from .blas import (gpu_dot22, GpuGemv, GpuGemm, GpuGer, GpuGemmBatch,
gpugemm_no_inplace, gpugemmbatch_no_inplace)
from .blocksparse import GpuSparseBlockGemv, GpuSparseBlockOuter
from .nnet import (GpuCrossentropySoftmaxArgmax1HotWithBias,
GpuCrossentropySoftmax1HotWithBiasDx, GpuSoftmaxWithBias,
GpuSoftmax)
from .elemwise import (GpuElemwise, GpuDimShuffle, GpuCAReduceCuda,
GpuCAReduceCPY)
from .subtensor import (GpuIncSubtensor, GpuSubtensor, GpuAdvancedSubtensor1,
GpuAdvancedIncSubtensor1,
GpuAdvancedIncSubtensor1_dev20)
from .opt_util import alpha_merge, output_merge
_logger = logging.getLogger("theano.gpuarray.opt")
gpu_optimizer = EquilibriumDB()
gpu_cut_copies = EquilibriumDB()
gpu_seqopt = SequenceDB()
# Don't register this right now
conv_groupopt = LocalGroupDB()
conv_groupopt.__name__ = "gpua_conv_opts"
gpu_seqopt.register('gpuarray_local_optimiziations', gpu_optimizer, 1,
'fast_compile', 'fast_run', 'gpuarray')
gpu_seqopt.register('gpuarray_cut_transfers', gpu_cut_copies, 2,
'fast_compile', 'fast_run', 'gpuarray')
# do not add 'fast_run' to these two as this would always enable gpuarray mode
optdb.register('gpuarray_opt', gpu_seqopt,
import stat
import sys
import theano
from theano.compat import get_unbound_function
from theano.compile import optdb
from theano.gof import EquilibriumDB, SequenceDB
from theano.gof.cmodule import get_lib_extension
from theano.gof.compilelock import get_lock, release_lock
from theano.configparser import config, AddConfigVar, StrParam, BoolParam
import nvcc_compiler
# ignore_newtrees is to speed the optimization as this is the pattern
# we use for optimization. Otherwise, we can iterate 100s of time on
# the graph and apply only a few optimizations each time.
gpu_optimizer = EquilibriumDB(ignore_newtrees=False)
gpu_seqopt = SequenceDB()
def register_opt(*tags, **kwargs):
if any([not isinstance(t, str) for t in tags]):
raise RuntimeError(
"Bad call to register_opt."
" All tags must be strings.", tags)
def f(local_opt):
name = (kwargs and kwargs.pop('name')) or local_opt.__name__
gpu_optimizer.register(name, local_opt, 'fast_run', 'fast_compile',
'gpu', *tags)
return local_opt
block = (inputs[0][0].shape[0], inputs[0][0].shape[1], 1)
pycuda_fct(inputs[0][0],
inputs[1][0],
z[0],
numpy.intc(inputs[1][0].size),
block=block,
grid=grid)
thunk.inputs = inputs
thunk.outputs = outputs
thunk.lazy = False
return thunk
pycuda_optimizer = EquilibriumDB()
gpu_seqopt.register("pycuda_optimizer", pycuda_optimizer, 1.5, "fast_run")
@local_optimizer([GpuElemwise])
def local_pycuda_gpu_elemwise(node):
"""
GpuElemwise -> PycudaElemwiseSourceModuleOp
"""
if isinstance(node.op, GpuElemwise):
if (not any([any(i.type.broadcastable) for i in node.inputs])
and all([i.ndim <= 2 for i in node.inputs])):
new_op = PycudaElemwiseSourceModuleOp(
node.op.scalar_op, node.op.inplace_pattern)(*node.inputs)
return [new_op]
