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 c_code(self, node, nodename, inp, out, sub):
dtype_x = node.inputs[0].dtype
dtype_b = node.inputs[1].dtype
dtype_z = node.outputs[0].dtype
work_x = work_dtype(dtype_x)
itemsize_x = np.dtype(dtype_x).itemsize
itemsize_b = np.dtype(dtype_b).itemsize
itemsize_z = np.dtype(dtype_z).itemsize
typecode = pygpu.gpuarray.dtype_to_typecode(node.outputs[0].dtype)
x, b = inp
(z, ) = out
fail = sub["fail"]
ctx = sub["params"]
err_check = ("""
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError, fmt_str, msg);
%(fail)s;
}
""" % locals())
return ("""
if (PyGpuArray_NDIM(%(x)s) != 2)
{
PyErr_SetString(PyExc_ValueError, "rank error input");
%(fail)s;
}
if (PyGpuArray_NDIM(%(b)s) != 1)
{
PyErr_SetString(PyExc_ValueError, "rank error for the bias");
%(fail)s;
}
if ((PyGpuArray_DIMS(%(x)s)[1] !=
PyGpuArray_DIMS(%(b)s)[0]))
{
PyErr_Format(PyExc_ValueError,
"number of columns in x (%%ld)"
" does not match length of b (%%ld)",
(long int)PyGpuArray_DIMS(%(x)s)[1],
(long int)PyGpuArray_DIMS(%(b)s)[0]);
%(fail)s;
}
if ((NULL == %(z)s)
|| (PyGpuArray_DIMS(%(z)s)[0] !=
PyGpuArray_DIMS(%(x)s)[0])
|| (PyGpuArray_DIMS(%(z)s)[1] !=
PyGpuArray_DIMS(%(x)s)[1]))
{
Py_XDECREF(%(z)s);
%(z)s = pygpu_empty(2, PyGpuArray_DIMS(%(x)s),
%(typecode)s, GA_C_ORDER,
%(ctx)s, Py_None);
if (!%(z)s) {
%(fail)s
}
}
{
size_t n_blocks[3] = {std::min(PyGpuArray_DIMS(%(x)s)[0], (size_t)(32*1024)), 1, 1};
//TODO, detect the maximum number of thread per block.
size_t threads_per_block[3] = {std::min(PyGpuArray_DIMS(%(x)s)[1], (size_t)256), 1, 1}; // TODO: Read GA_CTX_PROP_MAXLSIZE0
size_t shmem_sz = PyGpuArray_DIMS(%(x)s)[1] *
2 * sizeof(npy_%(work_x)s);
ssize_t stride_X0 = PyGpuArray_STRIDES(%(x)s)[0] / %(itemsize_x)s;
ssize_t stride_X1 = PyGpuArray_STRIDES(%(x)s)[1] / %(itemsize_x)s;
ssize_t stride_B0 = PyGpuArray_STRIDES(%(b)s)[0] / %(itemsize_b)s;
ssize_t stride_Z0 = PyGpuArray_STRIDES(%(z)s)[0] / %(itemsize_z)s;
ssize_t stride_Z1 = PyGpuArray_STRIDES(%(z)s)[1] / %(itemsize_z)s;
const char *fmt_str, *msg;
void *kernel_params[] = {
(void *)&PyGpuArray_DIMS(%(x)s)[0],
(void *)&PyGpuArray_DIMS(%(x)s)[1],
(void *)%(x)s->ga.data, (void *)&%(x)s->ga.offset,
(void *)&stride_X0, (void *)&stride_X1,
(void *)%(b)s->ga.data, (void *)&%(b)s->ga.offset,
(void *)&stride_B0,
(void *)%(z)s->ga.data, (void *)&%(z)s->ga.offset,
(void *)&stride_Z0, (void *)&stride_Z1};
int err = GA_NO_ERROR;
if (PyGpuArray_DIMS(%(x)s)[0] > 0)
{
if(shmem_sz < (32 * 1024 - 500)){
err = GpuKernel_call(&kSoftmaxWithBias_%(nodename)s, 3,
n_blocks, threads_per_block, shmem_sz,
kernel_params);
fmt_str = "gpuarray error: kSoftmaxWithBias_%(nodename)s: %%s";
msg = GpuKernel_error(&kSoftmaxWithBias_%(nodename)s, err);
}else{
err = GpuKernel_call(&kSoftmaxWithBias_fixed_shared%(nodename)s,
3, n_blocks, threads_per_block,
threads_per_block[0] * sizeof(npy_%(work_x)s),
kernel_params);
fmt_str = "gpuarray error: kSoftmaxWithBias_fixed_shared%(nodename)s: %%s";
msg = GpuKernel_error(&kSoftmaxWithBias_fixed_shared%(nodename)s, err);
}
%(err_check)s
}
}
assert(%(z)s);
""" % locals())
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, 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 c_code(self, node, nodename, inp, out, sub):
itemsize_x = np.dtype(node.inputs[0].dtype).itemsize
worksize_x = np.dtype(work_dtype(node.inputs[0].dtype)).itemsize
itemsize_b = np.dtype(node.inputs[1].dtype).itemsize
itemsize_y_idx = np.dtype(node.inputs[2].dtype).itemsize
itemsize_nll = np.dtype(node.outputs[0].dtype).itemsize
itemsize_sm = np.dtype(node.outputs[1].dtype).itemsize
itemsize_am = np.dtype(node.outputs[2].dtype).itemsize
x, b, y_idx = inp
nll, sm, am = out
fail = sub["fail"]
ctx = sub["params"]
k_var = f"k_xent_sm_1hot_bias_{nodename}"
err_check = ("""
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
"gpuarray error: %(k_var)s: %%s.",
GpuKernel_error(&%(k_var)s, err));
%(fail)s;
}
""" % locals())
sio = StringIO()
print(
"""
if (PyGpuArray_DIMS(%(x)s)[0] !=
PyGpuArray_DIMS(%(y_idx)s)[0])
{
PyErr_SetString(PyExc_ValueError,
"dimension mismatch in x,y_idx arguments");
%(fail)s;
}
if (PyGpuArray_DIMS(%(x)s)[1] != PyGpuArray_DIMS(%(b)s)[0])
{
PyErr_SetString(PyExc_ValueError,
"dimension mismatch in x,b arguments");
%(fail)s;
}
if (aesara_prep_output(&%(nll)s, 1, PyGpuArray_DIMS(%(y_idx)s), %(x)s->ga.typecode, GA_C_ORDER, %(ctx)s)) %(fail)s
if (aesara_prep_output(&%(sm)s, 2, PyGpuArray_DIMS(%(x)s), %(x)s->ga.typecode, GA_C_ORDER, %(ctx)s)) %(fail)s
if (aesara_prep_output(&%(am)s, 1, PyGpuArray_DIMS(%(y_idx)s), %(y_idx)s->ga.typecode, GA_C_ORDER, %(ctx)s)) %(fail)s
{
size_t n_blocks = std::min(PyGpuArray_DIM(%(x)s, 0), (size_t)4096);
size_t n_threads = std::min(PyGpuArray_DIM(%(x)s, 1), (size_t)256);
size_t n_shared = n_threads * %(worksize_x)s;
//TODO: launch more threads per row and do parallel sum and max reductions
int err = k_xent_sm_1hot_bias_call(
1, &n_blocks, &n_threads, n_shared,
PyGpuArray_DIMS(%(x)s)[0],
PyGpuArray_DIMS(%(x)s)[1],
%(x)s->ga.data, %(x)s->ga.offset,
PyGpuArray_STRIDE(%(x)s, 0) / %(itemsize_x)s,
PyGpuArray_STRIDE(%(x)s, 1) / %(itemsize_x)s,
%(b)s->ga.data, %(b)s->ga.offset,
PyGpuArray_STRIDE(%(b)s, 0) / %(itemsize_b)s,
%(y_idx)s->ga.data, %(y_idx)s->ga.offset,
PyGpuArray_STRIDE(%(y_idx)s, 0) / %(itemsize_y_idx)s,
%(nll)s->ga.data, %(nll)s->ga.offset,
PyGpuArray_STRIDE(%(nll)s, 0) / %(itemsize_nll)s,
%(sm)s->ga.data, %(sm)s->ga.offset,
PyGpuArray_STRIDE(%(sm)s, 0) / %(itemsize_sm)s,
PyGpuArray_STRIDE(%(sm)s, 1) / %(itemsize_sm)s,
%(am)s->ga.data, %(am)s->ga.offset,
PyGpuArray_STRIDE(%(am)s, 0) / %(itemsize_am)s);
%(err_check)s
}
""" % locals(),
file=sio,
)
return sio.getvalue()
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,
)
]