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Merged
kuke merged 5 commits into from
Sep 26, 2017
Merged

Modify multiplex_op #4360

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f94109d
replace LoDTensor in multiplex_op
Sep 25, 2017
236af56
separate index tensor from candidate tensors in multiplex_op
Sep 25, 2017
089f8e2
Merge branch 'develop' of upstream into multiplex_modify_dev
Sep 26, 2017
58ac8f4
apply more general dims for multiplex_op
Sep 26, 2017
e9dbc85
to trigger ci for multiplex_op
Sep 26, 2017
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62 changes: 34 additions & 28 deletions paddle/operators/multiplex_op.cc
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Expand Up @@ -18,32 +18,38 @@ namespace paddle {
namespace operators {

using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;

class MultiplexOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;

protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Ids"),
"Input(Ids) shouldn't be null.");
PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
"Input(X) should not be null");
"MultiInput(X) shouldn't be empty.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) shouldn't be null.");
auto ids_dim = ctx.Input<Tensor>("Ids")->dims();
PADDLE_ENFORCE(
ids_dim.size() == 2 && ids_dim[1] == 1,
"The index tensor must be a vector with size batchSize x 1.");

auto ins = ctx.MultiInput<Tensor>("X");
auto *out = ctx.Output<LoDTensor>("Out");
auto *out = ctx.Output<Tensor>("Out");
auto num_ins = ins.size();
PADDLE_ENFORCE(num_ins > 2,
"multiplex operator should have more than 2 inputs.");
PADDLE_ENFORCE_EQ(ins[0]->dims().size(), 1,
"The first input must be a index vector.");
auto in_dim = ins[1]->dims();

for (size_t i = 2; i < num_ins; i++) {
PADDLE_ENFORCE(num_ins > 1,
"multiplex operator should have more than "
"one candidate input tensors.");

auto in_dim = ins[0]->dims();
PADDLE_ENFORCE(in_dim.size() >= 2,
"The rank of candidate tensors must be not less than 2.");
for (size_t i = 1; i < num_ins; i++) {
auto dim = ins[i]->dims();
PADDLE_ENFORCE(
in_dim == dim,
"All the input tensors except the first one must have the same size");
PADDLE_ENFORCE(in_dim == dim,
"All the candidate tensors must have the same size.");
}
out->Resize(in_dim);
}
Expand All @@ -54,25 +60,25 @@ class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
MultiplexOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensors of multiplex operator.").AsDuplicable();
AddInput("Ids", "The index tensor of multiplex operator.");
AddInput("X", "The candidate tensors of multiplex operator.")
.AsDuplicable();
AddOutput("Out", "The output tensor of multiplex operator.");
AddComment(R"DOC(Multiplex operator

Multiplex multiple tensors according to the index provided by the first
input tensor.
Multiplex multiple tensors according to the index provided by the index tensor.

ins[0]: the index tensor.
ins[1:N]: the candidate output tensors.
Ids: the index tensor.
X[0 : N - 1]: the candidate tensors for output (N >= 2).
For each index i from 0 to batchSize - 1, the output is the i-th row of the
the (index[i] + 1)-th tensor.
the (Ids[i])-th tensor.

For i-th row of the output tensor:

y[i][j] = x_{k}[i][j], j = 0,1, ... , (x_{1}.width - 1)
y[i] = x_{k}[i]

where y is the output tensor. `x_{k}` is the k-th input tensor
and `k = x{0}[i] + 1`.

and `k = Ids[i]`.
)DOC");
}
};
Expand All @@ -84,15 +90,15 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
"Input(X) should not be null");
"Input(X) should not be null.");
PADDLE_ENFORCE(!ctx.MultiOutputVar(framework::GradVarName("X")).empty(),
"Output(X@Grad) should not be null");
"Output(X@Grad) should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto d_ins = ctx.MultiOutput<LoDTensor>(framework::GradVarName("X"));
"Input(Out@GRAD) should not be null.");
auto d_ins = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
auto ins = ctx.MultiInput<Tensor>("X");
// don't compute gradient for index (ins[0])
for (size_t i = 1; i < ins.size(); i++) {
// No need to compute gradient for Input(Ids)
for (size_t i = 0; i < ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->Resize(ins[i]->dims());
}
Expand Down
43 changes: 23 additions & 20 deletions paddle/operators/multiplex_op.cu
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Expand Up @@ -18,27 +18,30 @@
namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

template <typename Place, typename T>
class MultiplexGPUKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto* out = ctx.Output<framework::LoDTensor>("Out");

auto ins = ctx.MultiInput<Tensor>("X");
auto* ids = ctx.Input<Tensor>("Ids");
auto* out = ctx.Output<Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());

auto rows = ins[1]->dims()[0];
auto cols = ins[1]->dims()[1];
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
// copy index to cpu
framework::Tensor index_t_cpu;
index_t_cpu.CopyFrom<T>(*(ins[0]), platform::CPUPlace());
auto* index = index_t_cpu.data<T>();
Tensor index_t_cpu;
index_t_cpu.CopyFrom<int32_t>(*ids, platform::CPUPlace());
auto* index = index_t_cpu.data<int32_t>();
auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream();
Place place = boost::get<Place>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
int k = (int)index[i] + 1;
int32_t k = index[i];
PADDLE_ENFORCE_GE(k, 0, "index must be nonnegative.");
PADDLE_ENFORCE_LT(k, ins.size(),
"index exceeds the number of candidate tensors.");
memory::Copy(place, out->data<T>() + i * cols, place,
Expand All @@ -51,31 +54,31 @@ template <typename Place, typename T>
class MultiplexGradGPUKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto d_ins =
ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
for (size_t i = 1; i < d_ins.size(); i++) {
auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<Tensor>("X");
auto* ids = ctx.Input<Tensor>("Ids");
auto d_ins = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
for (size_t i = 0; i < d_ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
t.device(ctx.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
}
}

auto rows = ins[1]->dims()[0];
auto cols = ins[1]->dims()[1];
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
// copy index to cpu
framework::Tensor index_t_cpu;
index_t_cpu.CopyFrom<T>(*(ins[0]), platform::CPUPlace());
auto* index = index_t_cpu.data<T>();
Tensor index_t_cpu;
index_t_cpu.CopyFrom<int32_t>(*ids, platform::CPUPlace());
auto* index = index_t_cpu.data<int32_t>();

auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream();
Place place = boost::get<Place>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
int k = (int)index[i] + 1;
size_t k = static_cast<size_t>(index[i]);
if (d_ins[k]) {
memory::Copy(place, d_ins[k]->data<T>() + i * cols, place,
d_out->data<T>() + i * cols, cols * sizeof(T), stream);
Expand Down
23 changes: 13 additions & 10 deletions paddle/operators/multiplex_op.h
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Expand Up @@ -27,16 +27,18 @@ class MultiplexCPUKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto* out = ctx.Output<framework::LoDTensor>("Out");
auto ids = ctx.Input<framework::Tensor>("Ids");
auto* out = ctx.Output<framework::Tensor>("Out");

out->mutable_data<T>(ctx.GetPlace());

auto rows = ins[1]->dims()[0];
auto cols = ins[1]->dims()[1];
auto* index = ins[0]->data<T>();
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
auto index = ids->data<int32_t>();
Place place = boost::get<Place>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
int k = (int)index[i] + 1;
int32_t k = index[i];
PADDLE_ENFORCE_GE(k, 0, "index must be nonnegative.");
PADDLE_ENFORCE_LT(static_cast<size_t>(k), ins.size(),
"index exceeds the number of candidate tensors.");
memory::Copy(place, out->data<T>() + i * cols, place,
Expand All @@ -50,23 +52,24 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* ids = ctx.Input<framework::Tensor>("Ids");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto d_ins =
ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
for (size_t i = 1; i < d_ins.size(); i++) {
for (size_t i = 0; i < d_ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
t.device(ctx.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
}
}

auto rows = ins[1]->dims()[0];
auto cols = ins[1]->dims()[1];
auto* index = ins[0]->data<T>();
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
auto* index = ids->data<int32_t>();
Place place = boost::get<Place>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
int k = (int)index[i] + 1;
size_t k = static_cast<size_t>(index[i]);
if (d_ins[k]) {
memory::Copy(place, d_ins[k]->data<T>() + i * cols, place,
d_out->data<T>() + i * cols, cols * sizeof(T));
Expand Down
12 changes: 7 additions & 5 deletions python/paddle/v2/framework/tests/test_multiplex_op.py
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Expand Up @@ -6,20 +6,22 @@
class TestMultiplexOp(OpTest):
def setUp(self):
self.op_type = "multiplex"
rows = 3
index = np.array([3, 1, 0])
rows = 4
index = np.arange(0, rows).astype('int32')
np.random.shuffle(index)
index = np.reshape(index, (rows, 1))
ins1 = np.random.random((rows, 10)).astype("float32")
ins2 = np.random.random((rows, 10)).astype("float32")
ins3 = np.random.random((rows, 10)).astype("float32")
ins4 = np.random.random((rows, 10)).astype("float32")
self.inputs = {
'X': [('index', index), ('x1', ins1), ('x2', ins2), ('x3', ins3),
('x4', ins4)]
'Ids': index,
'X': [('x1', ins1), ('x2', ins2), ('x3', ins3), ('x4', ins4)]
}
# multiplex output
output = np.zeros_like(ins1)
for i in range(0, rows):
k = index[i] + 1
k = index[i][0]
output[i] = self.inputs['X'][k][1][i]
self.outputs = {'Out': output}

Expand Down