def verify(target="llvm",
algorithm=nnpack.ConvolutionAlgorithm.AUTO,
with_bias=True):
if not tvm.module.enabled(target):
pytest.skip("%s is not enabled..." % target)
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)
transformed_kernel = nnpack.convolution_inference_weight_transform(
kernel, algorithm=algorithm)
output = nnpack.convolution_inference_without_weight_transform(
data, transformed_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.random.uniform(size=bshape).astype(bias.dtype) if with_bias else 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)
transformed_kernel = nnpack.convolution_inference_weight_transform(
kernel, algorithm=algorithm)
output = nnpack.convolution_inference_without_weight_transform(
data, transformed_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.random.uniform(size=bshape).astype(bias.dtype) if with_bias else 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 conv2d_winograd_nnpack_weight_transform(kernel, convolution_algorithm, out_dtype):
"""Weight transformation for winograd
Parameters
----------
kernel: Tensor
The raw kernel tensor with layout "NCHW". Only 3x3 kernel is supported for now.
convolution_algorithm: int
The convolution algorithm for Winograd NNPACK.
Returns
-------
output : tvm.Tensor
4-D with shape [alpha, alpha, CO, CI]
"""
from tvm.contrib import nnpack
return nnpack.convolution_inference_weight_transform(
kernel, algorithm=convolution_algorithm, dtype=out_dtype)
