[Kernel] [Helion] [6/N] Add num_tokens dimension to silu_mul autotuning and dispatching (#34185)

Signed-off-by: Yanan Cao <gmagogsfm@gmail.com>

tests/kernels/helion/test_silu_mul_fp8.py

@@ -54,8 +54,8 @@ def reset_config_manager_singleton():
 class TestSiluMulFp8ConfigPicker:
     def test_config_picker_exact_match(self):
         config_keys = [
-            "intermediate_2048_batchsize_256",
-            "intermediate_4096_batchsize_256",
+            "intermediate_2048_numtokens_256",
+            "intermediate_4096_numtokens_256",
         ]
 
         input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
@@ -63,12 +63,12 @@ class TestSiluMulFp8ConfigPicker:
         args = (input_tensor, scale)
 
         selected_key = pick_silu_mul_fp8_config(args, config_keys)
-        assert selected_key == "intermediate_2048_batchsize_256"
+        assert selected_key == "intermediate_2048_numtokens_256"
 
     def test_config_picker_closest_match(self):
         config_keys = [
-            "intermediate_2048_batchsize_256",
-            "intermediate_4096_batchsize_256",
+            "intermediate_2048_numtokens_256",
+            "intermediate_4096_numtokens_256",
         ]
         # Use 7000 (intermediate_size=3500) which is closer to 4096 than 2048
         input_tensor = torch.randn(32, 7000, dtype=torch.bfloat16, device="cuda")
@@ -76,10 +76,10 @@ class TestSiluMulFp8ConfigPicker:
         args = (input_tensor, scale)
 
         selected_key = pick_silu_mul_fp8_config(args, config_keys)
-        assert selected_key == "intermediate_4096_batchsize_256"
+        assert selected_key == "intermediate_4096_numtokens_256"
 
     def test_config_picker_fallback_to_default(self):
-        config_keys = ["default", "some_other_key"]
+        config_keys = ["default"]
 
         input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
         scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
@@ -101,9 +101,9 @@ class TestSiluMulFp8ConfigPicker:
     @pytest.mark.parametrize("intermediate_size", [2048, 4096, 5120])
     def test_config_picker_different_sizes(self, intermediate_size):
         config_keys = [
-            "intermediate_2048_batchsize_256",
-            "intermediate_4096_batchsize_256",
-            "intermediate_5120_batchsize_256",
+            "intermediate_2048_numtokens_256",
+            "intermediate_4096_numtokens_256",
+            "intermediate_5120_numtokens_256",
         ]
 
         input_tensor = torch.randn(
@@ -113,9 +113,73 @@ class TestSiluMulFp8ConfigPicker:
         args = (input_tensor, scale)
 
         selected_key = pick_silu_mul_fp8_config(args, config_keys)
-        expected_key = f"intermediate_{intermediate_size}_batchsize_256"
+        expected_key = f"intermediate_{intermediate_size}_numtokens_256"
         assert selected_key == expected_key
 
+    def test_config_picker_numtokens_ceiling(self):
+        """Pick the smallest numtokens >= input num_tokens."""
+        config_keys = [
+            "intermediate_4096_numtokens_8",
+            "intermediate_4096_numtokens_32",
+            "intermediate_4096_numtokens_128",
+            "intermediate_4096_numtokens_256",
+        ]
+        # 20 tokens -> should pick numtokens_32 (smallest >= 20)
+        input_tensor = torch.randn(20, 8192, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        selected_key = pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
+        assert selected_key == "intermediate_4096_numtokens_32"
+
+    def test_config_picker_numtokens_exact(self):
+        """Exact num_tokens match is preferred over ceiling."""
+        config_keys = [
+            "intermediate_4096_numtokens_8",
+            "intermediate_4096_numtokens_32",
+            "intermediate_4096_numtokens_128",
+        ]
+        input_tensor = torch.randn(32, 8192, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        selected_key = pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
+        assert selected_key == "intermediate_4096_numtokens_32"
+
+    def test_config_picker_numtokens_fallback_to_largest(self):
+        """Fall back to the largest numtokens when input exceeds all."""
+        config_keys = [
+            "intermediate_4096_numtokens_8",
+            "intermediate_4096_numtokens_32",
+            "intermediate_4096_numtokens_128",
+        ]
+        # 512 tokens -> exceeds all available, should pick largest (128)
+        input_tensor = torch.randn(512, 8192, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        selected_key = pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
+        assert selected_key == "intermediate_4096_numtokens_128"
+
+    def test_config_picker_malformed_key_raises(self):
+        """Malformed config keys should raise ValueError."""
+        config_keys = ["intermediate_4096_badformat_256"]
+        input_tensor = torch.randn(32, 8192, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        with pytest.raises(ValueError, match="Malformed config key"):
+            pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
+
+    def test_config_picker_default_ignored_when_valid_keys_exist(self):
+        """'default' is skipped in favor of a real match."""
+        config_keys = [
+            "default",
+            "intermediate_4096_numtokens_32",
+            "intermediate_4096_numtokens_128",
+        ]
+        input_tensor = torch.randn(64, 8192, dtype=torch.bfloat16, device="cuda")
+        scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
+
+        selected_key = pick_silu_mul_fp8_config((input_tensor, scale), config_keys)
+        assert selected_key == "intermediate_4096_numtokens_128"
+
 
 class TestSiluMulFp8Correctness:
     @pytest.mark.parametrize("batch_size", [1, 8, 32, 128])

vllm/kernels/helion/ops/silu_mul_fp8.py

@@ -3,6 +3,7 @@
 
 from typing import Any
 
+import regex as re
 import torch
 
 from vllm.logger import init_logger
@@ -53,44 +54,78 @@ def silu_mul_fp8(input: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
     return out.view(output_shape)
 
 
+@silu_mul_fp8.register_input_generator  # type: ignore[misc]
+def generate_silu_mul_fp8_inputs() -> dict[str, tuple[Any, ...]]:
+    intermediate_sizes = [2048, 2880, 4096, 8192, 11008, 14336]
+
+    # Use the same num_tokens values as vLLM's default cudagraph capture sizes.
+    # See vllm/config/vllm.py _set_cudagraph_sizes() for the canonical formula.
+    num_tokens_list = [1, 2, 4] + list(range(8, 256, 8)) + list(range(256, 513, 16))
+
+    inputs = {}
+    for num_tokens in num_tokens_list:
+        for intermediate_size in intermediate_sizes:
+            # Input tensor has shape (num_tokens, 2 * intermediate_size)
+            # because silu_mul splits it into two halves
+            input_tensor = torch.randn(
+                num_tokens,
+                2 * intermediate_size,
+                device="cuda",
+                dtype=torch.bfloat16,
+            )
+            scale = torch.tensor([1.0], device="cuda", dtype=torch.float32)
+
+            config_key = f"intermediate_{intermediate_size}_numtokens_{num_tokens}"
+            inputs[config_key] = (input_tensor, scale)
+
+    return inputs
+
+
 @silu_mul_fp8.register_config_picker  # type: ignore[misc]
 def pick_silu_mul_fp8_config(
     args: tuple[Any, ...], config_keys: list[str]
 ) -> str | None:
+    """Pick the best pre-tuned config for the given input shape.
+
+    Selection strategy:
+      1. Find the closest intermediate_size among available configs
+         (exact match preferred).
+      2. Among the num_tokens values tuned for that intermediate_size, pick
+         the smallest num_tokens >= the input's num_tokens. If the input is
+         larger than all available num_tokens, fall back to the largest.
+
+    Config keys must be "default" or follow the format
+    "intermediate_{int}_numtokens_{int}".
+    """
     if not config_keys:
         return None
 
-    input_tensor, scale = args
+    input_tensor, _scale = args
     intermediate_size = input_tensor.shape[-1] // 2
-
-    # TODO(gmagosfm): Rerun autotuning to capture config for
-    # other batch sizes.
-    target_key = f"intermediate_{intermediate_size}_batchsize_256"
-    if target_key in config_keys:
-        return target_key
-
-    intermediate_sizes = []
+    num_tokens = input_tensor.view(-1, input_tensor.shape[-1]).shape[0]
+    configs: dict[int, list[int]] = {}
     for key in config_keys:
-        if key.startswith("intermediate_") and "_batchsize_256" in key:
-            try:
-                size_str = key.split("_")[1]
-                size = int(size_str)
-                intermediate_sizes.append((abs(size - intermediate_size), key))
-            except (ValueError, IndexError):
+        if key == "default":
             continue
+        match = re.fullmatch(r"intermediate_(\d+)_numtokens_(\d+)", key)
+        if not match:
+            raise ValueError(
+                f"Malformed config key '{key}', "
+                f"expected format 'intermediate_{{int}}_numtokens_{{int}}'"
+            )
+        isize_str, ntokens_str = match.groups()
+        configs.setdefault(int(isize_str), []).append(int(ntokens_str))
 
-    if intermediate_sizes:
-        _, best_key = min(intermediate_sizes)
-        logger.debug(
-            "No exact config for intermediate_size=%d, using closest match: %s",
-            intermediate_size,
-            best_key,
+    if not configs:
+        return "default" if "default" in config_keys else None
+
+    best_isize = min(configs, key=lambda s: abs(s - intermediate_size))
+    available_ntokens = sorted(configs[best_isize])
+    best_ntokens = next(
+        (n for n in available_ntokens if n >= num_tokens), available_ntokens[-1]
     )
-        return best_key
-    if "default" in config_keys:
-        return "default"
 
-    return None
+    return f"intermediate_{best_isize}_numtokens_{best_ntokens}"
 
 
 def silu_mul_fp8_baseline(input: torch.Tensor, scale: torch.Tensor) -> torch.Tensor: