From 9c87e137eeba744f44255790cf4e778b28923cf1 Mon Sep 17 00:00:00 2001 From: Yuan Luo Date: Wed, 18 Mar 2026 13:20:07 +0800 Subject: [PATCH] [GDN] Support GDN packed decode (#20627) --- .../bench_gdn_decode.py | 488 ++++++++++++++++++ .../layers/attention/fla/fused_recurrent.py | 221 ++++++++ .../layers/attention/linear/gdn_backend.py | 62 ++- .../attention/linear/kernels/gdn_triton.py | 60 +++ 4 files changed, 829 insertions(+), 2 deletions(-) create mode 100644 benchmark/bench_linear_attention/bench_gdn_decode.py diff --git a/benchmark/bench_linear_attention/bench_gdn_decode.py b/benchmark/bench_linear_attention/bench_gdn_decode.py new file mode 100644 index 000000000..816c6d978 --- /dev/null +++ b/benchmark/bench_linear_attention/bench_gdn_decode.py @@ -0,0 +1,488 @@ +""" +Benchmark & Correctness: GDN Packed Decode vs Baseline Decode. + +Compares: + - Baseline: split(mixed_qkv) → view → fused_sigmoid_gating_delta_rule_update + - Packed: fused_recurrent_gated_delta_rule_packed_decode (single kernel) + +The packed path eliminates: + - torch.split() + .view() tensor materialization + - Separate gating kernel launches + - Intermediate tensor allocations + +Reports correctness (output & state matching) and performance (ms, speedup). + +Usage: + python bench_gdn_decode.py # default sweep + python bench_gdn_decode.py --mode bench # benchmark only + python bench_gdn_decode.py --mode correctness # correctness only + python bench_gdn_decode.py --preset qwen3.5-35b # Qwen3.5-35B-A3B config +""" + +import argparse +import os +import sys +import time + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "python")) + +import torch +import triton + +from sglang.srt.layers.attention.fla.fused_recurrent import ( + fused_recurrent_gated_delta_rule_packed_decode, +) +from sglang.srt.layers.attention.fla.fused_sigmoid_gating_recurrent import ( + fused_sigmoid_gating_delta_rule_update, +) + +# --------------------------------------------------------------------------- +# Input factory +# --------------------------------------------------------------------------- + + +def make_inputs( + B: int, + H: int, + HV: int, + K: int, + V: int, + pool_size: int, + device: str, + dtype: torch.dtype, + seed: int = 42, +): + """Create all input tensors for a single benchmark / correctness run.""" + torch.manual_seed(seed) + + qkv_dim = 2 * H * K + HV * V + mixed_qkv = torch.randn(B, qkv_dim, device=device, dtype=dtype) + a = torch.randn(B, HV, device=device, dtype=dtype) + b = torch.randn(B, HV, device=device, dtype=dtype) + A_log = torch.randn(HV, device=device, dtype=dtype) + dt_bias = torch.randn(HV, device=device, dtype=dtype) + + ssm_states = torch.randn(pool_size, HV, V, K, device=device, dtype=dtype) * 0.1 + cache_indices = torch.arange(B, device=device, dtype=torch.int32) + + cu_seqlens = torch.arange(B + 1, device=device, dtype=torch.long) + + return dict( + B=B, + H=H, + HV=HV, + K=K, + V=V, + qkv_dim=qkv_dim, + pool_size=pool_size, + mixed_qkv=mixed_qkv, + a=a, + b=b, + A_log=A_log, + dt_bias=dt_bias, + ssm_states=ssm_states, + cache_indices=cache_indices, + cu_seqlens=cu_seqlens, + ) + + +# --------------------------------------------------------------------------- +# Runner wrappers +# --------------------------------------------------------------------------- + + +def run_baseline(inp): + """Baseline path: split → view → fused_sigmoid_gating_delta_rule_update. + + This mirrors the FULL original decode path in GDNAttnBackend.forward_decode, + including the split, view, and kernel call. + """ + B, H, HV, K, V = inp["B"], inp["H"], inp["HV"], inp["K"], inp["V"] + mixed_qkv = inp["mixed_qkv"] + ssm_states = inp["ssm_states"].clone() + + # Step 1: split (same as forward_decode) + q_flat, k_flat, v_flat = torch.split(mixed_qkv, [H * K, H * K, HV * V], dim=-1) + + # Step 2: view + reshape (same as forward_decode) + q = q_flat.view(1, B, H, K) + k = k_flat.view(1, B, H, K) + v = v_flat.view(1, B, HV, V) + + # Step 3: fused gating + recurrent update + o = fused_sigmoid_gating_delta_rule_update( + A_log=inp["A_log"], + dt_bias=inp["dt_bias"], + q=q, + k=k, + v=v, + a=inp["a"], + b=inp["b"], + initial_state_source=ssm_states, + initial_state_indices=inp["cache_indices"], + cu_seqlens=inp["cu_seqlens"], + use_qk_l2norm_in_kernel=True, + softplus_beta=1.0, + softplus_threshold=20.0, + ) + + return o, ssm_states + + +def run_packed(inp): + """Packed path: single fused kernel directly on mixed_qkv.""" + B, HV, K, V = inp["B"], inp["HV"], inp["K"], inp["V"] + ssm_states = inp["ssm_states"].clone() + out = inp["mixed_qkv"].new_empty(B, 1, HV, V) + + fused_recurrent_gated_delta_rule_packed_decode( + mixed_qkv=inp["mixed_qkv"], + a=inp["a"], + b=inp["b"], + A_log=inp["A_log"], + dt_bias=inp["dt_bias"], + scale=inp["K"] ** -0.5, + initial_state=ssm_states, + out=out, + ssm_state_indices=inp["cache_indices"], + use_qk_l2norm_in_kernel=True, + ) + + # Convert [B, 1, HV, V] → [1, B, HV, V] to match baseline layout + return out.transpose(0, 1), ssm_states + + +# --------------------------------------------------------------------------- +# Correctness check +# --------------------------------------------------------------------------- + + +def check_correctness(B, H, HV, K, V, pool_size, device, dtype, seed=42): + """Run correctness check for a single config. Returns True if PASS.""" + tag = f"B={B:>4} H={H:>2} HV={HV:>2} K={K:>3} V={V:>3} pool={pool_size:>4}" + + inp = make_inputs(B, H, HV, K, V, pool_size, device, dtype, seed=seed) + + o_baseline, state_baseline = run_baseline(inp) + o_packed, state_packed = run_packed(inp) + + # Output comparison + atol = 2e-2 if dtype != torch.float32 else 1e-4 + rtol = 1e-2 if dtype != torch.float32 else 1e-4 + + try: + torch.testing.assert_close(o_packed, o_baseline, atol=atol, rtol=rtol) + output_ok = True + except AssertionError as e: + output_ok = False + out_diff = (o_packed - o_baseline).abs().max().item() + + # State comparison (only for slots that were updated) + indices = inp["cache_indices"] + try: + torch.testing.assert_close( + state_packed[indices], state_baseline[indices], atol=atol, rtol=rtol + ) + state_ok = True + except AssertionError: + state_ok = False + st_diff = (state_packed[indices] - state_baseline[indices]).abs().max().item() + + passed = output_ok and state_ok + + if passed: + print(f" [PASS] {tag}") + else: + details = [] + if not output_ok: + details.append(f"output max_diff={out_diff:.6f}") + if not state_ok: + details.append(f"state max_diff={st_diff:.6f}") + print(f" [FAIL] {tag} ({', '.join(details)})") + + return passed + + +# --------------------------------------------------------------------------- +# Benchmark +# --------------------------------------------------------------------------- + + +def bench_shape(B, H, HV, K, V, pool_size, device, dtype): + """Benchmark baseline vs packed for a single config.""" + inp = make_inputs(B, H, HV, K, V, pool_size, device, dtype) + + # ── Baseline: full path including split + view ── + def fn_baseline(): + q_flat, k_flat, v_flat = torch.split( + inp["mixed_qkv"], [H * K, H * K, HV * V], dim=-1 + ) + q = q_flat.view(1, B, H, K) + k = k_flat.view(1, B, H, K) + v = v_flat.view(1, B, HV, V) + fused_sigmoid_gating_delta_rule_update( + A_log=inp["A_log"], + dt_bias=inp["dt_bias"], + q=q, + k=k, + v=v, + a=inp["a"], + b=inp["b"], + initial_state_source=inp["ssm_states"], + initial_state_indices=inp["cache_indices"], + cu_seqlens=inp["cu_seqlens"], + use_qk_l2norm_in_kernel=True, + softplus_beta=1.0, + softplus_threshold=20.0, + ) + + # ── Packed: single kernel ── + out_buf = inp["mixed_qkv"].new_empty(B, 1, HV, V) + + def fn_packed(): + fused_recurrent_gated_delta_rule_packed_decode( + mixed_qkv=inp["mixed_qkv"], + a=inp["a"], + b=inp["b"], + A_log=inp["A_log"], + dt_bias=inp["dt_bias"], + scale=K**-0.5, + initial_state=inp["ssm_states"], + out=out_buf, + ssm_state_indices=inp["cache_indices"], + use_qk_l2norm_in_kernel=True, + ) + + # Warmup + for _ in range(10): + fn_baseline() + fn_packed() + torch.cuda.synchronize() + + quantiles = [0.5, 0.2, 0.8] + + try: + ms_baseline, ms_base_lo, ms_base_hi = triton.testing.do_bench( + fn_baseline, quantiles=quantiles, warmup=50, rep=200 + ) + ms_packed, ms_pack_lo, ms_pack_hi = triton.testing.do_bench( + fn_packed, quantiles=quantiles, warmup=50, rep=200 + ) + except Exception: + # Fallback to manual timing + torch.cuda.synchronize() + N = 200 + start = time.perf_counter() + for _ in range(N): + fn_baseline() + torch.cuda.synchronize() + ms_baseline = (time.perf_counter() - start) / N * 1000 + + start = time.perf_counter() + for _ in range(N): + fn_packed() + torch.cuda.synchronize() + ms_packed = (time.perf_counter() - start) / N * 1000 + + speedup = ms_baseline / ms_packed if ms_packed > 0 else float("inf") + saved_us = (ms_baseline - ms_packed) * 1000 + + print( + f" {B:>5} {H:>3} {HV:>3} {K:>3} {V:>3} | " + f"{ms_baseline * 1000:>10.1f} | " + f"{ms_packed * 1000:>10.1f} | " + f"{speedup:>7.2f}x | " + f"{saved_us:>+9.1f}" + ) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + + +def run_correctness(device, dtype): + print("=" * 70) + print("Correctness: Baseline GDN Decode vs Packed GDN Decode") + print("=" * 70) + + shapes = [ + # (B, H, HV, K, V, pool_size) + # --- Qwen3.5-35B-A3B style (TP=2: H=8, HV=16) --- + (1, 8, 16, 128, 128, 32), + (4, 8, 16, 128, 128, 32), + (16, 8, 16, 128, 128, 64), + (32, 8, 16, 128, 128, 128), + (64, 8, 16, 128, 128, 128), + (128, 8, 16, 128, 128, 256), + (256, 8, 16, 128, 128, 512), + # --- Qwen3.5-35B-A3B style (TP=1: H=16, HV=32) --- + (1, 16, 32, 128, 128, 32), + (32, 16, 32, 128, 128, 128), + (64, 16, 32, 128, 128, 128), + # --- Qwen3-Next-80B-A3B style --- + (32, 16, 16, 128, 128, 128), + (64, 16, 16, 128, 128, 128), + # --- With PAD_SLOT_ID --- + (32, 8, 16, 128, 128, 128), # some indices may be padded + # --- Edge cases --- + (1, 8, 16, 128, 128, 32), + (2, 8, 16, 128, 128, 32), + ] + + all_pass = True + for B, H, HV, K, V, pool_size in shapes: + if not check_correctness(B, H, HV, K, V, pool_size, device, dtype): + all_pass = False + + # PAD_SLOT_ID test + print("\n PAD_SLOT_ID test (indices with -1):") + inp = make_inputs(32, 8, 16, 128, 128, 128, device, dtype) + o_baseline, st_baseline = run_baseline(inp) + o_packed, st_packed = run_packed(inp) + + try: + torch.testing.assert_close(o_packed, o_baseline, atol=2e-2, rtol=1e-2) + print(" [PASS] PAD_SLOT_ID=-1 handling") + except AssertionError: + print(" [FAIL] PAD_SLOT_ID=-1 handling") + all_pass = False + + print() + if all_pass: + print("ALL PASSED.") + else: + print("SOME FAILED.") + return all_pass + + +def run_benchmark(device, dtype, args): + print() + print("=" * 85) + print("Benchmark: Baseline GDN Decode vs Packed GDN Decode") + print("=" * 85) + + K = args.head_size_k + V = args.head_size_v + pool_size = args.pool_size + + if args.preset == "qwen3.5-35b": + # Qwen3.5-35B-A3B: H_qk=16, H_v=32, K=128, V=128 + # After TP=2: H=8, HV=16 + bench_configs = [ + # (B, H, HV) — TP=2 config + (1, 8, 16), + (2, 8, 16), + (4, 8, 16), + (8, 8, 16), + (16, 8, 16), + (32, 8, 16), + (64, 8, 16), + (128, 8, 16), + (256, 8, 16), + (512, 8, 16), + # TP=1 config (full heads) + (1, 16, 32), + (8, 16, 32), + (32, 16, 32), + (64, 16, 32), + (128, 16, 32), + (256, 16, 32), + ] + elif args.preset == "qwen3-next-80b": + bench_configs = [ + # Qwen3-Next-80B-A3B: all same H=HV=16 after TP + (1, 16, 16), + (8, 16, 16), + (32, 16, 16), + (64, 16, 16), + (128, 16, 16), + (256, 16, 16), + ] + else: + bench_configs = [] + for B in args.batch_sizes: + for H in args.num_q_heads: + for HV in args.num_v_heads: + bench_configs.append((B, H, HV)) + + print(f" Config: K={K}, V={V}, pool_size={pool_size}, dtype={dtype}") + print( + f" {'B':>5} {'H':>3} {'HV':>3} {'K':>3} {'V':>3} | " + f"{'base (μs)':>10} | " + f"{'packed (μs)':>10} | " + f"{'speedup':>8} | " + f"{'saved (μs)':>10}" + ) + print(" " + "-" * 75) + + for B, H, HV in bench_configs: + actual_pool = max(pool_size, B + 16) + bench_shape(B, H, HV, K, V, actual_pool, device, dtype) + + +def main(): + parser = argparse.ArgumentParser( + description="Benchmark & Correctness: GDN Packed Decode vs Baseline" + ) + parser.add_argument( + "--mode", + choices=["all", "correctness", "bench"], + default="all", + help="Run mode (default: all)", + ) + parser.add_argument( + "--preset", + choices=["qwen3.5-35b", "qwen3-next-80b", "custom"], + default="qwen3.5-35b", + help="Preset config (default: qwen3.5-35b)", + ) + parser.add_argument( + "--dtype", + choices=["float16", "bfloat16", "float32"], + default="bfloat16", + ) + parser.add_argument("--head-size-k", type=int, default=128) + parser.add_argument("--head-size-v", type=int, default=128) + parser.add_argument("--pool-size", type=int, default=512) + parser.add_argument( + "--batch-sizes", + type=int, + nargs="+", + default=[1, 4, 8, 16, 32, 64, 128, 256, 512], + ) + parser.add_argument( + "--num-q-heads", + type=int, + nargs="+", + default=[8, 16], + ) + parser.add_argument( + "--num-v-heads", + type=int, + nargs="+", + default=[16, 32], + ) + args = parser.parse_args() + + device = "cuda" + dtype = getattr(torch, args.dtype) + + cap = torch.cuda.get_device_capability() + dev_name = torch.cuda.get_device_name() + print(f"Device: {dev_name} (SM {cap[0]}{cap[1]})") + + if args.mode in ("all", "correctness"): + all_pass = run_correctness(device, dtype) + if not all_pass and args.mode == "all": + print("\nSkipping benchmark due to correctness failures.") + return 1 + + if args.mode in ("all", "bench"): + run_benchmark(device, dtype, args) + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/python/sglang/srt/layers/attention/fla/fused_recurrent.py b/python/sglang/srt/layers/attention/fla/fused_recurrent.py index 2fe5e4244..44e42e2d6 100644 --- a/python/sglang/srt/layers/attention/fla/fused_recurrent.py +++ b/python/sglang/srt/layers/attention/fla/fused_recurrent.py @@ -181,6 +181,227 @@ def fused_recurrent_gated_delta_rule_fwd( return o, final_state +# Adapted from vllm project. +@triton.jit +def fused_recurrent_gated_delta_rule_packed_decode_kernel( + mixed_qkv, + a, + b, + A_log, + dt_bias, + o, + h0, + ht, + ssm_state_indices, + scale, + stride_mixed_qkv_tok: tl.constexpr, + stride_a_tok: tl.constexpr, + stride_b_tok: tl.constexpr, + stride_init_state_token: tl.constexpr, + stride_final_state_token: tl.constexpr, + stride_indices_seq: tl.constexpr, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + SOFTPLUS_THRESHOLD: tl.constexpr, + USE_QK_L2NORM_IN_KERNEL: tl.constexpr, +): + i_v, i_nh = tl.program_id(0), tl.program_id(1) + i_n, i_hv = i_nh // HV, i_nh % HV + i_h = i_hv // (HV // H) + + o_k = tl.arange(0, BK) + o_v = i_v * BV + tl.arange(0, BV) + mask_k = o_k < K + mask_v = o_v < V + mask_h = mask_v[:, None] & mask_k[None, :] + + state_idx = tl.load(ssm_state_indices + i_n * stride_indices_seq).to(tl.int64) + p_o = o + (i_n * HV + i_hv) * V + o_v + + if state_idx < 0: + zero = tl.zeros([BV], dtype=tl.float32).to(p_o.dtype.element_ty) + tl.store(p_o, zero, mask=mask_v) + return + + p_h0 = h0 + state_idx * stride_init_state_token + p_h0 = p_h0 + i_hv * V * K + o_v[:, None] * K + o_k[None, :] + b_h = tl.load(p_h0, mask=mask_h, other=0).to(tl.float32) + + p_mixed = mixed_qkv + i_n * stride_mixed_qkv_tok + q_off = i_h * K + o_k + k_off = (H * K) + i_h * K + o_k + v_off = (2 * H * K) + i_hv * V + o_v + b_q = tl.load(p_mixed + q_off, mask=mask_k, other=0).to(tl.float32) + b_k = tl.load(p_mixed + k_off, mask=mask_k, other=0).to(tl.float32) + b_v = tl.load(p_mixed + v_off, mask=mask_v, other=0).to(tl.float32) + + if USE_QK_L2NORM_IN_KERNEL: + b_q = b_q / tl.sqrt(tl.sum(b_q * b_q) + 1e-6) + b_k = b_k / tl.sqrt(tl.sum(b_k * b_k) + 1e-6) + b_q = b_q * scale + + a_val = tl.load(a + i_n * stride_a_tok + i_hv).to(tl.float32) + b_val = tl.load(b + i_n * stride_b_tok + i_hv).to(tl.float32) + A_log_val = tl.load(A_log + i_hv).to(tl.float32) + dt_bias_val = tl.load(dt_bias + i_hv).to(tl.float32) + x = a_val + dt_bias_val + softplus_x = tl.where(x <= SOFTPLUS_THRESHOLD, tl.log(1.0 + tl.exp(x)), x) + g_val = -tl.exp(A_log_val) * softplus_x + beta_val = tl.sigmoid(b_val).to(b.dtype.element_ty).to(tl.float32) + + b_h *= exp(g_val) + b_v -= tl.sum(b_h * b_k[None, :], 1) + b_v *= beta_val + b_h += b_v[:, None] * b_k[None, :] + b_o = tl.sum(b_h * b_q[None, :], 1) + tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v) + + p_ht = ht + state_idx * stride_final_state_token + p_ht = p_ht + i_hv * V * K + o_v[:, None] * K + o_k[None, :] + tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h) + + +def fused_recurrent_gated_delta_rule_packed_decode( + mixed_qkv: torch.Tensor, + a: torch.Tensor, + b: torch.Tensor, + A_log: torch.Tensor, + dt_bias: torch.Tensor, + scale: float, + initial_state: torch.Tensor, + out: torch.Tensor, + ssm_state_indices: torch.Tensor, + use_qk_l2norm_in_kernel: bool = False, +) -> tuple[torch.Tensor, torch.Tensor]: + if mixed_qkv.ndim != 2: + raise ValueError( + f"`mixed_qkv` must be a 2D tensor (got ndim={mixed_qkv.ndim})." + ) + if mixed_qkv.stride(-1) != 1: + raise ValueError("`mixed_qkv` must be contiguous in the last dim.") + if a.ndim != 2 or b.ndim != 2: + raise ValueError( + f"`a` and `b` must be 2D tensors (got a.ndim={a.ndim}, b.ndim={b.ndim})." + ) + if a.stride(-1) != 1 or b.stride(-1) != 1: + raise ValueError("`a`/`b` must be contiguous in the last dim.") + if A_log.ndim != 1 or dt_bias.ndim != 1: + raise ValueError("`A_log`/`dt_bias` must be 1D tensors.") + if A_log.stride(0) != 1 or dt_bias.stride(0) != 1: + raise ValueError("`A_log`/`dt_bias` must be contiguous.") + if ssm_state_indices.ndim != 1: + raise ValueError( + f"`ssm_state_indices` must be 1D for packed decode (got ndim={ssm_state_indices.ndim})." + ) + if not out.is_contiguous(): + raise ValueError("`out` must be contiguous.") + + dev = mixed_qkv.device + if any( + t.device != dev + for t in (a, b, A_log, dt_bias, initial_state, out, ssm_state_indices) + ): + raise ValueError("All inputs must be on the same device.") + + B = mixed_qkv.shape[0] + if a.shape[0] != B or b.shape[0] != B: + raise ValueError( + "Mismatched batch sizes: " + f"mixed_qkv.shape[0]={B}, a.shape[0]={a.shape[0]}, b.shape[0]={b.shape[0]}." + ) + if ssm_state_indices.shape[0] != B: + raise ValueError( + f"`ssm_state_indices` must have shape [B] (got {tuple(ssm_state_indices.shape)}; expected ({B},))." + ) + + if initial_state.ndim != 4: + raise ValueError( + f"`initial_state` must be a 4D tensor (got ndim={initial_state.ndim})." + ) + if initial_state.stride(-1) != 1: + raise ValueError("`initial_state` must be contiguous in the last dim.") + HV, V, K = initial_state.shape[-3:] + if a.shape[1] != HV or b.shape[1] != HV: + raise ValueError( + f"`a`/`b` must have shape [B, HV] with HV={HV} (got a.shape={tuple(a.shape)}, b.shape={tuple(b.shape)})." + ) + if A_log.numel() != HV or dt_bias.numel() != HV: + raise ValueError( + f"`A_log` and `dt_bias` must have {HV} elements (got A_log.numel()={A_log.numel()}, dt_bias.numel()={dt_bias.numel()})." + ) + if out.shape != (B, 1, HV, V): + raise ValueError( + f"`out` must have shape {(B, 1, HV, V)} (got out.shape={tuple(out.shape)})." + ) + + qkv_dim = mixed_qkv.shape[1] + qk_dim = qkv_dim - HV * V + if qk_dim <= 0 or qk_dim % 2 != 0: + raise ValueError( + f"Invalid packed `mixed_qkv` last dim={qkv_dim} for HV={HV}, V={V}." + ) + q_dim = qk_dim // 2 + if q_dim % K != 0: + raise ValueError(f"Invalid packed Q size {q_dim}: must be divisible by K={K}.") + H = q_dim // K + if H <= 0 or HV % H != 0: + raise ValueError( + f"Invalid head config inferred from mixed_qkv: H={H}, HV={HV}." + ) + + BK = triton.next_power_of_2(K) + if triton.cdiv(K, BK) != 1: + raise ValueError( + f"Packed decode kernel only supports NK=1 (got K={K}, BK={BK})." + ) + BV = min(triton.next_power_of_2(V), 32) + num_stages = 3 + num_warps = 1 + + stride_mixed_qkv_tok = mixed_qkv.stride(0) + stride_a_tok = a.stride(0) + stride_b_tok = b.stride(0) + stride_init_state_token = initial_state.stride(0) + stride_final_state_token = initial_state.stride(0) + stride_indices_seq = ssm_state_indices.stride(0) + + NV = triton.cdiv(V, BV) + grid = (NV, B * HV) + fused_recurrent_gated_delta_rule_packed_decode_kernel[grid]( + mixed_qkv=mixed_qkv, + a=a, + b=b, + A_log=A_log, + dt_bias=dt_bias, + o=out, + h0=initial_state, + ht=initial_state, + ssm_state_indices=ssm_state_indices, + scale=scale, + stride_mixed_qkv_tok=stride_mixed_qkv_tok, + stride_a_tok=stride_a_tok, + stride_b_tok=stride_b_tok, + stride_init_state_token=stride_init_state_token, + stride_final_state_token=stride_final_state_token, + stride_indices_seq=stride_indices_seq, + H=H, + HV=HV, + K=K, + V=V, + BK=BK, + BV=BV, + SOFTPLUS_THRESHOLD=20.0, + USE_QK_L2NORM_IN_KERNEL=use_qk_l2norm_in_kernel, + num_warps=num_warps, + num_stages=num_stages, + ) + return out, initial_state + + class FusedRecurrentFunction(torch.autograd.Function): @staticmethod diff --git a/python/sglang/srt/layers/attention/linear/gdn_backend.py b/python/sglang/srt/layers/attention/linear/gdn_backend.py index 1e3cefa47..54d783958 100644 --- a/python/sglang/srt/layers/attention/linear/gdn_backend.py +++ b/python/sglang/srt/layers/attention/linear/gdn_backend.py @@ -1,4 +1,4 @@ -from typing import Tuple, Union +from typing import Optional, Tuple, Union import torch @@ -111,10 +111,48 @@ class GDNKernelDispatcher: else: self.verify_kernel = triton_kernel + self.supports_packed_decode = getattr( + self.decode_kernel, "supports_packed_decode", False + ) + rank0_log( f"GDN kernel dispatcher: decode={self.decode_kernel.__class__.__name__}, " f"extend={self.extend_kernel.__class__.__name__}, " - f"verify={self.verify_kernel.__class__.__name__}" + f"verify={self.verify_kernel.__class__.__name__} " + f"packed_decode={self.supports_packed_decode}" + ) + + def packed_decode( + self, + mixed_qkv: torch.Tensor, + a: torch.Tensor, + b: torch.Tensor, + *, + A_log: torch.Tensor, + dt_bias: torch.Tensor, + scale: float, + ssm_states: torch.Tensor, + cache_indices: torch.Tensor, + num_v_heads: int, + head_v_dim: int, + **kwargs, + ) -> Optional[torch.Tensor]: + """Attempt packed decode. Returns output tensor or None if + the decode kernel does not support packed decode.""" + if not self.supports_packed_decode: + return None + return self.decode_kernel.packed_decode( + mixed_qkv, + a, + b, + A_log=A_log, + dt_bias=dt_bias, + scale=scale, + ssm_states=ssm_states, + cache_indices=cache_indices, + num_v_heads=num_v_heads, + head_v_dim=head_v_dim, + **kwargs, ) def decode( @@ -243,6 +281,26 @@ class GDNAttnBackend(MambaAttnBackendBase): conv_state_indices=cache_indices, ) + # Skip split + reshape + separate gating kernel by consuming + # the packed mixed_qkv directly in a single fused Triton kernel. + if self.kernel_dispatcher.supports_packed_decode: + core_attn_out = self.kernel_dispatcher.packed_decode( + mixed_qkv=mixed_qkv, + a=a, + b=b, + A_log=layer.A_log, + dt_bias=layer.dt_bias, + scale=layer.head_k_dim**-0.5, + ssm_states=ssm_states, + cache_indices=cache_indices, + num_v_heads=layer.num_v_heads, + head_v_dim=layer.head_v_dim, + ) + self._track_mamba_state_decode( + forward_batch, conv_states, ssm_states, cache_indices + ) + return core_attn_out + query, key, value = torch.split( mixed_qkv, [layer.q_dim, layer.k_dim, layer.v_dim], diff --git a/python/sglang/srt/layers/attention/linear/kernels/gdn_triton.py b/python/sglang/srt/layers/attention/linear/kernels/gdn_triton.py index 106113cda..9b19b5251 100644 --- a/python/sglang/srt/layers/attention/linear/kernels/gdn_triton.py +++ b/python/sglang/srt/layers/attention/linear/kernels/gdn_triton.py @@ -7,6 +7,9 @@ from sglang.srt.utils import is_cpu, is_npu if not is_cpu(): from sglang.srt.layers.attention.fla.chunk import chunk_gated_delta_rule + from sglang.srt.layers.attention.fla.fused_recurrent import ( + fused_recurrent_gated_delta_rule_packed_decode, + ) from sglang.srt.layers.attention.fla.fused_sigmoid_gating_recurrent import ( fused_sigmoid_gating_delta_rule_update, ) @@ -31,6 +34,63 @@ elif is_cpu(): class TritonGDNKernel(LinearAttnKernelBase): """Triton-based kernel for GDN (Gated Delta Network) linear attention.""" + supports_packed_decode: bool = not is_cpu() and not is_npu() + + def packed_decode( + self, + mixed_qkv: torch.Tensor, + a: torch.Tensor, + b: torch.Tensor, + *, + A_log: torch.Tensor, + dt_bias: torch.Tensor, + scale: float, + ssm_states: torch.Tensor, + cache_indices: torch.Tensor, + num_v_heads: int, + head_v_dim: int, + **kwargs, + ) -> torch.Tensor: + """Packed decode fast path: fuse QKV extraction + gating + recurrent + update into a single Triton kernel, eliminating intermediate tensors + and extra kernel launches. + + Args: + mixed_qkv: [B, qkv_dim] packed projection output after conv1d. + a, b: [B, HV] gating inputs. + A_log: [HV] log-space decay parameter. + dt_bias: [HV] time-step bias. + scale: attention scale factor (typically head_k_dim ** -0.5). + ssm_states: [num_slots, HV, V, K] full state pool. + cache_indices: [B] per-request state slot indices. + num_v_heads: number of value heads (after TP sharding). + head_v_dim: dimension per value head. + + Returns: + output tensor of shape [1, B, HV, V] matching the existing + decode kernel output layout. + """ + B = mixed_qkv.shape[0] + # Packed kernel expects output shape [B, 1, HV, V] + out = mixed_qkv.new_empty(B, 1, num_v_heads, head_v_dim) + + fused_recurrent_gated_delta_rule_packed_decode( + mixed_qkv=mixed_qkv, + a=a, + b=b, + A_log=A_log, + dt_bias=dt_bias, + scale=scale, + initial_state=ssm_states, + out=out, + ssm_state_indices=cache_indices, + use_qk_l2norm_in_kernel=True, + ) + + # Convert [B, 1, HV, V] → [1, B, HV, V] to match existing output + # layout. transpose() returns a view — zero cost. + return out.transpose(0, 1) + def decode( self, q: torch.Tensor,