Variable
*
var
,
framework
::
Variable
*
grad_var
,
platform
::
DeviceContext
*
dev_ctx
)
{
void
Init
Grad
(
VarBase
*
var
,
platform
::
DeviceContext
*
dev_ctx
)
{
PADDLE_ENFORCE_NOT_NULL
(
var
,
"Could not get valid var base"
);
PADDLE_ENFORCE_NOT_NULL
(
dev_ctx
,
"Could not get valid device from forward op"
);
auto
&
var_t
=
var
->
Get
<
framework
::
LoDTensor
>
();
grad_var
->
GetMutable
<
framework
::
LoDTensor
>
()
->
mutable_data
<
float
>
(
var_t
.
dims
(),
dev_ctx
->
GetPlace
());
operators
::
math
::
set_constant
(
*
dev_ctx
,
grad_var
->
GetMutable
<
framework
::
LoDTensor
>
(),
0.0
);
if
(
var
->
grads_
==
nullptr
)
{
auto
&
var_t
=
var
->
var_
->
Get
<
framework
::
LoDTensor
>
();
var
->
grads_
=
new
VarBase
(
var
->
GradName
(),
framework
::
proto
::
VarType
::
FP32
,
framework
::
vectorize
(
var_t
.
dims
()),
dev_ctx
->
GetPlace
(),
true
,
false
);
auto
grad_t
=
var
->
grads_
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
();
operators
::
math
::
set_constant
(
*
dev_ctx
,
grad_t
,
0.0
);
}
}
platform
::
Place
GetExpectedPlace
(
platform
::
Place
place
,
VarBasePtrMap
inputs
)
{
...
...
@@ -85,6 +89,62 @@ platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs) {
return
result
;
}
framework
::
VariableNameMap
CreateInputVarNameMap
(
const
OpBase
*
op
,
const
VarBasePtrMap
&
varbase_map
)
{
framework
::
VariableNameMap
result
;
auto
&
info_map
=
framework
::
OpInfoMap
::
Instance
();
auto
*
op_info
=
info_map
.
GetNullable
(
op
->
Type
());
if
(
op_info
==
nullptr
||
op_info
->
proto_
==
nullptr
)
{
return
result
;
}
for
(
auto
&
in
:
op_info
->
Proto
().
inputs
())
{
auto
it
=
varbase_map
.
find
(
in
.
name
());
if
(
it
==
varbase_map
.
end
())
{
PADDLE_ENFORCE
(
in
.
dispensable
());
result
[
in
.
name
()]
=
{};
}
else
{
auto
var_vector
=
it
->
second
;
std
::
vector
<
std
::
string
>
args
;
args
.
reserve
(
var_vector
.
size
());
for
(
VarBase
*
var_base
:
var_vector
)
{
args
.
emplace_back
(
var_base
->
Name
());
}
result
[
in
.
name
()]
=
args
;
}
}
return
result
;
}
framework
::
VariableNameMap
CreateOutputVarNameMap
(
const
OpBase
*
op
,
const
VarBasePtrMap
&
varbase_map
)
{
framework
::
VariableNameMap
result
;
auto
&
info_map
=
framework
::
OpInfoMap
::
Instance
();
auto
*
op_info
=
info_map
.
GetNullable
(
op
->
Type
());
if
(
op_info
==
nullptr
||
op_info
->
proto_
==
nullptr
)
{
return
result
;
}
for
(
auto
&
out
:
op_info
->
Proto
().
outputs
())
{
auto
it
=
varbase_map
.
find
(
out
.
name
());
if
(
it
==
varbase_map
.
end
())
{
PADDLE_ENFORCE
(
out
.
dispensable
());
result
[
out
.
name
()]
=
{};
}
else
{
auto
var_vector
=
it
->
second
;
std
::
vector
<
std
::
string
>
args
;
args
.
reserve
(
var_vector
.
size
());
for
(
VarBase
*
var_base
:
var_vector
)
{
args
.
emplace_back
(
var_base
->
Name
());
}
result
[
out
.
name
()]
=
args
;
}
}
return
result
;
}
Tracer
::
Tracer
(
framework
::
BlockDesc
*
root_block
)
:
root_block_
(
root_block
)
{
if
(
!
FLAGS_tracer_profile_fname
.
empty
())
{
std
::
call_once
(
gTracerProfileOnce
,
[]
{
...
...
@@ -101,7 +161,7 @@ Tracer::Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {
std
::
set
<
std
::
string
>
Tracer
::
Trace
(
OpBase
*
op
,
const
VarBasePtrMap
&
inputs
,
const
VarBasePtrMap
&
outputs
,
framework
::
BlockDesc
*
block
,
framework
::
AttributeMap
attrs_map
,
const
platform
::
Place
expected_place
,
const
bool
stop_gradient
)
{
#ifdef WITH_GPERFTOOLS
...
...
@@ -110,40 +170,27 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
}
#endif
std
::
map
<
std
::
string
,
VarBase
*>
vars
;
framework
::
OpDesc
*
op_desc
=
op
->
op_desc_
;
VLOG
(
3
)
<<
"tracer tracing "
<<
op_desc
->
Type
()
<<
" trace id "
<<
op
->
trace_id_
;
op_desc
->
InferShape
(
*
block
);
op_desc
->
InferVarType
(
block
);
std
::
unique_ptr
<
framework
::
OperatorBase
>
op_base
=
framework
::
OpRegistry
::
CreateOp
(
*
op_desc
);
framework
::
VariableValueMap
invars_map
;
framework
::
VariableValueMap
outvars_map
;
// Construct input_vars_map and output_vars_map
std
::
map
<
std
::
string
,
VarBase
*>
current_vars_map
;
op
->
input_vars_
=
inputs
;
for
(
auto
it
:
op
->
input_vars_
)
{
auto
&
invars
=
invars_map
[
it
.
first
];
invars
.
reserve
(
it
.
second
.
size
());
for
(
VarBase
*
inp
:
it
.
second
)
{
PADDLE_ENFORCE_NOT_NULL
(
inp
->
var_
,
"op %s input %s nullptr"
,
op
->
op_desc_
->
Type
(),
inp
->
var_desc_
->
Name
());
PADDLE_ENFORCE_NOT_NULL
(
inp
->
var_
,
"op %s input %s nullptr"
,
op
->
Type
(),
inp
->
Name
());
invars
.
emplace_back
(
inp
->
var_
);
vars
[
inp
->
var_desc_
->
Name
()]
=
inp
;
if
(
inp
->
PreOp
()
&&
!
inp
->
IsStopGradient
())
{
op
->
pre_ops_
[
it
.
first
].
push_back
(
inp
->
PreOp
());
op
->
pre_ops_out_idx_
[
it
.
first
].
push_back
(
inp
->
PreOpOutIdx
());
VLOG
(
3
)
<<
"add pre op "
<<
inp
->
PreOp
()
->
op_desc_
->
Type
();
}
else
{
op
->
pre_ops_
[
it
.
first
].
push_back
(
nullptr
);
op
->
TrackPreOp
(
inp
,
it
.
first
);
if
(
!
stop_gradient
)
{
current_vars_map
[
inp
->
Name
()]
=
inp
;
}
VLOG
(
3
)
<<
"input v
name "
<<
inp
->
var_desc_
->
Name
()
<<
" "
<<
inp
->
var_
->
IsInitialized
()
<<
" stop_gradient "
<<
inp
->
IsStopGradient
();
VLOG
(
3
)
<<
"input v
ar name: "
<<
inp
->
Name
()
<<
" inited: "
<<
inp
->
var_
->
IsInitialized
()
<<
" stop_grad: "
<<
inp
->
IsStopGradient
();
}
}
...
...
@@ -152,25 +199,38 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
auto
&
outvars
=
outvars_map
[
it
.
first
];
const
std
::
vector
<
VarBase
*>&
outputs
=
it
.
second
;
outvars
.
reserve
(
outputs
.
size
());
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
for
(
size_t
i
=
0
U
;
i
<
outputs
.
size
();
++
i
)
{
VarBase
*
out
=
outputs
[
i
];
outvars
.
emplace_back
(
out
->
var_
);
vars
[
out
->
var_desc_
->
Name
()]
=
out
;
framework
::
VarDesc
*
var_desc
=
block
->
FindVar
(
out
->
var_desc_
->
Name
());
if
(
var_desc
->
GetType
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
out
->
var_
->
GetMutable
<
framework
::
LoDTensor
>
();
}
else
{
LOG
(
ERROR
)
<<
"tracer doesn't support yet"
;
}
out
->
TrackPreOp
(
op
,
it
.
first
,
i
,
stop_gradient
);
if
(
!
stop_gradient
)
{
current_vars_map
[
out
->
Name
()]
=
out
;
}
VLOG
(
3
)
<<
"output vname "
<<
out
->
var_desc_
->
Name
()
<<
" "
<<
out
->
var_
->
IsInitialized
();
VLOG
(
3
)
<<
"input var name: "
<<
out
->
Name
()
<<
" inited: "
<<
out
->
var_
->
IsInitialized
()
<<
" stop_grad: "
<<
out
->
IsStopGradient
();
}
}
VLOG
(
3
)
<<
"tracer running "
<<
op_desc
->
Type
();
// Check attrs and create op
framework
::
VariableNameMap
invars_name_map
=
CreateInputVarNameMap
(
op
,
inputs
);
framework
::
VariableNameMap
outvars_name_map
=
CreateOutputVarNameMap
(
op
,
outputs
);
auto
&
info
=
framework
::
OpInfoMap
::
Instance
().
Get
(
op
->
Type
());
if
(
info
.
Checker
()
!=
nullptr
)
{
info
.
Checker
()
->
Check
(
&
attrs_map
);
}
std
::
unique_ptr
<
framework
::
OperatorBase
>
op_base
=
framework
::
OpRegistry
::
CreateOp
(
op
->
Type
(),
invars_name_map
,
outvars_name_map
,
attrs_map
);
// TODO(minqiyang): Support infer var type in imperative mode
// Run forward op
VLOG
(
3
)
<<
"tracer running "
<<
op
->
Type
();
framework
::
RuntimeContext
ctx
(
invars_map
,
outvars_map
);
// TODO(panyx0718): Cache p.
...
...
@@ -186,36 +246,44 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
framework
::
ExecutionContext
(
prepared_op
.
op
,
scope
,
*
prepared_op
.
dev_ctx
,
prepared_op
.
ctx
,
prepared_op
.
kernel_configs
));
// construct backward op
std
::
set
<
std
::
string
>
vars_saved_for_backward
;
if
(
!
stop_gradient
)
{
VLOG
(
5
)
<<
"start construct backward op"
;
// construct grad op descs
std
::
unique_ptr
<
framework
::
OpDesc
>
fwd_op_desc
(
new
framework
::
OpDesc
(
op
->
Type
(),
invars_name_map
,
outvars_name_map
,
attrs_map
));
std
::
unique_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
string
>>
grad_to_var
(
new
std
::
unordered_map
<
std
::
string
,
std
::
string
>
());
CreateGradOp
(
*
op_desc
,
{},
{
block
},
&
op
->
grad_op_descs_
,
grad_to_var
.
get
());
// NOTE(minqiyang): We don't support control flow op in imperative now
// Add grad_block_ when we want to support it
CreateGradOp
(
*
fwd_op_desc
,
{},
{},
&
op
->
grad_op_descs_
,
grad_to_var
.
get
());
op
->
grad_input_vars_
.
resize
(
op
->
grad_op_descs_
.
size
());
op
->
grad_output_vars_
.
resize
(
op
->
grad_op_descs_
.
size
());
VLOG
(
5
)
<<
"create grad op desc: "
<<
op
->
grad_op_descs_
[
0
]
->
Type
();
for
(
size_t
i
=
0
;
i
<
op
->
grad_op_descs_
.
size
();
++
i
)
{
const
size_t
grad_op_count
=
op
->
grad_op_descs_
.
size
();
op
->
grad_input_vars_
.
resize
(
grad_op_count
);
op
->
grad_output_vars_
.
resize
(
grad_op_count
);
for
(
size_t
i
=
0
;
i
<
grad_op_count
;
++
i
)
{
framework
::
OpDesc
*
grad_op_desc
=
op
->
grad_op_descs_
[
i
];
for
(
auto
it
:
grad_op_desc
->
Inputs
())
{
auto
&
grad_in_vars
=
op
->
grad_input_vars_
[
i
][
it
.
first
];
grad_in_vars
.
reserve
(
it
.
second
.
size
());
for
(
const
std
::
string
&
grad_invar
:
it
.
second
)
{
block
->
FindRecursiveOrCreateVar
(
grad_invar
);
auto
var_it
=
grad_to_var
->
find
(
grad_invar
);
if
(
var_it
==
grad_to_var
->
end
())
{
auto
fwd_var_it
=
vars
.
find
(
grad_invar
);
PADDLE_ENFORCE
(
fwd_var_it
!=
vars
.
end
());
auto
fwd_var_it
=
current_vars_map
.
find
(
grad_invar
);
PADDLE_ENFORCE
(
fwd_var_it
!=
current_vars_map
.
end
());
// Forward inputs or outputs.
grad_in_vars
.
push
_back
(
fwd_var_it
->
second
->
var_
);
grad_in_vars
.
emplace
_back
(
fwd_var_it
->
second
->
var_
);
}
else
{
VarBase
*
var
=
vars
[
var_it
->
second
];
if
(
!
var
->
grads_
->
var_
->
IsInitialized
())
{
InitVar
(
var
->
var_
,
var
->
grads_
->
var_
,
prepared_op
.
GetDeviceContext
());
}
VarBase
*
var
=
current_vars_map
[
var_it
->
second
];
InitGrad
(
var
,
prepared_op
.
GetDeviceContext
());
// Douts.
grad_in_vars
.
push
_back
(
var
->
grads_
->
var_
);
grad_in_vars
.
emplace
_back
(
var
->
grads_
->
var_
);
}
vars_saved_for_backward
.
insert
(
it
.
first
);
...
...
@@ -225,48 +293,48 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
for
(
auto
it
:
grad_op_desc
->
Outputs
())
{
auto
&
grad_out_vars
=
op
->
grad_output_vars_
[
i
][
it
.
first
];
for
(
const
std
::
string
&
grad_outvar
:
it
.
second
)
{
block
->
FindRecursiveOrCreateVar
(
grad_outvar
);
auto
var_it
=
grad_to_var
->
find
(
grad_outvar
);
PADDLE_ENFORCE
(
var_it
!=
grad_to_var
->
end
(),
"Could not found the grad op output var, should this "
"operator %s's stop gradient be True"
,
op_desc
->
Type
());
VarBase
*
var
=
vars
[
var_it
->
second
];
if
(
!
var
->
grads_
->
var_
->
IsInitialized
())
{
InitVar
(
var
->
var_
,
var
->
grads_
->
var_
,
prepared_op
.
GetDeviceContext
());
}
op
->
Type
());
VarBase
*
var
=
current_vars_map
[
var_it
->
second
];
InitGrad
(
var
,
prepared_op
.
GetDeviceContext
());
grad_out_vars
.
push_back
(
var
->
grads_
->
var_
);
}
}
}
}
op
->
block_
=
block
;
return
vars_saved_for_backward
;
}
std
::
vector
<
VarBase
*>
Tracer
::
PyTrace
(
OpBase
*
op
,
const
std
::
vector
<
VarBase
*>&
inputs
,
bool
stop_gradient
)
{
VLOG
(
3
)
<<
"py_trace"
;
VLOG
(
3
)
<<
"py_trace "
<<
op
->
Type
();
op
->
input_vars_
[
PyLayer
::
kFwdInp
]
=
inputs
;
op
->
output_vars_
[
PyLayer
::
kFwdOut
]
=
PyLayer
::
Apply
(
op
->
forward_id_
,
inputs
);
std
::
vector
<
framework
::
Variable
*>
ret_vars
=
PyLayer
::
Apply
(
op
->
forward_id_
,
inputs
);
for
(
VarBase
*
inp
:
inputs
)
{
if
(
inp
->
PreOp
()
&&
!
inp
->
IsStopGradient
())
{
op
->
pre_ops_
[
PyLayer
::
kFwdInp
].
push_back
(
inp
->
PreOp
());
op
->
pre_ops_out_idx_
[
PyLayer
::
kFwdInp
].
push_back
(
inp
->
PreOpOutIdx
());
}
else
{
op
->
pre_ops_
[
PyLayer
::
kFwdInp
].
push_back
(
nullptr
);
}
op
->
TrackPreOp
(
inp
,
PyLayer
::
kFwdInp
);
}
auto
&
outputs
=
op
->
output_vars_
[
PyLayer
::
kFwdOut
];
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
VarBase
*
out
=
outputs
[
i
];
std
::
vector
<
VarBase
*>&
outputs
=
op
->
output_vars_
[
PyLayer
::
kFwdOut
];
outputs
.
reserve
(
ret_vars
.
size
());
for
(
size_t
i
=
0U
;
i
!=
ret_vars
.
size
();
++
i
)
{
framework
::
Variable
*
v
=
ret_vars
[
i
];
VarBase
*
out
=
new
VarBase
(
string
::
Sprintf
(
"%s_out_%d"
,
op
->
Type
(),
i
),
v
,
nullptr
,
stop_gradient
);
outputs
.
emplace_back
(
out
);
out
->
TrackPreOp
(
op
,
PyLayer
::
kFwdOut
,
i
,
stop_gradient
);
}
if
(
!
stop_gradient
)
{
VLOG
(
5
)
<<
"start construct backward op"
;
op
->
grad_input_vars_
.
resize
(
1
);
op
->
grad_output_vars_
.
resize
(
1
);
auto
&
grad_input_vars
=
...
...
@@ -281,23 +349,16 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
grad_input_vars
.
push_back
(
out
->
var_
);
}
// TODO(minqiyang): Add GPU support for PyLayer, only support CPU now
platform
::
CPUPlace
place
;
for
(
VarBase
*
out
:
outputs
)
{
InitGrad
(
out
,
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
));
grad_input_vars
.
push_back
(
out
->
grads_
->
var_
);
if
(
!
grad_input_vars
.
back
()
->
IsInitialized
())
{
// TODO(minqiyang): Add GPU support for PyLayer, only support CPU now
InitVar
(
out
->
var_
,
grad_input_vars
.
back
(),
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
));
}
}
for
(
const
VarBase
*
inp
:
inputs
)
{
for
(
VarBase
*
inp
:
inputs
)
{
InitGrad
(
inp
,
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
));
grad_output_vars
.
push_back
(
inp
->
grads_
->
var_
);
if
(
!
grad_output_vars
.
back
()
->
IsInitialized
())
{
// TODO(minqiyang): Add GPU support for PyLayer, only support CPU now
InitVar
(
inp
->
var_
,
grad_output_vars
.
back
(),
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
));
}
}
}
return
outputs
;
...
...
paddle/fluid/imperative/tracer.h
浏览文件 @
ac0e0f51
...
...
@@ -17,6 +17,8 @@
#include <map>
#include <set>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/op_desc.h"
...
...
@@ -34,7 +36,8 @@ void CreateGradOp(const framework::OpDesc& op_desc,
framework
::
OpDesc
**
grad_op_desc
,
std
::
unordered_map
<
std
::
string
,
std
::
string
>*
grad_to_var
);
void
InitVar
(
framework
::
Variable
*
var
,
framework
::
Variable
*
grad_var
);
void
InitVar
(
const
VarBase
*
var
,
framework
::
Variable
*
grad_var
,
platform
::
DeviceContext
*
dev_ctx
);
platform
::
Place
GetExpectedPlace
(
platform
::
Place
place
,
VarBasePtrMap
inputs
);
...
...
@@ -46,7 +49,7 @@ class Tracer {
std
::
set
<
std
::
string
>
Trace
(
OpBase
*
op
,
const
VarBasePtrMap
&
inputs
,
const
VarBasePtrMap
&
outputs
,
framework
::
BlockDesc
*
block
,
framework
::
AttributeMap
attrs_map
,
const
platform
::
Place
expected_place
,
const
bool
stop_gradient
=
false
);
...
...
paddle/fluid/inference/api/details/zero_copy_tensor.cc
浏览文件 @
ac0e0f51
...
...
@@ -126,15 +126,20 @@ void ZeroCopyTensor::copy_to_cpu(T *data) {
}
template
void
ZeroCopyTensor
::
copy_from_cpu
<
float
>(
const
float
*
data
);
template
void
ZeroCopyTensor
::
copy_from_cpu
<
int64_t
>(
const
int64_t
*
data
);
template
void
ZeroCopyTensor
::
copy_from_cpu
<
int32_t
>(
const
int32_t
*
data
);
template
void
ZeroCopyTensor
::
copy_to_cpu
<
float
>(
float
*
data
);
template
void
ZeroCopyTensor
::
copy_to_cpu
<
int64_t
>(
int64_t
*
data
);
template
void
ZeroCopyTensor
::
copy_to_cpu
<
int32_t
>(
int32_t
*
data
);
template
float
*
ZeroCopyTensor
::
data
<
float
>(
PaddlePlace
*
place
,
int
*
size
)
const
;
template
int64_t
*
ZeroCopyTensor
::
data
<
int64_t
>(
PaddlePlace
*
place
,
int
*
size
)
const
;
template
int32_t
*
ZeroCopyTensor
::
data
<
int32_t
>(
PaddlePlace
*
place
,
int
*
size
)
const
;
template
float
*
ZeroCopyTensor
::
mutable_data
<
float
>(
PaddlePlace
place
);
template
int64_t
*
ZeroCopyTensor
::
mutable_data
<
int64_t
>(
PaddlePlace
place
);
template
int32_t
*
ZeroCopyTensor
::
mutable_data
<
int32_t
>(
PaddlePlace
place
);
void
*
ZeroCopyTensor
::
FindTensor
()
const
{
PADDLE_ENFORCE
(
!
name_
.
empty
(),
...
...
paddle/fluid/inference/api/helper.h
浏览文件 @
ac0e0f51
...
...
@@ -139,9 +139,8 @@ static void TensorAssignData(PaddleTensor *tensor,
}
template
<
typename
T
>
static
int
ZeroCopyTensorAssignData
(
ZeroCopyTensor
*
tensor
,
const
std
::
vector
<
std
::
vector
<
T
>>
&
data
)
{
int
size
{
0
};
static
void
ZeroCopyTensorAssignData
(
ZeroCopyTensor
*
tensor
,
const
std
::
vector
<
std
::
vector
<
T
>>
&
data
)
{
auto
*
ptr
=
tensor
->
mutable_data
<
T
>
(
PaddlePlace
::
kCPU
);
int
c
=
0
;
for
(
const
auto
&
f
:
data
)
{
...
...
@@ -149,7 +148,15 @@ static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
ptr
[
c
++
]
=
v
;
}
}
return
size
;
}
template
<
typename
T
>
static
void
ZeroCopyTensorAssignData
(
ZeroCopyTensor
*
tensor
,
const
PaddleBuf
&
data
)
{
auto
*
ptr
=
tensor
->
mutable_data
<
T
>
(
PaddlePlace
::
kCPU
);
for
(
size_t
i
=
0
;
i
<
data
.
length
()
/
sizeof
(
T
);
i
++
)
{
ptr
[
i
]
=
*
(
reinterpret_cast
<
T
*>
(
data
.
data
())
+
i
);
}
}
static
bool
CompareTensor
(
const
PaddleTensor
&
a
,
const
PaddleTensor
&
b
)
{
...
...
paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc
浏览文件 @
ac0e0f51
...
...
@@ -107,6 +107,9 @@ void SetConfig(AnalysisConfig *cfg) {
cfg
->
DisableGpu
();
cfg
->
SwitchSpecifyInputNames
();
cfg
->
SwitchIrOptim
();
if
(
FLAGS_zero_copy
)
{
cfg
->
SwitchUseFeedFetchOps
(
false
);
}
}
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
...
...
@@ -131,7 +134,7 @@ TEST(Analyzer_Pyramid_DNN, profile) {
TestPrediction
(
reinterpret_cast
<
const
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
)
{
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
&&
!
FLAGS_zero_copy
)
{
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
1UL
);
size_t
size
=
GetSize
(
outputs
[
0
]);
PADDLE_ENFORCE_GT
(
size
,
0
);
...
...
@@ -166,6 +169,19 @@ TEST(Analyzer_Pyramid_DNN, compare) {
reinterpret_cast
<
const
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
);
}
// Compare result of AnalysisConfig and AnalysisConfig + ZeroCopy
TEST
(
Analyzer_Pyramid_DNN
,
compare_zero_copy
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
std
::
vector
<
std
::
string
>
outputs_name
;
outputs_name
.
emplace_back
(
"cos_sim_2.tmp_0"
);
CompareAnalysisAndZeroCopy
(
reinterpret_cast
<
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
,
outputs_name
);
}
// Compare Deterministic result
TEST
(
Analyzer_Pyramid_DNN
,
compare_determine
)
{
AnalysisConfig
cfg
;
...
...
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
浏览文件 @
ac0e0f51
...
...
@@ -207,6 +207,9 @@ void SetConfig(AnalysisConfig *cfg) {
cfg
->
DisableGpu
();
cfg
->
SwitchSpecifyInputNames
();
cfg
->
SwitchIrOptim
();
if
(
FLAGS_zero_copy
)
{
cfg
->
SwitchUseFeedFetchOps
(
false
);
}
}
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
...
...
@@ -285,133 +288,17 @@ TEST(Analyzer_rnn1, multi_thread) {
input_slots_all
,
&
outputs
,
2
/* multi_thread */
);
}
// Validate that the AnalysisPredictor + ZeroCopyTensor really works by testing
// on the complex RNN1 model.
TEST
(
Analyzer_rnn1
,
ZeroCopy
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
false
);
PaddlePlace
place
;
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
);
config
.
SwitchUseFeedFetchOps
(
true
);
auto
native_predictor
=
CreatePaddlePredictor
<
NativeConfig
>
(
config
.
ToNativeConfig
());
config
.
SwitchUseFeedFetchOps
(
true
);
// the analysis predictor needs feed/fetch.
auto
analysis_predictor
=
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
);
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
NEW_TENSOR
(
data_lod_attention
);
NEW_TENSOR
(
cell_init
);
NEW_TENSOR
(
data
);
NEW_TENSOR
(
week
);
NEW_TENSOR
(
minute
);
NEW_TENSOR
(
hidden_init
);
// Prepare data for AnalysisPredictor
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
PrepareZeroCopyInputs
(
data_lod_attention_tensor
.
get
(),
cell_init_tensor
.
get
(),
data_tensor
.
get
(),
hidden_init_tensor
.
get
(),
week_tensor
.
get
(),
minute_tensor
.
get
(),
&
data
,
FLAGS_batch_size
);
// Prepare data for NativePredictor
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
native_inputs
;
SetInput
(
&
native_inputs
);
std
::
vector
<
PaddleTensor
>
native_outputs
;
std
::
vector
<
PaddleTensor
>
analysis_outputs
;
auto
output_tensor
=
predictor
->
GetOutputTensor
(
"final_output.tmp_1"
);
// Run analysis predictor
int
num_ops
;
auto
fuse_statis
=
GetFuseStatis
(
predictor
.
get
(),
&
num_ops
);
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_fuse"
));
ASSERT_EQ
(
fuse_statis
.
at
(
"fc_fuse"
),
1
);
ASSERT_EQ
(
fuse_statis
.
at
(
"fc_nobias_lstm_fuse"
),
2
);
// bi-directional LSTM
ASSERT_EQ
(
fuse_statis
.
at
(
"seq_concat_fc_fuse"
),
1
);
ASSERT_EQ
(
num_ops
,
13
);
// After graph optimization, only 13 operators exists.
Timer
timer
;
double
total_time
{
0
};
for
(
int
i
=
0
;
i
<
FLAGS_repeat
;
i
++
)
{
timer
.
tic
();
predictor
->
ZeroCopyRun
();
total_time
+=
timer
.
toc
();
}
LOG
(
INFO
)
<<
"ZeroCopy output: "
<<
DescribeZeroCopyTensor
(
*
output_tensor
);
ASSERT_TRUE
(
native_predictor
->
Run
(
native_inputs
.
front
(),
&
native_outputs
));
LOG
(
INFO
)
<<
"native output "
<<
DescribeTensor
(
native_outputs
.
front
());
int
output_size
{
0
};
// this is the number of elements not memory size
auto
*
zero_copy_data
=
output_tensor
->
data
<
float
>
(
&
place
,
&
output_size
);
auto
*
native_data
=
static_cast
<
float
*>
(
native_outputs
.
front
().
data
.
data
());
for
(
int
i
=
0
;
i
<
output_size
;
i
++
)
{
EXPECT_NEAR
(
zero_copy_data
[
i
],
native_data
[
i
],
1e-3
);
}
}
TEST
(
Analyzer_rnn1
,
ZeroCopyMultiThread
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
false
);
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
std
::
vector
<
std
::
unique_ptr
<
PaddlePredictor
>>
predictors
;
predictors
.
emplace_back
(
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
));
for
(
int
tid
=
1
;
tid
<
FLAGS_num_threads
;
tid
++
)
{
predictors
.
emplace_back
(
predictors
.
front
()
->
Clone
());
}
double
total_time_of_threads
{
0
};
std
::
vector
<
std
::
thread
>
threads
;
for
(
int
tid
=
0
;
tid
<
FLAGS_num_threads
;
tid
++
)
{
threads
.
emplace_back
([
&
,
tid
]
{
auto
&
predictor
=
predictors
[
tid
];
NEW_TENSOR
(
data_lod_attention
);
NEW_TENSOR
(
cell_init
);
NEW_TENSOR
(
data
);
NEW_TENSOR
(
week
);
NEW_TENSOR
(
minute
);
NEW_TENSOR
(
hidden_init
);
// Prepare data for AnalysisPredictor
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
Timer
timer
;
double
total_time
{
0
};
for
(
int
i
=
0
;
i
<
FLAGS_repeat
;
i
++
)
{
PrepareZeroCopyInputs
(
data_lod_attention_tensor
.
get
(),
cell_init_tensor
.
get
(),
data_tensor
.
get
(),
hidden_init_tensor
.
get
(),
week_tensor
.
get
(),
minute_tensor
.
get
(),
&
data
,
FLAGS_batch_size
);
timer
.
tic
();
predictor
->
ZeroCopyRun
();
total_time
+=
timer
.
toc
();
}
total_time_of_threads
+=
total_time
;
LOG
(
INFO
)
<<
"thread time: "
<<
total_time
/
FLAGS_repeat
;
});
}
for
(
auto
&
t
:
threads
)
{
t
.
join
();
}
// Compare result of AnalysisConfig and AnalysisConfig + ZeroCopy
TEST
(
Analyzer_rnn1
,
compare_zero_copy
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
LOG
(
INFO
)
<<
"average time: "
<<
total_time_of_threads
/
FLAGS_num_threads
/
FLAGS_repeat
;
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
std
::
vector
<
std
::
string
>
outputs_name
;
outputs_name
.
emplace_back
(
"final_output.tmp_1"
);
CompareAnalysisAndZeroCopy
(
reinterpret_cast
<
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
,
outputs_name
);
}
}
// namespace inference
...
...
paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
浏览文件 @
ac0e0f51
...
...
@@ -144,6 +144,9 @@ void SetConfig(AnalysisConfig *cfg, bool use_mkldnn = false) {
cfg
->
SwitchSpecifyInputNames
();
cfg
->
SwitchIrDebug
();
cfg
->
SetCpuMathLibraryNumThreads
(
FLAGS_paddle_num_threads
);
if
(
FLAGS_zero_copy
)
{
cfg
->
SwitchUseFeedFetchOps
(
false
);
}
if
(
use_mkldnn
)
{
cfg
->
EnableMKLDNN
();
}
...
...
@@ -184,10 +187,10 @@ TEST(Analyzer_seq_pool1, compare_determine) {
input_slots_all
);
}
void
analysis_fuse_statis
(
bool
use_zerocopy
)
{
// Check the fuse status
TEST
(
Analyzer_seq_pool1
,
fuse_statis
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
cfg
.
SwitchUseFeedFetchOps
(
!
use_zerocopy
);
int
num_ops
;
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
>
(
cfg
);
auto
fuse_statis
=
GetFuseStatis
(
predictor
.
get
(),
&
num_ops
);
...
...
@@ -203,137 +206,17 @@ void analysis_fuse_statis(bool use_zerocopy) {
EXPECT_EQ
(
num_ops
,
171
);
}
// Check the fuse status
TEST
(
Analyzer_seq_pool1
,
fuse_statis
)
{
analysis_fuse_statis
(
false
);
}
void
PrepareZeroCopyInputs
(
const
std
::
unique_ptr
<
PaddlePredictor
>
&
predictor
,
std
::
vector
<
std
::
unique_ptr
<
ZeroCopyTensor
>>
*
inputs
)
{
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
// only feed one batch
const
auto
&
one_batch
=
data
.
NextBatch
();
inputs
->
clear
();
for
(
size_t
i
=
0
;
i
<
one_batch
.
size
();
++
i
)
{
auto
&
slot
=
one_batch
[
i
];
auto
tensor
=
predictor
->
GetInputTensor
(
slot
.
name
+
"_embed"
);
tensor
->
Reshape
(
slot
.
shape
);
tensor
->
SetLoD
({
slot
.
lod
});
ZeroCopyTensorAssignData
<
float
>
(
tensor
.
get
(),
slot
.
data
);
inputs
->
emplace_back
(
std
::
move
(
tensor
));
}
}
// return the output values
std
::
vector
<
float
>
zerocopy_profile
(
int
repeat_times
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
false
);
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
);
std
::
vector
<
std
::
unique_ptr
<
ZeroCopyTensor
>>
inputs
;
PrepareZeroCopyInputs
(
predictor
,
&
inputs
);
auto
output_tensor
=
predictor
->
GetOutputTensor
(
out_var_name
);
Timer
timer
;
LOG
(
INFO
)
<<
"Warm up run..."
;
timer
.
tic
();
predictor
->
ZeroCopyRun
();
PrintTime
(
FLAGS_batch_size
,
1
,
1
,
0
,
timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
LOG
(
INFO
)
<<
"Run "
<<
repeat_times
<<
" times..."
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
repeat_times
;
i
++
)
{
predictor
->
ZeroCopyRun
();
}
PrintTime
(
FLAGS_batch_size
,
repeat_times
,
1
,
0
,
timer
.
toc
()
/
repeat_times
,
1
);
LOG
(
INFO
)
<<
"ZeroCopy output: "
<<
DescribeZeroCopyTensor
(
*
output_tensor
);
PaddlePlace
place
;
int
output_size
{
0
};
auto
*
pdata
=
output_tensor
->
data
<
float
>
(
&
place
,
&
output_size
);
std
::
vector
<
float
>
res
(
output_size
);
for
(
int
i
=
0
;
i
<
output_size
;
++
i
)
{
res
[
i
]
=
pdata
[
i
];
}
return
res
;
}
TEST
(
Analyzer_seq_pool1
,
zerocopy_profile
)
{
zerocopy_profile
(
FLAGS_repeat
);
}
TEST
(
Analyzer_seq_pool1
,
zerocopy_profile_threads
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
false
);
std
::
vector
<
std
::
unique_ptr
<
PaddlePredictor
>>
predictors
;
predictors
.
emplace_back
(
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
));
for
(
int
tid
=
1
;
tid
<
FLAGS_num_threads
;
tid
++
)
{
predictors
.
emplace_back
(
predictors
.
front
()
->
Clone
());
}
double
total_time_of_threads
{
0
};
std
::
vector
<
std
::
thread
>
threads
;
for
(
int
tid
=
0
;
tid
<
FLAGS_num_threads
;
tid
++
)
{
threads
.
emplace_back
([
&
,
tid
]
{
auto
&
predictor
=
predictors
[
tid
];
std
::
vector
<
std
::
unique_ptr
<
ZeroCopyTensor
>>
inputs
;
PrepareZeroCopyInputs
(
predictor
,
&
inputs
);
auto
output_tensor
=
predictor
->
GetOutputTensor
(
out_var_name
);
Timer
timer
;
double
total_time
{
0
};
LOG
(
INFO
)
<<
"Warm up run..."
;
timer
.
tic
();
predictor
->
ZeroCopyRun
();
PrintTime
(
FLAGS_batch_size
,
1
,
FLAGS_num_threads
,
tid
,
timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
int
repeat_times
=
FLAGS_repeat
;
LOG
(
INFO
)
<<
"Run "
<<
repeat_times
<<
" times..."
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
repeat_times
;
i
++
)
{
predictor
->
ZeroCopyRun
();
}
total_time
+=
timer
.
toc
();
total_time_of_threads
+=
total_time
;
LOG
(
INFO
)
<<
"thread time: "
<<
total_time
/
repeat_times
;
});
}
for
(
auto
&
t
:
threads
)
{
t
.
join
();
}
LOG
(
INFO
)
<<
"average time: "
<<
total_time_of_threads
/
FLAGS_num_threads
/
FLAGS_repeat
;
}
TEST
(
Analyzer_seq_pool1
,
zerocopy_fuse_statis
)
{
analysis_fuse_statis
(
true
);
}
// Compare result of AnalysisConfig and AnalysisConfig + ZeroCopy
TEST
(
Analyzer_seq_pool1
,
compare_zero_copy
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
TEST
(
Analyzer_seq_pool1
,
zerocopy_compare_native
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
true
);
auto
predictor
=
CreatePaddlePredictor
<
NativeConfig
>
(
config
.
ToNativeConfig
());
std
::
vector
<
PaddleTensor
>
native_outputs
;
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
ASSERT_TRUE
(
predictor
->
Run
(
input_slots_all
[
0
],
&
native_outputs
));
EXPECT_EQ
(
native_outputs
.
size
(),
1UL
);
auto
zerocopy_output
=
zerocopy_profile
(
1
);
EXPECT_EQ
(
zerocopy_output
.
size
()
*
sizeof
(
float
),
native_outputs
.
front
().
data
.
length
());
auto
*
native_data
=
static_cast
<
float
*>
(
native_outputs
.
front
().
data
.
data
());
for
(
size_t
i
=
0
;
i
<
zerocopy_output
.
size
();
++
i
)
{
EXPECT_LT
(
std
::
fabs
((
zerocopy_output
[
i
]
-
native_data
[
i
])
/
zerocopy_output
[
i
]),
1e-3
);
}
std
::
vector
<
std
::
string
>
outputs_name
;
outputs_name
.
emplace_back
(
out_var_name
);
CompareAnalysisAndZeroCopy
(
reinterpret_cast
<
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
,
outputs_name
);
}
}
// namespace analysis
...
...
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @
ac0e0f51
...
...
@@ -50,6 +50,7 @@ DEFINE_bool(use_analysis, true,
DEFINE_bool
(
record_benchmark
,
false
,
"Record benchmark after profiling the model"
);
DEFINE_double
(
accuracy
,
1e-3
,
"Result Accuracy."
);
DEFINE_bool
(
zero_copy
,
false
,
"Use ZeroCopy to speedup Feed/Fetch."
);
DECLARE_bool
(
profile
);
DECLARE_int32
(
paddle_num_threads
);
...
...
@@ -67,6 +68,7 @@ void PrintConfig(const PaddlePredictor::Config *config, bool use_analysis) {
LOG
(
INFO
)
<<
analysis_config
->
ToNativeConfig
();
}
// Compare result between two PaddleTensor
void
CompareResult
(
const
std
::
vector
<
PaddleTensor
>
&
outputs
,
const
std
::
vector
<
PaddleTensor
>
&
ref_outputs
)
{
EXPECT_GT
(
outputs
.
size
(),
0UL
);
...
...
@@ -108,6 +110,50 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
}
}
// Compare result between a PaddleTensor and a ZeroCopyTensor
void
CompareResult
(
const
std
::
vector
<
PaddleTensor
>
&
outputs
,
const
std
::
vector
<
ZeroCopyTensor
>
&
ref_outputs
)
{
EXPECT_GT
(
outputs
.
size
(),
0UL
);
EXPECT_EQ
(
outputs
.
size
(),
ref_outputs
.
size
());
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
auto
&
out
=
outputs
[
i
];
auto
&
ref_out
=
ref_outputs
[
i
];
size_t
size
=
VecReduceToInt
(
out
.
shape
);
EXPECT_GT
(
size
,
0UL
);
int
ref_size
=
0
;
// this is the number of elements not memory size
PaddlePlace
place
;
switch
(
out
.
dtype
)
{
case
PaddleDType
::
INT64
:
{
int64_t
*
pdata
=
static_cast
<
int64_t
*>
(
out
.
data
.
data
());
int64_t
*
pdata_ref
=
ref_out
.
data
<
int64_t
>
(
&
place
,
&
ref_size
);
EXPECT_EQ
(
size
,
ref_size
);
for
(
size_t
j
=
0
;
j
<
size
;
++
j
)
{
EXPECT_EQ
(
pdata_ref
[
j
],
pdata
[
j
]);
}
break
;
}
case
PaddleDType
::
FLOAT32
:
{
float
*
pdata
=
static_cast
<
float
*>
(
out
.
data
.
data
());
float
*
pdata_ref
=
ref_out
.
data
<
float
>
(
&
place
,
&
ref_size
);
EXPECT_EQ
(
size
,
ref_size
);
for
(
size_t
j
=
0
;
j
<
size
;
++
j
)
{
CHECK_LE
(
std
::
abs
(
pdata_ref
[
j
]
-
pdata
[
j
]),
FLAGS_accuracy
);
}
break
;
}
case
PaddleDType
::
INT32
:
{
int32_t
*
pdata
=
static_cast
<
int32_t
*>
(
out
.
data
.
data
());
int32_t
*
pdata_ref
=
ref_out
.
data
<
int32_t
>
(
&
place
,
&
ref_size
);
EXPECT_EQ
(
size
,
ref_size
);
for
(
size_t
j
=
0
;
j
<
size
;
++
j
)
{
EXPECT_EQ
(
pdata_ref
[
j
],
pdata
[
j
]);
}
break
;
}
}
}
}
std
::
unique_ptr
<
PaddlePredictor
>
CreateTestPredictor
(
const
PaddlePredictor
::
Config
*
config
,
bool
use_analysis
=
true
)
{
const
auto
*
analysis_config
=
...
...
@@ -205,61 +251,106 @@ void GetInputPerBatch(const std::vector<std::vector<int64_t>> &in,
}
}
void
TestOneThreadPrediction
(
const
PaddlePredictor
::
Config
*
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
bool
use_analysis
=
true
)
{
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
auto
predictor
=
CreateTestPredictor
(
config
,
use_analysis
);
void
ConvertPaddleTensorToZeroCopyTensor
(
PaddlePredictor
*
predictor
,
const
std
::
vector
<
PaddleTensor
>
&
inputs
)
{
for
(
size_t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
auto
input
=
inputs
[
i
];
auto
tensor
=
predictor
->
GetInputTensor
(
input
.
name
);
tensor
->
Reshape
(
input
.
shape
);
tensor
->
SetLoD
({
input
.
lod
});
if
(
input
.
dtype
==
PaddleDType
::
INT64
)
{
ZeroCopyTensorAssignData
<
int64_t
>
(
tensor
.
get
(),
input
.
data
);
}
else
if
(
input
.
dtype
==
PaddleDType
::
FLOAT32
)
{
ZeroCopyTensorAssignData
<
float
>
(
tensor
.
get
(),
input
.
data
);
}
else
if
(
input
.
dtype
==
PaddleDType
::
INT32
)
{
ZeroCopyTensorAssignData
<
int32_t
>
(
tensor
.
get
(),
input
.
data
);
}
else
{
LOG
(
ERROR
)
<<
"unsupported feed type "
<<
input
.
dtype
;
}
}
}
// warmup run
LOG
(
INFO
)
<<
"Warm up run..."
;
{
Timer
warmup_timer
;
warmup_timer
.
tic
();
void
PredictionWarmUp
(
PaddlePredictor
*
predictor
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
,
int
tid
)
{
int
batch_size
=
FLAGS_batch_size
;
LOG
(
INFO
)
<<
"Running thread "
<<
tid
<<
", warm up run..."
;
if
(
FLAGS_zero_copy
)
{
ConvertPaddleTensorToZeroCopyTensor
(
predictor
,
inputs
[
0
]);
}
Timer
warmup_timer
;
warmup_timer
.
tic
();
if
(
!
FLAGS_zero_copy
)
{
predictor
->
Run
(
inputs
[
0
],
outputs
,
batch_size
);
PrintTime
(
batch_size
,
1
,
1
,
0
,
warmup_timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
}
else
{
predictor
->
ZeroCopyRun
();
}
PrintTime
(
batch_size
,
1
,
num_threads
,
tid
,
warmup_timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
}
LOG
(
INFO
)
<<
"Run "
<<
num_times
<<
" times..."
;
{
Timer
run_timer
;
run_timer
.
tic
();
void
PredictionRun
(
PaddlePredictor
*
predictor
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
,
int
tid
)
{
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
LOG
(
INFO
)
<<
"Thread "
<<
tid
<<
" run "
<<
num_times
<<
" times..."
;
Timer
run_timer
;
double
elapsed_time
=
0
;
#ifdef WITH_GPERFTOOLS
ProfilerStart
(
"paddle_inference.prof"
);
ProfilerStart
(
"paddle_inference.prof"
);
#endif
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
for
(
size_t
j
=
0
;
j
<
inputs
.
size
();
j
++
)
{
predictor
->
Run
(
inputs
[
j
],
outputs
,
batch_size
);
if
(
!
FLAGS_zero_copy
)
{
run_timer
.
tic
();
for
(
size_t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
for
(
int
j
=
0
;
j
<
num_times
;
j
++
)
{
predictor
->
Run
(
inputs
[
i
],
outputs
,
batch_size
);
}
}
elapsed_time
=
run_timer
.
toc
();
}
else
{
for
(
size_t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
ConvertPaddleTensorToZeroCopyTensor
(
predictor
,
inputs
[
i
]);
run_timer
.
tic
();
for
(
int
j
=
0
;
j
<
num_times
;
j
++
)
{
predictor
->
ZeroCopyRun
();
}
elapsed_time
+=
run_timer
.
toc
();
}
}
#ifdef WITH_GPERFTOOLS
ProfilerStop
();
ProfilerStop
();
#endif
double
latency
=
run_timer
.
toc
()
/
(
num_times
>
1
?
num_times
:
1
);
PrintTime
(
batch_size
,
num_times
,
1
,
0
,
latency
,
inputs
.
size
());
if
(
FLAGS_record_benchmark
)
{
Benchmark
benchmark
;
benchmark
.
SetName
(
FLAGS_model_name
);
benchmark
.
SetBatchSize
(
batch_size
);
benchmark
.
SetLatency
(
latency
);
benchmark
.
PersistToFile
(
"benchmark_record.txt"
);
}
PrintTime
(
batch_size
,
num_times
,
num_threads
,
tid
,
elapsed_time
/
num_times
,
inputs
.
size
());
if
(
FLAGS_record_benchmark
)
{
Benchmark
benchmark
;
benchmark
.
SetName
(
FLAGS_model_name
);
benchmark
.
SetBatchSize
(
batch_size
);
benchmark
.
SetLatency
(
elapsed_time
/
num_times
);
benchmark
.
PersistToFile
(
"benchmark_record.txt"
);
}
}
void
TestOneThreadPrediction
(
const
PaddlePredictor
::
Config
*
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
bool
use_analysis
=
true
)
{
auto
predictor
=
CreateTestPredictor
(
config
,
use_analysis
);
PredictionWarmUp
(
predictor
.
get
(),
inputs
,
outputs
,
1
,
0
);
PredictionRun
(
predictor
.
get
(),
inputs
,
outputs
,
1
,
0
);
}
void
TestMultiThreadPrediction
(
const
PaddlePredictor
::
Config
*
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
,
bool
use_analysis
=
true
)
{
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
std
::
vector
<
std
::
thread
>
threads
;
std
::
vector
<
std
::
unique_ptr
<
PaddlePredictor
>>
predictors
;
predictors
.
emplace_back
(
CreateTestPredictor
(
config
,
use_analysis
));
...
...
@@ -267,7 +358,6 @@ void TestMultiThreadPrediction(
predictors
.
emplace_back
(
predictors
.
front
()
->
Clone
());
}
size_t
total_time
{
0
};
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
threads
.
emplace_back
([
&
,
tid
]()
{
// Each thread should have local inputs and outputs.
...
...
@@ -280,34 +370,8 @@ void TestMultiThreadPrediction(
->
SetMkldnnThreadID
(
static_cast
<
int
>
(
tid
)
+
1
);
}
#endif
// warmup run
LOG
(
INFO
)
<<
"Running thread "
<<
tid
<<
", warm up run..."
;
{
Timer
warmup_timer
;
warmup_timer
.
tic
();
predictor
->
Run
(
inputs
[
0
],
outputs
,
batch_size
);
PrintTime
(
batch_size
,
1
,
num_threads
,
tid
,
warmup_timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
}
LOG
(
INFO
)
<<
"Thread "
<<
tid
<<
" run "
<<
num_times
<<
" times..."
;
{
Timer
timer
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
for
(
const
auto
&
input
:
inputs
)
{
ASSERT_TRUE
(
predictor
->
Run
(
input
,
&
outputs_tid
));
}
}
auto
time
=
timer
.
toc
();
total_time
+=
time
;
PrintTime
(
batch_size
,
num_times
,
num_threads
,
tid
,
time
/
num_times
,
inputs
.
size
());
}
PredictionWarmUp
(
predictor
.
get
(),
inputs
,
outputs
,
num_threads
,
tid
);
PredictionRun
(
predictor
.
get
(),
inputs
,
outputs
,
num_threads
,
tid
);
});
}
for
(
int
i
=
0
;
i
<
num_threads
;
++
i
)
{
...
...
@@ -367,6 +431,31 @@ void CompareNativeAndAnalysis(
CompareResult
(
analysis_outputs
,
native_outputs
);
}
void
CompareAnalysisAndZeroCopy
(
PaddlePredictor
::
Config
*
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
const
std
::
vector
<
std
::
string
>
&
outputs_name
)
{
int
batch_size
=
FLAGS_batch_size
;
// analysis
std
::
vector
<
PaddleTensor
>
analysis_outputs
;
auto
predictor
=
CreateTestPredictor
(
config
,
true
);
predictor
->
Run
(
inputs
[
0
],
&
analysis_outputs
,
batch_size
);
// analysis + zero_copy
std
::
vector
<
ZeroCopyTensor
>
zerocopy_outputs
;
reinterpret_cast
<
AnalysisConfig
*>
(
config
)
->
SwitchUseFeedFetchOps
(
false
);
predictor
=
CreateTestPredictor
(
config
,
true
);
ConvertPaddleTensorToZeroCopyTensor
(
predictor
.
get
(),
inputs
[
0
]);
predictor
->
ZeroCopyRun
();
for
(
size_t
i
=
0
;
i
<
outputs_name
.
size
();
i
++
)
{
ZeroCopyTensor
zerocopy_output
=
*
predictor
->
GetOutputTensor
(
outputs_name
[
i
]).
get
();
zerocopy_outputs
.
emplace_back
(
zerocopy_output
);
LOG
(
INFO
)
<<
"ZeroCopy output: "
<<
DescribeZeroCopyTensor
(
zerocopy_output
);
}
// compare
CompareResult
(
analysis_outputs
,
zerocopy_outputs
);
}
template
<
typename
T
>
std
::
string
LoDTensorSummary
(
const
framework
::
LoDTensor
&
tensor
)
{
std
::
stringstream
ss
;
...
...
paddle/fluid/inference/tests/test.cmake
浏览文件 @
ac0e0f51
...
...
@@ -30,19 +30,20 @@ function(inference_download_and_uncompress INSTALL_DIR URL FILENAME)
${
EXTERNAL_PROJECT_NAME
}
${
EXTERNAL_PROJECT_LOG_ARGS
}
PREFIX
${
INSTALL_DIR
}
URL
${
URL
}
/
${
FILENAME
}
DOWNLOAD_COMMAND wget -q -O
${
INSTALL_DIR
}
/
${
FILENAME
}
${
URL
}
/
${
FILENAME
}
&&
${
CMAKE_COMMAND
}
-E tar xzf
${
INSTALL_DIR
}
/
${
FILENAME
}
DOWNLOAD_DIR
${
INSTALL_DIR
}
DOWNLOAD_NO_PROGRESS 1
CONFIGURE_COMMAND
""
BUILD_COMMAND
""
UPDATE_COMMAND
""
INSTALL_COMMAND
${
CMAKE_COMMAND
}
-E copy_directory
${
UNPACK_DIR
}
${
INSTALL_DIR
}
INSTALL_COMMAND
""
)
endfunction
()
set
(
WORD2VEC_INSTALL_DIR
"
${
INFERENCE_DEMO_INSTALL_DIR
}
/word2vec"
)
if
(
NOT EXISTS
${
WORD2VEC_INSTALL_DIR
}
)
inference_download_and_uncompress
(
${
WORD2VEC_INSTALL_DIR
}
${
INFERENCE_URL
}
"word2vec.inference.model.tar.gz"
)
if
(
NOT EXISTS
${
WORD2VEC_INSTALL_DIR
}
AND NOT WIN32
)
inference_download_and_uncompress
(
${
WORD2VEC_INSTALL_DIR
}
${
INFERENCE_URL
}
"word2vec.inference.model.tar.gz"
)
endif
()
set
(
WORD2VEC_MODEL_DIR
"
${
WORD2VEC_INSTALL_DIR
}
/word2vec.inference.model"
)
...
...
paddle/fluid/memory/CMakeLists.txt
浏览文件 @
ac0e0f51
add_subdirectory
(
detail
)
add_subdirectory
(
allocation
)
cc_library
(
malloc SRCS malloc.cc DEPS place enforce allocator_facade
)
cc_library
(
malloc SRCS malloc.cc DEPS place enforce allocator_facade
profiler
)
cc_library
(
memcpy SRCS memcpy.cc DEPS place
)
cc_library
(
memory
...
...
paddle/fluid/memory/allocation/CMakeLists.txt
浏览文件 @
ac0e0f51
...
...
@@ -3,7 +3,7 @@ cc_library(cpu_allocator SRCS cpu_allocator.cc DEPS allocator)
cc_library
(
best_fit_allocator SRCS best_fit_allocator.cc DEPS allocator
)
cc_library
(
locked_allocator SRCS locked_allocator.cc DEPS allocator
)
cc_library
(
buffered_allocator SRCS buffered_allocator.cc DEPS allocator
)
cc_library
(
legacy_allocator SRCS legacy_allocator.cc DEPS allocator buddy_allocator
)
cc_library
(
legacy_allocator SRCS legacy_allocator.cc DEPS allocator buddy_allocator
profiler
)
cc_test
(
buffered_allocator_test SRCS buffered_allocator_test.cc DEPS best_fit_allocator locked_allocator buffered_allocator cpu_allocator
)
if
(
WITH_GPU
)
...
...
paddle/fluid/memory/allocation/legacy_allocator.cc
浏览文件 @
ac0e0f51
...
...
@@ -12,8 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/memory/allocation/legacy_allocator.h"
#include <memory>
#include <string>
#include <utility>
#include <vector>
...
...
@@ -23,9 +22,11 @@
#endif
#include "glog/logging.h"
#include "paddle/fluid/memory/allocation/legacy_allocator.h"
#include "paddle/fluid/memory/detail/buddy_allocator.h"
#include "paddle/fluid/memory/detail/system_allocator.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/string/printf.h"
#include "paddle/fluid/string/split.h"
...
...
@@ -328,18 +329,22 @@ size_t Usage::operator()(const platform::CUDAPinnedPlace &cuda_pinned) const {
}
// namespace legacy
namespace
allocation
{
LegacyMemMonitor
GPUMemMonitor
;
Allocation
*
LegacyAllocator
::
AllocateImpl
(
size_t
size
,
Allocator
::
Attr
attr
)
{
void
*
ptr
=
boost
::
apply_visitor
(
legacy
::
AllocVisitor
(
size
),
place_
);
return
new
Allocation
(
ptr
,
size
,
place_
);
auto
*
tmp_alloc
=
new
Allocation
(
ptr
,
size
,
place_
);
platform
::
MemEvenRecorder
::
Instance
().
PushMemRecord
(
static_cast
<
void
*>
(
tmp_alloc
),
place_
,
size
);
return
tmp_alloc
;
}
void
LegacyAllocator
::
Free
(
Allocation
*
allocation
)
{
boost
::
apply_visitor
(
legacy
::
FreeVisitor
(
allocation
->
ptr
(),
allocation
->
size
()),
allocation
->
place
());
platform
::
MemEvenRecorder
::
Instance
().
PopMemRecord
(
static_cast
<
void
*>
(
allocation
),
place_
);
delete
allocation
;
}
...
...
paddle/fluid/memory/memcpy.cc
浏览文件 @
ac0e0f51
...
...
@@ -15,6 +15,7 @@ limitations under the License. */
#include "paddle/fluid/memory/memcpy.h"
#include <cstring> // for memcpy
#include "paddle/fluid/platform/profiler.h"
namespace
paddle
{
namespace
memory
{
...
...
@@ -29,14 +30,23 @@ void Copy<platform::CPUPlace, platform::CPUPlace>(platform::CPUPlace, void* dst,
#ifdef PADDLE_WITH_CUDA
static
constexpr
size_t
kMaxGpuAsyncCopyBytes
=
64
*
1024
;
// 64K
// NOTE(zcd): Do not use GpuMemcpySync as much as possible.
// because GpuMemcpySync issues the copying command to the default stream,
// which will make two commands from different streams cannot run concurrently.
// Reference:
// https://devblogs.nvidia.com/gpu-pro-tip-cuda-7-streams-simplify-concurrency/
template
<
>
void
Copy
<
platform
::
CPUPlace
,
platform
::
CUDAPlace
>
(
platform
::
CPUPlace
dst_place
,
void
*
dst
,
platform
::
CUDAPlace
src_place
,
const
void
*
src
,
size_t
num
,
cudaStream_t
stream
)
{
platform
::
SetDeviceId
(
src_place
.
device
);
if
(
stream
)
{
platform
::
RecordEvent
record_event
(
"GpuMemcpyAsync:GPU->CPU"
);
platform
::
GpuMemcpyAsync
(
dst
,
src
,
num
,
cudaMemcpyDeviceToHost
,
stream
);
}
else
{
platform
::
RecordEvent
record_event
(
"GpuMemcpySync:GPU->CPU"
);
platform
::
GpuMemcpySync
(
dst
,
src
,
num
,
cudaMemcpyDeviceToHost
);
// FIXME(zjl): do we really need it?
if
(
num
<=
kMaxGpuAsyncCopyBytes
)
{
...
...
@@ -51,8 +61,10 @@ void Copy<platform::CUDAPlace, platform::CPUPlace>(
const
void
*
src
,
size_t
num
,
cudaStream_t
stream
)
{
platform
::
SetDeviceId
(
dst_place
.
device
);
if
(
stream
)
{
platform
::
RecordEvent
record_event
(
"GpuMemcpyAsync:CPU->GPU"
);
platform
::
GpuMemcpyAsync
(
dst
,
src
,
num
,
cudaMemcpyHostToDevice
,
stream
);
}
else
{
platform
::
RecordEvent
record_event
(
"GpuMemcpySync:CPU->GPU"
);
platform
::
GpuMemcpySync
(
dst
,
src
,
num
,
cudaMemcpyHostToDevice
);
// FIXME(zjl): do we really need it?
if
(
num
<=
kMaxGpuAsyncCopyBytes
)
{
...
...
@@ -68,15 +80,19 @@ void Copy<platform::CUDAPlace, platform::CUDAPlace>(
if
(
dst_place
==
src_place
)
{
platform
::
SetDeviceId
(
src_place
.
device
);
if
(
stream
)
{
platform
::
RecordEvent
record_event
(
"GpuMemcpyAsync(same_gpu):GPU->GPU"
);
platform
::
GpuMemcpyAsync
(
dst
,
src
,
num
,
cudaMemcpyDeviceToDevice
,
stream
);
}
else
{
platform
::
RecordEvent
record_event
(
"GpuMemcpySync(same_gpu):GPU->GPU"
);
platform
::
GpuMemcpySync
(
dst
,
src
,
num
,
cudaMemcpyDeviceToDevice
);
}
}
else
{
if
(
stream
)
{
platform
::
RecordEvent
record_event
(
"GpuMemcpyPeerAsync:GPU->GPU"
);
platform
::
GpuMemcpyPeerAsync
(
dst
,
dst_place
.
device
,
src
,
src_place
.
device
,
num
,
stream
);
}
else
{
platform
::
RecordEvent
record_event
(
"GpuMemcpyPeerSync:GPU->GPU"
);
platform
::
GpuMemcpyPeerSync
(
dst
,
dst_place
.
device
,
src
,
src_place
.
device
,
num
);
}
...
...
@@ -111,8 +127,10 @@ void Copy<platform::CUDAPinnedPlace, platform::CUDAPlace>(
cudaStream_t
stream
)
{
platform
::
SetDeviceId
(
src_place
.
device
);
if
(
stream
)
{
platform
::
RecordEvent
record_event
(
"GpuMemcpyAsync:GPU->CUDAPinned"
);
platform
::
GpuMemcpyAsync
(
dst
,
src
,
num
,
cudaMemcpyDeviceToHost
,
stream
);
}
else
{
platform
::
RecordEvent
record_event
(
"GpuMemcpySync:GPU->CUDAPinned"
);
platform
::
GpuMemcpySync
(
dst
,
src
,
num
,
cudaMemcpyDeviceToHost
);
}
}
...
...
@@ -124,8 +142,10 @@ void Copy<platform::CUDAPlace, platform::CUDAPinnedPlace>(
cudaStream_t
stream
)
{
platform
::
SetDeviceId
(
dst_place
.
device
);
if
(
stream
)
{
platform
::
RecordEvent
record_event
(
"GpuMemcpyAsync:CUDAPinned->GPU"
);
platform
::
GpuMemcpyAsync
(
dst
,
src
,
num
,
cudaMemcpyHostToDevice
,
stream
);
}
else
{
platform
::
RecordEvent
record_event
(
"GpuMemcpySync:CUDAPinned->GPU"
);
platform
::
GpuMemcpySync
(
dst
,
src
,
num
,
cudaMemcpyHostToDevice
);
}
}
...
...
paddle/fluid/operators/activation_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -13,7 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/activation_op.h"
#include <memory>
#include <string>
#include <unordered_map>
#include "paddle/fluid/operators/mkldnn/mkldnn_activation_op.h"
#include "paddle/fluid/platform/port.h"
#ifdef PADDLE_WITH_CUDA
...
...
@@ -269,6 +271,48 @@ $$out = \\frac{x}{1 + \|x\|}$$
)DOC"
;
class
AcosOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"Input of acos operator"
);
AddOutput
(
"Out"
,
"Output of acos operator"
);
AddComment
(
R"DOC(
Arccosine Activation Operator.
$$out = \cos^{-1}(x)$$
)DOC"
);
}
};
class
AsinOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"Input of asin operator"
);
AddOutput
(
"Out"
,
"Output of asin operator"
);
AddComment
(
R"DOC(
Arcsine Activation Operator.
$$out = \sin^{-1}(x)$$
)DOC"
);
}
};
class
AtanOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"Input of atan operator"
);
AddOutput
(
"Out"
,
"Output of atan operator"
);
AddComment
(
R"DOC(
Arctanh Activation Operator.
$$out = \tanh^{-1}(x)$$
)DOC"
);
}
};
class
LeakyReluOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
...
...
@@ -543,7 +587,10 @@ namespace ops = paddle::operators;
__macro(SoftShrink, softshrink); \
__macro(Abs, abs); \
__macro(Cos, cos); \
__macro(Acos, acos); \
__macro(Sin, sin); \
__macro(Asin, asin); \
__macro(Atan, atan); \
__macro(Round, round); \
__macro(Log, log); \
__macro(Square, square); \
...
...
paddle/fluid/operators/activation_op.h
浏览文件 @
ac0e0f51
...
...
@@ -39,9 +39,8 @@ namespace operators {
Please refer to the layer_helper.py and get the details.
*/
static
std
::
unordered_set
<
std
::
string
>
InplaceOpSet
=
{
"sigmoid"
,
"exp"
,
"relu"
,
"tanh"
,
"sqrt"
,
"ceil"
,
"floor"
,
"reciprocal"
,
"relu6"
,
"soft_relu"
,
"hard_sigmoid"
,
};
"sigmoid"
,
"exp"
,
"relu"
,
"tanh"
,
"sqrt"
,
"ceil"
,
"floor"
,
"reciprocal"
,
"relu6"
,
"soft_relu"
,
"hard_sigmoid"
};
static
bool
IsInplace
(
const
std
::
string
&
op
)
{
bool
inplace
=
InplaceOpSet
.
count
(
op
);
...
...
@@ -553,6 +552,101 @@ struct SinFunctor : public BaseActivationFunctor<T> {
}
};
template
<
typename
T
>
struct
Acos
{
HOSTDEVICE
T
operator
()(
const
T
&
val
)
const
{
return
acos
(
val
);
}
};
template
<
>
struct
Acos
<
platform
::
float16
>
{
HOSTDEVICE
platform
::
float16
operator
()(
const
platform
::
float16
&
val
)
const
{
return
platform
::
float16
(
acos
(
static_cast
<
float
>
(
val
)));
}
};
// Acos(x) = acos(x)
template
<
typename
T
>
struct
AcosFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Out
>
void
operator
()(
Device
d
,
X
x
,
Out
out
)
const
{
out
.
device
(
d
)
=
x
.
unaryExpr
(
Acos
<
T
>
());
}
};
// acos'(x) = -1/sqrt(1-x^2)
template
<
typename
T
>
struct
AcosGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Out
,
typename
dOut
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Out
out
,
dOut
dout
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
-
dout
*
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
-
x
.
square
()).
sqrt
();
}
};
template
<
typename
T
>
struct
Asin
{
HOSTDEVICE
T
operator
()(
const
T
&
val
)
const
{
return
asin
(
val
);
}
};
template
<
>
struct
Asin
<
platform
::
float16
>
{
HOSTDEVICE
platform
::
float16
operator
()(
const
platform
::
float16
&
val
)
const
{
return
platform
::
float16
(
asin
(
static_cast
<
float
>
(
val
)));
}
};
// Asin(x) = asin(x)
template
<
typename
T
>
struct
AsinFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Out
>
void
operator
()(
Device
d
,
X
x
,
Out
out
)
const
{
out
.
device
(
d
)
=
x
.
unaryExpr
(
Asin
<
T
>
());
}
};
// asin'(x) = 1/sqrt(1-x^2)
template
<
typename
T
>
struct
AsinGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Out
,
typename
dOut
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Out
out
,
dOut
dout
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dout
*
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
-
x
.
square
()).
sqrt
();
}
};
template
<
typename
T
>
struct
Atan
{
HOSTDEVICE
T
operator
()(
const
T
&
val
)
const
{
return
atan
(
val
);
}
};
template
<
>
struct
Atan
<
platform
::
float16
>
{
HOSTDEVICE
platform
::
float16
operator
()(
const
platform
::
float16
&
val
)
const
{
return
platform
::
float16
(
atan
(
static_cast
<
float
>
(
val
)));
}
};
// Atan(x) = atan(x)
template
<
typename
T
>
struct
AtanFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Out
>
void
operator
()(
Device
d
,
X
x
,
Out
out
)
const
{
out
.
device
(
d
)
=
x
.
unaryExpr
(
Atan
<
T
>
());
}
};
// atan'(x) = 1 / (1 + x^2)
template
<
typename
T
>
struct
AtanGradFunctor
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
Device
,
typename
X
,
typename
Out
,
typename
dOut
,
typename
dX
>
void
operator
()(
Device
d
,
X
x
,
Out
out
,
dOut
dout
,
dX
dx
)
const
{
dx
.
device
(
d
)
=
dout
*
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
+
x
.
square
());
}
};
// round(x) = [x]
template
<
typename
T
>
struct
RoundFunctor
:
public
BaseActivationFunctor
<
T
>
{
...
...
@@ -1001,13 +1095,16 @@ struct SwishGradFunctor : public BaseActivationFunctor<T> {
__macro(relu, ReluFunctor, ReluGradFunctor); \
__macro(gelu, GeluFunctor, GeluGradFunctor); \
__macro(tanh, TanhFunctor, TanhGradFunctor); \
__macro(atan, AtanFunctor, AtanGradFunctor); \
__macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor); \
__macro(sqrt, SqrtFunctor, SqrtGradFunctor); \
__macro(abs, AbsFunctor, AbsGradFunctor); \
__macro(ceil, CeilFunctor, ZeroGradFunctor); \
__macro(floor, FloorFunctor, ZeroGradFunctor); \
__macro(cos, CosFunctor, CosGradFunctor); \
__macro(acos, AcosFunctor, AcosGradFunctor); \
__macro(sin, SinFunctor, SinGradFunctor); \
__macro(asin, AsinFunctor, AsinGradFunctor); \
__macro(round, RoundFunctor, ZeroGradFunctor); \
__macro(reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \
__macro(log, LogFunctor, LogGradFunctor); \
...
...
paddle/fluid/operators/controlflow/CMakeLists.txt
浏览文件 @
ac0e0f51
include
(
operators
)
register_operators
(
DEPS naive_executor
)
cc_library
(
while_op_helper SRCS while_op_helper.cc DEPS operator
)
file
(
APPEND
${
pybind_file
}
"USE_OP(less_than);
\n
USE_OP(logical_and);
\n
USE_NO_KERNEL_OP(read_from_array);
\n
"
)
paddle/fluid/operators/controlflow/while_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -18,6 +18,7 @@
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/controlflow/while_op_helper.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
namespace
paddle
{
...
...
@@ -26,14 +27,6 @@ namespace operators {
using
StepScopeVar
=
std
::
vector
<
framework
::
Scope
*>
;
using
LoDTensor
=
framework
::
LoDTensor
;
static
constexpr
char
kStepBlock
[]
=
"sub_block"
;
static
constexpr
char
kCondition
[]
=
"Condition"
;
static
constexpr
char
kStepScopes
[]
=
"StepScopes"
;
static
constexpr
char
kX
[]
=
"X"
;
static
constexpr
char
kXGRAD
[]
=
"X@GRAD"
;
static
constexpr
char
kOutputs
[]
=
"Out"
;
static
constexpr
char
kSkipEagerDeletionVars
[]
=
"skip_eager_deletion_vars"
;
namespace
{
// NOLINT
static
std
::
string
GetSkipEagerDeletionVarsDebugString
(
const
std
::
vector
<
std
::
string
>
&
vars
)
{
...
...
paddle/fluid/operators/controlflow/while_op_helper.cc
0 → 100644
浏览文件 @
ac0e0f51
// Copyright (c) 2019 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/controlflow/while_op_helper.h"
#include <string>
#include <unordered_set>
#include <utility>
#include "paddle/fluid/framework/program_desc.h"
namespace
paddle
{
namespace
operators
{
// OpVariant is a wrapper class of OpDesc and OperatorBase
// So that API would be the same.
class
OpVariant
{
struct
InputsVisitor
:
public
boost
::
static_visitor
<
const
framework
::
VariableNameMap
*>
{
template
<
typename
OpType
>
const
framework
::
VariableNameMap
*
operator
()(
const
OpType
*
op
)
const
{
return
&
(
op
->
Inputs
());
}
};
struct
OutputsVisitor
:
public
boost
::
static_visitor
<
const
framework
::
VariableNameMap
*>
{
template
<
typename
OpType
>
const
framework
::
VariableNameMap
*
operator
()(
const
OpType
*
op
)
const
{
return
&
(
op
->
Outputs
());
}
};
struct
AttributeMapVisitor
:
public
boost
::
static_visitor
<
const
framework
::
AttributeMap
*>
{
const
framework
::
AttributeMap
*
operator
()(
const
framework
::
OpDesc
*
op
)
const
{
return
&
(
op
->
GetAttrMap
());
}
const
framework
::
AttributeMap
*
operator
()(
const
framework
::
OperatorBase
*
op
)
const
{
return
&
(
op
->
Attrs
());
}
};
struct
RawPointerVisitor
:
public
boost
::
static_visitor
<
const
void
*>
{
template
<
typename
OpType
>
const
void
*
operator
()(
const
OpType
*
op
)
const
{
return
op
;
}
};
public:
OpVariant
(
const
framework
::
OperatorBase
*
op
)
:
op_
(
op
)
{}
// NOLINT
OpVariant
(
const
framework
::
OpDesc
*
op
)
:
op_
(
op
)
{}
// NOLINT
const
framework
::
VariableNameMap
&
Inputs
()
const
{
return
*
boost
::
apply_visitor
(
InputsVisitor
(),
op_
);
}
const
framework
::
VariableNameMap
&
Outputs
()
const
{
return
*
boost
::
apply_visitor
(
OutputsVisitor
(),
op_
);
}
const
framework
::
AttributeMap
&
Attrs
()
const
{
return
*
boost
::
apply_visitor
(
AttributeMapVisitor
(),
op_
);
}
template
<
typename
AttrType
>
const
AttrType
&
Attr
(
const
std
::
string
&
name
)
const
{
auto
&
attrs
=
Attrs
();
auto
it
=
attrs
.
find
(
name
);
PADDLE_ENFORCE
(
it
!=
attrs
.
end
(),
"Cannot find attribute %s"
,
name
);
return
boost
::
get
<
AttrType
>
(
it
->
second
);
}
bool
operator
==
(
const
OpVariant
&
other
)
const
{
return
RawPointer
()
==
other
.
RawPointer
();
}
const
void
*
RawPointer
()
const
{
return
boost
::
apply_visitor
(
RawPointerVisitor
(),
op_
);
}
int
which
()
const
{
return
static_cast
<
int
>
(
op_
.
which
());
}
struct
Hasher
{
size_t
operator
()(
const
OpVariant
&
op
)
const
{
return
reinterpret_cast
<
size_t
>
(
op
.
RawPointer
());
}
};
private:
const
boost
::
variant
<
const
framework
::
OperatorBase
*
,
const
framework
::
OpDesc
*>
op_
;
};
static
std
::
string
GetDebugString
(
const
std
::
vector
<
std
::
string
>
&
names
)
{
if
(
names
.
empty
())
return
""
;
std
::
string
ret
=
names
[
0
];
for
(
size_t
i
=
1
;
i
<
names
.
size
();
++
i
)
{
ret
+=
(
" "
+
names
[
i
]);
}
return
ret
;
}
// Set skip variables of while_op and while_grad_op
// These variables should be skipped when eager deletion enables.
// It is because:
// 1. while_grad_op needs some variables defined in while_op.
// 2. while_grad_op needs variables from the previous time step.
static
void
SetSkipVars
(
const
OpVariant
&
op
,
std
::
vector
<
std
::
string
>
attr
)
{
auto
&
attrs
=
const_cast
<
framework
::
AttributeMap
&>
(
op
.
Attrs
());
VLOG
(
2
)
<<
"Prepare to skip "
<<
attr
.
size
()
<<
" var(s): "
<<
GetDebugString
(
attr
);
attrs
[
kSkipEagerDeletionVars
]
=
std
::
move
(
attr
);
}
// Check whether the forward while_op and while_grad_op match
// The program may have many while_ops.
static
bool
IsMatchedWhileOpAndWhileGradOp
(
const
OpVariant
&
fwd_op
,
const
OpVariant
&
grad_op
)
{
return
fwd_op
.
Inputs
().
at
(
kX
)
==
grad_op
.
Inputs
().
at
(
kX
)
&&
fwd_op
.
Outputs
().
at
(
kOutputs
)
==
grad_op
.
Inputs
().
at
(
kOutputs
);
}
// Test whether the variable is skippable in forward while_op
// The variable is skippable in while_op when the variable used in while_grad
// is not from grad_block.
static
bool
IsSkippableVar
(
const
std
::
string
&
name
,
framework
::
BlockDesc
*
grad_block
)
{
return
name
!=
framework
::
kEmptyVarName
&&
!
grad_block
->
HasVar
(
name
);
}
static
void
ModifyWhileOpAndWhileGradOpAttr
(
const
OpVariant
&
fwd_op
,
const
OpVariant
&
bwd_op
)
{
auto
*
grad_block
=
bwd_op
.
Attr
<
framework
::
BlockDesc
*>
(
kStepBlock
);
// Find all skippable variables in forward while_op
std
::
unordered_set
<
std
::
string
>
forward_skip_vars
;
for
(
auto
*
op_desc
:
grad_block
->
AllOps
())
{
for
(
auto
&
in_arg_name
:
op_desc
->
InputArgumentNames
())
{
if
(
IsSkippableVar
(
in_arg_name
,
grad_block
))
{
forward_skip_vars
.
insert
(
in_arg_name
);
}
}
for
(
auto
&
out_arg_name
:
op_desc
->
OutputArgumentNames
())
{
if
(
IsSkippableVar
(
out_arg_name
,
grad_block
))
{
forward_skip_vars
.
insert
(
out_arg_name
);
}
}
}
SetSkipVars
(
fwd_op
,
std
::
vector
<
std
::
string
>
(
forward_skip_vars
.
begin
(),
forward_skip_vars
.
end
()));
// Find all skippable variables in while_grad_op
// The skipped variables are those which would be used across time steps.
auto
&
fwd_input
=
fwd_op
.
Inputs
().
at
(
kX
);
auto
&
in_grads
=
bwd_op
.
Outputs
().
at
(
framework
::
GradVarName
(
kX
));
PADDLE_ENFORCE_EQ
(
fwd_input
.
size
(),
in_grads
.
size
(),
"Backward input gradient number does not match forward input number."
);
std
::
unordered_set
<
std
::
string
>
backward_skip_vars
;
for
(
size_t
i
=
0
;
i
<
in_grads
.
size
();
++
i
)
{
if
(
in_grads
[
i
]
==
framework
::
kEmptyVarName
)
{
continue
;
}
backward_skip_vars
.
insert
(
in_grads
[
i
]);
backward_skip_vars
.
insert
(
framework
::
GradVarName
(
fwd_input
[
i
]));
}
SetSkipVars
(
bwd_op
,
std
::
vector
<
std
::
string
>
(
backward_skip_vars
.
begin
(),
backward_skip_vars
.
end
()));
}
// Find all while_ops and while_grad_ops in the graph or program
// The while_grad_op and while_op may located in different blocks
// So we should traverse all blocks in the program and find them out.
static
void
FindAllWhileAndWhileGradOp
(
std
::
vector
<
OpVariant
>
*
while_ops
,
std
::
vector
<
OpVariant
>
*
while_grad_ops
)
{
PADDLE_ENFORCE_GE
(
while_ops
->
size
(),
while_grad_ops
->
size
());
if
(
while_ops
->
empty
())
return
;
const
auto
*
program
=
while_ops
->
front
().
Attr
<
framework
::
BlockDesc
*>
(
kStepBlock
)
->
Program
();
for
(
size_t
i
=
1
;
i
<
program
->
Size
();
++
i
)
{
auto
&
block
=
program
->
Block
(
i
);
for
(
size_t
j
=
0
;
j
<
block
.
OpSize
();
++
j
)
{
auto
*
op
=
block
.
Op
(
j
);
if
(
op
->
Type
()
==
"while"
)
{
while_ops
->
emplace_back
(
op
);
}
else
if
(
op
->
Type
()
==
"while_grad"
)
{
while_grad_ops
->
emplace_back
(
op
);
}
}
}
PADDLE_ENFORCE_GE
(
while_ops
->
size
(),
while_grad_ops
->
size
(),
"There are extra while_grad ops in the graph or program"
);
}
static
void
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOpImpl
(
std
::
vector
<
OpVariant
>
*
while_ops
,
std
::
vector
<
OpVariant
>
*
while_grad_ops
)
{
FindAllWhileAndWhileGradOp
(
while_ops
,
while_grad_ops
);
VLOG
(
2
)
<<
"Found while op num: "
<<
while_ops
->
size
()
<<
", while grad op num: "
<<
while_grad_ops
->
size
();
if
(
while_grad_ops
->
empty
())
{
return
;
}
std
::
unordered_set
<
OpVariant
,
OpVariant
::
Hasher
>
while_op_set
(
while_ops
->
begin
(),
while_ops
->
end
());
for
(
auto
&
bwd_op
:
*
while_grad_ops
)
{
const
OpVariant
*
matched_fwd_op
=
nullptr
;
for
(
auto
&
fwd_op
:
while_op_set
)
{
if
(
IsMatchedWhileOpAndWhileGradOp
(
fwd_op
,
bwd_op
))
{
PADDLE_ENFORCE
(
matched_fwd_op
==
nullptr
,
"Found multiple matched while ops"
);
matched_fwd_op
=
&
fwd_op
;
}
}
PADDLE_ENFORCE_NOT_NULL
(
matched_fwd_op
,
"Cannot find matched forward while op."
);
ModifyWhileOpAndWhileGradOpAttr
(
*
matched_fwd_op
,
bwd_op
);
while_op_set
.
erase
(
*
matched_fwd_op
);
}
}
void
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp
(
int
block_id
,
const
std
::
vector
<
std
::
unique_ptr
<
framework
::
OperatorBase
>>
&
all_ops
)
{
// If block_id is not 0, returns
// This is because all while_ops and while_grad_ops in the whole program
// would be processed when block_id is 0 (i.e. when Executor::Run() or
// ParallelExecutor constructs).
// What's more, all while_ops and while_grad_ops must be processed when
// block_id is zero. If not, while_op may run first and erase variables
// used in while_grad_op, and in this moment, while_grad_ops may be not
// constructed yet.
if
(
block_id
!=
0
)
return
;
std
::
vector
<
OpVariant
>
fwd_ops
,
bwd_ops
;
for
(
auto
&
op
:
all_ops
)
{
if
(
op
->
Type
()
==
"while"
)
{
fwd_ops
.
emplace_back
(
op
.
get
());
}
else
if
(
op
->
Type
()
==
"while_grad"
)
{
bwd_ops
.
emplace_back
(
op
.
get
());
}
}
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOpImpl
(
&
fwd_ops
,
&
bwd_ops
);
}
void
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp
(
const
std
::
vector
<
framework
::
OperatorBase
*>
&
while_ops
,
const
std
::
vector
<
framework
::
OperatorBase
*>
&
while_grad_ops
)
{
std
::
vector
<
OpVariant
>
fwd_ops
,
bwd_ops
;
fwd_ops
.
reserve
(
while_ops
.
size
());
for
(
auto
*
op
:
while_ops
)
{
fwd_ops
.
emplace_back
(
op
);
}
bwd_ops
.
reserve
(
while_grad_ops
.
size
());
for
(
auto
*
op
:
while_grad_ops
)
{
bwd_ops
.
emplace_back
(
op
);
}
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOpImpl
(
&
fwd_ops
,
&
bwd_ops
);
}
}
// namespace operators
}
// namespace paddle
paddle/fluid/
framework/details/eager_deletion_pass
.h
→
paddle/fluid/
operators/controlflow/while_op_helper
.h
浏览文件 @
ac0e0f51
// Copyright (c) 201
8
PaddlePaddle Authors. All Rights Reserved.
// Copyright (c) 201
9
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.
...
...
@@ -14,19 +14,30 @@
#pragma once
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/variant.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
namespace
operators
{
class
EagerDeletionPass
:
public
ir
::
Pass
{
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
override
;
};
static
constexpr
char
kStepBlock
[]
=
"sub_block"
;
static
constexpr
char
kCondition
[]
=
"Condition"
;
static
constexpr
char
kStepScopes
[]
=
"StepScopes"
;
static
constexpr
char
kX
[]
=
"X"
;
static
constexpr
char
kXGRAD
[]
=
"X@GRAD"
;
static
constexpr
char
kOutputs
[]
=
"Out"
;
static
constexpr
char
kSkipEagerDeletionVars
[]
=
"skip_eager_deletion_vars"
;
}
// namespace details
}
// namespace framework
void
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp
(
int
block_id
,
const
std
::
vector
<
std
::
unique_ptr
<
framework
::
OperatorBase
>>
&
all_ops
);
void
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp
(
const
std
::
vector
<
framework
::
OperatorBase
*>
&
while_ops
,
const
std
::
vector
<
framework
::
OperatorBase
*>
&
while_grad_ops
);
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/crf_decoding_op.h
浏览文件 @
ac0e0f51
...
...
@@ -82,8 +82,9 @@ class CRFDecodingOpKernel : public framework::OpKernel<T> {
Tensor
track
;
int
*
track_value
=
track
.
mutable_data
<
int
>
(
emission_dims
,
platform
::
CPUPlace
());
auto
ker
=
jit
::
Get
<
jit
::
kCRFDecoding
,
jit
::
CRFDecodingTuples
<
T
>
,
platform
::
CPUPlace
>
(
tag_num
);
auto
ker
=
jit
::
KernelFuncs
<
jit
::
CRFDecodingTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
()
.
At
(
tag_num
);
ker
(
static_cast
<
int
>
(
seq_len
),
x
,
w
,
alpha_value
,
track_value
,
tag_num
);
T
max_score
=
-
std
::
numeric_limits
<
T
>::
max
();
int
max_i
=
0
;
...
...
paddle/fluid/operators/detection/box_coder_op.h
浏览文件 @
ac0e0f51
...
...
@@ -20,7 +20,7 @@ namespace operators {
enum
class
BoxCodeType
{
kEncodeCenterSize
=
0
,
kDecodeCenterSize
=
1
};
inline
BoxCodeType
GetBoxCodeType
(
const
std
::
string
&
type
)
{
inline
BoxCodeType
GetBoxCodeType
(
const
std
::
string
&
type
)
{
if
(
type
==
"encode_center_size"
)
{
return
BoxCodeType
::
kEncodeCenterSize
;
}
else
if
(
type
==
"decode_center_size"
)
{
...
...
@@ -32,24 +32,23 @@ inline BoxCodeType GetBoxCodeType(const std::string& type) {
template
<
typename
DeviceContext
,
typename
T
>
class
BoxCoderKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
EncodeCenterSize
(
const
framework
::
Tensor
*
target_box
,
const
framework
::
Tensor
*
prior_box
,
const
framework
::
Tensor
*
prior_box_var
,
void
EncodeCenterSize
(
const
framework
::
Tensor
*
target_box
,
const
framework
::
Tensor
*
prior_box
,
const
framework
::
Tensor
*
prior_box_var
,
const
bool
normalized
,
const
std
::
vector
<
float
>
variance
,
T
*
output
)
const
{
const
std
::
vector
<
float
>
variance
,
T
*
output
)
const
{
int64_t
row
=
target_box
->
dims
()[
0
];
int64_t
col
=
prior_box
->
dims
()[
0
];
int64_t
len
=
prior_box
->
dims
()[
1
];
auto
*
target_box_data
=
target_box
->
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
->
data
<
T
>
();
const
T
*
prior_box_var_data
=
nullptr
;
if
(
prior_box_var
)
prior_box_var_data
=
prior_box_var
->
data
<
T
>
();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
auto
*
target_box_data
=
target_box
->
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
->
data
<
T
>
();
size_t
offset
=
i
*
col
*
len
+
j
*
len
;
T
prior_box_width
=
prior_box_data
[
j
*
len
+
2
]
-
prior_box_data
[
j
*
len
]
+
(
normalized
==
false
);
T
prior_box_height
=
prior_box_data
[
j
*
len
+
3
]
-
...
...
@@ -69,7 +68,6 @@ class BoxCoderKernel : public framework::OpKernel<T> {
target_box_data
[
i
*
len
+
1
]
+
(
normalized
==
false
);
size_t
offset
=
i
*
col
*
len
+
j
*
len
;
output
[
offset
]
=
(
target_box_center_x
-
prior_box_center_x
)
/
prior_box_width
;
output
[
offset
+
1
]
=
...
...
@@ -78,44 +76,61 @@ class BoxCoderKernel : public framework::OpKernel<T> {
std
::
log
(
std
::
fabs
(
target_box_width
/
prior_box_width
));
output
[
offset
+
3
]
=
std
::
log
(
std
::
fabs
(
target_box_height
/
prior_box_height
));
if
(
prior_box_var
)
{
int
prior_var_offset
=
j
*
len
;
output
[
offset
]
/=
prior_box_var_data
[
prior_var_offset
];
output
[
offset
+
1
]
/=
prior_box_var_data
[
prior_var_offset
+
1
];
output
[
offset
+
2
]
/=
prior_box_var_data
[
prior_var_offset
+
2
];
output
[
offset
+
3
]
/=
prior_box_var_data
[
prior_var_offset
+
3
];
}
else
if
(
!
(
variance
.
empty
()))
{
}
}
if
(
prior_box_var
)
{
const
T
*
prior_box_var_data
=
prior_box_var
->
data
<
T
>
();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(3)
#endif
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
for
(
int
k
=
0
;
k
<
4
;
++
k
)
{
size_t
offset
=
i
*
col
*
len
+
j
*
len
;
int
prior_var_offset
=
j
*
len
;
output
[
offset
+
k
]
/=
prior_box_var_data
[
prior_var_offset
+
k
];
}
}
}
}
else
if
(
!
(
variance
.
empty
()))
{
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(3)
#endif
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
for
(
int
k
=
0
;
k
<
4
;
++
k
)
{
size_t
offset
=
i
*
col
*
len
+
j
*
len
;
output
[
offset
+
k
]
/=
static_cast
<
T
>
(
variance
[
k
]);
}
}
}
}
}
template
<
int
axis
,
int
var_size
>
void
DecodeCenterSize
(
const
framework
::
Tensor
*
target_box
,
const
framework
::
Tensor
*
prior_box
,
const
framework
::
Tensor
*
prior_box_var
,
void
DecodeCenterSize
(
const
framework
::
Tensor
*
target_box
,
const
framework
::
Tensor
*
prior_box
,
const
framework
::
Tensor
*
prior_box_var
,
const
bool
normalized
,
std
::
vector
<
float
>
variance
,
T
*
output
)
const
{
T
*
output
)
const
{
int64_t
row
=
target_box
->
dims
()[
0
];
int64_t
col
=
target_box
->
dims
()[
1
];
int64_t
len
=
target_box
->
dims
()[
2
];
auto
*
target_box_data
=
target_box
->
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
->
data
<
T
>
();
const
T
*
prior_box_var_data
=
nullptr
;
if
(
var_size
==
2
)
prior_box_var_data
=
prior_box_var
->
data
<
T
>
();
int
prior_box_offset
=
0
;
T
var_data
[
4
]
=
{
1.
,
1.
,
1.
,
1.
};
T
*
var_ptr
=
var_data
;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
auto
*
target_box_data
=
target_box
->
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
->
data
<
T
>
();
T
var_data
[
4
]
=
{
1.
,
1.
,
1.
,
1.
};
T
*
var_ptr
=
var_data
;
size_t
offset
=
i
*
col
*
len
+
j
*
len
;
prior_box_offset
=
axis
==
0
?
j
*
len
:
i
*
len
;
int
prior_box_offset
=
axis
==
0
?
j
*
len
:
i
*
len
;
T
prior_box_width
=
prior_box_data
[
prior_box_offset
+
2
]
-
prior_box_data
[
prior_box_offset
]
+
(
normalized
==
false
);
...
...
@@ -131,10 +146,10 @@ class BoxCoderKernel : public framework::OpKernel<T> {
T
target_box_width
=
0
,
target_box_height
=
0
;
int
prior_var_offset
=
axis
==
0
?
j
*
len
:
i
*
len
;
if
(
var_size
==
2
)
{
std
::
memcpy
(
var_ptr
,
prior_box_var
_data
+
prior_var_offset
,
std
::
memcpy
(
var_ptr
,
prior_box_var
->
data
<
T
>
()
+
prior_var_offset
,
4
*
sizeof
(
T
));
}
else
if
(
var_size
==
1
)
{
var_ptr
=
reinterpret_cast
<
T
*>
(
variance
.
data
());
var_ptr
=
reinterpret_cast
<
T
*>
(
variance
.
data
());
}
T
box_var_x
=
*
var_ptr
;
T
box_var_y
=
*
(
var_ptr
+
1
);
...
...
@@ -162,11 +177,11 @@ class BoxCoderKernel : public framework::OpKernel<T> {
}
}
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
prior_box
=
context
.
Input
<
framework
::
Tensor
>
(
"PriorBox"
);
auto
*
prior_box_var
=
context
.
Input
<
framework
::
Tensor
>
(
"PriorBoxVar"
);
auto
*
target_box
=
context
.
Input
<
framework
::
LoDTensor
>
(
"TargetBox"
);
auto
*
output_box
=
context
.
Output
<
framework
::
Tensor
>
(
"OutputBox"
);
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
prior_box
=
context
.
Input
<
framework
::
Tensor
>
(
"PriorBox"
);
auto
*
prior_box_var
=
context
.
Input
<
framework
::
Tensor
>
(
"PriorBoxVar"
);
auto
*
target_box
=
context
.
Input
<
framework
::
LoDTensor
>
(
"TargetBox"
);
auto
*
output_box
=
context
.
Output
<
framework
::
Tensor
>
(
"OutputBox"
);
std
::
vector
<
float
>
variance
=
context
.
Attr
<
std
::
vector
<
float
>>
(
"variance"
);
const
int
axis
=
context
.
Attr
<
int
>
(
"axis"
);
if
(
target_box
->
lod
().
size
())
{
...
...
@@ -194,7 +209,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
output_box
->
mutable_data
<
T
>
({
row
,
col
,
len
},
context
.
GetPlace
());
T
*
output
=
output_box
->
data
<
T
>
();
T
*
output
=
output_box
->
data
<
T
>
();
if
(
code_type
==
BoxCodeType
::
kEncodeCenterSize
)
{
EncodeCenterSize
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
output
);
...
...
paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -110,8 +110,9 @@ class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
constexpr
int
simd_width
=
16
;
int
C
=
c
/
simd_width
;
auto
multiply
=
jit
::
Get
<
jit
::
kNCHW16CMulNC
,
jit
::
NCHW16CMulNCTuples
<
T
>
,
platform
::
CPUPlace
>
(
0
);
auto
multiply
=
jit
::
KernelFuncs
<
jit
::
NCHW16CMulNCTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
()
.
At
(
0
);
#pragma omp parallel for collapse(2)
for
(
int
ni
=
0
;
ni
<
n
;
ni
++
)
{
for
(
int
ci
=
0
;
ci
<
C
;
ci
++
)
{
...
...
paddle/fluid/operators/fake_dequantize_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/fake_dequantize_op.h"
#include <string>
#include <vector>
namespace
paddle
{
namespace
operators
{
...
...
@@ -76,6 +77,63 @@ $$Out = \frac{scale*X}{ max_range }$$
}
};
class
FakeChannelWiseDequantizeMaxAbsOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of FakeChannelWiseDequantizeMaxAbsOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
"Scales"
),
"Input(Scales) of FakeChannelWiseDequantizeMaxAbsOp "
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of FakeChannelWiseDequantizeMaxAbsOp should not be null."
);
ctx
->
ShareDim
(
"X"
,
/*->*/
"Out"
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
class
FakeChannelWiseDequantizeMaxAbsOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor) The input with float-32/64 type is the "
"low precision tensor."
);
AddInput
(
"Scales"
,
"(Tensors) The scales in quantization stage. "
"Now, `Scales` is a vector with at most two tensors. "
"If Scales has two elements, the second tensor should only have "
"one value."
)
.
AsDuplicable
();
AddOutput
(
"Out"
,
"(Tensor) The output is the dequantized high "
"precision tensor."
);
AddAttr
<
std
::
vector
<
int
>>
(
"quant_bits"
,
"Quantization bit numbers in quantization stage. "
"The size of `quant_bits` should be equal to the size of `Scales`."
)
.
SetDefault
({
8
});
AddComment
(
R"DOC(
FakeChannelWiseDequantizeMaxAbsOp operator.
This calculation is an opposite operation of FakeChannelWiseQuantizeMaxAbsOp:
$$Out_c = \frac{X_c\prod_{i=1}^{n}Scales_{ic}}{\prod_{i=1}^{n}(2^{quant\_bits_i-1}-1)}$$
In the above formula, the range value of $c$ can be represented as $0 \leq c \lt \ the\ channel\ number\ of\ X$.
Besides, the size of $quant\_bits$ should be equal to the size of $Scales$, and it is called $n$ in the formula.
Notes: In general, the per-channel quantization is only applied to weights and the activations use per-layer quantization.
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
...
...
@@ -88,3 +146,11 @@ REGISTER_OPERATOR(fake_dequantize_max_abs, ops::FakeDequantizeMaxAbsOp,
REGISTER_OP_CPU_KERNEL
(
fake_dequantize_max_abs
,
ops
::
FakeDequantizeMaxAbsKernel
<
CPU
,
float
>
,
ops
::
FakeDequantizeMaxAbsKernel
<
CPU
,
double
>
);
REGISTER_OPERATOR
(
fake_channel_wise_dequantize_max_abs
,
ops
::
FakeChannelWiseDequantizeMaxAbsOp
,
ops
::
FakeChannelWiseDequantizeMaxAbsOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
fake_channel_wise_dequantize_max_abs
,
ops
::
FakeChannelWiseDequantizeMaxAbsKernel
<
CPU
,
float
>
,
ops
::
FakeChannelWiseDequantizeMaxAbsKernel
<
CPU
,
double
>
);
paddle/fluid/operators/fake_dequantize_op.cu
浏览文件 @
ac0e0f51
...
...
@@ -55,3 +55,7 @@ using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL
(
fake_dequantize_max_abs
,
ops
::
FakeDequantizeMaxAbsKernel
<
CUDA
,
float
>
,
ops
::
FakeDequantizeMaxAbsKernel
<
CUDA
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
fake_channel_wise_dequantize_max_abs
,
ops
::
FakeChannelWiseDequantizeMaxAbsKernel
<
CUDA
,
float
>
,
ops
::
FakeChannelWiseDequantizeMaxAbsKernel
<
CUDA
,
double
>
);
paddle/fluid/operators/fake_dequantize_op.h
浏览文件 @
ac0e0f51
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
...
...
@@ -45,5 +46,42 @@ class FakeDequantizeMaxAbsKernel : public framework::OpKernel<T> {
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
FakeChannelWiseDequantizeMaxAbsKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
virtual
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
in
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
scales
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"Scales"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
PADDLE_ENFORCE_EQ
(
scales
[
0
]
->
numel
(),
in
->
dims
()[
0
],
"The number of first scale values must be the same with "
"first dimension value of Input(X)."
);
auto
quant_bits
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"quant_bits"
);
int
max_range
=
std
::
pow
(
2
,
quant_bits
[
0
]
-
1
)
-
1
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
out
->
mutable_data
<
T
>
(
dev_ctx
.
GetPlace
());
auto
dequant
=
DequantizeFunctor
<
DeviceContext
,
T
>
();
for
(
int64_t
i
=
0
;
i
<
in
->
dims
()[
0
];
i
++
)
{
framework
::
Tensor
one_channel_in
=
in
->
Slice
(
i
,
i
+
1
);
framework
::
Tensor
one_channel_out
=
out
->
Slice
(
i
,
i
+
1
);
framework
::
Tensor
one_channel_scale
=
scales
[
0
]
->
Slice
(
i
,
i
+
1
);
dequant
(
dev_ctx
,
&
one_channel_in
,
&
one_channel_scale
,
static_cast
<
T
>
(
max_range
),
&
one_channel_out
);
}
if
(
scales
.
size
()
==
2
)
{
PADDLE_ENFORCE_EQ
(
scales
[
1
]
->
numel
(),
1
,
"The second scale tensor should only have one value at now."
);
max_range
=
std
::
pow
(
2
,
quant_bits
[
1
]
-
1
)
-
1
;
dequant
(
dev_ctx
,
out
,
scales
[
1
],
static_cast
<
T
>
(
max_range
),
out
);
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/fake_quantize_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -134,6 +134,60 @@ $$Out = round(X/scale * range)$$
}
};
class
FakeChannelWiseQuantizeAbsMaxOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of FakeChannelWiseQuantizeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of FakeChannelWiseQuantizeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"OutScales"
),
"Output(Scales) of FakeChannelWiseQuantizeOp should not be null."
);
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
ctx
->
SetOutputDim
(
"OutScales"
,
{
ctx
->
GetInputDim
(
"X"
)[
0
]});
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
(),
ctx
.
GetPlace
());
}
};
class
FakeChannelWiseQuantizeAbsMaxOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor) Input is float data type."
);
AddOutput
(
"Out"
,
"(Tensor) Output of quantized low level tensor, "
"but also saved as float data type."
);
AddOutput
(
"OutScales"
,
"(Tensor) Current channel wise scale"
);
AddAttr
<
int
>
(
"bit_length"
,
"(int, default 8)"
)
.
SetDefault
(
8
)
.
AddCustomChecker
([](
const
int
&
bit_length
)
{
PADDLE_ENFORCE
(
bit_length
>=
1
&&
bit_length
<=
16
,
"'bit_length' should be between 1 and 16."
);
});
AddComment
(
R"DOC(
The scale of FakeChannelWiseQuantize operator is a vector.
In detail, each channel of the input X has a scale value.
$$scale_c = max(abs(X_c))$$
$$range = 2^{bit\_length - 1} - 1$$
$$Out_c = round(\frac{X_c * range} {scale_c})$$
In above three formulas, the range value of c is as follow:
$$0 \leq c \lt \ the\ channel\ number\ of\ X$$
)DOC"
);
}
};
class
FakeQuantizeRangeAbsMaxOp
:
public
framework
::
OperatorWithKernel
{
public:
FakeQuantizeRangeAbsMaxOp
(
const
std
::
string
&
type
,
...
...
@@ -218,3 +272,10 @@ REGISTER_OPERATOR(fake_quantize_range_abs_max, ops::FakeQuantizeRangeAbsMaxOp,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
fake_quantize_range_abs_max
,
ops
::
FakeQuantizeRangeAbsMaxKernel
<
CPU
,
float
>
);
REGISTER_OPERATOR
(
fake_channel_wise_quantize_abs_max
,
ops
::
FakeChannelWiseQuantizeAbsMaxOp
,
ops
::
FakeChannelWiseQuantizeAbsMaxOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
fake_channel_wise_quantize_abs_max
,
ops
::
FakeChannelWiseQuantizeAbsMaxKernel
<
CPU
,
float
>
);
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @
ac0e0f51
...
...
@@ -174,5 +174,7 @@ namespace ops = paddle::operators;
using
CUDA
=
paddle
::
platform
::
CUDADeviceContext
;
REGISTER_OP_CUDA_KERNEL
(
fake_quantize_abs_max
,
ops
::
FakeQuantizeAbsMaxKernel
<
CUDA
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
fake_channel_wise_quantize_abs_max
,
ops
::
FakeChannelWiseQuantizeAbsMaxKernel
<
CUDA
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
fake_quantize_range_abs_max
,
ops
::
FakeQuantizeRangeAbsMaxKernel
<
CUDA
,
float
>
);
paddle/fluid/operators/fake_quantize_op.h
浏览文件 @
ac0e0f51
...
...
@@ -63,6 +63,39 @@ class FakeQuantizeAbsMaxKernel : public framework::OpKernel<T> {
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
FakeChannelWiseQuantizeAbsMaxKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
out_scales
=
context
.
Output
<
framework
::
Tensor
>
(
"OutScales"
);
T
*
out_scales_data
=
out_scales
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
bit_length
=
context
.
Attr
<
int
>
(
"bit_length"
);
int
bin_cnt
=
std
::
pow
(
2
,
bit_length
-
1
)
-
1
;
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
auto
find_abs_max
=
FindAbsMaxFunctor
<
DeviceContext
,
T
>
();
for
(
int64_t
i
=
0
;
i
<
in
->
dims
()[
0
];
i
++
)
{
framework
::
Tensor
one_channel
=
in
->
Slice
(
i
,
i
+
1
);
const
T
*
one_channel_data
=
one_channel
.
data
<
T
>
();
find_abs_max
(
dev_ctx
,
one_channel_data
,
one_channel
.
numel
(),
&
out_scales_data
[
i
]);
}
auto
clip_quant
=
ClipAndFakeQuantFunctor
<
DeviceContext
,
T
>
();
for
(
int64_t
i
=
0
;
i
<
in
->
dims
()[
0
];
i
++
)
{
framework
::
Tensor
one_channel_in
=
in
->
Slice
(
i
,
i
+
1
);
framework
::
Tensor
one_channel_out
=
out
->
Slice
(
i
,
i
+
1
);
framework
::
Tensor
one_channel_scale
=
out_scales
->
Slice
(
i
,
i
+
1
);
clip_quant
(
dev_ctx
,
one_channel_in
,
one_channel_scale
,
bin_cnt
,
&
one_channel_out
);
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
FakeQuantizeRangeAbsMaxKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -23,9 +23,6 @@ class FusedEmbeddingSeqPoolOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
if
(
ctx
->
IsRuntime
())
{
return
;
}
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"W"
),
"Input W of FusedEmbeddingSeqPoolOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Ids"
),
...
...
@@ -91,6 +88,8 @@ class FusedEmbeddingSeqPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"(boolean, default false) "
"Sparse update."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
framework
::
kAllKernelsMustComputeRuntimeShape
,
""
)
.
SetDefault
(
true
);
AddComment
(
R"DOC(
FusedEmbeddingSeqPool Operator.
...
...
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h
浏览文件 @
ac0e0f51
...
...
@@ -52,8 +52,9 @@ struct EmbeddingVSumFunctor {
out_width
,
jit
::
SeqPoolType
::
kSum
);
for
(
size_t
i
=
0
;
i
!=
ids_lod
.
size
()
-
1
;
++
i
)
{
attr
.
index_height
=
ids_lod
[
i
+
1
]
-
ids_lod
[
i
];
auto
emb_seqpool
=
jit
::
Get
<
jit
::
kEmbSeqPool
,
jit
::
EmbSeqPoolTuples
<
T
>
,
platform
::
CPUPlace
>
(
attr
);
auto
emb_seqpool
=
jit
::
KernelFuncs
<
jit
::
EmbSeqPoolTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
()
.
At
(
attr
);
emb_seqpool
(
table
,
ids
+
ids_lod
[
i
]
*
idx_width
,
output
+
i
*
out_width
,
&
attr
);
}
...
...
@@ -120,6 +121,8 @@ class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
auto
*
ids
=
context
.
Input
<
LoDTensor
>
(
"Ids"
);
auto
*
d_output
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_table
=
context
.
Output
<
SelectedRows
>
(
framework
::
GradVarName
(
"W"
));
// runtime shape
d_table
->
set_height
(
table_dim
[
0
]);
auto
*
ids_data
=
ids
->
data
<
int64_t
>
();
int64_t
ids_num
=
ids
->
numel
();
...
...
@@ -135,8 +138,9 @@ class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
T
*
d_table_data
=
d_table_value
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
d_output_data
=
d_output
->
data
<
T
>
();
auto
vbroadcast
=
jit
::
Get
<
jit
::
kVBroadcast
,
jit
::
VBroadcastTuples
<
T
>
,
platform
::
CPUPlace
>
(
out_width
);
auto
vbroadcast
=
jit
::
KernelFuncs
<
jit
::
VBroadcastTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
()
.
At
(
out_width
);
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
.
size
())
-
1
;
++
i
)
{
int64_t
h
=
static_cast
<
int64_t
>
(
lod
[
i
+
1
]
-
lod
[
i
]);
const
T
*
src
=
d_output_data
+
i
*
out_width
;
...
...
paddle/fluid/operators/fused/fusion_gru_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -182,29 +182,32 @@ class FusionGRUKernel : public framework::OpKernel<T> {
const int total_T = x_dims[0]; \
const int D3 = wh_dims[1]
#define INIT_OTHER_DEFINES \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D2 = D * 2; \
const jit::gru_attr_t attr( \
D, jit::to_kerneltype(ctx.Attr<std::string>("gate_activation")), \
jit::to_kerneltype(ctx.Attr<std::string>("activation"))); \
jit::gru_t one_step; \
auto ComputeH1 = \
jit::Get<jit::kGRUH1, jit::GRUTuples<T>, platform::CPUPlace>(attr); \
auto ComputeHtPart1 = \
jit::Get<jit::kGRUHtPart1, jit::GRUTuples<T>, platform::CPUPlace>(attr); \
auto ComputeHtPart2 = \
jit::Get<jit::kGRUHtPart2, jit::GRUTuples<T>, platform::CPUPlace>(attr); \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
auto place = ctx.GetPlace(); \
#define INIT_OTHER_DEFINES \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D2 = D * 2; \
const jit::gru_attr_t attr( \
D, jit::to_kerneltype(ctx.Attr<std::string>("gate_activation")), \
jit::to_kerneltype(ctx.Attr<std::string>("activation"))); \
jit::gru_t one_step; \
auto ComputeH1 = \
jit::KernelFuncs<jit::GRUH1Tuple<T>, platform::CPUPlace>::Cache().At( \
attr); \
auto ComputeHtPart1 = \
jit::KernelFuncs<jit::GRUHtPart1Tuple<T>, platform::CPUPlace>::Cache() \
.At(attr); \
auto ComputeHtPart2 = \
jit::KernelFuncs<jit::GRUHtPart2Tuple<T>, platform::CPUPlace>::Cache() \
.At(attr); \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
auto place = ctx.GetPlace(); \
T* xx_data = xx->mutable_data<T>(place)
void
SeqCompute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
...
...
paddle/fluid/operators/fused/fusion_lstm_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -235,32 +235,34 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
const int D = wh_dims[0]; \
const int D4 = wh_dims[1]
#define INIT_OTHER_DEFINES \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
/* diagonal weight*/
\
const T* wp_data = bias->data<T>() + D4; \
/* for peephole only*/
\
T* checked_cell_data = nullptr; \
auto place = ctx.GetPlace(); \
if (use_peepholes) { \
/* w_ic * Ct-1, w_fc * Ct-1 ; w_oc * Ct => ih*/
\
auto* checked_cell = ctx.Output<Tensor>("CheckedCell"); \
checked_cell_data = checked_cell->mutable_data<T>(place); \
} \
const jit::lstm_attr_t attr( \
D, jit::to_kerneltype(ctx.Attr<std::string>("gate_activation")), \
jit::to_kerneltype(ctx.Attr<std::string>("candidate_activation")), \
jit::to_kerneltype(ctx.Attr<std::string>("cell_activation")), \
use_peepholes); \
jit::lstm_t one_step; \
one_step.wp = wp_data; \
one_step.checked = checked_cell_data; \
auto ComputeC1H1 = \
jit::Get<jit::kLSTMC1H1, jit::LSTMTuples<T>, platform::CPUPlace>(attr); \
auto ComputeCtHt = \
jit::Get<jit::kLSTMCtHt, jit::LSTMTuples<T>, platform::CPUPlace>(attr)
#define INIT_OTHER_DEFINES \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
/* diagonal weight*/
\
const T* wp_data = bias->data<T>() + D4; \
/* for peephole only*/
\
T* checked_cell_data = nullptr; \
auto place = ctx.GetPlace(); \
if (use_peepholes) { \
/* w_ic * Ct-1, w_fc * Ct-1 ; w_oc * Ct => ih*/
\
auto* checked_cell = ctx.Output<Tensor>("CheckedCell"); \
checked_cell_data = checked_cell->mutable_data<T>(place); \
} \
const jit::lstm_attr_t attr( \
D, jit::to_kerneltype(ctx.Attr<std::string>("gate_activation")), \
jit::to_kerneltype(ctx.Attr<std::string>("candidate_activation")), \
jit::to_kerneltype(ctx.Attr<std::string>("cell_activation")), \
use_peepholes); \
jit::lstm_t one_step; \
one_step.wp = wp_data; \
one_step.checked = checked_cell_data; \
auto ComputeC1H1 = \
jit::KernelFuncs<jit::LSTMC1H1Tuple<T>, platform::CPUPlace>::Cache().At( \
attr); \
auto ComputeCtHt = \
jit::KernelFuncs<jit::LSTMCtHtTuple<T>, platform::CPUPlace>::Cache().At( \
attr)
// Wh GEMM
#define GEMM_WH_ADDON(bs, prev, out) \
...
...
paddle/fluid/operators/fused/fusion_repeated_fc_relu_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -82,9 +82,11 @@ template <typename T>
static
void
fc_relu
(
const
T
*
x
,
const
T
*
w
,
const
T
*
b
,
T
*
y
,
const
jit
::
matmul_attr_t
&
attr
)
{
auto
matmul
=
jit
::
Get
<
jit
::
kMatMul
,
jit
::
MatMulTuples
<
T
>
,
platform
::
CPUPlace
>
(
attr
);
jit
::
KernelFuncs
<
jit
::
MatMulTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
attr
);
auto
addbias_relu
=
jit
::
Get
<
jit
::
kVAddRelu
,
jit
::
XYZNTuples
<
T
>
,
platform
::
CPUPlace
>
(
attr
.
n
);
jit
::
KernelFuncs
<
jit
::
VAddReluTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
attr
.
n
);
matmul
(
x
,
w
,
y
,
&
attr
);
T
*
dst
=
y
;
for
(
int
i
=
0
;
i
<
attr
.
m
;
++
i
)
{
...
...
paddle/fluid/operators/fused/fusion_seqpool_concat_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -98,7 +98,7 @@ class FusionSeqPoolConcatKernel : public framework::OpKernel<T> {
attr
.
type
=
jit
::
SeqPoolType
::
kSqrt
;
}
auto
seqpool
=
jit
::
Get
<
jit
::
kSeqPool
,
jit
::
SeqPoolTuples
<
T
>
,
platform
::
CPUPlace
>
(
jit
::
KernelFuncs
<
jit
::
SeqPoolTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
attr
);
size_t
n
=
ins
.
size
();
size_t
dst_step_size
=
n
*
w
;
...
...
paddle/fluid/operators/fused/fusion_squared_mat_sub_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -94,19 +94,23 @@ class FusionSquaredMatSubKernel : public framework::OpKernel<T> {
int
o_numel
=
attr
.
m
*
attr
.
n
;
auto
vsquare_x
=
jit
::
Get
<
jit
::
kVSquare
,
jit
::
XYNTuples
<
T
>
,
platform
::
CPUPlace
>
(
attr
.
m
*
attr
.
k
);
jit
::
KernelFuncs
<
jit
::
VSquareTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
attr
.
m
*
attr
.
k
);
auto
vsquare_y
=
jit
::
Get
<
jit
::
kVSquare
,
jit
::
XYNTuples
<
T
>
,
platform
::
CPUPlace
>
(
attr
.
k
*
attr
.
n
);
jit
::
KernelFuncs
<
jit
::
VSquareTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
attr
.
k
*
attr
.
n
);
auto
vsquare_xy
=
jit
::
Get
<
jit
::
kVSquare
,
jit
::
XYNTuples
<
T
>
,
platform
::
CPUPlace
>
(
o_numel
);
jit
::
KernelFuncs
<
jit
::
VSquareTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
o_numel
);
auto
vsub
=
jit
::
Get
<
jit
::
kVSub
,
jit
::
XYZNTuples
<
T
>
,
platform
::
CPUPlace
>
(
o_numel
);
jit
::
KernelFuncs
<
jit
::
VSubTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
o_numel
);
auto
vscal
=
jit
::
Get
<
jit
::
kVScal
,
jit
::
AXYNTuples
<
T
>
,
platform
::
CPUPlace
>
(
o_numel
);
jit
::
KernelFuncs
<
jit
::
VScalTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
o_numel
);
auto
matmul
=
jit
::
Get
<
jit
::
kMatMul
,
jit
::
MatMulTuples
<
T
>
,
platform
::
CPUPlace
>
(
attr
);
jit
::
KernelFuncs
<
jit
::
MatMulTuple
<
T
>
,
platform
::
CPUPlace
>::
Cache
().
At
(
attr
);
const
T
*
x_data
=
x
->
data
<
T
>
();
const
T
*
y_data
=
y
->
data
<
T
>
();
...
...
paddle/fluid/operators/hash_op.cc
浏览文件 @
ac0e0f51
...
...
@@ -26,9 +26,6 @@ class HashOp : public framework::OperatorWithKernel {
:
OperatorWithKernel
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
if
(
ctx
->
IsRuntime
())
{
return
;
}
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of HashOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
...
...
@@ -57,6 +54,8 @@ $$Out = scale * X$$
)DOC"
);
AddAttr
<
int
>
(
"num_hash"
,
""
).
SetDefault
(
1
);
AddAttr
<
int
>
(
"mod_by"
,
""
).
SetDefault
(
100000
);
AddAttr
<
bool
>
(
framework
::
kAllKernelsMustComputeRuntimeShape
,
""
)
.
SetDefault
(
true
);
}
};
...
...
paddle/fluid/operators/jit/CMakeLists.txt
浏览文件 @
ac0e0f51
...
...
@@ -5,7 +5,7 @@ file(APPEND ${jit_file} "\#pragma once\n")
file
(
APPEND
${
jit_file
}
"
\#
include
\"
paddle/fluid/operators/jit/helper.h
\"\n
"
)
file
(
APPEND
${
jit_file
}
"
\#
include
\"
paddle/fluid/operators/jit/registry.h
\"\n\n
"
)
set
(
JIT_KERNEL_DEPS cpu_info cblas gflags enforce place
)
set
(
JIT_KERNEL_DEPS cpu_info cblas gflags enforce place
xxhash
)
file
(
GLOB jit_kernel_cc_srcs RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"*.cc"
)
list
(
REMOVE_ITEM jit_kernel_cc_srcs test.cc benchmark.cc
)
...
...
paddle/fluid/operators/jit/benchmark.cc
浏览文件 @
ac0e0f51
...
...
@@ -59,8 +59,6 @@ BenchJITKernel* InsertBenchmark(BenchJITKernel* b) {
InsertBenchmark(new BenchJITKernel_##name##_##dtype##_##place##_()); \
void BenchJITKernel_##name##_##dtype##_##place##_::Run()
#define BENCH_FP32_CPU(name) BENCH_JITKERNEL(name, FP32, CPU)
void
RUN_ALL_BENCHMARK
()
{
for
(
auto
p
:
g_all_benchmarks
)
{
if
(
!
FLAGS_filter
.
empty
()
&&
FLAGS_filter
!=
p
->
Name
())
{
...
...
@@ -90,11 +88,11 @@ std::vector<int> TestSizes() {
return
s
;
}
template
<
typename
KernelTuple
s
,
typename
...
Args
>
template
<
typename
KernelTuple
,
typename
...
Args
>
struct
BenchFunc
{
// return this function avg time
// TODO(TJ): clear cache every time
double
operator
()(
const
typename
KernelTuple
s
::
func_type
tgt
,
Args
...
args
)
{
double
operator
()(
const
typename
KernelTuple
::
func_type
tgt
,
Args
...
args
)
{
for
(
int
i
=
0
;
i
<
FLAGS_burning
;
++
i
)
{
tgt
(
args
...);
}
...
...
@@ -109,40 +107,17 @@ struct BenchFunc {
namespace
jit
=
paddle
::
operators
::
jit
;
template
<
jit
::
KernelType
KT
,
typename
KernelTuples
,
typename
PlaceType
,
typename
...
Args
>
void
BenchAllImpls
(
const
typename
KernelTuples
::
attr_type
&
attr
,
Args
...
args
)
{
BenchFunc
<
KernelTuples
,
Args
...
>
benchmark
;
template
<
typename
KernelTuple
,
typename
PlaceType
,
typename
...
Args
>
void
BenchAllImpls
(
const
typename
KernelTuple
::
attr_type
&
attr
,
Args
...
args
)
{
BenchFunc
<
KernelTuple
,
Args
...
>
benchmark
;
std
::
vector
<
std
::
pair
<
std
::
string
,
double
>>
infos
;
// test refer
auto
refer
=
jit
::
GetRefer
<
KT
,
KernelTuples
>
();
if
(
!
refer
)
{
LOG
(
FATAL
)
<<
"Refer can not be empty!"
;
auto
funcs
=
jit
::
GetAllCandidateFuncsWithTypes
<
KernelTuple
,
PlaceType
>
(
attr
);
for
(
auto
f
:
funcs
)
{
infos
.
push_back
(
std
::
make_pair
(
f
.
first
,
benchmark
(
f
.
second
,
args
...)));
}
infos
.
push_back
(
std
::
make_pair
(
"Refer"
,
benchmark
(
refer
,
args
...)));
// test jitcode
auto
jitcode
=
jit
::
GetJitCode
<
KT
,
KernelTuples
,
PlaceType
>
(
attr
);
if
(
jitcode
)
{
infos
.
push_back
(
std
::
make_pair
(
"JitCode"
,
benchmark
(
jitcode
,
args
...)));
}
// test all impls in more
jit
::
KernelKey
kkey
(
KT
,
PlaceType
());
auto
&
pool
=
jit
::
KernelPool
().
Instance
().
AllKernels
();
auto
iter
=
pool
.
find
(
kkey
);
if
(
iter
!=
pool
.
end
())
{
auto
&
impls
=
iter
->
second
;
for
(
auto
&
impl
:
impls
)
{
auto
i
=
dynamic_cast
<
const
jit
::
KernelMore
<
KernelTuples
>*>
(
impl
.
get
());
if
(
i
&&
i
->
UseMe
(
attr
))
{
auto
more
=
i
->
GetFunc
();
infos
.
push_back
(
std
::
make_pair
(
i
->
ImplType
(),
benchmark
(
more
,
args
...)));
}
}
}
// Test result from Get function
auto
tgt
=
jit
::
Get
<
KT
,
KernelTuples
,
PlaceType
>
(
attr
);
auto
tgt
=
jit
::
KernelFuncs
<
KernelTuple
,
PlaceType
>::
Cache
().
At
(
attr
);
if
(
!
tgt
)
{
LOG
(
FATAL
)
<<
"Target can not be empty!"
;
}
...
...
@@ -150,7 +125,8 @@ void BenchAllImpls(const typename KernelTuples::attr_type& attr, Args... args) {
// print
std
::
ostringstream
loginfos
;
loginfos
<<
"Kernel Type "
<<
jit
::
to_string
(
KT
)
<<
": "
<<
attr
<<
": "
;
loginfos
<<
"Kernel Type "
<<
jit
::
to_string
(
KernelTuple
::
kernel_type
)
<<
": "
<<
attr
<<
": "
;
for
(
auto
pair
:
infos
)
{
loginfos
<<
pair
.
first
<<
" takes "
<<
pair
.
second
<<
" us; "
;
}
...
...
@@ -159,8 +135,9 @@ void BenchAllImpls(const typename KernelTuples::attr_type& attr, Args... args) {
using
Tensor
=
paddle
::
framework
::
Tensor
;
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchXYZNKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelXYZN
()
{
using
T
=
typename
KernelTuple
::
data_type
;
for
(
int
d
:
TestSizes
())
{
Tensor
x
,
y
,
z
;
x
.
Resize
({
d
});
...
...
@@ -171,16 +148,16 @@ void BenchXYZNKernel() {
T
*
z_data
=
z
.
mutable_data
<
T
>
(
PlaceType
());
RandomVec
<
T
>
(
d
,
x_data
);
RandomVec
<
T
>
(
d
,
y_data
);
BenchAllImpls
<
K
T
,
jit
::
XYZNTuples
<
T
>
,
PlaceType
>
(
d
,
x
.
data
<
T
>
()
,
y
.
data
<
T
>
(),
z_data
,
d
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
d
,
x
.
data
<
T
>
(),
y
.
data
<
T
>
(),
z_data
,
d
);
// test inplace
BenchAllImpls
<
KT
,
jit
::
XYZNTuples
<
T
>
,
PlaceType
>
(
d
,
x
.
data
<
T
>
(),
z_data
,
z_data
,
d
);
BenchAllImpls
<
KernelTuple
,
PlaceType
>
(
d
,
x
.
data
<
T
>
(),
z_data
,
z_data
,
d
);
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchAXYNKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelAXYN
()
{
using
T
=
typename
KernelTuple
::
data_type
;
for
(
int
d
:
TestSizes
())
{
const
T
a
=
static_cast
<
T
>
(
3
);
Tensor
x
,
y
;
...
...
@@ -189,26 +166,26 @@ void BenchAXYNKernel() {
T
*
x_data
=
x
.
mutable_data
<
T
>
(
PlaceType
());
T
*
y_data
=
y
.
mutable_data
<
T
>
(
PlaceType
());
RandomVec
<
T
>
(
d
,
x_data
);
BenchAllImpls
<
KT
,
jit
::
AXYNTuples
<
T
>
,
PlaceType
>
(
d
,
&
a
,
x
.
data
<
T
>
(),
y_data
,
d
);
BenchAllImpls
<
KernelTuple
,
PlaceType
>
(
d
,
&
a
,
x
.
data
<
T
>
(),
y_data
,
d
);
// test inplace
BenchAllImpls
<
KT
,
jit
::
AXYNTuples
<
T
>
,
PlaceType
>
(
d
,
&
a
,
x
.
data
<
T
>
(),
x_data
,
d
);
BenchAllImpls
<
KernelTuple
,
PlaceType
>
(
d
,
&
a
,
x
.
data
<
T
>
(),
x_data
,
d
);
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchXRNKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelXRN
()
{
using
T
=
typename
KernelTuple
::
data_type
;
for
(
int
d
:
TestSizes
())
{
Tensor
x
;
RandomVec
<
T
>
(
d
,
x
.
mutable_data
<
T
>
({
d
},
PlaceType
()));
T
res
;
BenchAllImpls
<
K
T
,
jit
::
XRNTuples
<
T
>
,
PlaceType
>
(
d
,
x
.
data
<
T
>
(),
&
res
,
d
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
d
,
x
.
data
<
T
>
(),
&
res
,
d
);
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchXYNKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelXYN
()
{
using
T
=
typename
KernelTuple
::
data_type
;
for
(
int
d
:
TestSizes
())
{
Tensor
x
,
y
;
x
.
Resize
({
d
});
...
...
@@ -216,12 +193,13 @@ void BenchXYNKernel() {
T
*
x_data
=
x
.
mutable_data
<
T
>
(
PlaceType
());
T
*
y_data
=
y
.
mutable_data
<
T
>
(
PlaceType
());
RandomVec
<
T
>
(
d
,
x_data
);
BenchAllImpls
<
K
T
,
jit
::
XYNTuples
<
T
>
,
PlaceType
>
(
d
,
x
.
data
<
T
>
(),
y_data
,
d
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
d
,
x
.
data
<
T
>
(),
y_data
,
d
);
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchLSTMKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelLSTM
()
{
using
T
=
typename
KernelTuple
::
data_type
;
for
(
bool
use_peephole
:
{
true
,
false
})
{
for
(
int
d
:
TestSizes
())
{
const
jit
::
lstm_attr_t
attr
(
d
,
jit
::
kVSigmoid
,
jit
::
kVTanh
,
jit
::
kVTanh
,
...
...
@@ -252,13 +230,14 @@ void BenchLSTMKernel() {
step
.
wp
=
wp_data
;
step
.
checked
=
checked_data
;
}
BenchAllImpls
<
K
T
,
jit
::
LSTMTuples
<
T
>
,
PlaceType
>
(
attr
,
&
step
,
&
attr
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
attr
,
&
step
,
&
attr
);
}
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchGRUKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelGRU
()
{
using
T
=
typename
KernelTuple
::
data_type
;
for
(
int
d
:
TestSizes
())
{
const
jit
::
gru_attr_t
attr
(
d
,
jit
::
kVSigmoid
,
jit
::
kVTanh
);
auto
place
=
PlaceType
();
...
...
@@ -275,12 +254,13 @@ void BenchGRUKernel() {
step
.
gates
=
x_data
;
step
.
ht_1
=
ht_1_data
;
step
.
ht
=
ht_data
;
BenchAllImpls
<
K
T
,
jit
::
GRUTuples
<
T
>
,
PlaceType
>
(
attr
,
&
step
,
&
attr
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
attr
,
&
step
,
&
attr
);
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchSeqPoolKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelSeqPool
()
{
using
T
=
typename
KernelTuple
::
data_type
;
std
::
vector
<
jit
::
SeqPoolType
>
pool_types
=
{
jit
::
SeqPoolType
::
kSum
,
jit
::
SeqPoolType
::
kAvg
,
jit
::
SeqPoolType
::
kSqrt
};
for
(
auto
type
:
pool_types
)
{
...
...
@@ -294,15 +274,15 @@ void BenchSeqPoolKernel() {
RandomVec
<
T
>
(
h
*
w
,
x
.
mutable_data
<
T
>
(
PlaceType
()),
-
2.
f
,
2.
f
);
const
T
*
x_data
=
x
.
data
<
T
>
();
T
*
y_data
=
y
.
mutable_data
<
T
>
(
PlaceType
());
BenchAllImpls
<
KT
,
jit
::
SeqPoolTuples
<
T
>
,
PlaceType
>
(
attr
,
x_data
,
y_data
,
&
attr
);
BenchAllImpls
<
KernelTuple
,
PlaceType
>
(
attr
,
x_data
,
y_data
,
&
attr
);
}
}
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchEmbSeqPoolKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelEmbSeqPool
()
{
using
T
=
typename
KernelTuple
::
data_type
;
std
::
vector
<
jit
::
SeqPoolType
>
pool_types
=
{
jit
::
SeqPoolType
::
kSum
};
int64_t
tbl_h
=
1e4
;
for
(
int
tbl_w
:
{
10
,
16
,
256
})
{
...
...
@@ -324,16 +304,17 @@ void BenchEmbSeqPoolKernel() {
tbl_h
-
1
);
const
int64_t
*
idx_data
=
idx
.
data
<
int64_t
>
();
T
*
o_data
=
out
.
mutable_data
<
T
>
(
PlaceType
());
BenchAllImpls
<
K
T
,
jit
::
EmbSeqPoolTuples
<
T
>
,
PlaceType
>
(
attr
,
table_data
,
idx_data
,
o_data
,
&
attr
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
attr
,
table_data
,
idx_data
,
o_data
,
&
attr
);
}
}
}
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchSgdKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelSgd
()
{
using
T
=
typename
KernelTuple
::
data_type
;
const
T
lr
=
0.1
;
auto
UnDuplicatedRandomVec
=
[](
int
n
,
const
int64_t
lower
,
const
int64_t
upper
)
->
std
::
vector
<
int64_t
>
{
...
...
@@ -364,15 +345,16 @@ void BenchSgdKernel() {
const
T
*
grad_data
=
grad
.
data
<
T
>
();
const
int64_t
*
rows_data
=
rows
.
data
();
jit
::
sgd_attr_t
attr
(
param_h
,
grad_w
,
rows_size
,
grad_w
,
rows_size
);
BenchAllImpls
<
K
T
,
jit
::
SgdTuples
<
T
>
,
PlaceType
>
(
attr
,
&
lr
,
param_data
,
grad_data
,
rows_data
,
param_data
,
&
attr
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
attr
,
&
lr
,
param_data
,
grad_data
,
rows_data
,
param_data
,
&
attr
);
}
}
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchMatMulKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelMatMul
()
{
using
T
=
typename
KernelTuple
::
data_type
;
for
(
int
m
:
{
1
,
2
,
3
,
4
})
{
for
(
int
n
:
TestSizes
())
{
for
(
int
k
:
TestSizes
())
{
...
...
@@ -386,15 +368,16 @@ void BenchMatMulKernel() {
const
T
*
b_data
=
b
.
data
<
T
>
();
T
*
c_data
=
c
.
mutable_data
<
T
>
(
PlaceType
());
const
jit
::
matmul_attr_t
attr
{
m
,
n
,
k
};
BenchAllImpls
<
K
T
,
jit
::
MatMulTuples
<
T
>
,
PlaceType
>
(
attr
,
a_data
,
b
_data
,
c_data
,
&
attr
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
attr
,
a_data
,
b_data
,
c
_data
,
&
attr
);
}
}
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchSoftmaxKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelSoftmax
()
{
using
T
=
typename
KernelTuple
::
data_type
;
for
(
int
bs
:
{
1
,
2
,
10
})
{
for
(
int
n
:
TestSizes
())
{
Tensor
x
,
y
;
...
...
@@ -403,14 +386,14 @@ void BenchSoftmaxKernel() {
RandomVec
<
T
>
(
bs
*
n
,
x
.
mutable_data
<
T
>
(
PlaceType
()),
-
2.
f
,
2.
f
);
const
T
*
x_data
=
x
.
data
<
T
>
();
T
*
y_data
=
y
.
mutable_data
<
T
>
(
PlaceType
());
BenchAllImpls
<
KT
,
jit
::
SoftmaxTuples
<
T
>
,
PlaceType
>
(
n
,
x_data
,
y_data
,
n
,
bs
);
BenchAllImpls
<
KernelTuple
,
PlaceType
>
(
n
,
x_data
,
y_data
,
n
,
bs
);
}
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchLayerNormKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelLayerNorm
()
{
using
T
=
typename
KernelTuple
::
data_type
;
const
T
epsilon
=
9.99999975e-06
;
for
(
int
n
:
{
1
,
2
,
10
})
{
for
(
int
x_dim_0
:
{
1
,
9
,
17
,
50
})
{
...
...
@@ -439,16 +422,17 @@ void BenchLayerNormKernel() {
T
*
var_data
=
var
.
data
<
T
>
();
T
*
out_data
=
out
.
mutable_data
<
T
>
(
PlaceType
());
BenchAllImpls
<
K
T
,
jit
::
LayerNormTuples
<
T
>
,
PlaceType
>
(
right
,
x_data
,
out_data
,
mean_data
,
var_data
,
scale_data
,
bias
_data
,
left
,
epsilon
,
right
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
right
,
x_data
,
out_data
,
mean_data
,
var_data
,
scale
_data
,
bias_data
,
left
,
epsilon
,
right
);
}
}
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchCRFDecodingKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelCRFDecoding
()
{
using
T
=
typename
KernelTuple
::
data_type
;
constexpr
int
state_trans_base_idx
=
2
;
for
(
int
seq_len
:
{
1
,
11
,
17
,
50
})
{
for
(
int
tag_num
:
TestSizes
())
{
...
...
@@ -468,14 +452,15 @@ void BenchCRFDecodingKernel() {
T
*
alpha_data
=
alpha
.
mutable_data
<
T
>
(
PlaceType
());
int
*
track_data
=
track
.
mutable_data
<
int
>
(
PlaceType
());
BenchAllImpls
<
K
T
,
jit
::
CRFDecodingTuples
<
T
>
,
PlaceType
>
(
tag_num
,
seq_len
,
x_data
,
w_data
,
alpha_data
,
track_data
,
tag_num
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
tag_num
,
seq_len
,
x_data
,
w_data
,
alpha_data
,
track_data
,
tag_num
);
}
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchVBroadcastKernel
()
{
template
<
typename
KernelTuple
,
typename
PlaceType
>
void
BenchKernelVBroadcast
()
{
using
T
=
typename
KernelTuple
::
data_type
;
for
(
int64_t
w
:
{
1
,
16
,
64
,
100
,
256
})
{
Tensor
x
;
x
.
Resize
({
w
});
...
...
@@ -485,78 +470,86 @@ void BenchVBroadcastKernel() {
Tensor
y
;
y
.
Resize
({
h
*
w
});
T
*
y_data
=
y
.
mutable_data
<
T
>
(
PlaceType
());
BenchAllImpls
<
K
T
,
jit
::
VBroadcastTuples
<
T
>
,
PlaceType
>
(
w
,
x_data
,
y_data
,
static_cast
<
int64_t
>
(
h
),
w
);
BenchAllImpls
<
K
ernelTuple
,
PlaceType
>
(
w
,
x_data
,
y_data
,
static_cast
<
int64_t
>
(
h
),
w
);
}
}
}
using
T
=
float
;
using
CPUPlace
=
paddle
::
platform
::
CPUPlace
;
#define BenchKernelVMul BenchKernelXYZN
#define BenchKernelVAdd BenchKernelXYZN
#define BenchKernelVAddRelu BenchKernelXYZN
#define BenchKernelVSub BenchKernelXYZN
// xyzn
BENCH_FP32_CPU
(
kVMul
)
{
BenchXYZNKernel
<
jit
::
kVMul
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVAdd
)
{
BenchXYZNKernel
<
jit
::
kVAdd
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVAddRelu
)
{
BenchXYZNKernel
<
jit
::
kVAddRelu
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVSub
)
{
BenchXYZNKernel
<
jit
::
kVSub
,
T
,
CPUPlace
>
();
}
#define BenchKernelVScal BenchKernelAXYN
#define BenchKernelVAddBias BenchKernelAXYN
// axyn
BENCH_FP32_CPU
(
kVScal
)
{
BenchAXYNKernel
<
jit
::
kVScal
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVAddBias
)
{
BenchAXYNKernel
<
jit
::
kVAddBias
,
T
,
CPUPlace
>
();
}
#define BenchKernelVRelu BenchKernelXYN
#define BenchKernelVIdentity BenchKernelXYN
#define BenchKernelVSquare BenchKernelXYN
#define BenchKernelVExp BenchKernelXYN
#define BenchKernelVSigmoid BenchKernelXYN
#define BenchKernelVTanh BenchKernelXYN
#define BenchKernelVCopy BenchKernelXYN
// xrn
BENCH_FP32_CPU
(
kHSum
)
{
BenchXRNKernel
<
jit
::
kHSum
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kHMax
)
{
BenchXRNKernel
<
jit
::
kHMax
,
T
,
CPUPlace
>
();
}
#define BenchKernelHMax BenchKernelXRN
#define BenchKernelHSum BenchKernelXRN
// xyn
BENCH_FP32_CPU
(
kVRelu
)
{
BenchXYNKernel
<
jit
::
kVRelu
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVIdentity
)
{
BenchXYNKernel
<
jit
::
kVIdentity
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVSquare
)
{
BenchXYNKernel
<
jit
::
kVSquare
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVExp
)
{
BenchXYNKernel
<
jit
::
kVExp
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVSigmoid
)
{
BenchXYNKernel
<
jit
::
kVSigmoid
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVTanh
)
{
BenchXYNKernel
<
jit
::
kVTanh
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVCopy
)
{
BenchXYNKernel
<
jit
::
kVCopy
,
T
,
CPUPlace
>
();
}
// lstm and peephole
BENCH_FP32_CPU
(
kLSTMCtHt
)
{
BenchLSTMKernel
<
jit
::
kLSTMCtHt
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kLSTMC1H1
)
{
BenchLSTMKernel
<
jit
::
kLSTMC1H1
,
T
,
CPUPlace
>
();
}
// gru functions
BENCH_FP32_CPU
(
kGRUH1
)
{
BenchGRUKernel
<
jit
::
kGRUH1
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kGRUHtPart1
)
{
BenchGRUKernel
<
jit
::
kGRUHtPart1
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kGRUHtPart2
)
{
BenchGRUKernel
<
jit
::
kGRUHtPart2
,
T
,
CPUPlace
>
();
}
// seq pool function
BENCH_FP32_CPU
(
kSeqPool
)
{
BenchSeqPoolKernel
<
jit
::
kSeqPool
,
T
,
CPUPlace
>
();
}
// embedding seq pool function
BENCH_FP32_CPU
(
kEmbSeqPool
)
{
BenchEmbSeqPoolKernel
<
jit
::
kEmbSeqPool
,
T
,
CPUPlace
>
();
}
#define BenchKernelLSTMCtHt BenchKernelLSTM
#define BenchKernelLSTMC1H1 BenchKernelLSTM
// sgd function
BENCH_FP32_CPU
(
kSgd
)
{
BenchSgdKernel
<
jit
::
kSgd
,
T
,
CPUPlace
>
();
}
#define BenchKernelGRUH1 BenchKernelGRU
#define BenchKernelGRUHtPart1 BenchKernelGRU
#define BenchKernelGRUHtPart2 BenchKernelGRU
// matmul
BENCH_FP32_CPU
(
kMatMul
)
{
BenchMatMulKernel
<
jit
::
kMatMul
,
T
,
CPUPlace
>
();
}
using
CPUPlace
=
paddle
::
platform
::
CPUPlace
;
// softmax
BENCH_FP32_CPU
(
kSoftmax
)
{
BenchSoftmaxKernel
<
jit
::
kSoftmax
,
T
,
CPUPlace
>
();
}
#define BENCH_FP32_CPU(name) \
BENCH_JITKERNEL(name, FP32, CPU) { \
BenchKernel##name<jit::name##Tuple<float>, CPUPlace>(); \
}
// layernorm
BENCH_FP32_CPU
(
kLayerNorm
)
{
BenchLayerNormKernel
<
jit
::
kLayerNorm
,
T
,
CPUPlace
>
();
}
// xyzn
BENCH_FP32_CPU
(
VMul
);
BENCH_FP32_CPU
(
VAdd
);
BENCH_FP32_CPU
(
VAddRelu
);
BENCH_FP32_CPU
(
VSub
);
// crfdecoding
BENCH_FP32_CPU
(
kCRFDecoding
)
{
BenchCRFDecodingKernel
<
jit
::
kCRFDecoding
,
T
,
CPUPlace
>
();
}
// axyn
BENCH_FP32_CPU
(
VScal
);
BENCH_FP32_CPU
(
VAddBias
);
// vbroadcast function
BENCH_FP32_CPU
(
kVBroadcast
)
{
BenchVBroadcastKernel
<
jit
::
kVBroadcast
,
T
,
CPUPlace
>
();
}
// xyn
BENCH_FP32_CPU
(
VRelu
);
BENCH_FP32_CPU
(
VIdentity
);
BENCH_FP32_CPU
(
VSquare
);
BENCH_FP32_CPU
(
VExp
);
BENCH_FP32_CPU
(
VSigmoid
);
BENCH_FP32_CPU
(
VTanh
);
BENCH_FP32_CPU
(
VCopy
);
// xrn
BENCH_FP32_CPU
(
HMax
);
BENCH_FP32_CPU
(
HSum
);
// LSTM
BENCH_FP32_CPU
(
LSTMCtHt
);
BENCH_FP32_CPU
(
LSTMC1H1
);
// GRU
BENCH_FP32_CPU
(
GRUH1
);
BENCH_FP32_CPU
(
GRUHtPart1
);
BENCH_FP32_CPU
(
GRUHtPart2
);
BENCH_FP32_CPU
(
LayerNorm
);
BENCH_FP32_CPU
(
CRFDecoding
);
BENCH_FP32_CPU
(
SeqPool
);
BENCH_FP32_CPU
(
EmbSeqPool
);
BENCH_FP32_CPU
(
MatMul
);
BENCH_FP32_CPU
(
Softmax
);
BENCH_FP32_CPU
(
Sgd
);
BENCH_FP32_CPU
(
VBroadcast
);
// Benchmark all jit kernels including jitcode, mkl and refer.
// To use this tool, run command: ./benchmark [options...]
...
...
paddle/fluid/operators/jit/gen/act.cc
浏览文件 @
ac0e0f51
...
...
@@ -13,6 +13,7 @@
* limitations under the License. */
#include "paddle/fluid/operators/jit/gen/act.h"
#include <memory>
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
...
...
@@ -81,7 +82,7 @@ void VActJitCode::genCode() {
#define DECLARE_ACT_CREATOR(name) \
class name##Creator : public JitCodeCreator<int> { \
public: \
bool
UseMe(const int& attr) const override;
\
bool
CanBeUsed(const int& attr) const override;
\
size_t CodeSize(const int& d) const override; \
std::unique_ptr<GenBase> CreateJitCode(const int& attr) const override { \
return make_unique<name##JitCode>(attr, CodeSize(attr)); \
...
...
@@ -96,27 +97,27 @@ DECLARE_ACT_CREATOR(VSigmoid);
DECLARE_ACT_CREATOR
(
VTanh
);
// TODO(TJ): tuning use me
bool
VReluCreator
::
UseMe
(
const
int
&
d
)
const
{
bool
VReluCreator
::
CanBeUsed
(
const
int
&
d
)
const
{
return
platform
::
MayIUse
(
platform
::
avx
);
}
bool
VSquareCreator
::
UseMe
(
const
int
&
d
)
const
{
bool
VSquareCreator
::
CanBeUsed
(
const
int
&
d
)
const
{
return
platform
::
MayIUse
(
platform
::
avx
);
}
bool
VIdentityCreator
::
UseMe
(
const
int
&
d
)
const
{
bool
VIdentityCreator
::
CanBeUsed
(
const
int
&
d
)
const
{
return
platform
::
MayIUse
(
platform
::
avx
);
}
bool
VExpCreator
::
UseMe
(
const
int
&
d
)
const
{
bool
VExpCreator
::
CanBeUsed
(
const
int
&
d
)
const
{
return
platform
::
MayIUse
(
platform
::
avx
)
&&
d
<
32
;
}
bool
VSigmoidCreator
::
UseMe
(
const
int
&
d
)
const
{
bool
VSigmoidCreator
::
CanBeUsed
(
const
int
&
d
)
const
{
return
platform
::
MayIUse
(
platform
::
avx
);
}
bool
VTanhCreator
::
UseMe
(
const
int
&
d
)
const
{
bool
VTanhCreator
::
CanBeUsed
(
const
int
&
d
)
const
{
return
platform
::
MayIUse
(
platform
::
avx
);
}
...
...
paddle/fluid/operators/jit/gen/blas.cc
浏览文件 @
ac0e0f51
...
...
@@ -13,6 +13,7 @@
* limitations under the License. */
#include "paddle/fluid/operators/jit/gen/blas.h"
#include <memory>
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
...
...
@@ -142,7 +143,7 @@ void NCHW16CMulNCJitCode::genCode() {
class
NCHW16CMulNCCreator
:
public
JitCodeCreator
<
int
>
{
public:
bool
UseMe
(
const
int
&
attr
)
const
override
{
bool
CanBeUsed
(
const
int
&
attr
)
const
override
{
return
platform
::
MayIUse
(
platform
::
avx512f
);
}
size_t
CodeSize
(
const
int
&
d
)
const
override
{
return
256
*
1024
;
}
...
...
@@ -154,7 +155,7 @@ class NCHW16CMulNCCreator : public JitCodeCreator<int> {
#define DECLARE_BLAS_CREATOR(name) \
class name##Creator : public JitCodeCreator<int> { \
public: \
bool
UseMe(const int& attr) const override {
\
bool
CanBeUsed(const int& attr) const override {
\
return platform::MayIUse(platform::avx) && attr <= 1024; \
} \
size_t CodeSize(const int& d) const override { \
...
...
paddle/fluid/operators/jit/gen/embseqpool.cc
浏览文件 @
ac0e0f51
...
...
@@ -14,6 +14,7 @@
#include "paddle/fluid/operators/jit/gen/embseqpool.h"
#include <stddef.h> // offsetof
#include <memory>
#include <vector>
#include "paddle/fluid/operators/jit/gen/act.h" // for exp_float_consts ones
#include "paddle/fluid/operators/jit/registry.h"
...
...
@@ -121,7 +122,7 @@ void EmbSeqPoolJitCode::genCode() {
class
EmbSeqPoolCreator
:
public
JitCodeCreator
<
emb_seq_pool_attr_t
>
{
public:
bool
UseMe
(
const
emb_seq_pool_attr_t
&
attr
)
const
override
{
bool
CanBeUsed
(
const
emb_seq_pool_attr_t
&
attr
)
const
override
{
return
platform
::
MayIUse
(
platform
::
avx
)
&&
attr
.
table_width
%
YMM_FLOAT_BLOCK
==
0
;
}
...
...
paddle/fluid/operators/jit/gen/gru.cc
浏览文件 @
ac0e0f51
...
...
@@ -14,6 +14,7 @@
#include "paddle/fluid/operators/jit/gen/gru.h"
#include <stddef.h> // offsetof
#include <memory>
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
...
...
@@ -86,7 +87,7 @@ void GRUJitCode::genCode() {
class name##Creator : public JitCodeCreator<gru_attr_t> { \
public: \
/* TODO(TJ): enable more */
\
bool
UseMe(const gru_attr_t& attr) const override {
\
bool
CanBeUsed(const gru_attr_t& attr) const override {
\
return platform::MayIUse(platform::avx) && attr.d % 8 == 0; \
} \
size_t CodeSize(const gru_attr_t& attr) const override { \
...
...
paddle/fluid/operators/jit/gen/hopv.cc
浏览文件 @
ac0e0f51
...
...
@@ -13,6 +13,7 @@
* limitations under the License. */
#include "paddle/fluid/operators/jit/gen/hopv.h"
#include <memory>
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
...
...
@@ -76,7 +77,7 @@ void HOPVJitCode::genCode() {
#define DECLARE_HOP_CREATOR(name) \
class name##Creator : public JitCodeCreator<int> { \
public: \
bool
UseMe(const int& attr) const override {
\
bool
CanBeUsed(const int& attr) const override {
\
return platform::MayIUse(platform::avx); \
} \
size_t CodeSize(const int& d) const override { \
...
...
paddle/fluid/operators/jit/gen/jitcode.h
浏览文件 @
ac0e0f51
...
...
@@ -73,7 +73,7 @@ class JitCode : public GenBase, public Xbyak::CodeGenerator {
virtual
void
genCode
()
=
0
;
size_t
getSize
()
const
override
{
return
CodeGenerator
::
getSize
();
}
const
unsigned
char
*
getCodeInternal
()
override
{
const
unsigned
char
*
getCodeInternal
()
const
override
{
const
Xbyak
::
uint8
*
code
=
CodeGenerator
::
getCode
();
return
code
;
}
...
...
paddle/fluid/operators/jit/gen/lstm.cc
浏览文件 @
ac0e0f51
...
...
@@ -14,6 +14,7 @@
#include "paddle/fluid/operators/jit/gen/lstm.h"
#include <stddef.h> // offsetof
#include <memory>
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
...
...
@@ -114,7 +115,7 @@ void LSTMJitCode::genCode() {
class name##Creator : public JitCodeCreator<lstm_attr_t> { \
public: \
/* TODO(TJ): enable more */
\
bool
UseMe(const lstm_attr_t& attr) const override {
\
bool
CanBeUsed(const lstm_attr_t& attr) const override {
\
return platform::MayIUse(platform::avx) && attr.d % 8 == 0; \
} \
size_t CodeSize(const lstm_attr_t& attr) const override { \
...
...
paddle/fluid/operators/jit/gen/matmul.cc
浏览文件 @
ac0e0f51
...
...
@@ -14,8 +14,8 @@
#include "paddle/fluid/operators/jit/gen/matmul.h"
#include <stddef.h> // offsetof
#include <memory>
#include <vector>
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
...
...
@@ -98,7 +98,7 @@ void MatMulJitCode::genCode() {
class
MatMulCreator
:
public
JitCodeCreator
<
matmul_attr_t
>
{
public:
bool
UseMe
(
const
matmul_attr_t
&
attr
)
const
override
{
bool
CanBeUsed
(
const
matmul_attr_t
&
attr
)
const
override
{
return
attr
.
m
==
1
&&
platform
::
MayIUse
(
platform
::
avx512f
)
&&
attr
.
n
%
ZMM_FLOAT_BLOCK
==
0
&&
attr
.
k
<
512
;
}
...
...
paddle/fluid/operators/jit/gen/seqpool.cc
浏览文件 @
ac0e0f51
...
...
@@ -13,6 +13,7 @@
* limitations under the License. */
#include "paddle/fluid/operators/jit/gen/seqpool.h"
#include <memory>
#include "paddle/fluid/operators/jit/gen/act.h" // for exp_float_consts ones
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
...
...
@@ -57,7 +58,7 @@ void SeqPoolJitCode::genCode() {
class
SeqPoolCreator
:
public
JitCodeCreator
<
seq_pool_attr_t
>
{
public:
bool
UseMe
(
const
seq_pool_attr_t
&
attr
)
const
override
{
bool
CanBeUsed
(
const
seq_pool_attr_t
&
attr
)
const
override
{
return
platform
::
MayIUse
(
platform
::
avx
);
}
size_t
CodeSize
(
const
seq_pool_attr_t
&
attr
)
const
override
{
...
...
paddle/fluid/operators/jit/gen/sgd.cc
浏览文件 @
ac0e0f51
...
...
@@ -14,6 +14,7 @@
#include "paddle/fluid/operators/jit/gen/sgd.h"
#include <stddef.h> // offsetof
#include <memory>
#include <vector>
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
...
...
@@ -104,7 +105,7 @@ void SgdJitCode::genCode() {
class
SgdCreator
:
public
JitCodeCreator
<
sgd_attr_t
>
{
public:
bool
UseMe
(
const
sgd_attr_t
&
attr
)
const
override
{
bool
CanBeUsed
(
const
sgd_attr_t
&
attr
)
const
override
{
return
platform
::
MayIUse
(
platform
::
avx
)
&&
attr
.
grad_width
%
YMM_FLOAT_BLOCK
==
0
;
}
...
...
paddle/fluid/operators/jit/gen/vbroadcast.cc
浏览文件 @
ac0e0f51
...
...
@@ -69,7 +69,7 @@ void VBroadcastJitCode::genCode() {
class
VBroadcastCreator
:
public
JitCodeCreator
<
int64_t
>
{
public:
bool
UseMe
(
const
int64_t
&
w
)
const
override
{
bool
CanBeUsed
(
const
int64_t
&
w
)
const
override
{
return
platform
::
MayIUse
(
platform
::
avx
)
&&
w
%
YMM_FLOAT_BLOCK
==
0
;
}
size_t
CodeSize
(
const
int64_t
&
w
)
const
override
{
...
...
paddle/fluid/operators/jit/gen_base.cc
浏览文件 @
ac0e0f51
...
...
@@ -31,7 +31,7 @@ namespace paddle {
namespace
operators
{
namespace
jit
{
// refer do not need
useme
, it would be the last one.
// refer do not need
CanBeUsed
, it would be the last one.
void
GenBase
::
dumpCode
(
const
unsigned
char
*
code
)
const
{
if
(
code
)
{
static
int
counter
=
0
;
...
...
paddle/fluid/operators/jit/gen_base.h
浏览文件 @
ac0e0f51
...
...
@@ -31,9 +31,10 @@ class GenBase : public Kernel {
virtual
~
GenBase
()
=
default
;
virtual
std
::
string
name
()
const
=
0
;
virtual
size_t
getSize
()
const
=
0
;
virtual
const
unsigned
char
*
getCodeInternal
()
=
0
;
virtual
const
unsigned
char
*
getCodeInternal
()
const
=
0
;
const
char
*
ImplType
()
const
override
{
return
"JitCode"
;
}
template
<
typename
Func
>
Func
getCode
()
{
Func
getCode
()
const
{
const
unsigned
char
*
code
=
this
->
getCodeInternal
();
if
(
FLAGS_dump_jitcode
)
{
this
->
dumpCode
(
code
);
...
...
@@ -65,7 +66,7 @@ class JitCodeCreator : public GenCreator {
virtual
~
JitCodeCreator
()
=
default
;
// condition when this jit code can be used.
virtual
bool
UseMe
(
const
Attr
&
attr
)
const
=
0
;
virtual
bool
CanBeUsed
(
const
Attr
&
attr
)
const
=
0
;
// estimate this code size
virtual
size_t
CodeSize
(
const
Attr
&
attr
)
const
=
0
;
...
...
paddle/fluid/operators/jit/helper.h
浏览文件 @
ac0e0f51
...
...
@@ -16,6 +16,8 @@
#include <iostream>
#include <string>
#include <unordered_map>
#include <utility> // for std::move
#include <vector>
#include "paddle/fluid/operators/jit/gen_base.h"
#include "paddle/fluid/operators/jit/kernel_base.h"
...
...
@@ -27,35 +29,34 @@ namespace paddle {
namespace
operators
{
namespace
jit
{
template
<
KernelType
KT
,
typename
KernelTuples
,
typename
PlaceType
>
template
<
typename
KernelTuple
,
typename
PlaceType
>
inline
typename
std
::
enable_if
<
std
::
is_same
<
typename
KernelTuple
s
::
data_type
,
float
>::
value
&&
std
::
is_same
<
typename
KernelTuple
::
data_type
,
float
>::
value
&&
std
::
is_same
<
PlaceType
,
platform
::
CPUPlace
>::
value
,
typename
KernelTuples
::
func_type
>::
type
GetJitCode
(
const
typename
KernelTuples
::
attr_type
&
attr
)
{
using
Func
=
typename
KernelTuples
::
func_type
;
using
Attr
=
typename
KernelTuples
::
attr_type
;
size_t
key
=
JitCodeKey
<
Attr
>
(
attr
);
auto
&
codes
=
JitCodePool
<
KT
>
().
Instance
();
const
Kernel
*>::
type
GetJitCode
(
const
typename
KernelTuple
::
attr_type
&
attr
)
{
using
Attr
=
typename
KernelTuple
::
attr_type
;
int64_t
key
=
JitCodeKey
<
Attr
>
(
attr
);
auto
&
codes
=
JitCodePool
<
KernelTuple
::
kernel_type
>::
Instance
();
if
(
codes
.
Has
(
key
))
{
return
codes
.
AllKernels
().
at
(
key
)
->
template
getCode
<
Func
>
();
return
codes
.
AllKernels
().
at
(
key
)
.
get
();
}
// creator is not related with attr, so can use KernelKey as key
KernelKey
kkey
(
K
T
,
PlaceType
());
KernelKey
kkey
(
K
ernelTuple
::
kernel_type
,
PlaceType
());
// pool: (KernelKey(type, place), vector<GenCreatorPtr>)
auto
&
creator_map
=
JitCodeCreatorPool
().
Instance
().
AllCreators
();
auto
&
creator_map
=
JitCodeCreatorPool
::
Instance
().
AllCreators
();
auto
iter
=
creator_map
.
find
(
kkey
);
if
(
iter
!=
creator_map
.
end
())
{
auto
&
creators
=
iter
->
second
;
for
(
auto
&
cur
:
creators
)
{
auto
i
=
dynamic_cast
<
const
JitCodeCreator
<
Attr
>*>
(
cur
.
get
());
if
(
i
&&
i
->
UseMe
(
attr
))
{
if
(
i
&&
i
->
CanBeUsed
(
attr
))
{
auto
p
=
i
->
CreateJitCode
(
attr
);
if
(
p
)
{
auto
f
=
p
->
template
getCode
<
Func
>
();
auto
res
=
p
.
get
();
codes
.
Insert
(
key
,
std
::
move
(
p
));
return
f
;
return
res
;
}
}
}
...
...
@@ -63,87 +64,153 @@ GetJitCode(const typename KernelTuples::attr_type& attr) {
return
nullptr
;
}
template
<
KernelType
KT
,
typename
KernelTuples
,
typename
PlaceType
>
template
<
typename
KernelTuple
,
typename
PlaceType
>
inline
typename
std
::
enable_if
<
!
std
::
is_same
<
typename
KernelTuple
s
::
data_type
,
float
>::
value
||
!
std
::
is_same
<
typename
KernelTuple
::
data_type
,
float
>::
value
||
!
std
::
is_same
<
PlaceType
,
platform
::
CPUPlace
>::
value
,
typename
KernelTuples
::
func_type
>::
type
GetJitCode
(
const
typename
KernelTuple
s
::
attr_type
&
attr
)
{
const
Kernel
*
>::
type
GetJitCode
(
const
typename
KernelTuple
::
attr_type
&
attr
)
{
return
nullptr
;
}
// Refer code do not related with attr, which is just for cast
// Refer is always on CPUPlace
template
<
KernelType
KT
,
typename
KernelTuples
>
inline
typename
KernelTuples
::
func_type
GetRefer
()
{
auto
&
ref_pool
=
ReferKernelPool
().
Instance
().
AllKernels
();
KernelKey
kkey
(
K
T
,
platform
::
CPUPlace
());
template
<
typename
KernelTuple
>
inline
const
Kernel
*
GetReferKernel
()
{
auto
&
ref_pool
=
ReferKernelPool
::
Instance
().
AllKernels
();
KernelKey
kkey
(
K
ernelTuple
::
kernel_type
,
platform
::
CPUPlace
());
auto
ref_iter
=
ref_pool
.
find
(
kkey
);
PADDLE_ENFORCE
(
ref_iter
!=
ref_pool
.
end
(),
"Every Kernel should have reference function."
);
auto
&
ref_impls
=
ref_iter
->
second
;
for
(
auto
&
impl
:
ref_impls
)
{
auto
i
=
dynamic_cast
<
const
ReferKernel
<
KernelTuple
s
>*>
(
impl
.
get
());
auto
i
=
dynamic_cast
<
const
ReferKernel
<
KernelTuple
>*>
(
impl
.
get
());
if
(
i
)
{
return
i
->
GetFunc
()
;
return
i
;
}
}
return
nullptr
;
}
template
<
KernelType
KT
,
typename
KernelTuples
,
typename
PlaceType
=
platform
::
CPUPlace
>
typename
KernelTuples
::
func_type
Get
(
const
typename
KernelTuples
::
attr_type
&
attr
)
{
auto
jitfunc
=
GetJitCode
<
KT
,
KernelTuples
,
PlaceType
>
(
attr
);
if
(
jitfunc
)
{
return
jitfunc
;
template
<
typename
KernelTuple
>
inline
typename
KernelTuple
::
func_type
GetReferFunc
()
{
auto
ker
=
GetReferKernel
<
KernelTuple
>
();
auto
p
=
dynamic_cast
<
const
ReferKernel
<
KernelTuple
>*>
(
ker
);
PADDLE_ENFORCE
(
p
,
"The Refer kernel should exsit"
);
return
p
->
GetFunc
();
}
// Return all Kernels that can be used
template
<
typename
KernelTuple
,
typename
PlaceType
>
std
::
vector
<
const
Kernel
*>
GetAllCandidateKernels
(
const
typename
KernelTuple
::
attr_type
&
attr
)
{
// the search order shoudl be jitcode > more > refer
std
::
vector
<
const
Kernel
*>
res
;
auto
jitker
=
GetJitCode
<
KernelTuple
,
PlaceType
>
(
attr
);
if
(
jitker
)
{
res
.
emplace_back
(
jitker
);
}
// pool: (KernelKey(type, place), vector<KernelPtr>)
KernelKey
kkey
(
K
T
,
PlaceType
());
auto
&
pool
=
KernelPool
().
Instance
().
AllKernels
();
//
more kernel
pool: (KernelKey(type, place), vector<KernelPtr>)
KernelKey
kkey
(
K
ernelTuple
::
kernel_type
,
PlaceType
());
auto
&
pool
=
KernelPool
::
Instance
().
AllKernels
();
auto
iter
=
pool
.
find
(
kkey
);
if
(
iter
!=
pool
.
end
())
{
auto
&
impls
=
iter
->
second
;
for
(
auto
&
impl
:
impls
)
{
auto
i
=
dynamic_cast
<
const
KernelMore
<
KernelTuple
s
>*>
(
impl
.
get
());
if
(
i
&&
i
->
UseMe
(
attr
))
{
re
turn
i
->
GetFunc
(
);
auto
i
=
dynamic_cast
<
const
KernelMore
<
KernelTuple
>*>
(
impl
.
get
());
if
(
i
&&
i
->
CanBeUsed
(
attr
))
{
re
s
.
emplace_back
(
i
);
}
}
}
// The last implementation should be reference function on CPUPlace.
return
GetRefer
<
KT
,
KernelTuples
>
();
auto
ref
=
GetReferKernel
<
KernelTuple
>
();
PADDLE_ENFORCE
(
ref
!=
nullptr
,
"Refer Kernel can not be empty."
);
res
.
emplace_back
(
ref
);
return
res
;
}
template
<
typename
KernelTuple
,
typename
PlaceType
=
platform
::
CPUPlace
>
std
::
vector
<
std
::
pair
<
std
::
string
,
typename
KernelTuple
::
func_type
>>
GetAllCandidateFuncsWithTypes
(
const
typename
KernelTuple
::
attr_type
&
attr
)
{
using
Func
=
typename
KernelTuple
::
func_type
;
auto
kers
=
GetAllCandidateKernels
<
KernelTuple
,
PlaceType
>
(
attr
);
std
::
vector
<
std
::
pair
<
std
::
string
,
Func
>>
res
;
for
(
auto
k
:
kers
)
{
std
::
string
name
=
k
->
ImplType
();
if
(
name
==
"JitCode"
)
{
auto
i
=
dynamic_cast
<
const
GenBase
*>
(
k
);
PADDLE_ENFORCE
(
i
,
"jitcode kernel cast can not fail."
);
res
.
emplace_back
(
std
::
make_pair
(
name
,
i
->
template
getCode
<
Func
>()));
}
else
{
auto
i
=
dynamic_cast
<
const
KernelMore
<
KernelTuple
>*>
(
k
);
PADDLE_ENFORCE
(
i
,
"kernel cast can not fail."
);
res
.
emplace_back
(
std
::
make_pair
(
name
,
i
->
GetFunc
()));
}
}
return
res
;
}
template
<
typename
KernelTuple
,
typename
PlaceType
=
platform
::
CPUPlace
>
std
::
vector
<
typename
KernelTuple
::
func_type
>
GetAllCandidateFuncs
(
const
typename
KernelTuple
::
attr_type
&
attr
)
{
auto
funcs
=
GetAllCandidateFuncsWithTypes
<
KernelTuple
,
PlaceType
>
(
attr
);
std
::
vector
<
typename
KernelTuple
::
func_type
>
res
;
for
(
auto
&
i
:
funcs
)
{
res
.
emplace_back
(
i
.
second
);
}
return
res
;
}
template
<
typename
KernelTuple
,
typename
PlaceType
=
platform
::
CPUPlace
>
typename
KernelTuple
::
func_type
GetDefaultBestFunc
(
const
typename
KernelTuple
::
attr_type
&
attr
)
{
auto
funcs
=
GetAllCandidateFuncs
<
KernelTuple
,
PlaceType
>
(
attr
);
PADDLE_ENFORCE_GE
(
funcs
.
size
(),
1UL
);
// Here could do some runtime benchmark of this attr and return the best one.
// But yet just get the first one as the default best one,
// which is searched in order and tuned by offline.
return
funcs
[
0
];
}
template
<
KernelType
KT
,
typename
KernelTuples
,
typename
PlaceType
>
template
<
typename
KernelTuple
,
typename
PlaceType
>
class
KernelFuncs
{
public:
KernelFuncs
()
=
default
;
static
KernelFuncs
&
Cache
()
{
static
thread_local
KernelFuncs
<
K
T
,
KernelTuples
,
PlaceType
>
g_func_cache
;
static
thread_local
KernelFuncs
<
K
ernelTuple
,
PlaceType
>
g_func_cache
;
return
g_func_cache
;
}
bool
Has
(
int
key
)
const
{
return
funcs_
.
find
(
key
)
!=
funcs_
.
end
();
}
void
Insert
(
int
key
,
typename
KernelTuples
::
func_type
func
)
{
funcs_
.
emplace
(
key
,
func
);
}
typename
KernelTuples
::
func_type
At
(
int
key
)
{
// the exposed interface to use
typename
KernelTuple
::
func_type
At
(
const
typename
KernelTuple
::
attr_type
&
attr
)
{
// Maybe here is not good enough, not all kernels should have jitcode
int64_t
key
=
JitCodeKey
<
typename
KernelTuple
::
attr_type
>
(
attr
);
if
(
Has
(
key
))
{
return
funcs_
.
at
(
key
);
}
auto
func
=
Get
<
KT
,
KernelTuples
,
PlaceType
>
(
key
);
// If do not have this attr in cache then get the default best
auto
func
=
GetDefaultBestFunc
<
KernelTuple
,
PlaceType
>
(
attr
);
Insert
(
key
,
func
);
return
func
;
}
typename
KernelTuple
::
func_type
operator
[](
const
typename
KernelTuple
::
attr_type
&
attr
)
{
return
At
(
attr
);
}
protected:
bool
Has
(
int64_t
key
)
const
{
return
funcs_
.
find
(
key
)
!=
funcs_
.
end
();
}
void
Insert
(
int64_t
key
,
typename
KernelTuple
::
func_type
func
)
{
funcs_
.
emplace
(
key
,
func
);
}
private:
std
::
unordered_map
<
int
,
typename
KernelTuples
::
func_type
>
funcs_
;
std
::
unordered_map
<
int
64_t
,
typename
KernelTuple
::
func_type
>
funcs_
;
DISABLE_COPY_AND_ASSIGN
(
KernelFuncs
);
};
...
...
paddle/fluid/operators/jit/kernel_base.h
浏览文件 @
ac0e0f51
...
...
@@ -62,26 +62,55 @@ typedef enum {
kSqrt
,
}
SeqPoolType
;
// x, y, z, n
template
<
typename
T
>
struct
XYZNTuple
s
{
struct
XYZNTuple
{
typedef
T
data_type
;
typedef
int
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
const
T
*
,
T
*
,
int
);
};
// a, x, y, n
template
<
typename
T
>
struct
AXYNTuple
s
:
public
XYZNTuples
<
T
>
{};
struct
AXYNTuple
:
public
XYZNTuple
<
T
>
{};
// x, y, n
template
<
typename
T
>
struct
XYNTuple
s
{
struct
XYNTuple
{
typedef
T
data_type
;
typedef
int
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
T
*
,
int
);
};
// x, return
and int
// x, return
ed value, n
template
<
typename
T
>
struct
XRNTuples
:
public
XYNTuples
<
T
>
{};
struct
XRNTuple
:
public
XYNTuple
<
T
>
{};
#define DECLARE_KERNELTUPLE(kernel_tuple, type) \
template <typename T> \
struct type##Tuple : public kernel_tuple<T> { \
static constexpr KernelType kernel_type = k##type; \
}
// Tuple should be corresponding to the KernelType
DECLARE_KERNELTUPLE
(
XYZNTuple
,
VMul
);
DECLARE_KERNELTUPLE
(
XYZNTuple
,
VAdd
);
DECLARE_KERNELTUPLE
(
XYZNTuple
,
VAddRelu
);
DECLARE_KERNELTUPLE
(
XYZNTuple
,
VSub
);
DECLARE_KERNELTUPLE
(
AXYNTuple
,
VScal
);
DECLARE_KERNELTUPLE
(
AXYNTuple
,
VAddBias
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VRelu
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VIdentity
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VSquare
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VExp
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VSigmoid
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VTanh
);
DECLARE_KERNELTUPLE
(
XYNTuple
,
VCopy
);
DECLARE_KERNELTUPLE
(
XRNTuple
,
HMax
);
DECLARE_KERNELTUPLE
(
XRNTuple
,
HSum
);
typedef
struct
{
void
*
gates
;
// gates: x_ch, x_ih, x_fh, x_oh
...
...
@@ -122,21 +151,31 @@ typedef struct rnn_attr_s gru_attr_t;
typedef
struct
lstm_attr_s
lstm_attr_t
;
template
<
typename
T
>
struct
LSTMTuple
s
{
struct
LSTMTuple
{
typedef
T
data_type
;
typedef
lstm_attr_t
attr_type
;
typedef
void
(
*
func_type
)(
lstm_t
*
,
const
lstm_attr_t
*
);
};
template
<
typename
T
>
struct
GRUTuple
s
{
struct
GRUTuple
{
typedef
T
data_type
;
typedef
gru_attr_t
attr_type
;
typedef
void
(
*
func_type
)(
gru_t
*
,
const
gru_attr_t
*
);
};
DECLARE_KERNELTUPLE
(
LSTMTuple
,
LSTMCtHt
);
DECLARE_KERNELTUPLE
(
LSTMTuple
,
LSTMC1H1
);
DECLARE_KERNELTUPLE
(
GRUTuple
,
GRUH1
);
DECLARE_KERNELTUPLE
(
GRUTuple
,
GRUHtPart1
);
DECLARE_KERNELTUPLE
(
GRUTuple
,
GRUHtPart2
);
#undef DECLARE_KERNELTUPLE
template
<
typename
T
>
struct
VBroadcastTuples
{
struct
VBroadcastTuple
{
static
constexpr
KernelType
kernel_type
=
kVBroadcast
;
typedef
T
data_type
;
typedef
int64_t
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
T
*
,
int64_t
,
int64_t
);
...
...
@@ -151,7 +190,8 @@ typedef struct seq_pool_attr_s {
}
seq_pool_attr_t
;
template
<
typename
T
>
struct
SeqPoolTuples
{
struct
SeqPoolTuple
{
static
constexpr
KernelType
kernel_type
=
kSeqPool
;
typedef
T
data_type
;
typedef
seq_pool_attr_t
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
T
*
,
const
seq_pool_attr_t
*
);
...
...
@@ -176,7 +216,8 @@ typedef struct emb_seq_pool_attr_s {
}
emb_seq_pool_attr_t
;
template
<
typename
T
>
struct
EmbSeqPoolTuples
{
struct
EmbSeqPoolTuple
{
static
constexpr
KernelType
kernel_type
=
kEmbSeqPool
;
typedef
T
data_type
;
typedef
emb_seq_pool_attr_t
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
const
int64_t
*
,
T
*
,
...
...
@@ -198,7 +239,8 @@ typedef struct sgd_attr_s {
}
sgd_attr_t
;
template
<
typename
T
>
struct
SgdTuples
{
struct
SgdTuple
{
static
constexpr
KernelType
kernel_type
=
kSgd
;
typedef
T
data_type
;
typedef
sgd_attr_t
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
const
T
*
,
const
T
*
,
const
int64_t
*
,
T
*
,
...
...
@@ -214,21 +256,24 @@ typedef struct matmul_attr_s {
}
matmul_attr_t
;
template
<
typename
T
>
struct
MatMulTuples
{
struct
MatMulTuple
{
static
constexpr
KernelType
kernel_type
=
kMatMul
;
typedef
T
data_type
;
typedef
matmul_attr_t
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
const
T
*
,
T
*
,
const
matmul_attr_t
*
);
};
template
<
typename
T
>
struct
CRFDecodingTuples
{
struct
CRFDecodingTuple
{
static
constexpr
KernelType
kernel_type
=
kCRFDecoding
;
typedef
T
data_type
;
typedef
int
attr_type
;
typedef
void
(
*
func_type
)(
const
int
,
const
T
*
,
const
T
*
,
T
*
,
int
*
,
int
);
};
template
<
typename
T
>
struct
LayerNormTuples
{
struct
LayerNormTuple
{
static
constexpr
KernelType
kernel_type
=
kLayerNorm
;
typedef
T
data_type
;
typedef
int
attr_type
;
typedef
void
(
*
func_type
)(
T
*
,
T
*
,
T
*
,
T
*
,
const
T
*
,
const
T
*
,
int
,
...
...
@@ -236,7 +281,8 @@ struct LayerNormTuples {
};
template
<
typename
T
>
struct
SoftmaxTuples
{
struct
SoftmaxTuple
{
static
constexpr
KernelType
kernel_type
=
kSoftmax
;
typedef
T
data_type
;
typedef
int
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
T
*
,
int
,
int
);
...
...
@@ -244,7 +290,8 @@ struct SoftmaxTuples {
// nChw16c = nChw16c .* NC
template
<
typename
T
>
struct
NCHW16CMulNCTuples
{
struct
NCHW16CMulNCTuple
{
static
constexpr
KernelType
kernel_type
=
kNCHW16CMulNC
;
typedef
T
data_type
;
typedef
int
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
const
T
*
,
T
*
,
int
,
int
);
...
...
@@ -255,28 +302,29 @@ class Kernel {
public:
Kernel
()
=
default
;
virtual
~
Kernel
()
=
default
;
virtual
const
char
*
ImplType
()
const
=
0
;
DISABLE_COPY_AND_ASSIGN
(
Kernel
);
};
template
<
typename
KernelTuple
s
>
template
<
typename
KernelTuple
>
class
KernelMore
:
public
Kernel
{
public:
using
T
=
typename
KernelTuple
s
::
data_type
;
using
Func
=
typename
KernelTuple
s
::
func_type
;
using
Attr
=
typename
KernelTuple
s
::
attr_type
;
using
T
=
typename
KernelTuple
::
data_type
;
using
Func
=
typename
KernelTuple
::
func_type
;
using
Attr
=
typename
KernelTuple
::
attr_type
;
virtual
Func
GetFunc
()
const
{
return
func
;
}
virtual
bool
UseMe
(
const
Attr
&
attr
)
const
=
0
;
virtual
const
char
*
ImplType
(
)
const
=
0
;
// specify this kernel can be used, means it should not fail if use it.
virtual
bool
CanBeUsed
(
const
Attr
&
attr
)
const
=
0
;
protected:
Func
func
{
nullptr
};
};
template
<
typename
KernelTuple
s
>
class
ReferKernel
:
public
KernelMore
<
KernelTuple
s
>
{
template
<
typename
KernelTuple
>
class
ReferKernel
:
public
KernelMore
<
KernelTuple
>
{
public:
// Refer code can always be used
bool
UseMe
(
const
typename
KernelTuples
::
attr_type
&
attr
)
const
override
{
bool
CanBeUsed
(
const
typename
KernelTuple
::
attr_type
&
attr
)
const
override
{
return
true
;
}
const
char
*
ImplType
()
const
override
{
return
"Refer"
;
}
...
...
paddle/fluid/operators/jit/kernel_key.cc
浏览文件 @
ac0e0f51
...
...
@@ -13,6 +13,7 @@
* limitations under the License. */
#include "paddle/fluid/operators/jit/kernel_key.h"
#include <xxhash.h> // XXH64: 13.8 GB/s
#include "paddle/fluid/platform/enforce.h"
namespace
paddle
{
...
...
@@ -20,71 +21,46 @@ namespace operators {
namespace
jit
{
template
<
>
size
_t
JitCodeKey
<
int
>
(
const
int
&
d
)
{
int64
_t
JitCodeKey
<
int
>
(
const
int
&
d
)
{
return
d
;
}
template
<
>
size
_t
JitCodeKey
<
int64_t
>
(
const
int64_t
&
d
)
{
int64
_t
JitCodeKey
<
int64_t
>
(
const
int64_t
&
d
)
{
return
d
;
}
// TODO(TJ): refine and benchmark JitCodeKey generatation
constexpr
int
act_type_shift
=
3
;
// suppot 2^3 act types
static
inline
int
act_type_convert
(
KernelType
type
)
{
if
(
type
==
kVIdentity
)
{
return
0
;
}
else
if
(
type
==
kVExp
)
{
return
1
;
}
else
if
(
type
==
kVRelu
)
{
return
2
;
}
else
if
(
type
==
kVSigmoid
)
{
return
3
;
}
else
if
(
type
==
kVTanh
)
{
return
4
;
}
PADDLE_THROW
(
"Unsupported act type %d"
,
type
);
return
0
;
}
template
<
>
size_t
JitCodeKey
<
lstm_attr_t
>
(
const
lstm_attr_t
&
attr
)
{
size_t
key
=
attr
.
d
;
int
gate_key
=
act_type_convert
(
attr
.
act_gate
)
<<
1
;
int
cand_key
=
act_type_convert
(
attr
.
act_cand
)
<<
(
1
+
act_type_shift
);
int
cell_key
=
act_type_convert
(
attr
.
act_cell
)
<<
(
1
+
act_type_shift
*
2
);
return
(
key
<<
(
1
+
act_type_shift
*
3
))
+
gate_key
+
cand_key
+
cell_key
+
attr
.
use_peephole
;
int64_t
JitCodeKey
<
gru_attr_t
>
(
const
gru_attr_t
&
attr
)
{
return
XXH64
(
&
attr
,
sizeof
(
gru_attr_t
),
0
);
}
template
<
>
size_t
JitCodeKey
<
gru_attr_t
>
(
const
gru_attr_t
&
attr
)
{
size_t
key
=
attr
.
d
;
return
(
key
<<
(
act_type_shift
*
2
))
+
act_type_convert
(
attr
.
act_gate
)
+
(
act_type_convert
(
attr
.
act_cand
)
<<
act_type_shift
);
int64_t
JitCodeKey
<
lstm_attr_t
>
(
const
lstm_attr_t
&
attr
)
{
int
keys
[
5
]
=
{
attr
.
d
,
static_cast
<
int
>
(
attr
.
act_gate
),
static_cast
<
int
>
(
attr
.
act_cand
),
static_cast
<
int
>
(
attr
.
act_cell
),
static_cast
<
int
>
(
attr
.
use_peephole
)};
return
XXH64
(
keys
,
sizeof
(
int
)
*
5
,
0
);
}
template
<
>
size_t
JitCodeKey
<
seq_pool_attr_t
>
(
const
seq_pool_attr_t
&
attr
)
{
size_t
key
=
attr
.
w
;
constexpr
int
pool_type_shift
=
3
;
return
(
key
<<
pool_type_shift
)
+
static_cast
<
int
>
(
attr
.
type
);
int64_t
JitCodeKey
<
seq_pool_attr_t
>
(
const
seq_pool_attr_t
&
attr
)
{
int
keys
[
2
]
=
{
attr
.
w
,
static_cast
<
int
>
(
attr
.
type
)};
return
XXH64
(
keys
,
sizeof
(
int
)
*
2
,
0
);
}
template
<
>
size_t
JitCodeKey
<
matmul_attr_t
>
(
const
matmul_attr_t
&
attr
)
{
size_t
key
=
attr
.
m
;
constexpr
int
shift
=
21
;
return
(
key
<<
shift
*
2
)
+
((
static_cast
<
size_t
>
(
attr
.
n
))
<<
shift
)
+
attr
.
k
;
int64_t
JitCodeKey
<
matmul_attr_t
>
(
const
matmul_attr_t
&
attr
)
{
return
XXH64
(
&
attr
,
sizeof
(
int
)
*
3
,
0
);
// m, n, k
}
template
<
>
size
_t
JitCodeKey
<
emb_seq_pool_attr_t
>
(
const
emb_seq_pool_attr_t
&
attr
)
{
int64
_t
JitCodeKey
<
emb_seq_pool_attr_t
>
(
const
emb_seq_pool_attr_t
&
attr
)
{
return
attr
.
table_width
;
}
template
<
>
size
_t
JitCodeKey
<
sgd_attr_t
>
(
const
sgd_attr_t
&
attr
)
{
int64
_t
JitCodeKey
<
sgd_attr_t
>
(
const
sgd_attr_t
&
attr
)
{
return
attr
.
grad_width
;
}
...
...
paddle/fluid/operators/jit/kernel_key.h
浏览文件 @
ac0e0f51
...
...
@@ -46,7 +46,7 @@ struct KernelKey {
// Every JitCode should have a method to get the key from attribution
template
<
typename
Attr
>
size
_t
JitCodeKey
(
const
Attr
&
attr
);
int64
_t
JitCodeKey
(
const
Attr
&
attr
);
}
// namespace jit
}
// namespace operators
...
...
paddle/fluid/operators/jit/kernel_pool.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/more/intrinsic/crf_decoding.cc
浏览文件 @
ac0e0f51
...
...
@@ -161,7 +161,7 @@ void CRFDecoding(const int seq_len, const float* x, const float* w,
}
}
bool
CRFDecodingKernel
::
UseMe
(
const
int
&
d
)
const
{
bool
CRFDecodingKernel
::
CanBeUsed
(
const
int
&
d
)
const
{
#ifdef __AVX512F__
constexpr
int
block
=
ZMM_FLOAT_BLOCK
;
#else
...
...
paddle/fluid/operators/jit/more/intrinsic/crf_decoding.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/more/intrinsic/layer_norm.cc
浏览文件 @
ac0e0f51
...
...
@@ -153,7 +153,7 @@ void LayerNorm(float* x, float* out, float* mean, float* var,
}
}
bool
LayerNormKernel
::
UseMe
(
const
int
&
d
)
const
{
bool
LayerNormKernel
::
CanBeUsed
(
const
int
&
d
)
const
{
return
platform
::
MayIUse
(
platform
::
avx
)
&&
d
>=
YMM_FLOAT_BLOCK
;
}
...
...
paddle/fluid/operators/jit/more/intrinsic/layer_norm.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/more/mix/mix.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/more/mix/mix.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/more/mkl/mkl.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/more/mkl/mkl.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/refer/refer.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/refer/refer.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/registry.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/jit/test.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/layer_norm_op.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/math/fc_compute.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/math/softmax_impl.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/ngraph/ngraph_engine.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/ngraph/ngraph_engine.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/ngraph/ngraph_engine_op.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/ngraph/ngraph_engine_op.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/optimizers/sgd_op.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/reader/buffered_reader.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/recurrent_op.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/platform/device_tracer.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/platform/device_tracer.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/platform/event.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/platform/profiler.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/platform/profiler.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/platform/profiler.proto
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/pybind/imperative.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/pybind/imperative.h
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/pybind/ir.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/pybind/protobuf.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/pybind/pybind.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/pybind/pybind_boost_headers.h
0 → 100644
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/pybind/recordio.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/fluid/recordio/scanner.cc
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
paddle/scripts/paddle_build.sh
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/__init__.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/compiler.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/contrib/slim/tests/test_graph.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/executor.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/framework.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/imperative/layers.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/imperative/nn.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/imperative/tracer.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/layers/nn.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/layers/ops.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/optimizer.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_accuracy_op.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_activation_op.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_eager_deletion_transformer.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_eager_deletion_while_op.py
0 → 100644
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_fake_quantize_op.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_gru_op.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_imperative_basic.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_partial_eager_deletion_transformer.py
0 → 100644
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_random_crop_op.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
tools/diff_api.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
tools/print_signatures.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
tools/timeline.py
浏览文件 @
ac0e0f51
此差异已折叠。
点击以展开。
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录