[inductor] update numbytes_hint for NoneLayout to allow more fusions (#141766)

We found that [this commit](6eca0aee76) caused a ~6% performance drop in ViT INT8. This was due to changes to the `numbytes_hint` for `NoneLayout`. In this PR, we reverted the changes in `numbytes_hint` to allow more fusions.

```
class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.dense = torch.nn.Linear(768, 768)
        self.layernorm = torch.nn.LayerNorm(768, eps=1e-12)
    def forward(self, context_layer, hidden_states):
        attention_output = self.dense(context_layer)
        hidden_states = attention_output + hidden_states
        layer_output = self.layernorm(hidden_states)
        return layer_output
```
The generated code before (left) and after (right) this PR is as follows:
![image](https://github.com/user-attachments/assets/0ec65ae5-103e-4e2c-bf7c-e8bed24fc179)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141766
Approved by: https://github.com/leslie-fang-intel, https://github.com/jgong5, https://github.com/jansel

test/inductor/test_cpu_repro.py

@@ -4782,6 +4782,33 @@ class CPUReproTests(TestCase):
             code
         )
 
+    @config.patch(freezing=True)
+    def test_add_layernorm(self):
+        class Model(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.dense = torch.nn.Linear(768, 768)
+                self.layernorm = torch.nn.LayerNorm(768, eps=1e-12)
+
+            def forward(self, context_layer, hidden_states):
+                attention_output = self.dense(context_layer)
+                hidden_states = attention_output + hidden_states
+                layer_output = self.layernorm(hidden_states)
+                return layer_output
+
+        model = Model()
+        example_batch = (torch.rand(1, 197, 768), torch.rand(1, 197, 768))
+        from torch.testing._internal.common_quantization import (
+            _generate_qdq_quantized_model,
+        )
+
+        with torch.no_grad():
+            converted_model = _generate_qdq_quantized_model(model, example_batch)
+            torch.ao.quantization.move_exported_model_to_eval(converted_model)
+            metrics.reset()
+            torch.compile(converted_model)(*example_batch)
+            check_metrics_vec_kernel_count(3)
+
 
 if __name__ == "__main__":
     from torch._inductor.test_case import run_tests

torch/_inductor/graph.py

@@ -812,6 +812,16 @@ class GraphLowering(torch.fx.Interpreter):
     def get_dtype(self, buffer_name: str) -> torch.dtype:
         if buffer_name in self.constants:
             return self.constants[buffer_name].dtype
+        # For a mutation op we should return the dtype of the buffer being mutated
+        if (
+            hasattr(self.scheduler, "mutation_real_name")
+            and buffer_name in self.scheduler.mutation_real_name
+        ):
+            mutated_buf = self.scheduler.mutation_real_name[buffer_name]
+            if mutated_buf in self.name_to_buffer:
+                return self.name_to_buffer[mutated_buf].get_dtype()
+            if mutated_buf in self.graph_inputs:
+                return self.graph_inputs[mutated_buf].get_dtype()
         if buffer_name in self.name_to_buffer:
             return self.name_to_buffer[buffer_name].get_dtype()
         if buffer_name in self.graph_inputs: