Merge pull request pytorch#1088 from pytorch/fix-quant-blogpost

Svetlana Karslioglu · web-flow · commit 5a6e89e86f2f · 2022-07-28T07:30:38.000-07:00
Fix bug in PTQ code example
diff --git a/_posts/2022-2-8-quantization-in-practice.md b/_posts/2022-2-8-quantization-in-practice.md
@@ -257,17 +257,20 @@ PTQ also pre-quantizes model weights but instead of calibrating activations on-t
 
 import torch
 from torch import nn
+import copy
 
 backend = "fbgemm"  # running on a x86 CPU. Use "qnnpack" if running on ARM.
 
-m = nn.Sequential(
+model = nn.Sequential(
      nn.Conv2d(2,64,3),
      nn.ReLU(),
      nn.Conv2d(64, 128, 3),
      nn.ReLU()
 )
 
 ## EAGER MODE
+m = copy.deepcopy(model)
+m.eval()
 """Fuse
 - Inplace fusion replaces the first module in the sequence with the fused module, and the rest with identity modules
 """
@@ -300,10 +303,11 @@ print(m[[1]].weight().element_size()) # 1 byte instead of 4 bytes for FP32
 
 ## FX GRAPH
 from torch.quantization import quantize_fx
+m = copy.deepcopy(model)
 m.eval()
 qconfig_dict = {"": torch.quantization.get_default_qconfig(backend)}
 # Prepare
-model_prepared = quantize_fx.prepare_fx(model_to_quantize, qconfig_dict)
+model_prepared = quantize_fx.prepare_fx(m, qconfig_dict)
 # Calibrate - Use representative (validation) data.
 with torch.inference_mode():
   for _ in range(10):
