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Fix pure reduce expansion for squeezed output memlets. #1709

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2 changes: 1 addition & 1 deletion dace/libraries/standard/nodes/reduce.py
Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,7 @@ def expansion(node: 'Reduce', state: SDFGState, sdfg: SDFG):
'reduce_init', {'_o%d' % i: '0:%s' % symstr(d)
for i, d in enumerate(outedge.data.subset.size())}, {},
'__out = %s' % node.identity,
{'__out': dace.Memlet.simple('_out', ','.join(['_o%d' % i for i in range(output_dims)]))},
{'__out': dace.Memlet.simple('_out', ','.join(['_o%d' % i for i in osqdim]))},
external_edges=True)
else:
nstate = nsdfg.add_state()
Expand Down
74 changes: 61 additions & 13 deletions tests/library/reduce_test.py
Original file line number Diff line number Diff line change
@@ -1,35 +1,82 @@
# Copyright 2019-2021 ETH Zurich and the DaCe authors. All rights reserved.
import dace
import numpy as np
import pytest

import dace
import dace.libraries.standard as std
from dace import SDFG, Memlet

C_in, C_out, H, K, N, W = (dace.symbol(s, dace.int64) for s in ('C_in', 'C_out', 'H', 'K', 'N', 'W'))


def make_sdfg():
g = SDFG('prog')
g.add_array('A', (N, 1, 1, C_in, C_out), dace.float32,
strides=(C_in * C_out, C_in * C_out, C_in * C_out, C_out, 1))
g.add_array('C', (N, H, W, C_out), dace.float32,
strides=(C_out * H * W, C_out * W, C_out, 1))

st0 = g.add_state('st0', is_start_block=True)
st = st0

A = st.add_access('A')
C = st.add_access('C')
R = st.add_reduce('lambda x, y: x + y', [1, 2, 3], 0)
st.add_memlet_path(A, R, memlet=Memlet(expr='A[0:N, 0, 0, 0:C_in, 0:C_out]'))
st.add_memlet_path(R, C, memlet=Memlet(expr='C[0:N, 5, 5, 0:C_out]'))
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return g, R


def test_library_node_expand_reduce_pure():
n, cin, cout = 7, 7, 7
h, k, w = 25, 35, 45
A = np.ones((n, 1, 1, cin, cout), np.float32)

g, R = make_sdfg()
R.implementation = 'pure-seq'
g.validate()
g.compile()

wantC = np.ones((n, h, w, cout), np.float32) * 42
g(A=A, C=wantC, N=n, C_in=cin, C_out=cout, H=h, K=k, W=w)

g, R = make_sdfg()
R.implementation = 'pure'
g.validate()
g.compile()

gotC = np.ones((n, h, w, cout), np.float32) * 42
g(A=A, C=gotC, N=n, C_in=cin, C_out=cout, H=h, K=k, W=w)
assert np.allclose(wantC, gotC)


_params = ['pure', 'CUDA (device)', 'pure-seq', 'GPUAuto']



@pytest.mark.gpu
@pytest.mark.parametrize('impl', _params)
def test_multidim_gpu(impl):

test_cases = [([1, 64, 60, 60], (0, 2, 3), [64], np.float32),
([8, 512, 4096], (0,1), [4096], np.float32),
([8, 512, 4096], (0,1), [4096], np.float64),
([1024, 8], (0), [8], np.float32),
([111, 111, 111], (0,1), [111], np.float64),
([111, 111, 111], (1,2), [111], np.float64),
([1000000], (0), [1], np.float64),
([1111111], (0), [1], np.float64),
([123,21,26,8], (1,2), [123,8], np.float32),
([2, 512, 2], (0,2), [512], np.float32),
([512, 555, 257], (0,2), [555], np.float64)]
([8, 512, 4096], (0, 1), [4096], np.float32),
([8, 512, 4096], (0, 1), [4096], np.float64),
([1024, 8], (0), [8], np.float32),
([111, 111, 111], (0, 1), [111], np.float64),
([111, 111, 111], (1, 2), [111], np.float64),
([1000000], (0), [1], np.float64),
([1111111], (0), [1], np.float64),
([123, 21, 26, 8], (1, 2), [123, 8], np.float32),
([2, 512, 2], (0, 2), [512], np.float32),
([512, 555, 257], (0, 2), [555], np.float64)]

for in_shape, ax, out_shape, dtype in test_cases:
print(in_shape, ax, out_shape, dtype)
axes = ax

@dace.program
def multidimred(a, b):
b[:] = np.sum(a, axis=axes)

a = np.random.rand(*in_shape).astype(dtype)
b = np.random.rand(*out_shape).astype(dtype)
sdfg = multidimred.to_sdfg(a, b)
Expand All @@ -45,3 +92,4 @@ def multidimred(a, b):
if __name__ == '__main__':
for p in _params:
test_multidim_gpu(p)
test_library_node_expand_reduce_pure()
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