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
def op_symbolic_kwargs(cls, module: QuantLinear):
linear_symbolic_kwargs = {
'input_scale': module.quant_input_scale(),
'input_zero_point': cls.quant_input_zero_point(module),
'int_weight': cls.int_weight(module).t(),
'weight_scale': module.quant_weight_scale(),
'weight_zero_point': cls.quant_weight_zero_point(module),
'output_scale': module.quant_output_scale(),
'output_zero_point': cls.quant_output_zero_point(module),
'output_dtype': cls.torch_8b_dtype(module.is_quant_output_signed),
'out_shape': cls.quant_output_shape(module)}
return linear_symbolic_kwargs
def op_symbolic_kwargs(cls, module: QuantLinear):
linear_symbolic_kwargs = {
'input_scale': module.quant_input_scale(),
'input_zero_point': cls.quant_input_zero_point(module),
'int_weight': cls.int_weight(module),
'weight_scale': module.quant_weight_scale(),
'weight_zero_point': cls.quant_weight_zero_point(module),
'output_scale': module.quant_output_scale(),
'output_zero_point': cls.quant_output_zero_point(module),
'out_shape': cls.quant_output_shape(module),
'in_features': module.in_features,
'out_features': module.out_features
}
return linear_symbolic_kwargs
def test_module_init_scale_impl_type_override(self):
mod = QuantLinear(
out_features=OUTPUT_FEATURES,
in_features=INPUT_FEATURES,
bias=True, weight_scaling_impl_type='HE')
assert mod.quant_weight_scale()