def test_fully_connected_inference():
n = 1024
l = 128
m = 235
bias = tvm.var('bias', dtype=tvm.float32)
A = tvm.placeholder((l, ), name='A')
B = tvm.placeholder((m, l), name='B')
C = nnpack.fully_connected_inference(A, B)
D = tvm.compute(C.shape, lambda i: C[i] + bias, name="D")
s = tvm.create_schedule(D.op)
def verify(target="llvm"):
if not tvm.module.enabled(target):
print("skip because %s is not enabled..." % target)
return
if not tvm.get_global_func(
"tvm.contrib.nnpack.fully_connected_inference", True):
print("skip because extern function is not available")
return
if not nnpack.is_available():
return
ctx = tvm.cpu(0)
f = tvm.build(s, [A, B, D, bias], target)
a = tvm.nd.array(np.random.uniform(size=(l)).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=(m, l)).astype(B.dtype), ctx)
d = tvm.nd.array(np.zeros((m, ), dtype=D.dtype), ctx)
bb = 10.0
f(a, b, d, bb)
tvm.testing.assert_allclose(d.asnumpy(),
np.dot(a.asnumpy(),
b.asnumpy().T) + bb,
rtol=1e-5)
verify()
def test_fully_connected_inference():
n = 1024
l = 128
m = 235
bias = te.var("bias", dtype="float32")
A = te.placeholder((l,), name="A")
B = te.placeholder((m, l), name="B")
C = nnpack.fully_connected_inference(A, B)
D = te.compute(C.shape, lambda i: C[i] + bias, name="D")
s = te.create_schedule(D.op)
def verify(target="llvm"):
if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_inference", True):
pytest.skip("extern function is not available")
if not nnpack.is_available():
pytest.skip("nnpack is not available")
ctx = tvm.cpu(0)
f = tvm.build(s, [A, B, D, bias], target)
a = tvm.nd.array(np.random.uniform(size=(l)).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=(m, l)).astype(B.dtype), ctx)
d = tvm.nd.array(np.zeros((m,), dtype=D.dtype), ctx)
bb = 10.0
f(a, b, d, bb)
tvm.testing.assert_allclose(d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy().T) + bb, rtol=1e-5)
verify()
def test_fully_connected_inference():
n = 1024
l = 128
m = 235
bias = tvm.var('bias', dtype=tvm.float32)
A = tvm.placeholder((l, ), name='A')
B = tvm.placeholder((m, l), name='B')
C = nnpack.fully_connected_inference(A, B)
D = tvm.compute(C.shape, lambda i: C[i] + bias, name="D")
s = tvm.create_schedule(D.op)
def verify(target="llvm"):
if not tvm.module.enabled(target):
print("skip because %s is not enabled..." % target)
return
if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_inference", True):
print("skip because extern function is not available")
return
if not nnpack.is_available():
return
ctx = tvm.cpu(0)
f = tvm.build(s, [A, B, D, bias], target)
a = tvm.nd.array(np.random.uniform(size=(l)).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=(m, l)).astype(B.dtype), ctx)
d = tvm.nd.array(np.zeros((m, ), dtype=D.dtype), ctx)
bb = 10.0
f(a, b, d, bb)
tvm.testing.assert_allclose(
d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy().T) + bb, rtol=1e-5)
verify()
