Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Revert "Revert "[Phi] trans logsumexp op"" #41109

Merged
merged 1 commit into from
Mar 30, 2022
Merged

Revert "Revert "[Phi] trans logsumexp op"" #41109

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
93 changes: 8 additions & 85 deletions paddle/fluid/operators/reduce_ops/logsumexp_op.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,91 +12,20 @@
// See the License for the specific language governing permissions and
// limitations under the License.

#include "paddle/fluid/operators/reduce_ops/logsumexp_op.h"
#include <algorithm>
#include <string>
#include <vector>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/operators/reduce_ops/reduce_op_function.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/unary.h"

namespace paddle {
namespace operators {

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

void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "logsumexp");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "logsumexp");
auto x_dims = ctx->GetInputDim("X");
auto x_rank = x_dims.size();
PADDLE_ENFORCE_LE(x_rank, 4,
platform::errors::InvalidArgument(
"The input tensor X's dimensions of logsumexp "
"should be less or equal than 4. But received X's "
"dimensions = %d, X's shape = [%s].",
x_rank, x_dims));
auto axis = ctx->Attrs().Get<std::vector<int>>("axis");
PADDLE_ENFORCE_GT(
axis.size(), 0,
platform::errors::InvalidArgument(
"The size of axis of logsumexp "
"should be greater than 0. But received the size of axis "
"of logsumexp is %d.",
axis.size()));

for (size_t i = 0; i < axis.size(); i++) {
PADDLE_ENFORCE_LT(axis[i], x_rank,
platform::errors::InvalidArgument(
"axis[%d] should be in the "
"range [-D, D), where D is the dimensions of X and "
"D is %d. But received axis[%d] = %d.",
i, x_rank, i, axis[i]));
PADDLE_ENFORCE_GE(axis[i], -x_rank,
platform::errors::InvalidArgument(
"axis[%d] should be in the "
"range [-D, D), where D is the dimensions of X and "
"D is %d. But received axis[%d] = %d.",
i, x_rank, i, axis[i]));
if (axis[i] < 0) {
axis[i] += x_rank;
}
}

bool keepdim = ctx->Attrs().Get<bool>("keepdim");
bool reduce_all = ctx->Attrs().Get<bool>("reduce_all");
auto dims_vector = vectorize(x_dims);
if (reduce_all) {
if (keepdim)
ctx->SetOutputDim("Out",
phi::make_ddim(std::vector<int64_t>(x_rank, 1)));
else
ctx->SetOutputDim("Out", {1});
} else {
auto dims_vector = vectorize(x_dims);
if (keepdim) {
for (size_t i = 0; i < axis.size(); ++i) {
dims_vector[axis[i]] = 1;
}
} else {
const int kDelFlag = -1;
for (size_t i = 0; i < axis.size(); ++i) {
dims_vector[axis[i]] = kDelFlag;
}
dims_vector.erase(
std::remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
dims_vector.end());
}
if (!keepdim && dims_vector.size() == 0) {
dims_vector.push_back(1);
}
auto out_dims = phi::make_ddim(dims_vector);
ctx->SetOutputDim("Out", out_dims);
if (axis.size() > 0 && axis[0] != 0) {
// Only pass LoD when not reducing on the first dim.
ctx->ShareLoD("X", /*->*/ "Out");
}
}
}
};

class LogsumexpOpMaker : public framework::OpProtoAndCheckerMaker {
Expand Down Expand Up @@ -164,16 +93,10 @@ class LogsumexpGradOpMaker : public framework::SingleGradOpMaker<T> {
} // namespace paddle

namespace ops = paddle::operators;

DECLARE_INFER_SHAPE_FUNCTOR(logsumexp, LogsumexpInferShapeFunctor,
PD_INFER_META(phi::LogsumexpInferMeta));
REGISTER_OPERATOR(logsumexp, ops::LogsumexpOp, ops::LogsumexpOpMaker,
ops::LogsumexpGradOpMaker<paddle::framework::OpDesc>,
ops::LogsumexpGradOpMaker<paddle::imperative::OpBase>);
ops::LogsumexpGradOpMaker<paddle::imperative::OpBase>,
LogsumexpInferShapeFunctor);
REGISTER_OPERATOR(logsumexp_grad, ops::LogsumexpGrapOp);

REGISTER_OP_CPU_KERNEL(
logsumexp, ops::LogsumexpKernel<paddle::platform::CPUDeviceContext, float>,
ops::LogsumexpKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
logsumexp_grad,
ops::LogsumexpGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::LogsumexpGradKernel<paddle::platform::CPUDeviceContext, double>);
170 changes: 0 additions & 170 deletions paddle/fluid/operators/reduce_ops/logsumexp_op.h
View file
Open in desktop

This file was deleted.

2 changes: 1 addition & 1 deletion paddle/fluid/operators/reduce_ops/logsumexp_op_xpu.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@

#ifdef PADDLE_WITH_XPU

#include "paddle/fluid/operators/reduce_ops/logsumexp_op.h"
#include "paddle/fluid/operators/reduce_ops/reduce_op_function.h"
#include "paddle/fluid/platform/device/xpu/xpu_header.h"
#include "paddle/fluid/platform/device_context.h"

Expand Down
85 changes: 85 additions & 0 deletions paddle/phi/infermeta/unary.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -804,6 +804,91 @@ void KthvalueInferMeta(const MetaTensor& x,
indices->set_dtype(x.dtype());
}

void LogsumexpInferMeta(const MetaTensor& input,
const std::vector<int64_t>& axis,
bool keepdim,
bool reduce_all,
MetaTensor* out) {
auto x_dims = input.dims();
auto x_rank = x_dims.size();
std::vector<int64_t> formated_axis = axis;
PADDLE_ENFORCE_LE(x_rank,
4,
errors::InvalidArgument(
"The input tensor X's dimensions of logsumexp "
"should be less or equal than 4. But received X's "
"dimensions = %d, X's shape = [%s].",
x_rank,
x_dims));
PADDLE_ENFORCE_GT(
axis.size(),
0,
errors::InvalidArgument(
"The size of axis of logsumexp "
"should be greater than 0. But received the size of axis "
"of logsumexp is %d.",
axis.size()));

for (size_t i = 0; i < axis.size(); i++) {
PADDLE_ENFORCE_LT(axis[i],
x_rank,
errors::InvalidArgument(
"axis[%d] should be in the "
"range [-D, D), where D is the dimensions of X and "
"D is %d. But received axis[%d] = %d.",
i,
x_rank,
i,
axis[i]));
PADDLE_ENFORCE_GE(axis[i],
-x_rank,
errors::InvalidArgument(
"axis[%d] should be in the "
"range [-D, D), where D is the dimensions of X and "
"D is %d. But received axis[%d] = %d.",
i,
x_rank,
i,
axis[i]));
if (axis[i] < 0) {
formated_axis[i] += x_rank;
}
}

auto dims_vector = vectorize(x_dims);
if (reduce_all) {
if (keepdim)
out->set_dims(phi::make_ddim(std::vector<int64_t>(x_rank, 1)));
else
out->set_dims({1});
} else {
auto dims_vector = vectorize(x_dims);
if (keepdim) {
for (size_t i = 0; i < formated_axis.size(); ++i) {
dims_vector[formated_axis[i]] = 1;
}
} else {
const int kDelFlag = -1;
for (size_t i = 0; i < formated_axis.size(); ++i) {
dims_vector[formated_axis[i]] = kDelFlag;
}
dims_vector.erase(
std::remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
dims_vector.end());
}
if (!keepdim && dims_vector.size() == 0) {
dims_vector.push_back(1);
}
auto out_dims = phi::make_ddim(dims_vector);
out->set_dims(out_dims);
if (formated_axis.size() > 0 && formated_axis[0] != 0) {
// Only pass LoD when not reducing on the first dim.
out->share_lod(input);
}
}
out->set_dtype(input.dtype());
}

void MatrixPowerInferMeta(const MetaTensor& x, int n, MetaTensor* out) {
auto dims = x.dims();
auto n_dim = dims.size();
Expand Down
Loading