[AMD][DSV4] perf: use full 1024-thread block for indexer top-k on ROCm (#36004)

python/sglang/kernels/aot/csrc/elementwise/deepseek_v4_topk.cu

@@ -28,7 +28,16 @@ limitations under the License.
 namespace {
 
 constexpr uint32_t kMaxTopK = 1024;
+#ifdef USE_ROCM
+// CDNA3/CDNA4: this kernel is one block per row and is latency-bound on its
+// O(c4_len) histogram and emit passes. A full 1024-thread block (16 wavefronts
+// of 64 lanes) instead of 512 doubles the per-block scan parallelism, which is
+// ~1.6x faster at 128k context (c4_len = 32768) and never slower at short
+// context. The selected index set is unchanged. CUDA keeps 512.
+constexpr uint32_t kBlockSize = 1024;
+#else
 constexpr uint32_t kBlockSize = 512;
+#endif
 
 #ifdef SGL_TOPK_DYNAMIC_SMEM_BYTES
 constexpr size_t kSMEM = static_cast<size_t>(SGL_TOPK_DYNAMIC_SMEM_BYTES);

python/sglang/kernels/aot/tests/test_topk.py

@@ -249,5 +249,48 @@ def test_topk_transform_ragged_kernel(
     )
 
 
+@pytest.mark.skipif(
+    torch.version.hip is None,
+    reason="deepseek_v4_topk_transform_512 is only built on ROCm",
+)
+@pytest.mark.parametrize("bs", [1, 48])
+@pytest.mark.parametrize("c4_len", [2048, 8192, 32768])
+@torch.inference_mode()
+def test_deepseek_v4_topk_transform(bs: int, c4_len: int) -> None:
+    # c4_len 32768 is the 128k-context decode shape, i.e. the longest scan the
+    # kernel runs and the one most sensitive to the block size it launches with.
+    from sgl_kernel import deepseek_v4_topk_transform_512
+
+    torch.manual_seed(42)
+    topk, page_size = 1024, 64
+
+    scores = torch.randn(bs, c4_len, dtype=torch.float32, device="cuda")
+    seq_lens = torch.full((bs,), c4_len, dtype=torch.int32, device="cuda")
+    # Identity page table, so emitted paged slots equal raw token positions and
+    # can be compared against torch.topk indices directly.
+    num_pages = (c4_len + page_size - 1) // page_size
+    page_table = (
+        torch.arange(num_pages, dtype=torch.int32, device="cuda")
+        .unsqueeze(0)
+        .expand(bs, -1)
+        .contiguous()
+    )
+    page_indices = torch.full((bs, topk), -1, dtype=torch.int32, device="cuda")
+
+    deepseek_v4_topk_transform_512(
+        scores, seq_lens, page_table, page_indices, page_size
+    )
+
+    indices_ref = torch.topk(scores, topk, dim=-1, sorted=False).indices
+    assert_equal(
+        scores,
+        torch.sort(indices_ref, dim=-1).values,
+        torch.sort(page_indices, dim=-1).values,
+        bs,
+        topk,
+        c4_len,
+    )
+
+
 if __name__ == "__main__":
     sys.exit(pytest.main([__file__]))