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提交 5c9d3118 authored 作者: notoraptor's avatar notoraptor
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Wrap Op params for theano.gpuarray.neighbours.GpuImages2Neibs:

- mode (enum list) - context (gpu_context_type) Add Kernel param for theano.gpuarray.neighbours.GpuImages2Neibs: - mode (ga_int) That helps very much to reduce C code variability (from 18 individual ops to about 6 modules with more than 1 op).
上级 1433cacb
隐藏空白字符变更
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正在显示 包含 78 行增加 和 66 行删除
theano/gpuarray/neighbours.py
浏览文件 @ 5c9d3118
from __future__ import absolute_import, print_function, division
import numpy as np
from theano import Op, Apply, config
from theano.gof import ParamsType
from theano.tensor.nnet.neighbours import Images2Neibs
import theano.tensor as T
try:
import pygpu
from pygpu import gpuarray
except ImportError:
pass
......@@ -14,7 +13,7 @@ except ImportError:
from .basic_ops import (as_gpuarray_variable, GpuKernelBase, Kernel,
infer_context_name)
from .opt import register_opt2, op_lifter, register_opt
from .type import GpuArrayType
from .type import GpuArrayType, gpu_context_type
class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
......@@ -22,13 +21,10 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
Images2Neibs for the GPU.
"""
def __init__(self, mode='valid'):
if mode not in ['valid', 'half', 'full',
'ignore_borders', 'wrap_centered']:
raise NotImplementedError("Only the mode valid, half, full, "
"ignore_borders and wrap_centered have "
"been implemented for GpuImages2Neibs")
self.mode = mode
params_type = ParamsType(mode=Images2Neibs.params_type, context=gpu_context_type)
def get_params(self, node):
return self.params_type.get_params(self, context=node.inputs[0].type.context)
def make_node(self, ten4, neib_shape, neib_step=None):
ten4 = as_gpuarray_variable(ten4, infer_context_name(ten4))
......@@ -50,7 +46,7 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
context_name=ten4.type.context_name)()])
def c_code_cache_version(self):
return (12,)
return (13,)
def c_headers(self):
return ['<numpy_compat.h>', '<gpuarray/types.h>']
......@@ -61,13 +57,17 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
flags = Kernel.get_flags(dtype_ten4, dtype_z)
type_ten4 = gpuarray.dtype_to_ctype(dtype_ten4)
type_z = gpuarray.dtype_to_ctype(dtype_z)
mode = self.mode
# Params type 'mode' is an enum list which c_support_code()
# contains C constants definitions that are useful here.
mode_constants = self.params_type.get_type('mode').c_support_code()
kernels = []
kname = "k_multi_warp_less"
k_var = "k_multi_warp_less_" + nodename
code = """
// a version that uses less registers but doesn't work in all cases.
// a version that uses less registers but doesn't work in all cases.
%(mode_constants)s
KERNEL void %(kname)s(
const ga_int mode,
const ga_int nb_batch,
const ga_int nb_stack,
const ga_int height,
......@@ -110,29 +110,29 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
ga_int i = LID_1; // loop over c
{
ga_int ten4_2 = i + a * step_x;
if("%(mode)s"=="wrap_centered"){
if(%(mode)s == MODE_WRAP_CENTERED) {
ten4_2 -= wrap_centered_half_idx_shift_x;
if ( ten4_2 < 0 )
ten4_2 += height;
else if (ten4_2 >= height)
ten4_2 -= height;
} else if ("%(mode)s"=="half"){
} else if (%(mode)s == MODE_HALF) {
ten4_2 -= wrap_centered_half_idx_shift_x;
} else if ("%(mode)s"=="full"){
} else if (%(mode)s == MODE_FULL) {
ten4_2 -= c - 1;
}
ga_int j = LID_0; // loop over d
{
ga_int ten4_3 = j + b * step_y;
if("%(mode)s"=="wrap_centered"){
if(%(mode)s == MODE_WRAP_CENTERED){
ten4_3 -= wrap_centered_half_idx_shift_y;
if ( ten4_3 < 0 )
ten4_3 += width;
else if (ten4_3 >= width)
ten4_3 -= width;
} else if ("%(mode)s"=="half"){
} else if (%(mode)s == MODE_HALF) {
ten4_3 -= wrap_centered_half_idx_shift_y;
} else if ("%(mode)s"=="full"){
} else if (%(mode)s == MODE_FULL) {
ten4_3 -= d - 1;
}
......@@ -150,8 +150,9 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
}
}
}
}""" % locals()
}""" % dict(kname=kname, type_ten4=type_ten4, type_z=type_z, mode_constants=mode_constants)
params = [
'intc',
'intc', 'intc', 'intc', 'intc', 'intc', 'intc',
'intc', 'intc', 'intc', 'intc',
'uintp', 'uintp', 'uintp', 'uintp',
......@@ -165,7 +166,9 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
kname = "k_multi_warp"
k_var = "k_multi_warp_" + nodename
code = """
%(mode_constants)s
KERNEL void %(kname)s(
const ga_int mode,
const ga_int nb_batch,
const ga_int nb_stack,
const ga_int height,
......@@ -209,30 +212,30 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
for (ga_int i = LID_1; i < c; i+=LDIM_1)
{
ga_int ten4_2 = i + a * step_x;
if("%(mode)s"=="wrap_centered"){
if(%(mode)s == MODE_WRAP_CENTERED) {
ten4_2 -= wrap_centered_half_idx_shift_x;
if ( ten4_2 < 0 )
ten4_2 += height;
else if (ten4_2 >= height)
ten4_2 -= height;
} else if ("%(mode)s"=="half"){
} else if (%(mode)s == MODE_HALF) {
ten4_2 -= wrap_centered_half_idx_shift_x;
} else if ("%(mode)s"=="full"){
} else if (%(mode)s == MODE_FULL) {
ten4_2 -= c - 1;
}
// loop over d
for (ga_int j = LID_0; j < d; j+=LDIM_0)
{
ga_int ten4_3 = j + b * step_y;
if("%(mode)s"=="wrap_centered"){
if(%(mode)s == MODE_WRAP_CENTERED) {
ten4_3 -= wrap_centered_half_idx_shift_y;
if ( ten4_3 < 0 )
ten4_3 += width;
else if (ten4_3 >= width)
ten4_3 -= width;
} else if ("%(mode)s"=="half"){
} else if (%(mode)s == MODE_HALF) {
ten4_3 -= wrap_centered_half_idx_shift_y;
} else if ("%(mode)s"=="full"){
} else if (%(mode)s == MODE_FULL) {
ten4_3 -= d - 1;
}
......@@ -251,8 +254,9 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
}
}
}
""" % locals()
""" % dict(kname=kname, type_ten4=type_ten4, type_z=type_z, mode_constants=mode_constants)
params = [
'intc',
'intc', 'intc', 'intc', 'intc', 'intc', 'intc',
'intc', 'intc', 'intc', 'intc',
'uintp', 'uintp', 'uintp', 'uintp',
......@@ -274,18 +278,6 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
"""
def c_code(self, node, name, inp, out, sub):
dtype_ten4 = node.inputs[0].dtype
dtype_neib_shape = node.inputs[1].dtype
dtype_neib_step = node.inputs[2].dtype
dtype_z = node.outputs[0].dtype
itemsize_ten4 = np.dtype(dtype_ten4).itemsize
itemsize_z = np.dtype(dtype_z).itemsize
typecode_z = pygpu.gpuarray.dtype_to_typecode(node.outputs[0].dtype)
ten4, neib_shape, neib_step = inp
z, = out
fail = sub['fail']
ctx = sub['params']
mode = self.mode
err_check = """
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError,
......@@ -293,16 +285,23 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
GpuKernel_error(fptr, err));
%(fail)s;
}
""" % locals()
""" % dict(fail=sub['fail'])
sync = ""
if config.gpuarray.sync:
sync = """
err = GpuArray_sync(&%(z)s->ga);
%(err_check)s
""" % locals()
""" % dict(z=out[0], err_check=err_check)
# NB: To reduce C code variability:
# For itemsize_ten4, I use GpuArray_ITEMSIZE(&ten4->ga) instead of np.dtype(node.inputs[0].dtype).itemsize
# For itemsize_z, I use itemsize_ten4, as ten4 and z have same type properties (deduced from make_node)
# For typecode_z, I use ten4->ga.typecode (for same reason as above)
return """
int grid_c = -1;
int grid_d = -1;
size_t itemsize_ten4 = GpuArray_ITEMSIZE(&%(ten4)s->ga);
size_t itemsize_z = itemsize_ten4;
int typecode_z = %(ten4)s->ga.typecode;
{
if (PyGpuArray_NDIM(%(ten4)s) != 4)
......@@ -351,10 +350,10 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
%(fail)s;
}
if ( "%(mode)s" == "wrap_centered") {
if (%(params)s->mode == MODE_WRAP_CENTERED) {
if (c%%2!=1 || d%%2!=1){
PyErr_Format(PyExc_TypeError,
"GpuImages2Neibs: in mode wrap_centered need patch with odd shapes");
"GpuImages2Neibs: in mode wrap_centered need patch with odd shapes");
%(fail)s;
}
if ( PyGpuArray_DIMS(%(ten4)s)[2] < c ||
......@@ -375,7 +374,7 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
(size_t)step_y);
}else if ( "%(mode)s" == "valid") {
} else if (%(params)s->mode == MODE_VALID) {
if ( ((PyGpuArray_DIMS(%(ten4)s))[2] < c) ||
((((PyGpuArray_DIMS(%(ten4)s))[2]-c) %% step_x)!=0))
{
......@@ -400,12 +399,12 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
grid_c = 1+(((PyGpuArray_DIMS(%(ten4)s))[2]-c)/step_x);
//number of patch in width
grid_d = 1+(((PyGpuArray_DIMS(%(ten4)s))[3]-d)/step_y);
}else if ( "%(mode)s" == "ignore_borders") {
} else if (%(params)s->mode == MODE_IGNORE_BORDERS) {
//number of patch in height
grid_c = 1+(((PyGpuArray_DIMS(%(ten4)s))[2]-c)/step_x);
//number of patch in width
grid_d = 1+(((PyGpuArray_DIMS(%(ten4)s))[3]-d)/step_y);
}else if ( "%(mode)s" == "half") {
} else if (%(params)s->mode == MODE_HALF) {
if ( ((PyGpuArray_DIMS(%(ten4)s))[2] < c) ||
((((PyGpuArray_DIMS(%(ten4)s))[2]-(c%%2)) %% step_x)!=0))
{
......@@ -430,7 +429,7 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
grid_c = 1+(((PyGpuArray_DIMS(%(ten4)s))[2]-(c%%2))/step_x);
//number of patch in width
grid_d = 1+(((PyGpuArray_DIMS(%(ten4)s))[3]-(d%%2))/step_y);
}else if ( "%(mode)s" == "full") {
} else if (%(params)s->mode == MODE_FULL) {
if ( ((PyGpuArray_DIMS(%(ten4)s))[2] < c) ||
( (((PyGpuArray_DIMS(%(ten4)s))[2]+c-2) %% step_x)!=0))
{
......@@ -455,9 +454,9 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
grid_c = 1+(((PyGpuArray_DIMS(%(ten4)s))[2]+c-2)/step_x);
//number of patch in width
grid_d = 1+(((PyGpuArray_DIMS(%(ten4)s))[3]+d-2)/step_y);
}else{
} else {
PyErr_Format(PyExc_TypeError,
"GpuImages2Neibs:: unknown mode '%(mode)s'");
"GpuImages2Neibs:: unknown mode %%d", %(params)s->mode);
%(fail)s;
}
......@@ -476,8 +475,8 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
size_t dims[2];
dims[0] = z_dim0;
dims[1] = z_dim1;
%(z)s = pygpu_empty(2, dims, %(typecode_z)s,
GA_C_ORDER, %(ctx)s, Py_None);
%(z)s = pygpu_empty(2, dims, typecode_z,
GA_C_ORDER, %(params)s->context, Py_None);
if (!%(z)s)
{
PyErr_SetString(PyExc_MemoryError, "GpuImages2Neibs:"
......@@ -490,6 +489,7 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
{ // NESTED SCOPE
const int mode = %(params)s->mode;
const int nb_batch = PyGpuArray_DIMS(%(ten4)s)[0];
const int nb_stack = PyGpuArray_DIMS(%(ten4)s)[1];
const int height = PyGpuArray_DIMS(%(ten4)s)[2];
......@@ -507,7 +507,7 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
size_t threads_per_block[3] = {d, c, 1};
//get the max threads per blocks
size_t max_threads_dim;
int err = gpucontext_property(%(ctx)s->ctx, GA_CTX_PROP_MAXLSIZE, &max_threads_dim);
int err = gpucontext_property(%(params)s->context->ctx, GA_CTX_PROP_MAXLSIZE, &max_threads_dim);
if (err != GA_NO_ERROR){
PyErr_SetString(PyExc_RuntimeError, "Could not fetch max_threads_dims");
%(fail)s;
......@@ -535,14 +535,19 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
}else{
fptr = &k_multi_warp_%(name)s;
}
// printf("%%zu %%zu %%zu %%zu %%zu %%zu %%zu\\n", max_threads_dim, threads_per_block[0], threads_per_block[1], threads_per_block[2], n_blocks[0], n_blocks[1], n_blocks[2]);
size_t stride_A0 = PyGpuArray_STRIDES(%(ten4)s)[0] / %(itemsize_ten4)s;
size_t stride_A1 = PyGpuArray_STRIDES(%(ten4)s)[1] / %(itemsize_ten4)s;
size_t stride_A2 = PyGpuArray_STRIDES(%(ten4)s)[2] / %(itemsize_ten4)s;
size_t stride_A3 = PyGpuArray_STRIDES(%(ten4)s)[3] / %(itemsize_ten4)s;
size_t stride_Z0 = PyGpuArray_STRIDES(%(z)s)[0] / %(itemsize_z)s;
size_t stride_Z1 = PyGpuArray_STRIDES(%(z)s)[1] / %(itemsize_z)s;
void *kernel_params[] = {(void *)&nb_batch,
/*
printf("%%zu %%zu %%zu %%zu %%zu %%zu %%zu\\n",
max_threads_dim, threads_per_block[0], threads_per_block[1], threads_per_block[2],
n_blocks[0], n_blocks[1], n_blocks[2]);
*/
size_t stride_A0 = PyGpuArray_STRIDES(%(ten4)s)[0] / itemsize_ten4;
size_t stride_A1 = PyGpuArray_STRIDES(%(ten4)s)[1] / itemsize_ten4;
size_t stride_A2 = PyGpuArray_STRIDES(%(ten4)s)[2] / itemsize_ten4;
size_t stride_A3 = PyGpuArray_STRIDES(%(ten4)s)[3] / itemsize_ten4;
size_t stride_Z0 = PyGpuArray_STRIDES(%(z)s)[0] / itemsize_z;
size_t stride_Z1 = PyGpuArray_STRIDES(%(z)s)[1] / itemsize_z;
void *kernel_params[] = {(void *)&mode,
(void *)&nb_batch,
(void *)&nb_stack,
(void *)&height, (void *)&width,
(void *)&c, (void *)&d,
......@@ -562,11 +567,18 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
%(err_check)s
%(sync)s
} // END NESTED SCOPE
""" % locals()
def perform(self, node, inp, out, ctx):
""" % dict(ten4=inp[0], neib_shape=inp[1], neib_step=inp[2], z=out[0],
dtype_neib_shape=node.inputs[1].dtype,
dtype_neib_step=node.inputs[2].dtype,
err_check=err_check,
sync=sync,
name=name,
params=sub['params'],
fail=sub['fail'])
def perform(self, node, inp, out, params):
# Disable the perform method from the CPU version
Op.perform(self, node, inp, out, ctx)
Op.perform(self, node, inp, out, params)
@register_opt('fast_compile')
......
theano/tensor/nnet/neighbours.py
浏览文件 @ 5c9d3118
......@@ -52,8 +52,8 @@ class Images2Neibs(Op):
def __init__(self, mode='valid'):
implemented_modes = self.params_type.get_aliases()
if mode not in implemented_modes:
raise NotImplementedError("Only modes %s have been implemented for Images2Neibs"
% ', '.join(implemented_modes))
raise NotImplementedError("Only modes %s have been implemented for %s"
% (', '.join(implemented_modes), type(self).__name__))
self.mode = mode
def __str__(self):
......
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