def test_structure_type(thr):
shape = (100, 100)
dtype = dtypes.align(
numpy.dtype([('i1', numpy.uint32),
('nested', numpy.dtype([
('v', numpy.uint64),
])), ('i2', numpy.uint32)]))
a = get_test_array(shape, dtype)
a_dev = thr.to_device(a)
# Have to construct the resulting array manually,
# since numpy cannot reduce arrays with struct dtypes.
b_ref = numpy.empty(100, dtype)
b_ref['i1'] = a['i1'].sum(0)
b_ref['nested']['v'] = a['nested']['v'].sum(0)
b_ref['i2'] = a['i2'].sum(0)
predicate = Predicate(
Snippet.create(lambda v1, v2: """
${ctype} result = ${v1};
result.i1 += ${v2}.i1;
result.nested.v += ${v2}.nested.v;
result.i2 += ${v2}.i2;
return result;
""",
render_kwds=dict(ctype=dtypes.ctype_module(dtype))),
numpy.zeros(1, dtype)[0])
rd = Reduce(a_dev, predicate, axes=(0, ))
b_dev = thr.empty_like(rd.parameter.output)
rdc = rd.compile(thr)
rdc(b_dev, a_dev)
b_res = b_dev.get()
# Array.get() runs numpy.lib.stride_tricks.as_strided() on the array,
# which adds dummy variables instead of custom offsets (and our `dtype` has them),
# making the result dtype different, and failing the test.
# For now we will just note the difference and convert the result
# back to the original dtype (they are still compatible).
# When the behavior changes, the test will start to fail and we will notice.
# See inducer/compyte issue #26.
wrong_dtype = b_res.dtype != b_dev.dtype
b_res = b_res.astype(dtype)
assert diff_is_negligible(b_res, b_ref)
if wrong_dtype:
pytest.xfail("as_strided() still corrupts the datatype")
else:
pytest.fail("as_strided() does not corrupt the datatype anymore, "
"we can remove the `astype()` now")
def test_nondefault_function(thr):
shape = (100, 100)
a = get_test_array(shape, numpy.int64)
a_dev = thr.to_device(a)
b_ref = a.sum(0)
predicate = Predicate(
Snippet.create(lambda v1, v2: "return ${v1} + ${v2};"), 0)
rd = Reduce(a_dev, predicate, axes=(0, ))
b_dev = thr.empty_like(rd.parameter.output)
rdc = rd.compile(thr)
rdc(b_dev, a_dev)
assert diff_is_negligible(b_dev.get(), b_ref)
def test_scan_structure_type(thr, exclusive):
shape = (100, 100)
dtype = dtypes.align(
numpy.dtype([('i1', numpy.uint32),
('nested', numpy.dtype([
('v', numpy.uint64),
])), ('i2', numpy.uint32)]))
a = get_test_array(shape, dtype)
a_dev = thr.to_device(a)
# Have to construct the resulting array manually,
# since numpy cannot scan arrays with struct dtypes.
b_ref = numpy.empty(shape, dtype)
b_ref['i1'] = ref_scan(a['i1'], axes=0, exclusive=exclusive)
b_ref['nested']['v'] = ref_scan(a['nested']['v'],
axes=0,
exclusive=exclusive)
b_ref['i2'] = ref_scan(a['i2'], axes=0, exclusive=exclusive)
predicate = Predicate(
Snippet.create(lambda v1, v2: """
${ctype} result = ${v1};
result.i1 += ${v2}.i1;
result.nested.v += ${v2}.nested.v;
result.i2 += ${v2}.i2;
return result;
""",
render_kwds=dict(ctype=dtypes.ctype_module(dtype))),
numpy.zeros(1, dtype)[0])
scan = Scan(a_dev, predicate, axes=(0, ), exclusive=exclusive)
b_dev = thr.empty_like(scan.parameter.output)
scanc = scan.compile(thr)
scanc(b_dev, a_dev)
b_res = b_dev.get()
assert diff_is_negligible(b_res, b_ref)
return repeat_kernel(img, prg.border, 1)
# Create LUT and stringify into preamble of map kernel
LUT = np.zeros(256, np.int32)
for b in xrange(8):
LUT[(np.arange(256) & (1 << b)) != 0] += 1
strLUT = "constant int LUT[256] = {" + ",".join(map(str, LUT)) + "};\n"
byte_to_count = Transformation([
Parameter('output', Annotation(Type(np.int32, (1, )), 'o')),
Parameter('input', Annotation(Type(np.uint8, (1, )), 'i'))
], strLUT + """
${output.store_same}(LUT[${input.load_same}]);
""")
predicate = Predicate(
Snippet.create(lambda v1, v2: """return ${v1} + ${v2}"""), np.int32(0))
sum_bits_reduction = Reduce(byte_to_count.output, predicate)
sum_bits_reduction.parameter.input.connect(byte_to_count,
byte_to_count.output,
new_input=byte_to_count.input)
sum_bits = sum_bits_reduction.compile(thr)
#sum_byte_count = ReductionKernel(cx, np.int32, neutral="0",
# reduce_expr="a+b", map_expr="LUT[bytes[i]]",
# arguments="__global unsigned char *bytes",
# preamble=strLUT)
#def count_bits(img):
# return sum_byte_count(img).get().item()
#
#pixel_inds = GenericScanKernel(cx, np.int32,
# arguments="__global unsigned char *bytes, "
]))
mmc_c_decl = dtypes.ctype_module(mmc_dtype)
# Create the "empty" element for our minmax monoid, that is
# x `minmax` empty == empty `minmax` x == x.
empty = numpy.empty(1, mmc_dtype)[0]
empty["cur_min"] = 1 << 30
empty["cur_max"] = -(1 << 30)
# Reduction predicate for the minmax.
# v1 and v2 get the names of two variables to be processed.
predicate = Predicate(
Snippet.create(lambda v1, v2: """
${ctype} result = ${v1};
if (${v2}.cur_min < result.cur_min)
result.cur_min = ${v2}.cur_min;
if (${v2}.cur_max > result.cur_max)
result.cur_max = ${v2}.cur_max;
return result;
""",
render_kwds=dict(ctype=mmc_c_decl)), empty)
# Test array
arr = numpy.random.randint(0, 10**6, 20000)
# A transformation that creates initial minmax structures for the given array of integers
to_mmc = Transformation([
Parameter('output', Annotation(Type(mmc_dtype, arr.shape), 'o')),
Parameter('input', Annotation(arr, 'i'))
], """
${output.ctype} res;
res.cur_min = ${input.load_same};
def test_scan_custom_predicate(thr):
predicate = Predicate(
Snippet.create(lambda v1, v2: "return ${v1} + ${v2};"), 0)
check_scan(thr, (10, 20, 30, 40), axes=(1, 2), predicate=predicate)