def test_parameter_from_stats_update():
config.IGNORE_MISSING_KEYS = True
linear = nn.Linear(10, 5, bias=False)
q_linear = QuantLinear(10,
5,
bias=False,
weight_quant_type='binary',
weight_scaling_impl_type='parameter_from_stats')
l_max = linear.weight.abs().max()
old_scale = q_linear.quant_weight_scale()
old_ql_max = q_linear.weight.abs().max()
q_linear.load_state_dict(linear.state_dict())
new_scale = q_linear.quant_weight_scale()
new_ql_max = q_linear.weight.abs().max()
assert old_scale == old_ql_max
assert new_scale == l_max
assert new_scale == new_ql_max
def test_parameter_from_stats_state_dict():
q_linear1 = QuantLinear(10,
5,
bias=False,
weight_quant_type='binary',
weight_scaling_impl_type='parameter',
weight_scaling_init=0.1)
q_linear2 = QuantLinear(10,
5,
bias=False,
weight_quant_type='binary',
weight_scaling_impl_type='parameter',
weight_scaling_init=0.001)
q_linear1_old_scale = q_linear1.quant_weight_scale()
q_linear1.load_state_dict(q_linear2.state_dict())
q_linear1_new_scale = q_linear1.quant_weight_scale()
q_linear2_scale = q_linear2.quant_weight_scale()
assert q_linear1_old_scale != q_linear2_scale
assert q_linear1_old_scale != q_linear1_new_scale
assert q_linear1_new_scale == q_linear2_scale
