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Delete Ref & VectorRef and add GetDataSafely (#22997)

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* delete invalid check inferface Ref & VectorRef, test=develop

* fix vector ref delete error, test=develop

* try the new check inferface, test=develop

* change all related code with new check macro, test=develop

* remove static assert, test=develop

* polish detail, test=develop

* skip coverage problem, test=develop

* add new check macro, test=develop
revert-23830-2.0-beta
Chen Weihang 5 years ago committed by GitHub
parent 4c675a450f
commit 16315d3d9e
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
37 changed files with 286 additions and 260 deletions
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4
paddle/fluid/operators/activation_cudnn.cu.cc
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@ -31,8 +31,8 @@ class CudnnActivationKernel
ExtractActivationTensor(context, X, Out);
ActivationDescriptor act_desc;
TensorDescriptor x_desc, out_desc;
x_desc.set(detail::Ref(X));
out_desc.set(detail::Ref(Out));
x_desc.set(GET_DATA_SAFELY(X, "Input", "X", "CudnnActivation"));
out_desc.set(GET_DATA_SAFELY(Out, "Output", "Out", "CudnnActivation");
}
};

11
paddle/fluid/operators/activation_cudnn_op.cu.cc
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@ -37,7 +37,7 @@ struct CudnnActivationFunctor {
act_desc.set(mode_, coef_);
TensorDescriptor x_desc, out_desc;
x_desc.set(x);
out_desc.set(detail::Ref(out));
out_desc.set(GET_DATA_SAFELY(out, "Output", "Out", "CudnnActivation"));
PADDLE_ENFORCE(platform::dynload::cudnnActivationForward(
ctx_.cudnn_handle(), act_desc.desc(),
platform::CudnnDataType<T>::kOne(), x_desc.desc(), x.data<T>(),
@ -63,7 +63,7 @@ struct CudnnActivationGradFunctor {
x_desc.set(x);
out_desc.set(out);
dout_desc.set(dout);
dx_desc.set(detail::Ref(dx));
dx_desc.set(GET_DATA_SAFELY(dx, "Output", "X@GRAD", "CudnnActivationGrad"));
PADDLE_ENFORCE(platform::dynload::cudnnActivationBackward(
ctx_.cudnn_handle(), act_desc.desc(),
platform::CudnnDataType<T>::kOne(), out_desc.desc(), out.data<T>(),
@ -141,7 +141,7 @@ class CudnnActivationKernel
Out->mutable_data<T>(context.GetPlace());
auto& dev_ctx = context.template device_context<CUDADeviceContext>();
Functor functor(dev_ctx);
functor(detail::Ref(X), Out);
functor(GET_DATA_SAFELY(X, "Input", "X", "CudnnActivation"), Out);
}
};
@ -161,7 +161,10 @@ class CudnnActivationGradKernel
dX->mutable_data<T>(context.GetPlace());
auto& dev_ctx = context.template device_context<CUDADeviceContext>();
Functor functor(dev_ctx);
functor(detail::Ref(X), detail::Ref(Out), detail::Ref(dOut), dX);
functor(GET_DATA_SAFELY(X, "Input", "X", "CudnnActivationGrad"),
GET_DATA_SAFELY(Out, "Input", "Out", "CudnnActivationGrad"),
GET_DATA_SAFELY(dOut, "Input", "Out@GRAD", "CudnnActivationGrad"),
dX);
}
};

100
paddle/fluid/operators/activation_op.h
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@ -26,7 +26,6 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/platform/float16.h"
@ -156,8 +155,10 @@ class ActivationKernel
ExtractActivationTensor(context, &X, &Out);
Out->mutable_data<T>(context.GetPlace());
auto x = framework::EigenVector<T>::Flatten(detail::Ref(X));
auto out = framework::EigenVector<T>::Flatten(detail::Ref(Out));
auto x = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(X, "Input", "X", "Activation"));
auto out = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(Out, "Output", "Out", "Activation"));
auto* place =
context.template device_context<DeviceContext>().eigen_device();
Functor functor;
@ -182,10 +183,14 @@ class ActivationGradKernel
ExtractActivationGradTensor<Functor::FwdDeps()>(context, &X, &Out, &dOut,
&dX);
dX->mutable_data<T>(context.GetPlace());
auto dout = framework::EigenVector<T>::Flatten(detail::Ref(dOut));
auto out = framework::EigenVector<T>::Flatten(detail::Ref(Out));
auto dx = framework::EigenVector<T>::Flatten(detail::Ref(dX));
auto x = framework::EigenVector<T>::Flatten(detail::Ref(X));
auto dout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dOut, "Input", "Out@GRAD", "ActivationGrad"));
auto out = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(Out, "Input", "Out", "ActivationGrad"));
auto dx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dX, "Input", "X@GRAD", "ActivationGrad"));
auto x = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(X, "Input", "X", "ActivationGrad"));
auto* place =
context.template device_context<DeviceContext>().eigen_device();
Functor functor;
@ -1285,10 +1290,13 @@ struct ReluGradGradFunctor : public BaseActivationFunctor<T> {
framework::Tensor* ddOut, framework::Tensor* dOut,
framework::Tensor* dX) const {
auto* d = dev.eigen_device();
auto ddx = framework::EigenVector<T>::Flatten(detail::Ref(ddX));
auto out = framework::EigenVector<T>::Flatten(detail::Ref(Out));
auto ddx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddX, "Input", "DDX", "ReluGradGrad"));
auto out = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(Out, "Output", "Out", "ReluGradGrad"));
if (ddOut) {
auto ddout = framework::EigenVector<T>::Flatten(detail::Ref(ddOut));
auto ddout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddOut, "Output", "DDOut", "ReluGradGrad"));
ddout.device(*d) = ddx * (out > static_cast<T>(0)).template cast<T>();
}
}
@ -1308,9 +1316,12 @@ struct LeakyReluGradGradFunctor : public BaseActivationFunctor<T> {
framework::Tensor* dX) const {
if (ddOut) {
auto* d = dev.eigen_device();
auto ddx = framework::EigenVector<T>::Flatten(detail::Ref(ddX));
auto out = framework::EigenVector<T>::Flatten(detail::Ref(Out));
auto ddout = framework::EigenVector<T>::Flatten(detail::Ref(ddOut));
auto ddx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddX, "Input", "DDX", "LeakyReluGradGrad"));
auto out = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(Out, "Output", "Out", "LeakyReluGradGrad"));
auto ddout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddOut, "Output", "DOut", "LeakyReluGradGrad"));
ddout.device(*d) = ddx *
((out > static_cast<T>(0)).template cast<T>() +
static_cast<T>(alpha) *
@ -1332,18 +1343,23 @@ struct ELUGradGradFunctor : public BaseActivationFunctor<T> {
const framework::Tensor* ddX, framework::Tensor* ddOut,
const framework::Tensor* dOut, framework::Tensor* dX) const {
auto* d = dev.eigen_device();
auto ddx = framework::EigenVector<T>::Flatten(detail::Ref(ddX));
auto x = framework::EigenVector<T>::Flatten(detail::Ref(X));
auto ddx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddX, "Input", "DDX", "ELUGradGrad"));
auto x = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(X, "Input", "X", "ELUGradGrad"));
if (dX) {
auto dx = framework::EigenVector<T>::Flatten(detail::Ref(dX));
auto dout = framework::EigenVector<T>::Flatten(detail::Ref(dOut));
auto dx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dX, "Output", "DX", "ELUGradGrad"));
auto dout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dOut, "Output", "DOut", "ELUGradGrad"));
dx.device(*d) = ddx * dout * static_cast<T>(alpha) * x.exp() *
(x < static_cast<T>(0)).template cast<T>();
}
if (ddOut) {
auto ddout = framework::EigenVector<T>::Flatten(detail::Ref(ddOut));
auto ddout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddOut, "Output", "DDOut", "ELUGradGrad"));
ddout.device(*d) = ddx *
((x > static_cast<T>(0)).template cast<T>() +
static_cast<T>(alpha) * x.exp() *
@ -1361,17 +1377,22 @@ struct SqrtGradGradFunctor : public BaseActivationFunctor<T> {
const framework::Tensor* ddX, framework::Tensor* ddOut,
framework::Tensor* dOut, const framework::Tensor* dX) const {
auto* d = dev.eigen_device();
auto ddx = framework::EigenVector<T>::Flatten(detail::Ref(ddX));
auto out = framework::EigenVector<T>::Flatten(detail::Ref(Out));
auto ddx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddX, "Input", "DDX", "SqrtGradGrad"));
auto out = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(Out, "Output", "Out", "SqrtGradGrad"));
// sqrt GradGrad: ddy = 0.5 * ddx / y, dy = -1 * dx * ddx
// calculate dy first, so ddy can inplace ddx
if (dOut) {
auto dx = framework::EigenVector<T>::Flatten(detail::Ref(dX));
auto dout = framework::EigenVector<T>::Flatten(detail::Ref(dOut));
auto dx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dX, "Output", "DX", "SqrtGradGrad"));
auto dout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dOut, "Output", "DOut", "SqrtGradGrad"));
dout.device(*d) = dx * ddx * static_cast<T>(-1) / out;
}
if (ddOut) {
auto ddout = framework::EigenVector<T>::Flatten(detail::Ref(ddOut));
auto ddout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddOut, "Output", "DDOut", "SqrtGradGrad"));
ddout.device(*d) = ddx * static_cast<T>(0.5) / out;
}
}
@ -1385,17 +1406,22 @@ struct SquareGradGradFunctor : public BaseActivationFunctor<T> {
const framework::Tensor* ddX, framework::Tensor* ddOut,
const framework::Tensor* dOut, framework::Tensor* dX) const {
auto* d = dev.eigen_device();
auto ddx = framework::EigenVector<T>::Flatten(detail::Ref(ddX));
auto x = framework::EigenVector<T>::Flatten(detail::Ref(X));
auto ddx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddX, "Input", "DDX", "SquareGradGrad"));
auto x = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(X, "Input", "X", "SquareGradGrad"));
// square GradGrad: ddy=2x*ddx, dx=2dy*ddx
// calculate dx first, so ddy can inplace ddx
if (dX) {
auto dx = framework::EigenVector<T>::Flatten(detail::Ref(dX));
auto dout = framework::EigenVector<T>::Flatten(detail::Ref(dOut));
auto dx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dX, "Output", "DX", "SquareGradGrad"));
auto dout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dOut, "Output", "DOut", "SquareGradGrad"));
dx.device(*d) = ddx * static_cast<T>(2) * dout;
}
if (ddOut) {
auto ddout = framework::EigenVector<T>::Flatten(detail::Ref(ddOut));
auto ddout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(ddOut, "Output", "DDOut", "SquareGradGrad"));
ddout.device(*d) = ddx * static_cast<T>(2) * x;
}
}
@ -1557,8 +1583,10 @@ class PowKernel : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
ExtractActivationTensor(context, &X, &Out);
Out->mutable_data<T>(context.GetPlace());
auto x = framework::EigenVector<T>::Flatten(detail::Ref(X));
auto out = framework::EigenVector<T>::Flatten(detail::Ref(Out));
auto x = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(X, "Input", "X", "Pow"));
auto out = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(Out, "Output", "Out", "Pow"));
auto* place =
context.template device_context<DeviceContext>().eigen_device();
Functor functor;
@ -1602,10 +1630,14 @@ class PowGradKernel
ExtractActivationGradTensor<Functor::FwdDeps()>(context, &X, &Out, &dOut,
&dX);
dX->mutable_data<T>(context.GetPlace());
auto dout = framework::EigenVector<T>::Flatten(detail::Ref(dOut));
auto out = framework::EigenVector<T>::Flatten(detail::Ref(Out));
auto dx = framework::EigenVector<T>::Flatten(detail::Ref(dX));
auto x = framework::EigenVector<T>::Flatten(detail::Ref(X));
auto dout = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dOut, "Input", "Out@GRAD", "PowGrad"));
auto out = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(Out, "Input", "Out", "PowGrad"));
auto dx = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(dX, "Output", "X@GRAD", "PowGrad"));
auto x = framework::EigenVector<T>::Flatten(
GET_DATA_SAFELY(X, "Input", "X", "PowGrad"));
auto* place =
context.template device_context<DeviceContext>().eigen_device();
Functor functor;

1
paddle/fluid/operators/add_position_encoding_op.h
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@ -15,7 +15,6 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {

6
paddle/fluid/operators/controlflow/get_places_op.cc
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@ -14,7 +14,6 @@ limitations under the License. */
#include <thread> // NOLINT
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/platform/place.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/gpu_info.h"
@ -56,9 +55,8 @@ class GetPlacesOp : public framework::OperatorBase {
is_gpu ? "GPU" : "CPU");
auto out_var_name = Output("Out");
auto &places =
*(detail::Ref(scope.FindVar(out_var_name),
"Output variable %s cannot be found", out_var_name)
auto &places = *(GET_DATA_SAFELY(scope.FindVar(out_var_name), "Output",
"Out", "GetPlaces")
.GetMutable<platform::PlaceList>());
places.reserve(device_count);
if (is_gpu) {

1
paddle/fluid/operators/controlflow/tensor_array_read_write_op.cc
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@ -12,7 +12,6 @@ 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/array_operator.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {

14
paddle/fluid/operators/controlflow/while_op.cc
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@ -19,7 +19,6 @@
#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 {
namespace operators {
@ -198,23 +197,18 @@ class WhileGradOp : public framework::OperatorBase {
continue;
}
auto &og_outside =
detail::Ref(scope.FindVar(outside_og_name),
"Cannot find Outside Gradient %s", outside_og_name);
auto &og_inside =
detail::Ref(cur_scope.Var(inside_og_name),
"Cannot find inside gradient %s", inside_og_name);
auto &og_outside = *scope.FindVar(outside_og_name);
auto &og_inside = *cur_scope.Var(inside_og_name);
if (og_outside.IsType<framework::LoDTensor>()) {
auto &outside_tensor = og_outside.Get<framework::LoDTensor>();
auto &inside_tensor =
detail::Ref(og_inside.GetMutable<framework::LoDTensor>());
auto &inside_tensor = *og_inside.GetMutable<framework::LoDTensor>();
inside_tensor.set_lod(outside_tensor.lod());
inside_tensor.ShareDataWith(outside_tensor);
} else if (og_outside.IsType<framework::LoDTensorArray>()) {
auto outside_array =
og_outside.GetMutable<framework::LoDTensorArray>();
auto &inside_array =
detail::Ref(og_inside.GetMutable<framework::LoDTensorArray>());
*og_inside.GetMutable<framework::LoDTensorArray>();
inside_array.clear();
inside_array.resize(outside_array->size());
VLOG(8) << outside_og_name << " size = " << outside_array->size();

6
paddle/fluid/operators/conv_op.h
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@ -20,7 +20,6 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/depthwise_conv.h"
#include "paddle/fluid/operators/math/im2col.h"
@ -674,9 +673,8 @@ class GemmConvDoubleGradKernel : public framework::OpKernel<T> {
Tensor* ddY = ctx.Output<Tensor>("DDOutput");
Tensor* dW = ctx.Output<Tensor>("DFilter");
Tensor* dX = ctx.Output<Tensor>("DInput");
Tensor W = detail::Ref(ctx.Input<Tensor>("Filter"),
"Cannot find input Filter(%s) in scope)",
ctx.InputNames("Filter")[0]);
Tensor W = GET_DATA_SAFELY(ctx.Input<Tensor>("Filter"), "Input", "Filter",
"GemmConvDoubleGrad");
if (!ddY && !dW && !dX) return;
const int groups = ctx.Attr<int>("groups");

13
paddle/fluid/operators/cum_op.h
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@ -18,7 +18,6 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
@ -29,13 +28,11 @@ class CumKernel : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
using T = typename Functor::ELEMENT_TYPE;
void Compute(const framework::ExecutionContext& context) const override {
auto& X = detail::Ref(context.Input<framework::Tensor>("X"),
"Cannot get input tensor X, variable name = %s",
context.InputName("X"));
auto& X = GET_DATA_SAFELY(context.Input<framework::Tensor>("X"), "Input",
"X", "Cum");
auto& Out = detail::Ref(context.Output<framework::Tensor>("Out"),
"Cannot get output tensor Out, variable name = %s",
context.OutputName("Out"));
auto& Out = GET_DATA_SAFELY(context.Output<framework::Tensor>("Out"),
"Output", "Out", "Cum");
int axis = context.Attr<int>("axis");
bool exclusive = context.Attr<bool>("exclusive");
bool reverse = context.Attr<bool>("reverse");
@ -46,7 +43,7 @@ class CumKernel : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
PADDLE_ENFORCE_LT(
axis, x_dims.size(),
"axis should be less than the dimensiotn of the input tensor");
Out.mutable_data<T>(context.GetPlace());
Out.template mutable_data<T>(context.GetPlace());
int pre = 1;
int post = 1;

45
paddle/fluid/operators/detail/safe_ref.h
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@ -1,45 +0,0 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace operators {
namespace detail {
/**
* Get Reference From Pointer with check. The error message is printf format,
* and passed by `args`
*/
template <typename T, typename... ARGS>
inline T& Ref(T* ptr, ARGS&&... args) {
PADDLE_ENFORCE_NOT_NULL(ptr, ::paddle::string::Sprintf(args...));
return *ptr;
}
template <typename T, typename... ARGS>
inline std::vector<std::reference_wrapper<T>> VectorRef(
const std::vector<T*>& vec, ARGS&&... args) {
std::vector<std::reference_wrapper<T>> result;
result.reserve(vec.size());
for (auto* ptr : vec) {
result.emplace_back(Ref(ptr, args...));
}
return result;
}
} // namespace detail
} // namespace operators
} // namespace paddle

1
paddle/fluid/operators/detection/collect_fpn_proposals_op.h
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@ -20,7 +20,6 @@ limitations under the License.*/
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/math_function.h"

1
paddle/fluid/operators/detection/distribute_fpn_proposals_op.h
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@ -20,7 +20,6 @@ limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/math_function.h"

11
paddle/fluid/operators/detection/generate_proposals_op.cc
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@ -17,7 +17,6 @@ limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/math_function.h"
@ -293,12 +292,10 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
auto *scores = context.Input<Tensor>("Scores");
auto *bbox_deltas = context.Input<Tensor>("BboxDeltas");
auto *im_info = context.Input<Tensor>("ImInfo");
auto anchors = detail::Ref(context.Input<Tensor>("Anchors"),
"Cannot find input Anchors(%s) in scope",
context.InputNames("Anchors")[0]);
auto variances = detail::Ref(context.Input<Tensor>("Variances"),
"Cannot find input Variances(%s) in scope",
context.InputNames("Variances")[0]);
auto anchors = GET_DATA_SAFELY(context.Input<Tensor>("Anchors"), "Input",
"Anchors", "GenerateProposals");
auto variances = GET_DATA_SAFELY(context.Input<Tensor>("Variances"),
"Input", "Variances", "GenerateProposals");
auto *rpn_rois = context.Output<LoDTensor>("RpnRois");
auto *rpn_roi_probs = context.Output<LoDTensor>("RpnRoiProbs");

11
paddle/fluid/operators/detection/generate_proposals_op.cu
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@ -20,7 +20,6 @@ limitations under the License. */
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/gather.cu.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/for_range.h"
@ -367,12 +366,10 @@ class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
auto *scores = context.Input<Tensor>("Scores");
auto *bbox_deltas = context.Input<Tensor>("BboxDeltas");
auto *im_info = context.Input<Tensor>("ImInfo");
auto anchors = detail::Ref(context.Input<Tensor>("Anchors"),
"Cannot find input Anchors(%s) in scope",
context.InputNames("Anchors")[0]);
auto variances = detail::Ref(context.Input<Tensor>("Variances"),
"Cannot find input Variances(%s) in scope",
context.InputNames("Variances")[0]);
auto anchors = GET_DATA_SAFELY(context.Input<Tensor>("Anchors"), "Input",
"Anchors", "GenerateProposals");
auto variances = GET_DATA_SAFELY(context.Input<Tensor>("Variances"),
"Input", "Variances", "GenerateProposals");
auto *rpn_rois = context.Output<LoDTensor>("RpnRois");
auto *rpn_roi_probs = context.Output<LoDTensor>("RpnRoiProbs");

7
paddle/fluid/operators/fill_op.h
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@ -19,7 +19,6 @@ limitations under the License. */
#include <algorithm>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
@ -44,10 +43,8 @@ template <typename T>
class FillKernel : public framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext &ctx) const override {
auto &out =
detail::Ref(ctx.Output<framework::LoDTensor>("Out"),
"Cannot get output lod tensor Out, variable name = %s",
ctx.OutputName("Out"));
auto &out = GET_DATA_SAFELY(ctx.Output<framework::LoDTensor>("Out"),
"Output", "Out", "Fill");
out.Resize(framework::make_ddim(ctx.Attr<std::vector<int>>("shape")));
auto dtype =
static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype"));

11
paddle/fluid/operators/fused/fused_elemwise_activation_op.h
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@ -18,7 +18,6 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/compound_functors.h"
#include "paddle/fluid/operators/math/functors.h"
@ -383,12 +382,10 @@ template <typename DeviceContext, typename T>
class FusedElemwiseActivationKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto &in_x = detail::Ref(ctx.Input<framework::Tensor>("X"),
"Cannot get input tensor %s, variable name = %s",
"X", ctx.InputName("X"));
auto &in_y = detail::Ref(ctx.Input<framework::Tensor>("Y"),
"Cannot get input tensor %s, variable name = %s",
"Y", ctx.InputName("Y"));
auto &in_x = GET_DATA_SAFELY(ctx.Input<framework::Tensor>("X"), "Input",
"X", "FusedElemwiseActivation");
auto &in_y = GET_DATA_SAFELY(ctx.Input<framework::Tensor>("Y"), "Input",
"Y", "FusedElemwiseActivation");
PADDLE_ENFORCE(ctx.HasOutput("Out"), "The output(Out) should not be empty");
auto output = ctx.Output<framework::Tensor>("Out");

1
paddle/fluid/operators/fused/fused_embedding_eltwise_layernorm_op.cc
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@ -14,7 +14,6 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/platform/errors.h"
namespace paddle {

1
paddle/fluid/operators/fused/fused_embedding_eltwise_layernorm_op.cu
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@ -19,7 +19,6 @@
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/bert_encoder_functor.h"
#include "paddle/fluid/operators/math/blas.h"

1
paddle/fluid/operators/fused/multihead_matmul_op.cc
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@ -14,7 +14,6 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/platform/errors.h"
namespace paddle {

8
paddle/fluid/operators/fused/multihead_matmul_op.cu
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@ -17,7 +17,6 @@
#include <algorithm>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/bert_encoder_functor.h"
#include "paddle/fluid/operators/math/blas.h"
@ -142,14 +141,13 @@ class MultiHeadMatMulV2Kernel : public framework::OpKernel<T> {
auto *input = context.Input<framework::Tensor>("Input");
auto *w = context.Input<framework::Tensor>("W");
auto *bias = context.Input<framework::Tensor>("Bias");
auto &bias_qk = detail::Ref(context.Input<framework::Tensor>("BiasQK"),
"Cannot find QK");
auto &bias_qk = GET_DATA_SAFELY(context.Input<framework::Tensor>("BiasQK"),
"Input", "BiasQK", "MultiHeadMatMulV2");
auto *input_d = input->data<T>();
auto *w_d = w->data<T>();
auto *bias_d = bias->data<T>();
auto *bias_qk_d = bias_qk.data<T>();
auto *bias_qk_d = bias_qk.template data<T>();
T scale = static_cast<T>(context.Attr<float>("alpha"));
int head_number = context.Attr<int>("head_number");

77
paddle/fluid/operators/hierarchical_sigmoid_op.h
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@ -24,7 +24,6 @@ limitations under the License. */
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/clip_op.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/matrix_bit_code.h"
#include "paddle/fluid/platform/transform.h"
@ -40,8 +39,9 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
using platform::Transform;
using framework::LoDTensor;
static std::vector<int64_t> PathToRows(const framework::LoDTensor& path) {
static std::vector<int64_t> PathToRows(const LoDTensor& path) {
std::set<int64_t> rows;
const int64_t* paths = path.data<int64_t>();
for (int64_t i = 0; i < path.numel(); ++i) {
@ -57,14 +57,17 @@ template <typename DeviceContext, typename T>
class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& in = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
auto& w = detail::Ref(ctx.Input<framework::LoDTensor>("W"));
auto* path = ctx.Input<framework::LoDTensor>("PathTable");
auto* code = ctx.Input<framework::LoDTensor>("PathCode");
auto& label = detail::Ref(ctx.Input<framework::LoDTensor>("Label"));
auto* bias = ctx.Input<framework::LoDTensor>("Bias");
auto* out = ctx.Output<framework::LoDTensor>("Out");
auto* pre_out = ctx.Output<framework::LoDTensor>("PreOut");
auto& in = GET_DATA_SAFELY(ctx.Input<LoDTensor>("X"), "Input", "X",
"HierarchicalSigmoid");
auto& w = GET_DATA_SAFELY(ctx.Input<LoDTensor>("W"), "Input", "W",
"HierarchicalSigmoid");
auto* path = ctx.Input<LoDTensor>("PathTable");
auto* code = ctx.Input<LoDTensor>("PathCode");
auto& label = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Label"), "Input",
"Label", "HierarchicalSigmoid");
auto* bias = ctx.Input<LoDTensor>("Bias");
auto* out = ctx.Output<LoDTensor>("Out");
auto* pre_out = ctx.Output<LoDTensor>("PreOut");
size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
// for remote prefetch
@ -75,7 +78,7 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
int64_t code_length =
path ? path->dims()[1] : math::FindLastSet(num_classes - 1);
int64_t batch_size = in.dims()[0];
framework::LoDTensor sum;
LoDTensor sum;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto* pre_out_data = pre_out->mutable_data<T>(
framework::make_ddim({batch_size, code_length}), ctx.GetPlace());
@ -89,11 +92,11 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
std::unique_ptr<math::MatrixBitCodeFunctor<T>> bit_code;
if (!is_custom) {
bit_code.reset(new math::MatrixBitCodeFunctor<T>(num_classes,
label.data<int64_t>()));
bit_code.reset(new math::MatrixBitCodeFunctor<T>(
num_classes, label.template data<int64_t>()));
} else {
bit_code.reset(new math::MatrixBitCodeFunctor<T>(*path, *code,
label.data<int64_t>()));
bit_code.reset(new math::MatrixBitCodeFunctor<T>(
*path, *code, label.template data<int64_t>()));
}
std::vector<int64_t> sum_dims({batch_size, 1UL});
@ -126,20 +129,24 @@ template <typename DeviceContext, typename T>
class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& in = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
auto& w = detail::Ref(ctx.Input<framework::LoDTensor>("W"));
auto* path = ctx.Input<framework::LoDTensor>("PathTable");
auto* code = ctx.Input<framework::LoDTensor>("PathCode");
auto* in_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto& in = GET_DATA_SAFELY(ctx.Input<LoDTensor>("X"), "Input", "X",
"HierarchicalSigmoidGrad");
auto& w = GET_DATA_SAFELY(ctx.Input<LoDTensor>("W"), "Input", "W",
"HierarchicalSigmoidGrad");
auto* path = ctx.Input<LoDTensor>("PathTable");
auto* code = ctx.Input<LoDTensor>("PathCode");
auto* in_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
bool is_sparse = ctx.Attr<bool>("is_sparse");
auto& dev_ctx = ctx.template device_context<DeviceContext>();
math::SetConstant<DeviceContext, T> zero;
auto& label = detail::Ref(ctx.Input<framework::LoDTensor>("Label"));
auto& pre_out = detail::Ref(ctx.Input<framework::LoDTensor>("PreOut"));
auto& out_grad = detail::Ref(
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out")));
framework::LoDTensor pre_out_grad;
auto& label = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Label"), "Input",
"Label", "HierarchicalSigmoidGrad");
auto& pre_out = GET_DATA_SAFELY(ctx.Input<LoDTensor>("PreOut"), "Input",
"PreOut", "HierarchicalSigmoidGrad");
auto& out_grad = GET_DATA_SAFELY(
ctx.Input<LoDTensor>(framework::GradVarName("Out")), "Input",
framework::GradVarName("Out"), "HierarchicalSigmoidGrad");
LoDTensor pre_out_grad;
pre_out_grad.mutable_data<T>(pre_out.dims(), ctx.GetPlace());
in_grad->mutable_data<T>(ctx.GetPlace());
@ -154,11 +161,11 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
std::unique_ptr<math::MatrixBitCodeFunctor<T>> bit_code;
if (!is_custom) {
bit_code.reset(new math::MatrixBitCodeFunctor<T>(num_classes,
label.data<int64_t>()));
bit_code.reset(new math::MatrixBitCodeFunctor<T>(
num_classes, label.template data<int64_t>()));
} else {
bit_code.reset(new math::MatrixBitCodeFunctor<T>(*path, *code,
label.data<int64_t>()));
bit_code.reset(new math::MatrixBitCodeFunctor<T>(
*path, *code, label.template data<int64_t>()));
}
// softrelu derivative
@ -166,7 +173,7 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
auto blas = math::GetBlas<DeviceContext, T>(ctx);
auto* pre_out_grad_data = pre_out_grad.data<T>();
auto* pre_out_data = pre_out.data<T>();
auto* pre_out_data = pre_out.template data<T>();
auto n = pre_out.numel();
blas.VEXP(n, pre_out_data, pre_out_grad_data);
blas.VINV(n, pre_out_grad_data, pre_out_grad_data);
@ -174,7 +181,7 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
pre_out_grad_data[i] = 1.0 - pre_out_grad_data[i];
}
bit_code->Sub(&pre_out_grad); // the gradient of clip(w * x + b)
auto* out_grad_data = out_grad.data<T>();
auto* out_grad_data = out_grad.template data<T>();
int64_t dim0 = pre_out_grad.dims()[0];
int64_t dim1 = pre_out_grad.dims()[1];
@ -184,16 +191,14 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
}
// TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
// be consistent with the clipping in forward.
auto* bias_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Bias"));
auto* bias_grad = ctx.Output<LoDTensor>(framework::GradVarName("Bias"));
if (bias_grad) {
bias_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, bias_grad, static_cast<T>(0.0));
bit_code->AddGrad(pre_out_grad, bias_grad);
}
if (!is_sparse) {
auto* w_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("W"));
auto* w_grad = ctx.Output<LoDTensor>(framework::GradVarName("W"));
w_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, w_grad, static_cast<T>(0.0));
bit_code->MulGradWeight(pre_out_grad, w_grad, in);

13
paddle/fluid/operators/lod_tensor_to_array_op.cc
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@ -16,7 +16,6 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/port.h"
@ -95,13 +94,15 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &x = detail::Ref(scope.FindVar(Input("X")), "Cannot find input %s",
Input("X"))
auto &x = GET_DATA_SAFELY(scope.FindVar(Input("X")), "Input", "X",
"LoDTensorToArray")
.Get<framework::LoDTensor>();
auto &rank_table = detail::Ref(scope.FindVar(Input("RankTable")))
auto &rank_table = GET_DATA_SAFELY(scope.FindVar(Input("RankTable")),
"Input", "RankTable", "LoDTensorToArray")
.Get<framework::LoDRankTable>();
auto &out = *detail::Ref(scope.FindVar(Output("Out")))
.GetMutable<framework::LoDTensorArray>();
auto &out = *(GET_DATA_SAFELY(scope.FindVar(Output("Out")), "Output", "Out",
"LoDTensorToArray")
.GetMutable<framework::LoDTensorArray>());
auto &items = rank_table.items();
auto max_seq_len = items[0].length;
auto rank_level = rank_table.level();

9
paddle/fluid/operators/matmul_op.cc
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@ -16,7 +16,6 @@ limitations under the License. */
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/blas.h"
namespace paddle {
@ -58,10 +57,10 @@ template <typename DeviceContext, typename T>
class MatMulKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto &x =
detail::Ref(context.Input<framework::Tensor>("X"), "Cannot find X");
auto &y =
detail::Ref(context.Input<framework::Tensor>("Y"), "Cannot find Y");
auto &x = GET_DATA_SAFELY(context.Input<framework::Tensor>("X"), "Input",
"X", "MatMul");
auto &y = GET_DATA_SAFELY(context.Input<framework::Tensor>("Y"), "Input",
"Y", "MatMul");
auto *out = context.Output<framework::Tensor>("Out");
out->mutable_data<T>(context.GetPlace());

1
paddle/fluid/operators/mkldnn/mkldnn_activation_op.h
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@ -17,7 +17,6 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"

1
paddle/fluid/operators/optimizers/adam_op.cu
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@ -128,7 +128,6 @@ class AdamOpCUDAKernel : public framework::OpKernel<T> {
framework::ToTypeName(param_var->Type())));
using paddle::framework::LoDTensor;
using paddle::operators::detail::Ref;
int64_t min_row_size_to_use_multithread =
ctx.Attr<int64_t>("min_row_size_to_use_multithread");

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