[quant][docs] quantized model save/load instructions (#69789)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69789

Add details on how to save and load quantized models without hitting errors

Test Plan:
CI autogenerated docs

Imported from OSS

Reviewed By: jerryzh168

Differential Revision: D33030991

fbshipit-source-id: 8ec4610ae6d5bcbdd3c5e3bb725f2b06af960d52

docs/source/quantization.rst

@@ -816,6 +816,63 @@ An e2e example::
   # turn off quantization for conv2
   m.conv2.qconfig = None
 
+Saving and Loading Quantized models
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+When calling ``torch.load`` on a quantized model, if you see an error like::
+
+  AttributeError: 'LinearPackedParams' object has no attribute '_modules'
+
+This is because directly saving and loading a quantized model using ``torch.save`` and ``torch.load``
+is not supported. To save/load quantized models, the following ways can be used:
+
+1. Saving/Loading the quantized model state_dict
+
+An example::
+
+  class M(torch.nn.Module):
+      def __init__(self):
+          super().__init__()
+          self.linear = nn.Linear(5, 5)
+          self.relu = nn.ReLU()
+
+      def forward(self, x):
+          x = self.linear(x)
+          x = self.relu(x)
+          return x
+
+  m = M().eval()
+  prepare_orig = prepare_fx(m, {'' : default_qconfig})
+  prepare_orig(torch.rand(5, 5))
+  quantized_orig = convert_fx(prepare_orig)
+
+  # Save/load using state_dict
+  b = io.BytesIO()
+  torch.save(quantized_orig.state_dict(), b)
+
+  m2 = M().eval()
+  prepared = prepare_fx(m2, {'' : default_qconfig})
+  quantized = convert_fx(prepared)
+  b.seek(0)
+  quantized.load_state_dict(torch.load(b))
+
+2. Saving/Loading scripted quantized models using ``torch.jit.save`` and ``torch.jit.load``
+
+An example::
+
+  # Note: using the same model M from previous example
+  m = M().eval()
+  prepare_orig = prepare_fx(m, {'' : default_qconfig})
+  prepare_orig(torch.rand(5, 5))
+  quantized_orig = convert_fx(prepare_orig)
+
+  # save/load using scripted model
+  scripted = torch.jit.script(quantized_orig)
+  b = io.BytesIO()
+  torch.jit.save(scripted, b)
+  b.seek(0)
+  scripted_quantized = torch.jit.load(b)
+
 Numerical Debugging (prototype)
 -------------------------------