def patch_model_output_dequant(model):
def patched_forward(self, input):
out = self._original_forward(input)
out = self.output_dequant(out)
return out
model.add_module('output_dequant', nnq.DeQuantize())
model._original_forward = model.forward
# https://stackoverflow.com/questions/972/adding-a-method-to-an-existing-object-instance#comment66379065_2982
model.forward = patched_forward.__get__(model)
def init(self, C, M, N, dtype, mode):
assert(mode in ('Q', 'D'))
self.input = torch.rand(C, M, N)
self.dtype = dtype
self.op = nnq.Quantize(scale=1.0, zero_point=0, dtype=dtype)
self.set_module_name('QuantizePerTensor')
if mode == 'D':
self.input = self.op(self.input)
self.op = nnq.DeQuantize()
self.set_module_name('DequantizePerTensor')
def test_quant_dequant_api(self):
r = torch.tensor([[1., -1.], [1., -1.]], dtype=torch.float)
scale, zero_point, dtype = 1.0, 2, torch.qint8
# testing Quantize API
qr = torch.quantize_linear(r, scale, zero_point, dtype)
quant_m = nnq.Quantize(scale, zero_point, dtype)
qr2 = quant_m(r)
self.assertEqual(qr, qr2)
# testing Dequantize API
rqr = qr.dequantize()
dequant_m = nnq.DeQuantize()
rqr2 = dequant_m(qr2)
self.assertEqual(rqr, rqr2)
def __init__(self, q_module, float_module, Logger):
super(Shadow, self).__init__()
self.orig_module = q_module
self.shadow_module = float_module
self.dequant = nnq.DeQuantize()
self.logger = Logger()
