[Inductor][FP8] Validate exhaustive autotuning for FP8 Inductor templates (pytorch#161442)

Summary:
X-link: meta-pytorch/tritonbench#355


Validate exhaustive autotuning for FP8 Inductor templates: scaled MM templates require `block_k >= 32`. Before, exhaustive autotuning defaulted to a limited set of autotuning configs, as limitations for exhaustively autotuning on FP8 shapes had not been tested.

Test Plan:
```
CUDA_VISIBLE_DEVICES=0 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM_SEARCH_SPACE=DEFAULT buck2 run mode/{opt,inplace} pytorch/t
ritonbench:run -- --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --input-loader=/home/jananisriram/personal/exhaustive_autotune_rowwise_persistent_tma/json_fi
les/rowwise_ptma_0.json --output="/home/jananisriram/personal/exhaustive_autotune_rowwise_persistent_tma/autotune/gpu0_bench.csv" --atol=1e-2 --rtol=0.5 2>&1 | tee ~/personal/exhaustive_
autotune_rowwise_persistent_tma/autotune/gpu0.log
```
autotunes on the maximum configs available, rather than the defaults, and skips configs not compatible with TMA.

Rollback Plan:

Differential Revision: D80958642

Author: jananisriram

torch/_inductor/template_heuristics.py

@@ -1522,9 +1522,9 @@ class ScaledTMAConfigMixin(ScaledMMConfigMixin):
 
     def _filter_configs(self, configs: list[BaseConfig]) -> list[BaseConfig]:
         """
-        TMA specific filtering, as num_warps=2 not safe for TMA
+        TMA specific filtering
         """
-        configs = [c for c in configs if c.num_warps != 2]
+        configs = [c for c in configs if c.num_warps != 2 and getattr(c, "block_k", None) is not None and c.block_k >= 32]
         return super()._filter_configs(configs)
 
     def get_template_configs(
@@ -1571,7 +1571,6 @@ def __init__(self) -> None:
         self.mm_configs = self.extra_mm_configs
         self.exhaustive_configs = self.extra_mm_configs
 
-
 # TODO(coconutruben): replace with template.name once templates are importable
 @register_template_heuristic(
     "mm_persistent_tma", "cuda", register=torch.version.hip is None
@@ -1596,11 +1595,10 @@ def __init__(self) -> None:
         super().__init__()
         # Override mm_configs to use scaled_mm_configs
         self.mm_configs = self.scaled_mm_configs
-        # NOTE: overriding exhaustive configs here to be the same as mm_configs
-        # as we haven't validated exhaustive support here yet
-        # TODO(coconutruben): remove this once we have validated exhaustive support
-        # for scaled_mm
-        self.exhaustive_configs = self.scaled_mm_configs
+
+    def _filter_configs(self, configs: list[BaseConfig]) -> list[BaseConfig]:
+        configs = [c for c in configs if getattr(c, "block_k", None) is not None and c.block_k >= 32]
+        return super()._filter_configs(configs)
 
 
 # TODO(coconutruben): replace with template.name once templates are importable
@@ -1614,11 +1612,6 @@ def __init__(self) -> None:
         super().__init__()
         # Override mm_configs to use scaled_persistent_mm_configs for TMA
         self.mm_configs = self.scaled_persistent_mm_configs
-        # NOTE: overriding exhaustive configs here to be the same as mm_configs
-        # as we haven't validated exhaustive support here yet
-        # TODO(coconutruben): remove this once we have validated exhaustive support
-        # for scaled_mm
-        self.exhaustive_configs = self.scaled_persistent_mm_configs
 
 
 # TODO(coconutruben): replace with template.name once templates are importable