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data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_inductor/autoheuristic/artifacts/_MMRankingA100.py

@@ -0,0 +1,296 @@
+# flake8: noqa: B950
+# fmt: off
+# This file was generated by AutoHeuristic. Do not modify it manually!
+# To regenerate this file, take a look at the steps in the README.md file inside torchgen/_autoheuristic/mm/
+from typing import List, Optional, Tuple
+
+from torch._inductor.autoheuristic.autoheuristic_utils import (
+    AHContext,
+    AHMetadata,
+    Choice,
+)
+from torch._inductor.autoheuristic.learnedheuristic_interface import (
+    LearnedHeuristicDecision,
+)
+
+
+class MMRankingA100(LearnedHeuristicDecision):
+
+    def __init__(self) -> None:
+        self.choices: List[Choice] = []
+        self.fill_choices()
+
+    def check_precondition(self, metadata: AHMetadata, context: AHContext,) -> bool:
+        return (
+            metadata.name == self.get_name()
+            and metadata.shared_memory == 166912
+            and str(metadata.device_capa) == "(8, 0)"
+        )
+
+    def get_confidence_threshold(self) -> float:
+        return 0.0
+
+    def get_choice(self, idx: int) -> Optional[str]:
+        if idx < len(self.choices):
+            return self.choices[idx]
+        return None
+
+    def fill_choices(self) -> None:
+        self.choices.append('extern_mm')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=128_BLOCK-N=16_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=128_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=128_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=1')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=1_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=3_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=4_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=64_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=1')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=16_numstages=1_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=32_numstages=1_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=32_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=64_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=16_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=16_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=64_numstages=5_numwarps=4')
+
+    def get_name(self) -> str:
+        return 'mm'
+
+    def get_best_choices(self, context: AHContext) -> Optional[List[tuple[float, int]]]:
+        if context.get_value('arith_intensity') <= 52.6245059967041:
+            if context.get_value('n') <= 34.0:
+                if context.get_value('n') <= 18.0:
+                    if context.get_value('k*n') <= 312.0:
+                        return [(0.093, 12), (0.081, 16), (0.081, 148), (0.070, 10), (0.070, 17), (0.070, 149), (0.070, 151), (0.070, 150), (0.070, 14), (0.058, 11), (0.058, 15), (0.058, 13), (0.058, 122), (0.047, 121), (0.035, 123), (0.012, 92)]
+                    else:
+                        if context.get_value('k') <= 40.0:
+                            return [(0.083, 42), (0.083, 46), (0.083, 44), (0.083, 40), (0.083, 128), (0.067, 45), (0.067, 43), (0.067, 41), (0.067, 169), (0.067, 171), (0.067, 168), (0.067, 129), (0.067, 170), (0.033, 103), (0.017, 121)]
+                        else:
+                            return [(0.112, 137), (0.104, 136), (0.101, 0), (0.081, 1), (0.073, 135), (0.069, 67), (0.066, 187), (0.058, 41), (0.050, 71), (0.046, 68), (0.046, 70), (0.031, 44), (0.027, 43), (0.027, 170), (0.019, 189), (0.019, 188), (0.015, 169), (0.015, 171), (0.012, 115), (0.012, 168), (0.012, 69), (0.004, 103)]
+                else:
+                    if context.get_value('mat1_stride_0') <= 20.0:
+                        return [(0.069, 0), (0.059, 157), (0.059, 22), (0.059, 153), (0.059, 155), (0.059, 25), (0.059, 23), (0.059, 19), (0.044, 21), (0.044, 18), (0.044, 152), (0.044, 158), (0.044, 154), (0.044, 156), (0.044, 20), (0.044, 124), (0.044, 24), (0.030, 125), (0.029, 126), (0.015, 97), (0.015, 95), (0.015, 96), (0.010, 2), (0.010, 75)]
+                    else:
+                        if context.get_value('k') <= 68.0:
+                            return [(0.087, 72), (0.087, 74), (0.087, 73), (0.086, 76), (0.077, 75), (0.067, 192), (0.058, 190), (0.048, 47), (0.048, 193), (0.048, 49), (0.048, 51), (0.048, 191), (0.038, 53), (0.019, 133), (0.019, 50), (0.019, 175), (0.019, 172), (0.019, 48), (0.019, 174), (0.010, 173), (0.010, 177), (0.010, 52), (0.010, 54), (0.010, 178), (0.010, 176)]
+                        else:
+                            return [(0.154, 52), (0.154, 72), (0.102, 75), (0.087, 49), (0.087, 73), (0.086, 51), (0.057, 176), (0.045, 2), (0.038, 191), (0.038, 178), (0.038, 190), (0.029, 173), (0.029, 76), (0.026, 138), (0.013, 139), (0.013, 140), (0.003, 0)]
+            else:
+                if context.get_value('k') <= 35.0:
+                    if context.get_value('k') <= 18.0:
+                        if context.get_value('m*n') <= 19505152.0:
+                            return [(0.151, 159), (0.140, 160), (0.129, 164), (0.055, 127), (0.051, 29), (0.044, 161), (0.044, 147), (0.040, 146), (0.040, 31), (0.037, 145), (0.026, 28), (0.022, 90), (0.022, 93), (0.022, 94), (0.022, 100), (0.022, 125), (0.022, 158), (0.022, 157), (0.011, 87), (0.011, 88), (0.011, 89), (0.011, 91), (0.011, 95), (0.011, 96), (0.011, 98), (0.011, 99)]
+                        else:
+                            return [(0.069, 7), (0.069, 5), (0.067, 147), (0.066, 8), (0.061, 145), (0.058, 146), (0.052, 124), (0.049, 29), (0.049, 159), (0.046, 31), (0.043, 157), (0.041, 9), (0.041, 4), (0.040, 6), (0.035, 164), (0.035, 160), (0.026, 158), (0.017, 125), (0.017, 28), (0.017, 32), (0.017, 162), (0.017, 27), (0.017, 30), (0.017, 161), (0.009, 33), (0.009, 26), (0.009, 163), (0.006, 0)]
+                    else:
+                        if context.get_value('n') <= 68.0:
+                            return [(0.101, 182), (0.101, 59), (0.088, 57), (0.076, 184), (0.076, 61), (0.076, 179), (0.076, 62), (0.076, 58), (0.063, 180), (0.063, 60), (0.051, 56), (0.050, 181), (0.025, 130), (0.025, 177), (0.025, 183), (0.013, 178), (0.013, 55)]
+                        else:
+                            return [(0.089, 180), (0.079, 60), (0.066, 35), (0.066, 181), (0.066, 38), (0.066, 58), (0.066, 179), (0.066, 57), (0.062, 184), (0.053, 37), (0.044, 166), (0.040, 55), (0.040, 39), (0.040, 36), (0.040, 165), (0.040, 167), (0.027, 177), (0.027, 34), (0.022, 159)]
+                else:
+                    if context.get_value('m*n') <= 309760.0:
+                        return [(0.298, 0), (0.097, 140), (0.080, 83), (0.072, 86), (0.044, 84), (0.036, 178), (0.036, 117), (0.036, 82), (0.032, 120), (0.032, 85), (0.028, 119), (0.024, 130), (0.024, 109), (0.020, 108), (0.020, 118), (0.012, 104), (0.012, 116), (0.012, 141), (0.012, 144), (0.008, 105), (0.008, 106), (0.008, 111), (0.008, 114), (0.008, 107), (0.008, 132), (0.004, 101), (0.004, 102), (0.004, 110), (0.004, 112), (0.004, 113), (0.004, 131)]
+                    else:
+                        if context.get_value('n') <= 72.0:
+                            return [(0.227, 77), (0.118, 78), (0.102, 194), (0.086, 80), (0.059, 57), (0.054, 81), (0.049, 196), (0.048, 197), (0.048, 59), (0.043, 79), (0.032, 195), (0.027, 180), (0.022, 3), (0.021, 141), (0.016, 60), (0.016, 142), (0.011, 183), (0.011, 0), (0.011, 144)]
+                        else:
+                            return [(0.140, 186), (0.132, 185), (0.109, 63), (0.085, 65), (0.078, 37), (0.077, 35), (0.062, 197), (0.047, 194), (0.046, 165), (0.046, 57), (0.039, 78), (0.039, 79), (0.039, 66), (0.039, 64), (0.016, 195), (0.008, 159)]
+        else:
+            if str(context.get_value('using_tf32')) != 'False':
+                if context.get_value('m*n') <= 815360.0:
+                    if context.get_value('k') <= 1184.0:
+                        return [(0.218, 140), (0.205, 0), (0.154, 144), (0.115, 141), (0.051, 185), (0.051, 104), (0.039, 78), (0.038, 116), (0.026, 165), (0.026, 130), (0.026, 178), (0.013, 57), (0.013, 195), (0.013, 167), (0.013, 186)]
+                    else:
+                        return [(0.901, 0), (0.030, 144), (0.030, 134), (0.016, 3), (0.006, 78), (0.006, 77), (0.002, 57), (0.002, 194), (0.002, 59), (0.002, 60), (0.002, 143)]
+                else:
+                    if context.get_value('arith_intensity') <= 187.23922729492188:
+                        if context.get_value('mat1_stride_0') <= 198.0:
+                            return [(0.273, 63), (0.158, 37), (0.152, 35), (0.127, 57), (0.097, 165), (0.053, 185), (0.031, 0), (0.028, 64), (0.014, 60), (0.014, 78), (0.009, 55), (0.008, 134), (0.005, 34), (0.005, 167), (0.005, 179), (0.005, 65), (0.005, 66), (0.005, 186), (0.005, 194), (0.002, 166)]
+                        else:
+                            return [(0.296, 63), (0.235, 0), (0.132, 64), (0.074, 37), (0.069, 78), (0.051, 185), (0.051, 35), (0.030, 57), (0.020, 77), (0.016, 194), (0.008, 66), (0.007, 65), (0.003, 3), (0.003, 165), (0.003, 141), (0.001, 134), (0.001, 166)]
+                    else:
+                        return [(0.405, 0), (0.246, 37), (0.177, 63), (0.145, 35), (0.005, 185), (0.005, 65), (0.005, 64), (0.004, 57), (0.003, 66), (0.002, 165), (0.001, 78), (0.001, 55)]
+            else:
+                return [(0.357, 0), (0.112, 165), (0.101, 57), (0.094, 179), (0.086, 64), (0.074, 167), (0.067, 60), (0.064, 159), (0.033, 35), (0.007, 195), (0.002, 180), (0.001, 34), (0.001, 166), (0.001, 78)]

data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_inductor/autoheuristic/artifacts/_MMRankingH100.py

@@ -0,0 +1,321 @@
+# flake8: noqa: B950
+# fmt: off
+# This file was generated by AutoHeuristic. Do not modify it manually!
+# To regenerate this file, take a look at the steps in the README.md file inside torchgen/_autoheuristic/mm/
+from typing import List, Optional, Tuple
+
+from torch._inductor.autoheuristic.autoheuristic_utils import (
+    AHContext,
+    AHMetadata,
+    Choice,
+)
+from torch._inductor.autoheuristic.learnedheuristic_interface import (
+    LearnedHeuristicDecision,
+)
+
+
+class MMRankingH100(LearnedHeuristicDecision):
+
+    def __init__(self) -> None:
+        self.choices: List[Choice] = []
+        self.fill_choices()
+
+    def check_precondition(self, metadata: AHMetadata, context: AHContext,) -> bool:
+        return (
+            metadata.name == self.get_name()
+            and metadata.shared_memory == 232448
+            and str(metadata.device_capa) == "(9, 0)"
+        )
+
+    def get_confidence_threshold(self) -> float:
+        return 0.0
+
+    def get_choice(self, idx: int) -> Optional[str]:
+        if idx < len(self.choices):
+            return self.choices[idx]
+        return None
+
+    def fill_choices(self) -> None:
+        self.choices.append('extern_mm')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=128_BLOCK-N=16_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=128_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=128_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=16_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=32_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=16_numstages=3_numwarps=1')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=16_numstages=4_numwarps=1')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=1')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=1_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=3_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=4_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=64_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=64_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=128_numstages=2_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=16_numstages=4_numwarps=1')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=1')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=32_numstages=4_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=64_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=16_numstages=1_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=32_numstages=1_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=32_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=32_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=64_BLOCK-N=16_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=64_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=128_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=16_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=32_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=16_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=16_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=32_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=16_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=16_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=16_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=32_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=32_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=32_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=64_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=64_numstages=5_numwarps=4')
+
+    def get_name(self) -> str:
+        return 'mm'
+
+    def get_best_choices(self, context: AHContext) -> Optional[List[tuple[float, int]]]:
+        if context.get_value('arith_intensity') <= 29.89772129058838:
+            if context.get_value('n') <= 34.0:
+                if context.get_value('n') <= 18.0:
+                    if context.get_value('k*n') <= 432.0:
+                        if context.get_value('arith_intensity') <= 7.8700292110443115:
+                            return [(0.098, 128), (0.098, 129), (0.098, 127), (0.073, 14), (0.073, 16), (0.073, 12), (0.073, 154), (0.073, 156), (0.073, 157), (0.073, 155), (0.049, 10), (0.049, 94), (0.049, 95), (0.048, 96)]
+                        else:
+                            return [(0.091, 154), (0.073, 10), (0.073, 15), (0.073, 13), (0.073, 11), (0.073, 17), (0.073, 16), (0.073, 14), (0.073, 12), (0.055, 127), (0.054, 157), (0.054, 156), (0.054, 155), (0.036, 129), (0.036, 128), (0.018, 41), (0.018, 43)]
+                    else:
+                        if context.get_value('k') <= 40.0:
+                            return [(0.070, 39), (0.069, 45), (0.069, 41), (0.069, 43), (0.069, 111), (0.069, 112), (0.056, 38), (0.056, 40), (0.056, 42), (0.056, 44), (0.056, 174), (0.056, 173), (0.056, 175), (0.056, 134), (0.056, 172), (0.056, 135), (0.014, 154), (0.014, 127)]
+                        else:
+                            return [(0.147, 144), (0.119, 143), (0.087, 142), (0.083, 0), (0.073, 191), (0.059, 69), (0.050, 67), (0.046, 70), (0.041, 1), (0.036, 174), (0.032, 43), (0.032, 123), (0.028, 40), (0.027, 42), (0.027, 173), (0.023, 175), (0.018, 66), (0.014, 192), (0.014, 193), (0.014, 139), (0.014, 68), (0.014, 127)]
+                else:
+                    if context.get_value('mat1_stride_0') <= 40.0:
+                        if context.get_value('mat1_stride_0') <= 20.0:
+                            return [(0.109, 23), (0.109, 21), (0.109, 20), (0.088, 0), (0.087, 131), (0.066, 18), (0.065, 130), (0.065, 132), (0.065, 159), (0.065, 160), (0.065, 161), (0.065, 158), (0.022, 22), (0.022, 19)]
+                        else:
+                            return [(0.065, 46), (0.064, 52), (0.064, 50), (0.064, 48), (0.064, 51), (0.064, 49), (0.064, 47), (0.064, 53), (0.064, 181), (0.064, 177), (0.064, 179), (0.064, 176), (0.038, 130), (0.038, 136), (0.026, 182), (0.026, 178), (0.026, 180), (0.026, 137), (0.025, 158), (0.013, 114), (0.013, 113)]
+                    else:
+                        if context.get_value('mat1_stride_0') <= 68.0:
+                            return [(0.138, 140), (0.125, 195), (0.100, 71), (0.100, 74), (0.100, 196), (0.100, 194), (0.100, 197), (0.075, 75), (0.062, 72), (0.062, 73), (0.012, 180), (0.012, 51), (0.012, 182)]
+                        else:
+                            return [(0.124, 180), (0.124, 182), (0.114, 75), (0.103, 74), (0.093, 51), (0.093, 71), (0.072, 72), (0.062, 194), (0.052, 145), (0.052, 195), (0.021, 48), (0.021, 50), (0.021, 47), (0.020, 124), (0.010, 147), (0.010, 146), (0.010, 46)]
+            else:
+                if context.get_value('k') <= 18.0:
+                    if context.get_value('m*k') <= 528.0:
+                        return [(0.097, 88), (0.087, 92), (0.077, 90), (0.058, 105), (0.058, 103), (0.058, 104), (0.058, 99), (0.058, 100), (0.058, 106), (0.058, 93), (0.057, 91), (0.057, 97), (0.057, 98), (0.057, 101), (0.048, 102), (0.029, 87), (0.029, 89)]
+                    else:
+                        if context.get_value('n') <= 80.0:
+                            return [(0.057, 161), (0.057, 130), (0.057, 24), (0.056, 164), (0.056, 163), (0.056, 166), (0.056, 168), (0.056, 30), (0.056, 28), (0.056, 26), (0.056, 25), (0.056, 27), (0.056, 29), (0.056, 31), (0.042, 131), (0.028, 99), (0.028, 101), (0.028, 100), (0.028, 167), (0.028, 165), (0.028, 133)]
+                        else:
+                            return [(0.110, 164), (0.108, 163), (0.106, 168), (0.069, 161), (0.066, 151), (0.060, 152), (0.055, 165), (0.050, 27), (0.050, 29), (0.048, 131), (0.043, 153), (0.037, 133), (0.037, 130), (0.028, 8), (0.028, 5), (0.027, 7), (0.026, 26), (0.016, 162), (0.012, 9), (0.007, 4), (0.005, 100), (0.005, 6), (0.005, 24)]
+                else:
+                    if context.get_value('k') <= 36.0:
+                        if context.get_value('n') <= 68.0:
+                            return [(0.097, 184), (0.097, 56), (0.086, 186), (0.086, 183), (0.086, 188), (0.086, 58), (0.086, 60), (0.065, 54), (0.043, 187), (0.043, 185), (0.043, 57), (0.043, 61), (0.032, 55), (0.032, 130), (0.032, 59), (0.011, 181), (0.011, 163), (0.011, 136), (0.011, 138)]
+                        else:
+                            return [(0.117, 184), (0.117, 170), (0.117, 169), (0.107, 183), (0.106, 188), (0.075, 181), (0.064, 130), (0.064, 56), (0.053, 171), (0.032, 57), (0.032, 59), (0.032, 185), (0.011, 163), (0.011, 32), (0.011, 37), (0.011, 34), (0.011, 33), (0.011, 35), (0.011, 36), (0.011, 54)]
+                    else:
+                        if context.get_value('mat2_stride_0') <= 384.0:
+                            return [(0.244, 0), (0.061, 76), (0.061, 79), (0.030, 3), (0.030, 183), (0.030, 189), (0.030, 187), (0.030, 64), (0.030, 190), (0.030, 62), (0.030, 198), (0.030, 201), (0.030, 77), (0.030, 200), (0.030, 80), (0.030, 199), (0.030, 78), (0.030, 184), (0.020, 86), (0.020, 84), (0.020, 120), (0.020, 81), (0.020, 121), (0.020, 85), (0.020, 122), (0.010, 83), (0.010, 118), (0.010, 119), (0.010, 82)]
+                        else:
+                            return [(0.274, 83), (0.171, 86), (0.152, 0), (0.071, 85), (0.061, 125), (0.050, 84), (0.020, 109), (0.020, 117), (0.020, 81), (0.020, 118), (0.020, 121), (0.020, 108), (0.020, 115), (0.020, 116), (0.010, 110), (0.010, 120), (0.010, 103), (0.010, 107), (0.010, 119), (0.010, 122)]
+        else:
+            if context.get_value('arith_intensity') <= 56.995582580566406:
+                if context.get_value('n') <= 68.0:
+                    if context.get_value('k*n') <= 4448.0:
+                        if context.get_value('m*n') <= 29626368.0:
+                            return [(0.107, 198), (0.107, 200), (0.107, 201), (0.107, 199), (0.106, 76), (0.106, 79), (0.064, 197), (0.063, 56), (0.043, 184), (0.043, 187), (0.042, 80), (0.042, 77), (0.042, 183), (0.021, 78)]
+                        else:
+                            return [(0.073, 201), (0.073, 198), (0.073, 200), (0.073, 199), (0.073, 197), (0.073, 56), (0.073, 58), (0.073, 79), (0.073, 76), (0.072, 59), (0.072, 78), (0.072, 77), (0.072, 80), (0.018, 184), (0.018, 55), (0.018, 54)]
+                    else:
+                        if context.get_value('k') <= 348.0:
+                            return [(0.206, 76), (0.183, 77), (0.169, 198), (0.160, 199), (0.053, 59), (0.046, 56), (0.038, 3), (0.030, 148), (0.030, 58), (0.030, 187), (0.023, 184), (0.015, 0), (0.008, 55), (0.008, 54)]
+                        else:
+                            return [(0.146, 198), (0.145, 199), (0.145, 148), (0.126, 0), (0.084, 76), (0.084, 77), (0.042, 80), (0.042, 79), (0.021, 149), (0.021, 150), (0.021, 3), (0.014, 46), (0.014, 74), (0.014, 75), (0.014, 124), (0.014, 194), (0.014, 195), (0.007, 145), (0.007, 146), (0.007, 2), (0.007, 72), (0.007, 147), (0.007, 71)]
+                else:
+                    if context.get_value('m') <= 3264.0:
+                        return [(0.247, 147), (0.115, 197), (0.066, 199), (0.066, 201), (0.066, 198), (0.049, 0), (0.049, 169), (0.049, 171), (0.033, 140), (0.033, 125), (0.033, 114), (0.016, 126), (0.016, 183), (0.016, 184), (0.016, 185), (0.016, 182), (0.016, 188), (0.016, 78), (0.016, 148), (0.016, 138), (0.016, 77), (0.016, 56), (0.016, 59)]
+                    else:
+                        if context.get_value('k') <= 62.5:
+                            return [(0.226, 190), (0.226, 189), (0.122, 62), (0.122, 64), (0.055, 77), (0.055, 78), (0.037, 198), (0.036, 201), (0.036, 33), (0.024, 163), (0.018, 56), (0.018, 35), (0.018, 169), (0.006, 171)]
+                        else:
+                            return [(0.162, 35), (0.118, 33), (0.096, 189), (0.096, 190), (0.088, 169), (0.074, 62), (0.073, 56), (0.066, 171), (0.051, 198), (0.051, 201), (0.044, 59), (0.037, 64), (0.029, 63), (0.007, 0), (0.007, 77)]
+            else:
+                if context.get_value('m*n') <= 1097728.0:
+                    return [(0.403, 0), (0.179, 141), (0.134, 150), (0.086, 147), (0.051, 148), (0.048, 3), (0.024, 189), (0.020, 199), (0.017, 64), (0.010, 65), (0.010, 77), (0.007, 114), (0.003, 138), (0.003, 59), (0.003, 182)]
+                else:
+                    if context.get_value('m*n') <= 3244032.0:
+                        return [(0.295, 189), (0.176, 64), (0.157, 65), (0.090, 0), (0.069, 62), (0.059, 63), (0.046, 77), (0.039, 169), (0.023, 199), (0.020, 35), (0.013, 33), (0.010, 171), (0.003, 141)]
+                    else:
+                        if context.get_value('n') <= 136.0:
+                            return [(0.197, 189), (0.197, 63), (0.161, 77), (0.157, 62), (0.061, 33), (0.044, 65), (0.039, 35), (0.039, 64), (0.030, 169), (0.026, 0), (0.017, 199), (0.017, 148), (0.009, 56), (0.004, 3)]
+                        else:
+                            return [(0.460, 0), (0.145, 62), (0.138, 63), (0.081, 35), (0.047, 33), (0.043, 189), (0.023, 64), (0.018, 77), (0.013, 169), (0.009, 65), (0.009, 56), (0.005, 32), (0.005, 59), (0.002, 183), (0.002, 163)]

data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_inductor/autoheuristic/artifacts/_MixedMMA100.py

@@ -0,0 +1,150 @@
+# flake8: noqa: B950
+# fmt: off
+# This file was generated by AutoHeuristic. Do not modify it manually!
+# To regenerate this file, take a look at the steps in the README.md file inside torchgen/_autoheuristic/mixed_mm/
+from typing import List, Optional, Tuple
+
+from torch._inductor.autoheuristic.autoheuristic_utils import (
+    AHContext,
+    AHMetadata,
+    Choice,
+)
+from torch._inductor.autoheuristic.learnedheuristic_interface import (
+    LearnedHeuristicDecision,
+)
+
+
+class MixedMMA100(LearnedHeuristicDecision):
+
+    def __init__(self) -> None:
+        self.choices: List[Choice] = []
+        self.fill_choices()
+
+    def check_precondition(self, metadata: AHMetadata, context: AHContext,) -> bool:
+        return (
+            metadata.name == self.get_name()
+            and metadata.shared_memory == 166912
+            and str(metadata.device_capa) == "(8, 0)"
+        )
+
+    def get_confidence_threshold(self) -> float:
+        return 0.0
+
+    def get_choice(self, idx: int) -> Optional[str]:
+        if idx < len(self.choices):
+            return self.choices[idx]
+        return None
+
+    def fill_choices(self) -> None:
+        self.choices.append('extern_fallback_mixed_mm')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=256_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=256_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=128_numstages=4_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=8')
+
+    def get_name(self) -> str:
+        return 'mixed_mm'
+
+    def get_best_choices(self, context: AHContext) -> Optional[List[tuple[float, int]]]:
+        if str(context.get_value('1LEQmLEQ16')) != 'True':
+            if context.get_value('m') <= 32.5:
+                if context.get_value('n') <= 6976.0:
+                    if context.get_value('n') <= 3520.0:
+                        if context.get_value('m*n') <= 37632.0:
+                            return None
+                        else:
+                            return [(1.000, 13)]
+                    else:
+                        if context.get_value('m*k') <= 452352.0:
+                            return [(0.590, 13), (0.256, 8), (0.103, 7), (0.051, 11)]
+                        else:
+                            return [(0.778, 8), (0.222, 13)]
+                else:
+                    if context.get_value('k*n') <= 102776832.0:
+                        if context.get_value('n') <= 14656.0:
+                            return [(1.000, 11)]
+                        else:
+                            return [(0.889, 11), (0.111, 13)]
+                    else:
+                        return [(1.000, 11)]
+            else:
+                if context.get_value('m*n') <= 446464.0:
+                    if context.get_value('m*n') <= 223424.0:
+                        if context.get_value('mat1_stride_0') <= 3968.0:
+                            return None
+                        else:
+                            return None
+                    else:
+                        if context.get_value('m*n') <= 346112.0:
+                            return [(0.960, 16), (0.040, 7)]
+                        else:
+                            return [(0.750, 16), (0.136, 14), (0.114, 7)]
+                else:
+                    if str(context.get_value('33LEQmLEQ64')) != 'True':
+                        if context.get_value('n') <= 6976.0:
+                            return [(1.000, 14)]
+                        else:
+                            return [(0.753, 2), (0.222, 1), (0.015, 7), (0.007, 16), (0.004, 12)]
+                    else:
+                        if context.get_value('n') <= 13888.0:
+                            return [(0.710, 14), (0.275, 21), (0.014, 12)]
+                        else:
+                            return [(0.374, 19), (0.339, 20), (0.106, 21), (0.101, 16), (0.066, 17), (0.009, 14), (0.004, 18)]
+        else:
+            if context.get_value('n') <= 3520.0:
+                if context.get_value('arith_intensity') <= 3.994754433631897:
+                    if str(context.get_value('mat2_dtype')) != 'torch.uint8':
+                        if context.get_value('m*k') <= 18944.0:
+                            return [(0.577, 5), (0.423, 6)]
+                        else:
+                            return [(0.988, 5), (0.012, 6)]
+                    else:
+                        if context.get_value('arith_intensity') <= 2.9899919033050537:
+                            return None
+                        else:
+                            return None
+                else:
+                    if context.get_value('arith_intensity') <= 7.956453561782837:
+                        if context.get_value('k*n') <= 9244032.0:
+                            return [(0.822, 5), (0.178, 6)]
+                        else:
+                            return [(0.977, 5), (0.023, 0)]
+                    else:
+                        if context.get_value('m*k') <= 978944.0:
+                            return [(1.000, 5)]
+                        else:
+                            return [(0.971, 5), (0.029, 0)]
+            else:
+                if context.get_value('n') <= 13632.0:
+                    if context.get_value('n') <= 6976.0:
+                        return [(1.000, 6)]
+                    else:
+                        if context.get_value('k') <= 3968.0:
+                            return [(0.617, 3), (0.111, 5), (0.099, 7), (0.086, 9), (0.062, 6), (0.025, 8)]
+                        else:
+                            return [(0.779, 8), (0.119, 5), (0.053, 7), (0.035, 6), (0.013, 3)]
+                else:
+                    if context.get_value('k*n') <= 39518208.0:
+                        return [(0.385, 4), (0.327, 3), (0.192, 6), (0.038, 7), (0.038, 10), (0.019, 5)]
+                    else:
+                        if context.get_value('n') <= 20800.0:
+                            return [(0.821, 6), (0.121, 7), (0.029, 4), (0.014, 5), (0.007, 3), (0.007, 8)]
+                        else:
+                            return [(0.530, 7), (0.386, 6), (0.046, 8), (0.021, 3), (0.015, 4), (0.002, 5)]

data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_inductor/autoheuristic/artifacts/_MixedMMH100.py

@@ -0,0 +1,149 @@
+# flake8: noqa: B950
+# fmt: off
+# This file was generated by AutoHeuristic. Do not modify it manually!
+# To regenerate this file, take a look at the steps in the README.md file inside torchgen/_autoheuristic/mixed_mm/
+from typing import List, Optional, Tuple
+
+from torch._inductor.autoheuristic.autoheuristic_utils import (
+    AHContext,
+    AHMetadata,
+    Choice,
+)
+from torch._inductor.autoheuristic.learnedheuristic_interface import (
+    LearnedHeuristicDecision,
+)
+
+
+class MixedMMH100(LearnedHeuristicDecision):
+
+    def __init__(self) -> None:
+        self.choices: List[Choice] = []
+        self.fill_choices()
+
+    def check_precondition(self, metadata: AHMetadata, context: AHContext,) -> bool:
+        return (
+            metadata.name == self.get_name()
+            and metadata.shared_memory == 232448
+            and str(metadata.device_capa) == "(9, 0)"
+        )
+
+    def get_confidence_threshold(self) -> float:
+        return 0.0
+
+    def get_choice(self, idx: int) -> Optional[str]:
+        if idx < len(self.choices):
+            return self.choices[idx]
+        return None
+
+    def fill_choices(self) -> None:
+        self.choices.append('extern_fallback_mixed_mm')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=32_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=128_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=32_numstages=2_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=2')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=256_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=256_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=128_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=16_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=32_numstages=2_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=32_BLOCK-K=32_BLOCK-N=64_numstages=5_numwarps=8')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=128_numstages=4_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=32_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=128_BLOCK-N=64_numstages=5_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=128_numstages=3_numwarps=4')
+        self.choices.append('type=triton_BLOCK-M=64_BLOCK-K=64_BLOCK-N=64_numstages=3_numwarps=8')
+
+    def get_name(self) -> str:
+        return 'mixed_mm'
+
+    def get_best_choices(self, context: AHContext) -> Optional[List[tuple[float, int]]]:
+        if context.get_value('arith_intensity') <= 15.988086223602295:
+            if context.get_value('n') <= 25280.0:
+                if context.get_value('n') <= 1344.0:
+                    if context.get_value('mat1_stride_0') <= 7808.0:
+                        return [(0.581, 7), (0.419, 6)]
+                    else:
+                        if context.get_value('m*n') <= 7680.0:
+                            return [(0.875, 0), (0.125, 6)]
+                        else:
+                            return [(0.833, 0), (0.167, 7)]
+                else:
+                    if context.get_value('n') <= 8512.0:
+                        if str(context.get_value('mat2_dtype')) != 'torch.int8':
+                            return [(0.763, 6), (0.237, 7)]
+                        else:
+                            return [(0.725, 7), (0.275, 6)]
+                    else:
+                        if str(context.get_value('mat1_dtype')) != 'torch.bfloat16':
+                            return [(0.736, 7), (0.197, 9), (0.048, 6), (0.014, 8), (0.005, 10)]
+                        else:
+                            return [(0.473, 7), (0.398, 6), (0.097, 9), (0.032, 10)]
+            else:
+                if context.get_value('n') <= 42254.0:
+                    if context.get_value('n') <= 33856.0:
+                        if context.get_value('k*n') <= 68157440.0:
+                            return [(0.370, 4), (0.370, 5), (0.074, 7), (0.074, 8), (0.074, 11), (0.037, 6)]
+                        else:
+                            return [(0.916, 8), (0.036, 7), (0.036, 9), (0.012, 4)]
+                    else:
+                        return [(0.659, 5), (0.341, 6)]
+                else:
+                    if context.get_value('k*n') <= 326052992.0:
+                        if context.get_value('n') <= 55232.0:
+                            return [(0.571, 6), (0.321, 7), (0.036, 4), (0.036, 8), (0.036, 9)]
+                        else:
+                            return [(0.506, 6), (0.325, 8), (0.104, 7), (0.039, 5), (0.026, 9)]
+                    else:
+                        if context.get_value('n') <= 57024.0:
+                            return [(0.462, 9), (0.385, 7), (0.115, 6), (0.038, 8)]
+                        else:
+                            return [(0.598, 8), (0.223, 9), (0.107, 6), (0.071, 7)]
+        else:
+            if context.get_value('m*n') <= 543936.0:
+                if str(context.get_value('17LEQmLEQ32')) != 'True':
+                    if context.get_value('m*n') <= 262272.0:
+                        if context.get_value('n') <= 1592.5:
+                            return [(0.860, 0), (0.140, 9)]
+                        else:
+                            return None
+                    else:
+                        if context.get_value('m*k') <= 1294336.0:
+                            return [(0.833, 17), (0.150, 18), (0.017, 15)]
+                        else:
+                            return [(0.917, 17), (0.083, 8)]
+                else:
+                    if context.get_value('n') <= 12416.0:
+                        if context.get_value('m*n') <= 43008.0:
+                            return None
+                        else:
+                            return [(0.853, 14), (0.147, 9)]
+                    else:
+                        return [(0.625, 12), (0.375, 14)]
+            else:
+                if context.get_value('m') <= 32.5:
+                    if context.get_value('mat2_stride_1') <= 6656.0:
+                        if context.get_value('n') <= 69184.0:
+                            return [(0.611, 12), (0.361, 14), (0.028, 13)]
+                        else:
+                            return [(1.000, 12)]
+                    else:
+                        if context.get_value('mat2_stride_1') <= 20864.0:
+                            return [(1.000, 12)]
+                        else:
+                            return [(0.958, 12), (0.042, 9)]
+                else:
+                    if context.get_value('m*n') <= 1085440.0:
+                        if context.get_value('n') <= 9152.0:
+                            return [(1.000, 18)]
+                        else:
+                            return [(0.780, 18), (0.160, 16), (0.060, 20)]
+                    else:
+                        if context.get_value('m') <= 67.0:
+                            return [(0.650, 16), (0.203, 19), (0.122, 18), (0.016, 20), (0.008, 1)]
+                        else:
+                            return [(0.561, 3), (0.185, 16), (0.096, 20), (0.083, 19), (0.076, 2)]

data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_inductor/autoheuristic/artifacts/_PadMMA100.py

@@ -0,0 +1,109 @@
+# flake8: noqa: B950
+# fmt: off
+# This file was generated by AutoHeuristic. Do not modify it manually!
+# To regenerate this file, take a look at the steps in the README.md file inside torchgen/_autoheuristic/pad_mm/
+from torch._inductor.autoheuristic.autoheuristic_utils import AHContext, AHMetadata, Choice, CHOICE_COL
+from torch._inductor.autoheuristic.learnedheuristic_interface import (
+    LearnedHeuristicRegression,
+)
+
+
+class PadMMA100(LearnedHeuristicRegression):
+
+    def __init__(self) -> None:
+        pass
+
+    def check_precondition(self, metadata: AHMetadata, context: AHContext,) -> bool:
+        return (
+            metadata.name == self.get_name()
+            and metadata.shared_memory == 166912
+            and str(metadata.device_capa) == "(8, 0)"
+        )
+
+    def get_feedback(self, context: AHContext, choice: Choice) -> float:
+        context.context_dict[CHOICE_COL] = choice
+        return self.predict(context)
+
+    def get_confidence_threshold(self) -> float:
+        return 1.7025303314066
+
+    def get_name(self) -> str:
+        return 'pad_mm'
+
+    def predict(self, context: AHContext) -> float:
+        if str(context.get_value('choice')) != 'pad':
+            if str(context.get_value('using_tf32')) != 'False':
+                if context.get_value('m*n') <= 4171264.0:
+                    if context.get_value('m*k') <= 3999308.0:
+                        return 1.8751469764071178
+                    else:
+                        if str(context.get_value('n_multiple_32')) != 'True':
+                            return 0.9117231355626345
+                        else:
+                            return 1.1607689608873861
+                else:
+                    if str(context.get_value('n_multiple_2')) != 'True':
+                        if str(context.get_value('using_tf32')) != 'True':
+                            return 0.7430382200435992
+                        else:
+                            return 0.8531269794448678
+                    else:
+                        if str(context.get_value('k_multiple_2')) != 'True':
+                            return 0.7577181972719917
+                        else:
+                            return 0.8977349440424219
+            else:
+                if context.get_value('m*n') <= 1299712.0:
+                    return 1.1669723418995592
+                else:
+                    if context.get_value('mat2_stride_1') <= 45217.5:
+                        if context.get_value('m*n') <= 55884158.0:
+                            return 1.0262769936909601
+                        else:
+                            return 1.0022677428470845
+                    else:
+                        if context.get_value('m') <= 18478.0:
+                            return 1.1127066261894312
+                        else:
+                            return 1.0337740659894263
+        else:
+            if str(context.get_value('mat1_dtype')) != 'torch.float32':
+                if str(context.get_value('n_multiple_2')) != 'False':
+                    if str(context.get_value('k_multiple_2')) != 'True':
+                        if context.get_value('mat1_stride_0') <= 561.0:
+                            return 1.2900382135142956
+                        else:
+                            return 1.5761737616057887
+                    else:
+                        if context.get_value('num_dims_needs_padding') <= 1.5:
+                            return 1.0472263310239422
+                        else:
+                            return 1.1727673465762514
+                else:
+                    if context.get_value('k') <= 28238.5:
+                        if context.get_value('k/(m*n)') <= 0.00026227018679492176:
+                            return 1.6770542505397175
+                        else:
+                            return 1.3974785435105923
+                    else:
+                        if str(context.get_value('mat1_dtype')) != 'torch.bfloat16':
+                            return 1.3952699800111992
+                        else:
+                            return 1.5759286511628336
+            else:
+                if str(context.get_value('using_tf32')) != 'False':
+                    if context.get_value('m*n') <= 14119424.0:
+                        return 0.8875772670422478
+                    else:
+                        if str(context.get_value('mat2_innermost_needs_padding')) != 'True':
+                            return 1.1467728924377265
+                        else:
+                            return 1.215842963532998
+                else:
+                    if context.get_value('arith_intensity') <= 396.8774871826172:
+                        return 0.89940161869551
+                    else:
+                        if context.get_value('mat2_stride_1') <= 45217.5:
+                            return 0.9964328169353532
+                        else:
+                            return 0.9493479238294826