def __init__(self, quantized=False):
super(ModForWrapping, self).__init__()
self.qconfig = default_qconfig
if quantized:
self.mycat = nnq.QFunctional()
self.myadd = nnq.QFunctional()
else:
self.mycat = nnq.FloatFunctional()
self.myadd = nnq.FloatFunctional()
self.mycat.observer = DummyObserver()
self.myadd.observer = DummyObserver()
def init(self, M, N, K, L, dim, contig, dtype):
f_input = (torch.rand(M, N, K) - 0.5) * 256
self.qf = nnq.QFunctional()
scale = 1.0
zero_point = 0
self.qf.scale = scale
self.qf.zero_point = zero_point
assert(contig in ('none', 'one', 'all'))
q_input = torch.quantize_per_tensor(f_input, scale, zero_point, dtype)
permute_dims = tuple(range(q_input.ndim - 1, -1, -1))
q_input_non_contig = q_input.permute(permute_dims).contiguous()
q_input_non_contig = q_input_non_contig.permute(permute_dims)
if contig == 'all':
self.input = (q_input, q_input)
elif contig == 'one':
self.input = (q_input, q_input_non_contig)
elif contig == 'none':
self.input = (q_input_non_contig, q_input_non_contig)
self.inputs = {
"input": self.input,
"dim": dim
}
self.set_module_name('qcat')
def __init__(self):
super(QFunctionalWrapper, self).__init__()
self.qfunc = nnq.QFunctional()