def collect(self, size, top_of_stack):
"""
Collect garbage by copying all live data to tospace, starting with
`roots`. If there is no or little garbage and we're running out of heap
space, grow the heap. `size` indicates the size of the requested memory.
This method may raise MemoryError.
For every root, we determine the trace function and call it on the root.
The trace function traces the object itself (since only it knows its
size), and traces its children:
def __flypy_trace__(self, gc):
obj = gc.trace((int8 *) self, sizeof(self))
obj.field1 = obj.field1.trace(gc)
...
obj.fieldN = obj.fieldN.trace(gc)
return obj
"""
# Copy all live data
for item in roots.find_roots(top_of_stack):
obj = head(item)
trace = head(tail(item))
trace(obj)
# Swap spaces and adjust heap if necessary
self.fromspace, self.tospace = self.tospace, self.fromspace
if self.resize_heap(size):
self.init_forwarding_table()
else:
self.forwarding_table.clear()
def add(a, b):
arrays = broadcast(a, b)
a = head(arrays)
b = head(tail(arrays))
out = np.empty_like(a, unify(a.dtype, b.dtype))
_add(a, b, out)
return out
def add(a, b):
arrays = broadcast(a, b)
a = head(arrays)
b = head(tail(arrays))
out = np.empty_like(a, unify(a.dtype, b.dtype))
_add(a, b, out)
return out
def collect(self, size, top_of_stack):
"""
Collect garbage by copying all live data to tospace, starting with
`roots`. If there is no or little garbage and we're running out of heap
space, grow the heap. `size` indicates the size of the requested memory.
This method may raise MemoryError.
For every root, we determine the trace function and call it on the root.
The trace function traces the object itself (since only it knows its
size), and traces its children:
def __flypy_trace__(self, gc):
obj = gc.trace((int8 *) self, sizeof(self))
obj.field1 = obj.field1.trace(gc)
...
obj.fieldN = obj.fieldN.trace(gc)
return obj
"""
# Copy all live data
for item in roots.find_roots(top_of_stack):
obj = head(item)
trace = head(tail(item))
trace(obj)
# Swap spaces and adjust heap if necessary
self.fromspace, self.tospace = self.tospace, self.fromspace
if self.resize_heap(size):
self.init_forwarding_table()
else:
self.forwarding_table.clear()
def slice_dim(dim, p, indices, dtype):
# TODO: wraparound
s = head(indices)
data = p
extent = dim.extent
stride = dim.stride
t = normalize(s, extent)
start = t[0]
stop = t[1]
step = t[2] # TODO: tuple unpacking
extent = len(xrange(start, stop, step))
# Process start
if s.start is not None:
data += start * stride
# Process step
if s.step is not None:
stride *= step
array = dim.base.index(data, tail(indices), dtype)
# TODO: Without 'step', ContigDim should remain a ContigDim
dims = Dimension(array.dims, extent, stride)
return NDArray(array.data, dims, dtype)
def index(self, p, indices, dtype):
idx = head(indices)
if 0 <= idx < self.base.extent:
return self.base.index(p, indices, dtype)
# TODO: Exceptions
print("Index out of bounds: index", end="")
print(idx, end=", extent ")
print(self.base.extent)
def index(self, p, indices, dtype):
idx = head(indices)
if 0 <= idx < self.base.extent:
return self.base.index(p, indices, dtype)
# TODO: Exceptions
print("Index out of bounds: index", end="")
print(idx, end=", extent ")
print(self.base.extent)
def slice_dim(dim, p, indices, dtype):
# TODO: wraparound
s = head(indices)
data = p
extent = dim.extent
stride = dim.stride
t = normalize(s, extent)
start = t[0] ; stop = t[1] ; step = t[2] # TODO: tuple unpacking
extent = len(range(start, stop, step))
# Process start
if s.start is not None:
data += start * stride
# Process step
if s.step is not None:
stride *= step
array = dim.base.index(data, tail(indices), dtype)
# TODO: Without 'step', ContigDim should remain a ContigDim
dims = Dimension(array.dims, extent, stride)
return NDArray(array.data, dims, dtype)
def c_layout_from_shape(shape, dtype):
extent = head(shape)
return Dimension(EmptyDim(), extent, 1) # TODO: ContigDim
def c_layout_from_shape(shape, dtype):
"""Construct dimensions for an array of shape `shape` with a C layout"""
dim = c_layout_from_shape(tail(shape), dtype)
extent = head(shape)
stride = dim.stride * dim.extent
return Dimension(dim, extent, stride)
def index(self, p, indices, dtype):
idx = head(indices)
#if idx < 0 or idx > self.extent:
# print("Index out of bounds!")
# return self.base.index(p, tail(indices))
return self.base.index(p + idx * self.stride, tail(indices), dtype)
def index(self, p, indices, dtype):
idx = head(indices)
#if idx < 0 or idx > self.extent:
# print("Index out of bounds!")
# return self.base.index(p, tail(indices))
return self.base.index(p + idx * self.stride, tail(indices), dtype)
def c_layout_from_shape(shape, dtype):
extent = head(shape)
return Dimension(EmptyDim(), extent, 1) # TODO: ContigDim
def c_layout_from_shape(shape, dtype):
"""Construct dimensions for an array of shape `shape` with a C layout"""
dim = c_layout_from_shape(tail(shape), dtype)
extent = head(shape)
stride = dim.stride * dim.extent
return Dimension(dim, extent, stride)
