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merged 2 commits into from
Apr 28, 2025
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[API] isnan isinf isfinite support bigtensor #72517

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669d041
isnan isinf isfinite support bigtensor
wanghuancoder Apr 27, 2025
4cac3b4
refine
wanghuancoder Apr 28, 2025
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163 changes: 89 additions & 74 deletions paddle/phi/kernels/impl/isfinite_kernel_impl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -78,8 +78,8 @@ struct IsfiniteFunctor<
const DenseTensor& in,
DenseTensor* output) {
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
out_data[i] = true;
}
}
Expand All @@ -95,8 +95,8 @@ struct IsfiniteFunctor<
DenseTensor* output) {
auto* in_a = in.data<T>();
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
const T& a = in_a[i];
out_data[i] = std::isfinite(a);
}
Expand All @@ -113,8 +113,8 @@ struct IsfiniteFunctor<
DenseTensor* output) {
auto* in_a = in.data<T>();
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
const T& a = in_a[i];
out_data[i] = phi::dtype::isfinite(a);
}
Expand All @@ -131,8 +131,8 @@ struct IsfiniteFunctor<
DenseTensor* output) {
auto* in_a = in.data<T>();
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
const T& a = in_a[i];
out_data[i] = std::isfinite(a.real) && std::isfinite(a.imag);
}
Expand All @@ -157,8 +157,8 @@ struct IsnanFunctor<
const DenseTensor& in,
DenseTensor* output) {
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
out_data[i] = false;
}
}
Expand All @@ -174,8 +174,8 @@ struct IsnanFunctor<
DenseTensor* output) {
auto* in_a = in.data<T>();
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
const T& a = in_a[i];
out_data[i] = std::isnan(a);
}
Expand All @@ -191,8 +191,8 @@ struct IsnanFunctor<phi::CPUContext,
DenseTensor* output) {
auto* in_a = in.data<T>();
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
const T& a = in_a[i];
out_data[i] = phi::dtype::isnan(a);
}
Expand All @@ -209,8 +209,8 @@ struct IsnanFunctor<
DenseTensor* output) {
auto* in_a = in.data<T>();
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
const T& a = in_a[i];
out_data[i] = std::isnan(a.real) || std::isnan(a.imag);
}
Expand All @@ -236,7 +236,7 @@ struct IsinfFunctor<
DenseTensor* output) {
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
for (int64_t i = 0; i < num; i++) {
out_data[i] = false;
}
}
Expand All @@ -252,8 +252,8 @@ struct IsinfFunctor<
DenseTensor* output) {
auto* in_a = in.data<T>();
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
const T& a = in_a[i];
out_data[i] = std::isinf(a);
}
Expand All @@ -269,8 +269,8 @@ struct IsinfFunctor<phi::CPUContext,
DenseTensor* output) {
auto* in_a = in.data<T>();
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
const T& a = in_a[i];
out_data[i] = phi::dtype::isinf(a);
}
Expand All @@ -287,8 +287,8 @@ struct IsinfFunctor<
DenseTensor* output) {
auto* in_a = in.data<T>();
auto* out_data = ctx.template Alloc<bool>(output);
auto num = in.numel();
for (int i = 0; i < num; i++) {
int64_t num = in.numel();
for (int64_t i = 0; i < num; i++) {
const T& a = in_a[i];
out_data[i] = std::isinf(a.real) || std::isinf(a.imag);
}
Expand All @@ -297,117 +297,117 @@ struct IsinfFunctor<

#if defined(__NVCC__) || defined(__HIPCC__)
/* IsfiniteFunctor */
template <typename T>
template <typename T, typename IndexType>
__global__ void IsfiniteCUDAKernel(
const T* in_data,
int num,
IndexType num,
bool* out_data,
typename std::enable_if<std::is_floating_point<T>::value>::type* = 0) {
unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = idx; i < num; i += blockDim.x * gridDim.x) {
IndexType idx = threadIdx.x + blockIdx.x * blockDim.x;
for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
const T& a = in_data[i];
out_data[i] = isfinite(a);
}
}

template <typename T>
template <typename T, typename IndexType>
__global__ void IsfiniteCUDAKernel(
const T* in_data,
int num,
IndexType num,
bool* out_data,
typename std::enable_if<std::is_integral<T>::value>::type* = 0) {
unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = idx; i < num; i += blockDim.x * gridDim.x) {
IndexType idx = threadIdx.x + blockIdx.x * blockDim.x;
for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
out_data[i] = true;
}
}

template <typename T>
template <typename T, typename IndexType>
__global__ void IsfiniteCUDAKernel(
const T* in_data,
int num,
IndexType num,
bool* out_data,
typename std::enable_if<is_complex64_or_complex128<T>::value>::type* = 0) {
unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = idx; i < num; i += blockDim.x * gridDim.x) {
IndexType idx = threadIdx.x + blockIdx.x * blockDim.x;
for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
const T& a = in_data[i];
out_data[i] = isfinite(a.real) && isfinite(a.imag);
}
}

/* IsnanFunctor */
template <typename T>
template <typename T, typename IndexType>
__global__ void IsnanCUDAKernel(
const T* in_data,
int num,
IndexType num,
bool* out_data,
typename std::enable_if<std::is_floating_point<T>::value>::type* = 0) {
unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = idx; i < num; i += blockDim.x * gridDim.x) {
IndexType idx = threadIdx.x + blockIdx.x * blockDim.x;
for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
const T& a = in_data[i];
out_data[i] = isnan(a);
}
}

template <typename T>
template <typename T, typename IndexType>
__global__ void IsnanCUDAKernel(
const T* in_data,
int num,
IndexType num,
bool* out_data,
typename std::enable_if<std::is_integral<T>::value>::type* = 0) {
unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = idx; i < num; i += blockDim.x * gridDim.x) {
IndexType idx = threadIdx.x + blockIdx.x * blockDim.x;
for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
out_data[i] = false;
}
}

template <typename T>
template <typename T, typename IndexType>
__global__ void IsnanCUDAKernel(
const T* in_data,
int num,
IndexType num,
bool* out_data,
typename std::enable_if<is_complex64_or_complex128<T>::value>::type* = 0) {
unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = idx; i < num; i += blockDim.x * gridDim.x) {
IndexType idx = threadIdx.x + blockIdx.x * blockDim.x;
for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
const T& a = in_data[i];
out_data[i] = isnan(a.real) || isnan(a.imag);
}
}

/* IsinfFunctor */
template <typename T>
template <typename T, typename IndexType>
__global__ void IsinfCUDAKernel(
const T* in_data,
int num,
IndexType num,
bool* out_data,
typename std::enable_if<std::is_floating_point<T>::value>::type* = 0) {
unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = idx; i < num; i += blockDim.x * gridDim.x) {
IndexType idx = threadIdx.x + blockIdx.x * blockDim.x;
for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
const T& a = in_data[i];
out_data[i] = isinf(a);
}
}

template <typename T>
template <typename T, typename IndexType>
__global__ void IsinfCUDAKernel(
const T* in_data,
int num,
IndexType num,
bool* out_data,
typename std::enable_if<std::is_integral<T>::value>::type* = 0) {
unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = idx; i < num; i += blockDim.x * gridDim.x) {
IndexType idx = threadIdx.x + blockIdx.x * blockDim.x;
for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
out_data[i] = false;
}
}

template <typename T>
template <typename T, typename IndexType>
__global__ void IsinfCUDAKernel(
const T* in_data,
int num,
IndexType num,
bool* out_data,
typename std::enable_if<is_complex64_or_complex128<T>::value>::type* = 0) {
unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = idx; i < num; i += blockDim.x * gridDim.x) {
IndexType idx = threadIdx.x + blockIdx.x * blockDim.x;
for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
const T& a = in_data[i];
out_data[i] = isinf(a.real) || isinf(a.imag);
}
Expand All @@ -418,14 +418,19 @@ struct IsfiniteFunctor<phi::GPUContext, T> {
void operator()(const phi::GPUContext& dev_ctx,
const DenseTensor& in,
DenseTensor* output) {
int num = in.numel();
int64_t num = in.numel();
const T* in_data = in.data<T>();
bool* out_data = dev_ctx.template Alloc<bool>(output);
int block = 1024;
int grid = (block - 1 + num) / block;
int64_t block = 1024;
int64_t grid = (block - 1 + num) / block;
grid = (grid > block) ? block : grid;
IsfiniteCUDAKernel<T>
<<<grid, block, 0, dev_ctx.stream()>>>(in_data, num, out_data);
if (num + block * grid + 1 > std::numeric_limits<unsigned int>::max()) {
IsfiniteCUDAKernel<T, int64_t>
<<<grid, block, 0, dev_ctx.stream()>>>(in_data, num, out_data);
} else {
IsfiniteCUDAKernel<T, unsigned int>
<<<grid, block, 0, dev_ctx.stream()>>>(in_data, num, out_data);
}
}
};

Expand All @@ -434,14 +439,19 @@ struct IsnanFunctor<phi::GPUContext, T> {
void operator()(const phi::GPUContext& dev_ctx,
const DenseTensor& in,
DenseTensor* output) {
int num = in.numel();
int64_t num = in.numel();
const T* in_data = in.data<T>();
bool* out_data = dev_ctx.template Alloc<bool>(output);
int block = 1024;
int grid = (block - 1 + num) / block;
int64_t block = 1024;
int64_t grid = (block - 1 + num) / block;
grid = (grid > block) ? block : grid;
IsnanCUDAKernel<T>
<<<grid, block, 0, dev_ctx.stream()>>>(in_data, num, out_data);
if (num + block * grid + 1 > std::numeric_limits<unsigned int>::max()) {
IsnanCUDAKernel<T, int64_t>
<<<grid, block, 0, dev_ctx.stream()>>>(in_data, num, out_data);
} else {
IsnanCUDAKernel<T, unsigned int>
<<<grid, block, 0, dev_ctx.stream()>>>(in_data, num, out_data);
}
}
};

Expand All @@ -450,14 +460,19 @@ struct IsinfFunctor<phi::GPUContext, T> {
void operator()(const phi::GPUContext& dev_ctx,
const DenseTensor& in,
DenseTensor* output) {
int num = in.numel();
int64_t num = in.numel();
const T* in_data = in.data<T>();
bool* out_data = dev_ctx.template Alloc<bool>(output);
int block = 1024;
int grid = (block - 1 + num) / block;
int64_t block = 1024;
int64_t grid = (block - 1 + num) / block;
grid = (grid > block) ? block : grid;
IsinfCUDAKernel<T>
<<<grid, block, 0, dev_ctx.stream()>>>(in_data, num, out_data);
if (num + block * grid + 1 > std::numeric_limits<unsigned int>::max()) {
IsinfCUDAKernel<T, int64_t>
<<<grid, block, 0, dev_ctx.stream()>>>(in_data, num, out_data);
} else {
IsinfCUDAKernel<T, unsigned int>
<<<grid, block, 0, dev_ctx.stream()>>>(in_data, num, out_data);
}
}
};
#endif
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