def verify(target="llvm", algorithm=nnpack.ConvolutionAlgorithm.AUTO, with_bias=True):
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)
output = nnpack.convolution_inference(
data,
kernel,
bias if with_bias else None,
[PAD, PAD, PAD, PAD],
[STRIDE, STRIDE],
algorithm=algorithm,
)
s = te.create_schedule(output.op)
f = tvm.build(s, [data, kernel, bias, output], target)
na = np.random.uniform(size=dshape).astype(data.dtype)
nb = np.random.uniform(size=kshape).astype(kernel.dtype)
nc = np.zeros(bshape, dtype=bias.dtype)
ta = tvm.nd.array(na, ctx)
tb = tvm.nd.array(nb, ctx)
tc = tvm.nd.array(nc, ctx)
td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx)
f(ta, tb, tc, td)
nd = np_conv(np.reshape(na, (BATCH, IC, IH, IW)), nb, PAD, STRIDE) + nc.reshape(
1, bshape[0], 1, 1
)
tvm.testing.assert_allclose(td.asnumpy(), nd.reshape(BATCH, IC, IH, IW), rtol=1e-5)
def verify(target="llvm",
algorithm=nnpack.ConvolutionAlgorithm.AUTO,
with_bias=True):
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)
output = nnpack.convolution_inference(
data, kernel, bias if with_bias else None,
[PAD, PAD, PAD, PAD], [STRIDE, STRIDE],
algorithm=algorithm)
s = tvm.create_schedule(output.op)
f = tvm.build(s, [data, kernel, bias, output], target)
na = np.random.uniform(size=dshape).astype(data.dtype)
nb = np.random.uniform(size=kshape).astype(kernel.dtype)
nc = np.zeros(bshape, dtype=bias.dtype)
ta = tvm.nd.array(na, ctx)
tb = tvm.nd.array(nb, ctx)
tc = tvm.nd.array(nc, ctx)
td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx)
f(ta, tb, tc, td)
nd = np_conv(np.reshape(na, (BATCH, IC, IH, IW)), nb, PAD, STRIDE) + nc.reshape(1, bshape[0], 1, 1)
tvm.testing.assert_allclose(
td.asnumpy(), nd.reshape(BATCH, IC, IH, IW), rtol=1e-5)
def test_convolution_inference():
BATCH = 32
IH = 48
IW = 48
IC = 16
OC = 16
K = 3
PAD = 1
STRIDE = 1
OH = (IH + 2 * PAD - K) + 1
OW = (IW + 2 * PAD - K) + 1
dshape = (IC, IH, IW)
kshape = (OC, IC, K, K)
bshape = (OC, )
oshape = (OC, OH, OW)
data = tvm.placeholder(dshape, name='data')
kernel = tvm.placeholder(kshape, name='kernel')
bias = tvm.placeholder(bshape, name='bias')
output = nnpack.convolution_inference(data, kernel, bias,
[PAD, PAD, PAD, PAD],
[STRIDE, STRIDE])
s = tvm.create_schedule(output.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
ctx = tvm.cpu(0)
f = tvm.build(s, [data, kernel, bias, output], target)
na = np.random.uniform(size=dshape).astype(data.dtype)
nb = np.random.uniform(size=kshape).astype(kernel.dtype)
nc = np.zeros(bshape, dtype=bias.dtype)
ta = tvm.nd.array(na, ctx)
tb = tvm.nd.array(nb, ctx)
tc = tvm.nd.array(nc, ctx)
td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx)
f(ta, tb, tc, td)
nd = np_conv(np.reshape(na, (1, IC, IH, IW)), nb, PAD, STRIDE)
np.testing.assert_allclose(td.asnumpy(),
nd.reshape(IC, IH, IW),
rtol=1e-5)
verify()
def test_convolution_inference():
BATCH = 32
IH = 48
IW = 48
IC = 16
OC = 16
K = 3
PAD = 1
STRIDE = 1
OH = (IH + 2*PAD - K) + 1
OW = (IW + 2*PAD - K) + 1
dshape = (IC, IH, IW)
kshape = (OC, IC, K, K)
bshape = (OC, )
oshape = (OC, OH, OW)
data = tvm.placeholder(dshape, name='data')
kernel = tvm.placeholder(kshape, name='kernel')
bias = tvm.placeholder(bshape, name='bias')
output = nnpack.convolution_inference(data, kernel, bias,
[PAD, PAD, PAD, PAD], [STRIDE, STRIDE])
s = tvm.create_schedule(output.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 avalable")
return
ctx = tvm.cpu(0)
f = tvm.build(s, [data, kernel, bias, output], target)
na = np.random.uniform(size=dshape).astype(data.dtype)
nb = np.random.uniform(size=kshape).astype(kernel.dtype)
nc = np.zeros(bshape, dtype=bias.dtype)
ta = tvm.nd.array(na, ctx)
tb = tvm.nd.array(nb, ctx)
tc = tvm.nd.array(nc, ctx)
td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx)
f(ta, tb, tc, td)
nd = np_conv(np.reshape(na, (1, IC, IH, IW)), nb, PAD, STRIDE)
np.testing.assert_allclose(
td.asnumpy(), nd.reshape(IC, IH, IW), rtol=1e-5)
verify()
