[CI] Removes debug print statements from the example. (#1030)

* [CI] Removes debug print statements from the example.

* add parse args

* [Lint]: [pre-commit.ci] auto fixes [...]

* format

---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

examples/dequantize_gemm/example_dequant_groupedgemm_bf16_mxfp4_hopper.py

@@ -6,6 +6,7 @@ from tvm import DataType
 import torch
 from dequantize_utils import torch_convert_bit_twiddling, assert_similar
 from tilelang.autotuner import set_autotune_inputs
+import argparse
 
 
 def get_configs():
@@ -433,13 +434,18 @@ def get_data(m, n, k, qk, scale_size, topk, E, block_M):
     expert_ids = torch.tensor(expert_ids, dtype=torch.int32, device='cuda')  # （padding_M,）
     padding_M = sorted_token_ids.shape[0]  # padding_M: token number after padding
 
-    print(f'{sorted_token_ids=}')
-    print(f'{expert_ids=}')
-
     return A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, padding_M
 
 
-def main(m=256, n=256, k=256, scale_size=32, fast_dequant=True, with_bias=False, topk=4, E=32):
+def main(m=256,
+         n=256,
+         k=256,
+         scale_size=32,
+         topk=4,
+         E=32,
+         fast_dequant=True,
+         with_bias=False,
+         tune=False):
     # Tunable parameters
     block_M, block_N, block_K = 128, 256, 128  # noqa: F841
     num_stages = 1  # noqa: F841
@@ -453,8 +459,25 @@ def main(m=256, n=256, k=256, scale_size=32, fast_dequant=True, with_bias=False,
     A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, padding_M = get_data(
         m, n, k, qk, scale_size, topk, E, block_M)
 
-    with set_autotune_inputs([A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids]):
-        # Autotune with inputs manually composed
+    if tune:
+        with set_autotune_inputs([A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids]):
+            # Autotune with inputs manually composed
+            kernel = matmul(
+                m,
+                n,
+                k,
+                topk,
+                E,
+                padding_M,
+                "bfloat16",
+                "bfloat16",
+                "float32",
+                num_bits=num_bits,
+                scale_size=scale_size,
+                fast_dequant=fast_dequant,
+                with_bias=with_bias,
+            )
+    else:
         kernel = matmul(
             m,
             n,
@@ -469,8 +492,13 @@ def main(m=256, n=256, k=256, scale_size=32, fast_dequant=True, with_bias=False,
             scale_size=scale_size,
             fast_dequant=fast_dequant,
             with_bias=with_bias,
+            block_M=block_M,
+            block_N=block_N,
+            block_K=block_K,
+            num_stages=num_stages,
+            threads=threads,
+            split=split,
         )
-        print(f'Best config: {kernel.config}')
 
     output = kernel(
         A,
@@ -504,8 +532,25 @@ def main(m=256, n=256, k=256, scale_size=32, fast_dequant=True, with_bias=False,
 
 
 if __name__ == "__main__":
-    M, N, K = 16384, 5760, 2944  # From gpt-oss-20b MoE's first gemm
-    scale_size = 32
-    topk = 4  # experts activated for each token
-    E = 32  # number of experts
-    main(M, N, K, scale_size, fast_dequant=True, with_bias=True, topk=topk, E=E)
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--M", type=int, default=16384, help="M")  # From gpt-oss-20b MoE's first gemm
+    parser.add_argument("--N", type=int, default=5760, help="N")
+    parser.add_argument("--K", type=int, default=2944, help="K")
+    parser.add_argument("--scale_size", type=int, default=32, help="scale size")
+    parser.add_argument(
+        "--topk", type=int, default=4, help="topk")  # experts activated for each token
+    parser.add_argument("--E", type=int, default=32, help="E")  # number of experts
+    parser.add_argument("--tune", action="store_true", help="tune configs")
+    args = parser.parse_args()
+
+    main(
+        args.M,
+        args.N,
+        args.K,
+        args.scale_size,
+        topk=args.topk,
+        E=args.E,
+        fast_dequant=True,
+        with_bias=True,
+        tune=args.tune)