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PaddleDetection
未验证 提交 4d8e8ee2 编写于 作者: T tensor-tang 提交者： GitHub
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Merge pull request #11628 from PaddlePaddle/revert-11102-mozga-intel/Sum_mkldnn_layout 

Revert "MKLDNN layout: Support for sum operator"
上级 d6a9f005 90780e22
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paddle/fluid/operators/parallel_do_op.cc
浏览文件 @ 4d8e8ee2
......@@ -295,7 +295,7 @@ class ParallelDoGradOp : public framework::OperatorBase {
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {s, tmp_name}}}, {{"Out", {s}}},
framework::AttributeMap{{"use_mkldnn", {false}}});
framework::AttributeMap{});
VLOG(10) << sum_op->DebugStringEx(sub_scopes[0]);
sum_op->Run(*sub_scopes[0], places[0]);
WaitOnPlace(places[0]);
......
paddle/fluid/operators/recurrent_op.cc
浏览文件 @ 4d8e8ee2
......@@ -429,8 +429,7 @@ class RecurrentGradOp : public RecurrentBase {
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {pg_names[param_id], new_inside_name}}},
{{"Out", {pg_names[param_id]}}},
framework::AttributeMap{{"use_mkldnn", {false}}});
{{"Out", {pg_names[param_id]}}}, framework::AttributeMap{});
sum_op->Run(cur_scope, place);
cur_scope.Rename(new_inside_name, inside_grad_name);
......
paddle/fluid/operators/sum_mkldnn_op.cc 已删除 100644 → 0
浏览文件 @ d6a9f005
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
/*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 "mkldnn.hpp"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/operators/sum_op.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::CPUDeviceContext;
using framework::DataLayout;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::stream;
using mkldnn::sum;
using mkldnn::reorder;
using platform::to_void_cast;
template <typename T>
class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
auto in_vars = ctx.MultiInputVar("X");
const int N = in_vars.size();
auto out_var = ctx.OutputVar("Out");
bool in_place = out_var == in_vars[0];
if (out_var->IsType<framework::LoDTensor>()) {
LoDTensor* output = ctx.Output<LoDTensor>("Out");
T* output_data = output->mutable_data<T>(ctx.GetPlace());
std::vector<int> dst_tz = framework::vectorize2int(output->dims());
auto src_tz = dst_tz;
memory::format output_format{memory::format::format_undef};
std::vector<float> scales;
std::vector<memory::primitive_desc> srcs_mpd;
std::vector<mkldnn::memory> srcs_mem;
PADDLE_ENFORCE(in_vars[0]->IsType<LoDTensor>(),
"Input[0] must be LoDTensors");
auto& input0 = in_vars[0]->Get<LoDTensor>();
PADDLE_ENFORCE(input0.layout() == DataLayout::kMKLDNN &&
input0.format() != memory::format::format_undef,
"Wrong layout/format for inputs[0]");
memory::format input_format = input0.format();
if (src_tz.size() == 1 && (input_format == memory::format::nchw ||
input_format == memory::format::nhwc)) {
input_format = memory::format::x;
}
if (src_tz.size() == 2 && (input_format == memory::format::nchw ||
input_format == memory::format::nhwc)) {
input_format = memory::format::nc;
}
for (int i = in_place ? 1 : 0; i < N; i++) {
PADDLE_ENFORCE(in_vars[i]->IsType<LoDTensor>(),
"all inputs must be all LoDTensors");
auto& input = in_vars[i]->Get<LoDTensor>();
PADDLE_ENFORCE(input.layout() == DataLayout::kMKLDNN &&
input.format() != memory::format::format_undef,
"Wrong layout/format for inputs");
if (input.numel() == 0) {
continue;
}
const T* input_data = input.data<T>();
auto src_md =
memory::desc(src_tz, memory::data_type::f32, input_format);
auto src_mpd = memory::primitive_desc(src_md, mkldnn_engine);
auto src_mem = memory(src_mpd, to_void_cast(input_data));
srcs_mpd.push_back(src_mpd);
srcs_mem.push_back(src_mem);
scales.push_back(1.0);
}
auto dst_md =
memory::desc(dst_tz, memory::data_type::f32, memory::format::any);
auto sum_pd = sum::primitive_desc(dst_md, scales, srcs_mpd);
std::shared_ptr<memory> dst_mem;
if (in_place) {
dst_mem.reset(new memory(sum_pd.dst_primitive_desc()));
} else {
dst_mem.reset(new memory(sum_pd.dst_primitive_desc(), output_data));
}
std::vector<mkldnn::primitive::at> inputs;
for (size_t i = 0; i < srcs_mem.size(); ++i) {
inputs.push_back(srcs_mem[i]);
}
auto sum_prim = mkldnn::sum(sum_pd, inputs, *dst_mem);
output_format = (memory::format)platform::GetMKLDNNFormat(sum_pd);
primitive reorder_prim;
std::shared_ptr<memory> target_mem;
if (in_place) {
output_format = input_format;
target_mem.reset(new memory(
{{{src_tz}, memory::data_type::f32, output_format}, mkldnn_engine},
output_data));
reorder_prim = reorder(*dst_mem, *target_mem);
}
std::vector<primitive> pipeline;
pipeline.push_back(sum_prim);
if (in_place) pipeline.push_back(reorder_prim);
stream(stream::kind::eager).submit(pipeline).wait();
output->set_layout(DataLayout::kMKLDNN);
output->set_format(output_format);
} else if (out_var->IsType<framework::SelectedRows>()) {
// TODO(@mozga-intel) Add MKLDNN SelectedRows support
std::unique_ptr<framework::SelectedRows> in0;
if (in_place) {
// If is in_place, we store the input[0] to in0
auto& in_sel0 = in_vars[0]->Get<SelectedRows>();
auto& rows = in_sel0.rows();
in0.reset(new framework::SelectedRows(rows, in_sel0.height()));
in0->mutable_value()->ShareDataWith(in_sel0.value());
}
auto get_selected_row = [&](size_t i) -> const SelectedRows& {
if (i == 0 && in0) {
return *in0.get();
} else {
return in_vars[i]->Get<SelectedRows>();
}
};
auto* out = ctx.Output<SelectedRows>("Out");
out->mutable_rows()->clear();
auto* out_value = out->mutable_value();
// Runtime InferShape
size_t first_dim = 0;
for (int i = 0; i < N; i++) {
auto& sel_row = get_selected_row(i);
first_dim += sel_row.rows().size();
}
auto in_dim =
framework::vectorize(get_selected_row(N - 1).value().dims());
in_dim[0] = static_cast<int64_t>(first_dim);
out_value->Resize(framework::make_ddim(in_dim));
// if all the input sparse vars are empty, no need to
// merge these vars.
if (first_dim == 0UL) {
return;
}
out_value->mutable_data<T>(ctx.GetPlace());
math::SelectedRowsAddTo<CPUDeviceContext, T> functor;
int64_t offset = 0;
for (int i = 0; i < N; i++) {
auto& sel_row = get_selected_row(i);
if (sel_row.rows().size() == 0) {
continue;
}
PADDLE_ENFORCE_EQ(out->height(), sel_row.height());
functor(ctx.template device_context<CPUDeviceContext>(), sel_row,
offset, out);
offset += sel_row.value().numel();
}
} else if (out_var->IsType<framework::LoDTensorArray>()) {
// TODO(@mozga-intel) Add MKLDNN LoDTensorArray support
auto& out_array = *out_var->GetMutable<framework::LoDTensorArray>();
for (size_t i = in_place ? 1 : 0; i < in_vars.size(); ++i) {
PADDLE_ENFORCE(in_vars[i]->IsType<framework::LoDTensorArray>(),
"Only support all inputs are TensorArray");
auto& in_array = in_vars[i]->Get<framework::LoDTensorArray>();
for (size_t i = 0; i < in_array.size(); ++i) {
if (in_array[i].numel() != 0) {
if (i >= out_array.size()) {
out_array.resize(i + 1);
}
if (out_array[i].numel() == 0) {
framework::TensorCopy(in_array[i], in_array[i].place(),
ctx.device_context(), &out_array[i]);
out_array[i].set_lod(in_array[i].lod());
} else {
PADDLE_ENFORCE(out_array[i].lod() == in_array[i].lod());
auto in = EigenVector<T>::Flatten(in_array[i]);
auto result = EigenVector<T>::Flatten(out_array[i]);
result.device(*ctx.template device_context<MKLDNNDeviceContext>()
.eigen_device()) = result + in;
}
}
}
}
} else {
PADDLE_THROW("Unexpected branch, output variable type is %s",
out_var->Type().name());
}
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_KERNEL(sum, MKLDNN, ::paddle::platform::CPUPlace,
paddle::operators::SumMKLDNNOpKernel<float>);
paddle/fluid/operators/sum_op.cc
浏览文件 @ 4d8e8ee2
......@@ -18,10 +18,6 @@ limitations under the License. */
#include "paddle/fluid/framework/var_type_inference.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
using framework::Tensor;
......@@ -67,18 +63,6 @@ class SumOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto x_vars = ctx.MultiInputVar("X");
framework::LibraryType library{framework::LibraryType::kPlain};
framework::DataLayout layout{framework::DataLayout::kAnyLayout};
#ifdef PADDLE_WITH_MKLDNN
if (library == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN;
}
#endif
if (x_vars[0]->IsType<framework::LoDTensor>()) {
int dtype = -1;
for (auto& x_var : x_vars) {
......@@ -96,27 +80,26 @@ class SumOp : public framework::OperatorWithKernel {
"Sum operator should have at least one tensor");
return framework::OpKernelType(
static_cast<framework::proto::VarType::Type>(dtype), ctx.GetPlace(),
layout, library);
static_cast<framework::proto::VarType::Type>(dtype),
ctx.device_context());
} else if (x_vars[0]->IsType<framework::SelectedRows>()) {
for (auto& var : x_vars) {
auto& value = var->Get<framework::SelectedRows>().value();
if (value.IsInitialized()) {
return framework::OpKernelType(framework::ToDataType(value.type()),
ctx.device_context(), layout, library);
ctx.device_context());
}
}
// if input sparse vars are not initialized, use an default kernel type.
return framework::OpKernelType(framework::proto::VarType::FP32,
ctx.device_context(), layout, library);
ctx.device_context());
} else if (x_vars[0]->IsType<framework::LoDTensorArray>()) {
for (auto& x_var : x_vars) {
auto& array = x_var->Get<framework::LoDTensorArray>();
for (auto& each : array) {
if (each.numel() != 0) {
return framework::OpKernelType(framework::ToDataType(each.type()),
ctx.device_context(), layout,
library);
ctx.device_context());
}
}
}
......@@ -133,9 +116,6 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
.AsDuplicable();
AddOutput("Out", "(Tensor) The output tensor of sum operator.").Reuse("X");
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddComment(R"DOC(
Sum operator.
......@@ -152,6 +132,7 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
framework::BlockDesc* block) const override {
auto& inputs = op_desc.Input("X");
auto var_type = framework::proto::VarType::SELECTED_ROWS;
for (auto& name : op_desc.Input("X")) {
VLOG(10) << name << " "
<< block->FindRecursiveOrCreateVar(name).GetType();
......@@ -225,7 +206,6 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(sum, ops::SumOp, ops::SumOpMaker, ops::SumGradMaker,
ops::SumOpVarTypeInference);
REGISTER_OP_CPU_KERNEL(
sum, ops::SumKernel<paddle::platform::CPUDeviceContext, float>,
ops::SumKernel<paddle::platform::CPUDeviceContext, double>,
......
paddle/fluid/operators/while_op.cc
浏览文件 @ 4d8e8ee2
......@@ -203,11 +203,11 @@ class WhileGradOp : public framework::OperatorBase {
->set_lod(inside_tensor.lod());
}
}
auto new_inside_name = cur_scope.Rename(inside_grad_name);
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {pg_names[param_id], new_inside_name}}},
{{"Out", {pg_names[param_id]}}},
framework::AttributeMap{{"use_mkldnn", {false}}});
{{"Out", {pg_names[param_id]}}}, framework::AttributeMap{});
sum_op->Run(cur_scope, dev_place);
cur_scope.Rename(new_inside_name, inside_grad_name);
}
......
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @ 4d8e8ee2
......@@ -99,11 +99,5 @@ inline mkldnn::memory::format GetMKLDNNFormat(const mkldnn::memory memory) {
memory.get_primitive_desc().desc().data.format);
}
inline mkldnn::memory::format GetMKLDNNFormat(
const mkldnn::sum::primitive_desc& memory) {
return static_cast<mkldnn::memory::format>(
memory.dst_primitive_desc().desc().data.format);
}
} // namespace platform
} // namespace paddle
python/paddle/fluid/backward.py
浏览文件 @ 4d8e8ee2
......@@ -132,9 +132,9 @@ def _addup_repetitive_outputs_(op_descs):
for idx, op_desc in enumerate(op_descs):
for var_name in op_desc.input_arg_names():
if len(renamed_vars[var_name]) > 1:
pending_sum_ops.append((_create_op_desc_(
"sum", {"X": renamed_vars[var_name]}, {"Out": [var_name]},
{"use_mkldnn": False}), idx))
pending_sum_ops.append(
(_create_op_desc_("sum", {"X": renamed_vars[var_name]},
{"Out": [var_name]}, {}), idx))
renamed_vars[var_name] = [var_name]
for var_name in op_desc.output_arg_names():
if var_name == core.empty_var_name(
......@@ -161,9 +161,8 @@ def _addup_repetitive_outputs_(op_descs):
renamed_vars[var_name].append(new_name)
for var_name, inputs in renamed_vars.iteritems():
if len(inputs) > 1:
pending_sum_ops.append(
(_create_op_desc_("sum", {"X": inputs}, {"Out": [var_name]},
{"use_mkldnn": False}), len(op_descs)))
pending_sum_ops.append((_create_op_desc_(
"sum", {"X": inputs}, {"Out": [var_name]}, {}), len(op_descs)))
# sum_op descs are sorted according to their insert position
for p in reversed(pending_sum_ops):
op_descs.insert(p[1], p[0])
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 4d8e8ee2
......@@ -198,10 +198,7 @@ def fc(input,
else:
pre_bias = helper.create_tmp_variable(dtype)
helper.append_op(
type="sum",
inputs={"X": mul_results},
outputs={"Out": pre_bias},
attrs={"use_mkldnn": use_mkldnn})
type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias})
# add bias
pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims)
# add activation
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 4d8e8ee2
......@@ -230,11 +230,7 @@ def sums(input, out=None):
helper = LayerHelper('sum', **locals())
if out is None:
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='sum',
inputs={'X': input},
outputs={'Out': out},
attrs={'use_mkldnn': False})
helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
return out
......
python/paddle/fluid/tests/unittests/test_sum_mkldnn_op.py 已删除 100644 → 0
浏览文件 @ d6a9f005
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from test_sum_op import TestSumOp
class TestMKLDNN(TestSumOp):
def init_kernel_type(self):
self.use_mkldnn = True
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_sum_op.py
浏览文件 @ 4d8e8ee2
......@@ -20,15 +20,12 @@ from op_test import OpTest
class TestSumOp(OpTest):
def setUp(self):
self.op_type = "sum"
self.use_mkldnn = False
self.init_kernel_type()
x0 = np.random.random((3, 4)).astype('float32')
x1 = np.random.random((3, 4)).astype('float32')
x2 = np.random.random((3, 4)).astype('float32')
self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]}
y = x0 + x1 + x2
self.outputs = {'Out': y}
self.attrs = {'use_mkldnn': self.use_mkldnn}
def test_check_output(self):
self.check_output()
......@@ -36,9 +33,6 @@ class TestSumOp(OpTest):
def test_check_grad(self):
self.check_grad(['x0'], 'Out')
def init_kernel_type(self):
pass
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 4d8e8ee2
......@@ -872,8 +872,7 @@ class DistributeTranspiler(object):
table_opt_block.append_op(
type="sum",
inputs={"X": pserver_side_table_grad_list},
outputs={"Out": [grad_var]},
attrs={"use_mkldnn": False})
outputs={"Out": [grad_var]})
else:
# in async_mode, for table gradient, it also need to be splited to each parameter server
origin_grad_name = grad_var.name
......@@ -1105,8 +1104,7 @@ class DistributeTranspiler(object):
optimize_block.append_op(
type="sum",
inputs={"X": vars2merge},
outputs={"Out": merged_var},
attrs={"use_mkldnn": False})
outputs={"Out": merged_var})
# TODO(panyx0718): What if it's SELECTED_ROWS.
if not merged_var.type == core.VarDesc.VarType.SELECTED_ROWS:
optimize_block.append_op(
......
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