[AMD][MLA] Fix mla autotune for rocm (#861)

* Refactor matmul example to include ReLU activation and update batch size in benchmark script

* lint fix

* Enhance autotuning capabilities in benchmark script and update argument defaults

- Introduced a new `get_configs` function to generate autotuning configurations for the benchmark.
- Updated the default batch size and kv context length in the argument parser for improved performance.
- Renamed the `--auto_tune` argument to `--autotune` for consistency.
- Modified the kernel invocation logic to support autotuning based on the new configurations.

* lint fix

examples/deepseek_mla/amd/benchmark_mla_decode_amd_tilelang.py

 import torch
 import torch.nn.functional as F
 import tilelang
-from tilelang.autotuner import *
 import tilelang.language as T
 from einops import rearrange, einsum
 import argparse
@@ -9,6 +8,24 @@ import argparse
 tilelang.disable_cache()
 
 
+def get_configs():
+    import itertools
+    BLOCK_N = [16, 32, 64, 128]
+    BLOCK_H = [16, 32, 64, 128]
+    num_split = [1, 2, 4, 8, 16, 32]
+    threads = [128, 256]
+
+    _configs = list(itertools.product(BLOCK_N, BLOCK_H, num_split, threads))
+
+    return [{
+        "block_N": c[0],
+        "block_H": c[1],
+        "num_split": c[2],
+        "threads": c[3],
+    } for c in _configs]
+
+
+@tilelang.autotune(configs=get_configs())
 @tilelang.jit(
     out_idx=[6], pass_configs={
         tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
@@ -273,16 +290,16 @@ def ref_program(q, q_pe, kv, k_pe, glse, Output_partial):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--batch', type=int, default=1, help='batch size')
+    parser.add_argument('--batch', type=int, default=128, help='batch size')
     parser.add_argument('--heads', type=int, default=128, help='q heads number')
     parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number')
-    parser.add_argument('--kv_ctx', type=int, default=1024, help='kv context length')
+    parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length')
     parser.add_argument('--dim', type=int, default=512, help='head dim')
     parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim')
-    parser.add_argument('--auto_tune', action='store_true', help='auto tune')
+    parser.add_argument('--autotune', action='store_true', help='auto tune')
     args = parser.parse_args()
     batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim
-    enable_autotune = args.auto_tune
+    enable_autotune = args.autotune
 
     qk_flops = 2 * batch * heads * kv_ctx * (dim + pe_dim)
     pv_flops = 2 * batch * heads * kv_ctx * dim
@@ -290,9 +307,22 @@ if __name__ == "__main__":
     BLOCK_N = 32
     BLOCK_H = 64
     num_split = 4
+    threads = 128
 
-    kernel = flashmla_decode(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H,
-                             num_split)
+    if enable_autotune:
+        kernel = flashmla_decode(batch, heads, kv_heads, kv_ctx, dim, pe_dim)
+    else:
+        kernel = flashmla_decode(
+            batch,
+            heads,
+            kv_heads,
+            kv_ctx,
+            dim,
+            pe_dim,
+            BLOCK_N,
+            BLOCK_H,
+            num_split,
+            threads=threads)
     profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn)
     input_tensors = profiler._get_inputs()
     tilelang_output = kernel(*input_tensors)
@@ -303,35 +333,3 @@ if __name__ == "__main__":
     latency = profiler.do_bench(warmup=500)
     print(f"Latency: {latency} ms")
     print(f"TFlops: {total_flops / latency * 1e-9} TFlops")
-
-    # Enable Auto Tuning
-
-
-    def get_configs():
-        import itertools
-        BLOCK_N = [16, 32, 64, 128]
-        BLOCK_H = [16, 32, 64, 128]
-        num_split = [1, 2, 4, 8, 16, 32]
-        thread_num = [128, 256]
-
-        _configs = list(itertools.product(BLOCK_N, BLOCK_H, num_split, thread_num))
-
-        return [{
-            "block_N": c[0],
-            "block_H": c[1],
-            "num_split": c[2],
-            "thread_num": c[3],
-        } for c in _configs]
-
-    def wrapped_kernel(block_N=None, block_H=None, num_split=None, thread_num=None):
-        return flashmla_decode(batch, heads, kv_heads, kv_ctx, dim, pe_dim, block_N, block_H,
-                               num_split, thread_num)
-
-    if enable_autotune:
-        autotuner = AutoTuner.from_kernel(kernel=wrapped_kernel, configs=get_configs())
-        tune_result = autotuner.run(warmup=3, rep=20)
-        best_latency = tune_result.latency
-        best_config = tune_result.config
-        print(f"Best latency: {best_latency} ms")
-        print(f"Best TFlops: {total_flops / best_latency * 1e-9} TFlops")
-        print(f"Best config: {best_config}")

tilelang/autotuner/tuner.py

@@ -104,6 +104,7 @@ class AutoTuner:
     profile_args = ProfileArgs()
 
     _kernel_parameters: Optional[Tuple[str, ...]] = None
+    _function_parameters: Optional[Dict[str, Any]] = None
     _lock = threading.Lock()  # For thread safety
     _memory_cache = {}  # In-memory cache dictionary
     cache_dir: Path = Path(env.TILELANG_CACHE_DIR) / "autotuner"
@@ -222,9 +223,10 @@ class AutoTuner:
 
         return self
 
-    def set_kernel_parameters(self, parameters: Tuple[str, ...]):
+    def set_kernel_parameters(self, k_parameters: Tuple[str, ...], f_parameters: Dict[str, Any]):
         # for cache key generation
-        self._kernel_parameters = parameters
+        self._kernel_parameters = k_parameters
+        self._function_parameters = f_parameters
 
     def generate_cache_key(self, parameters: Dict[str, Any]) -> Optional[AutotuneResult]:
         """Generate a cache key for the auto-tuning process.
@@ -417,8 +419,15 @@ class AutoTuner:
             key_args_tuple, key_kwargs_tuple = self._kernel_parameters
             tunable_arguments = [key for key, _ in top_config.items()]
 
+            def check_tunable_argument_value(key, parameters, key_args_tuple) -> bool:
+                params_list = list(parameters.keys())
+                assert key in params_list, f"Tunable argument {key} not found in function parameters"
+                return params_list.index(key) < len(key_args_tuple)
+
             # Check if all tunable arguments have been tuned by comparing config keys with key_kwargs_tuple
-            if any(key in top_config for key, _ in key_kwargs_tuple):
+            if any(key in top_config for key, _ in key_kwargs_tuple) or any(
+                    check_tunable_argument_value(key, self._function_parameters, key_args_tuple)
+                    for key in tunable_arguments):
                 logger.warning(
                     f"Tunable parameters {tunable_arguments} already provided during auto-tuning. Skipping compilation and using direct JIT"
                 )
@@ -676,7 +685,7 @@ class _AutoTunerImplementation:
                     )
 
                 autotuner.jit_compile = jit_compile
-                autotuner.set_kernel_parameters(key)
+                autotuner.set_kernel_parameters(key, inspect.signature(fn).parameters)
 
                 autotuner.run = partial(autotuner.run, warmup, rep, timeout)