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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into scope

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cblas_new
qiaolongfei 8 years ago
parent 5d134a03f1 61ebacbcd3
commit 65880f7e9c
20 changed files with 289 additions and 185 deletions
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3
paddle/cuda/src/hl_cuda_sequence.cu
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@ -269,8 +269,7 @@ void hl_sequence2batch_copy_padding(real* batch,
int blockDimY = CUDA_BLOCK_SIZE / blockDimX;
dim3 threads(blockDimX, blockDimY);
int gridDimX = (maxSequenceLength * blockDimX + CUDA_BLOCK_SIZE - 1) /
CUDA_BLOCK_SIZE;
int gridDimX = (maxSequenceLength + blockDimY - 1) / blockDimY;
int gridDimY = numSequences;
dim3 grid(gridDimX, gridDimY);

4
paddle/framework/operator.cc
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@ -20,7 +20,7 @@ namespace paddle {
namespace framework {
template <>
Eigen::DefaultDevice* KernelContext::GetEigenDevice<
Eigen::DefaultDevice* ExecutionContext::GetEigenDevice<
platform::CPUPlace, Eigen::DefaultDevice>() const {
return device_context_.get_eigen_device<Eigen::DefaultDevice>();
}
@ -28,7 +28,7 @@ Eigen::DefaultDevice* KernelContext::GetEigenDevice<
#ifndef PADDLE_ONLY_CPU
template <>
Eigen::GpuDevice*
KernelContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
return device_context_.get_eigen_device<Eigen::GpuDevice>();
}
#endif

181
paddle/framework/operator.h
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File diff suppressed because it is too large Load Diff

32
paddle/framework/operator_test.cc
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@ -73,6 +73,7 @@ TEST(OperatorBase, all) {
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
scope.NewVar("OUT1");
ASSERT_EQ(paddle::framework::op_run_num, 0);
op->InferShape(scope);
op->Run(scope, device_context);
ASSERT_EQ(paddle::framework::op_run_num, 1);
}
@ -97,14 +98,13 @@ static int cpu_kernel_run_num = 0;
class OpWithKernelTest : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor*>& inputs,
const std::vector<Tensor*>& outputs) const override {}
void InferShape(const framework::InferShapeContext& ctx) const override {}
};
template <typename T1, typename T2>
class CPUKernelTest : public OpKernel {
public:
void Compute(const KernelContext& ctx) const {
void Compute(const ExecutionContext& ctx) const {
std::cout << "this is cpu kernel" << std::endl;
std::cout << ctx.op_.DebugString() << std::endl;
cpu_kernel_run_num++;
@ -149,13 +149,31 @@ class OpKernelTestMultiInputsProtoAndCheckerMaker
class CPUKernalMultiInputsTest : public OpKernel {
public:
void Compute(const KernelContext& ctx) const {
void Compute(const ExecutionContext& ctx) const {
auto xs = ctx.op_.Inputs("xs");
ASSERT_EQ(xs.size(), 3UL);
ASSERT_EQ(xs[0], "x0");
ASSERT_EQ(xs[1], "x1");
ASSERT_EQ(xs[2], "x2");
auto inVar0 = ctx.MultiInputVar("xs");
ASSERT_EQ(inVar0.size(), 3);
auto intVar1 = ctx.InputVar("k");
ASSERT_NE(intVar1, nullptr);
auto outVar0 = ctx.MultiOutputVar("ys");
ASSERT_EQ(outVar0.size(), 2);
auto inTensor0 = ctx.MultiInput<Tensor>("xs");
ASSERT_EQ(inTensor0.size(), 3);
auto intTensor1 = ctx.Input<Tensor>("k");
ASSERT_NE(intTensor1, nullptr);
auto outTensor0 = ctx.MultiOutput<Tensor>("ys");
ASSERT_EQ(outTensor0.size(), 2);
auto k = ctx.op_.Input("k");
ASSERT_EQ(k, "k0");
@ -233,6 +251,12 @@ TEST(OpKernel, multi_inputs) {
paddle::platform::CPUDeviceContext cpu_device_context;
paddle::framework::Scope scope;
scope.NewVar("x0")->GetMutable<Tensor>();
scope.NewVar("x1")->GetMutable<Tensor>();
scope.NewVar("x2")->GetMutable<Tensor>();
scope.NewVar("k0")->GetMutable<Tensor>();
scope.NewVar("y0")->GetMutable<Tensor>();
scope.NewVar("y1")->GetMutable<Tensor>();
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
op->Run(scope, cpu_device_context);

60
paddle/math/tests/test_matrixCompare.cpp
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@ -1141,4 +1141,64 @@ TEST(CpuMatrix, copyFrom) {
TensorCheckEqual(cpu, copy);
}
void testBatch2seqPadding(int batchSize, int inputDim) {
MatrixPtr cpuInput = std::make_shared<CpuMatrix>(batchSize, inputDim);
MatrixPtr gpuInput = std::make_shared<GpuMatrix>(batchSize, inputDim);
cpuInput->randomizeUniform();
gpuInput->copyFrom(*cpuInput);
IVectorPtr cpuSequence;
generateSequenceStartPositions(batchSize, cpuSequence);
IVectorPtr gpuSequence = IVector::create(cpuSequence->getSize(), true);
gpuSequence->copyFrom(*cpuSequence);
size_t numSeq = cpuSequence->getSize() - 1;
size_t maxSeqLen = *std::max_element(cpuSequence->getData(),
cpuSequence->getData() + numSeq);
MatrixPtr cBatch = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
MatrixPtr gBatch = std::make_shared<GpuMatrix>(numSeq * maxSeqLen, inputDim);
MatrixPtr cCheck = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
hl_sequence2batch_copy_padding(gBatch->getData(),
gpuInput->getData(),
cpuSequence->getData(),
inputDim,
maxSeqLen,
numSeq,
false,
true);
cCheck->copyFrom(*gBatch);
int* seqStart = cpuSequence->getData();
float* batchData = cBatch->getData();
float* seqData = cpuInput->getData();
for (size_t i = 0; i < maxSeqLen; i++) {
for (size_t j = 0; j < numSeq; j++) {
size_t sequenceStart = seqStart[j];
size_t sequenceLength = seqStart[j + 1] - seqStart[j];
if (i < sequenceLength) {
memcpy(batchData + (i * numSeq + j) * inputDim,
seqData + (sequenceStart + i) * inputDim,
inputDim * sizeof(real));
} else {
memset(batchData + (i * numSeq + j) * inputDim,
0,
inputDim * sizeof(real));
}
}
}
TensorCheckErr(*cBatch, *cCheck);
}
TEST(Matrix, warpCTC) {
for (auto batchSize : {51, 526, 2884}) {
for (auto inputDim : {32, 512, 2026}) {
VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim;
testBatch2seqPadding(batchSize, inputDim);
}
}
}
#endif

21
paddle/operators/add_op.cc
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@ -19,16 +19,16 @@ namespace operators {
class AddOp : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 2, "Input size of AddOp must be two");
PADDLE_ENFORCE(outputs.size() == 1, "Output size of AddOp must be one");
PADDLE_ENFORCE(
inputs[0] != nullptr && inputs[1] != nullptr && outputs[0] != nullptr,
"Inputs/Outputs of AddOp must all be set");
PADDLE_ENFORCE(inputs[0]->dims() == inputs[1]->dims(),
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2, "Input size of AddOp must be two");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of AddOp must be one");
PADDLE_ENFORCE(ctx.InputVar(0) != nullptr && ctx.InputVar(1) != nullptr,
"Inputs of AddOp must all be set");
PADDLE_ENFORCE(ctx.OutputVar(0) != nullptr,
"Outputs of AddOp must all be set");
PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims() == ctx.Input<Tensor>(1)->dims(),
"Two input of Add Op's dimension must be same.");
outputs[0]->Resize(inputs[0]->dims());
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
}
};
@ -49,8 +49,7 @@ The equation is: Out = X + Y
class AddOpGrad : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {}
void InferShape(const InferShapeContext &ctx) const override {}
std::string DebugString() const override {
LOG(INFO) << "AddOpGrad";
return "";

11
paddle/operators/add_op.h
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@ -21,16 +21,17 @@ namespace operators {
template <typename Place, typename T>
class AddKernel : public OpKernel {
public:
void Compute(const KernelContext& context) const override {
auto input0 = context.Input(0)->Get<Tensor>();
auto input1 = context.Input(1)->Get<Tensor>();
auto output = context.Output(0)->GetMutable<Tensor>();
void Compute(const ExecutionContext& context) const override {
auto input0 = context.Input<Tensor>(0);
auto input1 = context.Input<Tensor>(1);
auto output = context.Output<Tensor>(0);
output->mutable_data<T>(context.GetPlace());
EigenVector<T>::Flatten(*output).device(
*(context.GetEigenDevice<Place>())) =
EigenVector<T>::Flatten(input0) + EigenVector<T>::Flatten(input1);
framework::EigenVector<T>::Flatten(*input0) +
framework::EigenVector<T>::Flatten(*input1);
}
};

18
paddle/operators/cross_entropy_op.cc
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@ -19,20 +19,20 @@ namespace operators {
class OnehotCrossEntropyOp : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 2,
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2,
"Input size of OnehotCrossEntropyOp must be two");
PADDLE_ENFORCE(outputs.size() == 1,
PADDLE_ENFORCE(ctx.OutputSize() == 1,
"Output size of OnehotCrossEntropyOp must be one");
PADDLE_ENFORCE(inputs[0] != nullptr && inputs[1] != nullptr,
PADDLE_ENFORCE(ctx.InputVar(0) != nullptr && ctx.InputVar(1) != nullptr,
"Inputs of OnehotCrossEntropyOp must all be set");
PADDLE_ENFORCE(outputs[0] != nullptr,
PADDLE_ENFORCE(ctx.OutputVar(0) != nullptr,
"Outputs of OnehotCrossEntropyOp must all be set");
PADDLE_ENFORCE(inputs[0]->dims().size() == 2, "X's dimension must be 2.");
PADDLE_ENFORCE(outputs[0]->dims().size() == 1,
PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims().size() == 2,
"X's dimension must be 2.");
PADDLE_ENFORCE(ctx.Output<Tensor>(0)->dims().size() == 1,
"label's dimension must be 1.");
outputs[0]->Resize({inputs[0]->dims()[0]});
ctx.Output<Tensor>(0)->Resize({ctx.Input<Tensor>(0)->dims()[0]});
}
};

16
paddle/operators/cross_entropy_op.h
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@ -23,18 +23,18 @@ class OnehotCrossEntropyOpKernel : public OpKernel {
public:
constexpr T LOG_THRESHOLD() const { return static_cast<T>(1e-20); }
void Compute(const KernelContext& context) const override {
auto X = context.Input(0)->Get<Tensor>();
const T* X_data = X.data<T>();
const int* label_data = context.Input(1)->Get<Tensor>().data<int>();
auto* Y = context.Output(0)->GetMutable<Tensor>();
void Compute(const ExecutionContext& ctx) const override {
auto X = ctx.Input<Tensor>(0);
const T* X_data = X->data<T>();
const int* label_data = ctx.Input<Tensor>(1)->data<int>();
auto Y = ctx.Output<Tensor>(0);
Y->mutable_data<T>(context.GetPlace());
Y->mutable_data<T>(ctx.GetPlace());
T* Y_data = Y->data<T>();
int batch_size = X.dims()[0];
int class_num = X.dims()[1];
int batch_size = X->dims()[0];
int class_num = X->dims()[1];
// Y[i] = -log(X[i][j])
for (int i = 0; i < batch_size; ++i) {

16
paddle/operators/mul_op.cc
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@ -19,18 +19,17 @@ namespace operators {
class MulOp : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 2, "The mul op must take two inputs");
auto dim0 = inputs[0]->dims();
auto dim1 = inputs[1]->dims();
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs");
auto dim0 = ctx.Input<Tensor>(0)->dims();
auto dim1 = ctx.Input<Tensor>(1)->dims();
PADDLE_ENFORCE(dim0.size() == 2 && dim1.size() == 2,
"The input of mul op must be matrix");
PADDLE_ENFORCE(
dim0[1] == dim1[0],
"First matrix's width must be equal with second matrix's height.");
PADDLE_ENFORCE(outputs.size() == 1, "The mul op must take one output");
outputs[0]->Resize({dim0[0], dim1[1]});
PADDLE_ENFORCE(ctx.OutputSize() == 1, "The mul op must take one output");
ctx.Output<Tensor>(0)->Resize({dim0[0], dim1[1]});
}
};
@ -51,8 +50,7 @@ The equation is: Out = X * Y
class MulOpGrad : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {}
void InferShape(const InferShapeContext &ctx) const override {}
std::string DebugString() const override {
LOG(INFO) << "MulGrad";
return "";

12
paddle/operators/mul_op.h
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@ -22,19 +22,17 @@ namespace operators {
template <typename Place, typename T>
class MulKernel : public OpKernel {
public:
void Compute(const KernelContext& context) const override {
void Compute(const ExecutionContext& context) const override {
Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
{Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
auto input0 = context.Input(0)->Get<Tensor>();
auto input1 = context.Input(1)->Get<Tensor>();
auto* output = context.Output(0)->GetMutable<Tensor>();
auto output = context.Output<Tensor>(0);
output->mutable_data<T>(context.GetPlace());
EigenMatrix<T>::From(*output).device(*(context.GetEigenDevice<Place>())) =
EigenMatrix<T>::From(input0).contract(EigenMatrix<T>::From(input1),
dim_pair);
EigenMatrix<T>::From(*context.Input<Tensor>("X"))
.contract(EigenMatrix<T>::From(*context.Input<Tensor>("Y")),
dim_pair);
}
};
} // namespace operators

14
paddle/operators/rowwise_add_op.cc
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@ -18,17 +18,17 @@ namespace operators {
class RowWiseAddOp : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 2UL, "Two inputs is needed by rowwise add");
auto dim0 = inputs[0]->dims();
auto dim1 = inputs[1]->dims();
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2UL,
"Two inputs is needed by rowwise add");
auto dim0 = ctx.Input<Tensor>(0)->dims();
auto dim1 = ctx.Input<Tensor>(1)->dims();
PADDLE_ENFORCE(dim0.size() == 2, "Input 0 must be matrix");
PADDLE_ENFORCE(dim1.size() == 1, "The second input must be vector");
PADDLE_ENFORCE(dim0[1] == dim1[0], "The width of two input must be same");
PADDLE_ENFORCE(outputs.size() == 1, "The output size must be 1");
outputs[0]->Resize(inputs[0]->dims());
PADDLE_ENFORCE(ctx.OutputSize() == 1, "The output size must be 1");
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
}
};

10
paddle/operators/rowwise_add_op.h
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@ -21,14 +21,12 @@ namespace operators {
template <typename Place, typename T>
class RowWiseAddKernel : public OpKernel {
public:
void Compute(const KernelContext& context) const override {
auto in0 = context.Input(0)->Get<Tensor>();
auto in1 = context.Input(1)->Get<Tensor>();
auto* out = context.Output(0)->GetMutable<Tensor>();
void Compute(const ExecutionContext& context) const override {
auto out = context.Output<Tensor>(0);
out->mutable_data<T>(context.GetPlace());
auto input = EigenMatrix<T>::From(in0);
auto bias = EigenVector<T>::From(in1);
auto input = EigenMatrix<T>::From(*context.Input<Tensor>(0));
auto bias = EigenVector<T>::From(*context.Input<Tensor>(1));
auto output = EigenMatrix<T>::From(*out);
const int bias_size = bias.dimension(0);

17
paddle/operators/sgd_op.cc
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@ -19,16 +19,15 @@ namespace operators {
class SGDOp : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 2, "Input size of SGDOp must be two");
PADDLE_ENFORCE(outputs.size() == 1, "Output size of SGDOp must be one");
PADDLE_ENFORCE(inputs[0] != nullptr, "inputs[0] mast be set");
PADDLE_ENFORCE(inputs[1] != nullptr, "inputs[1] mast be set");
PADDLE_ENFORCE(outputs[0] != nullptr, "outputs[0] mast be set");
PADDLE_ENFORCE(inputs[0]->dims() == inputs[1]->dims(),
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2, "Input size of SGDOp must be two");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of SGDOp must be one");
PADDLE_ENFORCE(ctx.InputVar(0) != nullptr, "inputs[0] mast be set");
PADDLE_ENFORCE(ctx.InputVar(1) != nullptr, "inputs[1] mast be set");
PADDLE_ENFORCE(ctx.OutputVar(0) != nullptr, "outputs[0] mast be set");
PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims() == ctx.Input<Tensor>(1)->dims(),
"Two input of SGD Op's dimension must be same.");
outputs[0]->Resize(inputs[0]->dims());
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
}
};

10
paddle/operators/sgd_op.h
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@ -21,16 +21,16 @@ namespace operators {
template <typename Place, typename T>
class SGDOpKernel : public OpKernel {
public:
void Compute(const KernelContext& ctx) const override {
auto param = ctx.Input("param")->Get<Tensor>();
auto grad = ctx.Input("grad")->Get<Tensor>();
auto* param_out = ctx.Output(0)->GetMutable<Tensor>();
void Compute(const ExecutionContext& ctx) const override {
auto param = ctx.Input<Tensor>("param");
auto grad = ctx.Input<Tensor>("grad");
auto param_out = ctx.Output<Tensor>(0);
float lr = ctx.op_.GetAttr<float>("learning_rate");
param_out->mutable_data<T>(ctx.GetPlace());
EigenVector<T>::Flatten(*param_out).device(*(ctx.GetEigenDevice<Place>())) =
EigenVector<T>::Flatten(param) - lr * EigenVector<T>::Flatten(grad);
EigenVector<T>::Flatten(*param) - lr * EigenVector<T>::Flatten(*grad);
}
};

12
paddle/operators/sigmoid_op.cc
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@ -18,11 +18,10 @@ namespace operators {
class SigmoidOp : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 1, "Sigmoid Op only have one input");
PADDLE_ENFORCE(outputs.size() == 1, "Sigmoid Op only have one output");
outputs[0]->Resize(inputs[0]->dims());
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1, "Sigmoid Op only have one input");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Sigmoid Op only have one output");
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
}
};
@ -38,8 +37,7 @@ public:
class SigmoidOpGrad : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {}
void InferShape(const InferShapeContext &ctx) const override {}
std::string DebugString() const override {
LOG(INFO) << "SigmoidGrad";
return "";

9
paddle/operators/sigmoid_op.h
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@ -22,15 +22,14 @@ namespace operators {
template <typename Place, typename T>
class SigmoidKernel : public OpKernel {
public:
void Compute(const KernelContext& context) const override {
auto input = context.Input(0)->Get<Tensor>();
auto* output = context.Output(0)->GetMutable<Tensor>();
void Compute(const ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
output->mutable_data<T>(context.GetPlace());
EigenVector<T>::Flatten(*output).device(
*(context.GetEigenDevice<Place>())) =
1.0 / (1.0 + (-1.0 * EigenVector<T>::Flatten(input)).exp());
1.0 / (1.0 + (-1.0 * EigenVector<T>::Flatten(*input)).exp());
}
};
} // namespace operators

16
paddle/operators/softmax_op.cc
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@ -18,14 +18,13 @@ namespace operators {
class SoftmaxOp : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 1, "Only one input is need for softmax");
PADDLE_ENFORCE(inputs[0]->dims().size() == 2,
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1, "Only one input is need for softmax");
PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims().size() == 2,
"The input of softmax op must be matrix");
PADDLE_ENFORCE(outputs.size() == 1, "Only one output is need for softmax");
outputs[0]->Resize(inputs[0]->dims());
PADDLE_ENFORCE(ctx.OutputSize() == 1,
"Only one output is need for softmax");
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
}
};
@ -41,8 +40,7 @@ public:
class SoftmaxOpGrad : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {}
void InferShape(const InferShapeContext &ctx) const override {}
std::string DebugString() const override {
LOG(INFO) << "SoftmaxOpGrad";
return "";

8
paddle/operators/softmax_op.h
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@ -22,12 +22,12 @@ namespace operators {
template <typename Place, typename T>
class SoftmaxKernel : public OpKernel {
public:
void Compute(const KernelContext& context) const override {
auto input = context.Input(0)->Get<Tensor>();
auto* output = context.Output(0)->GetMutable<Tensor>();
void Compute(const ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
output->mutable_data<T>(context.GetPlace());
auto logits = EigenMatrix<T>::From(input);
auto logits = EigenMatrix<T>::From(*input);
auto softmax = EigenMatrix<T>::From(*output);
const int kBatchDim = 0;

4
paddle/operators/type_alias.h
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@ -22,7 +22,9 @@ namespace paddle {
namespace operators {
using OpKernel = framework::OpKernel;
using KernelContext = framework::KernelContext;
using InferShapeContext = framework::InferShapeContext;
using ExecutionContext = framework::ExecutionContext;
using Variable = framework::Variable;
template <typename T,
int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>

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