test_quant_conv2d.py

import torch
import torch.nn as nn
from math_model import QuantConv2d

torch.manual_seed(0)

batch_size = 1
out_ch = 128
in_ch = 64
k = 3
h = 5
w = 5

i = 2*torch.rand((batch_size,in_ch,h,w)) - 1.
l = nn.Conv2d(in_ch, out_ch, k, bias=True)

quant_params = {
    'smoothquant_mul': torch.rand((in_ch,)),
    'smoothquant_mul_shape': (1,in_ch,1,1),
    'weight_scale': torch.rand((out_ch,)),
    'weight_scale': torch.max(torch.abs(torch.flatten(l.weight, start_dim=1)), dim=1).values / 128.,
    'weight_scale_shape': (out_ch,1,1,1),
    'weight_zp': torch.clamp(torch.round((torch.mean((l.weight), dim=(1,2,3))) * (128 / torch.max(torch.abs(torch.flatten(l.weight, start_dim=1)), dim=1).values)), -128, 127),
    'weight_zp_shape': (out_ch,1,1,1),
    '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 = QuantConv2d(in_ch, out_ch, k, quant_params)
ql.conv2d.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)