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“626abfc33ac373b2c18ef1b26d0b1470eb8f94c0”上不存在“paddle/phi/kernels/reduce_sum_grad_kernel.h”
未验证 提交 5c66338f 编写于 作者: X xiongkun 提交者： GitHub
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[pten] trans diagonal kernel into pten (#39575) 

* trans diagonal kernel into pten

* fix by code review
上级 7d6d3848
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Showing with 687 addition and 446 deletion
paddle/fluid/operators/diagonal_op.cc
浏览文件 @ 5c66338f
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/diagonal_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
......@@ -169,18 +169,10 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERER(DiagonalGradNoNeedBufferVarsInferer,
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(diagonal, ops::DiagonalOp, ops::DiagonalOpMaker,
ops::DiagonalGradOpMaker<paddle::framework::OpDesc>,
ops::DiagonalGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(diagonal_grad, ops::DiagonalGradOp,
ops::DiagonalGradNoNeedBufferVarsInferer)
REGISTER_OP_CPU_KERNEL(diagonal, ops::DiagonalKernel<int>,
ops::DiagonalKernel<int64_t>, ops::DiagonalKernel<float>,
ops::DiagonalKernel<double>, ops::DiagonalKernel<bool>);
REGISTER_OP_CPU_KERNEL(diagonal_grad, ops::DiagonalGradKernel<int>,
ops::DiagonalGradKernel<int64_t>,
ops::DiagonalGradKernel<float>,
ops::DiagonalGradKernel<double>);
paddle/fluid/operators/diagonal_op.cu 已删除 100644 → 0
浏览文件 @ 7d6d3848
/* Copyright (c) 2021 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/framework/op_registry.h"
#include "paddle/fluid/operators/diagonal_op.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
namespace paddle {
namespace operators {
using platform::PADDLE_CUDA_NUM_THREADS;
template <typename T, int X_DIM_SIZE, int OUT_DIM_SIZE>
__global__ void Diagonal(const T* data1, T* data2, const int64_t offset_,
int64_t axis1_, int64_t axis2_, int64_t* x_stride,
int64_t* out_stride, int64_t numel, bool is_grad) {
CUDA_KERNEL_LOOP(idx, numel) {
int64_t idx_dim[X_DIM_SIZE] = {0};
int64_t temp = 0;
for (size_t i = 0; i < X_DIM_SIZE - 1; i++) {
idx_dim[i] = (idx - temp) / x_stride[i];
temp = temp + idx_dim[i] * x_stride[i];
}
idx_dim[X_DIM_SIZE - 1] = idx - temp;
int64_t axis1_dim = idx_dim[axis1_];
int64_t axis2_dim = idx_dim[axis2_];
int64_t out_dim[OUT_DIM_SIZE] = {0};
int temp_pos = 0;
for (int i = 0; i < X_DIM_SIZE; i++) {
if (i != axis1_ && i != axis2_) {
out_dim[temp_pos] = idx_dim[i];
temp_pos++;
}
}
bool flag = false;
if (offset_ == 0 && axis1_dim == axis2_dim) {
out_dim[temp_pos] = axis1_dim;
flag = true;
} else if (offset_ > 0 && (axis1_dim + offset_) == axis2_dim) {
out_dim[temp_pos] = axis1_dim;
flag = true;
} else if (offset_ < 0 && (axis1_dim + offset_) == axis2_dim) {
out_dim[temp_pos] = axis2_dim;
flag = true;
}
if (!is_grad) {
if (flag) {
int64_t idx_output = 0;
for (size_t i = 0; i < OUT_DIM_SIZE - 1; i++) {
idx_output = idx_output + out_dim[i] * out_stride[i];
}
idx_output = idx_output + out_dim[OUT_DIM_SIZE - 1];
data2[idx_output] = data1[idx];
}
} else {
if (flag) {
int64_t idx_output = 0;
for (size_t i = 0; i < OUT_DIM_SIZE - 1; i++) {
idx_output = idx_output + out_dim[i] * out_stride[i];
}
idx_output = idx_output + out_dim[OUT_DIM_SIZE - 1];
data2[idx] = data1[idx_output];
} else {
data2[idx] = static_cast<T>(0);
}
}
}
}
template <typename T>
class DiagonalCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input = context.Input<framework::Tensor>("Input");
const auto* input_data = input->data<T>();
auto input_dim = input->dims().Get();
auto input_dim_size = input->dims().size();
std::vector<int64_t> res_in = vectorize(framework::stride(input->dims()));
paddle::framework::Tensor input_stride_tensor;
framework::TensorFromVector<int64_t>(res_in, context.device_context(),
&input_stride_tensor);
int64_t* input_stride = input_stride_tensor.data<int64_t>();
auto* output = context.Output<framework::Tensor>("Out");
auto* output_data = output->mutable_data<T>(context.GetPlace());
auto output_dim = output->dims().Get();
auto output_dim_size = output->dims().size();
std::vector<int64_t> res_out = vectorize(framework::stride(output->dims()));
paddle::framework::Tensor output_stride_tensor;
framework::TensorFromVector<int64_t>(res_out, context.device_context(),
&output_stride_tensor);
int64_t* output_stride = output_stride_tensor.data<int64_t>();
const int64_t offset_ = context.Attr<int>("offset");
const int64_t axis1 = context.Attr<int>("axis1");
int64_t axis1_ = axis1 < 0 ? input_dim_size + axis1 : axis1;
const int64_t axis2 = context.Attr<int>("axis2");
int64_t axis2_ = axis2 < 0 ? input_dim_size + axis2 : axis2;
int64_t numel = input->numel();
int threads = PADDLE_CUDA_NUM_THREADS;
int blocks = (numel + threads - 1) / threads;
switch (input_dim_size) {
case 2:
Diagonal<T, 2, 1><<<blocks, threads>>>(input_data, output_data, offset_,
axis1_, axis2_, input_stride,
output_stride, numel, false);
break;
case 3:
Diagonal<T, 3, 2><<<blocks, threads>>>(input_data, output_data, offset_,
axis1_, axis2_, input_stride,
output_stride, numel, false);
break;
case 4:
Diagonal<T, 4, 3><<<blocks, threads>>>(input_data, output_data, offset_,
axis1_, axis2_, input_stride,
output_stride, numel, false);
break;
case 5:
Diagonal<T, 5, 4><<<blocks, threads>>>(input_data, output_data, offset_,
axis1_, axis2_, input_stride,
output_stride, numel, false);
break;
case 6:
Diagonal<T, 6, 5><<<blocks, threads>>>(input_data, output_data, offset_,
axis1_, axis2_, input_stride,
output_stride, numel, false);
break;
case 7:
Diagonal<T, 7, 6><<<blocks, threads>>>(input_data, output_data, offset_,
axis1_, axis2_, input_stride,
output_stride, numel, false);
break;
case 8:
Diagonal<T, 8, 7><<<blocks, threads>>>(input_data, output_data, offset_,
axis1_, axis2_, input_stride,
output_stride, numel, false);
break;
case 9:
Diagonal<T, 9, 8><<<blocks, threads>>>(input_data, output_data, offset_,
axis1_, axis2_, input_stride,
output_stride, numel, false);
break;
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"The rank of input should be less than 10, but received %d.",
input_dim_size));
}
}
};
template <typename T>
class DiagonalGradCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const auto* dout =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
const auto* dout_data = dout->data<T>();
auto dout_dim = dout->dims().Get();
auto dout_dim_size = dout->dims().size();
std::vector<int64_t> res_dout = vectorize(framework::stride(dout->dims()));
paddle::framework::Tensor dout_stride_tensor;
framework::TensorFromVector<int64_t>(res_dout, context.device_context(),
&dout_stride_tensor);
int64_t* dout_stride = dout_stride_tensor.data<int64_t>();
auto* dx =
context.Output<framework::Tensor>(framework::GradVarName("Input"));
auto* dx_data = dx->mutable_data<T>(context.GetPlace());
auto dx_dim = dx->dims().Get();
auto dx_dim_size = dx->dims().size();
std::vector<int64_t> res_dx = vectorize(framework::stride(dx->dims()));
paddle::framework::Tensor dx_stride_tensor;
framework::TensorFromVector<int64_t>(res_dx, context.device_context(),
&dx_stride_tensor);
int64_t* dx_stride = dx_stride_tensor.data<int64_t>();
const int64_t offset_ = context.Attr<int>("offset");
const int64_t axis1 = context.Attr<int>("axis1");
int64_t axis1_ = axis1 < 0 ? dx_dim_size + axis1 : axis1;
const int64_t axis2 = context.Attr<int>("axis2");
int64_t axis2_ = axis2 < 0 ? dx_dim_size + axis2 : axis2;
int64_t numel = dx->numel();
int threads = PADDLE_CUDA_NUM_THREADS;
int blocks = (numel + threads - 1) / threads;
switch (dx_dim_size) {
case 2:
Diagonal<T, 2, 1><<<blocks, threads>>>(dout_data, dx_data, offset_,
axis1_, axis2_, dx_stride,
dout_stride, numel, true);
break;
case 3:
Diagonal<T, 3, 2><<<blocks, threads>>>(dout_data, dx_data, offset_,
axis1_, axis2_, dx_stride,
dout_stride, numel, true);
break;
case 4:
Diagonal<T, 4, 3><<<blocks, threads>>>(dout_data, dx_data, offset_,
axis1_, axis2_, dx_stride,
dout_stride, numel, true);
break;
case 5:
Diagonal<T, 5, 4><<<blocks, threads>>>(dout_data, dx_data, offset_,
axis1_, axis2_, dx_stride,
dout_stride, numel, true);
break;
case 6:
Diagonal<T, 6, 5><<<blocks, threads>>>(dout_data, dx_data, offset_,
axis1_, axis2_, dx_stride,
dout_stride, numel, true);
break;
case 7:
Diagonal<T, 7, 6><<<blocks, threads>>>(dout_data, dx_data, offset_,
axis1_, axis2_, dx_stride,
dout_stride, numel, true);
break;
case 8:
Diagonal<T, 8, 7><<<blocks, threads>>>(dout_data, dx_data, offset_,
axis1_, axis2_, dx_stride,
dout_stride, numel, true);
break;
case 9:
Diagonal<T, 9, 8><<<blocks, threads>>>(dout_data, dx_data, offset_,
axis1_, axis2_, dx_stride,
dout_stride, numel, true);
break;
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"The rank of output(input@Grad) should be less than 10, but "
"received %d.",
dx_dim_size));
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(diagonal, ops::DiagonalCUDAKernel<int>,
ops::DiagonalCUDAKernel<int64_t>,
ops::DiagonalCUDAKernel<float>,
ops::DiagonalCUDAKernel<double>,
ops::DiagonalCUDAKernel<plat::float16>,
ops::DiagonalCUDAKernel<bool>);
REGISTER_OP_CUDA_KERNEL(diagonal_grad, ops::DiagonalGradCUDAKernel<int>,
ops::DiagonalGradCUDAKernel<int64_t>,
ops::DiagonalGradCUDAKernel<float>,
ops::DiagonalGradCUDAKernel<double>,
ops::DiagonalGradCUDAKernel<plat::float16>);
paddle/fluid/operators/diagonal_op.h 已删除 100644 → 0
浏览文件 @ 7d6d3848
// Copyright (c) 2021 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 <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename T>
std::vector<T> ComputeDimStride(const std::vector<T> dim) {
size_t dim_size = dim.size();
std::vector<T> dim_strides;
dim_strides.resize(dim_size);
for (size_t i = 0; i < dim_size - 1; i++) {
size_t temp_stride = 1;
for (size_t j = i + 1; j < dim_size; j++) {
temp_stride = temp_stride * dim[j];
}
dim_strides[i] = temp_stride;
}
dim_strides[dim_size - 1] = 1;
return dim_strides;
}
template <typename T>
class DiagonalKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input = context.Input<framework::Tensor>("Input");
const T* input_data = input->data<T>();
auto input_dim = vectorize(input->dims());
auto input_dim_size = input_dim.size();
auto* output = context.Output<framework::Tensor>("Out");
T* output_data = output->mutable_data<T>(context.GetPlace());
auto output_dim = vectorize(output->dims());
const int64_t offset_ = context.Attr<int>("offset");
const int64_t axis1 = context.Attr<int>("axis1");
int64_t axis1_ = axis1 < 0 ? input_dim_size + axis1 : axis1;
const int64_t axis2 = context.Attr<int>("axis2");
int64_t axis2_ = axis2 < 0 ? input_dim_size + axis2 : axis2;
std::vector<int64_t> input_stride = ComputeDimStride(input_dim);
std::vector<int64_t> output_stride = ComputeDimStride(output_dim);
int64_t numel = input->numel();
for (int64_t idx = 0; idx < numel; idx++) {
std::vector<int64_t> idx_dim(input_dim_size);
int64_t temp = 0;
for (size_t i = 0; i < input_dim_size; i++) {
idx_dim[i] = (idx - temp) / input_stride[i];
temp = temp + idx_dim[i] * input_stride[i];
}
int64_t axis1_dim = idx_dim[axis1_];
int64_t axis2_dim = idx_dim[axis2_];
idx_dim.erase(idx_dim.begin() + std::max(axis1_, axis2_));
idx_dim.erase(idx_dim.begin() + std::min(axis1_, axis2_));
bool flag = false;
if (offset_ == 0 && axis1_dim == axis2_dim) {
idx_dim.push_back(axis1_dim);
flag = true;
} else if (offset_ > 0 && (axis1_dim + offset_) == axis2_dim) {
idx_dim.push_back(axis1_dim);
flag = true;
} else if (offset_ < 0 && (axis1_dim + offset_) == axis2_dim) {
idx_dim.push_back(axis2_dim);
flag = true;
}
if (flag) {
int64_t idx_output = 0;
for (size_t i = 0; i < idx_dim.size(); i++) {
idx_output = idx_output + idx_dim[i] * output_stride[i];
}
output_data[idx_output] = input_data[idx];
}
}
}
};
template <typename T>
class DiagonalGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const auto* dout =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
const T* dout_data = dout->data<T>();
auto dout_dim = vectorize(dout->dims());
auto* dx =
context.Output<framework::Tensor>(framework::GradVarName("Input"));
T* dx_data = dx->mutable_data<T>(context.GetPlace());
auto dx_dim = vectorize(dx->dims());
auto dx_dim_size = dx_dim.size();
const int64_t offset_ = context.Attr<int>("offset");
const int64_t axis1 = context.Attr<int>("axis1");
int64_t axis1_ = axis1 < 0 ? dx_dim_size + axis1 : axis1;
const int64_t axis2 = context.Attr<int>("axis2");
int64_t axis2_ = axis2 < 0 ? dx_dim_size + axis2 : axis2;
std::vector<int64_t> dout_stride = ComputeDimStride(dout_dim);
std::vector<int64_t> dx_stride = ComputeDimStride(dx_dim);
int64_t numel = dx->numel();
for (int64_t idx = 0; idx < numel; idx++) {
std::vector<int64_t> idx_dim(dx_dim_size);
int64_t temp = 0;
for (size_t i = 0; i < dx_dim_size; i++) {
idx_dim[i] = (idx - temp) / dx_stride[i];
temp = temp + idx_dim[i] * dx_stride[i];
}
int64_t axis1_dim = idx_dim[axis1_];
int64_t axis2_dim = idx_dim[axis2_];
idx_dim.erase(idx_dim.begin() + std::max(axis1_, axis2_));
idx_dim.erase(idx_dim.begin() + std::min(axis1_, axis2_));
bool flag = false;
if (offset_ == 0 && axis1_dim == axis2_dim) {
idx_dim.push_back(axis1_dim);
flag = true;
} else if (offset_ > 0 && (axis1_dim + offset_) == axis2_dim) {
idx_dim.push_back(axis1_dim);
flag = true;
} else if (offset_ < 0 && (axis1_dim + offset_) == axis2_dim) {
idx_dim.push_back(axis2_dim);
flag = true;
}
if (flag) {
int64_t idx_output = 0;
for (size_t i = 0; i < idx_dim.size(); i++) {
idx_output = idx_output + idx_dim[i] * dout_stride[i];
}
dx_data[idx] = dout_data[idx_output];
} else {
dx_data[idx] = static_cast<T>(0);
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/pten/kernels/cpu/diagonal_grad_kernel.cc 0 → 100644
浏览文件 @ 5c66338f
// Copyright (c) 2022 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/pten/kernels/diagonal_grad_kernel.h"
#include "paddle/pten/backends/cpu/cpu_context.h"
#include "paddle/pten/core/kernel_registry.h"
#include "paddle/pten/kernels/funcs/diagonal.h"
namespace pten {
template <typename T, typename Context>
void DiagonalGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
int offset,
int axis1,
int axis2,
DenseTensor* in_grad) {
const auto* dout = &out_grad;
const T* dout_data = dout->data<T>();
auto dout_dim = vectorize(dout->dims());
auto* dx = in_grad;
T* dx_data = dev_ctx.template Alloc<T>(dx);
auto dx_dim = vectorize(dx->dims());
auto dx_dim_size = dx_dim.size();
const int64_t offset_ = offset;
int64_t axis1_ = axis1 < 0 ? dx_dim_size + axis1 : axis1;
int64_t axis2_ = axis2 < 0 ? dx_dim_size + axis2 : axis2;
std::vector<int64_t> dout_stride = funcs::ComputeDimStride(dout_dim);
std::vector<int64_t> dx_stride = funcs::ComputeDimStride(dx_dim);
int64_t numel = dx->numel();
for (int64_t idx = 0; idx < numel; idx++) {
std::vector<int64_t> idx_dim(dx_dim_size);
int64_t temp = 0;
for (size_t i = 0; i < dx_dim_size; i++) {
idx_dim[i] = (idx - temp) / dx_stride[i];
temp = temp + idx_dim[i] * dx_stride[i];
}
int64_t axis1_dim = idx_dim[axis1_];
int64_t axis2_dim = idx_dim[axis2_];
idx_dim.erase(idx_dim.begin() + std::max(axis1_, axis2_));
idx_dim.erase(idx_dim.begin() + std::min(axis1_, axis2_));
bool flag = false;
if (offset_ == 0 && axis1_dim == axis2_dim) {
idx_dim.push_back(axis1_dim);
flag = true;
} else if (offset_ > 0 && (axis1_dim + offset_) == axis2_dim) {
idx_dim.push_back(axis1_dim);
flag = true;
} else if (offset_ < 0 && (axis1_dim + offset_) == axis2_dim) {
idx_dim.push_back(axis2_dim);
flag = true;
}
if (flag) {
int64_t idx_output = 0;
for (size_t i = 0; i < idx_dim.size(); i++) {
idx_output = idx_output + idx_dim[i] * dout_stride[i];
}
dx_data[idx] = dout_data[idx_output];
} else {
dx_data[idx] = static_cast<T>(0);
}
}
}
} // namespace pten
PT_REGISTER_KERNEL(diagonal_grad,
CPU,
ALL_LAYOUT,
pten::DiagonalGradKernel,
float,
double,
int,
int64_t) {}
paddle/pten/kernels/cpu/diagonal_kernel.cc 0 → 100644
浏览文件 @ 5c66338f
// Copyright (c) 2022 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/pten/kernels/diagonal_kernel.h"
#include "paddle/pten/backends/cpu/cpu_context.h"
#include "paddle/pten/core/kernel_registry.h"
#include "paddle/pten/kernels/funcs/diagonal.h"
namespace pten {
template <typename T, typename Context>
void DiagonalKernel(const Context& dev_ctx,
const DenseTensor& x,
int offset,
int axis1,
int axis2,
DenseTensor* out) {
auto* input = &x;
const T* input_data = input->data<T>();
auto input_dim = vectorize(input->dims());
auto input_dim_size = input_dim.size();
auto* output = out;
T* output_data = dev_ctx.template Alloc<T>(output);
auto output_dim = vectorize(output->dims());
const int64_t offset_ = offset;
int64_t axis1_ = axis1 < 0 ? input_dim_size + axis1 : axis1;
int64_t axis2_ = axis2 < 0 ? input_dim_size + axis2 : axis2;
std::vector<int64_t> input_stride = funcs::ComputeDimStride(input_dim);
std::vector<int64_t> output_stride = funcs::ComputeDimStride(output_dim);
int64_t numel = input->numel();
for (int64_t idx = 0; idx < numel; idx++) {
std::vector<int64_t> idx_dim(input_dim_size);
int64_t temp = 0;
for (size_t i = 0; i < input_dim_size; i++) {
idx_dim[i] = (idx - temp) / input_stride[i];
temp = temp + idx_dim[i] * input_stride[i];
}
int64_t axis1_dim = idx_dim[axis1_];
int64_t axis2_dim = idx_dim[axis2_];
idx_dim.erase(idx_dim.begin() + std::max(axis1_, axis2_));
idx_dim.erase(idx_dim.begin() + std::min(axis1_, axis2_));
bool flag = false;
if (offset_ == 0 && axis1_dim == axis2_dim) {
idx_dim.push_back(axis1_dim);
flag = true;
} else if (offset_ > 0 && (axis1_dim + offset_) == axis2_dim) {
idx_dim.push_back(axis1_dim);
flag = true;
} else if (offset_ < 0 && (axis1_dim + offset_) == axis2_dim) {
idx_dim.push_back(axis2_dim);
flag = true;
}
if (flag) {
int64_t idx_output = 0;
for (size_t i = 0; i < idx_dim.size(); i++) {
idx_output = idx_output + idx_dim[i] * output_stride[i];
}
output_data[idx_output] = input_data[idx];
}
}
}
} // namespace pten
PT_REGISTER_KERNEL(diagonal,
CPU,
ALL_LAYOUT,
pten::DiagonalKernel,
float,
double,
int,
int64_t,
bool) {}
paddle/pten/kernels/diagonal_grad_kernel.h 0 → 100644
浏览文件 @ 5c66338f
// Copyright (c) 2022 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 "paddle/pten/core/dense_tensor.h"
namespace pten {
template <typename T, typename Context>
void DiagonalGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
int offset,
int axis1,
int axis2,
DenseTensor* in_grad);
} // namespace pten
paddle/pten/kernels/diagonal_kernel.h 0 → 100644
浏览文件 @ 5c66338f
// Copyright (c) 2022 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 "paddle/pten/core/dense_tensor.h"
namespace pten {
template <typename T, typename Context>
void DiagonalKernel(const Context& dev_ctx,
const DenseTensor& x,
int offset,
int axis1,
int axis2,
DenseTensor* out);
} // pten
paddle/pten/kernels/funcs/diagonal.h
浏览文件 @ 5c66338f
......@@ -17,11 +17,13 @@
#if defined(__NVCC__) || defined(__HIPCC__)
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include "paddle/pten/kernels/primitive/kernel_primitives.h"
#endif
#include <algorithm>
#include "paddle/fluid/platform/for_range.h"
#include "paddle/pten/core/dense_tensor.h"
namespace pten {
namespace funcs {
......@@ -126,5 +128,88 @@ DenseTensor Diagonal(const DeviceContext& context,
}
}
template <typename T>
std::vector<T> ComputeDimStride(const std::vector<T> dim) {
size_t dim_size = dim.size();
std::vector<T> dim_strides;
dim_strides.resize(dim_size);
for (size_t i = 0; i < dim_size - 1; i++) {
size_t temp_stride = 1;
for (size_t j = i + 1; j < dim_size; j++) {
temp_stride = temp_stride * dim[j];
}
dim_strides[i] = temp_stride;
}
dim_strides[dim_size - 1] = 1;
return dim_strides;
}
#if defined(__NVCC__) || defined(__HIPCC__)
template <typename T, int X_DIM_SIZE, int OUT_DIM_SIZE>
__global__ void DiagonalCuda(const T* data1,
T* data2,
const int64_t offset_,
int64_t axis1_,
int64_t axis2_,
int64_t* x_stride,
int64_t* out_stride,
int64_t numel,
bool is_grad) {
CUDA_KERNEL_LOOP(idx, numel) {
int64_t idx_dim[X_DIM_SIZE] = {0};
int64_t temp = 0;
for (size_t i = 0; i < X_DIM_SIZE - 1; i++) {
idx_dim[i] = (idx - temp) / x_stride[i];
temp = temp + idx_dim[i] * x_stride[i];
}
idx_dim[X_DIM_SIZE - 1] = idx - temp;
int64_t axis1_dim = idx_dim[axis1_];
int64_t axis2_dim = idx_dim[axis2_];
int64_t out_dim[OUT_DIM_SIZE] = {0};
int temp_pos = 0;
for (int i = 0; i < X_DIM_SIZE; i++) {
if (i != axis1_ && i != axis2_) {
out_dim[temp_pos] = idx_dim[i];
temp_pos++;
}
}
bool flag = false;
if (offset_ == 0 && axis1_dim == axis2_dim) {
out_dim[temp_pos] = axis1_dim;
flag = true;
} else if (offset_ > 0 && (axis1_dim + offset_) == axis2_dim) {
out_dim[temp_pos] = axis1_dim;
flag = true;
} else if (offset_ < 0 && (axis1_dim + offset_) == axis2_dim) {
out_dim[temp_pos] = axis2_dim;
flag = true;
}
if (!is_grad) {
if (flag) {
int64_t idx_output = 0;
for (size_t i = 0; i < OUT_DIM_SIZE - 1; i++) {
idx_output = idx_output + out_dim[i] * out_stride[i];
}
idx_output = idx_output + out_dim[OUT_DIM_SIZE - 1];
data2[idx_output] = data1[idx];
}
} else {
if (flag) {
int64_t idx_output = 0;
for (size_t i = 0; i < OUT_DIM_SIZE - 1; i++) {
idx_output = idx_output + out_dim[i] * out_stride[i];
}
idx_output = idx_output + out_dim[OUT_DIM_SIZE - 1];
data2[idx] = data1[idx_output];
} else {
data2[idx] = static_cast<T>(0);
}
}
}
}
#endif
} // namespace funcs
} // namespace pten
paddle/pten/kernels/gpu/diagonal_grad_kernel.cu 0 → 100644
浏览文件 @ 5c66338f
// Copyright (c) 2022 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/framework/tensor_util.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/pten/core/kernel_registry.h"
#include "paddle/pten/kernels/diagonal_grad_kernel.h"
#include "paddle/pten/kernels/funcs/diagonal.h"
namespace pten {
using paddle::platform::PADDLE_CUDA_NUM_THREADS;
template <typename T, typename Context>
void DiagonalGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
int offset,
int axis1,
int axis2,
DenseTensor* in_grad) {
const auto* dout = &out_grad;
const auto* dout_data = dout->data<T>();
auto dout_dim = dout->dims().Get();
auto dout_dim_size = dout->dims().size();
std::vector<int64_t> res_dout = vectorize(framework::stride(dout->dims()));
DenseTensor dout_stride_tensor;
paddle::framework::TensorFromVector<int64_t>(
res_dout, dev_ctx, &dout_stride_tensor);
int64_t* dout_stride = dout_stride_tensor.data<int64_t>();
auto* dx = in_grad;
auto* dx_data = dev_ctx.template Alloc<T>(dx);
auto dx_dim = dx->dims().Get();
auto dx_dim_size = dx->dims().size();
std::vector<int64_t> res_dx = vectorize(framework::stride(dx->dims()));
DenseTensor dx_stride_tensor;
paddle::framework::TensorFromVector<int64_t>(
res_dx, dev_ctx, &dx_stride_tensor);
int64_t* dx_stride = dx_stride_tensor.data<int64_t>();
const int64_t offset_ = offset;
int64_t axis1_ = axis1 < 0 ? dx_dim_size + axis1 : axis1;
int64_t axis2_ = axis2 < 0 ? dx_dim_size + axis2 : axis2;
int64_t numel = dx->numel();
int threads = PADDLE_CUDA_NUM_THREADS;
int blocks = (numel + threads - 1) / threads;
switch (dx_dim_size) {
case 2:
funcs::DiagonalCuda<T, 2, 1><<<blocks, threads>>>(dout_data,
dx_data,
offset_,
axis1_,
axis2_,
dx_stride,
dout_stride,
numel,
true);
break;
case 3:
funcs::DiagonalCuda<T, 3, 2><<<blocks, threads>>>(dout_data,
dx_data,
offset_,
axis1_,
axis2_,
dx_stride,
dout_stride,
numel,
true);
break;
case 4:
funcs::DiagonalCuda<T, 4, 3><<<blocks, threads>>>(dout_data,
dx_data,
offset_,
axis1_,
axis2_,
dx_stride,
dout_stride,
numel,
true);
break;
case 5:
funcs::DiagonalCuda<T, 5, 4><<<blocks, threads>>>(dout_data,
dx_data,
offset_,
axis1_,
axis2_,
dx_stride,
dout_stride,
numel,
true);
break;
case 6:
funcs::DiagonalCuda<T, 6, 5><<<blocks, threads>>>(dout_data,
dx_data,
offset_,
axis1_,
axis2_,
dx_stride,
dout_stride,
numel,
true);
break;
case 7:
funcs::DiagonalCuda<T, 7, 6><<<blocks, threads>>>(dout_data,
dx_data,
offset_,
axis1_,
axis2_,
dx_stride,
dout_stride,
numel,
true);
break;
case 8:
funcs::DiagonalCuda<T, 8, 7><<<blocks, threads>>>(dout_data,
dx_data,
offset_,
axis1_,
axis2_,
dx_stride,
dout_stride,
numel,
true);
break;
case 9:
funcs::DiagonalCuda<T, 9, 8><<<blocks, threads>>>(dout_data,
dx_data,
offset_,
axis1_,
axis2_,
dx_stride,
dout_stride,
numel,
true);
break;
default:
PADDLE_THROW(errors::InvalidArgument(
"The rank of output(input@Grad) should be less than 10, but "
"received %d.",
dx_dim_size));
}
}
} // namespace pten
PT_REGISTER_KERNEL(diagonal_grad,
GPU,
ALL_LAYOUT,
pten::DiagonalGradKernel,
float,
double,
int,
int64_t) {}
paddle/pten/kernels/gpu/diagonal_kernel.cu 0 → 100644
浏览文件 @ 5c66338f
// Copyright (c) 2022 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/framework/tensor_util.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/pten/core/kernel_registry.h"
#include "paddle/pten/kernels/diagonal_kernel.h"
#include "paddle/pten/kernels/funcs/diagonal.h"
namespace pten {
using paddle::platform::PADDLE_CUDA_NUM_THREADS;
template <typename T, typename Context>
void DiagonalKernel(const Context& dev_ctx,
const DenseTensor& x,
int offset,
int axis1,
int axis2,
DenseTensor* out) {
auto* input = &x;
const auto* input_data = input->data<T>();
auto input_dim = input->dims().Get();
auto input_dim_size = input->dims().size();
std::vector<int64_t> res_in = vectorize(framework::stride(input->dims()));
DenseTensor input_stride_tensor;
paddle::framework::TensorFromVector<int64_t>(
res_in, dev_ctx, &input_stride_tensor);
int64_t* input_stride = input_stride_tensor.data<int64_t>();
auto* output = out;
auto* output_data = dev_ctx.template Alloc<T>(out);
auto output_dim = output->dims().Get();
auto output_dim_size = output->dims().size();
std::vector<int64_t> res_out = vectorize(framework::stride(output->dims()));
DenseTensor output_stride_tensor;
paddle::framework::TensorFromVector<int64_t>(
res_out, dev_ctx, &output_stride_tensor);
int64_t* output_stride = output_stride_tensor.data<int64_t>();
const int64_t offset_ = offset;
int64_t axis1_ = axis1 < 0 ? input_dim_size + axis1 : axis1;
int64_t axis2_ = axis2 < 0 ? input_dim_size + axis2 : axis2;
int64_t numel = input->numel();
int threads = PADDLE_CUDA_NUM_THREADS;
int blocks = (numel + threads - 1) / threads;
switch (input_dim_size) {
case 2:
funcs::DiagonalCuda<T, 2, 1><<<blocks, threads>>>(input_data,
output_data,
offset_,
axis1_,
axis2_,
input_stride,
output_stride,
numel,
false);
break;
case 3:
funcs::DiagonalCuda<T, 3, 2><<<blocks, threads>>>(input_data,
output_data,
offset_,
axis1_,
axis2_,
input_stride,
output_stride,
numel,
false);
break;
case 4:
funcs::DiagonalCuda<T, 4, 3><<<blocks, threads>>>(input_data,
output_data,
offset_,
axis1_,
axis2_,
input_stride,
output_stride,
numel,
false);
break;
case 5:
funcs::DiagonalCuda<T, 5, 4><<<blocks, threads>>>(input_data,
output_data,
offset_,
axis1_,
axis2_,
input_stride,
output_stride,
numel,
false);
break;
case 6:
funcs::DiagonalCuda<T, 6, 5><<<blocks, threads>>>(input_data,
output_data,
offset_,
axis1_,
axis2_,
input_stride,
output_stride,
numel,
false);
break;
case 7:
funcs::DiagonalCuda<T, 7, 6><<<blocks, threads>>>(input_data,
output_data,
offset_,
axis1_,
axis2_,
input_stride,
output_stride,
numel,
false);
break;
case 8:
funcs::DiagonalCuda<T, 8, 7><<<blocks, threads>>>(input_data,
output_data,
offset_,
axis1_,
axis2_,
input_stride,
output_stride,
numel,
false);
break;
case 9:
funcs::DiagonalCuda<T, 9, 8><<<blocks, threads>>>(input_data,
output_data,
offset_,
axis1_,
axis2_,
input_stride,
output_stride,
numel,
false);
break;
default:
PADDLE_THROW(errors::InvalidArgument(
"The rank of input should be less than 10, but received %d.",
input_dim_size));
}
}
} // namespace pten
PT_REGISTER_KERNEL(diagonal,
GPU,
ALL_LAYOUT,
pten::DiagonalKernel,
float,
double,
int,
int64_t,
bool) {}
paddle/pten/ops/compat/diagonal_sig.cc 0 → 100644
浏览文件 @ 5c66338f
// Copyright (c) 2022 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/pten/core/compat/op_utils.h"
namespace pten {
KernelSignature DiagonalGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("diagonal_grad",
{"Input", GradVarName("Out")},
{"offset", "axis1", "axis2"},
{GradVarName("Input")});
}
} // namespace pten
PT_REGISTER_ARG_MAPPING_FN(diagonal_grad, pten::DiagonalGradOpArgumentMapping);
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