CUTLASS: tensor_elementwise.h Source File

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device/kernel/tensor_elementwise.h
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1 /***************************************************************************************************
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24 **************************************************************************************************/
25
26 #pragma once
27
28 #include <curand_kernel.h>
29
30 #include "cutlass/cutlass.h"
31
32 namespace cutlass {
33 namespace reference {
34 namespace device {
35 namespace kernel {
36
38
40 template <typename T>
41 __global__ void TensorInitializeUniform(
42 Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm) {
43 __shared__ curandState_t rng_state[1024];
44
45 uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;
46
47 curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);
48
49 int c_idx = blockIdx.x * blockDim.x + threadIdx.x;
50 int s_idx = blockIdx.y * blockDim.x;
51
52 tensor += s_idx * ldm + c_idx;
53
54 for (int s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {
55 if (s_idx < dim_strided && c_idx < dim_contiguous) {
56 double range = dist.uniform.max - dist.uniform.min;
57
58 double rnd = curand_uniform(&rng_state[threadIdx.x]);
59
60 rnd = dist.uniform.min + range * rnd;
61
62 // Random values are cast to integer after scaling by a power of two to facilitate error
63 // testing
64 if (dist.int_scale >= 0) {
65 rnd = double(int(rnd * double(1 << dist.int_scale)));
66 *tensor = T(rnd / double(1 << dist.int_scale));
67 } else {
68 *tensor = T(rnd);
69 }
70
71 tensor += ldm;
72 }
73 }
74 }
75
77
79 template <typename T>
80 __global__ void TensorInitializeGaussian(
81 Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm) {
82 __shared__ curandState_t rng_state[1024];
83
84 uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;
85
86 curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);
87
88 int c_idx = blockIdx.x * blockDim.x + threadIdx.x;
89 int s_idx = blockIdx.y * blockDim.x;
90
91 tensor += s_idx * ldm + c_idx;
92
93 for (int s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {
94 if (s_idx < dim_strided && c_idx < dim_contiguous) {
95 // Random values are cast to integer after scaling by a power of two to facilitate error
96 // testing
97
98 double rnd = curand_normal(&rng_state[threadIdx.x]);
99
100 rnd = dist.gaussian.mean + dist.gaussian.stddev * rnd;
101
102 if (dist.int_scale >= 0) {
103 rnd = double(int(rnd * double(1 << dist.int_scale)));
104 *tensor = T(rnd / double(1 << dist.int_scale));
105 } else {
106 *tensor = T(rnd);
107 }
108 }
109 }
110 }
111
113 template <typename T>
114 __global__ void TensorInitializeLinear(
115 Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm) {
116 __shared__ curandState_t rng_state[1024];
117
118 uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;
119
120 curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);
121
122 int c_idx = blockIdx.x * blockDim.x + threadIdx.x;
123 int s_idx = blockIdx.y * blockDim.x;
124
125 tensor += s_idx * ldm + c_idx;
126
127 for (int s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {
128 if (s_idx < dim_strided && c_idx < dim_contiguous) {
129 *tensor =
130 dist.linear.offset + dist.linear.delta_row * c_idx + dist.linear.delta_column * s_idx;
131 }
132 }
133 }
134
136 template <typename T>
137 __global__ void TensorInitializeIdentity(
138 Distribution dist, int64_t seed, int dim_contiguous, int dim_strided, T *tensor, int ldm) {
139 __shared__ curandState_t rng_state[1024];
140
141 uint64_t gtid = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * gridDim.x * blockDim.x;
142
143 curand_init(seed, gtid, 0, &rng_state[threadIdx.x]);
144
145 int c_idx = blockIdx.x * blockDim.x + threadIdx.x;
146 int s_idx = blockIdx.y * blockDim.x;
147
148 tensor += s_idx * ldm + c_idx;
149
150 for (int s_offset = 0; s_offset < blockDim.x; ++s_offset, ++s_idx) {
151 if (s_idx < dim_strided && c_idx < dim_contiguous) {
152 *tensor = (c_idx == s_idx ? T(1) : T(0));
153 }
154 }
155 }
156
158
159 } // namespace kernel
160 } // namespace device
161 } // namespace reference
162 } // 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.