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.sandbox.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, optdb.__position__.get("add_destroy_handler", 49.5) - 1, "gpuarray")
def register_opt(*tags, **kwargs):
def f(local_opt):
name = (kwargs and kwargs.pop("name")) or local_opt.__name__
GpuAlloc,
GpuShape,
GpuReshape,
GpuEye)
from theano.sandbox.gpuarray.blas import gpu_dot22, GpuGemv, GpuGemm
from theano.sandbox.gpuarray.nnet import (GpuCrossentropySoftmaxArgmax1HotWithBias,
GpuCrossentropySoftmax1HotWithBiasDx)
from theano.sandbox.gpuarray.elemwise import (GpuElemwise, _is_scalar,
GpuDimShuffle, GpuCAReduce)
from theano.sandbox.gpuarray.subtensor import GpuIncSubtensor, GpuSubtensor
from theano.sandbox.gpuarray.type import GpuArrayConstant
gpu_optimizer = EquilibriumDB()
gpu_cut_copies = EquilibriumDB()
gpu_seqopt = SequenceDB()
gpu_seqopt.register('gpuarray_local_optimiziations', gpu_optimizer, 1,
'fast_run', 'inplace', 'gpuarray')
gpu_seqopt.register('gpuarray_cut_transfers', gpu_cut_copies, 2,
'fast_run', 'gpuarray')
# do not add 'fast_run' to these two as this would always enable gpuarray mode
optdb.register('gpuarray_opt', gpu_seqopt,
optdb.__position__.get('add_destroy_handler', 49.5) - 1,
'gpuarray')
def register_opt(*tags, **kwargs):
def f(local_opt):
name = (kwargs and kwargs.pop('name')) or local_opt.__name__
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, BoolParam, FloatParam,
StrParam)
from . import nvcc_compiler
from theano.tensor.basic import register_transfer
# 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, **kwargs)
return local_opt
return f
gpu_crossentropy_softmax_argmax_1hot_with_bias,
gpu_softmax_with_bias, gpu_softmax)
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,
optdb.__position__.get('add_destroy_handler', 49.5) - 1,
'gpuarray')
)
from theano.sandbox.cuda.blas import GpuDownsampleFactorMax, GpuDownsampleFactorMaxGrad
from theano.sandbox.cuda.nnet import (
GpuCrossentropySoftmaxArgmax1HotWithBias,
GpuCrossentropySoftmax1HotWithBiasDx,
GpuSoftmax,
GpuSoftmaxWithBias,
)
from theano.compile import optdb
from theano.tensor.blas import _is_real_vector, _is_real_matrix
# optdb.print_summary() # shows what is currently registered
gpu_optimizer = EquilibriumDB()
gpu_cut_copies = EquilibriumDB()
gpu_seqopt = SequenceDB()
gpu_seqopt.register("gpu_local_optimizations", gpu_optimizer, 1, "fast_run", "inplace")
gpu_seqopt.register("gpu_cut_transfers", gpu_cut_copies, 2, "fast_run", "gpu")
optdb.register("gpu_opt", gpu_seqopt, optdb.__position__.get("add_destroy_handler", 49.5) - 1, "gpu")
# This second pass is needed as the fusion can put all the non float32 code
# inside the elemwise. When it there is no float64 op, this is working.
optdb.register("gpu_after_fusion", ProxyDB(gpu_seqopt), optdb.__position__.get("elemwise_fusion", 71) + 0.1, "gpu")
def register_opt(*tags, **kwargs):
def f(local_opt):
name = (kwargs and kwargs.pop("name")) or local_opt.__name__
gpu_optimizer.register(name, local_opt, "fast_run", "inplace", *tags)
return local_opt
return f
import theano
from theano import config, gof
from six import integer_types
from theano.gof.cmodule import Compiler
from theano.sandbox.mkl.mkl_helper import header_text
from theano.gof import EquilibriumDB, SequenceDB
from theano.tensor.blas import ldflags
_logger_name = 'theano.sandbox.mkl'
_logger = logging.getLogger(_logger_name)
mkl_optimizer = EquilibriumDB(ignore_newtrees=False)
mkl_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__
mkl_optimizer.register(name, local_opt, 'fast_run', 'fast_compile',
'mkl', *tags, **kwargs)
return local_opt
return f
