def opt(node):
if (isinstance(node.op, GpuElemwise)
and node.op.scalar_op == scal.add and node.nin == 2):
targ = find_node(node.inputs[0], cls)
W = node.inputs[1]
if targ is None:
targ = find_node(node.inputs[1], cls)
W = node.inputs[0]
if targ is None:
return None
if W.dtype != targ.outputs[0].dtype:
return None
if not is_equal(targ.inputs[beta_in], 0.0):
# other cases are too complex for now
return None
if W.broadcastable != targ.inputs[out_in].broadcastable:
# Would need to explicitly tile the output to fill
# the full shape here. Disable for now.
return None
inputs = list(targ.inputs)
inputs[out_in] = W
dtype = inputs[beta_in].dtype
one = scal.constant(np.asarray(1.0, dtype=dtype))
inputs[beta_in] = one
with inherit_stack_trace(node.outputs):
return maker(targ, *inputs)
def opt(node):
if (isinstance(node.op, GpuElemwise) and
node.op.scalar_op == scal.add and
node.nin == 2):
targ = find_node(node.inputs[0], cls)
W = node.inputs[1]
if targ is None:
targ = find_node(node.inputs[1], cls)
W = node.inputs[0]
if targ is None:
return None
if W.dtype != targ.outputs[0].dtype:
return None
if not is_equal(targ.inputs[beta_in], 0.0):
# other cases are too complex for now
return None
if W.broadcastable != targ.inputs[out_in].broadcastable:
# Would need to explicitly tile the output to fill
# the full shape here. Disable for now.
return None
inputs = list(targ.inputs)
inputs[out_in] = W
dtype = inputs[beta_in].dtype
one = scal.constant(np.asarray(1.0, dtype=dtype))
inputs[beta_in] = one
with inherit_stack_trace(node.outputs):
return maker(targ, *inputs)
def ensure_float(val, name):
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name, ))
if not val.type.dtype == 'float32':
raise TypeError("%s: type is not float32" % (name, ))
return val
def ensure_float(val, name):
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name,))
if not val.type.dtype == 'float32':
raise TypeError("%s: type is not float32" % (name,))
return val
def ensure_dt(val, default, name, dtype):
if val is None:
val = default.clone()
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name,))
if not val.type.dtype == dtype:
val = val.astype(dtype)
return val
def ensure_dt(val, default, name, dtype):
if val is None:
val = default.clone()
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name,))
if not val.type.dtype == dtype:
val = val.astype(dtype)
return val
from __future__ import absolute_import, print_function, division
from functools import wraps
import numpy as np
from theano import tensor, scalar as scal, Constant
from theano.gof import local_optimizer
from theano.tensor import (DimShuffle, get_scalar_constant_value,
NotScalarConstantError)
from .basic_ops import GpuFromHost, HostFromGpu, GpuAllocEmpty, GpuReshape
from .elemwise import GpuDimShuffle, GpuElemwise
_one = scal.constant(np.asarray(1.0, dtype='float32'))
def grab_cpu_scalar(v, nd):
"""
Get a scalar variable value from the tree at `v`.
This function will dig through transfers and dimshuffles to get
the constant value. If no such constant is found, it returns None.
Parameters
----------
v
Theano variable to extract the constant value from.
nd : int
Expected number of dimensions for the variable (for
broadcasted constants).
from functools import wraps
import numpy
from theano import scalar as scal, Constant
from theano.gof import local_optimizer
from theano.tensor import (DimShuffle, get_scalar_constant_value,
NotScalarConstantError)
from theano.sandbox.cuda.basic_ops import (GpuFromHost, HostFromGpu,
host_from_gpu, GpuDimShuffle,
GpuElemwise)
_one = scal.constant(numpy.asarray(1.0, dtype='float32'))
def grab_cpu_scalar(v, nd):
if v.owner is not None:
n = v.owner
if (isinstance(n.op, GpuDimShuffle)
and n.op.new_order == ('x', ) * nd):
return host_from_gpu(n.inputs[0])
elif (isinstance(n.op, DimShuffle) and n.op.new_order == ('x', ) * nd):
return n.inputs[0]
elif isinstance(n.op, GpuFromHost):
return grab_cpu_scalar(n.inputs[0], nd=nd)
else:
return None
else:
if (isinstance(v, Constant) and v.broadcastable == (True, ) * nd):
return v.dimshuffle(())
if len(self.subsample) > 2:
sub2 = str(self.subsample[2])
else:
sub2 = '0'
return [('NB_DIMS', str(len(self.subsample))),
('BORDER_MODE', bmode),
('PAD_0', pad0), ('PAD_1', pad1), ('PAD_2', pad2),
('CONV_MODE', conv_flag),
('SUB_0', sub0), ('SUB_1', sub1), ('SUB_2', sub2)]
def c_code_cache_version(self):
return (super(GpuDnnConvDesc, self).c_code_cache_version(), version())
# scalar constants
_zero = constant(numpy.asarray(0.0, dtype='float64'))
_one = constant(numpy.asarray(1.0, dtype='float64'))
def ensure_dt(val, default, name, dtype):
if val is None:
val = default.clone()
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name,))
if not val.type.dtype == dtype:
val = val.astype(dtype)
return val
from __future__ import absolute_import, print_function, division
from functools import wraps
import numpy
from theano import scalar as scal, Constant
from theano.gof import local_optimizer
from theano.tensor import DimShuffle, get_scalar_constant_value, NotScalarConstantError
from .basic_ops import GpuFromHost, HostFromGpu, GpuAllocEmpty
from .elemwise import GpuDimShuffle, GpuElemwise
_one = scal.constant(numpy.asarray(1.0, dtype="float64"))
def grab_cpu_scalar(v, nd):
"""
Get a scalar variable value from the tree at `v`.
This function will dig through transfers and dimshuffles to get
the constant value. If no such constant is found, it returns None.
Parameters
----------
v
Theano variable to extract the constant value from.
nd : int
Expected number of dimensions for the variable (for
broadcasted constants).
"""
if len(self.subsample) > 2:
sub2 = str(self.subsample[2])
else:
sub2 = '0'
return [('NB_DIMS', str(len(self.subsample))),
('BORDER_MODE', bmode),
('PAD_0', pad0), ('PAD_1', pad1), ('PAD_2', pad2),
('CONV_MODE', conv_flag),
('SUB_0', sub0), ('SUB_1', sub1), ('SUB_2', sub2)]
def c_code_cache_version(self):
return (super(GpuDnnConvDesc, self).c_code_cache_version(), version())
# scalar constants
_zero = constant(numpy.asarray(0.0, dtype='float64'))
_one = constant(numpy.asarray(1.0, dtype='float64'))
def ensure_dt(val, default, name, dtype):
if val is None:
val = default.clone()
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name,))
if not val.type.dtype == dtype:
val = val.astype(dtype)
return val
from functools import wraps
import numpy
from theano import scalar as scal, Constant
from theano.gof import local_optimizer
from theano.tensor import (DimShuffle, get_scalar_constant_value,
NotScalarConstantError)
from .basic_ops import GpuFromHost, HostFromGpu, GpuAllocEmpty
from .elemwise import GpuDimShuffle, GpuElemwise
_one = scal.constant(numpy.asarray(1.0, dtype='float64'))
def grab_cpu_scalar(v, nd):
"""
Get a scalar variable value from the tree at `v`.
This function will dig through transfers and dimshuffles to get
the constant value. If no such constant is found, it returns None.
Parameters
----------
v : variable
Theano variable to extract the constant value from.
nd : int
Expected number of dimensions for the variable (for
broadcasted constants).
"""
from __future__ import absolute_import, print_function, division
from functools import wraps
import numpy
from theano import scalar as scal, Constant, config
from theano.gof import local_optimizer
from theano.tensor import (DimShuffle, get_scalar_constant_value,
NotScalarConstantError)
from .basic_ops import GpuFromHost, HostFromGpu, GpuAllocEmpty
from .elemwise import GpuDimShuffle, GpuElemwise
_one = scal.constant(numpy.asarray(1.0, dtype=config.floatX))
def grab_cpu_scalar(v, nd):
"""
Get a scalar variable value from the tree at `v`.
This function will dig through transfers and dimshuffles to get
the constant value. If no such constant is found, it returns None.
Parameters
----------
v
Theano variable to extract the constant value from.
nd : int
Expected number of dimensions for the variable (for
broadcasted constants).
from __future__ import absolute_import, print_function, division
from functools import wraps
import numpy
from theano import scalar as scal, Constant, config
from theano.gof import local_optimizer
from theano.tensor import (DimShuffle, get_scalar_constant_value,
NotScalarConstantError)
from .basic_ops import GpuFromHost, HostFromGpu, GpuAllocEmpty
from .elemwise import GpuDimShuffle, GpuElemwise
_one = scal.constant(numpy.asarray(1.0, dtype=config.floatX))
def grab_cpu_scalar(v, nd):
"""
Get a scalar variable value from the tree at `v`.
This function will dig through transfers and dimshuffles to get
the constant value. If no such constant is found, it returns None.
Parameters
----------
v
Theano variable to extract the constant value from.
nd : int
Expected number of dimensions for the variable (for
broadcasted constants).
