def local_gpualloc(node):
replace = False
if node.op == tensor.alloc:
if node.inputs[0].owner and node.inputs[0].owner.op == host_from_gpu:
replace = True
elif all([c != 'output' and c.op == gpu_from_host
for c, idx in node.outputs[0].clients]):
replace = True
elif all([c != 'output' and c.op == tensor.join and
all([i.owner and i.owner.op in [host_from_gpu, tensor.alloc]
for i in c.inputs[1:]])
for c, idx in node.outputs[0].clients]):
replace = True
if replace:
val = node.inputs[0]
shp = node.inputs[1:]
old_out = node.outputs[0]
val2 = tensor.shape_padleft(val, len(shp) - val.ndim)
new_out = host_from_gpu(gpu_alloc(val, *shp))
if new_out.type != old_out.type:
assert new_out.type.ndim == old_out.type.ndim
assert new_out.type.dtype == old_out.type.dtype
for b_old, b_new in zip(old_out.type.broadcastable,
new_out.type.broadcastable):
assert b_new or (not b_old)
new_out = tensor.patternbroadcast(new_out. old_out.broadcastable)
return [new_out]
def test_multiple_out_grad(self):
# Tests that we can compute the gradients through lazy if
x1 = tensor.vector('x1')
x2 = tensor.vector('x2')
y1 = tensor.vector('y1')
y2 = tensor.vector('y2')
c = tensor.iscalar('c')
z = ifelse(c, (x1, x2), (y1, y2))
grads = tensor.grad(z[0].sum() + z[1].sum(),
[x1, x2, y1, y2])
f = theano.function([c, x1, x2, y1, y2], grads)
rng = numpy.random.RandomState(utt.fetch_seed())
lens = [rng.randint(200) for i in range(4)]
values = [numpy.asarray(rng.uniform(size=(l,)), theano.config.floatX)
for l in lens]
outs_1 = f(1, *values)
assert all([x.shape[0] == y for x, y in zip(outs_1, lens)])
assert numpy.all(outs_1[0] == 1.)
assert numpy.all(outs_1[1] == 1.)
assert numpy.all(outs_1[2] == 0.)
assert numpy.all(outs_1[3] == 0.)
outs_0 = f(0, *values)
assert all([x.shape[0] == y for x, y in zip(outs_1, lens)])
assert numpy.all(outs_0[0] == 0.)
assert numpy.all(outs_0[1] == 0.)
assert numpy.all(outs_0[2] == 1.)
assert numpy.all(outs_0[3] == 1.)
def local_gpuaalloc2(node):
"""
Join(axis, Alloc, Alloc, ...) -> Join(axis, GpuAlloc, Alloc, ...)
Moves an alloc that is an input to join to the gpu.
"""
if isinstance(node.op, tensor.Alloc) and all(
c != "output"
and c.op == tensor.join
and all(i.owner and i.owner.op in [host_from_gpu, tensor.alloc] for i in c.inputs[1:])
for c, idx in node.outputs[0].clients
):
return [host_from_gpu(gpu_alloc(*node.inputs))]
def test_zeros_basic():
for shp in [(3,4,5), (300,), (), (0,7)]:
_a = cuda_ndarray.CudaNdarray.zeros(shp)
_n = numpy.zeros(shp, dtype="float32")
assert numpy.allclose(numpy.asarray(_a), _n)
assert _a.shape == _n.shape
assert all(_a._strides == numpy.asarray(_n.strides)/4)
# TODO:The following don't have the same stride!
# This should be fixed with the new GpuNdArray.
for shp in [(3,0), (4,1,5)]:
_a = cuda_ndarray.CudaNdarray.zeros(shp)
_n = numpy.zeros(shp, dtype="float32")
assert numpy.allclose(numpy.asarray(_a), _n)
assert _a.shape == _n.shape
try:
_n = numpy.zeros()
except TypeError:
pass
else:
raise Exception("An error was expected!")
try:
_a = cuda_ndarray.CudaNdarray.zeros()
except TypeError:
pass
else:
raise Exception("An error was expected!")
def guess_n_streams(size, warn=True):
"""
Return a guess at a good number of streams.
:param warn: If True, warn when a guess cannot be made (in which case
we return 60 * 256).
"""
# TODO: a smart way of choosing the number of streams, see #612.
# Note that this code was moved out of `MRG_RandomStreams` so that it can
# be easily accessed from tests, where we want to disable the warning.
if (isinstance(size, (tuple, list)) and
all([isinstance(i, int) for i in size])):
# We can make a guess.
r = 1
for s in size:
r *= s
if r > 6:
r = r // 6 # chosen as fastest for rbm_benchmark
# The purpose of sampling from many streams is to be able to use
# the GPU to its full capacity. It just wastes RAM and stream-initialization time to
# allocate more streams than necessary for the GPU.
# XXX: This number is chosen to be good for 280 and 480 architectures,
# Better would be to use pycuda to query the number of
# processors on the GPU device,
# rather than guessing 60.
return min(r, 60 * 256)
else:
if warn:
warnings.warn((
"MRG_RandomStreams Can't determine #streams from "
"size (%s), guessing 60*256") % str(size),
stacklevel=3)
return 60 * 256
def is_updates(elem):
if isinstance(elem, dict):
return True
# Dictionaries can be given as lists of tuples
if isinstance(elem, (list, tuple)) and all([isinstance(x, (list, tuple)) and len(x) == 2 for x in elem]):
return True
return False
def guess_n_streams(size, warn=True):
"""
Return a guess at a good number of streams.
:param warn: If True, warn when a guess cannot be made (in which case
we return 30 * 256).
"""
# TODO: a smart way of choosing the number of streams, see #612.
# Note that this code was moved out of `MRG_RandomStreams` so that it can
# be easily accessed from tests, where we want to disable the warning.
if (isinstance(size, (tuple, list)) and
all([isinstance(i, int) for i in size])):
# We can make a guess.
r = 1
for s in size:
r *= s
if r > 6:
r = r/6 # chosen as fastest for rbm_benchmark
return r
else:
if warn:
warnings.warn((
"MRG_RandomStreams Can't determine #streams from "
"size (%s), guessing 30*256") % str(size),
stacklevel=3)
return 30 * 256
def is_outputs(elem):
if (isinstance(elem, (list, tuple)) and
all([isinstance(x, theano.Variable) for x in elem])):
return True
if isinstance(elem, theano.Variable):
return True
return False
def test_give_variables_names_small():
x = theano.tensor.matrix('x')
y = theano.tensor.dot(x, x)
fgraph = theano.FunctionGraph((x,), (y,))
give_variables_names(fgraph.variables)
assert all(var.name for var in fgraph.variables)
assert unique([var.name for var in fgraph.variables])
def normal(self, size=None, avg=0.0, std=1.0, ndim=None,
dtype=config.floatX):
"""
Return symbolic tensor of normally-distributed numbers.
:param: size: Can be a list of integer or Theano variable(ex: the shape
of other Theano Variable)
"""
if isinstance(size, tuple):
msg = "size must be a tuple of int or a Theano variable"
assert all([isinstance(i, int) or isinstance(i, Variable)
for i in size]), msg
else:
msg = "size must be a tuple of int or a Theano variable"
assert isinstance(size, Variable) and size.ndim == 1, msg
generator = theano.shared(False) # makes a generic
s_size = theano.tensor.as_tensor_variable(size)
u = CURAND_Normal.new_auto_update(generator, ndim, dtype, s_size,
self.next_seed())
self.state_updates.append(u.update)
rval = u * std + avg
if u.type.broadcastable != rval.type.broadcastable:
raise NotImplementedError(
'Increase the size to match the broadcasting pattern of `low`'
'and `high` arguments'
)
return rval
def test_mpi_tag_ordering():
x = recv((2, 2), "float32", 1, 12)
y = recv((2, 2), "float32", 1, 11)
z = recv((2, 2), "float32", 1, 13)
f = theano.function([], [x, y, z], mode=mpi_mode)
nodes = f.maker.linker.make_all()[-1]
assert all(node.op.tag == tag for node, tag in zip(nodes, (11, 12, 13, 11, 12, 13)))
def test_give_variables_names():
x = theano.tensor.matrix('x')
y = x + 1
z = theano.tensor.dot(x, y)
variables = (x, y, z)
give_variables_names(variables)
assert all(var.name for var in variables)
assert unique([var.name for var in variables])
def is_updates(elem):
if isinstance(elem, dict):
return True
# Dictionaries can be given as lists of tuples
if (isinstance(elem, (list, tuple)) and all(
[isinstance(x, (list, tuple)) and len(x) == 2 for x in elem])):
return True
return False
def test_give_variables_names():
x = theano.tensor.matrix('x')
y = x + 1
z = theano.tensor.dot(x, y)
variables = (x, y, z)
give_variables_names(variables)
assert all(var.name for var in variables)
assert unique([var.name for var in variables])
def test_mpi_tag_ordering():
x = recv((2, 2), 'float32', 1, 12)
y = recv((2, 2), 'float32', 1, 11)
z = recv((2, 2), 'float32', 1, 13)
f = theano.function([], [x, y, z], mode=mpi_mode)
nodes = f.maker.linker.make_all()[-1]
assert all(node.op.tag == tag
for node, tag in zip(nodes, (11, 12, 13, 11, 12, 13)))
def __setup_node__(self, node):
# sets up node so it belongs to this fgraph
if hasattr(node, 'fgraph') and node.fgraph is not self:
raise Exception("%s is already owned by another fgraph" % node)
if (hasattr(node.op, 'view_map') and
not all([isinstance(view, (list, tuple))
for view in node.op.view_map.values()])):
raise Exception("Op '%s' have a bad view map '%s',"
" the values must be tuples or lists." % (
str(node.op), str(node.op.view_map)))
if (hasattr(node.op, 'destroy_map') and
not all([isinstance(destroy, (list, tuple))
for destroy in node.op.destroy_map.values()])):
raise Exception("Op '%s' have a bad destroy map '%s',"
" the values must be tuples or lists." % (
str(node.op), str(node.op.destroy_map)))
node.fgraph = self
node.deps = {}
def apply_node_merge(self, env):
# we clear the dicts because the Constants signatures are not necessarily hashable
# and it's more efficient to give them an integer like the other Variables
nodes_seen = {}
for node_idx, node in enumerate(_list_of_nodes(env)):
#
# these asserts ensure that the env has set the clients field properly the clients
# should at least contain `node` itself!
#
if node.inputs:
assert len(node.inputs[0].clients) > 0
assert (node, 0) in node.inputs[0].clients
merge_candidates = [(nodes_seen[c], c)
for (c, i) in node.inputs[0].clients
if c in nodes_seen]
else:
merge_candidates = []
merge_candidates.sort()
nodes_seen[node] = node_idx
#print 'NODE', node, merge_candidates, node.inputs[0].clients
for candidate_idx, candidate in merge_candidates:
if len(node.inputs) != len(candidate.inputs):
continue
inputs_match = all(
node_in is cand_in
for node_in, cand_in in zip(node.inputs, candidate.inputs))
if inputs_match and node.op == candidate.op:
assert node is not candidate
#
#transfer clients from node to candidate
#
success = True
assert len(node.outputs) == len(candidate.outputs)
pairs = zip(node.outputs, candidate.outputs)
#transfer names
for node_output, cand_output in pairs:
#clobber old name with new one
#it's arbitrary... one of the names has to go
if node_output.name:
cand_output.name = node_output.name
try:
env.replace_all_validate(pairs, reason="Merge")
except InconsistencyError, e:
success = False
if success:
#break out of the candidate loop
break
else:
#try the next candidate
pass
def test_mpi_schedule():
x = theano.tensor.matrix("x")
y = send(x, 1, 11)
z = x + x
waitnode = y.owner
sendnode = y.owner.inputs[0].owner
addnode = z.owner
f = theano.function([x], [y, z], mode=mpi_mode)
nodes = f.maker.linker.make_all()[-1]
optypes = [MPISend, theano.tensor.Elemwise, MPISendWait]
assert all(isinstance(node.op, optype) for node, optype in zip(nodes, optypes))
def with_linker(self, linker):
for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)),
((1, 2, 3), (1, 2), (2, 3)),
((1, 2, 1, 3), (1, 3), (2, 3)),
((2, 3, 4), (2, 1, 0), (4, 3, 2)),
((2, 3, 4), ('x', 2, 1, 0, 'x'),
(1, 4, 3, 2, 1)),
((1, 4, 3, 2, 1), (3, 2, 1), (2, 3, 4)),
((1, 1, 4), (1, 2), (1, 4)),
((1, 1, 1), (), ()),
((1,), ('x', 'x'), (1, 1))]:
ib = [(entry == 1) for entry in xsh]
x = TensorType('float64', ib)('x')
e = DimShuffle(ib, shuffle)(x)
f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
assert f(numpy.ones(xsh)).shape == zsh
#test that DimShuffle.infer_shape work correctly
x = TensorType('float64', ib)('x')
e = DimShuffle(ib, shuffle)(x)
f = copy(linker).accept(FunctionGraph([x], [e.
shape])).make_function()
assert all(f(numpy.ones(xsh))) == all(zsh)
# Test when we drop a axis that is not broadcastable
ib = [False, True, False]
x = TensorType('float64', ib)('x')
self.assertRaises(ValueError, DimShuffle, ib, shuffle)
# Test when we drop a axis that don't have shape 1
ib = [True, True, False]
x = TensorType('float64', ib)('x')
e = DimShuffle(ib, (1, 2))(x)
f = copy(linker).accept(FunctionGraph([x], [e.shape])).make_function()
self.assertRaises(TypeError, f, numpy.ones((2, 1, 4)))
# Test that we can't take a dimensions multiple time
xsh, shuffle, zsh = ((1, 1, 4), (0, 1, 2, 0), (1, 4))
ib = [False, True, False]
x = TensorType('float64', ib)('x')
self.assertRaises(ValueError, DimShuffle, ib, shuffle)
def test_specify_shape_inplace(self):
# test that specify_shape don't break inserting inplace op
dtype = self.dtype
if dtype is None:
dtype = theano.config.floatX
rng = numpy.random.RandomState(utt.fetch_seed())
a = numpy.asarray(rng.uniform(1, 2, [40, 40]), dtype=dtype)
a = self.cast_value(a)
a_shared = self.shared_constructor(a)
b = numpy.asarray(rng.uniform(1, 2, [40, 40]), dtype=dtype)
b = self.cast_value(b)
b_shared = self.shared_constructor(b)
s = numpy.zeros((40, 40), dtype=dtype)
s = self.cast_value(s)
s_shared = self.shared_constructor(s)
f = theano.function([], updates={s_shared: theano.dot(a_shared, b_shared) + s_shared})
topo = f.maker.env.toposort()
f()
# [Gemm{inplace}(<TensorType(float64, matrix)>, 0.01, <TensorType(float64, matrix)>, <TensorType(float64, matrix)>, 2e-06)]
if theano.config.mode != "FAST_COMPILE":
assert sum([node.op.__class__.__name__ in ["Gemm", "GpuGemm", "StructuredDot"] for node in topo]) == 1
assert all(
node.op == tensor.blas.gemm_inplace for node in topo if isinstance(node.op, tensor.blas.Gemm)
)
assert all(node.op.inplace for node in topo if node.op.__class__.__name__ == "GpuGemm")
# Their is no inplace gemm for sparse
# assert all(node.op.inplace for node in topo if node.op.__class__.__name__ == "StructuredDot")
s_shared_specify = tensor.specify_shape(s_shared, s_shared.get_value(borrow=True).shape)
# now test with the specify shape op in the output
f = theano.function(
[], s_shared.shape, updates={s_shared: theano.dot(a_shared, b_shared) + s_shared_specify}
)
topo = f.maker.env.toposort()
shp = f()
assert numpy.all(shp == (40, 40))
if theano.config.mode != "FAST_COMPILE":
assert sum([node.op.__class__.__name__ in ["Gemm", "GpuGemm", "StructuredDot"] for node in topo]) == 1
assert all(
node.op == tensor.blas.gemm_inplace for node in topo if isinstance(node.op, tensor.blas.Gemm)
)
assert all(node.op.inplace for node in topo if node.op.__class__.__name__ == "GpuGemm")
# now test with the specify shape op in the inputs and outputs
a_shared = tensor.specify_shape(a_shared, a_shared.get_value(borrow=True).shape)
b_shared = tensor.specify_shape(b_shared, b_shared.get_value(borrow=True).shape)
f = theano.function(
[], s_shared.shape, updates={s_shared: theano.dot(a_shared, b_shared) + s_shared_specify}
)
topo = f.maker.env.toposort()
shp = f()
assert numpy.all(shp == (40, 40))
if theano.config.mode != "FAST_COMPILE":
assert sum([node.op.__class__.__name__ in ["Gemm", "GpuGemm", "StructuredDot"] for node in topo]) == 1
assert all(
node.op == tensor.blas.gemm_inplace for node in topo if isinstance(node.op, tensor.blas.Gemm)
)
assert all(node.op.inplace for node in topo if node.op.__class__.__name__ == "GpuGemm")
def with_linker(self, linker):
for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)),
((1, 2, 3), (1, 2), (2, 3)),
((1, 2, 1, 3), (1, 3), (2, 3)),
((2, 3, 4), (2, 1, 0), (4, 3, 2)),
((2, 3, 4), ('x', 2, 1, 0, 'x'), (1, 4, 3, 2,
1)),
((1, 4, 3, 2, 1), (3, 2, 1), (2, 3, 4)),
((1, 1, 4), (1, 2), (1, 4)),
((1, 1, 1), (), ()),
((1, ), ('x', 'x'), (1, 1))]:
ib = [(entry == 1) for entry in xsh]
x = TensorType('float64', ib)('x')
e = self.op(ib, shuffle)(x)
f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
assert f(numpy.ones(xsh)).shape == zsh
#test that DimShuffle.infer_shape work correctly
x = TensorType('float64', ib)('x')
e = self.op(ib, shuffle)(x)
f = copy(linker).accept(FunctionGraph([x],
[e.shape])).make_function()
assert all(f(numpy.ones(xsh))) == all(zsh)
# Test when we drop a axis that is not broadcastable
ib = [False, True, False]
x = TensorType('float64', ib)('x')
self.assertRaises(ValueError, self.op, ib, shuffle)
# Test when we drop a axis that don't have shape 1
ib = [True, True, False]
x = TensorType('float64', ib)('x')
e = self.op(ib, (1, 2))(x)
f = copy(linker).accept(FunctionGraph([x], [e.shape])).make_function()
self.assertRaises(TypeError, f, numpy.ones((2, 1, 4)))
# Test that we can't take a dimensions multiple time
xsh, shuffle, zsh = ((1, 1, 4), (0, 1, 2, 0), (1, 4))
ib = [False, True, False]
x = TensorType('float64', ib)('x')
self.assertRaises(ValueError, DimShuffle, ib, shuffle)
def test_mpi_schedule():
x = theano.tensor.matrix('x')
y = send(x, 1, 11)
z = x + x
waitnode = y.owner
sendnode = y.owner.inputs[0].owner
addnode = z.owner
f = theano.function([x], [y, z], mode=mpi_mode)
nodes = f.maker.linker.make_all()[-1]
optypes = [MPISend, theano.tensor.Elemwise, MPISendWait]
assert all(
isinstance(node.op, optype) for node, optype in zip(nodes, optypes))
def is_updates(elem):
if isinstance(elem, dict):
# Make sure the updates will be applied in a deterministic order
if (not isinstance(elem, gof.python25.OrderedDict)
and len(elem) > 1):
warnings.warn("Expected OrderedDict or OrderedUpdates, got "\
+ str(type(elem)) + ". This can make your script non-"
"deterministic.")
return True
# Dictionaries can be given as lists of tuples
if (isinstance(elem, (list, tuple)) and all(
[isinstance(x, (list, tuple)) and len(x) == 2 for x in elem])):
return True
return False
def is_updates(elem):
if isinstance(elem, dict):
# Make sure the updates will be applied in a deterministic order
if not isinstance(elem, gof.python25.OrderedDict):
warnings.warn("Expected OrderedDict or OrderedUpdates, got "\
+str(type(elem))+". This can make your script non-"
"deterministic.")
return True
# Dictionaries can be given as lists of tuples
if (isinstance(elem, (list, tuple)) and
all([isinstance(x, (list, tuple)) and len(x) == 2
for x in elem])):
return True
return False
def apply_node_merge(self, env):
# we clear the dicts because the Constants signatures are not necessarily hashable
# and it's more efficient to give them an integer like the other Variables
nodes_seen = {}
for node_idx, node in enumerate(_list_of_nodes(env)):
#
# these asserts ensure that the env has set the clients field properly the clients
# should at least contain `node` itself!
#
if node.inputs:
assert len(node.inputs[0].clients) > 0
assert (node,0) in node.inputs[0].clients
merge_candidates = [(nodes_seen[c],c) for (c,i) in node.inputs[0].clients if c in nodes_seen]
else:
merge_candidates = []
merge_candidates.sort()
nodes_seen[node] = node_idx
#print 'NODE', node, merge_candidates, node.inputs[0].clients
for candidate_idx, candidate in merge_candidates:
if len(node.inputs) != len(candidate.inputs):
continue
inputs_match = all(node_in is cand_in for node_in, cand_in in zip(node.inputs, candidate.inputs))
if inputs_match and node.op == candidate.op:
assert node is not candidate
#
#transfer clients from node to candidate
#
success = True
assert len(node.outputs) == len(candidate.outputs)
pairs = zip(node.outputs, candidate.outputs)
#transfer names
for node_output, cand_output in pairs:
#clobber old name with new one
#it's arbitrary... one of the names has to go
if node_output.name:
cand_output.name = node_output.name
try:
env.replace_all_validate(pairs, reason="Merge")
except InconsistencyError, e:
success = False
if success:
#break out of the candidate loop
break
else:
#try the next candidate
pass
def test_infer_shape(self):
def mat(format, name, dtype):
if format == 'dense':
return theano.tensor.matrix(name, dtype=dtype)
else:
return theano.sparse.matrix(format, name, dtype=dtype)
params = [('float32', 'float64', 'int16', 'complex64', 'csc', 'dense'),
('float32', 'float64', 'int16', 'complex64', 'csr', 'dense')]
for dtype1, dtype2, dtype3, dtype4, format1, format2 in params:
if format1 == 'dense' and format2 == 'dense':
# Usmm won't be used!
continue
x = mat(format1, 'x', dtype1)
y = mat(format2, 'y', dtype2)
a = theano.tensor.scalar('a', dtype=dtype3)
z = theano.shared(numpy.asarray(self.z, dtype=dtype4).copy())
f_b = lambda z, a, x, y: z - a * (x * y)
x_data = numpy.asarray(self.x, dtype=dtype1)
if format1 != 'dense':
x_data = as_sparse_format(x_data, format1)
y_data = numpy.asarray(self.y, dtype=dtype2)
if format2 != 'dense':
y_data = as_sparse_format(y_data, format2)
a_data = numpy.asarray(1.5, dtype=dtype3)
z_data = numpy.asarray(self.z, dtype=dtype4)
f_b_out = f_b(z_data, a_data, x_data, y_data)
# Can it work inplace?
inplace = dtype4 == theano.scalar.upcast(dtype1, dtype2, dtype3)
# To make it easier to check the toposort
mode = theano.compile.mode.get_default_mode().excluding('fusion')
# test infer_shape of Dot got applied
f_shape = theano.function([a, x, y],
(z - a * theano.sparse.dot(x, y)).shape,
mode=mode)
assert all(f_shape(a_data, x_data, y_data) == f_b_out.shape)
topo = f_shape.maker.env.toposort()
if theano.config.mode != 'FAST_COMPILE':
nb = 0
else:
nb = 1
assert sum([
isinstance(node.op, (Dot, Usmm, UsmmCscDense)) for node in topo
]) == nb
def test_infer_shape(self):
def mat(format, name, dtype):
if format == 'dense':
return theano.tensor.matrix(name, dtype=dtype)
else:
return theano.sparse.matrix(format, name, dtype=dtype)
params = [('float32', 'float64', 'int16', 'complex64', 'csc', 'dense'),
('float32', 'float64', 'int16', 'complex64', 'csr', 'dense')]
for dtype1, dtype2, dtype3, dtype4, format1, format2 in params:
if format1 == 'dense' and format2 == 'dense':
# Usmm won't be used!
continue
x = mat(format1, 'x', dtype1)
y = mat(format2, 'y', dtype2)
a = theano.tensor.scalar('a', dtype=dtype3)
z = theano.shared(numpy.asarray(self.z, dtype=dtype4).copy())
f_b = lambda z, a, x, y: z - a * (x * y)
x_data = numpy.asarray(self.x, dtype=dtype1)
if format1 != 'dense':
x_data = as_sparse_format(x_data, format1)
y_data = numpy.asarray(self.y, dtype=dtype2)
if format2 != 'dense':
y_data = as_sparse_format(y_data, format2)
a_data = numpy.asarray(1.5, dtype=dtype3)
z_data = numpy.asarray(self.z, dtype=dtype4)
f_b_out = f_b(z_data, a_data, x_data, y_data)
# Can it work inplace?
inplace = dtype4 == theano.scalar.upcast(dtype1, dtype2, dtype3)
# To make it easier to check the toposort
mode = theano.compile.mode.get_default_mode().excluding('fusion')
# test infer_shape of Dot got applied
f_shape = theano.function([a, x, y],
(z - a * theano.sparse.dot(x, y)).shape,
mode=mode)
assert all(f_shape(a_data, x_data, y_data) == f_b_out.shape)
topo = f_shape.maker.env.toposort()
if theano.config.mode != 'FAST_COMPILE':
nb = 0
else:
nb = 1
assert sum([isinstance(node.op, (Dot, Usmm, UsmmCscDense))
for node in topo]) == nb
def filter(x):
"""
Ensure `x` is made only of allowed data types.
Return True iff `x` is made only of lists, tuples, dictionaries, Theano
variables or `theano.scan_module.until` objects.
"""
# Is `x` a container we can iterate on?
iter_on = None
if isinstance(x, list) or isinstance(x, tuple):
iter_on = x
elif isinstance(x, dict):
iter_on = x.iteritems()
if iter_on is not None:
return all(filter(y) for y in iter_on)
else:
return isinstance(x, theano.Variable) or isinstance(x, theano.scan_module.until)
def local_opt(node):
if type(node.op) is OP:
# This does not support nodes that have more than one output.
assert len(node.outputs) == 1
# either one of our inputs is on the gpu or
# all of our client are on the gpu
if (any([i.owner and i.owner.op == host_from_gpu
for i in node.inputs]) or
all([c != 'output' and c.op == gpu_from_host
for c, idx in node.outputs[0].clients])):
new_op = maker(node)
# This is needed as sometimes new_op inherit from OP.
if new_op and new_op != node.op:
if isinstance(new_op, theano.Op):
return [host_from_gpu(new_op(*node.inputs))]
else: # suppose it is a variable on the GPU
return [host_from_gpu(new_op)]
return False
def filter(x):
"""
Ensure `x` is made only of allowed data types.
Return True iff `x` is made only of lists, tuples, dictionaries, Theano
variables or `theano.scan_module.until` objects.
"""
# Is `x` a container we can iterate on?
iter_on = None
if isinstance(x, list) or isinstance(x, tuple):
iter_on = x
elif isinstance(x, dict):
iter_on = x.iteritems()
if iter_on is not None:
return all(filter(y) for y in iter_on)
else:
return (isinstance(x, theano.Variable)
or isinstance(x, theano.scan_module.until))
class T_picklefunction(unittest.TestCase):
def test_deepcopy(self):
a = T.scalar() # the a is for 'anonymous' (un-named).
x,s = T.scalars('xs')
f = function([x, In(a, value=1.0,name='a'), In(s, value=0.0, update=s+a*x, mutable=True)], s+a*x)
try:
g = copy.deepcopy(f)
except NotImplementedError, e:
if e[0].startswith('DebugMode is not picklable'):
return
else:
raise
#if they both return, assume that they return equivalent things.
#print [(k,id(k)) for k in f.finder.keys()]
#print [(k,id(k)) for k in g.finder.keys()]
self.assertFalse(g.container[0].storage is f.container[0].storage)
self.assertFalse(g.container[1].storage is f.container[1].storage)
self.assertFalse(g.container[2].storage is f.container[2].storage)
self.assertFalse(x in g.container)
self.assertFalse(x in g.value)
self.assertTrue(len(f.defaults) == len(g.defaults))
#print 'f.defaults = %s' % (f.defaults, )
#print 'g.defaults = %s' % (g.defaults, )
self.assertTrue(all([f_req == g_req and f_feed == g_feed and
f_val == g_val
for ((f_req, f_feed, f_val), (g_req, g_feed, g_val)) in zip(
f.defaults, g.defaults)]))
self.assertFalse(g.value[1] is f.value[1]) # should not have been copied
self.assertFalse(g.value[2] is f.value[2]) # should have been copied because it is mutable.
self.assertFalse((g.value[2] != f.value[2]).any()) # its contents should be identical
self.assertTrue(f(2, 1) == g(2)) #they should be in sync, default value should be copied.
self.assertTrue(f(2, 1) == g(2)) #they should be in sync, default value should be copied.
f(1,2) # put them out of sync
self.assertFalse(f(1, 2) == g(1, 2)) #they should not be equal anymore.
g(1, 2) # put them back in sync
self.assertTrue(f(3) == g(3)) # They should be in sync again.
def local_opt(node):
if type(node.op) in OP:
# Either one of our inputs is on the gpu or
# all of our client are on the gpu
if (any([i.owner and i.owner.op == host_from_gpu
for i in node.inputs]) or
all([c != 'output' and c.op == gpu_from_host
for c, idx in node.outputs[0].clients])):
new_op = maker(node)
# This is needed as sometimes new_op inherit from OP.
if new_op and new_op != node.op:
if isinstance(new_op, theano.Op):
return [safe_to_cpu(o) for o in
new_op(*node.inputs, return_list=True)]
elif isinstance(new_op, (tuple, list)):
return [safe_to_cpu(o) for o in new_op]
else: # suppose it is a variable on the GPU
return [host_from_gpu(new_op)]
return False
def local_opt(node):
if type(node.op) in OP:
# Either one of our inputs is on the gpu or
# all of our client are on the gpu
if (any([i.owner and i.owner.op == host_from_gpu
for i in node.inputs]) or
all([c != 'output' and c.op == gpu_from_host
for c, idx in node.outputs[0].clients])):
new_op = maker(node)
# This is needed as sometimes new_op inherit from OP.
if new_op and new_op != node.op:
if isinstance(new_op, theano.Op):
return [host_from_gpu(o) for o in
new_op(*node.inputs, return_list=True)]
elif isinstance(new_op, (tuple, list)):
return [host_from_gpu(o) for o in new_op]
else: # suppose it is a variable on the GPU
return [host_from_gpu(new_op)]
return False
def local_opt(node):
if type(node.op) is OP:
# This does not support nodes that have more than one output.
assert len(node.outputs) == 1
# either one of our inputs is on the gpu or
# all of our client are on the gpu
if (any([
i.owner and i.owner.op == host_from_gpu
for i in node.inputs
]) or all([
c != 'output' and c.op == gpu_from_host
for c, idx in node.outputs[0].clients
])):
new_op = maker(node)
# This is needed as sometimes new_op inherit from OP.
if new_op and new_op != node.op:
if isinstance(new_op, theano.Op):
return [host_from_gpu(new_op(*node.inputs))]
else: # suppose it is a variable on the GPU
return [host_from_gpu(new_op)]
return False
def uniform(self, size, low=0.0, high=1.0, ndim=None, dtype=config.floatX):
"""
Return symbolic tensor of uniform numbers.
"""
if isinstance(size, tuple):
msg = "size must be a tuple of int or a Theano variable"
assert all([
isinstance(i, int) or isinstance(i, Variable) for i in size
]), msg
else:
msg = "size must be a tuple of int or a Theano variable"
assert isinstance(size, Variable) and size.ndim == 1, msg
generator = theano.shared(False) # makes a generic
s_size = theano.tensor.as_tensor_variable(size)
u = CURAND_Uniform.new_auto_update(generator, ndim, dtype, s_size,
self.next_seed())
self.state_updates.append(u.update)
rval = u * (high - low) + low
if u.type.broadcastable != rval.type.broadcastable:
raise NotImplementedError(
'Increase the size to match the broadcasting pattern of '
'low and `high` arguments')
return rval
def uniform(self, size, low=0.0, high=1.0, ndim=None,
dtype=config.floatX):
"""
Return symbolic tensor of uniform numbers.
"""
if isinstance(size, tuple):
msg = "size must be a tuple of int or a Theano variable"
assert all([isinstance(i, int) or isinstance(i, Variable)
for i in size]), msg
else:
msg = "size must be a tuple of int or a Theano variable"
assert isinstance(size, Variable) and size.ndim == 1, msg
generator = theano.shared(False) # makes a generic
s_size = theano.tensor.as_tensor_variable(size)
u = CURAND_Uniform.new_auto_update(generator, ndim, dtype, s_size,
self.next_seed())
self.state_updates.append(u.update)
rval = u * (high - low) + low
if u.type.broadcastable != rval.type.broadcastable:
raise NotImplementedError(
'Increase the size to match the broadcasting pattern of '
'low and `high` arguments'
)
return rval
def __call__(self):
storage_map = self.storage_map
compute_map = self.compute_map
thunks = self.thunks
dependencies = self.dependencies
for k in self.storage_map:
compute_map[k][0] = (k.owner is None)
# apply_stack contains nodes
apply_stack = list(self.base_apply_stack)
last_apply_stack_len = -1
ls = []
while apply_stack:
# Make sure something happened last time round. This is
# just a safety check to make sure the op is written
# correctly apply_stack should either decrease in length
# by one (a thunk successfully applied), or increase in
# length (added dependencies over and above the original).
# NB: this doesn't catch cycles (would be too expensive/slow),
# just stalls.
apply_stack_len = len(apply_stack)
assert apply_stack_len != last_apply_stack_len
last_apply_stack_len = apply_stack_len
current_apply = apply_stack.pop()
current_inputs = current_apply.inputs
current_outputs = current_apply.outputs
current_deps = current_inputs + current_apply.destroy_dependencies
computed_ins = all(compute_map[v][0] for v in current_deps)
computed_outs = all(compute_map[v][0] for v in current_outputs)
if not thunks[self.node_idx[current_apply]].lazy:
#
# stack loop: Normal Non-Lazy Case
# ================================
#
# Check if all inputs are in place
# If so compute thunk and remove it from the apply_stack
# If not leave it in, and add to the apply_stack those
# that will produce you those inputs
if computed_ins and not computed_outs:
# -- Non-lazy case: have inputs, time to compute outputs
try:
_, dt = self.run_thunk_of_node(current_apply)
del _
if config.profile:
self.apply_time[current_apply] += dt
## Computing the memory footprint of the the op
# ?? What about inplace .. if the op is inplace
# you don't actually ask for more memory!
size = []
for (idx, o) in enumerate(thunks[
self.node_idx[current_apply]].outputs):
if not hasattr(o[0], 'size'):
size.append(-1)
continue
s = o[0].size
dtype = str(o[0].dtype)
dtype2 = dtype[-3:]
# KeyError here: couldn't determine
# the dtype memory size
s *= self.memory_size_map[dtype2]
size.append(s)
self.outputs_size[current_apply] = size
except Exception:
raise_with_op(current_apply)
for o in current_apply.outputs:
compute_map[o][0] = 1
if self.allow_gc:
for i in current_apply.inputs:
# Garbage Collection -> check if anybody else uses
# this input
if (dependencies[i] and i.owner
and i not in self.outputs):
if all(compute_map[v][0]
for v in dependencies[i]):
storage_map[i][0] = None
#DO NOT set compute_map to 0
#If values become False and the
#current_apply is still in the
#stack, this will cause it to be
#recomputed! This can cause wrong value
#with some combination of inplace op.
compute_map[i][0] = 2
if (config.warn.vm_gc_bug
and current_apply in apply_stack
and getattr(
current_apply.op,
'destroy_map', False)):
warnings.warn(
"There was a bug that existed in the default Theano configuration,"
" only in the development version between July 5th 2012"
" and July 30th 2012. This was not in a released version."
" The bug was affecting this script.",
#The stack level is not good when inside a Scan.
stacklevel=3)
elif not computed_ins:
# -- Non-lazy case, need inputs
apply_stack.append(current_apply)
apply_stack.extend(inp.owner for inp in current_deps
if inp.owner)
elif not computed_outs:
#
# stack loop: Lazy Evaluation Case
# ================================
#
# Lazy evaluation protocol is to run the thunk with the
# current storage_map and compute_map accessed via closure,
# and the thunk will return a list of variables from its input
# list that it requires.
try:
requires, dt = self.run_thunk_of_node(current_apply)
self.apply_time[current_apply] += dt
except Exception:
raise_with_op(current_apply)
if requires:
for r in requires:
# We are not done with this op .. so we added
# back and see to get the inputs we are
# missing
apply_stack.append(current_apply)
if current_apply.inputs[r].owner:
apply_stack.append(current_apply.inputs[r].owner)
else:
if config.profile:
size = []
for (idx, o) in enumerate(
thunks[self.node_idx[current_apply]].outputs):
if not hasattr(o[0], 'size'):
size.append(-1)
continue
s = o[0].size
dtype = str(o[0].dtype)
dtype2 = dtype[-2:]
# KeyError here: couldn't determine the
# dtype memory size
s *= self.memory_size_map[dtype2]
size.append(s)
self.outputs_size[current_apply] = size
if self.allow_gc:
for i in current_apply.inputs:
if (dependencies[i] and i.owner
and i not in self.outputs):
empty_storage_map = True
for x in dependencies[i]:
if not compute_map[x][0]:
empty_storage_map = False
break
if empty_storage_map:
storage_map[i][0] = None
#See the not lazy gc code for explanations
#of compute_map change
compute_map[i][0] = 2
# Hacky coarse gc final pass
# This is required until we have a proper gc algorithm for graphs with
# lazy evaluation. See discussion on theano-dev June 19 2012.
if self.allow_gc:
for v in storage_map:
if v.owner and not v in self.outputs:
storage_map[v][0] = None
def normal(self, size, avg=0.0, std=1.0, ndim=None,
dtype=None, nstreams=None):
"""
:param size: Can be a list of integers or Theano variables (ex: the
shape of another Theano Variable)
:param dtype: The output data type. If dtype is not specified, it will
be inferred from the dtype of low and high, but will be at least as
precise as floatX.
:param nstreams: Number of streams.
"""
# We need an even number of ]0,1[ samples. Then we split them
# in two halves. First half becomes our U1's for Box-Muller,
# second half our U2's. See Wikipedia page:
# http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
avg = as_tensor_variable(avg)
std = as_tensor_variable(std)
if dtype is None:
dtype = scal.upcast(config.floatX, avg.dtype, std.dtype)
avg = cast(avg, dtype)
std = cast(std, dtype)
evened = False
constant = False
if isinstance(size, tuple) and all([isinstance(i, (numpy.integer, int)) for i in size]):
constant = True
# Force dtype because it defaults to float when size is empty
n_samples = numpy.prod(size, dtype='int64')
if n_samples % 2 == 1:
n_samples += 1
evened = True
else:
#if even, don't change, if odd, +1
n_samples = prod(size) + (prod(size) % 2)
flattened = self.uniform(size=(n_samples,), dtype=dtype,
nstreams=nstreams)
if constant:
U1 = flattened[:n_samples // 2]
U2 = flattened[n_samples // 2:]
else:
U1 = flattened[:prod(flattened.shape) // 2]
U2 = flattened[prod(flattened.shape) // 2:]
#normal_samples = zeros_like(flattened)
sqrt_ln_U1 = sqrt(-2.0 * log(U1))
# TypeError: 'TensorVariable' object does not support item assignment
# so this doesn't work...
#normal_samples[:n_samples/2] = sqrt_ln_U1 * cos(2.0*numpy.pi*U2)
#normal_samples[n_samples/2:] = sqrt_ln_U1 * sin(2.0*numpy.pi*U2)
# so trying this instead
first_half = sqrt_ln_U1 * cos(numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
second_half = sqrt_ln_U1 * sin(numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
normal_samples = join(0, first_half, second_half)
final_samples = None
if evened:
final_samples = normal_samples[:-1]
elif constant:
final_samples = normal_samples
else:
final_samples = normal_samples[:prod(size)]
if not size:
# Force the dtype to be int64, otherwise reshape complains
size = tensor.constant(size, dtype='int64')
final_samples = final_samples.reshape(size)
final_samples = avg + std * final_samples
assert final_samples.dtype == dtype
return final_samples
def uniform(self, size, low=0.0, high=1.0, ndim=None, dtype=None,
nstreams=None):
"""
Sample a tensor of given size whose element from a uniform
distribution between low and high.
If the size argument is ambiguous on the number of dimensions,
ndim may be a plain integer to supplement the missing
information.
:param low: Lower bound of the interval on which values are sampled.
If the ``dtype`` arg is provided, ``low`` will be cast into dtype.
This bound is excluded.
:param high: Higher bound of the interval on which values are sampled.
If the ``dtype`` arg is provided, ``high`` will be cast into dtype.
This bound is excluded.
:param size: Can be a list of integer or Theano variable
(ex: the shape of other Theano Variable)
:param dtype: The output data type. If dtype is not specified, it will
be inferred from the dtype of low and high, but will be at least as
precise as floatX.
"""
low = as_tensor_variable(low)
high = as_tensor_variable(high)
if dtype is None:
dtype = scal.upcast(config.floatX, low.dtype, high.dtype)
low = cast(low, dtype=dtype)
high = cast(high, dtype=dtype)
if isinstance(size, tuple):
msg = "size must be a tuple of int or a Theano variable"
assert all([isinstance(i, (numpy.integer, int, Variable))
for i in size]), msg
if any([isinstance(i, (numpy.integer, int)) and i <= 0 for i in size]):
raise ValueError(
"The specified size contains a dimension with value <= 0",
size)
else:
if not (isinstance(size, Variable) and size.ndim == 1):
raise TypeError("size must be a tuple of int or a Theano "
"Variable with 1 dimension, got " + str(size) +
" of type " + str(type(size)))
if nstreams is None:
nstreams = self.n_streams(size)
if self.use_cuda and dtype == 'float32':
rstates = self.get_substream_rstates(nstreams)
rstates = rstates.flatten()
# HACK - we use fact that int32 and float32 have same size to
# sneak ints into the CudaNdarray type.
# these *SHOULD NEVER BE USED AS FLOATS*
tmp_float_buf = numpy.frombuffer(rstates.data, dtype='float32')
assert tmp_float_buf.shape == rstates.shape
assert (tmp_float_buf.view('int32') == rstates).all()
# transfer to device
node_rstate = float32_shared_constructor(tmp_float_buf)
assert isinstance(node_rstate.type, CudaNdarrayType)
# we can't use the normal mrg_uniform constructor + later
# optimization
# because of the tmp_float_buf hack above. There is
# currently no Theano node that will do a frombuffer
# reinterpretation.
u = self.pretty_return(node_rstate,
*GPU_mrg_uniform.new(node_rstate,
ndim, dtype, size))
else:
node_rstate = shared(self.get_substream_rstates(nstreams))
u = self.pretty_return(node_rstate,
*mrg_uniform.new(node_rstate,
ndim, dtype, size))
r = u * (high - low) + low
if u.type.broadcastable != r.type.broadcastable:
raise NotImplementedError(
'Increase the size to match the broadcasting pattern of '
'`low` and `high` arguments')
assert r.dtype == dtype
return r
def test_infer_shape(self):
f = theano.function([], softmax(numpy.random.rand(3, 4)).shape)
assert all(f() == [3, 4])
def __init__(self, *optimizers):
self.opts = optimizers
self.reentrant = any(
getattr(opt, 'reentrant', True) for opt in optimizers)
self.retains_inputs = all(
getattr(opt, 'retains_inputs', False) for opt in optimizers)
# Component -> itself
register_wrapper(lambda x: isinstance(x, Component), lambda x: x, no_warn=True)
# Variable -> Member
register_wrapper(lambda x: isinstance(x, gof.Variable) and not x.owner,
lambda x: Member(x),
no_warn=True)
# Variable -> External
register_wrapper(lambda x: isinstance(x, gof.Variable) and x.owner,
lambda x: External(x),
no_warn=True)
# [[Variable1], {Variable2}, Variable3...] -> ComponentList(Member(Variable1), Member(Variable2), ...)
register_wrapper(lambda x: isinstance(x, (list, tuple)) \
and all(wrapper(r) is not None for r in x),
lambda x: ComponentList(*map(wrap, x)), no_warn = True)
#{ "name1":{Component,Variable,list,tuple,dict},...} -> ComponentDict({Component,Variable,list,tuple,dict},...)
register_wrapper(lambda x: isinstance(x, dict) \
and all(wrapper(r) is not None for r in x.itervalues()),
lambda x: ComponentDict(dict_wrap(x)),no_warn = True)
class Curry:
def __init__(self, obj, name, arg):
deprecation_warning()
self.obj = obj
self.name = name
self.meth = getattr(self.obj, self.name)
self.arg = arg
def test_specify_shape_inplace(self):
#test that specify_shape don't break inserting inplace op
dtype = self.dtype
if dtype is None:
dtype = theano.config.floatX
rng = numpy.random.RandomState(utt.fetch_seed())
a = numpy.asarray(rng.uniform(1, 2, [40, 40]), dtype=dtype)
a = self.cast_value(a)
a_shared = self.shared_constructor(a)
b = numpy.asarray(rng.uniform(1, 2, [40, 40]), dtype=dtype)
b = self.cast_value(b)
b_shared = self.shared_constructor(b)
s = numpy.zeros((40, 40), dtype=dtype)
s = self.cast_value(s)
s_shared = self.shared_constructor(s)
f = theano.function(
[],
updates={s_shared: theano.dot(a_shared, b_shared) + s_shared})
topo = f.maker.env.toposort()
f()
#[Gemm{inplace}(<TensorType(float64, matrix)>, 0.01, <TensorType(float64, matrix)>, <TensorType(float64, matrix)>, 2e-06)]
if theano.config.mode != 'FAST_COMPILE':
assert sum([
node.op.__class__.__name__
in ["Gemm", "GpuGemm", "StructuredDot"] for node in topo
]) == 1
assert all(node.op == tensor.blas.gemm_inplace for node in topo
if isinstance(node.op, tensor.blas.Gemm))
assert all(node.op.inplace for node in topo
if node.op.__class__.__name__ == "GpuGemm")
#Their is no inplace gemm for sparse
#assert all(node.op.inplace for node in topo if node.op.__class__.__name__ == "StructuredDot")
s_shared_specify = tensor.specify_shape(
s_shared,
s_shared.get_value(borrow=True).shape)
#now test with the specify shape op in the output
f = theano.function([],
s_shared.shape,
updates={
s_shared:
theano.dot(a_shared, b_shared) +
s_shared_specify
})
topo = f.maker.env.toposort()
shp = f()
assert numpy.all(shp == (40, 40))
if theano.config.mode != 'FAST_COMPILE':
assert sum([
node.op.__class__.__name__
in ["Gemm", "GpuGemm", "StructuredDot"] for node in topo
]) == 1
assert all(node.op == tensor.blas.gemm_inplace for node in topo
if isinstance(node.op, tensor.blas.Gemm))
assert all(node.op.inplace for node in topo
if node.op.__class__.__name__ == "GpuGemm")
#now test with the specify shape op in the inputs and outputs
a_shared = tensor.specify_shape(
a_shared,
a_shared.get_value(borrow=True).shape)
b_shared = tensor.specify_shape(
b_shared,
b_shared.get_value(borrow=True).shape)
f = theano.function([],
s_shared.shape,
updates={
s_shared:
theano.dot(a_shared, b_shared) +
s_shared_specify
})
topo = f.maker.env.toposort()
shp = f()
assert numpy.all(shp == (40, 40))
if theano.config.mode != 'FAST_COMPILE':
assert sum([
node.op.__class__.__name__
in ["Gemm", "GpuGemm", "StructuredDot"] for node in topo
]) == 1
assert all(node.op == tensor.blas.gemm_inplace for node in topo
if isinstance(node.op, tensor.blas.Gemm))
assert all(node.op.inplace for node in topo
if node.op.__class__.__name__ == "GpuGemm")
# FOR EACH OUTPUT PIXEL...
for oy in N.arange(lbound[0], ubound[0],
dy): # loop over output image height
for ox in N.arange(lbound[1], ubound[1],
dx): # loop over output image width
l = 0 # kern[l] is filter value to apply at (oj,oi) for (iy,ix)
# ... ITERATE OVER INPUT UNITS IN RECEPTIVE FIELD
for ky in oy + N.arange(kshp[0]):
for kx in ox + N.arange(kshp[1]):
# verify if we are still within image boundaries. Equivalent to
# zero-padding of the input image
if all((ky, kx) >= topleft) and all(
(ky, kx) < botright):
# convert to "valid" input space coords
# used to determine column index to write to in sparse mat
iy, ix = N.array((ky, kx)) - topleft
# determine raster-index of input pixel...
col = iy*inshp[2]+ix +\
fmapi*N.prod(inshp[1:]) # taking into account multiple input features
# convert oy,ox values to output space coordinates
if mode == 'full':
(y, x) = (oy, ox)
else:
(y, x) = (oy, ox) - topleft
(y, x) = N.array([y, x]) / (
def __call__(self):
storage_map = self.storage_map
compute_map = self.compute_map
thunks = self.thunks
dependencies = self.dependencies
self.node_executed_order = []
self.node_cleared_order = []
for k in self.storage_map:
compute_map[k][0] = (k.owner is None)
# apply_stack contains nodes
apply_stack = list(self.base_apply_stack)
last_apply_stack_len = -1
#This record all function inputs/shared varibles and constants
for var, data in self.storage_map.iteritems():
if data[0] is None:
continue
if hasattr(var.type, 'get_shape_info'):
sh = var.type.get_shape_info(data[0])
else:
sh = 'input no shape'
self.variable_shape[var] = sh
st = getattr(data[0], 'strides', 'input no strides')
if getattr(data[0], 'flags', False) and data[0].flags.c_contiguous:
st = 'c'
elif (hasattr(data[0], 'is_c_contiguous')
and data[0].is_c_contiguous()):
st = "c"
self.variable_strides[var] = st
while apply_stack:
# Make sure something happened last time round. This is
# just a safety check to make sure the op is written
# correctly apply_stack should either decrease in length
# by one (a thunk successfully applied), or increase in
# length (added dependencies over and above the original).
# NB: this doesn't catch cycles (would be too expensive/slow),
# just stalls.
apply_stack_len = len(apply_stack)
assert apply_stack_len != last_apply_stack_len
last_apply_stack_len = apply_stack_len
current_apply = apply_stack.pop()
current_inputs = current_apply.inputs
current_outputs = current_apply.outputs
current_deps = current_inputs + current_apply.destroy_dependencies
computed_ins = all(compute_map[v][0] for v in current_deps)
computed_outs = all(compute_map[v][0] for v in current_outputs)
if not thunks[self.node_idx[current_apply]].lazy:
#
# stack loop: Normal Non-Lazy Case
# ================================
#
# Check if all inputs are in place
# If so compute thunk and remove it from the apply_stack
# If not leave it in, and add to the apply_stack those
# that will produce you those inputs
if computed_ins and not computed_outs:
# -- Non-lazy case: have inputs, time to compute outputs
try:
_, dt = self.run_thunk_of_node(current_apply)
del _
if config.profile:
current_idx = self.node_idx[current_apply]
self.call_counts[current_idx] += 1
self.call_times[current_idx] += dt
## Computing the memory footprint of the the op
# ?? What about inplace .. if the op is inplace
# you don't actually ask for more memory!
for (idx, o) in enumerate(thunks[
self.node_idx[current_apply]].outputs):
var = self.nodes[current_idx].outputs[idx]
if hasattr(var.type, 'get_shape_info'):
sh = var.type.get_shape_info(o[0])
else:
sh = 'input no shape'
self.variable_shape[var] = sh
st = getattr(o[0], 'strides',
'input no strides')
if (getattr(o[0], 'flags', False)
and o[0].flags.c_contiguous):
st = 'c'
elif (hasattr(data[0], 'is_c_contiguous')
and data[0].is_c_contiguous()):
st = "c"
self.variable_strides[var] = st
except Exception:
raise_with_op(
current_apply,
self.thunks[self.node_idx[current_apply]])
for o in current_apply.outputs:
compute_map[o][0] = 1
input_index = []
# A list store the index of inputs variables
if self.allow_gc:
for i in current_apply.inputs:
# Garbage Collection -> check if anybody else uses
# this input
if (dependencies[i] and i.owner
and i not in self.outputs):
if all(compute_map[v][0]
for v in dependencies[i]):
storage_map[i][0] = None
input_index.append(
current_apply.inputs.index(i))
#DO NOT set compute_map to 0
#If values become False and the
#current_apply is still in the
#stack, this will cause it to be
#recomputed! This can cause wrong value
#with some combination of inplace op.
compute_map[i][0] = 2
if (config.warn.vm_gc_bug
and current_apply in apply_stack
and getattr(
current_apply.op,
'destroy_map', False)):
warnings.warn(
"There was a bug that existed in the default Theano configuration,"
" only in the development version between July 5th 2012"
" and July 30th 2012. This was not in a released version."
" The bug was affecting this script.",
#The stack level is not good when inside a Scan.
stacklevel=3)
self.node_cleared_order.append(input_index)
elif not computed_ins:
# -- Non-lazy case, need inputs
apply_stack.append(current_apply)
apply_stack.extend(inp.owner for inp in current_deps
if inp.owner)
elif not computed_outs:
#
# stack loop: Lazy Evaluation Case
# ================================
#
# Lazy evaluation protocol is to run the thunk with the
# current storage_map and compute_map accessed via closure,
# and the thunk will return a list of variables from its input
# list that it requires.
try:
requires, dt = self.run_thunk_of_node(current_apply)
current_idx = self.node_idx[current_apply]
self.call_counts[current_idx] += 1
self.call_times[current_idx] += dt
except Exception:
raise_with_op(current_apply,
self.thunks[self.node_idx[current_apply]])
if requires:
for r in requires:
# We are not done with this op .. so we added
# back and see to get the inputs we are
# missing
apply_stack.append(current_apply)
if current_apply.inputs[r].owner:
apply_stack.append(current_apply.inputs[r].owner)
else:
if config.profile:
for (idx, o) in enumerate(
thunks[self.node_idx[current_apply]].outputs):
var = self.nodes[
self.node_idx[current_apply]].outputs[idx]
if hasattr(var.type, 'get_shape_info'):
sh = var.type.get_shape_info(o[0])
else:
sh = 'input no shape'
self.variable_shape[var] = sh
st = getattr(o[0], 'strides', 'input no strides')
if (getattr(o[0], 'flags', False)
and o[0].flags.c_contiguous):
st = 'c'
elif (hasattr(data[0], 'is_c_contiguous')
and data[0].is_c_contiguous()):
st = "c"
self.variable_strides[var] = st
input_index = []
if self.allow_gc:
for i in current_apply.inputs:
if (dependencies[i] and i.owner
and i not in self.outputs):
empty_storage_map = True
for x in dependencies[i]:
if not compute_map[x][0]:
empty_storage_map = False
break
if empty_storage_map:
storage_map[i][0] = None
input_index.append(
current_apply.inputs.index(i))
#See the not lazy gc code for explanations
#of compute_map change
compute_map[i][0] = 2
self.node_cleared_order.append(input_index)
# Hacky coarse gc final pass
# This is required until we have a proper gc algorithm for graphs with
# lazy evaluation. See discussion on theano-dev June 19 2012.
final_index = []
if self.allow_gc:
for v in storage_map:
if v.owner and not v in self.outputs:
if compute_map[v][0] == 2:
continue
else:
storage_map[v][0] = None
final_index.append(v)
compute_map[v][0] = 2
self.node_cleared_order.append(final_index)
def make_vm(self, nodes, thunks, input_storage, output_storage,
storage_map, post_thunk_clear, computed, compute_map,
updated_vars):
pre_call_clear = [storage_map[v] for v in self.no_recycling]
if (self.callback is not None
or (config.profile and config.profile_memory)):
if self.use_cloop and self.callback is not None:
logger.warn('CVM does not support callback, using Stack VM.')
if self.use_cloop and config.profile_memory:
warnings.warn(
'CVM does not support memory profile, using Stack VM.')
deps = None
if self.allow_gc:
deps = self.compute_gc_dependencies(storage_map)
vm = Stack(nodes,
thunks,
pre_call_clear,
storage_map,
compute_map,
self.fgraph,
self.allow_gc,
dependencies=deps,
callback=self.callback)
elif self.use_cloop:
# create a map from nodes to ints and vars to ints
nodes_idx = {}
vars_idx = {}
for i, node in enumerate(nodes):
nodes_idx[node] = i
for v in node.inputs + node.outputs:
vars_idx.setdefault(v, len(vars_idx))
for v in self.fgraph.inputs + self.fgraph.outputs:
vars_idx.setdefault(v, len(vars_idx))
nodes_idx_inv = {}
vars_idx_inv = {}
for (node, i) in nodes_idx.items():
nodes_idx_inv[i] = node
for (var, i) in vars_idx.items():
vars_idx_inv[i] = var
# put storage_map and compute_map into a int-based scheme
n_applies = len(nodes)
storage_map_list = [
storage_map[vars_idx_inv[i]] for i in xrange(len(vars_idx_inv))
]
compute_map_list = [
compute_map[vars_idx_inv[i]] for i in xrange(len(vars_idx_inv))
]
if nodes:
assert type(storage_map_list[0]) is list
assert type(compute_map_list[0]) is list
if self.allow_gc:
dependency_map = self.compute_gc_dependencies(storage_map)
dependency_map_list = [[
vars_idx[d] for d in dependency_map[vars_idx_inv[i]]
] for i in xrange(len(vars_idx_inv))]
else:
dependency_map_list = None
# build the pointers to node inputs and offsets
base_input_output_list = []
node_n_inputs = []
node_n_outputs = []
node_input_offset = []
node_output_offset = []
for node in nodes:
inputs_idx = [vars_idx[v] for v in node.inputs]
outputs_idx = [vars_idx[v] for v in node.outputs]
node_n_inputs.append(len(inputs_idx))
node_n_outputs.append(len(outputs_idx))
node_input_offset.append(len(base_input_output_list))
base_input_output_list.extend(inputs_idx)
node_output_offset.append(len(base_input_output_list))
base_input_output_list.extend(outputs_idx)
# build the var owner array
var_owner = [None] * len(vars_idx)
for (var, i) in vars_idx.items():
if var.owner:
var_owner[i] = nodes_idx[var.owner]
is_lazy_list = [int(th.lazy) for th in thunks]
output_vars = [vars_idx[v] for v in self.fgraph.outputs]
# builds the list of prereqs induced by e.g. destroy_handler
ords = self.fgraph.orderings()
node_prereqs = []
node_output_size = []
for i, node in enumerate(nodes):
node_output_size.append(0)
prereq_var_idxs = []
for prereq_node in ords.get(node, []):
prereq_var_idxs.extend(
[vars_idx[v] for v in prereq_node.outputs])
prereq_var_idxs = list(set(prereq_var_idxs))
prereq_var_idxs.sort() # TODO: why sort?
node_prereqs.append(prereq_var_idxs)
# Builds the list of input storage to update (according to update
# rules) when the outputs are computed.
# They are in the same order as the second part of output_vars
# (output_vars contains first the returned outputs, then the
# values of the update expressions).
update_storage = []
update_in_from_out = {}
for (ivar, ovar) in updated_vars.items():
update_in_from_out[vars_idx[ovar]] = vars_idx[ivar]
for oidx in output_vars:
if oidx in update_in_from_out:
update_storage.append(update_in_from_out[oidx])
c0 = sys.getrefcount(node_n_inputs)
vm = CVM(
nodes,
thunks,
pre_call_clear,
allow_gc=self.allow_gc,
call_counts=[0] * len(nodes),
call_times=[0.0] * len(nodes),
compute_map_list=compute_map_list,
storage_map_list=storage_map_list,
base_input_output_list=base_input_output_list,
node_n_inputs=node_n_inputs,
node_n_outputs=node_n_outputs,
node_input_offset=node_input_offset,
node_output_offset=node_output_offset,
var_owner=var_owner,
is_lazy_list=is_lazy_list,
output_vars=output_vars,
node_prereqs=node_prereqs,
node_output_size=node_output_size,
update_storage=update_storage,
dependencies=dependency_map_list,
)
assert c0 == sys.getrefcount(node_n_inputs)
else:
lazy = self.lazy
if lazy is None:
lazy = config.vm.lazy
if lazy is None:
lazy = not all([(not th.lazy) for th in thunks])
if not lazy:
# there is no conditional in the graph
if self.allow_gc:
vm = LoopGC(nodes, thunks, pre_call_clear,
post_thunk_clear)
else:
vm = Loop(nodes, thunks, pre_call_clear)
else:
deps = None
if self.allow_gc:
deps = self.compute_gc_dependencies(storage_map)
vm = Stack(nodes,
thunks,
pre_call_clear,
storage_map,
compute_map,
self.fgraph,
self.allow_gc,
dependencies=deps)
return vm
def test_infer_shape(self):
fff=theano.function([],outputs=softmax_with_bias(numpy.random.rand(3,4),numpy.random.rand(4)).shape)
assert all(fff()==[3,4])
def grad_sources_inputs(sources, graph_inputs, warn_type=True):
"""
:type sources: list of pairs of Variable: (v, gradient-on-v)
:param sources: gradients to back-propagate using chain rule
:type graph_inputs: list of Variable
:param graph_inputs: variables considered to be constant
(do not backpropagate through them)
:rtype: dictionary whose keys and values are of type `Variable`
:return: mapping from each Variable encountered in the backward
traversal to the gradient with respect to that Variable.
It is assumed that there is some objective J shared between all members of
sources, so that for each v, gradient-on-v is the gradient of J with
respect to v
"""
gmap = {}
for (r, g_r) in sources:
if not hasattr(r, 'type'):
raise TypeError('sources must be Variables', r)
if g_r is not None:
if r in gmap:
gmap[r] = gmap[r] + g_r
else:
gmap[r] = g_r
graph_outputs = gof.utils.uniq([r for r, g in sources])
if graph_inputs is None:
graph_inputs = gof.graph.inputs(graph_outputs)
for node in gof.graph.io_toposort(graph_inputs,
graph_outputs).__reversed__():
g_outputs = [gmap.get(o, None) for o in node.outputs]
#if all output gradients are None, continue
if all(map(lambda x: x is None, g_outputs)): continue
output_arg = g_outputs
input_arg = node.inputs
# Each Op's grad function requires inputs and output_grads
# If the Op destroys any input, but the grad expression uses it,
# then chances are the resulting graph will have a dependency
# cycle. We avoid this cycle by passing (symbolic) copies of
# each destroyed input.
try:
dinputs = [node.inputs[x[0]] for x in node.op.destroy_map.values()]
except AttributeError:
dinputs = []
new_input_arg = []
for input in input_arg:
if input in dinputs and hasattr(input, 'copy'):
new_input_arg.append(input.copy())
else:
new_input_arg.append(input)
input_arg = new_input_arg
#note that this function is not in a try-except block
# the rationale:
# If the op implements grad, then any exception should be passed to
# the caller
# If the op doesn't implement grad, this entire function should fail.
# Other possibilities:
# * return a partial back-prop
#
op_grad = node.op.grad(input_arg, output_arg)
if not isinstance(op_grad, (list, tuple)):
raise ValueError(_msg_retType, node.op)
g_inputs = op_grad
assert isinstance(g_inputs, (list, tuple))
if len(g_inputs) != len(node.inputs):
raise ValueError(_msg_badlen,
node.op,
len(g_inputs),
len(node.inputs))
for ii, (r, g_r) in enumerate(zip(node.inputs, g_inputs)):
if warn_type:
if g_r and (getattr(r, 'type', 0) != getattr(g_r, 'type', 1)):
r_type = getattr(r, 'type', None)
g_r_type = getattr(g_r, 'type', None)
_logger.warning('%s.grad returned a different type (%s) '
'for input %i of type (%s)',
node.op, g_r_type, ii, r_type)
if g_r and len(sources) == 1 and sources[0][0].name and r.name:
g_r.name = "(d%s/d%s)" % (sources[0][0].name, r.name)
if g_r is not None:
assert r is not None
if r in gmap:
gmap[r] = gmap[r] + g_r
else:
gmap[r] = g_r
return gmap
def test_infer_shape(self):
f=theano.function([],softmax(numpy.random.rand(3,4)).shape)
assert all(f()==[3,4])
def with_linker(self,
linker,
scalar_op=scalar.add,
dtype="floatX",
test_nan=False,
tensor_op=None):
for xsh, tosum in [((5, 6), None), ((5, 6), (0, 1)), ((5, 6), (0, )),
((5, 6), (1, )), ((5, 6), (-1, )), ((5, 6), (-2, )),
((5, 6), ()), ((2, 3, 4, 5), (0, 1, 3)),
((2, 3, 4, 5), (-2, -3)), ((5, 0), None),
((5, 0), (0, )), ((5, 0), (1, )), ((5, 0), ()),
((), None), ((), ())]:
if dtype == "floatX":
dtype = theano.config.floatX
x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
if tensor_op is None:
e = as_tensor_variable(self.op(scalar_op, axis=tosum)(x))
else:
e = as_tensor_variable(tensor_op(x, axis=tosum))
if tosum is None:
tosum = range(len(xsh))
f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
xv = numpy.asarray(numpy.random.rand(*xsh))
if not "int" in dtype:
xv = numpy.asarray(xv, dtype=dtype)
else:
xv = numpy.asarray(xv < 0.5, dtype=dtype)
if test_nan and xv.size > 0:
if len(xsh) > 0:
xv = xv.flatten()
xv[0] = numpy.nan
xv = xv.reshape(*xsh)
else:
xv = numpy.asarray(numpy.nan, dtype=dtype)
zv = xv
numpy_raised = False
if len(tosum) > 1 and any([a < 0 for a in tosum]):
#In that case, we need to use the good order of axis
#in the reduction.
axis2 = []
for a in tosum:
if a < 0:
axis2.append(a + len(xsh))
else:
axis2.append(a)
assert len(axis2) == len(tosum)
tosum = tuple(axis2)
if tensor_op == tensor.all:
for axis in reversed(sorted(tosum)):
zv = numpy.all(zv, axis)
if len(tosum) == 0:
zv = zv != 0
elif tensor_op == tensor.any:
for axis in reversed(sorted(tosum)):
zv = numpy.any(zv, axis)
if len(tosum) == 0:
zv = zv != 0
elif scalar_op == scalar.add:
for axis in reversed(sorted(tosum)):
zv = numpy.add.reduce(zv, axis)
elif scalar_op == scalar.mul:
for axis in reversed(sorted(tosum)):
zv = numpy.multiply.reduce(zv, axis)
elif scalar_op == scalar.maximum:
try:
for axis in reversed(sorted(tosum)):
zv = numpy.maximum.reduce(zv, axis)
except ValueError:
numpy_raised = True
elif scalar_op == scalar.minimum:
try:
for axis in reversed(sorted(tosum)):
zv = numpy.minimum.reduce(zv, axis)
except ValueError:
numpy_raised = True
elif scalar_op == scalar.or_:
for axis in reversed(sorted(tosum)):
zv = numpy.bitwise_or.reduce(zv, axis)
elif scalar_op == scalar.and_:
for axis in reversed(sorted(tosum)):
zv = numpy.bitwise_and.reduce(zv, axis)
elif scalar_op == scalar.xor:
# There is no identity value for the xor function
# So we can't support shape of dimensions 0.
if numpy.prod(zv.shape) == 0:
continue
for axis in reversed(sorted(tosum)):
zv = numpy.bitwise_xor.reduce(zv, axis)
else:
raise Exception(
"Test for CAReduce with scalar_op %s not implemented" %
str(scalar_op))
if scalar_op in [scalar.maximum, scalar.minimum] and numpy_raised:
try:
out = f(xv)
assert out.dtype == dtype
except ValueError:
pass
else:
self.fail()
else:
# numpy.{all,any} return bool type,
# but theano ops return an int8 array instead
if scalar_op in [scalar.and_, scalar.or_]:
zv = numpy.asarray(zv, dtype='int8')
if test_nan:
self.assertTrue(
theano.tensor.TensorType.values_eq(f(xv), zv),
(f(xv), zv))
else:
f_xv = f(xv)
self.assertTrue((f_xv.shape == zv.shape), (f_xv, zv))
self.assertTrue(numpy.allclose(f_xv, zv), (f_xv, zv))
#test CAReduce.infer_shape
#the Shape op don't implement c_code!
if isinstance(linker, gof.PerformLinker):
x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
if tensor_op is None:
e = self.op(scalar_op, axis=tosum)(x)
else:
e = tensor_op(x, axis=tosum)
if tosum is None:
tosum = range(len(xsh))
f = copy(linker).accept(FunctionGraph(
[x], [e.shape])).make_function()
if not (scalar_op in [scalar.maximum, scalar.minimum] and
((xsh == () or numpy.prod(xsh) == 0))):
assert all(f(xv) == zv.shape)
for oy in N.arange(lbound[0], ubound[0], dy):
# loop over output image width
for ox in N.arange(lbound[1], ubound[1], dx):
# kern[l] is filter value to apply at (oj,oi)
# for (iy,ix)
l = 0
# ... ITERATE OVER INPUT UNITS IN RECEPTIVE FIELD
for ky in oy + N.arange(kshp[0]):
for kx in ox + N.arange(kshp[1]):
# verify if we are still within image
# boundaries. Equivalent to
# zero-padding of the input image
if (all((ky, kx) >= topleft)
and all((ky, kx) < botright)):
# convert to "valid" input space
# coords used to determine column
# index to write to in sparse mat
iy, ix = N.array((ky, kx)) - topleft
# determine raster-index of input pixel...
# taking into account multiple
# input features
col = iy * inshp[2] + ix + \
fmapi * N.prod(inshp[1:])
# convert oy,ox values to output
# space coordinates
def test_infer_shape(self):
var = self.op(numpy.random.rand(3,5),numpy.random.rand(5), y_idx=numpy.random.randint(
low=0, high=5, size=3))
assert theano.function([],var[0].shape)() == [3]
assert all(theano.function([],var[1].shape)() == [3,5])
assert theano.function([],var[2].shape)() == [3]
def make_vm(self, nodes, thunks, input_storage, output_storage,
storage_map, post_thunk_clear, computed, compute_map,
updated_vars):
pre_call_clear = [storage_map[v] for v in self.no_recycling]
if self.callback is not None:
if self.use_cloop:
logger.warn('CLoop does not support callback, using Stack VM.')
deps = None
if self.allow_gc:
deps = self.compute_gc_dependencies(storage_map)
vm = Stack(nodes,
thunks,
pre_call_clear,
storage_map,
compute_map,
self.env,
self.allow_gc,
dependencies=deps,
callback=self.callback)
elif self.use_cloop:
# create a map from nodes to ints and vars to ints
nodes_idx = {}
vars_idx = {}
for i, node in enumerate(nodes):
nodes_idx[node] = i
for v in node.inputs + node.outputs:
vars_idx.setdefault(v, len(vars_idx))
for v in self.env.inputs + self.env.outputs:
vars_idx.setdefault(v, len(vars_idx))
nodes_idx_inv = {}
vars_idx_inv = {}
for (node, i) in nodes_idx.items():
nodes_idx_inv[i] = node
for (var, i) in vars_idx.items():
vars_idx_inv[i] = var
# put storage_map and compute_map into a int-based scheme
n_applies = len(nodes)
storage_map_list = [
storage_map[vars_idx_inv[i]] for i in xrange(len(vars_idx_inv))
]
compute_map_list = [
compute_map[vars_idx_inv[i]] for i in xrange(len(vars_idx_inv))
]
if nodes:
assert type(storage_map_list[0]) is list
assert type(compute_map_list[0]) is list
if self.allow_gc:
dependency_map = self.compute_gc_dependencies(storage_map)
dependency_map_list = [[
vars_idx[d] for d in dependency_map[vars_idx_inv[i]]
] for i in xrange(len(vars_idx_inv))]
else:
dependency_map_list = None
# build the pointers to node inputs and offsets
base_input_output_list = []
node_n_inputs = []
node_n_outputs = []
node_input_offset = []
node_output_offset = []
for node in nodes:
inputs_idx = [vars_idx[v] for v in node.inputs]
outputs_idx = [vars_idx[v] for v in node.outputs]
node_n_inputs.append(len(inputs_idx))
node_n_outputs.append(len(outputs_idx))
node_input_offset.append(len(base_input_output_list))
base_input_output_list.extend(inputs_idx)
node_output_offset.append(len(base_input_output_list))
base_input_output_list.extend(outputs_idx)
# build the var owner array
var_owner = [None] * len(vars_idx)
for (var, i) in vars_idx.items():
if var.owner:
var_owner[i] = nodes_idx[var.owner]
is_lazy_list = [int(th.lazy) for th in thunks]
output_vars = [vars_idx[v] for v in self.env.outputs]
# builds the list of prereqs induced by e.g. destroy_handler
ords = self.env.orderings()
node_prereqs = []
node_output_size = []
for i, node in enumerate(nodes):
node_output_size.append(0)
prereq_var_idxs = []
for prereq_node in ords.get(node, []):
prereq_var_idxs.extend(
[vars_idx[v] for v in prereq_node.outputs])
prereq_var_idxs = list(set(prereq_var_idxs))
prereq_var_idxs.sort() # TODO: why sort?
node_prereqs.append(prereq_var_idxs)
update_storage = []
for (ivar, ovar) in updated_vars.items():
if ivar != ovar:
update_storage.append(vars_idx[ivar]) # dst
update_storage.append(vars_idx[ovar]) # src
c0 = sys.getrefcount(node_n_inputs)
vm = CVM(
nodes,
thunks,
pre_call_clear,
allow_gc=self.allow_gc,
call_counts=[0] * len(nodes),
call_times=[0.0] * len(nodes),
compute_map_list=compute_map_list,
storage_map_list=storage_map_list,
base_input_output_list=base_input_output_list,
node_n_inputs=node_n_inputs,
node_n_outputs=node_n_outputs,
node_input_offset=node_input_offset,
node_output_offset=node_output_offset,
var_owner=var_owner,
is_lazy_list=is_lazy_list,
output_vars=output_vars,
node_prereqs=node_prereqs,
node_output_size=node_output_size,
update_storage=update_storage,
dependencies=dependency_map_list,
)
assert c0 == sys.getrefcount(node_n_inputs)
else:
if all([(not th.lazy) for th in thunks]):
# there is no conditional in the graph
if self.allow_gc:
vm = LoopGC(nodes, thunks, pre_call_clear,
post_thunk_clear)
else:
vm = Loop(nodes, thunks, pre_call_clear)
else:
deps = None
if self.allow_gc:
deps = self.compute_gc_dependencies(storage_map)
vm = Stack(nodes,
thunks,
pre_call_clear,
storage_map,
compute_map,
self.env,
self.allow_gc,
dependencies=deps)
return vm
def test_infer_shape(self):
fff = theano.function([],
outputs=softmax_with_bias(
numpy.random.rand(3, 4),
numpy.random.rand(4)).shape)
assert all(fff() == [3, 4])
def make_vm(self, nodes, thunks,
input_storage, output_storage, storage_map,
post_thunk_clear,
computed,
compute_map,
updated_vars
):
pre_call_clear = [storage_map[v] for v in self.no_recycling]
if (self.callback is not None or
(config.profile and config.profile_memory)):
if self.use_cloop and self.callback is not None:
logger.warn('CVM does not support callback, using Stack VM.')
if self.use_cloop and config.profile_memory:
warnings.warn(
'CVM does not support memory profile, using Stack VM.')
deps = None
if self.allow_gc:
deps = self.compute_gc_dependencies(storage_map)
vm = Stack(
nodes, thunks, pre_call_clear,
storage_map, compute_map,
self.fgraph, self.allow_gc,
dependencies=deps,
callback=self.callback)
elif self.use_cloop:
# create a map from nodes to ints and vars to ints
nodes_idx = {}
vars_idx = {}
for i, node in enumerate(nodes):
nodes_idx[node] = i
for v in node.inputs + node.outputs:
vars_idx.setdefault(v, len(vars_idx))
for v in self.fgraph.inputs + self.fgraph.outputs:
vars_idx.setdefault(v, len(vars_idx))
nodes_idx_inv = {}
vars_idx_inv = {}
for (node, i) in nodes_idx.items():
nodes_idx_inv[i] = node
for (var, i) in vars_idx.items():
vars_idx_inv[i] = var
# put storage_map and compute_map into a int-based scheme
n_applies = len(nodes)
storage_map_list = [storage_map[vars_idx_inv[i]]
for i in xrange(len(vars_idx_inv))]
compute_map_list = [compute_map[vars_idx_inv[i]]
for i in xrange(len(vars_idx_inv))]
if nodes:
assert type(storage_map_list[0]) is list
assert type(compute_map_list[0]) is list
if self.allow_gc:
dependency_map = self.compute_gc_dependencies(storage_map)
dependency_map_list = [
[vars_idx[d] for d in dependency_map[vars_idx_inv[i]]]
for i in xrange(len(vars_idx_inv))]
else:
dependency_map_list = None
# build the pointers to node inputs and offsets
base_input_output_list = []
node_n_inputs = []
node_n_outputs = []
node_input_offset = []
node_output_offset = []
for node in nodes:
inputs_idx = [vars_idx[v] for v in node.inputs]
outputs_idx = [vars_idx[v] for v in node.outputs]
node_n_inputs.append(len(inputs_idx))
node_n_outputs.append(len(outputs_idx))
node_input_offset.append(len(base_input_output_list))
base_input_output_list.extend(inputs_idx)
node_output_offset.append(len(base_input_output_list))
base_input_output_list.extend(outputs_idx)
# build the var owner array
var_owner = [None] * len(vars_idx)
for (var, i) in vars_idx.items():
if var.owner:
var_owner[i] = nodes_idx[var.owner]
is_lazy_list = [int(th.lazy) for th in thunks]
output_vars = [vars_idx[v] for v in self.fgraph.outputs]
# builds the list of prereqs induced by e.g. destroy_handler
ords = self.fgraph.orderings()
node_prereqs = []
node_output_size = []
for i, node in enumerate(nodes):
node_output_size.append(0)
prereq_var_idxs = []
for prereq_node in ords.get(node, []):
prereq_var_idxs.extend(
[vars_idx[v] for v in prereq_node.outputs])
prereq_var_idxs = list(set(prereq_var_idxs))
prereq_var_idxs.sort() # TODO: why sort?
node_prereqs.append(prereq_var_idxs)
# Builds the list of input storage to update (according to update
# rules) when the outputs are computed.
# They are in the same order as the second part of output_vars
# (output_vars contains first the returned outputs, then the
# values of the update expressions).
update_storage = []
update_in_from_out = {}
for (ivar, ovar) in updated_vars.items():
update_in_from_out[vars_idx[ovar]] = vars_idx[ivar]
for oidx in output_vars:
if oidx in update_in_from_out:
update_storage.append(update_in_from_out[oidx])
c0 = sys.getrefcount(node_n_inputs)
vm = CVM(
nodes,
thunks,
pre_call_clear,
allow_gc=self.allow_gc,
call_counts=[0] * len(nodes),
call_times=[0.0] * len(nodes),
compute_map_list=compute_map_list,
storage_map_list=storage_map_list,
base_input_output_list=base_input_output_list,
node_n_inputs=node_n_inputs,
node_n_outputs=node_n_outputs,
node_input_offset=node_input_offset,
node_output_offset=node_output_offset,
var_owner=var_owner,
is_lazy_list=is_lazy_list,
output_vars=output_vars,
node_prereqs=node_prereqs,
node_output_size=node_output_size,
update_storage=update_storage,
dependencies=dependency_map_list,
)
assert c0 == sys.getrefcount(node_n_inputs)
else:
lazy = self.lazy
if lazy is None:
lazy = config.vm.lazy
if lazy is None:
lazy = not all([(not th.lazy) for th in thunks])
if not lazy:
# there is no conditional in the graph
if self.allow_gc:
vm = LoopGC(
nodes,
thunks,
pre_call_clear,
post_thunk_clear)
else:
vm = Loop(
nodes,
thunks,
pre_call_clear)
else:
deps = None
if self.allow_gc:
deps = self.compute_gc_dependencies(storage_map)
vm = Stack(
nodes, thunks, pre_call_clear,
storage_map, compute_map,
self.fgraph, self.allow_gc,
dependencies=deps
)
return vm
def __call__(self):
storage_map = self.storage_map
compute_map = self.compute_map
thunks = self.thunks
dependencies = self.dependencies
self.node_executed_order = []
self.node_cleared_order = []
for k in self.storage_map:
compute_map[k][0] = (k.owner is None)
# apply_stack contains nodes
apply_stack = list(self.base_apply_stack)
last_apply_stack_len = -1
#This record all function inputs/shared varibles and constants
for var, data in self.storage_map.iteritems():
if data[0] is None:
continue
if hasattr(var.type, 'get_shape_info'):
sh = var.type.get_shape_info(data[0])
else:
sh = 'input no shape'
self.variable_shape[var] = sh
st = getattr(data[0], 'strides', 'input no strides')
if getattr(data[0], 'flags', False) and data[0].flags.c_contiguous:
st = 'c'
elif (hasattr(data[0], 'is_c_contiguous') and
data[0].is_c_contiguous()):
st = "c"
self.variable_strides[var] = st
while apply_stack:
# Make sure something happened last time round. This is
# just a safety check to make sure the op is written
# correctly apply_stack should either decrease in length
# by one (a thunk successfully applied), or increase in
# length (added dependencies over and above the original).
# NB: this doesn't catch cycles (would be too expensive/slow),
# just stalls.
apply_stack_len = len(apply_stack)
assert apply_stack_len != last_apply_stack_len
last_apply_stack_len = apply_stack_len
current_apply = apply_stack.pop()
current_inputs = current_apply.inputs
current_outputs = current_apply.outputs
current_deps = current_inputs + current_apply.destroy_dependencies
computed_ins = all(compute_map[v][0] for v in current_deps)
computed_outs = all(compute_map[v][0] for v in current_outputs)
if not thunks[self.node_idx[current_apply]].lazy:
#
# stack loop: Normal Non-Lazy Case
# ================================
#
# Check if all inputs are in place
# If so compute thunk and remove it from the apply_stack
# If not leave it in, and add to the apply_stack those
# that will produce you those inputs
if computed_ins and not computed_outs:
# -- Non-lazy case: have inputs, time to compute outputs
try:
_, dt = self.run_thunk_of_node(current_apply)
del _
if config.profile:
current_idx = self.node_idx[current_apply]
self.call_counts[current_idx] += 1
self.call_times[current_idx] += dt
## Computing the memory footprint of the the op
# ?? What about inplace .. if the op is inplace
# you don't actually ask for more memory!
for (idx, o) in enumerate(
thunks[self.node_idx[
current_apply]].outputs):
var = self.nodes[current_idx].outputs[idx]
if hasattr(var.type, 'get_shape_info'):
sh = var.type.get_shape_info(o[0])
else:
sh = 'input no shape'
self.variable_shape[var] = sh
st = getattr(o[0], 'strides',
'input no strides')
if (getattr(o[0], 'flags', False) and
o[0].flags.c_contiguous):
st = 'c'
self.variable_strides[var] = st
except Exception:
raise_with_op(current_apply,
self.thunks[self.node_idx[current_apply]])
for o in current_apply.outputs:
compute_map[o][0] = 1
input_index = []
# A list store the index of inputs variables
if self.allow_gc:
for i in current_apply.inputs:
# Garbage Collection -> check if anybody else uses
# this input
if (dependencies[i]
and i.owner
and i not in self.outputs):
if all(compute_map[v][0]
for v in dependencies[i]):
storage_map[i][0] = None
input_index.append(current_apply.inputs.index(i))
#DO NOT set compute_map to 0
#If values become False and the
#current_apply is still in the
#stack, this will cause it to be
#recomputed! This can cause wrong value
#with some combination of inplace op.
compute_map[i][0] = 2
if (config.warn.vm_gc_bug and
current_apply in apply_stack and
getattr(current_apply.op,
'destroy_map',
False)):
warnings.warn(
"There was a bug that existed in the default Theano configuration,"
" only in the development version between July 5th 2012"
" and July 30th 2012. This was not in a released version."
" The bug was affecting this script.",
#The stack level is not good when inside a Scan.
stacklevel=3
)
self.node_cleared_order.append(input_index)
elif not computed_ins:
# -- Non-lazy case, need inputs
apply_stack.append(current_apply)
apply_stack.extend(inp.owner
for inp in current_deps
if inp.owner)
elif not computed_outs:
#
# stack loop: Lazy Evaluation Case
# ================================
#
# Lazy evaluation protocol is to run the thunk with the
# current storage_map and compute_map accessed via closure,
# and the thunk will return a list of variables from its input
# list that it requires.
try:
requires, dt = self.run_thunk_of_node(current_apply)
current_idx = self.node_idx[current_apply]
self.call_counts[current_idx] += 1
self.call_times[current_idx] += dt
except Exception:
raise_with_op(current_apply,
self.thunks[self.node_idx[current_apply]])
if requires:
for r in requires:
# We are not done with this op .. so we added
# back and see to get the inputs we are
# missing
apply_stack.append(current_apply)
if current_apply.inputs[r].owner:
apply_stack.append(current_apply.inputs[r].owner)
else:
if config.profile:
for (idx, o) in enumerate(thunks[
self.node_idx[current_apply]].outputs):
var = self.nodes[
self.node_idx[current_apply]].outputs[idx]
if hasattr(var.type, 'get_shape_info'):
sh = var.type.get_shape_info(o[0])
else:
sh = 'input no shape'
self.variable_shape[var] = sh
st = getattr(o[0], 'strides', 'input no strides')
if (getattr(o[0], 'flags', False) and
o[0].flags.c_contiguous):
st = 'c'
self.variable_strides[var] = st
input_index = []
if self.allow_gc:
for i in current_apply.inputs:
if (dependencies[i] and i.owner and
i not in self.outputs):
empty_storage_map = True
for x in dependencies[i]:
if not compute_map[x][0]:
empty_storage_map = False
break
if empty_storage_map:
storage_map[i][0] = None
input_index.append(current_apply.inputs.index(i))
#See the not lazy gc code for explanations
#of compute_map change
compute_map[i][0] = 2
self.node_cleared_order.append(input_index)
# Hacky coarse gc final pass
# This is required until we have a proper gc algorithm for graphs with
# lazy evaluation. See discussion on theano-dev June 19 2012.
final_index = []
if self.allow_gc:
for v in storage_map:
if v.owner and not v in self.outputs:
if compute_map[v][0] == 2:
continue
else:
storage_map[v][0] = None
final_index.append(v)
compute_map[v][0] = 2
self.node_cleared_order.append(final_index)
def uniform(self,
size,
low=0.0,
high=1.0,
ndim=None,
dtype=None,
nstreams=None):
"""
Sample a tensor of given size whose element from a uniform
distribution between low and high.
If the size argument is ambiguous on the number of dimensions,
ndim may be a plain integer to supplement the missing
information.
:param low: Lower bound of the interval on which values are sampled.
If the ``dtype`` arg is provided, ``low`` will be cast into dtype.
:param high: Higher bound of the interval on which values are sampled.
If the ``dtype`` arg is provided, ``high`` will be cast into dtype.
:param size: Can be a list of integer or Theano variable
(ex: the shape of other Theano Variable)
:param dtype: The output data type. If dtype is not specified, it will
be inferred from the dtype of low and high, but will be at least as
precise as floatX.
"""
low = as_tensor_variable(low)
high = as_tensor_variable(high)
if dtype is None:
dtype = scal.upcast(config.floatX, low.dtype, high.dtype)
low = cast(low, dtype=dtype)
high = cast(high, dtype=dtype)
if isinstance(size, tuple):
msg = "size must be a tuple of int or a Theano variable"
assert all([
isinstance(i, (numpy.integer, int, Variable)) for i in size
]), msg
if any(
[isinstance(i, (numpy.integer, int)) and i <= 0
for i in size]):
raise ValueError(
"The specified size contains a dimension with value <= 0",
size)
else:
if not (isinstance(size, Variable) and size.ndim == 1):
raise TypeError("size must be a tuple of int or a Theano "
"Variable with 1 dimension, got " + str(size) +
" of type " + str(type(size)))
if nstreams is None:
nstreams = self.n_streams(size)
if self.use_cuda and dtype == 'float32':
rstates = self.get_substream_rstates(nstreams)
rstates = rstates.flatten()
# HACK - we use fact that int32 and float32 have same size to
# sneak ints into the CudaNdarray type.
# these *SHOULD NEVER BE USED AS FLOATS*
tmp_float_buf = numpy.frombuffer(rstates.data, dtype='float32')
assert tmp_float_buf.shape == rstates.shape
assert (tmp_float_buf.view('int32') == rstates).all()
# transfer to device
node_rstate = float32_shared_constructor(tmp_float_buf)
assert isinstance(node_rstate.type, CudaNdarrayType)
# we can't use the normal mrg_uniform constructor + later
# optimization
# because of the tmp_float_buf hack above. There is
# currently no Theano node that will do a frombuffer
# reinterpretation.
u = self.pretty_return(
node_rstate,
*GPU_mrg_uniform.new(node_rstate, ndim, dtype, size))
else:
node_rstate = shared(self.get_substream_rstates(nstreams))
u = self.pretty_return(
node_rstate, *mrg_uniform.new(node_rstate, ndim, dtype, size))
r = u * (high - low) + low
if u.type.broadcastable != r.type.broadcastable:
raise NotImplementedError(
'Increase the size to match the broadcasting pattern of '
'`low` and `high` arguments')
assert r.dtype == dtype
return r
def normal(self,
size,
avg=0.0,
std=1.0,
ndim=None,
dtype=None,
nstreams=None):
"""
:param size: Can be a list of integers or Theano variables (ex: the
shape of another Theano Variable)
:param dtype: The output data type. If dtype is not specified, it will
be inferred from the dtype of low and high, but will be at least as
precise as floatX.
:param nstreams: Number of streams.
"""
# We need an even number of ]0,1[ samples. Then we split them
# in two halves. First half becomes our U1's for Box-Muller,
# second half our U2's. See Wikipedia page:
# http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
avg = as_tensor_variable(avg)
std = as_tensor_variable(std)
if dtype is None:
dtype = scal.upcast(config.floatX, avg.dtype, std.dtype)
avg = cast(avg, dtype)
std = cast(std, dtype)
evened = False
constant = False
if isinstance(size, tuple) and all(
[isinstance(i, (numpy.integer, int)) for i in size]):
constant = True
# Force dtype because it defaults to float when size is empty
n_samples = numpy.prod(size, dtype='int64')
if n_samples % 2 == 1:
n_samples += 1
evened = True
else:
#if even, don't change, if odd, +1
n_samples = prod(size) + (prod(size) % 2)
flattened = self.uniform(size=(n_samples, ),
dtype=dtype,
nstreams=nstreams)
if constant:
U1 = flattened[:n_samples // 2]
U2 = flattened[n_samples // 2:]
else:
U1 = flattened[:prod(flattened.shape) // 2]
U2 = flattened[prod(flattened.shape) // 2:]
#normal_samples = zeros_like(flattened)
sqrt_ln_U1 = sqrt(-2.0 * log(U1))
# TypeError: 'TensorVariable' object does not support item assignment
# so this doesn't work...
#normal_samples[:n_samples/2] = sqrt_ln_U1 * cos(2.0*numpy.pi*U2)
#normal_samples[n_samples/2:] = sqrt_ln_U1 * sin(2.0*numpy.pi*U2)
# so trying this instead
first_half = sqrt_ln_U1 * cos(
numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
second_half = sqrt_ln_U1 * sin(
numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
normal_samples = join(0, first_half, second_half)
final_samples = None
if evened:
final_samples = normal_samples[:-1]
elif constant:
final_samples = normal_samples
else:
final_samples = normal_samples[:prod(size)]
if not size:
# Force the dtype to be int64, otherwise reshape complains
size = tensor.constant(size, dtype='int64')
final_samples = final_samples.reshape(size)
final_samples = avg + std * final_samples
assert final_samples.dtype == dtype
return final_samples
def grad_sources_inputs(sources, graph_inputs, warn_type=True):
"""
A gradient source is a pair (``v``, ``g_v``), in which ``v`` is
a `Variable`, and ``g_v`` is a `Variable` that is a gradient wrt
``v``. More specifically, ``g_v`` is the gradient of an external
scalar cost, ``cost`` (that is not explicitly used), wrt ``v``.
This function traverses the graph backward from the ``r`` sources,
calling ``op.grad(...)`` for all ops with some non-None gradient
on an output, to compute gradients of ``cost`` wrt intermediate
variables and ``graph_inputs``.
The ``op.grad(...)`` functions are called like this:
.. code-block:: python
op.grad(op.inputs[:], [total_gradient(v) for v in op.outputs])
This call to ``op.grad`` should return a list or tuple: one symbolic
gradient per input. These gradients represent the gradients of
the same implicit ``cost`` mentionned above, wrt ``op.inputs``. Note
that this is **not** the same as the gradient of ``op.outputs`` wrt
``op.inputs``.
If ``op`` has a single input, then ``op.grad`` should return a list
or tuple of length 1.
For each input wrt to which ``op`` is not differentiable, it should
return ``None`` instead of a `Variable` instance.
If a source ``r`` receives a gradient from another source ``r2``,
then the effective gradient on ``r`` is the sum of both gradients.
:type sources: list of pairs of Variable: (v, gradient-on-v) to
initialize the total_gradient dictionary
:param sources: gradients to back-propagate using chain rule
:type graph_inputs: list of Variable
:param graph_inputs: variables considered to be constant
(do not backpropagate through them)
:type warn_type: bool
:param warn_type: True will trigger warnings via the logging module when
the gradient on an expression has a different type than the original
expression
:rtype: dictionary whose keys and values are of type Variable
:return: mapping from each Variable encountered in the backward
traversal to the gradient with respect to that Variable.
It is assumed that there is some objective J shared between all members of
sources, so that for each v, gradient-on-v is the gradient of J with
respect to v
"""
gmap = {}
for (r, g_r) in sources:
if not hasattr(r, 'type'):
raise TypeError('sources must be Variables', r)
if g_r is not None:
if r in gmap:
gmap[r] = gmap[r] + g_r
else:
gmap[r] = g_r
graph_outputs = gof.utils.uniq([r for r, g in sources])
if graph_inputs is None:
graph_inputs = gof.graph.inputs(graph_outputs)
for node in gof.graph.io_toposort(graph_inputs,
graph_outputs).__reversed__():
g_outputs = [gmap.get(o, None) for o in node.outputs]
#if all output gradients are None, continue
if all(map(lambda x: x is None, g_outputs)): continue
output_arg = g_outputs
input_arg = node.inputs
# Each Op's grad function requires inputs and output_grads
# If the Op destroys any input, but the grad expression uses it,
# then chances are the resulting graph will have a dependency
# cycle. We avoid this cycle by passing (symbolic) copies of
# each destroyed input.
try:
dinputs = [node.inputs[x[0]] for x in node.op.destroy_map.values()]
except AttributeError:
dinputs = []
new_input_arg = []
for input in input_arg:
if input in dinputs and hasattr(input, 'copy'):
new_input_arg.append(input.copy())
else:
new_input_arg.append(input)
input_arg = new_input_arg
#note that this function is not in a try-except block
# the rationale:
# If the op implements grad, then any exception should be passed to
# the caller
# If the op doesn't implement grad, this entire function should fail.
# Other possibilities:
# * return a partial back-prop
#
op_grad = node.op.grad(input_arg, output_arg)
if not isinstance(op_grad, (list, tuple)):
raise ValueError(_msg_retType, node.op)
g_inputs = op_grad
assert isinstance(g_inputs, (list, tuple))
if len(g_inputs) != len(node.inputs):
raise ValueError(_msg_badlen, node.op, len(g_inputs),
len(node.inputs))
for ii, (r, g_r) in enumerate(zip(node.inputs, g_inputs)):
if warn_type:
if g_r and (getattr(r, 'type', 0) != getattr(g_r, 'type', 1)):
r_type = getattr(r, 'type', None)
g_r_type = getattr(g_r, 'type', None)
_logger.warning(
'%s.grad returned a different type (%s) '
'for input %i of type (%s)', node.op, g_r_type, ii,
r_type)
if g_r and len(sources) == 1 and sources[0][0].name and r.name:
g_r.name = "(d%s/d%s)" % (sources[0][0].name, r.name)
if g_r is not None:
assert r is not None
if r in gmap:
gmap[r] = gmap[r] + g_r
else:
gmap[r] = g_r
return gmap