[Bugfix] Improve autotune from elementwise_add function in examples (#1445)

* Remove JIT decorator from elementwise_add function in examples

* fix kernel compilation without autotune

* Refactor main function to accept parameters and update tests for autotune option

* Refactor autotune test function for morden style

examples/elementwise/example_elementwise_add.py

@@ -3,13 +3,21 @@ import itertools
 import torch
 import tilelang
 import tilelang.language as T
-from tilelang.autotuner import AutoTuner
 
 
 def ref_program(x, y):
     return x + y
 
 
+def get_configs():
+    block_M = [64, 128, 256]
+    block_N = [64, 128, 256]
+    threads = [64, 128, 256]
+    configs = list(itertools.product(block_M, block_N, threads))
+    return [{"block_M": bm, "block_N": bn, "threads": th} for bm, bn, th in configs]
+
+
+@tilelang.autotune(configs=get_configs())
 @tilelang.jit(out_idx=[-1])
 def elementwise_add(M, N, block_M, block_N, in_dtype, out_dtype, threads):
     @T.prim_func
@@ -30,47 +38,12 @@ def elementwise_add(M, N, block_M, block_N, in_dtype, out_dtype, threads):
     return elem_add
 
 
-def get_configs(M, N):
-    block_M = [64, 128, 256]
-    block_N = [64, 128, 256]
-    threads = [64, 128, 256]
-    configs = list(itertools.product(block_M, block_N, threads))
-    return [{"block_M": bm, "block_N": bn, "threads": th} for bm, bn, th in configs]
-
-
-def get_best_config(M, N):
-    def kernel(block_M=None, block_N=None, threads=None):
-        return elementwise_add(M, N, block_M, block_N, "float32", "float32", threads)
-
-    autotuner = (
-        AutoTuner.from_kernel(kernel=kernel, configs=get_configs(M, N))
-        .set_compile_args(
-            out_idx=[-1],
-            target="cuda",
-        )
-        .set_profile_args(
-            supply_type=tilelang.TensorSupplyType.Auto,
-            ref_prog=ref_program,
-            skip_check=False,
-        )
-    )
-    return autotuner.run(warmup=3, rep=20)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--m", type=int, default=1024)
-    parser.add_argument("--n", type=int, default=1024)
-    parser.add_argument("--use_autotune", action="store_true", default=False)
-    args, _ = parser.parse_known_args()
-    M, N = args.m, args.n
-
+def main(M=1024, N=1024, use_autotune=False):
     a = torch.randn(M, N, dtype=torch.float32, device="cuda")
     b = torch.randn(M, N, dtype=torch.float32, device="cuda")
 
-    if args.use_autotune:
-        result = get_best_config(M, N)
-        kernel = result.kernel
+    if use_autotune:
+        kernel = elementwise_add(M, N, in_dtype="float32", out_dtype="float32")
     else:
         # Default config
         config = {"block_M": 32, "block_N": 32, "threads": 128}
@@ -81,4 +54,9 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--m", type=int, default=1024)
+    parser.add_argument("--n", type=int, default=1024)
+    parser.add_argument("--use_autotune", action="store_true", default=False)
+    args, _ = parser.parse_known_args()
+    main(args.m, args.n, args.use_autotune)

examples/elementwise/test_example_elementwise.py

@@ -6,5 +6,9 @@ def test_example_elementwise_add():
     example_elementwise_add.main()
 
 
+def test_example_elementwise_add_autotune():
+    example_elementwise_add.main(use_autotune=True)
+
+
 if __name__ == "__main__":
     tilelang.testing.main()