fix typo

Signed-off-by: Enwei Zhu <21126786+syuoni@users.noreply.github.com>

Author: syuoni

tensorrt_llm/_torch/autotuner.py

@@ -730,7 +730,7 @@ def choose_one(
             # Log the cache miss. Expect no cache miss in inference.
             if not is_cache_hit:
                 logger.warning_once(
-                    f"[AutoTunner] {custom_op} using the fallback tactic, due to cache miss on input shapes={input_shapes}",
+                    f"[AutoTuner] {custom_op} using the fallback tactic, due to cache miss on input shapes={input_shapes}",
                     key=(custom_op, "warning_autotuning_cache_miss_fallback"))
 
             return (best_runner, best_tactic)

tests/unittest/_torch/misc/test_autotuner.py

@@ -112,13 +112,13 @@ def forward(self,
                          mutates_args=())
 def get_best_gemm_tactic(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
     runners = [GemmRunner()]
-    tunner = AutoTuner.get()
+    tuner = AutoTuner.get()
     tuning_config = TuningConfig(dynamic_tensor_specs=(DynamicTensorSpec(
         input_idx=0,
         dim_idx=0,
         gen_tuning_buckets=get_power_of_2_num_tokens_buckets,
         map_to_tuning_buckets=next_positive_power_of_2), ), )
-    runner, tactic = tunner.choose_one(
+    runner, tactic = tuner.choose_one(
         "autotuner_test::get_best_gemm_tactic",
         runners,
         tuning_config,
@@ -175,20 +175,20 @@ def forward(self,
 
     x, w = torch.randn(M, 64), torch.randn(64, 128)
     runners = [PartialCrashedRunner()]
-    tunner = AutoTuner.get()
+    tuner = AutoTuner.get()
     tuning_config = TuningConfig(dynamic_tensor_specs=(DynamicTensorSpec(
         input_idx=0,
         dim_idx=0,
         gen_tuning_buckets=get_power_of_2_num_tokens_buckets,
         map_to_tuning_buckets=next_positive_power_of_2), ), )
     with autotune():
-        runner, tactic = tunner.choose_one("test_autotuner_try_block", runners,
-                                           tuning_config, [x, w])
+        runner, tactic = tuner.choose_one("test_autotuner_try_block", runners,
+                                          tuning_config, [x, w])
 
     m = M // 2
     while m >= 1:
-        _, tactic = tunner.choose_one("test_autotuner_try_block", runners,
-                                      tuning_config, [torch.randn(m, 64), w])
+        _, tactic = tuner.choose_one("test_autotuner_try_block", runners,
+                                     tuning_config, [torch.randn(m, 64), w])
         assert tactic in [
             -1, 0
         ], f"Expect only tactic -1, 0 being chosen, but got tactic {tactic}."