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[API] fix paddle.cross with big tensor #72652

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Merged
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May 19, 2025
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[API] fix paddle.cross with big tensor #72652

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24 changes: 12 additions & 12 deletions paddle/phi/kernels/cpu/cross_grad_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -74,13 +74,13 @@ void CrossGradKernel(const Context &dev_ctx,
"But received: Input(X/Y).dims() == [%s].",
input_x_dims));
}
auto outer_loops = 1;
for (auto i = 0; i < dim; i++) {
outer_loops *= static_cast<int>(input_x_dims[i]);
int64_t outer_loops = 1;
for (int i = 0; i < dim; i++) {
outer_loops *= input_x_dims[i];
}
auto slice_size = 1;
for (auto i = dim + 1; i < input_x_dims.size(); i++) {
slice_size *= static_cast<int>(input_x_dims[i]);
int64_t slice_size = 1;
for (int i = dim + 1; i < input_x_dims.size(); i++) {
slice_size *= input_x_dims[i];
}

int64_t numel = x.numel();
Expand Down Expand Up @@ -111,12 +111,12 @@ void CrossGradKernel(const Context &dev_ctx,
dev_ctx.template Alloc<T>(output_x_grad);
dev_ctx.template Alloc<T>(output_y_grad);

for (auto i = 0; i < outer_loops; i++) {
for (auto j = 0; j < 3; j++) {
auto dst_pos = (3 * i + j) * slice_size;
auto in_pos1 = (3 * i + ((j + 1) % 3)) * slice_size;
auto in_pos2 = (3 * i + ((j + 2) % 3)) * slice_size;
for (auto k = 0; k < slice_size; k++) {
for (int64_t i = 0; i < outer_loops; i++) {
for (int64_t j = 0; j < 3; j++) {
int64_t dst_pos = (3 * i + j) * slice_size;
int64_t in_pos1 = (3 * i + ((j + 1) % 3)) * slice_size;
int64_t in_pos2 = (3 * i + ((j + 2) % 3)) * slice_size;
for (int64_t k = 0; k < slice_size; k++) {
out_dx_vec[dst_pos + k] =
input_dout_vec[in_pos2 + k] * input_y_vec[in_pos1 + k] -
input_dout_vec[in_pos1 + k] * input_y_vec[in_pos2 + k];
Expand Down
20 changes: 10 additions & 10 deletions paddle/phi/kernels/cpu/cross_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -75,13 +75,13 @@ void CrossKernel(const Context& dev_ctx,
dev_ctx.template Alloc<T>(output);
return;
}
auto outer_loops = 1;
int64_t outer_loops = 1;
for (auto i = 0; i < dim; i++) {
outer_loops *= static_cast<int>(input_x_dims[i]);
outer_loops *= input_x_dims[i];
}
auto slice_size = 1;
int64_t slice_size = 1;
for (auto i = dim + 1; i < input_x_dims.size(); i++) {
slice_size *= static_cast<int>(input_x_dims[i]);
slice_size *= input_x_dims[i];
}

std::vector<T> input_x_vec, input_y_vec;
Expand All @@ -91,13 +91,13 @@ void CrossKernel(const Context& dev_ctx,

dev_ctx.template Alloc<T>(output);

for (auto i = 0; i < outer_loops; i++) {
for (auto j = 0; j < 3; j++) {
auto dst_pos = (3 * i + j) * slice_size;
auto in_pos1 = (3 * i + ((j + 1) % 3)) * slice_size;
auto in_pos2 = (3 * i + ((j + 2) % 3)) * slice_size;
for (int64_t i = 0; i < outer_loops; i++) {
for (int64_t j = 0; j < 3; j++) {
int64_t dst_pos = (3 * i + j) * slice_size;
int64_t in_pos1 = (3 * i + ((j + 1) % 3)) * slice_size;
int64_t in_pos2 = (3 * i + ((j + 2) % 3)) * slice_size;

for (auto k = 0; k < slice_size; k++) {
for (int64_t k = 0; k < slice_size; k++) {
out_vec[dst_pos + k] =
input_x_vec[in_pos1 + k] * input_y_vec[in_pos2 + k] -
input_x_vec[in_pos2 + k] * input_y_vec[in_pos1 + k];
Expand Down
10 changes: 6 additions & 4 deletions paddle/phi/kernels/gpu/class_center_sample_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -41,12 +41,14 @@ namespace cub = hipcub;
#include "paddle/phi/core/kernel_registry.h"

namespace phi {
#define CUDA_KERNEL_LOOP(i, n) \
for (int32_t i = blockIdx.x * blockDim.x + threadIdx.x, \
step = blockDim.x * gridDim.x; \
i < (n); \
#define CUDA_KERNEL_LOOP_TYPE(i, n, index_type) \
for (index_type i = blockIdx.x * blockDim.x + threadIdx.x, \
step = blockDim.x * gridDim.x; \
i < (n); \
i += step)

#define CUDA_KERNEL_LOOP(i, n) CUDA_KERNEL_LOOP_TYPE(i, n, int32_t)

static constexpr int kNumCUDAThreads = 512;
static constexpr int kNumMaximumNumBlocks = 4096;

Expand Down
44 changes: 22 additions & 22 deletions paddle/phi/kernels/gpu/cross_grad_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -25,20 +25,21 @@
namespace phi {

template <typename T>
__global__ void CrossGrad(const T* x,
const T* y,
const T* out,
T* out_dx,
T* out_dy,
const int64_t stride,
const int64_t N,
phi::funcs::IndexCalculator<int> index_calculator) {
CUDA_KERNEL_LOOP(i, N) {
__global__ void CrossGrad(
const T* x,
const T* y,
const T* out,
T* out_dx,
T* out_dy,
const int64_t stride,
const int64_t N,
phi::funcs::IndexCalculator<int64_t> index_calculator) {
CUDA_KERNEL_LOOP_TYPE(i, N, int64_t) {
int64_t offset = index_calculator(i);

auto pos0 = offset + 0 * stride;
auto pos1 = offset + 1 * stride;
auto pos2 = offset + 2 * stride;
int64_t pos0 = offset + 0 * stride;
int64_t pos1 = offset + 1 * stride;
int64_t pos2 = offset + 2 * stride;

using MPType = typename phi::dtype::MPTypeTrait<T>::Type;

Expand Down Expand Up @@ -127,16 +128,16 @@ void CrossGradKernel(const Context& dev_ctx,
std::vector<int64_t> full_strides;
std::vector<int64_t> merged_dims;

for (int64_t i = 0; i < dim; i++) {
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];
}
}
int64_t merge_axis = merged_dims.size();
int merge_axis = merged_dims.size();
merged_dims.push_back(input_x_dims[dim]);
for (int64_t i = dim + 1; i < input_x_dims.size(); i++) {
for (int i = dim + 1; i < input_x_dims.size(); i++) {
if (i == dim + 1) {
merged_dims.push_back(input_x_dims[i]);
} else {
Expand All @@ -145,7 +146,7 @@ void CrossGradKernel(const Context& dev_ctx,
}

int64_t full_dim = 1;
for (int64_t i = 0; i < merged_dims.size(); i++) {
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) {
Expand All @@ -154,7 +155,7 @@ void CrossGradKernel(const Context& dev_ctx,
cal_dims.push_back(i);
}
int64_t left_dim = 1;
for (int64_t i = merged_dims.size() - 1; i >= 0; i--) {
for (int i = merged_dims.size() - 1; i >= 0; i--) {
if (i == merge_axis) {
continue;
}
Expand All @@ -168,11 +169,11 @@ void CrossGradKernel(const Context& dev_ctx,
const auto* input_out_grad_data = input_out_grad.data<T>();
auto* output_x_grad_data = dev_ctx.template Alloc<T>(x_grad);
auto* output_y_grad_data = dev_ctx.template Alloc<T>(y_grad);
auto index_calculator = phi::funcs::IndexCalculator<int>(
merged_dims.size() - 1, cal_dims, left_strides, full_strides);

backends::gpu::GpuLaunchConfig config =
backends::gpu::GetGpuLaunchConfig1D(dev_ctx, numel / 3);
auto index_calculator = phi::funcs::IndexCalculator<int64_t>(
merged_dims.size() - 1, cal_dims, left_strides, full_strides);
if (IsComplexType(x.dtype())) {
DenseTensor x_conj, y_conj;
DenseTensorMeta meta_xy(x.dtype(), x.dims());
Expand All @@ -189,7 +190,6 @@ void CrossGradKernel(const Context& dev_ctx,
input_y_data, numel, input_y_conj_data);
for_range(functor_x);
for_range(functor_y);

CrossGrad<<<config.block_per_grid,
config.thread_per_block,
0,
Expand All @@ -199,7 +199,7 @@ void CrossGradKernel(const Context& dev_ctx,
output_x_grad_data,
output_y_grad_data,
full_strides[merge_axis],
numel / 3,
static_cast<int64_t>(numel / 3),
index_calculator);
} else {
CrossGrad<<<config.block_per_grid,
Expand All @@ -211,7 +211,7 @@ void CrossGradKernel(const Context& dev_ctx,
output_x_grad_data,
output_y_grad_data,
full_strides[merge_axis],
numel / 3,
static_cast<int64_t>(numel / 3),
index_calculator);
}
}
Expand Down
26 changes: 13 additions & 13 deletions paddle/phi/kernels/gpu/cross_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -29,13 +29,13 @@ __global__ void Cross(const T* x,
T* out,
const int64_t stride,
const int64_t N,
phi::funcs::IndexCalculator<int> index_calculator) {
CUDA_KERNEL_LOOP(i, N) {
phi::funcs::IndexCalculator<int64_t> index_calculator) {
CUDA_KERNEL_LOOP_TYPE(i, N, int64_t) {
int64_t offset = index_calculator(i);

auto pos0 = offset + 0 * stride;
auto pos1 = offset + 1 * stride;
auto pos2 = offset + 2 * stride;
int64_t pos0 = offset + 0 * stride;
int64_t pos1 = offset + 1 * stride;
int64_t pos2 = offset + 2 * stride;

using MPType = typename phi::dtype::MPTypeTrait<T>::Type;

Expand Down Expand Up @@ -111,16 +111,16 @@ void CrossKernel(const Context& dev_ctx,
std::vector<int64_t> full_strides;
std::vector<int64_t> merged_dims;

for (int64_t i = 0; i < dim; i++) {
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];
}
}
int64_t merge_axis = merged_dims.size();
int merge_axis = merged_dims.size();
merged_dims.push_back(input_x_dims[dim]);
for (int64_t i = dim + 1; i < input_x_dims.size(); i++) {
for (int i = dim + 1; i < input_x_dims.size(); i++) {
if (i == dim + 1) {
merged_dims.push_back(input_x_dims[i]);
} else {
Expand All @@ -129,7 +129,7 @@ void CrossKernel(const Context& dev_ctx,
}

int64_t full_dim = 1;
for (int64_t i = 0; i < merged_dims.size(); i++) {
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) {
Expand All @@ -138,7 +138,7 @@ void CrossKernel(const Context& dev_ctx,
cal_dims.push_back(i);
}
int64_t left_dim = 1;
for (int64_t i = merged_dims.size() - 1; i >= 0; i--) {
for (int i = merged_dims.size() - 1; i >= 0; i--) {
if (i == merge_axis) {
continue;
}
Expand All @@ -149,21 +149,21 @@ void CrossKernel(const Context& dev_ctx,
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);
auto index_calculator = phi::funcs::IndexCalculator<int>(
merged_dims.size() - 1, cal_dims, left_strides, full_strides);

int64_t numel = x.numel();
backends::gpu::GpuLaunchConfig config =
backends::gpu::GetGpuLaunchConfig1D(dev_ctx, numel / 3);

auto index_calculator = phi::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],
numel / 3,
static_cast<int64_t>(numel / 3),
index_calculator);
}
} // namespace phi
Expand Down
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