device/kernel/tensor_elementwise.h
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#pragma once
#include <curand_kernel.h>
#include "cutlass/cutlass.h"
namespace cutlass {
namespace reference {
namespace device {
namespace kernel {
template <typename T>
__global__ void TensorInitializeUniform(
Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm) {
__shared__ curandState_t rng_state[1024];
uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;
curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);
int c_idx = blockIdx.x * blockDim.x + threadIdx.x;
int s_idx = blockIdx.y * blockDim.x;
tensor += s_idx * ldm + c_idx;
for (int s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {
if (s_idx < dim_strided && c_idx < dim_contiguous) {
double range = dist.uniform.max - dist.uniform.min;
double rnd = curand_uniform(&rng_state[threadIdx.x]);
rnd = dist.uniform.min + range * rnd;
// Random values are cast to integer after scaling by a power of two to facilitate error
// testing
if (dist.int_scale >= 0) {
rnd = double(int(rnd * double(1 << dist.int_scale)));
*tensor = T(rnd / double(1 << dist.int_scale));
} else {
*tensor = T(rnd);
}
tensor += ldm;
}
}
}
template <typename T>
__global__ void TensorInitializeGaussian(
Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm) {
__shared__ curandState_t rng_state[1024];
uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;
curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);
int c_idx = blockIdx.x * blockDim.x + threadIdx.x;
int s_idx = blockIdx.y * blockDim.x;
tensor += s_idx * ldm + c_idx;
for (int s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {
if (s_idx < dim_strided && c_idx < dim_contiguous) {
// Random values are cast to integer after scaling by a power of two to facilitate error
// testing
double rnd = curand_normal(&rng_state[threadIdx.x]);
rnd = dist.gaussian.mean + dist.gaussian.stddev * rnd;
if (dist.int_scale >= 0) {
rnd = double(int(rnd * double(1 << dist.int_scale)));
*tensor = T(rnd / double(1 << dist.int_scale));
} else {
*tensor = T(rnd);
}
}
}
}
template <typename T>
__global__ void TensorInitializeLinear(
Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm) {
__shared__ curandState_t rng_state[1024];
uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;
curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);
int c_idx = blockIdx.x * blockDim.x + threadIdx.x;
int s_idx = blockIdx.y * blockDim.x;
tensor += s_idx * ldm + c_idx;
for (int s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {
if (s_idx < dim_strided && c_idx < dim_contiguous) {
*tensor =
dist.linear.offset + dist.linear.delta_row * c_idx + dist.linear.delta_column * s_idx;
}
}
}
template <typename T>
__global__ void TensorInitializeIdentity(
Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm) {
__shared__ curandState_t rng_state[1024];
uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;
curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);
int c_idx = blockIdx.x * blockDim.x + threadIdx.x;
int s_idx = blockIdx.y * blockDim.x;
tensor += s_idx * ldm + c_idx;
for (int s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {
if (s_idx < dim_strided && c_idx < dim_contiguous) {
*tensor = (c_idx == s_idx ? T(1) : T(0));
}
}
}
} // namespace kernel
} // namespace device
} // namespace reference
} // namespace cutlass
cutlass::reference::device::kernel::TensorInitializeUniform
__global__ void TensorInitializeUniform(Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm)
Kernel to initialize tensor to uniform random distribution.
Definition: device/kernel/tensor_elementwise.h:41
cutlass
Definition: aligned_buffer.h:35
cutlass::reference::device::kernel::TensorInitializeGaussian
__global__ void TensorInitializeGaussian(Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm)
Kernel to initialize tensor to uniform distribution.
Definition: device/kernel/tensor_elementwise.h:80
cutlass::Distribution::uniform
struct cutlass::Distribution::@18::@20 uniform
Uniform distribution.
cutlass::reference::device::kernel::TensorInitializeLinear
__global__ void TensorInitializeLinear(Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm)
Kernel to initialize tensor to an identity matrix.
Definition: device/kernel/tensor_elementwise.h:114
cutlass::Distribution::gaussian
struct cutlass::Distribution::@18::@21 gaussian
Gaussian distribution.
cutlass::reference::device::kernel::TensorInitializeIdentity
__global__ void TensorInitializeIdentity(Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm)
Kernel to initialize tensor to an identity matrix.
Definition: device/kernel/tensor_elementwise.h:137
cutlass::Distribution
Distribution type.
Definition: distribution.h:38
cutlass::Distribution::int_scale
int int_scale
Random values are cast to integer after scaling by this power of two.
Definition: distribution.h:67
cutlass.h
Basic include for CUTLASS.