[Autotune] Add autotune coverage for symbolic M and normalize cache key (#1075)

- extend matmul autotune test suite with a symbolic M case and allow run_autotune to accept concrete values for symbolic dims
  - sanitize _kernel_parameters when generating cache keys so symbolic vars serialize deterministically

testing/python/autotune/test_tilelang_autotune_with_inputs.py

@@ -116,10 +116,22 @@ def matmul(M,
     return main
 
 
-def run_autotune(M: int, N: int, K: int):
+def run_autotune(M, N, K, M_value=None, N_value=None, K_value=None):
     import torch
-    a = torch.randn(M, K, dtype=torch.float16).cuda()
-    b = torch.randn(N, K, dtype=torch.float16).cuda()
+
+    def _resolve(dim, provided, name):
+        if isinstance(dim, T.Var):
+            if provided is None:
+                raise ValueError(f"Dynamic dimension {name} requires a concrete value.")
+            return provided
+        return dim
+
+    actual_M = _resolve(M, M_value, "M")
+    actual_N = _resolve(N, N_value, "N")
+    actual_K = _resolve(K, K_value, "K")
+
+    a = torch.randn(actual_M, actual_K, dtype=torch.float16).cuda()
+    b = torch.randn(actual_N, actual_K, dtype=torch.float16).cuda()
 
     with set_autotune_inputs([a, b]):
         kernel = matmul(M, N, K)
@@ -140,5 +152,9 @@ def test_autotune_matmul():
     run_autotune(1024, 1024, 1024)
 
 
+def test_autotune_matmul_symbolic_m():
+    run_autotune(T.symbolic("m"), 1024, 1024, M_value=1024)
+
+
 if __name__ == "__main__":
     tilelang.testing.main()

tilelang/autotuner/tuner.py

@@ -231,6 +231,16 @@ class AutoTuner:
     def generate_cache_key(self, parameters: Dict[str, Any]) -> Optional[AutotuneResult]:
         """Generate a cache key for the auto-tuning process.
         """
+
+        def _normalize_param(value):
+            if isinstance(value, Var):
+                return str(value)
+            if isinstance(value, (list, tuple)):
+                return [_normalize_param(v) for v in value]
+            if isinstance(value, dict):
+                return {str(k): _normalize_param(v) for k, v in value.items()}
+            return value
+
         # extract parameters from the function signature
         op_parameters = []
         for _, default_value in parameters.items():
@@ -238,7 +248,7 @@ class AutoTuner:
                 op_parameters.append(default_value.default)
 
         if self._kernel_parameters is not None:
-            op_parameters += self._kernel_parameters
+            op_parameters += _normalize_param(self._kernel_parameters)
 
         func_source = inspect.getsource(self.fn)
         key_data = {