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fix_doc_bu
| Author | SHA1 | Date |
|---|---|---|
swtkiwi
|
13e906c162 | 6 years ago |
danleifeng
|
3f8746842b | 6 years ago |
Wilber
|
c008ae696a | 6 years ago |
lidanqing
|
56aba4b747 | 6 years ago |
wangguanzhong
|
86887b9b1f | 6 years ago |
|
wangguanzhong
|
5178d9f832 | 6 years ago |
liu zhengxi
|
5d94a5ca54 | 6 years ago |
李灿
|
4193ae0d02 | 6 years ago |
Zhou Wei
|
dc53ba879f | 6 years ago |
WangXi
|
739043c6a6 | 6 years ago |
Double_V
|
39745d448c | 6 years ago |
LielinJiang
|
172e76a852 | 6 years ago |
tianshuo78520a
|
f63f8d730c | 6 years ago |
 李灿
|
2477ccc378 | 6 years ago |
|
danleifeng
|
61162497b4 | 6 years ago |
|
LielinJiang
|
d4160941f3 | 6 years ago |
yinhaofeng
|
0b294906f9 | 6 years ago |
|
yinhaofeng
|
ea45fb90a4 | 6 years ago |
|
Double_V
|
4f43d51f43 | 6 years ago |
lilong12
|
c4e18dc093 | 6 years ago |
Kaipeng Deng
|
6375ad39cc | 6 years ago |
TeslaZhao
|
11adb0f373 | 6 years ago |
|
lilong12
|
957e6fbe5f | 6 years ago |
Chengmo
|
91727ac899 | 6 years ago |
liuyuhui
|
5c2852a330 | 6 years ago |
huangxu96
|
6bb6cb27b8 | 6 years ago |
xiaoting
|
905b076553 | 6 years ago |
|
Double_V
|
8600f47439 | 6 years ago |
Pei Yang
|
d89deae9e0 | 6 years ago |
|
lilong12
|
316c97c7eb | 6 years ago |
pangyoki
|
46a1f69b3c | 6 years ago |
|
pangyoki
|
b21409e0c0 | 6 years ago |
|
pangyoki
|
69ec13cdf0 | 6 years ago |
|
pangyoki
|
386429beb0 | 6 years ago |
MRXLT
|
e3513a6395 | 6 years ago |
LutaoChu
|
e3a88eb450 | 6 years ago |
littletomatodonkey
|
edbaa027fe | 6 years ago |
|
liuyuhui
|
77eddf9168 | 6 years ago |
|
Kaipeng Deng
|
f05b184f72 | 6 years ago |
|
Zhou Wei
|
e9a1669e92 | 6 years ago |
|
xiaoting
|
5c1babde56 | 6 years ago |
Guo Sheng
|
3f565903e8 | 6 years ago |
Jack Zhou
|
a6c18075d6 | 6 years ago |
chentianyu03
|
2ca53fa65f | 6 years ago |
LiuChiachi
|
4316bd4d5a | 6 years ago |
|
lidanqing
|
ea76fe310c | 6 years ago |
tangwei12
|
c0550b54a5 | 6 years ago |
Aurelius84
|
772c266822 | 6 years ago |
|
chentianyu03
|
77866effeb | 6 years ago |
|
chentianyu03
|
cafa35e1ea | 6 years ago |
|
chentianyu03
|
3251f9c1eb | 6 years ago |
Steffy-zxf
|
164b9aabc1 | 6 years ago |
mapingshuo
|
50d24899cf | 6 years ago |
|
Zhou Wei
|
b57254ed61 | 6 years ago |
123malin
|
c0061ff56f | 6 years ago |
Guanghua Yu
|
51dd268cfe | 6 years ago |
Feiyu Chan
|
429c0b62b1 | 6 years ago |
|
mapingshuo
|
39c31a20e5 | 6 years ago |
Qinghe JING
|
1f45c06e92 | 6 years ago |
Yiqun Liu
|
ef2b12f11c | 6 years ago |
|
tianshuo78520a
|
b7fd4f9224 | 6 years ago |
Thunderbrook
|
ba6a29070a | 6 years ago |
@ -0,0 +1,161 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
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|
||||
Licensed under the Apache License, Version 2.0 (the "License");
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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. */
|
||||
|
||||
#ifdef PADDLE_WITH_XPU
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#include "paddle/fluid/operators/assign_op.h"
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#include <string>
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namespace paddle {
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namespace framework {
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class OpDesc;
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class Variable;
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} // namespace framework
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namespace imperative {
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class OpBase;
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} // namespace imperative
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namespace platform {
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struct CPUPlace;
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struct CUDAPlace;
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struct float16;
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} // namespace platform
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} // namespace paddle
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namespace paddle {
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namespace operators {
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class AssignOp : public framework::OperatorWithKernel {
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public:
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AssignOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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void InferShape(framework::InferShapeContext *ctx) const override {
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if (ctx->HasInput("X")) {
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auto type = ctx->GetInputsVarType("X")[0];
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if (type == framework::proto::VarType::SELECTED_ROWS ||
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type == framework::proto::VarType::LOD_TENSOR) {
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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if (type == framework::proto::VarType::LOD_TENSOR) {
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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} else if (type == framework::proto::VarType::LOD_TENSOR_ARRAY) {
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if (ctx->IsRuntime()) {
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// The runtime output shape is determined in kernel.
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return;
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} else {
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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}
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}
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}
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}
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protected:
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framework::OpKernelType GetKernelTypeForVar(
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const std::string &var_name, const framework::Tensor &tensor,
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const framework::OpKernelType &expected_kernel_type) const override {
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return framework::OpKernelType(expected_kernel_type.data_type_,
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expected_kernel_type.place_,
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tensor.layout());
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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const framework::Variable *var = ctx.InputVar("X");
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if (var->IsType<framework::LoDTensorArray>()) {
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auto t_arr = var->Get<framework::LoDTensorArray>();
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// NOTE(liym27): Support an empty tensor array as Input.
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// And set the kernel type is float.
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if (t_arr.size() == 0) {
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return framework::OpKernelType(framework::proto::VarType::FP32,
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ctx.device_context());
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}
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}
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return framework::OpKernelType(
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OperatorWithKernel::IndicateVarDataType(ctx, "X"),
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ctx.device_context());
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}
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};
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class AssignInferVarType : public framework::VarTypeInference {
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public:
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void operator()(framework::InferVarTypeContext *ctx) const override {
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ctx->SyncTypeAndDataType("X", "Out");
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}
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};
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class AssignKernel {
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public:
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void operator()(const framework::ExecutionContext &ctx) const {
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auto *x = ctx.InputVar("X");
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if (x == nullptr) {
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return;
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}
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PADDLE_ENFORCE_EQ(
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ctx.HasOutput("Out"), true,
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platform::errors::NotFound("Output(Out) of assign_op is not found."));
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auto *out = ctx.OutputVar("Out");
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platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
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auto &dev_ctx = *pool.Get(ctx.GetPlace());
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framework::VisitVarType(*x, AssignFunctor(out, dev_ctx));
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}
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};
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class AssignOpProtoMaker : public framework::OpProtoAndCheckerMaker {
|
||||
public:
|
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void Make() override {
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AddInput("X",
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"(LoDTensor, SelectedRows or LoDTensorArray) The input variable "
|
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"could be LoDTensor, SelectedRows or LoDTensorArray.")
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.AsDispensable();
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AddOutput("Out",
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"(LoDTensor, SelectedRows or LoDTensorArray) The type of output "
|
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"is the same as input X.");
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AddComment(R"DOC(Assign Operator
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Out = X, when type in [LoDTensor/SelectedRows/LoDTensorArray]
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raise error if the type is not listed above.
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)DOC");
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}
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};
|
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template <typename T>
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class AssignGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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|
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("assign");
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op->SetInput("X", this->OutputGrad("Out"));
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op->SetOutput("Out", this->InputGrad("X"));
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}
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};
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DECLARE_INPLACE_OP_INFERER(AssignOpInplaceInferer, {"X", "Out"});
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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namespace plat = paddle::platform;
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REGISTER_OP_XPU_KERNEL_FUNCTOR(assign, float, ops::AssignKernel, double,
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ops::AssignKernel, int, ops::AssignKernel,
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int64_t, ops::AssignKernel, bool,
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ops::AssignKernel);
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#endif
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@ -0,0 +1,69 @@
|
||||
/* 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. */
|
||||
|
||||
#ifdef PADDLE_WITH_XPU
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#include "paddle/fluid/operators/cast_op.h"
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#include <memory>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/platform/float16.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename InT>
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class CastXPUKernel : public framework::OpKernel<InT> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<framework::Tensor>("X");
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auto* out = context.Output<framework::Tensor>("Out");
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auto in_type = static_cast<framework::proto::VarType::Type>(
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context.Attr<int>("in_dtype"));
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auto out_type = static_cast<framework::proto::VarType::Type>(
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context.Attr<int>("out_dtype"));
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auto* in_data = in->data<InT>();
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auto numel = in->numel();
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int r = -1;
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if (out_type == framework::proto::VarType::FP32) {
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auto* out_data = out->mutable_data<float>(context.GetPlace());
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r = xpu::cast<InT, float>(dev_ctx.x_context(), in_data, out_data, numel);
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} else if (out_type == framework::proto::VarType::INT32) {
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auto* out_data = out->mutable_data<int>(context.GetPlace());
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r = xpu::cast<InT, int>(dev_ctx.x_context(), in_data, out_data, numel);
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} else if (out_type == framework::proto::VarType::INT64) {
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auto* out_data = out->mutable_data<int64_t>(context.GetPlace());
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r = xpu::cast<InT, int64_t>(dev_ctx.x_context(), in_data, out_data,
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numel);
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} else {
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PADDLE_THROW(platform::errors::Unavailable("Not supported cast %d -> %d",
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in_type, out_type));
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}
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PADDLE_ENFORCE_EQ(
|
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r, XPU_SUCCESS,
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platform::errors::External(
|
||||
"XPU API return wrong value[%d], please check whether "
|
||||
"Baidu Kunlun Card is properly installed.",
|
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r));
|
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}
|
||||
};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_XPU_KERNEL(
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cast, ops::CastXPUKernel<paddle::platform::XPUDeviceContext, int>,
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ops::CastXPUKernel<paddle::platform::XPUDeviceContext, float>,
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ops::CastXPUKernel<paddle::platform::XPUDeviceContext, int64_t>);
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#endif
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@ -0,0 +1,185 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#include "paddle/fluid/operators/concat_op.h"
|
||||
|
||||
#include <memory>
|
||||
#include <string>
|
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#include <vector>
|
||||
|
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#ifdef PADDLE_WITH_MKLDNN
|
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#include <paddle/fluid/platform/mkldnn_helper.h>
|
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#endif
|
||||
|
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#ifdef PADDLE_WITH_XPU
|
||||
|
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namespace paddle {
|
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namespace operators {
|
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using Tensor = framework::Tensor;
|
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|
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template <typename DeviceContext, typename T>
|
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class ConcatXPUKernel : public framework::OpKernel<T> {
|
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public:
|
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void Compute(const framework::ExecutionContext& ctx) const override {
|
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auto ins = ctx.MultiInput<framework::Tensor>("X");
|
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framework::Tensor* out = ctx.Output<framework::Tensor>("Out");
|
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int axis = ctx.Attr<int>("axis");
|
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PADDLE_ENFORCE_NE(ins[0], nullptr, platform::errors::InvalidArgument(
|
||||
"The input should not be null."));
|
||||
PADDLE_ENFORCE_NE(ctx.HasInput("AxisTensor"), true,
|
||||
platform::errors::InvalidArgument(
|
||||
"XPU donot surpport AxisTensor for now"));
|
||||
axis = ComputeAxis(static_cast<int64_t>(axis),
|
||||
static_cast<int64_t>(ins[0]->dims().size()));
|
||||
PADDLE_ENFORCE_GE(
|
||||
axis, 0, platform::errors::InvalidArgument("concat: axis shoud >= 0!"));
|
||||
PADDLE_ENFORCE_LT(axis, ins[0]->dims().size(),
|
||||
platform::errors::InvalidArgument(
|
||||
"concat: axis shoud < ins[0]->dims()!"));
|
||||
auto place = ctx.GetPlace();
|
||||
out->mutable_data<T>(place);
|
||||
std::vector<int> choose_idx;
|
||||
int n = 0;
|
||||
for (unsigned int i = 0; i < ins.size(); ++i) {
|
||||
if (ins[i] && ins[i]->numel() > 0) {
|
||||
choose_idx.push_back(i);
|
||||
n++;
|
||||
}
|
||||
}
|
||||
PADDLE_ENFORCE_LE(n, 8, platform::errors::InvalidArgument(
|
||||
"XPU only surpport at most 8 tensors for now"));
|
||||
PADDLE_ENFORCE_GT(
|
||||
n, 0, platform::errors::InvalidArgument("No tensor need concat?"));
|
||||
int h = 1;
|
||||
int w_except_axis = 1;
|
||||
for (int i = 0; i < axis; ++i) {
|
||||
h *= (ins[choose_idx[0]]->dims())[i];
|
||||
}
|
||||
for (int i = axis + 1; i < ins[0]->dims().size(); ++i) {
|
||||
w_except_axis *= (ins[choose_idx[0]]->dims())[i];
|
||||
}
|
||||
for (int i = 1; i < n; ++i) {
|
||||
int hh = 1;
|
||||
int ww = 1;
|
||||
for (int j = 0; j < axis; ++j) {
|
||||
hh *= (ins[choose_idx[i]]->dims())[j];
|
||||
}
|
||||
for (int j = axis + 1; j < ins[i]->dims().size(); ++j) {
|
||||
ww *= (ins[choose_idx[i]]->dims())[j];
|
||||
}
|
||||
PADDLE_ENFORCE_EQ(hh, h, platform::errors::InvalidArgument(
|
||||
"concat: h should be eual!"));
|
||||
PADDLE_ENFORCE_EQ(ww, w_except_axis,
|
||||
platform::errors::InvalidArgument(
|
||||
"concat: w should be eual except for axis!"));
|
||||
}
|
||||
auto& dev_ctx = ctx.template device_context<DeviceContext>();
|
||||
std::unique_ptr<int[]> in_w_host(new int[n]);
|
||||
std::unique_ptr<const float* []> ptrs(new const float*[n]);
|
||||
for (int i = 0; i < n; ++i) {
|
||||
ptrs[i] = ins[choose_idx[i]]->data<T>();
|
||||
in_w_host[i] = w_except_axis * (ins[choose_idx[i]]->dims())[axis];
|
||||
}
|
||||
int r =
|
||||
xpu::concat<float>(dev_ctx.x_context(), h, (const int*)in_w_host.get(),
|
||||
n, (const float**)ptrs.get(), out->data<T>());
|
||||
PADDLE_ENFORCE_EQ(
|
||||
r, XPU_SUCCESS,
|
||||
platform::errors::External(
|
||||
"XPU API return wrong value[%d], please check whether "
|
||||
"Baidu Kunlun Card is properly installed.",
|
||||
r));
|
||||
}
|
||||
};
|
||||
template <typename DeviceContext, typename T>
|
||||
class ConcatGradXPUKernel : public framework::OpKernel<T> {
|
||||
public:
|
||||
void Compute(const framework::ExecutionContext& ctx) const {
|
||||
auto* out_grad =
|
||||
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
|
||||
auto ins = ctx.MultiInput<framework::LoDTensor>("X");
|
||||
auto out_var_names = ctx.OutputNames(framework::GradVarName("X"));
|
||||
auto outs =
|
||||
ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
|
||||
{
|
||||
auto dx = outs;
|
||||
auto x = ins;
|
||||
for (size_t i = 0; i < dx.size(); ++i) {
|
||||
if (dx[i] != nullptr) {
|
||||
dx[i]->set_lod(x[i]->lod());
|
||||
}
|
||||
}
|
||||
}
|
||||
PADDLE_ENFORCE_NE(ins[0], nullptr, platform::errors::InvalidArgument(
|
||||
"The input should not be null."));
|
||||
auto axis = ctx.Attr<int>("axis");
|
||||
if (ctx.HasInput("AxisTensor")) {
|
||||
auto* axis_tensor = ctx.Input<framework::Tensor>("AxisTensor");
|
||||
axis = GetDataFromTensor<int>(axis_tensor)[0];
|
||||
}
|
||||
axis = ComputeAxis(static_cast<int64_t>(axis),
|
||||
static_cast<int64_t>(ins[0]->dims().size()));
|
||||
// get output tensor that the name is not kEmptyVarName
|
||||
std::vector<framework::Tensor*> outputs;
|
||||
for (size_t j = 0; j < outs.size(); ++j) {
|
||||
if (out_var_names[j] != framework::kEmptyVarName &&
|
||||
outs[j]->numel() != 0UL) {
|
||||
outs[j]->mutable_data<T>(ctx.GetPlace());
|
||||
outputs.push_back(outs[j]);
|
||||
} else {
|
||||
outputs.push_back(nullptr);
|
||||
}
|
||||
}
|
||||
PADDLE_ENFORCE_GE(axis, 0, platform::errors::InvalidArgument(
|
||||
"concat_grad: axis shoud >= 0!"));
|
||||
PADDLE_ENFORCE_LT(axis, out_grad->dims().size(),
|
||||
platform::errors::InvalidArgument(
|
||||
"concat_grad: axis shoud < ins[0]->dims()!"));
|
||||
auto out_grad_stride = framework::stride_numel(out_grad->dims());
|
||||
int n = outputs.size();
|
||||
PADDLE_ENFORCE_LE(n, 16,
|
||||
platform::errors::InvalidArgument(
|
||||
"XPU only surpport at most 16 tensors for now"));
|
||||
int h = out_grad_stride[0] / out_grad_stride[axis];
|
||||
auto& dev_ctx = ctx.template device_context<DeviceContext>();
|
||||
std::unique_ptr<int[]> in_w_host(new int[n]);
|
||||
std::unique_ptr<float* []> ptrs(new float*[n]);
|
||||
for (int i = 0; i < n; ++i) {
|
||||
auto out_stride = framework::stride_numel(outputs[i]->dims());
|
||||
ptrs[i] = outputs[i]->data<T>();
|
||||
in_w_host[i] = out_stride[axis];
|
||||
}
|
||||
int r = xpu::concat_grad(dev_ctx.x_context(), h, in_w_host.get(), n,
|
||||
reinterpret_cast<float**>(ptrs.get()),
|
||||
out_grad->data<T>());
|
||||
PADDLE_ENFORCE_EQ(
|
||||
r, XPU_SUCCESS,
|
||||
platform::errors::External(
|
||||
"XPU API return wrong value[%d], please check whether "
|
||||
"Baidu Kunlun Card is properly installed.",
|
||||
r));
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
|
||||
namespace ops = paddle::operators;
|
||||
REGISTER_OP_XPU_KERNEL(
|
||||
concat, ops::ConcatXPUKernel<paddle::platform::XPUDeviceContext, float>);
|
||||
REGISTER_OP_XPU_KERNEL(
|
||||
concat_grad,
|
||||
ops::ConcatGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
|
||||
|
||||
#endif
|
||||
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Reference in new issue