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提交 26890672 authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Fix offset for multinomial.

上级 233f4bf8
隐藏空白字符变更
内嵌 并排
正在显示 包含 26 行增加 和 48 行删除
theano/gpuarray/multinomial.py
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......@@ -71,16 +71,22 @@ class GPUAMultinomialFromUniform(GpuKernelBase, Op):
KERNEL void k_multi_warp_multinomial(
const ga_size nb_multi,
const ga_size nb_outcomes,
GLOBAL_MEM %(in_ctype)s * global_pvals,
GLOBAL_MEM %(in_ctype)s *global_pvals,
const ga_size global_pvals_offset,
const ga_ssize pvals_row_stride,
const ga_ssize pvals_col_stride,
GLOBAL_MEM %(in_ctype)s * global_unis,
GLOBAL_MEM %(in_ctype)s *global_unis,
const ga_size global_unis_offset,
const ga_ssize unis_stride,
GLOBAL_MEM %(out_ctype)s * global_outs,
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 %(in_ctype)s *)(((char *)global_pvals) + global_pvals_offset);
global_unis = (GLOBAL_MEM %(in_ctype)s *)(((char *)global_unis) + global_unis_offset);
global_outs = (GLOBAL_MEM %(out_ctype)s *)(((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)
......@@ -113,11 +119,14 @@ KERNEL void 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),
......@@ -193,27 +202,8 @@ KERNEL void k_multi_warp_multinomial(
assert(nb_blocks*nb_threads >= nb_multi);
void *args[10];
ssize_t strides[5] = {
PyGpuArray_STRIDES(pvals)[0]/gpuarray_get_elsize(%(in_typecode)s),
PyGpuArray_STRIDES(pvals)[1]/gpuarray_get_elsize(%(in_typecode)s),
PyGpuArray_STRIDES(unis)[0]/gpuarray_get_elsize(%(in_typecode)s),
PyGpuArray_STRIDES(out)[0]/gpuarray_get_elsize(%(out_typecode)s),
PyGpuArray_STRIDES(out)[1]/gpuarray_get_elsize(%(out_typecode)s)
};
int err;
args[0] = (void*)&PyGpuArray_DIMS(out)[1];
args[1] = (void*)&PyGpuArray_DIMS(out)[0];
args[2] = pvals->ga.data; //PyGpuArray_DEV_DATA(pvals);
args[3] = (void*)&strides[0];
args[4] = (void*)&strides[1];
args[5] = unis->ga.data; //PyGpuArray_DEV_DATA(unis);
args[6] = (void*)&strides[2];
args[7] = out->ga.data; //PyGpuArray_DEV_DATA(out);
args[8] = (void*)&strides[3];
args[9] = (void*)&strides[4];
err = GpuKernel_call(&%(kname)s, 1, &nb_blocks, &nb_threads, 0, args);
int err = k_multi_warp_multinomial_call(1, &nb_blocks, &nb_threads, 0, PyGpuArray_DIMS(out)[1], PyGpuArray_DIMS(out)[0], pvals->ga.data, pvals->ga.offset, PyGpuArray_STRIDES(pvals)[0]/gpuarray_get_elsize(%(in_typecode)s), PyGpuArray_STRIDES(pvals)[1]/gpuarray_get_elsize(%(in_typecode)s), unis->ga.data, unis->ga.offset, PyGpuArray_STRIDES(unis)[0]/gpuarray_get_elsize(%(in_typecode)s), out->ga.data, out->ga.offset, PyGpuArray_STRIDES(out)[0]/gpuarray_get_elsize(%(out_typecode)s), PyGpuArray_STRIDES(out)[1]/gpuarray_get_elsize(%(out_typecode)s));
if (err != GA_NO_ERROR) {
PyErr_Format(
PyExc_RuntimeError,
......@@ -230,7 +220,7 @@ KERNEL void k_multi_warp_multinomial(
return s
def c_code_cache_version(self):
return (3,)
return (4,)
class GPUAChoiceFromUniform(GpuKernelBase, Op):
......@@ -295,15 +285,21 @@ KERNEL void k_multi_warp_multinomial_wor(
const ga_size nb_outcomes,
const ga_size n_samples,
GLOBAL_MEM float * global_pvals_copy,
const ga_size global_pvals_offset,
const ga_ssize pvals_row_stride,
const ga_ssize pvals_col_stride,
GLOBAL_MEM float * global_unis,
const ga_size global_unis_offset,
const ga_ssize unis_stride,
GLOBAL_MEM ga_long * global_outs,
const ga_size global_outs_offset,
const ga_ssize outs_row_stride,
const ga_ssize outs_col_stride
)
{
global_pvals_copy = (GLOBAL_MEM float *)(((char *)global_pvals_copy) + global_pvals_offset);
global_unis = (GLOBAL_MEM float *)(((char *)global_unis) + global_unis_offset);
global_outs = (GLOBAL_MEM ga_long *)(((char *)global_outs) + global_outs_offset);
// each thread takes care of one multinomial-wor n_samples-draw
int n = LDIM_0*GID_0 + LID_0;
......@@ -344,11 +340,14 @@ KERNEL void k_multi_warp_multinomial_wor(
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
],
......@@ -438,28 +437,7 @@ KERNEL void k_multi_warp_multinomial_wor(
assert(nb_blocks*nb_threads >= nb_multi);
void *args[11];
ssize_t strides[5] = {
PyGpuArray_STRIDES(pvals)[0]/sizeof(float),
PyGpuArray_STRIDES(pvals)[1]/sizeof(float),
PyGpuArray_STRIDES(unis)[0]/sizeof(float),
PyGpuArray_STRIDES(out)[0]/8,
PyGpuArray_STRIDES(out)[1]/8
};
int err;
args[0] = (void*)&PyGpuArray_DIMS(pvals)[0];
args[1] = (void*)&PyGpuArray_DIMS(pvals)[1];
args[2] = (void*)&n_samples;
args[3] = pvals_copy->ga.data; //PyGpuArray_DEV_DATA(pvals);
args[4] = (void*)&strides[0];
args[5] = (void*)&strides[1];
args[6] = unis->ga.data; //PyGpuArray_DEV_DATA(unis);
args[7] = (void*)&strides[2];
args[8] = out->ga.data; //PyGpuArray_DEV_DATA(out);
args[9] = (void*)&strides[3];
args[10] = (void*)&strides[4];
err = GpuKernel_call(&%(kname)s, 1, &nb_blocks, &nb_threads, 0, args);
int err = k_multi_warp_multinomial_wor_call(1, &nb_blocks, &nb_threads, 0, PyGpuArray_DIMS(pvals)[0], PyGpuArray_DIMS(pvals)[1], n_samples, pvals_copy->ga.data, pvals_copy->ga.offset, PyGpuArray_STRIDES(pvals)[0]/sizeof(float), PyGpuArray_STRIDES(pvals)[1]/sizeof(float), unis->ga.data, unis->ga.offset, PyGpuArray_STRIDES(unis)[0]/sizeof(float), out->ga.data, out->ga.offset, PyGpuArray_STRIDES(out)[0]/8, PyGpuArray_STRIDES(out)[1]/8);
if (err != GA_NO_ERROR) {
PyErr_Format(
PyExc_RuntimeError,
......@@ -477,7 +455,7 @@ KERNEL void k_multi_warp_multinomial_wor(
return s
def c_code_cache_version(self):
return (7,)
return (8,)
@register_opt('fast_compile')
......
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