Skip to content

P
pytensor
提交 1d7b9bdb authored 作者: sentient07's avatar sentient07 提交者： Reyhane Askari
浏览文件
操作

Removed caching instances

Conflicts: theano/gpuarray/basic_ops.py theano/gpuarray/dnn.py theano/gpuarray/elemwise.py theano/gpuarray/extra_ops.py theano/gpuarray/opt.py theano/gpuarray/opt_util.py
上级 eb2c7226
隐藏空白字符变更
内嵌 并排
正在显示 包含 64 行增加 和 127 行删除
theano/gpuarray/basic_ops.py
浏览文件 @ 1d7b9bdb
......@@ -73,7 +73,7 @@ def as_gpuarray_variable(x, context_name):
# If we couldn't deal with transfers, then maybe it's a tensor
if isinstance(x.type, tensor.TensorType):
return gpu_from_host(context_name)(x)
return GpuFromHost(context_name)(x)
# Try _as_GpuArrayVariable if possible
if hasattr(x, '_as_GpuArrayVariable'):
......@@ -617,7 +617,7 @@ class HostFromGpu(Op):
def grad(self, inputs, grads):
gz, = grads
return [gpu_from_host(inputs[0].type.context_name)(gz)]
return [GpuFromHost(inputs[0].type.context_name)(gz)]
def R_op(self, inputs, eval_points):
ev, = eval_points
......@@ -722,14 +722,6 @@ class GpuFromHost(Op):
return (9,)
# Caching GPUAlloc
def gpu_from_host(ctx):
if ctx not in gpu_alloc.cache:
gpu_from_host.cache[ctx] = GpuFromHost(ctx)
return gpu_from_host.cache[ctx]
gpu_from_host.cache = {}
class GpuToGpu(Op):
"""
Transfer data between GPUs.
......@@ -953,15 +945,6 @@ class GpuAlloc(HideC, Alloc):
return True
# Caching GPUAlloc
def gpu_alloc(ctx, memset_0=False):
key = (ctx, memset_0)
if key not in gpu_alloc.cache:
gpu_alloc.cache[key] = GpuAlloc(ctx, memset_0)
return gpu_alloc.cache[key]
gpu_alloc.cache = {}
class GpuAllocEmpty(HideC, AllocEmpty):
"""
Allocate uninitialized memory on the GPU.
......@@ -971,7 +954,7 @@ class GpuAllocEmpty(HideC, AllocEmpty):
_f16_ok = True
params_type = gpu_context_type
def __init__(self, dtype, context_name):
def __init__(self, dtype, context_name=None):
self.dtype = dtype
self.context_name = context_name
......@@ -1048,14 +1031,6 @@ def empty_like(var):
return GpuAllocEmpty(var.type.dtype, var.type.context_name)(*var.shape)
def gpu_alloc_empty(ctx, dtype):
key = (dtype, ctx)
if key not in gpu_alloc_empty.cache:
gpu_alloc_empty.cache[key] = GpuAllocEmpty(dtype, ctx)
return gpu_alloc_empty.cache[key]
gpu_alloc_empty.cache = {}
class GpuContiguous(Op):
"""
Return a C contiguous version of the input.
......
theano/gpuarray/dnn.py
浏览文件 @ 1d7b9bdb
......@@ -32,7 +32,7 @@ from . import pygpu
from .type import (get_context, gpu_context_type, list_contexts,
GpuArraySharedVariable)
from .basic_ops import (as_gpuarray_variable, infer_context_name,
gpu_contiguous, gpu_alloc_empty,
gpu_contiguous, GpuAllocEmpty,
empty_like, GpuArrayType, HostFromGpu)
from .elemwise import GpuElemwise
......@@ -466,18 +466,6 @@ class GpuDnnConvDesc(COp):
return (super(GpuDnnConvDesc, self).c_code_cache_version(), version())
def gpu_dnn_conv_desc(border_mode, subsample=(1, 1), conv_mode='conv',
precision="float32"):
key = (border_mode, subsample, conv_mode, precision)
if key not in gpu_dnn_conv_desc.cache:
gpu_dnn_conv_desc.cache[key] = GpuDnnConvDesc(border_mode,
subsample,
conv_mode,
precision)
return gpu_dnn_conv_desc.cache[key]
gpu_dnn_conv_desc.cache = {}
# scalar constants
_zero = constant(np.asarray(0.0, dtype='float64'))
_one = constant(np.asarray(1.0, dtype='float64'))
......@@ -613,8 +601,8 @@ class GpuDnnConv(DnnBase):
top = gpu_contiguous(top)
d_img = gpu_dnn_conv_gradI()(kerns, top, empty_like(img), desc)
d_kerns = gpu_dnn_conv_gradW()(img, top, empty_like(kerns), desc)
d_img = GpuDnnConvGradI()(kerns, top, empty_like(img), desc)
d_kerns = GpuDnnConvGradW()(img, top, empty_like(kerns), desc)
d_alpha = grad_not_implemented(self, 4, alpha)
d_beta = grad_not_implemented(self, 5, beta)
......@@ -651,14 +639,6 @@ class GpuDnnConv(DnnBase):
return [shape[2]]
def gpu_dnn_conv(algo=None, inplace=False):
key = (algo, inplace)
if key not in gpu_dnn_conv.cache:
gpu_dnn_conv.cache[key] = GpuDnnConv(algo, inplace)
return gpu_dnn_conv.cache[key]
gpu_dnn_conv.cache = {}
class GpuDnnConvGradW(DnnBase):
"""
......@@ -703,8 +683,8 @@ class GpuDnnConvGradW(DnnBase):
kerns = gpu_contiguous(kerns)
d_img = gpu_dnn_conv_gradI()(kerns, top, empty_like(img), desc)
d_top = gpu_dnn_conv()(img, kerns, empty_like(top), desc)
d_img = GpuDnnConvGradI()(kerns, top, empty_like(img), desc)
d_top = GpuDnnConv()(img, kerns, empty_like(top), desc)
d_alpha = grad_not_implemented(self, 4, alpha)
d_beta = grad_not_implemented(self, 5, beta)
......@@ -790,14 +770,6 @@ class GpuDnnConvGradW(DnnBase):
return [shape[2]]
def gpu_dnn_conv_gradW(algo=None, inplace=False):
key = (algo, inplace)
if key not in gpu_dnn_conv_gradW.cache:
gpu_dnn_conv_gradW.cache[key] = GpuDnnConvGradW(inplace, algo)
return gpu_dnn_conv_gradW.cache[key]
gpu_dnn_conv_gradW.cache = {}
class GpuDnnConvGradI(DnnBase):
"""
The convolution gradient with respect to the inputs.
......@@ -843,8 +815,8 @@ class GpuDnnConvGradI(DnnBase):
img = gpu_contiguous(img)
d_kerns = gpu_dnn_conv_gradW()(img, top, empty_like(kerns), desc)
d_top = gpu_dnn_conv()(img, kerns, empty_like(top), desc)
d_kerns = GpuDnnConvGradW()(img, top, empty_like(kerns), desc)
d_top = GpuDnnConv()(img, kerns, empty_like(top), desc)
d_alpha = grad_not_implemented(self, 4, alpha)
d_beta = grad_not_implemented(self, 5, beta)
......@@ -920,14 +892,6 @@ class GpuDnnConvGradI(DnnBase):
return [shape[2]]
def gpu_dnn_conv_gradI(algo=None, inplace=False):
key = (algo, inplace)
if key not in gpu_dnn_conv_gradI.cache:
gpu_dnn_conv_gradI.cache[key] = GpuDnnConvGradI(inplace, algo)
return gpu_dnn_conv_gradI.cache[key]
gpu_dnn_conv_gradI.cache = {}
def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
conv_mode='conv', direction_hint=None, workmem=None,
algo=None, precision=None):
......@@ -1002,10 +966,10 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
shape_i(img, 2, fgraph) - shape_i(kerns, 2, fgraph) + 1,
shape_i(img, 3, fgraph) - shape_i(kerns, 3, fgraph) + 1)
out_shp = assert_conv_shape(out_shp)
out = gpu_alloc_empty(ctx_name, dtype=img.dtype)(*out_shp)
out = GpuAllocEmpty(ctx_name, dtype=img.dtype)(*out_shp)
desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
conv_mode='cross', precision=precision)(out.shape)
conv = gpu_dnn_conv_gradW()(img, kerns, out, desc)
conv = GpuDnnConvGradW()(img, kerns, out, desc)
return as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3), ctx_name)
elif (border_mode == 'full' and subsample == (1, 1) and
......@@ -1021,18 +985,18 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
shape_i(img, 2, fgraph) + shape_i(kerns, 2, fgraph) - 1,
shape_i(img, 3, fgraph) + shape_i(kerns, 3, fgraph) - 1)
out_shp = assert_conv_shape(out_shp)
out = gpu_alloc_empty(ctx_name, dtype=img.dtype)(*out_shp)
out = GpuAllocEmpty(ctx_name, dtype=img.dtype)(*out_shp)
desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
conv_mode=conv_mode, precision=precision)(kerns.shape)
return gpu_dnn_conv_gradI()(kerns, img, out, desc)
return GpuDnnConvGradI()(kerns, img, out, desc)
# Standard case: We use GpuDnnConv with suitable padding.
# contig_version will return a gpu_contiguous copy
# if the img contains negative strides
img = gpu_contiguous(img)
kerns = gpu_contiguous(kerns)
desc = gpu_dnn_conv_desc(border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode, precision=precision)(kerns.shape)
desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode, precision=precision)(kerns.shape)
desc_op = desc.owner.op
# We can use Shape_i and bypass the infer_shape here as this is on
# the input of node and it will always be present.
......@@ -1042,8 +1006,8 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
desc_op.border_mode,
desc_op.subsample)
out_shp = assert_conv_shape(out_shp)
out = gpu_alloc_empty(ctx_name, dtype=img.dtype)(*out_shp)
return gpu_dnn_conv(algo=algo)(img, kerns, out, desc)
out = GpuAllocEmpty(ctx_name, dtype=img.dtype)(*out_shp)
return GpuDnnConv(algo=algo)(img, kerns, out, desc)
def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1),
......@@ -1114,10 +1078,10 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1),
shape_i(img, 3, fgraph) - shape_i(kerns, 3, fgraph) + 1,
shape_i(img, 4, fgraph) - shape_i(kerns, 4, fgraph) + 1)
out_shp = assert_conv_shape(out_shp)
out = gpu_alloc_empty(ctx_name, dtype=img.dtype)(*out_shp)
out = GpuAllocEmpty(ctx_name, dtype=img.dtype)(*out_shp)
desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1, 1),
conv_mode='cross', precision=precision)(out.shape)
conv = gpu_dnn_conv_gradW()(img, kerns, out, desc)
conv = GpuDnnConvGradW()(img, kerns, out, desc)
return as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3, 4), ctx_name)
elif (border_mode == 'full' and subsample == (1, 1, 1) and
......@@ -1134,18 +1098,18 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1),
shape_i(img, 3, fgraph) + shape_i(kerns, 3, fgraph) - 1,
shape_i(img, 4, fgraph) + shape_i(kerns, 4, fgraph) - 1)
out_shp = assert_conv_shape(out_shp)
out = gpu_alloc_empty(ctx_name, dtype=img.dtype)(*out_shp)
out = GpuAllocEmpty(ctx_name, dtype=img.dtype)(*out_shp)
desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1, 1),
conv_mode=conv_mode, precision=precision)(kerns.shape)
return gpu_dnn_conv_gradI()(kerns, img, out, desc)
return GpuDnnConvGradI()(kerns, img, out, desc)
# Standard case: We use GpuDnnConv with suitable padding.
# contig_version will return a gpu_contiguous copy
# if the img contains negative strides
img = gpu_contiguous(img)
kerns = gpu_contiguous(kerns)
desc = gpu_dnn_conv_desc(border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode, precision=precision)(kerns.shape)
desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode, precision=precision)(kerns.shape)
desc_op = desc.owner.op
# We can use Shape_i and bypass the infer_shape here as this is on
# the input of node and it will always be present.
......@@ -1155,8 +1119,8 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1),
desc_op.border_mode,
desc_op.subsample)
out_shp = assert_conv_shape(out_shp)
out = gpu_alloc_empty(ctx_name, dtype=img.dtype)(*out_shp)
return gpu_dnn_conv(algo=algo)(img, kerns, out, desc)
out = GpuAllocEmpty(ctx_name, dtype=img.dtype)(*out_shp)
return GpuDnnConv(algo=algo)(img, kerns, out, desc)
def dnn_gradweight(img, topgrad, kerns_shp, border_mode='valid',
......@@ -1172,11 +1136,11 @@ def dnn_gradweight(img, topgrad, kerns_shp, border_mode='valid',
kerns_shp = as_tensor_variable(kerns_shp)
precision = get_precision(precision, [img, topgrad])
desc = gpu_dnn_conv_desc(border_mode=border_mode, subsample=subsample,
desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode, precision=precision)(
kerns_shp)
out = gpu_alloc_empty(ctx_name, dtype=img.dtype)(*kerns_shp)
return gpu_dnn_conv_gradW()(img, topgrad, out, desc)
out = GpuAllocEmpty(ctx_name, dtype=img.dtype)(*kerns_shp)
return GpuDnnConvGradW()(img, topgrad, out, desc)
def dnn_gradweight3d(img, topgrad, kerns_shp, border_mode='valid',
......@@ -1201,11 +1165,11 @@ def dnn_gradinput(kerns, topgrad, img_shp, border_mode='valid',
img_shp = as_tensor_variable(img_shp)
precision = get_precision(precision, [kerns, topgrad])
desc = gpu_dnn_conv_desc(border_mode=border_mode, subsample=subsample,
desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode, precision=precision)(
kerns.shape)
out = gpu_alloc_empty(ctx_name, kerns.dtype)(*img_shp)
return gpu_dnn_conv_gradI()(kerns, topgrad, out, desc)
out = GpuAllocEmpty(ctx_name, kerns.dtype)(*img_shp)
return GpuDnnConvGradI()(kerns, topgrad, out, desc)
def dnn_gradinput3d(kerns, topgrad, img_shp, border_mode='valid',
......@@ -2849,17 +2813,17 @@ def local_abstractconv_gi_cudnn(node):
@inplace_allocempty(GpuDnnConv, 2)
def local_dnn_conv_inplace(node, inputs):
return [gpu_dnn_conv(algo=node.op.algo, inplace=True)(*inputs)]
return [GpuDnnConv(algo=node.op.algo, inplace=True)(*inputs)]
@inplace_allocempty(GpuDnnConvGradW, 2)
def local_dnn_convgw_inplace(node, inputs):
return [gpu_dnn_conv_gradW(algo=node.op.algo, inplace=True)(*inputs)]
return [GpuDnnConvGradW(algo=node.op.algo, inplace=True)(*inputs)]
@inplace_allocempty(GpuDnnConvGradI, 2)
def local_dnn_convgi_inplace(node, inputs):
return [gpu_dnn_conv_gradI(algo=node.op.algo, inplace=True)(*inputs)]
return [GpuDnnConvGradI(algo=node.op.algo, inplace=True)(*inputs)]
optdb.register('local_dnna_conv_inplace',
tensor.opt.in2out(local_dnn_conv_inplace,
......@@ -2872,40 +2836,40 @@ optdb.register('local_dnna_conv_inplace',
@register_opt('cudnn')
@alpha_merge(GpuDnnConv, alpha_in=4, beta_in=5)
def local_dnn_conv_alpha_merge(node, *inputs):
return [gpu_dnn_conv(algo=node.op.algo)(*inputs)]
return [GpuDnnConv(algo=node.op.algo)(*inputs)]
@register_opt('cudnn')
@alpha_merge(GpuDnnConvGradW, alpha_in=4, beta_in=5)
def local_dnn_convw_alpha_merge(node, *inputs):
return [gpu_dnn_conv_gradW(algo=node.op.algo)(*inputs)]
return [GpuDnnConvGradW(algo=node.op.algo)(*inputs)]
@register_opt('cudnn')
@alpha_merge(GpuDnnConvGradI, alpha_in=4, beta_in=5)
def local_dnn_convi_alpha_merge(node, *inputs):
return [gpu_dnn_conv_gradI(algo=node.op.algo)(*inputs)]
return [GpuDnnConvGradI(algo=node.op.algo)(*inputs)]
@register_opt('cudnn')
@output_merge(GpuDnnConv, alpha_in=4, beta_in=5, out_in=2)
def local_dnn_conv_output_merge(node, *inputs):
inputs = inputs[0:2] + (gpu_contiguous(inputs[2]),) + inputs[3:]
return [gpu_dnn_conv(algo=node.op.algo)(*inputs)]
return [GpuDnnConv(algo=node.op.algo)(*inputs)]
@register_opt('cudnn')
@output_merge(GpuDnnConvGradW, alpha_in=4, beta_in=5, out_in=2)
def local_dnn_convw_output_merge(node, *inputs):
inputs = inputs[0:2] + (gpu_contiguous(inputs[2]),) + inputs[3:]
return [gpu_dnn_conv_gradW(algo=node.op.algo)(*inputs)]
return [GpuDnnConvGradW(algo=node.op.algo)(*inputs)]
@register_opt('cudnn')
@output_merge(GpuDnnConvGradI, alpha_in=4, beta_in=5, out_in=2)
def local_dnn_convi_output_merge(node, *inputs):
inputs = inputs[0:2] + (gpu_contiguous(inputs[2]),) + inputs[3:]
return [gpu_dnn_conv_gradI(algo=node.op.algo)(*inputs)]
return [GpuDnnConvGradI(algo=node.op.algo)(*inputs)]
def local_gpua_pool_dnn_alternative(op, ctx_name, inputs, outputs):
......
theano/gpuarray/extra_ops.py
浏览文件 @ 1d7b9bdb
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import os
from theano import Apply, Op
from theano.tensor.extra_ops import CumOp
from .basic_ops import infer_context_name
try:
from pygpu import gpuarray
except ImportError:
......
theano/gpuarray/nerv.py
浏览文件 @ 1d7b9bdb
......@@ -10,7 +10,7 @@ from theano.scalar import as_scalar, constant
from . import opt
from .basic_ops import (as_gpuarray_variable, GpuAllocEmpty,
infer_context_name, gpu_alloc_empty)
infer_context_name)
from .type import gpu_context_type
from .opt_util import alpha_merge, output_merge
......@@ -157,8 +157,8 @@ def local_gpua_dot_to_gemm16(op, ctx_name, inputs, outputs):
B = inputs[1]
if (A.ndim == 2 and B.ndim == 2 and
A.dtype == 'float16' and B.dtype == 'float16'):
fgraph = getattr(outputs[0], 'fgraph', None)
C = gpu_alloc_empty(ctx_name, dtype='float16')(
fgraph = inputs[0].fgraph
C = GpuAllocEmpty('float16', ctx_name)(
shape_i(A, 0, fgraph), shape_i(B, 1, fgraph))
return Gemm16()(C, 1.0, A, B, 0.0)
......
theano/gpuarray/opt.py
浏览文件 @ 1d7b9bdb
......@@ -44,8 +44,7 @@ from .basic_ops import (as_gpuarray_variable, infer_context_name,
HostFromGpu, GpuFromHost,
GpuSplit, GpuContiguous, gpu_contiguous,
GpuAlloc, GpuAllocEmpty, GpuReshape,
GpuEye, gpu_join, GpuJoin, gpu_alloc_empty,
gpu_alloc, gpu_from_host)
GpuEye, gpu_join, GpuJoin)
from .blas import (gpu_dot22, GpuGemm, GpuGer, GpuGemmBatch,
gpugemm_no_inplace, gpugemm_inplace,
gpugemmbatch_no_inplace,
......@@ -61,7 +60,6 @@ from .blocksparse import (GpuSparseBlockGemv, GpuSparseBlockOuter,
from .nnet import (gpu_crossentropy_softmax_1hot_with_bias_dx,
gpu_crossentropy_softmax_argmax_1hot_with_bias,
gpu_softmax_with_bias, gpu_softmax)
from .elemwise import (GpuElemwise, GpuDimShuffle, GpuCAReduceCuda,
GpuCAReduceCPY, gpu_ca_reduce_cuda, gpu_erfinv, gpu_erfcinv,
max_inputs_to_GpuElemwise)
......@@ -165,7 +163,7 @@ gpu_optimizer.register('local_remove_all_assert',
def safe_to_gpu(x, ctx_name):
if isinstance(x.type, tensor.TensorType):
return gpu_from_host(ctx_name)(x)
return GpuFromHost(ctx_name)(x)
else:
return x
......@@ -269,7 +267,7 @@ class InputToGpuOptimizer(Optimizer):
continue
try:
new_input = gpu_from_host(target)(input).transfer('cpu')
new_input = GpuFromHost(target)(input).transfer('cpu')
fgraph.replace_validate(input, new_input,
"InputToGpuOptimizer")
except TypeError:
......@@ -600,14 +598,14 @@ def local_gpua_alloc2(node):
i.owner.op in [host_from_gpu, tensor.alloc]
for i in c.inputs[1:])
for c, idx in node.outputs[0].clients)):
return [gpu_alloc(None)(*node.inputs).transfer('cpu')]
return [GpuAlloc(None)(*node.inputs).transfer('cpu')]
@register_opt('fast_compile')
@op_lifter([tensor.Alloc])
@register_opt2([tensor.Alloc], 'fast_compile')
def local_gpua_alloc(op, context_name, inputs, outputs):
return gpu_alloc(context_name)
def local_gpuaalloc(op, context_name, inputs, outputs):
return GpuAlloc(context_name)(*inputs)
@register_opt('fast_compile')
......@@ -616,7 +614,7 @@ def local_gpua_alloc(op, context_name, inputs, outputs):
def local_gpua_alloc_empty(op, context_name, inputs, outputs):
# We use _props_dict() to make sure that the GPU op know all the
# CPU op props.
return gpu_alloc_empty(context_name, **op._props_dict())
return GpuAllocEmpty(**op._props_dict())(*inputs)
@register_opt()
......@@ -627,7 +625,7 @@ def local_gpualloc_memset_0(node):
if (isinstance(inp, GpuArrayConstant) and
inp.data.size == 1 and
(np.asarray(inp.data) == 0).all()):
new_op = gpu_alloc(node.op.context_name, memset_0=True)
new_op = GpuAlloc(node.op.context_name, memset_0=True)
return [new_op(*node.inputs)]
......@@ -637,8 +635,8 @@ def local_gpua_alloc_empty_to_zeros(node):
if isinstance(node.op, GpuAllocEmpty):
context_name = infer_context_name(*node.inputs)
z = np.asarray(0, dtype=node.outputs[0].dtype)
return [gpu_alloc(context_name)(as_gpuarray_variable(z, context_name),
*node.inputs)]
return [GpuAlloc(context_name)(as_gpuarray_variable(z, context_name),
*node.inputs)]
optdb.register('local_gpua_alloc_empty_to_zeros',
theano.tensor.opt.in2out(local_gpua_alloc_empty_to_zeros),
# After move to gpu and merge2, before inplace.
......@@ -1234,7 +1232,7 @@ def local_gpua_dot22scalar(op, context_name, inputs, outputs):
x, y, a = inputs
x = as_gpuarray_variable(x, context_name)
y = as_gpuarray_variable(y, context_name)
z = gpu_alloc_empty(context_name, dtype=x.dtype)(x.shape[0], y.shape[1])
z = GpuAllocEmpty(x.dtype, context_name)(x.shape[0], y.shape[1])
return [gpugemm_no_inplace(z, a, x, y, 0)]
......@@ -1804,10 +1802,10 @@ def local_gpu_elemwise_careduce(node):
isinstance(node.inputs[0].owner.op.scalar_op, scalar.basic.Sqr)):
op = node.op
inp = node.inputs[0].owner.inputs[0]
return [gpu_ca_reduce_cuda(scalar_op=op.scalar_op,
axis=op.axis,
reduce_mask=op.reduce_mask,
pre_scalar_op=scalar.basic.sqr)(inp)]
return [GpuCAReduceCuda(scalar_op=op.scalar_op,
axis=op.axis,
reduce_mask=op.reduce_mask,
pre_scalar_op=scalar.basic.sqr)(inp)]
@local_optimizer(None)
......
theano/gpuarray/opt_util.py
浏览文件 @ 1d7b9bdb
......@@ -8,7 +8,7 @@ from theano.gof import local_optimizer
from theano.tensor import (DimShuffle, get_scalar_constant_value,
NotScalarConstantError)
from .basic_ops import GpuFromHost, HostFromGpu, GpuAllocEmpty, GpuReshape, gpu_alloc_empty
from .basic_ops import GpuFromHost, HostFromGpu, GpuAllocEmpty, GpuReshape
from .elemwise import GpuDimShuffle, GpuElemwise
_one = scal.constant(np.asarray(1.0, dtype='float32'))
......@@ -324,7 +324,7 @@ def inplace_allocempty(op, idx):
if (alloc.owner and
isinstance(alloc.owner.op, GpuAllocEmpty) and
len(alloc.clients) > 1):
alloc_op = gpu_alloc_empty(alloc.owner.op.context_name, dtype=alloc.owner.op.dtype)
alloc_op = GpuAllocEmpty(alloc.owner.op.dtype, alloc.owner.op.context_name)
inputs[idx] = alloc_op(*alloc.owner.inputs)
return maker(node, inputs)
return opt
......
theano/gpuarray/type.py
浏览文件 @ 1d7b9bdb
......@@ -271,7 +271,7 @@ class GpuArrayType(Type):
return data
def filter_variable(self, other, allow_convert=True):
from theano.gpuarray.basic_ops import gpu_from_host
from theano.gpuarray.basic_ops import GpuFromHost
if hasattr(other, '_as_GpuArrayVariable'):
other = other._as_GpuArrayVariable(self.context_name)
......@@ -303,7 +303,7 @@ class GpuArrayType(Type):
str(self.broadcastable)))
other = other2
return gpu_from_host(self.context_name)(other)
return GpuFromHost(self.context_name)(other)
@staticmethod
def values_eq(a, b, force_same_dtype=True):
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论