def test_execution():
a = NDArray([1, 2, 3, 4], datashape('4, float32'))
b = NDArray([5, 6, 7, 8], datashape('4, float32'))
c = NDArray([9, 10, 11, 12], datashape('4, float32'))
out = NDArray([0, 0, 0, 0], datashape('4, float32'))
graph = a + b * c
out[:] = graph
print list(out.data.ca)
def test_conversion():
a = NDArray([1, 2, 3, 4], datashape('2, 2, int32'))
b = NDArray([5, 6, 7, 8], datashape('2, 2, float32'))
c = NDArray([9, 10, 11, 12], datashape('2, 2, int32'))
graph = a + b * c
result_graph, executors = convert_graph(graph)
assert result_graph.spine.label == 'Executor', result_graph.spine
assert len(executors) == 1
executor_id, executor = executors.popitem()
def test_conversion():
"""
>>> test_conversion()
[LLVMExecutor(chunked, (op0 + (op1 * op2)))('%0' '%1' '%2')]
['%0', '%1', '%2']
"""
a = NDArray([1, 2, 3, 4], datashape('2, 2, int32'))
b = NDArray([5, 6, 7, 8], datashape('2, 2, float32'))
c = NDArray([9, 10, 11, 12], datashape('2, 2, int32'))
graph = a + b * c
instructions, executors, symbols = convert_graph(graph)
assert len(instructions) == len(executors) == 1
print instructions
print sorted(symbols)
def test_execution():
"""
>>> test_execution()
[ 46. 62. 80. 100.]
"""
a = NDArray([1, 2, 3, 4], datashape('4, float32'))
b = NDArray([5, 6, 7, 8], datashape('4, float32'))
c = NDArray([9, 10, 11, 12], datashape('4, float32'))
out = NDArray([0, 0, 0, 0], datashape('4, float32'))
graph = a + b * c
out[:] = graph
# print list(out.data.ca), hex(out.data.ca.leftover_array.ctypes.data)
# print "*" * 100
# print out.data.ca.leftover_array.dtype
print out.data.ca
def get_datashape(dshape_aterm):
"Assemble datashape from aterm dshape"
args = []
for arg in dshape_aterm.args:
if isinstance(arg, paterm.AInt):
args.append(arg.n)
elif isinstance(arg, paterm.AString):
args.append(arg.s)
else:
raise NotImplementedError
return datashape(*args)
def get_datashape(term):
"Assemble datashape from aterm dshape"
type = term.annotation.ty
args = []
for arg in type.args:
if isinstance(arg, paterm.AInt):
args.append(arg.n)
elif isinstance(arg, paterm.AString):
args.append(arg.s)
else:
raise NotImplementedError
return datashape(*args)
def unannotate_dtype(aterm):
""" Takes a term with a datashape annotation and returns the NumPy
dtype associate with it
>>> term
x{dshape("2, 2, int32")}
>>> unannotate_dtype(term)
int32
"""
# unpack the annotation {'s': 'int32'}
unpack = paterm.matches('dshape(s);*', aterm.annotation['type'])
ds_str = unpack['s']
dshape = datashape(ds_str.s)
dtype = coretypes.to_dtype(dshape)
return dtype
def unannotate_dtype(aterm):
""" Takes a term with a datashape annotation and returns the NumPy
dtype associate with it
>>> term
x{dshape("2, 2, int32")}
>>> unannotate_dtype(term)
int32
"""
# unpack the annotation {'s': 'int32'}
unpack = paterm.matches('dshape(s);*', aterm.annotation['type'])
ds_str = unpack['s']
dshape = datashape(ds_str.s)
dtype = coretypes.to_dtype(dshape)
return dtype
dtype associate with it
>>> term
x{dshape("2, 2, int32")}
>>> unannotate_dtype(term)
int32
"""
# unpack the annotation {'s': 'int32'}
unpack = paterm.matches('dshape(s);*', aterm.annotation['type'])
ds_str = unpack['s']
dshape = datashape(ds_str.s)
dtype = coretypes.to_dtype(dshape)
return dtype
def minitype(dtype):
return minitypes.map_dtype(dtype)
def substitute_llvm_executors(aterm_graph, executors):
translator = ATermToAstTranslator(executors)
return translator.visit(aterm_graph)
if __name__ == '__main__':
a = NDArray([1, 2, 3, 4], datashape('2, 2, int'))
ops, funcdef = convert_graph(a + a)
# print getsource(funcdef)
# print result
# import doctest
# doctest.testmod()
