Enable epilogue fusion benchmarking internally (#125455)

Differential Revision: [D56920738](https://our.internmc.facebook.com/intern/diff/D56920738)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125455
Approved by: https://github.com/Chillee

test/inductor/test_benchmark_fusion.py

@@ -203,7 +203,7 @@ if HAS_CUDA and not TEST_WITH_ASAN:
                     {
                         "benchmark_kernel": True,
                         "benchmark_fusion": True,
-                        "benchmark_multi_templates": True,
+                        "benchmark_epilogue_fusion": True,
                     }
                 )
             )
@@ -231,7 +231,7 @@ if HAS_CUDA and not TEST_WITH_ASAN:
 
             torch._dynamo.reset()
             with unittest.mock.patch.object(
-                torch._inductor.config, "benchmark_multi_templates", False
+                torch._inductor.config, "benchmark_epilogue_fusion", False
             ):
                 foo_c = torch.compile(mode="max-autotune-no-cudagraphs")(foo)
                 with torch.no_grad():

torch/_inductor/config.py

@@ -318,15 +318,13 @@ debug_fusion = os.environ.get("TORCHINDUCTOR_DEBUG_FUSION") == "1"
 benchmark_fusion = os.environ.get("TORCHINDUCTOR_BENCHMARK_FUSION") == "1"
 enabled_metric_tables = os.environ.get("TORCHINDUCTOR_ENABLED_METRIC_TABLES", "")
 
-benchmark_multi_templates = (
-    os.environ.get(
-        "TORCHINDUCTOR_BENCHMARK_MULTI_TEMPLATES", "0" if is_fbcode() else "1"
-    )
-    == "1"
+# For Triton Templates, select fastest of best template + epilogue vs best template + separate epilogue kernel
+benchmark_epilogue_fusion = (
+    os.environ.get("TORCHINDUCTOR_BENCHMARK_EPILOGUE_FUSION", "1") == "1"
 )
 
 # Take how many of the top triton kernels to benchmark epilogue
-max_epilogue_benchmarked_choices = 3
+max_epilogue_benchmarked_choices = 1
 
 # how many nodes to allow into a single fusion
 max_fusion_size = 64

torch/_inductor/select_algorithm.py

@@ -1499,7 +1499,7 @@ def autotune_select_algorithm(*args, **kwargs):
     if "return_multi_template" not in kwargs:
         kwargs[
             "return_multi_template"
-        ] = torch._inductor.config.benchmark_multi_templates
+        ] = torch._inductor.config.benchmark_epilogue_fusion
 
     return _ALGORITHM_SELECTOR_CACHE(*args, **kwargs)