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提交 6366716c authored 作者: Frederic's avatar Frederic
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theano/sandbox/cuda/blas.py
浏览文件 @ 6366716c
import os
import StringIO
from theano import Op, Type, Apply, Variable, Constant
from theano import tensor, scalar
import StringIO, os
import cuda_ndarray.cuda_ndarray as cuda
from theano.sandbox.cuda.type import CudaNdarrayType
from theano.sandbox.cuda import GpuOp
class GpuDot22(GpuOp):
"""
Implement dot(2d, 2d) on the gpu.
"""
def __str__(self):
return 'GpuDot22'
def __eq__(self, other):
return type(self) == type(other)
......@@ -25,10 +28,10 @@ class GpuDot22(GpuOp):
raise TypeError(y)
otype = CudaNdarrayType(
(x.type.broadcastable[0], y.type.broadcastable[1]))
return Apply(self, [x,y], [otype()])
return Apply(self, [x, y], [otype()])
def c_code_cache_version(self):
return (1,1)
return (1, 1)
def c_code(self, node, nodename, inputs, outputs, sub):
x, y = inputs
......@@ -77,12 +80,14 @@ class GpuDot22(GpuOp):
""" % locals()
gpu_dot22 = GpuDot22()
class GpuDot22Scalar(GpuOp):
"""
Implement dot(2d, 2d) * scalar on the gpu.
"""
def __str__(self):
return 'GpuDot22Scalar'
def __eq__(self, other):
return type(self) == type(other)
......@@ -98,10 +103,10 @@ class GpuDot22Scalar(GpuOp):
raise TypeError(a)
otype = CudaNdarrayType(
(x.type.broadcastable[0], y.type.broadcastable[1]))
return Apply(self, [x,y,a], [otype()])
return Apply(self, [x, y, a], [otype()])
def c_code_cache_version(self):
return (1,1)
return (1, 1)
def c_code(self, node, name, inputs, outputs, sub):
x, y, a = inputs
......@@ -156,13 +161,14 @@ class GpuDot22Scalar(GpuOp):
""" % locals()
gpu_dot22scalar = GpuDot22Scalar()
class GpuGemm(GpuOp):
"""
implement the gemm on the gpu.
"""
def __init__(self, inplace):
self.__setstate__({'inplace':inplace})
self.__setstate__({'inplace': inplace})
def __str__(self):
if self.inplace:
......@@ -187,8 +193,8 @@ class GpuGemm(GpuOp):
return dict(inplace=self.inplace)
def make_node(self, z, a, x, y, b):
# the more complicated error checking performed by tensor.gemm is assumed to already
# have been done
# the more complicated error checking performed by tensor.gemm
# is assumed to already have been done
return Apply(self, [z, a, x, y, b], [z.type()])
def c_code_cache_version(self):
......@@ -270,13 +276,14 @@ class GpuGemm(GpuOp):
gpu_gemm_no_inplace = GpuGemm(inplace=False)
gpu_gemm_inplace = GpuGemm(inplace=True)
class GpuGemv(GpuOp):
"""
implement gemv on the gpu.
"""
def __init__(self, inplace):
self.__setstate__({'inplace':inplace})
self.__setstate__({'inplace': inplace})
def __str__(self):
if self.inplace:
......@@ -301,8 +308,8 @@ class GpuGemv(GpuOp):
return dict(inplace=self.inplace)
def make_node(self, z, a, x, y, b):
# the more complicated error checking performed by tensor.gemv is assumed to already
# have been done
# the more complicated error checking performed by tensor.gemv
# is assumed to already have been done
return Apply(self, [z, a, x, y, b], [z.type()])
def c_code_cache_version(self):
......@@ -364,13 +371,14 @@ class GpuGemv(GpuOp):
gpu_gemv_no_inplace = GpuGemv(inplace=False)
gpu_gemv_inplace = GpuGemv(inplace=True)
class GpuGer(GpuOp):
"""
implement ger on the gpu.
"""
def __init__(self, inplace):
self.__setstate__({'inplace':inplace})
self.__setstate__({'inplace': inplace})
def __str__(self):
if self.inplace:
......@@ -468,6 +476,7 @@ class GpuGer(GpuOp):
gpu_ger_no_inplace = GpuGer(inplace=False)
gpu_ger_inplace = GpuGer(inplace=True)
class GpuOuter(GpuOp):
""" Implement outer on the gpu."""
def make_node(self, x, y):
......@@ -554,10 +563,11 @@ class GpuOuter(GpuOp):
if (%(name)sres) {
%(fail)s;
}
"""%dict(x=x,y=y,A=A,fail=fail,name=name)
""" % dict(x=x, y=y, A=A, fail=fail, name=name)
gpu_outer = GpuOuter()
##
# Not really a BLAS operation, but whatever.
#
......@@ -574,7 +584,7 @@ class GpuConv(GpuOp):
raise ValueError(mode)
def __init__(self, border_mode,
subsample=(1,1),
subsample=(1, 1),
logical_img_hw=None,
logical_kern_hw=None,
logical_kern_align_top=True,
......@@ -591,30 +601,32 @@ class GpuConv(GpuOp):
the execution of the convolution. Mostly used for
optimization or debugging.
:param kshp: The size of the kernel. If provided, can genera
faster code. If the GpuConv op is automatically inserted,
faster code. If the GpuConv op is automatically
inserted,
we take its value automatically from the Conv op.
:param imshp: The size of the image. Not used for code generation but
allow to select an experimental new version in another repo.
allow to select an experimental new version in another
repo.
"""
self.border_mode = border_mode
self.subsample = subsample
if logical_img_hw is not None:
h,w = logical_img_hw
#TODO: reconsider this... since shapes are not given in constructor,
# maybe a multiplier + offset is a more appropriate way of passing this logical
# grid
h, w = logical_img_hw
#TODO: reconsider this... since shapes are not given in
# constructor, maybe a multiplier + offset is a more
# appropriate way of passing this logical grid
logical_img_hw = tuple(logical_img_hw)
self.logical_img_hw = logical_img_hw
if logical_kern_hw is not None:
h,w = logical_kern_hw
#TODO: reconsider this... since shapes are not given in constructor,
# maybe a multiplier + offset is a more appropriate way of passing this logical
# grid
h, w = logical_kern_hw
#TODO: reconsider this... since shapes are not given in
# constructor, maybe a multiplier + offset is a more
# appropriate way of passing this logical grid
logical_kern_hw = tuple(logical_kern_hw)
self.logical_kern_hw = logical_kern_hw
self.logical_kern_align_top = logical_kern_align_top
self.version=version
self.verbose=verbose
self.version = version
self.verbose = verbose
self.kshp = kshp
self.imshp = imshp
......@@ -632,11 +644,12 @@ class GpuConv(GpuOp):
def __setstate__(self, d):
self.__dict__.update(d)
if not hasattr(self,"imshp"):
if not hasattr(self, "imshp"):
self.imshp = None
def __hash__(self):
# don't use hash(self.version) as hash(-1)==-2 and hash(-2)==-2 in python!
# don't use hash(self.version) as hash(-1)==-2 and
# hash(-2)==-2 in python!
return hash(type(self)) \
^ hash(self.border_mode) \
^ hash(self.subsample) \
......@@ -649,14 +662,15 @@ class GpuConv(GpuOp):
^ hash(self.imshp)
def __str__(self):
return '%s{%s, %s, %s, %s, %s, %s, %s}' %(self.__class__.__name__,
self.border_mode,
str(self.subsample),
str(self.logical_img_hw),
str(self.logical_kern_hw),
str(self.logical_kern_align_top),
str(self.imshp),
str(self.kshp))
return '%s{%s, %s, %s, %s, %s, %s, %s}' % (
self.__class__.__name__,
self.border_mode,
str(self.subsample),
str(self.logical_img_hw),
str(self.logical_kern_hw),
str(self.logical_kern_align_top),
str(self.imshp),
str(self.kshp))
def make_node(self, img, kern):
if img.type.ndim != 4:
......@@ -664,26 +678,30 @@ class GpuConv(GpuOp):
if kern.type.ndim != 4:
raise TypeError('kern must be 4D tensor')
broadcastable = [img.type.broadcastable[0], kern.type.broadcastable[0], False, False]
broadcastable = [img.type.broadcastable[0], kern.type.broadcastable[0],
False, False]
return Apply(self, [img, kern], [CudaNdarrayType(broadcastable)()])
def c_compile_args(self):
nb = 0
if self.kshp is not None:
nb = self.kshp[1]
return ['-DTHEANO_KERN_WID='+str(nb)]#,'-g','-G']
return ['-DTHEANO_KERN_WID=' + str(nb)] # ,'-g','-G']
def c_headers(self):
return ['cuda_ndarray.cuh','<stdio.h>']
return ['cuda_ndarray.cuh', '<stdio.h>']
def c_code_cache_version(self):
return (0, 17) # raise this whenever modifying any of the support_code_files
# raise this whenever modifying any of the support_code_files
return (0, 17)
def c_support_code_apply(self, node, nodename):
# REMEMBER TO RAISE c_code_cache_version when changing any of these files
return open(os.path.join(os.path.split(__file__)[0],'conv_kernel.cu')).read()+\
open(os.path.join(os.path.split(__file__)[0],'conv_full_kernel.cu')).read()+\
open(os.path.join(os.path.split(__file__)[0],'conv.cu')).read()
# REMEMBER TO RAISE c_code_cache_version when changing any of
# these files
files = ['conv_kernel.cu', 'conv_full_kernel.cu', 'conv.cu']
codes = [open(os.path.join(os.path.split(__file__)[0], f)).read()
for f in files]
return reduce(str.__add__, codes)
def c_code(self, node, nodename, inp, out_, sub):
img, kern = inp
......@@ -724,7 +742,7 @@ class GpuConv(GpuOp):
mode, dx, dy, version, verbose);
Py_XDECREF(%(out)s);
%(out)s = out2;
"""%sub
""" % sub
class GpuDownsampleFactorMax(GpuOp):
......@@ -736,13 +754,17 @@ class GpuDownsampleFactorMax(GpuOp):
self.ignore_border = ignore_border
def __eq__(self, other):
return type(self) == type(other) and self.ds == other.ds and self.ignore_border == other.ignore_border
return (type(self) == type(other) and
self.ds == other.ds and
self.ignore_border == other.ignore_border)
def __hash__(self):
return hash(type(self)) ^ hash(self.ds) ^ hash(self.ignore_border)
def __str__(self):
return '%s{%s,%s}' % (self.__class__.__name__, self.ds, self.ignore_border)
return '%s{%s,%s}' % (self.__class__.__name__,
self.ds,
self.ignore_border)
def make_node(self, x):
if not isinstance(x.type, CudaNdarrayType):
......@@ -750,10 +772,12 @@ class GpuDownsampleFactorMax(GpuOp):
if not x.type.ndim == 4:
raise TypeError()
return Apply(self, [x], [x.type()])
#def perform(self, node, input_storage, output_storage):
#raise NotImplementedError('only C is implemented')
def c_code_cache_version(self):
return (3)
def c_code(self, node, nodename, inp, out, sub):
x, = inp
z, = out
......@@ -887,6 +911,7 @@ class GpuDownsampleFactorMax(GpuOp):
}
""" % locals()
class GpuDownsampleFactorMaxGrad(GpuOp):
"""
Implement the grad of downsample with max on the gpu.
......@@ -896,16 +921,21 @@ class GpuDownsampleFactorMaxGrad(GpuOp):
self.ignore_border = ignore_border
def __eq__(self, other):
return type(self) == type(other) and self.ds == other.ds and self.ignore_border == other.ignore_border
return (type(self) == type(other) and
self.ds == other.ds and
self.ignore_border == other.ignore_border)
def __hash__(self):
return hash(type(self)) ^ hash(self.ds) ^ hash(self.ignore_border)
def __str__(self):
return '%s{%s,%s}' % (self.__class__.__name__, self.ds, self.ignore_border)
return '%s{%s,%s}' % (self.__class__.__name__,
self.ds,
self.ignore_border)
def make_node(self, x, z, gz):
return Apply(self, [x, z, gz], [x.type()])
def c_code_cache_version(self):
#return ()
return (5,)
......@@ -988,12 +1018,14 @@ class GpuDownsampleFactorMaxGrad(GpuOp):
""" % locals()
def c_support_code_apply(self, node, nodename):
# This code considers every position in the output z, andthen computes the gradient for the
# input pixels that were downsampled to that z-position. It does so by running along every
# z row (sometimes plus one, to make sure every gx row gets totally filled), and by
# running along every x col. This code is not sensitive to the ignore_border flag along
# the row dimension (since it runs for every position in the output z), but it is sensitive
# along the col dimension.
# This code considers every position in the output z, andthen
# computes the gradient for the input pixels that were
# downsampled to that z-position. It does so by running along
# every z row (sometimes plus one, to make sure every gx row
# gets totally filled), and by running along every x col. This
# code is not sensitive to the ignore_border flag along the
# row dimension (since it runs for every position in the
# output z), but it is sensitive along the col dimension.
ignore_border = int(self.ignore_border)
return """
......
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