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import torch
import torch.nn as nn
from math_model import QuantLinear
torch.manual_seed(0)
batch_size = 1
out_ch = 128
in_ch = 64
i = 2*torch.rand((batch_size,in_ch)) - 1.
l = nn.Linear(in_ch, out_ch, bias=True)
quant_params = {
'smoothquant_mul': torch.rand((in_ch,)),
'smoothquant_mul_shape': (1,in_ch),
'weight_scale': torch.max(torch.abs(l.weight), dim=1).values / 128.,
'weight_scale_shape': (out_ch,1),
'weight_zp': torch.clamp(torch.round((torch.mean((l.weight), dim=1)) * (128 / torch.max(torch.abs(l.weight), dim=1).values)), -128, 127),
'weight_zp_shape': (out_ch,1),
'weight_zp_dtype': 'torch.int8',
'input_scale': torch.max(torch.abs(i)) / 128.,
'input_scale_shape': tuple(),
'input_zp': torch.zeros((1,)),
'input_zp_shape': tuple(),
'input_zp_dtype': 'torch.int8',
}
print(quant_params)
ql = QuantLinear(in_ch, out_ch, quant_params)
ql.linear.load_state_dict(l.state_dict())
o_qdq = ql(i)
o_qop = ql(i, qop=True)
print(o_qdq.shape)
print(o_qop.shape)
print(o_qdq - o_qop)
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