[Bugfix] FIx autotuning params (#585)

* [Enhancement] Update AutoTuner and Profiler for improved kernel handling and output validation

- Modified AutoTuner to store cache in a dedicated "autotuner" directory.
- Enhanced kernel source code saving logic in AutotuneResult and AutoTunerCache to check for None before writing.
- Updated Profiler to handle None outputs gracefully during tensor comparisons, improving robustness in output validation.

* lint fix

* [Enhancement] Improve error handling and documentation in AutoTuner

- Added traceback logging for exceptions during configuration testing to enhance debugging.
- Expanded the AutoTuner class docstring to include detailed descriptions of new parameters for input tensor generation and validation, improving clarity for users.

tilelang/autotuner/__init__.py

@@ -22,6 +22,7 @@ import signal
 import json
 import hashlib
 import threading
+import traceback
 from pathlib import Path
 
 from tilelang.env import TILELANG_CACHE_DIR, is_cache_enabled
@@ -103,7 +104,7 @@ class AutoTuner:
     _kernel_parameters: Optional[Tuple[str, ...]] = None
     _lock = threading.Lock()  # For thread safety
     _memory_cache = {}  # In-memory cache dictionary
-    cache_dir: Path = Path(TILELANG_CACHE_DIR)
+    cache_dir: Path = Path(TILELANG_CACHE_DIR) / "autotuner"
 
     def __init__(self, fn: Callable, configs):
         self.fn = fn
@@ -352,6 +353,7 @@ class AutoTuner:
                         max_mismatched_ratio=max_mismatched_ratio)
             latency = profiler.do_bench(
                 warmup=warmup, rep=rep, input_tensors=self.jit_input_tensors)
+
             if self.ref_latency_cache is None and ref_prog is not None:
                 self.ref_input_tensors = ref_input_tensors_supply()
                 self.ref_latency_cache = profiler.do_bench(
@@ -417,15 +419,13 @@ class AutoTuner:
                     f"A timeout occurred while testing config {config}, checkout autotuner.log for more details"
                 )
                 continue
-            except Exception as e:
+            except Exception:
                 logger.info(
                     f"An error occurred while testing config {config}, checkout autotuner.log for more details"
                 )
-                logger.debug(f"Error: {e}")
+                logger.debug(f"Error: {traceback.format_exc()}")
                 continue
 
-            logging.debug(f"Config {config} latency: {latency} at index {i}")
-
             if latency < best_latency:
                 best_latency = latency
                 best_config = config
@@ -515,13 +515,37 @@ class _AutoTunerImplementation:
             warmup: Number of warmup iterations before timing.
             rep: Number of repetitions for timing measurements.
             timeout: Maximum time (in seconds) allowed for each configuration.
+            supply_type: Strategy for generating input tensors (random/zeros/etc)
+            ref_prog: Reference implementation for validation
+            supply_prog: Custom function to provide input tensors
+            rtol: Relative tolerance for numerical validation
+            atol: Absolute tolerance for numerical validation
+            max_mismatched_ratio: Allowed percentage of mismatched values
+            skip_check: Bypass validation against reference implementation
+            manual_check_prog: Custom validation function
+            cache_input_tensors: Reuse input tensors across trials
         """
-        self.configs = configs
-        self.warmup = warmup
-        self.rep = rep
-        self.timeout = timeout
-
-        self._tuner_cache: Dict[tuple, tilelang.JITKernel] = {}
+        # Configuration and benchmarking parameters
+        self.configs = configs  # Search space of tuning configurations
+        self.warmup = warmup  # Warmup iterations for stable measurements
+        self.rep = rep  # Measurement repetitions for statistics
+        self.timeout = timeout  # Per-configuration timeout threshold
+
+        # Tensor handling and validation setup
+        self.supply_type = supply_type  # Input tensor generation strategy
+        self.ref_prog = ref_prog  # Ground truth implementation
+        self.supply_prog = supply_prog  # Custom input data provider
+        self.rtol = rtol  # Relative error tolerance
+        self.atol = atol  # Absolute error tolerance
+        self.max_mismatched_ratio = max_mismatched_ratio  # Allowed mismatch
+
+        # Validation control flags
+        self.skip_check = skip_check  # Bypass accuracy verification
+        self.manual_check_prog = manual_check_prog  # Custom validation
+        self.cache_input_tensors = cache_input_tensors  # Reuse inputs
+
+        # Cache for storing tuned kernel implementations
+        self._tuner_cache: Dict[tuple, tilelang.JITKernel] = {}  # (args, kwargs) -> compiled kernel
 
     # This tells the type checker what the *wrapper* function will return.
     # this is for linting, please do not remove it.