Update README.md (#649)

Author: jerryzh168

torchao/quantization/README.md

@@ -49,12 +49,14 @@ import pickle
 import torchao.quantization
 
 # After the first forward pass (when quantization was done)
+from torchao.quantization.autoquant import AUTOQUANT_CACHE
 with open("quantization-cache.pkl", "wb") as f:
-    pickle.dump(torchao.quantization.AUTOQUANT_CACHE)
+    pickle.dump(AUTOQUANT_CACHE)
 
 # On load
+from torchao.quantization.autoquant import AUTOQUANT_CACHE
 with open("quantization-cache.pkl", "rb") as f:
-    torchao.quantization.AUTOQUANT_CACHE.update(pickle.load(f))
+    AUTOQUANT_CACHE.update(pickle.load(f))
 ```
 ## Affine Quantization
 Affine quantization refers to the type of quantization that maps from floating point numbers to quantized numbers (typically integer) with an affine transformation, i.e.: `quantized_val = float_val / scale + zero_point` where `scale` and `zero_point` are quantization parameters for some granularity and based on some data.