def do_take1(shp, idx, offseted):
c, g = gen_gpuarray(shp, dtype='float32', ctx=ctx, order='c')
ci = numpy.asarray(idx)
gi = pygpu.asarray(ci, context=ctx)
rc = c.take(ci, axis=0)
rg = g.take1(gi)
check_content(rg, rc)
def do_take1(shp, idx, offseted):
c, g = gen_gpuarray(shp, dtype='float32', ctx=ctx, order='c')
ci = numpy.asarray(idx)
gi = pygpu.asarray(ci, context=ctx)
rc = c.take(ci, axis=0)
rg = g.take1(gi)
check_content(rg, rc)
def perform(self, node, inputs, out_):
out, = out_
x = inputs[0]
idx = inputs[1:]
# detect and transpose array indices
nidx = []
nshp = list(x.shape)
for k, i in enumerate(idx):
if i is None:
nidx.append(slice(None))
nshp.insert(k, 1)
else:
nidx.append(i)
x = x.reshape(nshp)
narrays = 0
transp = list(range(x.ndim))
p = 0
# ap gives the position of the array in case there is only one.
# if there are more than one (narray > 1) it should be ignored.
ap = 0
for k, i in enumerate(list(nidx)):
if (isinstance(i, np.ndarray) and
i.ndim != 0):
transp.remove(k)
transp.insert(p, k)
ap += k
i = nidx.pop(k)
nidx.insert(p, i)
p += 1
narrays += 1
else:
if narrays == 0:
try:
i.__index__()
# We shift back the position of the array by the
# number of dimensions that are removed by
# indexing. If ap is bigger than 0 it means we
# have encountered at least one array.
if ap >= 0:
ap -= 1
# If this index is before the first array then
# we will not move the array back to its
# position. Mark this by faking that there
# are more than two arrays. This is crazy
# numpy behaviour so blame them.
narrays = 2
except Exception:
pass
x = x.transpose(*transp)
idx_ = ([slice(None)] * p + nidx[p:])
x = x.__getitem__(idx_)
if p == 0:
# The only indexing was through slices and indices.
# This can happen with symbolic slices for instance.
# Since no view_map is set, we need to copy the returned value
out[0] = x.copy()
return
# flatten the array-indexed dimensions
shape = ((np.prod(x.shape[0: p]),) +
x.shape[p:])
input_flat = x.reshape(shape)
# build the strides
strides = [1]
for i in range(p - 1, 0, -1):
stride = x.shape[i] * strides[0]
strides.insert(0, stride)
# build the indices and use it
take_idx = sum((i * s for i, s in zip(nidx, strides)))
out_flat = input_flat.take1(pygpu.asarray(take_idx.flatten(),
context=x.context))
# finish up
out_flat_shp = take_idx.shape + x.shape[p:]
o = out_flat.reshape(out_flat_shp)
# If there was only one array we need to move the indexed
# dimension(s) back to the position of the array, which is
# stored in ap. Note that ap is invalid is narrays != 1.
if narrays == 1:
ntransp = list(range(take_idx.ndim, o.ndim))
ntransp[ap:ap] = list(range(take_idx.ndim))
o = o.transpose(*ntransp)
out[0] = o
def perform(self, node, inputs, out_):
out, = out_
x = inputs[0]
idx = inputs[1:]
# detect and transpose array indices
nidx = []
nshp = list(x.shape)
for k, i in enumerate(idx):
if i is None:
nidx.append(slice(None))
nshp.insert(k, 1)
else:
nidx.append(i)
x = x.reshape(nshp)
transp = list(range(x.ndim))
# number of array-indexed dimensions
p = 0
# ap represents the axis in the resulting array where the
# dimensions indexed by arrays and ints will be inserted.
# For instance, if all such dimensions are grouped together,
# it corresponds to the index of the first such dimension in the
# inital array. If these dimensions are split (with slices
# inbetween), then the resulting dimensions will be moved to the
# beginning, and ap will be 0.
# If no such dimension has been encountered, ap is None.
ap = None
# Indicates whether we have already encountered an index (array
# or number), and then a slice.
slice_after_idx = False
for k, i in enumerate(list(nidx)):
if (isinstance(i, np.ndarray) and i.ndim != 0):
transp.remove(k)
transp.insert(p, k)
i = nidx.pop(k)
nidx.insert(p, i)
p += 1
if ap is None:
# first non-slice index
ap = k
elif slice_after_idx:
# We already encountered at least an array or int, and then
# a slice. Array-indexed axes are not grouped,
# moving to the beginning
ap = 0
else:
try:
i.__index__()
if ap is None:
ap = k
# indices do not break the contiguity of
# array-indexed axes
except Exception:
# If we already encountered an array/int index, it
# means future ones will not be grouped.
if ap is not None:
slice_after_idx = True
x = x.transpose(*transp)
idx_ = ([slice(None)] * p + nidx[p:])
x = x.__getitem__(idx_)
if p == 0:
assert ap is None
# The only indexing was through slices and indices.
# This can happen with symbolic slices for instance.
# Since no view_map is set, we need to copy the returned value
out[0] = x.copy()
return
# At this point, we should have encountered at least one array
assert ap is not None
# flatten the array-indexed dimensions
shape = ((np.prod(x.shape[0: p]),) +
x.shape[p:])
input_flat = x.reshape(shape)
# build the strides
strides = [1]
for i in range(p - 1, 0, -1):
stride = x.shape[i] * strides[0]
strides.insert(0, stride)
# build the indices and use it
take_idx = sum((i * s for i, s in zip(nidx, strides)))
out_flat = input_flat.take1(pygpu.asarray(take_idx.flatten(),
context=x.context))
# finish up
out_flat_shp = take_idx.shape + x.shape[p:]
o = out_flat.reshape(out_flat_shp)
if ap != 0:
# Put the resulting indexing at the place that NumPy
# decided was the right one.
ntransp = list(range(take_idx.ndim, o.ndim))
ntransp[ap:ap] = list(range(take_idx.ndim))
o = o.transpose(*ntransp)
out[0] = o
def test_bool():
for data in [numpy.empty((0, 33)), [[1]], [[0]], [], [0], [1], 0, 1]:
assert (bool(pygpu.asarray(data, context=ctx)) ==
bool(numpy.asarray(data)))
def set_bhc_data_from_ary(self, ary):
self.clary[:] = pygpu.asarray(ary)
def test_bool():
for data in [numpy.empty((0, 33)), [[1]], [[0]], [], [0], [1], 0, 1]:
assert (bool(pygpu.asarray(data,
context=ctx)) == bool(numpy.asarray(data)))
def perform(self, node, inputs, out_):
out, = out_
x = inputs[0]
idx = inputs[1:]
# convert boolean masks to index arrays
idx = check_and_convert_boolean_masks(x, idx)
# detect and transpose array indices
nidx = []
nshp = list(x.shape)
for k, i in enumerate(idx):
if i is None:
nidx.append(slice(None))
nshp.insert(k, 1)
else:
nidx.append(i)
x = x.reshape(nshp)
transp = list(range(x.ndim))
# number of array-indexed dimensions
p = 0
# ap represents the axis in the resulting array where the
# dimensions indexed by arrays and ints will be inserted.
# For instance, if all such dimensions are grouped together,
# it corresponds to the index of the first such dimension in the
# inital array. If these dimensions are split (with slices
# inbetween), then the resulting dimensions will be moved to the
# beginning, and ap will be 0.
# If no such dimension has been encountered, ap is None.
ap = None
# Indicates whether we have already encountered an index (array
# or number), and then a slice.
slice_after_idx = False
for k, i in enumerate(list(nidx)):
if (isinstance(i, np.ndarray) and i.ndim != 0):
transp.remove(k)
transp.insert(p, k)
i = nidx.pop(k)
nidx.insert(p, i)
p += 1
if ap is None:
# first non-slice index
ap = k
elif slice_after_idx:
# We already encountered at least an array or int, and then
# a slice. Array-indexed axes are not grouped,
# moving to the beginning
ap = 0
else:
try:
i.__index__()
if ap is None:
ap = k
# indices do not break the contiguity of
# array-indexed axes
except Exception:
# If we already encountered an array/int index, it
# means future ones will not be grouped.
if ap is not None:
slice_after_idx = True
x = x.transpose(*transp)
idx_ = ([slice(None)] * p + nidx[p:])
x = x.__getitem__(idx_)
if p == 0:
assert ap is None
# The only indexing was through slices and indices.
# This can happen with symbolic slices for instance.
# Since no view_map is set, we need to copy the returned value
out[0] = x.copy()
return
# At this point, we should have encountered at least one array
assert ap is not None
# flatten the array-indexed dimensions
shape = ((np.prod(x.shape[0: p]),) +
x.shape[p:])
input_flat = x.reshape(shape)
# build the strides
strides = [1]
for i in range(p - 1, 0, -1):
stride = x.shape[i] * strides[0]
strides.insert(0, stride)
# build the indices and use it
take_idx = sum((i * s for i, s in zip(nidx, strides)))
out_flat = input_flat.take1(pygpu.asarray(take_idx.flatten(),
context=x.context))
# finish up
out_flat_shp = take_idx.shape + x.shape[p:]
o = out_flat.reshape(out_flat_shp)
if ap != 0:
# Put the resulting indexing at the place that NumPy
# decided was the right one.
ntransp = list(range(take_idx.ndim, o.ndim))
ntransp[ap:ap] = list(range(take_idx.ndim))
o = o.transpose(*ntransp)
out[0] = o
def perform(self, node, inputs, out_):
out, = out_
x = inputs[0]
idx = inputs[1:]
# detect and transpose array indices
nidx = []
nshp = list(x.shape)
for k, i in enumerate(idx):
if i is None:
nidx.append(slice(None))
nshp.insert(k, 1)
else:
nidx.append(i)
x = x.reshape(nshp)
narrays = 0
transp = list(range(x.ndim))
p = 0
# ap gives the position of the array in case there is only one.
# if there are more than one (narray > 1) it should be ignored.
ap = 0
for k, i in enumerate(list(nidx)):
if (isinstance(i, numpy.ndarray) and
i.ndim != 0):
transp.remove(k)
transp.insert(p, k)
ap += k
i = nidx.pop(k)
nidx.insert(p, i)
p += 1
narrays += 1
else:
if narrays == 0:
try:
i.__index__()
# We shift back the position of the array by the
# number of dimensions that are removed by
# indexing. If ap is bigger than 0 it means we
# have encountered at least one array.
if ap >= 0:
ap -= 1
# If this index is before the first array then
# we will not move the array back to its
# position. Mark this by faking that there
# are more than two arrays. This is crazy
# numpy behaviour so blame them.
narrays = 2
except Exception:
pass
x = x.transpose(*transp)
idx_ = ([slice(None)] * p + nidx[p:])
x = x.__getitem__(idx_)
# flatten the array-indexed dimensions
shape = ((numpy.prod(x.shape[0: p]),) +
x.shape[p:])
input_flat = x.reshape(shape)
# build the strides
strides = [1]
for i in range(p - 1, 0, -1):
stride = x.shape[i] * strides[-1]
strides.insert(0, stride)
# build the indices and use it
take_idx = sum((i * s for i, s in zip(nidx, strides)))
out_flat = input_flat.take1(pygpu.asarray(take_idx.flatten(),
context=x.context))
# finish up
out_flat_shp = take_idx.shape + x.shape[p:]
o = out_flat.reshape(out_flat_shp)
# If there was only one array we need to move the indexed
# dimension(s) back to the position of the array, which is
# stored in ap. Note that ap is invalid is narrays != 1.
if narrays == 1:
ntransp = list(range(take_idx.ndim, o.ndim))
ntransp[ap:ap] = list(range(take_idx.ndim))
o = o.transpose(*ntransp)
out[0] = o
_,ctx,arr,shared_x,shared_xx = init_device(device=device)
float2half = pygpu.elemwise.GpuElemwise(expr="a = b",
args=[pygpu.elemwise.arg("a", 'float16', write=True),\
pygpu.elemwise.arg("b", 'float32', read=True)],
convert_f16=True,
ctx=ctx)
numpy_float = np.array([1.444, 2.555, 7.999], dtype=np.float32)
numpy_half = np.array([0, 0, 0], dtype=np.float16)
ga_float = pygpu.asarray(numpy_float, dtype=np.float32,
context=ctx)
ga_half = pygpu.asarray(numpy_half, dtype=np.float16,
context=ctx)
print 'before convert', ga_float
float2half(ga_half, ga_float)
print 'after convert', ga_half
def set_bhc_data_from_ary(self, ary):
self.clary[:] = pygpu.asarray(ary)
