[executorch][cuda] Add a length-aware Triton SDPA over per-sequence KV columns

backends/cuda/BUCK

@@ -174,6 +174,7 @@ fbcode_target(
         "triton/kernels/quantized_gemm_family.py",
         "triton/kernels/quantized_gemm_utils.py",
         "triton/kernels/sdpa.py",
+        "triton/kernels/sdpa_varlen.py",
         "triton/kernels/topk.py",
         "triton/kernels/tq4_sdpa.py",
         "triton/kernels/triton_mutation_cache.py",

backends/cuda/tests/bench_sdpa_varlen.py

@@ -0,0 +1,109 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Benchmark triton::sdpa_varlen against the cell layout's triton::sdpa.
+
+Muse Glimmer 30B attention (H_q=32, H_kv=2, D=128). Each case is a step of
+the batching runtime: per sequence, the query rows it adds and the context it
+then holds. The cell baseline sweeps one shared pool holding every sequence,
+masked per row; sdpa_varlen reads each sequence's own column.
+
+usage: python bench_sdpa_varlen.py [--check]
+"""
+
+import argparse
+
+import torch
+import triton
+
+from executorch.backends.cuda.triton.kernels.sdpa import sdpa
+from executorch.backends.cuda.triton.kernels.sdpa_varlen import sdpa_varlen
+
+HQ, HKV, D = 32, 2, 128
+WINDOW = 2048
+
+# (name, [(query rows, context after the step)] per sequence)
+CASES = [
+    ("decode c1 ctx600", [(1, 600)]),
+    ("decode c4 ctx600", [(1, 600)] * 4),
+    ("decode c4 ctx4k", [(1, 4096)] * 4),
+    ("decode c4 mixed", [(1, 300), (1, 900), (1, 2500), (1, 4096)]),
+    ("verify c1 16x600", [(16, 600)]),
+    ("verify c4 16x600", [(16, 600)] * 4),
+    ("verify c4 16x4k", [(16, 4096)] * 4),
+    ("prefill 97", [(97, 97)]),
+    ("prefill 512 + 3 decode", [(512, 512), (1, 600), (1, 600), (1, 600)]),
+    ("prefill 4096", [(4096, 4096)]),
+]
+
+
+def _step(case, window, device="cuda"):
+    t = sum(q for q, _ in case)
+    cols = len(case) + 1
+    rows = max(c for _, c in case)
+    q = torch.randn(1, HQ, t, D, device=device, dtype=torch.bfloat16)
+    k_seq = torch.randn(rows, cols, HKV, D, device=device, dtype=torch.bfloat16)
+    v_seq = torch.randn_like(k_seq)
+    info, start = [], 0
+    for col, (q_len, ctx) in enumerate(case):
+        info.append((start, q_len, col, ctx))
+        start += q_len
+    info = torch.tensor(info, dtype=torch.int32, device=device)
+
+    # Cell layout: every sequence's context packed into one pool, each row's
+    # visibility in an explicit mask, the sweep bounded by the occupied extent.
+    pool = sum(c for _, c in case)
+    k_pool = torch.empty(1, pool, HKV, D, device=device, dtype=torch.bfloat16)
+    v_pool = torch.empty_like(k_pool)
+    mask = torch.zeros(1, 1, t, pool, device=device, dtype=torch.bool)
+    base, row = 0, 0
+    for col, (q_len, ctx) in enumerate(case):
+        k_pool[0, base : base + ctx] = k_seq[:ctx, col]
+        v_pool[0, base : base + ctx] = v_seq[:ctx, col]
+        for r in range(q_len):
+            p = ctx - q_len + r
+            lo = max(0, p - window + 1) if window else 0
+            mask[0, 0, row + r, base + lo : base + p + 1] = True
+        base += ctx
+        row += q_len
+    read_len = torch.tensor([pool], device=device, dtype=torch.int64)
+    return q, k_seq, v_seq, info, k_pool, v_pool, mask, read_len
+
+
+def _time(fn):
+    fn()
+    torch.cuda.synchronize()
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        fn()
+    return triton.testing.do_bench(graph.replay, warmup=25, rep=200)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--check", action="store_true")
+    args = parser.parse_args()
+    torch.manual_seed(0)
+    scale = D**-0.5
+    print(f"{'case':28s} {'window':>6s} {'cell ms':>9s} {'varlen ms':>10s} {'speedup':>8s}")
+    for name, case in CASES:
+        for window in (0, WINDOW):
+            q, k, v, info, k_pool, v_pool, mask, read_len = _step(case, window)
+            varlen = lambda: sdpa_varlen(q, k, v, info, scale, True, window, 0)
+            cell = lambda: sdpa(
+                q, k_pool.transpose(1, 2), v_pool.transpose(1, 2), mask, 0.0,
+                False, scale, True, read_len,
+            )
+            if args.check:
+                torch.testing.assert_close(
+                    varlen().float(), cell().float(), atol=2e-2, rtol=2e-2
+                )
+            t_cell, t_var = _time(cell), _time(varlen)
+            print(f"{name:28s} {window:6d} {t_cell:9.4f} {t_var:10.4f} {t_cell / t_var:7.2f}x")
+
+
+if __name__ == "__main__":
+    main()

backends/cuda/tests/targets.bzl

@@ -133,6 +133,25 @@ def define_common_targets(is_fbcode = False):
         ),
     )
 
+    python_unittest_remote_gpu(
+        name = "test_sdpa_varlen",
+        srcs = [
+            "test_sdpa_varlen.py",
+        ],
+        visibility = [
+            "//executorch/...",
+        ],
+        deps = [
+            "//caffe2:torch",
+            "//executorch/backends/cuda:triton_kernels",
+        ],
+        keep_gpu_sections = True,
+        remote_execution = re_test_utils.remote_execution(
+            platform = "gpu-remote-execution",
+            subplatform = "A100-exclusive",
+        ),
+    )
+
     python_unittest_remote_gpu(
         name = "test_int4_large_quantized_gemm",
         srcs = [