Re-sync with internal repository (#2271)

Add torchao_backend.py to pt2 benchmark runner

userbenchmark/dynamo/dynamobench/torchao_backend.py

@@ -0,0 +1,54 @@
+from typing import Any, Callable
+
+import torch
+
+
+def setup_baseline():
+    torch._dynamo.epilogue_fusion = False
+    torch._dynamo.config.automatic_dynamic_shapes = False
+    torch._dynamo.config.force_parameter_static_shapes = False
+    torch._dynamo.config.cache_size_limit = 10000
+    torch._inductor.config.force_fuse_int_mm_with_mul = True
+    torch._inductor.config.use_mixed_mm = True
+
+
+def torchao_optimize_ctx(quantization: str):
+    import torchao
+    from torchao.quantization import (
+        change_linear_weights_to_int4_woqtensors,
+        change_linear_weights_to_int8_dqtensors,
+        change_linear_weights_to_int8_woqtensors,
+    )
+
+    def inner(model_iter_fn: Callable):
+        def _torchao_apply(module: torch.nn.Module, example_inputs: Any):
+            if getattr(module, "_quantized", None) is None:
+                if quantization == "int8dynamic":
+                    change_linear_weights_to_int8_dqtensors(module)
+                elif quantization == "int8weightonly":
+                    change_linear_weights_to_int8_woqtensors(module)
+                elif quantization == "int4weightonly":
+                    change_linear_weights_to_int4_woqtensors(module)
+                elif quantization == "autoquant":
+                    torchao.autoquant(module, error_on_unseen=False)
+                    if isinstance(example_inputs, dict):
+                        module(**example_inputs)
+                    else:
+                        module(*example_inputs)
+                    from torchao.quantization.autoquant import AUTOQUANT_CACHE
+
+                    assert (
+                        len(AUTOQUANT_CACHE) > 0
+                    ), f"Err: found no autoquantizable layers in model {type(module)}, stopping autoquantization"
+                elif quantization == "noquant":
+                    pass
+                else:
+                    raise AssertionError(
+                        f"Unsupposed quantization mode {quantization}."
+                    )
+                setattr(module, "_quantized", True)  # noqa: B010
+            model_iter_fn(module, example_inputs)
+
+        return _torchao_apply
+
+    return inner