chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,182 @@
|
||||
// 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/phi/kernels/cross_kernel.h"
|
||||
|
||||
#include "paddle/phi/backends/gpu/gpu_context.h"
|
||||
#include "paddle/phi/backends/gpu/gpu_launch_config.h"
|
||||
#include "paddle/phi/common/amp_type_traits.h"
|
||||
#include "paddle/phi/core/dense_tensor.h"
|
||||
#include "paddle/phi/core/kernel_registry.h"
|
||||
#include "paddle/phi/kernels/funcs/index_calculator.h"
|
||||
|
||||
namespace phi {
|
||||
|
||||
template <typename T>
|
||||
__global__ void Cross(const T* x,
|
||||
const T* y,
|
||||
T* out,
|
||||
const int64_t stride,
|
||||
const int64_t N,
|
||||
funcs::IndexCalculator<int64_t> index_calculator) {
|
||||
CUDA_KERNEL_LOOP_TYPE(i, N, int64_t) {
|
||||
int64_t offset = index_calculator(i);
|
||||
|
||||
int64_t pos0 = offset + 0 * stride;
|
||||
int64_t pos1 = offset + 1 * stride;
|
||||
int64_t pos2 = offset + 2 * stride;
|
||||
|
||||
using MT = typename MPTypeTrait<T>::Type;
|
||||
|
||||
MT x_pos0_mp = static_cast<MT>(x[pos0]);
|
||||
MT x_pos1_mp = static_cast<MT>(x[pos1]);
|
||||
MT x_pos2_mp = static_cast<MT>(x[pos2]);
|
||||
MT y_pos0_mp = static_cast<MT>(y[pos0]);
|
||||
MT y_pos1_mp = static_cast<MT>(y[pos1]);
|
||||
MT y_pos2_mp = static_cast<MT>(y[pos2]);
|
||||
|
||||
out[pos0] = static_cast<T>(x_pos1_mp * y_pos2_mp - x_pos2_mp * y_pos1_mp);
|
||||
out[pos1] = static_cast<T>(x_pos2_mp * y_pos0_mp - x_pos0_mp * y_pos2_mp);
|
||||
out[pos2] = static_cast<T>(x_pos0_mp * y_pos1_mp - x_pos1_mp * y_pos0_mp);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T, typename Context>
|
||||
void CrossKernel(const Context& dev_ctx,
|
||||
const DenseTensor& x,
|
||||
const DenseTensor& y,
|
||||
int axis,
|
||||
DenseTensor* out) {
|
||||
auto& input_x = x;
|
||||
auto& input_y = y;
|
||||
auto* output = out;
|
||||
int dim = axis;
|
||||
|
||||
auto input_x_dims = input_x.dims();
|
||||
if (dim != DDim::kMaxRank) {
|
||||
PADDLE_ENFORCE_EQ(
|
||||
dim < input_x_dims.size() && dim >= (0 - input_x_dims.size()),
|
||||
true,
|
||||
common::errors::OutOfRange(
|
||||
"Attr(dim) is out of range, It's expected "
|
||||
"to be in range of [-%d, %d]. But received Attr(dim) = %d.",
|
||||
input_x_dims.size(),
|
||||
input_x_dims.size() - 1,
|
||||
dim));
|
||||
if (dim < 0) {
|
||||
dim += input_x_dims.size();
|
||||
}
|
||||
|
||||
PADDLE_ENFORCE_EQ(
|
||||
input_x_dims[dim] == 3,
|
||||
true,
|
||||
common::errors::InvalidArgument(
|
||||
"Input(X/Y).dims[dim] must be equal to 3. But received: "
|
||||
"Input(X/Y).dims[dim] = [%d].",
|
||||
input_x_dims[dim]));
|
||||
} else {
|
||||
for (auto i = 0; i < input_x_dims.size(); i++) {
|
||||
if (input_x_dims[i] == 3) {
|
||||
dim = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
PADDLE_ENFORCE_EQ(dim == DDim::kMaxRank,
|
||||
false,
|
||||
common::errors::InvalidArgument(
|
||||
"There must be at least one dimension 'd' so that "
|
||||
"Input(X/Y).dims()[d] is equal to 3. "
|
||||
"But received: Input(X/Y).dims() == [%s].",
|
||||
input_x_dims));
|
||||
}
|
||||
|
||||
if (input_x.numel() == 0 || input_y.numel() == 0) {
|
||||
output->Resize(input_x.dims());
|
||||
dev_ctx.template Alloc<T>(output);
|
||||
return;
|
||||
}
|
||||
std::vector<int64_t> cal_dims;
|
||||
std::vector<int64_t> left_strides;
|
||||
std::vector<int64_t> full_strides;
|
||||
std::vector<int64_t> merged_dims;
|
||||
|
||||
for (int i = 0; i < dim; i++) {
|
||||
if (i == 0) {
|
||||
merged_dims.push_back(input_x_dims[i]);
|
||||
} else {
|
||||
merged_dims[0] *= input_x_dims[i];
|
||||
}
|
||||
}
|
||||
int merge_axis = merged_dims.size();
|
||||
merged_dims.push_back(input_x_dims[dim]);
|
||||
for (int i = dim + 1; i < input_x_dims.size(); i++) {
|
||||
if (i == dim + 1) {
|
||||
merged_dims.push_back(input_x_dims[i]);
|
||||
} else {
|
||||
merged_dims[merge_axis + 1] *= input_x_dims[i];
|
||||
}
|
||||
}
|
||||
|
||||
int64_t full_dim = 1;
|
||||
for (int i = 0; i < merged_dims.size(); i++) {
|
||||
full_strides.insert(full_strides.begin(), full_dim);
|
||||
full_dim *= merged_dims[merged_dims.size() - i - 1];
|
||||
if (i == merge_axis) {
|
||||
continue;
|
||||
}
|
||||
cal_dims.push_back(i);
|
||||
}
|
||||
int64_t left_dim = 1;
|
||||
for (int i = merged_dims.size() - 1; i >= 0; i--) {
|
||||
if (i == merge_axis) {
|
||||
continue;
|
||||
}
|
||||
left_strides.insert(left_strides.begin(), left_dim);
|
||||
left_dim *= merged_dims[i];
|
||||
}
|
||||
|
||||
const auto* input_x_data = input_x.data<T>();
|
||||
const auto* input_y_data = input_y.data<T>();
|
||||
auto* out_data = dev_ctx.template Alloc<T>(out);
|
||||
|
||||
int64_t numel = x.numel();
|
||||
backends::gpu::GpuLaunchConfig config =
|
||||
backends::gpu::GetGpuLaunchConfig1D(dev_ctx, numel / 3);
|
||||
|
||||
auto index_calculator = funcs::IndexCalculator<int64_t>(
|
||||
merged_dims.size() - 1, cal_dims, left_strides, full_strides);
|
||||
Cross<<<config.block_per_grid,
|
||||
config.thread_per_block,
|
||||
0,
|
||||
dev_ctx.stream()>>>(input_x_data,
|
||||
input_y_data,
|
||||
out_data,
|
||||
full_strides[merge_axis],
|
||||
static_cast<int64_t>(numel / 3),
|
||||
index_calculator);
|
||||
}
|
||||
} // namespace phi
|
||||
|
||||
PD_REGISTER_KERNEL(cross,
|
||||
GPU,
|
||||
ALL_LAYOUT,
|
||||
phi::CrossKernel,
|
||||
phi::float16,
|
||||
phi::bfloat16,
|
||||
float,
|
||||
double,
|
||||
int,
|
||||
int64_t,
|
||||
phi::complex64,
|
||||
phi::complex128) {}
|
||||
Reference in New Issue
Block a user