conv-bn folding should be used for CNN-based Vision Models when AMP is used with oneDNN Graph (#2535)

* Update tuning guide to reflect folding conv-bn when oneDNN Graph is used with AMP

By default, conv-bn folding isn't done with CNN based models when AMP is used with oneDNN Graph. `torch.fx.experimental.optimize.fuse` should be used for such models
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Co-authored-by: Svetlana Karslioglu <svekars@meta.com>

Author: sanchitintel

recipes_source/recipes/tuning_guide.py

@@ -295,6 +295,10 @@ def fused_gelu(x):
 torch._C._jit_set_autocast_mode(False)
 
 with torch.no_grad(), torch.cpu.amp.autocast(cache_enabled=False, dtype=torch.bfloat16):
+    # Conv-BatchNorm folding for CNN-based Vision Models should be done with ``torch.fx.experimental.optimization.fuse`` when AMP is used
+    import torch.fx.experimental.optimization as optimization
+    # Please note that optimization.fuse need not be called when AMP is not used
+    model = optimization.fuse(model)
     model = torch.jit.trace(model, (example_input))
     model = torch.jit.freeze(model)
     # a couple of warm-up runs