def cli(n, tile_first):
N.set(n)
A = dace.ndarray([N], dtype=dace.float32)
B = dace.ndarray([N], dtype=dace.float32)
out_AB = dace.scalar(dace.float32)
print('Dot product %d' % (N.get()))
A[:] = np.random.rand(N.get()).astype(dace.float32.type)
B[:] = np.random.rand(N.get()).astype(dace.float32.type)
out_AB[0] = dace.float32(0)
sdfg = dot.to_sdfg()
if tile_first:
sdfg.apply_transformations(MapTiling)
sdfg.apply_transformations(FPGATransformSDFG)
else:
sdfg.apply_transformations(FPGATransformSDFG)
sdfg.apply_transformations(MapTiling)
sdfg(A=A, B=B, out=out_AB, N=N)
diff_ab = np.linalg.norm(np.dot(A, B) - out_AB) / float(N.get())
print("Difference (A*B):", diff_ab)
exit(0 if (diff_ab <= 1e-5) else 1)
def vector_reduce(x: dace.float32[N], s: dace.scalar(dace.float32)):
#transient
tmp = dace.define_local([N], dtype=x.dtype)
@dace.map
def sum(i: _[0:N]):
in_x << x[i]
out >> tmp[i]
out = in_x
dace.reduce(lambda a, b: a + b, tmp, s, axis=(0), identity=0)
def test_dot():
n = 64
N.set(n)
A = dace.ndarray([N], dtype=dace.float32)
out_AA = dace.scalar(dace.float64)
A[:] = np.random.rand(n).astype(dace.float32.type)
out_AA[0] = dace.float64(0)
dot(A, A, out_AA, N=n)
diff_aa = np.linalg.norm(np.dot(A, A) - out_AA) / float(n)
print("Difference:", diff_aa)
assert diff_aa <= 1e-5
def test_filter():
N.set(64)
ratio = np.float32(0.5)
print('Predicate-Based Filter. size=%d, ratio=%f' % (N.get(), ratio))
A = np.random.rand(N.get()).astype(np.float32)
B = np.zeros_like(A)
outsize = dace.scalar(dace.uint32)
outsize[0] = 0
sdfg = pbf.to_sdfg()
vectorize(sdfg, 'i')
sdfg(A=A, B=B, outsz=outsize, ratio=ratio, N=N)
if dace.Config.get_bool('profiling'):
dace.timethis('filter', 'numpy', 0, regression, A, ratio)
filtered = regression(A, ratio)
if len(filtered) != outsize[0]:
print(
"Difference in number of filtered items: %d (DaCe) vs. %d (numpy)"
% (outsize[0], len(filtered)))
totalitems = min(outsize[0], N.get())
print('DaCe:', B[:totalitems].view(type=np.ndarray))
print('Regression:', filtered.view(type=np.ndarray))
exit(1)
# Sort the outputs
filtered = np.sort(filtered)
B[:outsize[0]] = np.sort(B[:outsize[0]])
if len(filtered) == 0:
print("==== Program end ====")
exit(0)
diff = np.linalg.norm(filtered - B[:outsize[0]]) / float(outsize[0])
print("Difference:", diff)
if diff > 1e-5:
totalitems = min(outsize[0], N.get())
print('DaCe:', B[:totalitems].view(type=np.ndarray))
print('Regression:', filtered.view(type=np.ndarray))
print("==== Program end ====")
assert diff <= 1e-5
def test_vector_reduce():
N.set(24)
# Initialize arrays: X, Y and Z
X = np.random.rand(N.get()).astype(dace.float32.type)
s = dace.scalar(dace.float32)
sdfg = vector_reduce.to_sdfg()
sdfg.apply_transformations(FPGATransformSDFG)
sdfg(x=X, s=s, N=N)
# Compute expected result
s_exp = 0.0
for x in X:
s_exp += x
diff = np.linalg.norm(s_exp - s) / N.get()
assert diff <= 1e-5
return sdfg
def run_dot(n, tile_first):
N.set(n)
A = dace.ndarray([N], dtype=dace.float32)
B = dace.ndarray([N], dtype=dace.float32)
out_AB = dace.scalar(dace.float32)
A[:] = np.random.rand(N.get()).astype(dace.float32.type)
B[:] = np.random.rand(N.get()).astype(dace.float32.type)
out_AB[0] = dace.float32(0)
sdfg = dot.to_sdfg()
if tile_first:
sdfg.apply_transformations(MapTiling)
sdfg.apply_transformations(FPGATransformSDFG)
else:
sdfg.apply_transformations(FPGATransformSDFG)
sdfg.apply_transformations(MapTiling)
sdfg(A=A, B=B, out=out_AB, N=N)
diff_ab = np.linalg.norm(np.dot(A, B) - out_AB) / float(N.get())
assert diff_ab <= 1e-5
return sdfg
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("N", type=int)
parser.add_argument("ratio", type=float)
parser.add_argument("-specialize",
default=False,
action="store_true",
help="Fix all symbols at compile time/in hardware")
args = vars(parser.parse_args())
N.set(args["N"])
A = dace.ndarray([N], dtype=dace.float32)
B = dace.ndarray([N], dtype=dace.float32)
outsize = dace.scalar(dace.uint32)
outsize[0] = 0
ratio = np.float32(args["ratio"])
print("Predicate-Based Filter. size={}, ratio={} ({}specialized)".format(
N.get(), ratio, "" if args["specialize"] else "not "))
A[:] = np.random.rand(N.get()).astype(dace.float32.type)
B[:] = dace.float32(0)
sdfg = make_sdfg(args["specialize"])
sdfg.draw_to_file()
if args["specialize"]:
sdfg.specialize(dict(N=N))
sdfg(A=A, B=B, outsize=outsize, ratio=ratio)
def product(i: _[0:N]):
a << A[i]
b << B[i]
o >> out(1, lambda x, y: x + y)
o = a * b
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("N", type=int, nargs="?", default=64)
args = vars(parser.parse_args())
A = dace.ndarray([N], dtype=dace.float32)
B = dace.ndarray([N], dtype=dace.float32)
out_AB = dace.scalar(dace.float64)
out_AA = dace.scalar(dace.float64)
N.set(args["N"])
print('Dot product %d' % (N.get()))
A[:] = np.random.rand(N.get()).astype(dace.float32.type)
B[:] = np.random.rand(N.get()).astype(dace.float32.type)
out_AB[0] = dace.float64(0)
out_AA[0] = dace.float64(0)
cdot = dace.compile(dot, A, B, out_AB)
cdot(A, B, out_AB)
# To allow reloading the SDFG code file with the same name
def dot(A: dace.float32[N], B: dace.float32[N], out: dace.float32[1]):
@dace.map
def product(i: _[0:N]):
a << A[i]
b << B[i]
o >> out(1, lambda x, y: x + y)
o = a * b
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("N", type=int, nargs="?", default=64)
args = vars(parser.parse_args())
N.set(args["N"])
A = dace.ndarray([N], dtype=dace.float32)
B = dace.ndarray([N], dtype=dace.float32)
out_AB = dace.scalar(dace.float32)
print('Dot product %d' % (N.get()))
A[:] = np.random.rand(N.get()).astype(dace.float32.type)
B[:] = np.random.rand(N.get()).astype(dace.float32.type)
out_AB[0] = dace.float32(0)
dot(A, B, out_AB)
diff_ab = np.linalg.norm(np.dot(A, B) - out_AB) / float(N.get())
print("Difference (A*B):", diff_ab)
exit(0 if (diff_ab <= 1e-5) else 1)
else:
W.set(args["W"])
num_stages = 2 * W.get() - 1
sdfg = make_sdfg(False)
sdfg.specialize()
N.set(args["N"])
sdfg.add_constants({"num_stages": num_stages})
ratio = dtype(args["ratio"])
print("Predicate-Based Filter. size={}, ratio={} ({}specialized)".format(
N.get(), ratio, "" if args["specialize"] else "not "))
A = dace.ndarray([N], dtype=dtype)
B = dace.ndarray([N], dtype=dtype)
outsize = dace.scalar(dace.uint32, allow_conflicts=True)
outsize[0] = 0
A[:] = np.random.rand(N.get()).astype(dtype.type)
B[:] = dtype(0)
sdfg.draw_to_file()
if args["specialize"]:
sdfg.specialize()
sdfg(A=A, B=B, outsize=outsize, ratio=ratio)
else:
sdfg(A=A, B=B, outsize=outsize, ratio=ratio, N=N)
if dace.Config.get_bool('profiling'):
dace.timethis('filter', 'numpy', 0, regression, A, ratio)
if __name__ == "__main__":
print("==== Program start ====")
parser = argparse.ArgumentParser()
parser.add_argument("N", type=int, nargs="?", default=24)
parser.add_argument("--compile-only",
default=False,
action="store_true",
dest="compile-only")
args = vars(parser.parse_args())
dace.config.Config.set("compiler", "intel_fpga", "mode", value="emulator")
N.set(args["N"])
print('Vectors addition %d' % (N.get()))
# Initialize arrays: X, Y and Z
X = np.random.rand(N.get()).astype(dace.float32.type)
s = dace.scalar(dace.float32)
vector_reduce(X, s)
#compute expected result
s_exp = 0.0
for x in X:
s_exp += x
print(s)
print(s_exp)
diff = np.linalg.norm(s_exp - s) / N.get()
print("Difference:", diff)
print("==== Program end ====")
exit(0 if diff <= 1e-5 else 1)
