def gpu_kernels(self, node, name):
write = write_w(self.output_type.dtype)
if self.output_type.dtype == "float16":
otype = "ga_half"
# limit the values of the state that we use.
mask = "& 0x7fff"
offset = "+ 1"
NORM = "3.0458e-05f" # numpy.float16(1.0/(2**15+33))
# this was determined by finding the biggest number such that
# numpy.float16(number * ((M1 & 0x7fff) + 1)) < 1.0
elif self.output_type.dtype == "float32":
otype = "float"
mask = ""
offset = ""
NORM = "4.6566126e-10f" # numpy.float32(1.0/(2**31+65))
# this was determined by finding the biggest number such that
# numpy.float32(number * M1) < 1.0
elif self.output_type.dtype == "float64":
otype = "double"
mask = ""
offset = ""
NORM = "4.656612873077392578125e-10"
else:
raise ValueError("Unsupported data type for output",
self.output_type.dtype)
code = ("""#include "cluda.h"
KERNEL void mrg_uniform(
GLOBAL_MEM %(otype)s *sample_data,
ga_size sample_offset,
GLOBAL_MEM ga_int *state_data,
ga_size state_offset,
const ga_uint Nsamples,
const ga_uint Nstreams_used)
{
sample_data = (GLOBAL_MEM %(otype)s *)(((GLOBAL_MEM char *)sample_data) + sample_offset);
state_data = (GLOBAL_MEM ga_int *)(((GLOBAL_MEM char *)state_data) + state_offset);
/*
* The cluda backend makes sure that ga_int corresponds to
* a 32 bit signed type on the target device. It is not a
* variable width type.
*/
const ga_int i7 = 7;
const ga_int i9 = 9;
const ga_int i15 = 15;
const ga_int i16 = 16;
const ga_int i22 = 22;
const ga_int i24 = 24;
const ga_int M1 = 2147483647; //2^31 - 1
const ga_int M2 = 2147462579; //2^31 - 21069
const ga_int MASK12 = 511; //2^9 - 1
const ga_int MASK13 = 16777215; //2^24 - 1
const ga_int MASK2 = 65535; //2^16 - 1
const ga_int MULT2 = 21069;
const ga_uint idx = GID_0 * LDIM_0 + LID_0;
ga_int y1, y2, x11, x12, x13, x21, x22, x23;
if (idx < Nstreams_used)
{
x11 = state_data[idx*6+0];
x12 = state_data[idx*6+1];
x13 = state_data[idx*6+2];
x21 = state_data[idx*6+3];
x22 = state_data[idx*6+4];
x23 = state_data[idx*6+5];
for (ga_uint i = idx; i < Nsamples; i += Nstreams_used)
{
y1 = ((x12 & MASK12) << i22) + (x12 >> i9) + ((x13 & MASK13) << i7) + (x13 >> i24);
y1 -= (y1 < 0 || y1 >= M1) ? M1 : 0;
y1 += x13;
y1 -= (y1 < 0 || y1 >= M1) ? M1 : 0;
x13 = x12;
x12 = x11;
x11 = y1;
y1 = ((x21 & MASK2) << i15) + (MULT2 * (x21 >> i16));
y1 -= (y1 < 0 || y1 >= M2) ? M2 : 0;
y2 = ((x23 & MASK2) << i15) + (MULT2 * (x23 >> i16));
y2 -= (y2 < 0 || y2 >= M2) ? M2 : 0;
y2 += x23;
y2 -= (y2 < 0 || y2 >= M2) ? M2 : 0;
y2 += y1;
y2 -= (y2 < 0 || y2 >= M2) ? M2 : 0;
x23 = x22;
x22 = x21;
x21 = y2;
if (x11 <= x21) {
sample_data[i] = %(write)s((((x11 - x21 + M1) %(mask)s) %(offset)s) * %(NORM)s);
}
else
{
sample_data[i] = %(write)s((((x11 - x21) %(mask)s) %(offset)s) * %(NORM)s);
}
}
state_data[idx*6+0]= x11;
state_data[idx*6+1]= x12;
state_data[idx*6+2]= x13;
state_data[idx*6+3]= x21;
state_data[idx*6+4]= x22;
state_data[idx*6+5]= x23;
}
}
""" % locals())
# we shouldn't get to this line if it's about to fail
from pygpu import gpuarray
return [
Kernel(
code=code,
name="mrg_uniform",
params=[
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
"uint32",
"uint32",
],
flags=Kernel.get_flags(self.output_type.dtype, "int32"),
)
]
def gpu_kernels(self, node, nodename):
dtype_x = node.inputs[0].dtype
dtype_b = node.inputs[1].dtype
dtype_sm = node.outputs[0].dtype
load_x = load_w(node.inputs[0].dtype)
load_b = load_w(node.inputs[1].dtype)
write_sm = write_w(node.outputs[0].dtype)
work_sm = work_dtype(node.outputs[0].dtype)
flags = Kernel.get_flags(dtype_x, dtype_b, dtype_sm)
type_x = gpuarray.dtype_to_ctype(dtype_x)
type_b = gpuarray.dtype_to_ctype(dtype_b)
type_sm = gpuarray.dtype_to_ctype(dtype_sm)
type_acc = gpuarray.dtype_to_ctype(work_sm)
ctype = gpuarray.dtype_to_ctype(work_sm)
params = [
gpuarray.SIZE,
gpuarray.SIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.SSIZE,
]
kernels = []
kname = "kSoftmaxWithBias"
k_var = "kSoftmaxWithBias_" + nodename
code = ("""#include "cluda.h"
KERNEL void %(kname)s (const ga_size M, const ga_size N,
GLOBAL_MEM const %(type_x)s * x, const ga_size offset_x, const ga_ssize sx0, const ga_ssize sx1,
GLOBAL_MEM const %(type_b)s * b, const ga_size offset_b, const ga_ssize sb0,
GLOBAL_MEM %(type_sm)s * sm, const ga_size offset_sm, const ga_ssize sm_s0, const ga_ssize sm_s1 GA_DECL_SHARED_PARAM(%(type_acc)s, buf))
{
GA_DECL_SHARED_BODY(%(type_acc)s, buf);
LOCAL_MEM_ARG %(type_acc)s * buf2 = buf + N;
x = (GLOBAL_MEM const %(type_x)s *)(((GLOBAL_MEM char *)x)+offset_x);
b = (GLOBAL_MEM const %(type_b)s *)(((GLOBAL_MEM char *)b)+offset_b);
sm = (GLOBAL_MEM %(type_sm)s *)(((GLOBAL_MEM char *)sm)+offset_sm);
for (ga_int blockIDX = GID_0; blockIDX < M; blockIDX += GDIM_0){
for (ga_int tx = LID_0; tx< N; tx += LDIM_0){
buf[tx] = %(load_x)s(x[blockIDX * sx0 + tx * sx1]);
buf[tx] += %(load_b)s(b[tx * sb0]);
buf2[tx] = buf[tx];
}
local_barrier();
{
// This function trashes buf[1..GA_WARP_SIZE],
// leaving the reduction result in buf[0].
if (LID_0 < GA_WARP_SIZE) {
for (ga_int i = LID_0 + GA_WARP_SIZE; i < N; i += GA_WARP_SIZE)
{
buf[LID_0] = max(buf[LID_0], buf[i]);
}
}
local_barrier();
//reduce so that LID_0 0 has the reduction of everything
for (ga_uint _n = GA_WARP_SIZE / 2; _n > 0; _n /= 2) {
if (LID_0 < _n && LID_0 + _n < N)
buf[LID_0] = max(buf[LID_0], buf[LID_0+_n]);
local_barrier();
}
}
%(ctype)s row_max = buf[0];
local_barrier();
for(ga_int __i=LID_0; __i<N; __i+=LDIM_0){;
buf[__i] = exp(buf2[__i] - row_max);
buf2[__i] = buf[__i];
}
local_barrier();
{
// This function trashes buf[1..GA_WARP_SIZE],
// leaving the reduction result in buf[0].
if (LID_0 < GA_WARP_SIZE) {
for (ga_int i = LID_0 + GA_WARP_SIZE; i < N; i += GA_WARP_SIZE)
{
buf[LID_0] = buf[LID_0] + buf[i];
}
}
local_barrier();
//reduce so that LID_0 0 has the reduction of everything
for (ga_uint _n = GA_WARP_SIZE / 2; _n > 0; _n /= 2) {
if (LID_0 < _n && LID_0 + _n < N)
buf[LID_0] = buf[LID_0] + buf[LID_0+_n];
local_barrier();
}
}
%(ctype)s row_sum = buf[0];
local_barrier();
for(ga_int __i=LID_0; __i<N; __i+=LDIM_0){
buf[__i] = buf2[__i] / row_sum;
}
local_barrier();
for (ga_int tx = LID_0; tx< N; tx += LDIM_0){
sm[blockIDX * sm_s0 + tx * sm_s1] = %(write_sm)s(buf[tx]);
}
local_barrier();
}
}
""" % locals())
kernels.append(
Kernel(code=code,
name=kname,
params=params,
flags=flags,
objvar=k_var))
kname = "kSoftmaxWithBias_fixed_shared"
k_var = "kSoftmaxWithBias_fixed_shared" + nodename
code = ("""#include "cluda.h"
KERNEL void %(kname)s (const ga_size M, const ga_size N,
GLOBAL_MEM const %(type_x)s * x, const ga_size offset_x, const ga_ssize sx0, const ga_ssize sx1,
GLOBAL_MEM const %(type_b)s * b, const ga_size offset_b, const ga_ssize sb0,
GLOBAL_MEM %(type_sm)s * sm, const ga_size offset_sm, const ga_ssize sm_s0, const ga_ssize sm_s1 GA_DECL_SHARED_PARAM(%(type_acc)s, buf))
{
GA_DECL_SHARED_BODY(%(type_acc)s, buf);
x = (GLOBAL_MEM const %(type_x)s *)(((GLOBAL_MEM char *)x)+offset_x);
b = (GLOBAL_MEM const %(type_b)s *)(((GLOBAL_MEM char *)b)+offset_b);
sm = (GLOBAL_MEM %(type_sm)s *)(((GLOBAL_MEM char *)sm)+offset_sm);
for (ga_int blockIDX = GID_0; blockIDX < M; blockIDX += GDIM_0){
GLOBAL_MEM const %(type_x)s *x_ptr = &x[blockIDX * sx0];
GLOBAL_MEM %(type_sm)s *sm_ptr = &sm[blockIDX * sm_s0];
{
// This function trashes buf[1..n_threads],
// leaving the reduction result in buf[0].
%(ctype)s red = %(load_x)s(x_ptr[LID_0 * sx1]) + %(load_b)s(b[LID_0 * sb0]);
#pragma unroll 16
for (ga_int i = LID_0 + LDIM_0; i<N; i += LDIM_0) {
red = max(red, %(load_x)s(x_ptr[i * sx1]) + %(load_b)s(b[i * sb0]));
}
buf[LID_0] = red;
local_barrier();
if (LID_0 < GA_WARP_SIZE) {
for (ga_int i = LID_0 + GA_WARP_SIZE; i < LDIM_0; i += GA_WARP_SIZE) {
buf[LID_0] = max(buf[LID_0], buf[i]);
}
}
local_barrier();
//reduce so that LID_0 0 has the reduction of everything
for (ga_uint _n = GA_WARP_SIZE / 2; _n > 0; _n /= 2) {
if (LID_0 < _n && LID_0 + _n < N)
buf[LID_0] = max(buf[LID_0], buf[LID_0+_n]);
local_barrier();
}
}
%(ctype)s row_max = buf[0];
local_barrier();
{
// This function trashes buf[1..n_threads],
// leaving the reduction result in buf[0].
%(ctype)s red = exp(%(load_x)s(x_ptr[LID_0 * sx1]) + %(load_b)s(b[LID_0 * sb0]) - row_max);
#pragma unroll 16
for (ga_int i = LID_0 + LDIM_0; i<N; i += LDIM_0) {
red = red + exp(%(load_x)s(x_ptr[i * sx1]) + %(load_b)s(b[i * sb0]) - row_max);
}
buf[LID_0] = red;
local_barrier();
if (LID_0 < GA_WARP_SIZE) {
for (ga_int i = LID_0 + GA_WARP_SIZE; i < LDIM_0; i += GA_WARP_SIZE) {
buf[LID_0] = buf[LID_0] + buf[i];
}
}
local_barrier();
//reduce so that LID_0 0 has the reduction of everything
for (ga_uint _n = GA_WARP_SIZE / 2; _n > 0; _n /= 2) {
if (LID_0 < _n && LID_0 + _n < N)
buf[LID_0] = buf[LID_0] + buf[LID_0+_n];
local_barrier();
}
}
%(ctype)s row_sum = buf[0];
local_barrier();
for (ga_int tx = LID_0; tx< N; tx += LDIM_0){
sm_ptr[tx * sm_s1] = %(write_sm)s(exp(%(load_x)s(x_ptr[tx * sx1]) + %(load_b)s(b[tx * sb0]) - row_max) / row_sum);
}
local_barrier();
}
}
""" % locals())
kernels.append(
Kernel(code=code,
name=kname,
params=params,
flags=flags,
objvar=k_var))
return kernels
def gpu_kernels(self, node, nodename):
dtype_dnll = node.inputs[0].dtype
dtype_sm = node.inputs[1].dtype
dtype_y_idx = node.inputs[2].dtype
dtype_dx = node.outputs[0].dtype
work_dnll = work_dtype(dtype_dnll)
load_dnll = load_w(dtype_dnll)
load_sm = load_w(dtype_sm)
write_dx = write_w(dtype_dx)
flags = Kernel.get_flags(dtype_dnll, dtype_sm, dtype_y_idx, dtype_dx)
wtype_dnll = gpuarray.dtype_to_ctype(work_dnll)
type_dnll = gpuarray.dtype_to_ctype(dtype_dnll)
type_sm = gpuarray.dtype_to_ctype(dtype_sm)
type_y_idx = gpuarray.dtype_to_ctype(dtype_y_idx)
type_dx = gpuarray.dtype_to_ctype(dtype_dx)
kname = "kCrossEntropySoftmax1HotWithBiasDx"
k_var = "kCrossEntropySoftmax1HotWithBiasDx_" + nodename
params = [
gpuarray.SIZE,
gpuarray.SIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.SSIZE,
]
sio = StringIO()
print(
"""#include "cluda.h"
KERNEL void %(kname)s(
const ga_size N, const ga_size K,
GLOBAL_MEM const %(type_dnll)s* dnll, const ga_size offset_dnll, const ga_ssize dnll_s0,
GLOBAL_MEM const %(type_sm)s* sm, const ga_size offset_sm, const ga_ssize sm_s0, const ga_ssize sm_s1,
GLOBAL_MEM const %(type_y_idx)s* y_idx, const ga_size offset_y_idx, const ga_ssize y_idx_s0,
GLOBAL_MEM %(type_dx)s* dx, const ga_size offset_dx, const ga_ssize dx_s0, const ga_ssize dx_s1)
{
dnll = (GLOBAL_MEM const %(type_dnll)s *)(((GLOBAL_MEM char *)dnll)+offset_dnll);
sm = (GLOBAL_MEM const %(type_sm)s *)(((GLOBAL_MEM char *)sm)+offset_sm);
y_idx = (GLOBAL_MEM const %(type_y_idx)s *)(((GLOBAL_MEM char *)y_idx)+offset_y_idx);
dx = (GLOBAL_MEM %(type_dx)s *)(((GLOBAL_MEM char *)dx)+offset_dx);
for (ga_int i = GID_0; i < N; i += GDIM_0)
{
%(wtype_dnll)s dnll_i = %(load_dnll)s(dnll[i * dnll_s0]);
%(type_y_idx)s y_i = y_idx[i * y_idx_s0];
for (ga_int j = LID_0; j < K; j += LDIM_0)
{
if (y_i == j)
{
dx[i * dx_s0 + j * dx_s1] =
%(write_dx)s(dnll_i *
(%(load_sm)s(sm[i * sm_s0 + j * sm_s1]) - 1.0));
}
else
{
dx[i * dx_s0 + j * dx_s1] =
%(write_dx)s(dnll_i *
%(load_sm)s(sm[i * sm_s0 + j * sm_s1]));
}
}
}
}
""" % locals(),
file=sio,
)
return [
Kernel(
code=sio.getvalue(),
name=kname,
params=params,
flags=flags,
objvar=k_var,
)
]
def gpu_kernels(self, node, nodename):
dtype_x = node.inputs[0].dtype
dtype_b = node.inputs[1].dtype
dtype_y_idx = node.inputs[2].dtype
work_x = work_dtype(dtype_x)
work_b = work_dtype(dtype_b)
load_x = load_w(dtype_x)
load_b = load_w(dtype_b)
write_x = write_w(dtype_x)
write_b = write_w(dtype_b)
flags = Kernel.get_flags(dtype_x, dtype_b, dtype_y_idx)
type_x = gpuarray.dtype_to_ctype(dtype_x)
type_b = gpuarray.dtype_to_ctype(dtype_b)
work_x = gpuarray.dtype_to_ctype(work_x)
type_y_idx = gpuarray.dtype_to_ctype(dtype_y_idx)
kname = "k_xent_sm_1hot_bias"
k_var = "k_xent_sm_1hot_bias_" + nodename
if node.inputs[0].type.context.kind != b"cuda":
f = ""
else:
f = "" if dtype_x == "float64" else "f"
params = [
gpuarray.SIZE,
gpuarray.SIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
gpuarray.SSIZE,
gpuarray.GpuArray,
gpuarray.SIZE,
gpuarray.SSIZE,
]
sio = StringIO()
print(
"""#include "cluda.h"
KERNEL void %(kname)s(const ga_size M, const ga_size N,
GLOBAL_MEM const %(type_x)s* x_data, const ga_size offset_x, const ga_ssize xs0, const ga_ssize xs1,
GLOBAL_MEM const %(type_b)s* b, const ga_size offset_b, const ga_ssize bs0,
GLOBAL_MEM const %(type_y_idx)s* y_idx_data, const ga_size offset_y_idx, const ga_ssize y_idxs0,
GLOBAL_MEM %(type_x)s* nll_data, const ga_size offset_nll, const ga_ssize nlls0,
GLOBAL_MEM %(type_x)s* sm_data, const ga_size offset_sm, const ga_ssize sms0, const ga_ssize sms1,
GLOBAL_MEM %(type_y_idx)s* am_data, const ga_size offset_am, const ga_ssize ams0 GA_DECL_SHARED_PARAM(%(work_x)s, per_thread_values))
{
x_data = (GLOBAL_MEM const %(type_x)s *)(((GLOBAL_MEM char *)x_data)+offset_x);
b = (GLOBAL_MEM const %(type_b)s *)(((GLOBAL_MEM char *)b)+offset_b);
y_idx_data = (GLOBAL_MEM const %(type_y_idx)s *)(((GLOBAL_MEM char *)y_idx_data)+offset_y_idx);
nll_data = (GLOBAL_MEM %(type_x)s *)(((GLOBAL_MEM char *)nll_data)+offset_nll);
sm_data = (GLOBAL_MEM %(type_x)s *)(((GLOBAL_MEM char *)sm_data)+offset_sm);
am_data = (GLOBAL_MEM %(type_y_idx)s *)(((GLOBAL_MEM char *)am_data)+offset_am);
for (ga_int row = GID_0; row < M; row += GDIM_0){
GLOBAL_MEM const %(type_x)s* x = x_data + xs0 * row;
GLOBAL_MEM %(type_x)s* sm = sm_data + sms0 * row;
GA_DECL_SHARED_BODY(%(work_x)s, per_thread_values);
LOCAL_MEM %(work_x)s row_max, sum, sum_inv;
LOCAL_MEM ga_int row_max_threadIdx;
%(work_x)s per_thread_row_max, per_thread_sum;
ga_int per_thread_row_max_j;
// COMPUTE ROW MAX AND ARGMAX
// compute separate per-thread maximums and argmaxes
per_thread_row_max = NAN;
per_thread_row_max_j = 0;
for (ga_int j = LID_0; j < N; j += LDIM_0)
{
%(work_x)s row_ij = %(load_x)s(x[j * xs1]) + %(load_b)s(b[j * bs0]);
per_thread_row_max_j = (row_ij > per_thread_row_max) ? j : per_thread_row_max_j;
per_thread_row_max = fmax%(f)s(row_ij, per_thread_row_max);
}
per_thread_values[LID_0] = per_thread_row_max;
local_barrier();
if (LID_0 == 0) {
row_max = NAN;
row_max_threadIdx = 0;
for (ga_int j = 0; j < LDIM_0; j++)
{
%(work_x)s per_thread_max = per_thread_values[j];
row_max_threadIdx = (per_thread_max > row_max) ? j : row_max_threadIdx;
row_max = fmax%(f)s(per_thread_max, row_max);
}
}
local_barrier();
// The thread with the highest max writes out which of its
// values was the winner.
if (LID_0 == row_max_threadIdx) am_data[row * ams0] = per_thread_row_max_j;
// COMPUTE SOFTMAX
per_thread_sum = 0.0;
for (ga_int j = LID_0; j < N; j += LDIM_0)
{
%(work_x)s row_ij = %(load_x)s(x[j * xs1]) + %(load_b)s(b[j * bs0]);
%(work_x)s sm_ij = exp%(f)s(row_ij - row_max);
per_thread_sum += sm_ij;
sm[j * sms1] = %(write_x)s(sm_ij);
}
per_thread_values[LID_0] = per_thread_sum;
local_barrier();
if (LID_0 == 0) {
sum = 0.0;
for (ga_int j = 0; j < LDIM_0; j++) {
sum += per_thread_values[j];
}
sum_inv = 1.0 / sum;
}
local_barrier();
for (ga_int j = LID_0; j < N; j += LDIM_0) {
sm[j * sms1] = %(write_x)s(%(load_x)s(sm[j * sms1]) * sum_inv);
}
if (LID_0 == 0) {
const %(type_y_idx)s y_idx = (ga_int)y_idx_data[row * y_idxs0];
if ((y_idx >= N || y_idx < 0)) {
// raise some suspicion.
nll_data[row * nlls0] = %(write_x)s(0.0);
} else {
nll_data[row * nlls0] = %(write_x)s(
- %(load_x)s(x[y_idx * xs1])
- %(load_b)s(b[y_idx * bs0])
+ row_max + log%(f)s(sum));
}
}
}
}
""" % locals(),
file=sio,
)
return [
Kernel(
code=sio.getvalue(),
name=kname,
params=params,
flags=flags,
objvar=k_var,
)
]
def gpu_kernels(self, node, name):
out_ctype = pygpu.gpuarray.dtype_to_ctype(node.outputs[0].dtype)
pvals_ctype = pygpu.gpuarray.dtype_to_ctype(node.inputs[0].dtype)
unis_ctype = pygpu.gpuarray.dtype_to_ctype(node.inputs[1].dtype)
work_ctype = pygpu.gpuarray.dtype_to_ctype(work_dtype(node.inputs[0].dtype))
write_out_ctype = write_w(node.outputs[0].dtype)
load_in_ctype = load_w(node.inputs[0].dtype)
code = """#include "cluda.h"
KERNEL void k_multi_warp_multinomial(
const ga_size nb_multi,
const ga_size nb_outcomes,
GLOBAL_MEM %(pvals_ctype)s *global_pvals,
const ga_size global_pvals_offset,
const ga_ssize pvals_row_stride,
const ga_ssize pvals_col_stride,
GLOBAL_MEM %(unis_ctype)s *global_unis,
const ga_size global_unis_offset,
const ga_ssize unis_stride,
GLOBAL_MEM %(out_ctype)s *global_outs,
const ga_size global_outs_offset,
const ga_ssize outs_row_stride,
const ga_ssize outs_col_stride
)
{
global_pvals = (GLOBAL_MEM %(pvals_ctype)s *)(((GLOBAL_MEM char *)global_pvals) + global_pvals_offset);
global_unis = (GLOBAL_MEM %(unis_ctype)s *)(((GLOBAL_MEM char *)global_unis) + global_unis_offset);
global_outs = (GLOBAL_MEM %(out_ctype)s *)(((GLOBAL_MEM char *)global_outs) + global_outs_offset);
// each thread takes care of one multinomial draw
int n = LDIM_0*GID_0 + LID_0;
if (n < nb_multi)
{
%(work_ctype)s cummul = 0.;
bool done = false;
const %(work_ctype)s unis_n = %(load_in_ctype)s(global_unis[n*unis_stride]);
for (ga_size m = 0; m < nb_outcomes; ++m)
{
%(work_ctype)s current_out = 0;
if (!done)
{
cummul += %(load_in_ctype)s(global_pvals[m * pvals_col_stride + n * pvals_row_stride]);
if (unis_n < cummul)
{
current_out = 1;
done = true;
}
}
//write out transposed for speed.
global_outs[n * outs_col_stride +
m * outs_row_stride] = %(write_out_ctype)s(current_out);
}
}
}
""" % dict(
out_ctype=out_ctype,
write_out_ctype=write_out_ctype,
work_ctype=work_ctype,
pvals_ctype=pvals_ctype,
unis_ctype=unis_ctype,
load_in_ctype=load_in_ctype,
)
return [
Kernel(
code=code,
name="k_multi_warp_multinomial",
params=[
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.GpuArray,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.GpuArray,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.GpuArray,
pygpu.gpuarray.SIZE,
pygpu.gpuarray.SSIZE,
pygpu.gpuarray.SSIZE,
],
flags=Kernel.get_flags(node.outputs[0].dtype),
objvar="k_multi_warp_multinomial_" + name,
)
]