|
|
import torch |
|
|
import torch.nn as nn |
|
|
import torch.nn.functional as F |
|
|
import matplotlib.pyplot as plt |
|
|
|
|
|
|
|
|
COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], |
|
|
[0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]] |
|
|
|
|
|
def plot_results(model, pil_img, results): |
|
|
plt.figure(figsize=(16,10)) |
|
|
plt.imshow(pil_img) |
|
|
ax = plt.gca() |
|
|
scores, labels, boxes = results["scores"], results["labels"], results["boxes"] |
|
|
colors = COLORS * 100 |
|
|
for score, label, (xmin, ymin, xmax, ymax),c in zip(scores.tolist(), labels.tolist(), boxes.tolist(), colors): |
|
|
ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, |
|
|
fill=False, color=c, linewidth=3)) |
|
|
text = f'{model.config.id2label[label]}: {score:0.2f}' |
|
|
ax.text(xmin, ymin, text, fontsize=15, |
|
|
bbox=dict(facecolor='yellow', alpha=0.5)) |
|
|
plt.axis('off') |
|
|
plt.show() |
|
|
|
|
|
|
|
|
def w8_a16_forward(weight, input, scales, bias=None): |
|
|
|
|
|
casted_weights = weight.to(input.dtype) |
|
|
output = F.linear(input, casted_weights) * scales |
|
|
|
|
|
if bias is not None: |
|
|
output = output + bias |
|
|
|
|
|
return output |
|
|
|
|
|
class W8A16LinearLayer(nn.Module): |
|
|
def __init__(self, in_features, out_features, |
|
|
bias=True, dtype=torch.float32): |
|
|
super().__init__() |
|
|
|
|
|
|
|
|
self.register_buffer( |
|
|
"int8_weights", |
|
|
torch.randint( |
|
|
-128, 127, (out_features, in_features), dtype=torch.int8 |
|
|
) |
|
|
) |
|
|
|
|
|
self.register_buffer("scales", |
|
|
torch.randn((out_features), dtype=dtype)) |
|
|
|
|
|
if bias: |
|
|
self.register_buffer("bias", |
|
|
torch.randn((1, out_features), |
|
|
dtype=dtype)) |
|
|
|
|
|
else: |
|
|
self.bias = None |
|
|
|
|
|
def quantize(self, weights): |
|
|
w_fp32 = weights.clone().to(torch.float32) |
|
|
|
|
|
scales = w_fp32.abs().max(dim=-1).values / 127 |
|
|
scales = scales.to(weights.dtype) |
|
|
|
|
|
int8_weights = torch.round(weights |
|
|
/scales.unsqueeze(1)).to(torch.int8) |
|
|
|
|
|
self.int8_weights = int8_weights |
|
|
self.scales = scales |
|
|
|
|
|
def forward(self, input): |
|
|
return w8_a16_forward(self.int8_weights, |
|
|
input, self.scales, self.bias) |
|
|
|
|
|
|
|
|
def replace_linear_with_target_and_quantize(module, |
|
|
target_class, module_name_to_exclude): |
|
|
for name, child in module.named_children(): |
|
|
if isinstance(child, nn.Linear) and not \ |
|
|
any([x == name for x in module_name_to_exclude]): |
|
|
old_bias = child.bias |
|
|
old_weight = child.weight |
|
|
|
|
|
new_module = target_class(child.in_features, |
|
|
child.out_features, |
|
|
old_bias is not None, |
|
|
child.weight.dtype) |
|
|
setattr(module, name, new_module) |
|
|
|
|
|
getattr(module, name).quantize(old_weight) |
|
|
|
|
|
if old_bias is not None: |
|
|
getattr(module, name).bias = old_bias |
|
|
else: |
|
|
|
|
|
replace_linear_with_target_and_quantize(child, |
|
|
target_class, module_name_to_exclude) |
|
|
|
