[KDA] Support KDA packed decode (#26586)
Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com>
This commit is contained in:
Yuan Luo
@@ -0,0 +1,395 @@
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"""
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Benchmark & Correctness: KDA Packed Decode vs Baseline Decode.
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Compares:
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- Baseline: split(mixed_qkv) -> view -> fused_sigmoid_gating_delta_rule_update(is_kda=True)
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- Packed: fused_recurrent_kda_packed_decode (single fused kernel)
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Differences from the GDN packed decode benchmark:
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- KDA gate ``a`` is per-K with shape ``[B, HV * K]`` (instead of ``[B, HV]``).
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- KDA ``dt_bias`` is per-K with shape ``[HV * K]`` (instead of ``[HV]``).
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- State decay in the kernel is a per-K vector ``exp(g)`` (instead of a scalar).
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Reports correctness (output & state matching) and performance (us, speedup).
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Usage:
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python bench_kda_decode.py # default sweep
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python bench_kda_decode.py --mode bench # benchmark only
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python bench_kda_decode.py --mode correctness # correctness only
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"""
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import argparse
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import torch
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from sglang.srt.layers.attention.fla.fused_recurrent import (
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fused_recurrent_kda_packed_decode,
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)
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from sglang.srt.layers.attention.fla.fused_sigmoid_gating_recurrent import (
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fused_sigmoid_gating_delta_rule_update,
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)
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def make_inputs(
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B: int,
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H: int,
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HV: int,
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K: int,
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V: int,
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pool_size: int,
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device: str,
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dtype: torch.dtype,
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seed: int = 42,
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):
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"""Create all input tensors for a single benchmark / correctness run."""
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torch.manual_seed(seed)
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qkv_dim = 2 * H * K + HV * V
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mixed_qkv = torch.randn(B, qkv_dim, device=device, dtype=dtype) * 0.1
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# KDA per-K gate: a is [B, HV*K], dt_bias is [HV*K].
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a = torch.randn(B, HV * K, device=device, dtype=dtype) * 0.5 - 1.0
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b = torch.randn(B, HV, device=device, dtype=dtype) * 0.5
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A_log = torch.randn(HV, device=device, dtype=torch.float32) * 0.2
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dt_bias = torch.randn(HV * K, device=device, dtype=torch.float32) * 0.1
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ssm_states = torch.randn(pool_size, HV, V, K, device=device, dtype=dtype) * 0.01
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cache_indices = torch.arange(B, device=device, dtype=torch.int32)
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cu_seqlens = torch.arange(B + 1, device=device, dtype=torch.long)
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return dict(
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B=B,
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H=H,
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HV=HV,
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K=K,
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V=V,
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qkv_dim=qkv_dim,
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pool_size=pool_size,
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mixed_qkv=mixed_qkv.contiguous(),
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a=a.contiguous(),
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b=b.contiguous(),
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A_log=A_log,
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dt_bias=dt_bias,
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ssm_states=ssm_states.contiguous(),
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cache_indices=cache_indices,
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cu_seqlens=cu_seqlens,
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)
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def run_baseline(inp):
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"""Baseline path: split -> view -> fused_sigmoid_gating_delta_rule_update.
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Mirrors the existing decode path in ``KDAAttnBackend.forward_decode``
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(post conv1d, pre-packed-optimization).
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"""
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B, H, HV, K, V = inp["B"], inp["H"], inp["HV"], inp["K"], inp["V"]
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mixed_qkv = inp["mixed_qkv"]
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ssm_states = inp["ssm_states"].clone()
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q_flat, k_flat, v_flat = torch.split(mixed_qkv, [H * K, H * K, HV * V], dim=-1)
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q = q_flat.view(1, B, H, K)
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k = k_flat.view(1, B, H, K)
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v = v_flat.view(1, B, HV, V)
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o = fused_sigmoid_gating_delta_rule_update(
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A_log=inp["A_log"],
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dt_bias=inp["dt_bias"],
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q=q,
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k=k,
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v=v,
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a=inp["a"],
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b=inp["b"],
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initial_state_source=ssm_states,
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initial_state_indices=inp["cache_indices"],
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cu_seqlens=inp["cu_seqlens"],
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use_qk_l2norm_in_kernel=True,
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softplus_beta=1.0,
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softplus_threshold=20.0,
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is_kda=True,
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)
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return o, ssm_states
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def run_packed(inp):
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"""Packed path: single fused kernel directly on mixed_qkv."""
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B, HV, K, V = inp["B"], inp["HV"], inp["K"], inp["V"]
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ssm_states = inp["ssm_states"].clone()
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out = inp["mixed_qkv"].new_empty(B, 1, HV, V)
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fused_recurrent_kda_packed_decode(
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mixed_qkv=inp["mixed_qkv"],
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a=inp["a"],
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b=inp["b"],
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A_log=inp["A_log"],
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dt_bias=inp["dt_bias"],
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scale=inp["K"] ** -0.5,
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initial_state=ssm_states,
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out=out,
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ssm_state_indices=inp["cache_indices"],
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use_qk_l2norm_in_kernel=True,
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)
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return out.transpose(0, 1), ssm_states
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def check_correctness(B, H, HV, K, V, pool_size, device, dtype, seed=42):
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"""Run correctness check for a single config. Returns True if PASS."""
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tag = f"B={B:>4} H={H:>2} HV={HV:>2} K={K:>3} V={V:>3} pool={pool_size:>4}"
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inp = make_inputs(B, H, HV, K, V, pool_size, device, dtype, seed=seed)
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o_baseline, state_baseline = run_baseline(inp)
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o_packed, state_packed = run_packed(inp)
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atol = 2e-2 if dtype != torch.float32 else 1e-4
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rtol = 1e-2 if dtype != torch.float32 else 1e-4
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out_diff = (o_packed.float() - o_baseline.float()).abs().max().item()
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output_ok = out_diff <= max(atol, rtol * o_baseline.float().abs().max().item())
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indices = inp["cache_indices"]
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st_diff = (
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(state_packed[indices].float() - state_baseline[indices].float())
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.abs()
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.max()
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.item()
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)
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state_ok = st_diff <= max(
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atol, rtol * state_baseline[indices].float().abs().max().item()
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)
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passed = output_ok and state_ok
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if passed:
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print(
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f" [PASS] {tag} (out max_diff={out_diff:.2e}, state max_diff={st_diff:.2e})"
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)
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else:
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print(
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f" [FAIL] {tag} out max_diff={out_diff:.6f}, state max_diff={st_diff:.6f}"
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)
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return passed
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def bench_shape(B, H, HV, K, V, pool_size, device, dtype):
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"""Benchmark baseline vs packed for a single config."""
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inp = make_inputs(B, H, HV, K, V, pool_size, device, dtype)
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def fn_baseline():
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q_flat, k_flat, v_flat = torch.split(
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inp["mixed_qkv"], [H * K, H * K, HV * V], dim=-1
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)
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q = q_flat.view(1, B, H, K)
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k = k_flat.view(1, B, H, K)
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v = v_flat.view(1, B, HV, V)
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fused_sigmoid_gating_delta_rule_update(
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A_log=inp["A_log"],
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dt_bias=inp["dt_bias"],
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q=q,
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k=k,
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v=v,
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a=inp["a"],
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b=inp["b"],
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initial_state_source=inp["ssm_states"],
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initial_state_indices=inp["cache_indices"],
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cu_seqlens=inp["cu_seqlens"],
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use_qk_l2norm_in_kernel=True,
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softplus_beta=1.0,
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softplus_threshold=20.0,
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is_kda=True,
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)
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out_buf = inp["mixed_qkv"].new_empty(B, 1, HV, V)
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def fn_packed():
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fused_recurrent_kda_packed_decode(
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mixed_qkv=inp["mixed_qkv"],
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a=inp["a"],
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b=inp["b"],
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A_log=inp["A_log"],
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dt_bias=inp["dt_bias"],
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scale=K**-0.5,
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initial_state=inp["ssm_states"],
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out=out_buf,
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ssm_state_indices=inp["cache_indices"],
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use_qk_l2norm_in_kernel=True,
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)
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# Intentionally wall-clock CUDA-event timing, not the shared do_bench /
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# do_bench_cudagraph util: ~2/3 of the packed win is eager CPU dispatch
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# (split + 3x unflatten + extra launch), which graph capture / L2-flush
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# harnesses amortize away. Decode runs these ops eagerly every step, so
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# wall-clock is the production-relevant metric (~1.7x vs ~1.3x kernel-only).
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warmup, iters = 50, 200
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for _ in range(warmup):
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fn_baseline()
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fn_packed()
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torch.cuda.synchronize()
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def _time(fn):
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start = torch.cuda.Event(enable_timing=True)
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end = torch.cuda.Event(enable_timing=True)
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start.record()
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for _ in range(iters):
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fn()
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end.record()
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torch.cuda.synchronize()
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return start.elapsed_time(end) / iters # ms
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ms_baseline = _time(fn_baseline)
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ms_packed = _time(fn_packed)
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speedup = ms_baseline / ms_packed if ms_packed > 0 else float("inf")
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saved_us = (ms_baseline - ms_packed) * 1000
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print(
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f" {B:>5} {H:>3} {HV:>3} {K:>3} {V:>3} | "
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f"{ms_baseline * 1000:>10.1f} | "
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f"{ms_packed * 1000:>10.1f} | "
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f"{speedup:>7.2f}x | "
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f"{saved_us:>+9.1f}"
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)
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def run_correctness(device, dtype):
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print("=" * 80)
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print("Correctness: Baseline KDA Decode vs Packed KDA Decode")
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print("=" * 80)
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shapes = [
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# (B, H, HV, K, V, pool_size)
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(1, 16, 16, 128, 128, 32),
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(4, 16, 16, 128, 128, 32),
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(16, 16, 16, 128, 128, 64),
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(32, 16, 16, 128, 128, 128),
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(64, 16, 16, 128, 128, 128),
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(128, 16, 16, 128, 128, 256),
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(256, 16, 16, 128, 128, 512),
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# Asymmetric H vs HV
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(1, 32, 32, 128, 128, 32),
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(32, 32, 32, 128, 128, 128),
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(64, 32, 32, 128, 128, 128),
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# Edge case
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(1, 16, 16, 128, 128, 32),
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(2, 16, 16, 128, 128, 32),
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]
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all_pass = True
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for B, H, HV, K, V, pool_size in shapes:
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if not check_correctness(B, H, HV, K, V, pool_size, device, dtype):
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all_pass = False
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# PAD_SLOT_ID test: some indices < 0 should output zeros and skip state update.
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print("\n PAD_SLOT_ID test (indices with -1):")
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inp = make_inputs(32, 16, 16, 128, 128, 128, device, dtype)
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pad_mask = torch.zeros(32, device=device, dtype=torch.bool)
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pad_mask[::4] = True
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inp["cache_indices"] = torch.where(
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pad_mask,
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torch.tensor(-1, device=device, dtype=torch.int32),
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inp["cache_indices"],
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)
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o_baseline, _ = run_baseline(inp)
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o_packed, _ = run_packed(inp)
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try:
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torch.testing.assert_close(o_packed, o_baseline, atol=2e-2, rtol=1e-2)
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print(" [PASS] PAD_SLOT_ID=-1 handling")
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except AssertionError as e:
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print(f" [FAIL] PAD_SLOT_ID=-1 handling: {e}")
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all_pass = False
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print()
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print("ALL PASSED." if all_pass else "SOME FAILED.")
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return all_pass
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def run_benchmark(device, dtype, args):
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print()
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print("=" * 85)
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print("Benchmark: Baseline KDA Decode vs Packed KDA Decode")
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print("=" * 85)
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K = args.head_size_k
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V = args.head_size_v
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pool_size = args.pool_size
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bench_configs = []
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for B in args.batch_sizes:
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for H in args.num_q_heads:
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for HV in args.num_v_heads:
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bench_configs.append((B, H, HV))
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print(f" Config: K={K}, V={V}, pool_size={pool_size}, dtype={dtype}")
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print(
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f" {'B':>5} {'H':>3} {'HV':>3} {'K':>3} {'V':>3} | "
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f"{'base (us)':>10} | "
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f"{'packed (us)':>10} | "
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f"{'speedup':>8} | "
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f"{'saved (us)':>10}"
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)
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print(" " + "-" * 80)
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for B, H, HV in bench_configs:
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# Packed kernel requires HV % H == 0 (GVA / grouped query layout).
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if HV % H != 0:
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continue
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actual_pool = max(pool_size, B + 16)
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bench_shape(B, H, HV, K, V, actual_pool, device, dtype)
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def main():
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parser = argparse.ArgumentParser(
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description="Benchmark & Correctness: KDA Packed Decode vs Baseline"
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)
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parser.add_argument(
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"--mode",
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choices=["all", "correctness", "bench"],
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default="all",
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)
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parser.add_argument(
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"--dtype",
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choices=["float16", "bfloat16", "float32"],
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default="bfloat16",
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)
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parser.add_argument("--head-size-k", type=int, default=128)
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parser.add_argument("--head-size-v", type=int, default=128)
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parser.add_argument("--pool-size", type=int, default=512)
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parser.add_argument(
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"--batch-sizes",
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type=int,
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nargs="+",
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default=[1, 4, 8, 16, 32, 64, 128, 256],
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)
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parser.add_argument(
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"--num-q-heads",
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type=int,
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nargs="+",
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default=[16, 32],
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)
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parser.add_argument(
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"--num-v-heads",
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type=int,
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nargs="+",
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default=[16, 32],
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)
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args = parser.parse_args()
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device = "cuda"
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dtype = getattr(torch, args.dtype)
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cap = torch.cuda.get_device_capability()
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dev_name = torch.cuda.get_device_name()
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print(f"Device: {dev_name} (SM {cap[0]}{cap[1]})")
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if args.mode in ("all", "correctness"):
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all_pass = run_correctness(device, dtype)
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if not all_pass and args.mode == "all":
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print("\nSkipping benchmark due to correctness failures.")
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return 1
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if args.mode in ("all", "bench"):
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run_benchmark(device, dtype, args)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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@@ -402,6 +402,265 @@ def fused_recurrent_gated_delta_rule_packed_decode(
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return out, initial_state
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@triton.jit
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def fused_recurrent_kda_packed_decode_kernel(
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mixed_qkv,
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a,
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b,
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A_log,
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dt_bias,
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o,
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h0,
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ht,
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ssm_state_indices,
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scale,
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stride_mixed_qkv_tok: tl.constexpr,
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stride_a_tok: tl.constexpr,
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stride_b_tok: tl.constexpr,
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stride_init_state_token: tl.constexpr,
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stride_final_state_token: tl.constexpr,
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stride_indices_seq: tl.constexpr,
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H: tl.constexpr,
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HV: tl.constexpr,
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K: tl.constexpr,
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V: tl.constexpr,
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BK: tl.constexpr,
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BV: tl.constexpr,
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SOFTPLUS_THRESHOLD: tl.constexpr,
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USE_QK_L2NORM_IN_KERNEL: tl.constexpr,
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):
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"""KDA packed decode: same shape as the GDN packed decode kernel, but
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with a per-K gate (``a`` is ``[B, HV*K]`` and ``dt_bias`` is ``[HV*K]``),
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so the state decay is a per-K vector ``exp(g)`` rather than a scalar."""
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i_v, i_nh = tl.program_id(0), tl.program_id(1)
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i_n, i_hv = i_nh // HV, i_nh % HV
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i_h = i_hv // (HV // H)
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o_k = tl.arange(0, BK)
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o_v = i_v * BV + tl.arange(0, BV)
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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
|
||||
|
||||
# KDA per-K gate: load BK values of ``a`` and ``dt_bias`` for this head.
|
||||
p_a = a + i_n * stride_a_tok + i_hv * K + o_k
|
||||
p_dt = dt_bias + i_hv * K + o_k
|
||||
b_a = tl.load(p_a, mask=mask_k, other=0).to(tl.float32)
|
||||
b_dt = tl.load(p_dt, mask=mask_k, other=0).to(tl.float32)
|
||||
A_log_val = tl.load(A_log + i_hv).to(tl.float32)
|
||||
|
||||
x = b_a + b_dt
|
||||
softplus_x = tl.where(x <= SOFTPLUS_THRESHOLD, tl.log(1.0 + tl.exp(x)), x)
|
||||
b_g = -tl.exp(A_log_val) * softplus_x # [BK]
|
||||
|
||||
b_val = tl.load(b + i_n * stride_b_tok + i_hv).to(tl.float32)
|
||||
# Keep beta in fp32 (no bf16 round-trip) to match the generic decode
|
||||
# kernel `fused_sigmoid_gating_delta_rule_update`, which is the reference
|
||||
# validated against torch.
|
||||
beta_val = tl.sigmoid(b_val).to(tl.float32)
|
||||
|
||||
# Per-K decay: each K-row of the [V, K] state decays by its own exp(g_k).
|
||||
b_h *= exp(b_g)[None, :]
|
||||
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_kda_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]:
|
||||
"""KDA T=1 decode fast path. Mirrors ``fused_recurrent_gated_delta_rule_packed_decode``
|
||||
but the gate ``g`` is a per-K vector instead of a scalar.
|
||||
|
||||
Args:
|
||||
mixed_qkv: ``[B, 2*H*K + HV*V]`` packed projection output after conv1d.
|
||||
Requires ``num_q_heads == num_k_heads == H`` and ``head_q_dim == head_k_dim == K``.
|
||||
a: ``[B, HV*K]`` per-K gate input (typically reshaped from ``[B, HV, K]``).
|
||||
b: ``[B, HV]`` beta input (post-sigmoid scalar per head).
|
||||
A_log: ``[HV]`` log-space decay parameter.
|
||||
dt_bias: ``[HV*K]`` per-K time-step bias.
|
||||
scale: attention scale factor (typically ``head_k_dim ** -0.5``).
|
||||
initial_state: ``[num_slots, HV, V, K]`` full state pool, updated in place.
|
||||
out: ``[B, 1, HV, V]`` contiguous output buffer.
|
||||
ssm_state_indices: ``[B]`` per-request state slot indices (-1 = skip).
|
||||
use_qk_l2norm_in_kernel: apply per-head L2 norm to Q/K inside the kernel.
|
||||
"""
|
||||
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 * K:
|
||||
raise ValueError(
|
||||
f"`a` must have shape [B, HV*K] with HV={HV}, K={K} "
|
||||
f"(got a.shape={tuple(a.shape)})."
|
||||
)
|
||||
if b.shape[1] != HV:
|
||||
raise ValueError(
|
||||
f"`b` must have shape [B, HV] with HV={HV} (got b.shape={tuple(b.shape)})."
|
||||
)
|
||||
if A_log.numel() != HV:
|
||||
raise ValueError(f"`A_log` must have {HV} elements (got {A_log.numel()}).")
|
||||
if dt_bias.numel() != HV * K:
|
||||
raise ValueError(
|
||||
f"`dt_bias` must have {HV * K} elements (got {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}. "
|
||||
"KDA packed decode requires num_q_heads == num_k_heads and "
|
||||
"head_q_dim == head_k_dim."
|
||||
)
|
||||
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_kda_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
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from typing import Tuple, Union
|
||||
from typing import Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
|
||||
@@ -66,9 +66,47 @@ class KDAKernelDispatcher:
|
||||
"KDA currently only supports 'triton'."
|
||||
)
|
||||
|
||||
self.supports_packed_decode = getattr(
|
||||
self.decode_kernel, "supports_packed_decode", False
|
||||
)
|
||||
|
||||
rank0_log(
|
||||
f"KDA kernel dispatcher: decode={self.decode_kernel.__class__.__name__}, "
|
||||
f"extend={self.extend_kernel.__class__.__name__}"
|
||||
f"extend={self.extend_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(
|
||||
@@ -157,6 +195,35 @@ class KDAAttnBackend(MambaAttnBackendBase):
|
||||
activation="silu",
|
||||
conv_state_indices=cache_indices,
|
||||
)
|
||||
|
||||
# Skip split + reshape by consuming the packed mixed_qkv directly in a
|
||||
# single fused Triton kernel (KDA per-K gate variant of GDN PR #20627).
|
||||
#
|
||||
# The packed kernel hard-assumes one token per sequence (T=1): it has no
|
||||
# query_start_loc / per-sequence loop. forward_decode is only entered in
|
||||
# decode mode (see HybridLinearAttnBackend.forward dispatch), where each
|
||||
# request contributes exactly one token, so #tokens == #requests. Multi-
|
||||
# token-per-seq speculative paths (target_verify / draft_extend) go
|
||||
# through forward_extend instead. Assert the invariant so a future
|
||||
# routing change fails loudly rather than silently corrupting state.
|
||||
if self.kernel_dispatcher.supports_packed_decode:
|
||||
assert qkv.shape[0] == cache_indices.shape[0], (
|
||||
"KDA packed decode requires one token per sequence (T=1): "
|
||||
f"got {qkv.shape[0]} tokens for {cache_indices.shape[0]} requests."
|
||||
)
|
||||
return self.kernel_dispatcher.packed_decode(
|
||||
mixed_qkv=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,
|
||||
)
|
||||
|
||||
q, k, v = qkv.split([layer.q_dim, layer.k_dim, layer.v_dim], dim=-1)
|
||||
q = q.unflatten(-1, (-1, layer.head_q_dim)).unsqueeze(0) # n (h d) -> 1 n h d
|
||||
k = k.unflatten(-1, (-1, layer.head_k_dim)).unsqueeze(0) # n (h d) -> 1 n h d
|
||||
|
||||
@@ -5,9 +5,12 @@ import torch
|
||||
from sglang.srt.layers.attention.linear.kernels.kernel_backend import (
|
||||
LinearAttnKernelBase,
|
||||
)
|
||||
from sglang.srt.utils import is_cpu
|
||||
from sglang.srt.utils import is_cpu, is_npu
|
||||
|
||||
if not is_cpu():
|
||||
from sglang.srt.layers.attention.fla.fused_recurrent import (
|
||||
fused_recurrent_kda_packed_decode,
|
||||
)
|
||||
from sglang.srt.layers.attention.fla.fused_sigmoid_gating_recurrent import (
|
||||
fused_sigmoid_gating_delta_rule_update,
|
||||
)
|
||||
@@ -17,6 +20,53 @@ if not is_cpu():
|
||||
class TritonKDAKernel(LinearAttnKernelBase):
|
||||
"""Triton-based kernel for KDA (Kimi Delta Attention) 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: feed the conv-1d output ``mixed_qkv``
|
||||
straight into a single fused Triton kernel that does Q/K/V extraction,
|
||||
gate/beta computation, l2-norm, and the recurrent state update.
|
||||
|
||||
Returns output tensor of shape [1, B, HV, V] to match the existing
|
||||
decode kernel output layout.
|
||||
"""
|
||||
B = mixed_qkv.shape[0]
|
||||
# a may come in as [B, HV, K] (or [B, 1, HV*K]); b may come in as
|
||||
# [B, 1, HV]. Flatten both to the 2D shapes the kernel expects.
|
||||
if a.dim() != 2:
|
||||
a = a.reshape(B, -1)
|
||||
if b.dim() != 2:
|
||||
b = b.reshape(B, -1)
|
||||
out = mixed_qkv.new_empty(B, 1, num_v_heads, head_v_dim)
|
||||
fused_recurrent_kda_packed_decode(
|
||||
mixed_qkv=mixed_qkv,
|
||||
a=a,
|
||||
b=b,
|
||||
A_log=A_log.reshape(-1),
|
||||
dt_bias=dt_bias.reshape(-1),
|
||||
scale=scale,
|
||||
initial_state=ssm_states,
|
||||
out=out,
|
||||
ssm_state_indices=cache_indices,
|
||||
use_qk_l2norm_in_kernel=True,
|
||||
)
|
||||
# [B, 1, HV, V] -> [1, B, HV, V] view to match existing decode layout.
|
||||
return out.transpose(0, 1)
|
||||
|
||||
def decode(
|
||||
self,
|
||||
q: torch.Tensor,
|
||||
|
||||
@@ -3,6 +3,9 @@ import unittest
|
||||
import torch
|
||||
|
||||
from sglang.srt.layers.attention.fla.cumsum import chunk_local_cumsum
|
||||
from sglang.srt.layers.attention.fla.fused_recurrent import (
|
||||
fused_recurrent_kda_packed_decode,
|
||||
)
|
||||
from sglang.srt.layers.attention.fla.fused_sigmoid_gating_recurrent import (
|
||||
fused_sigmoid_gating_delta_rule_update,
|
||||
)
|
||||
@@ -241,5 +244,209 @@ class TestKDAGateChunkCumsum(unittest.TestCase):
|
||||
)
|
||||
|
||||
|
||||
@unittest.skipIf(not torch.cuda.is_available(), "Test requires CUDA")
|
||||
class TestKDAPackedDecode(unittest.TestCase):
|
||||
"""Verify ``fused_recurrent_kda_packed_decode`` matches the existing decode
|
||||
path (split + unflatten + ``fused_sigmoid_gating_delta_rule_update``)."""
|
||||
|
||||
@staticmethod
|
||||
def _make_inputs(B, H, HV, K, V, pool_size, dtype, device, seed=42):
|
||||
torch.manual_seed(seed)
|
||||
qkv_dim = 2 * H * K + HV * V
|
||||
mixed_qkv = (
|
||||
torch.randn(B, qkv_dim, dtype=dtype, device=device) * 0.1
|
||||
).contiguous()
|
||||
a = (
|
||||
torch.randn(B, HV * K, dtype=dtype, device=device) * 0.5 - 1.0
|
||||
).contiguous()
|
||||
b = (torch.randn(B, HV, dtype=dtype, device=device) * 0.5).contiguous()
|
||||
A_log = torch.randn(HV, dtype=torch.float32, device=device) * 0.2
|
||||
dt_bias = torch.randn(HV * K, dtype=torch.float32, device=device) * 0.1
|
||||
ssm_states = (
|
||||
torch.randn(pool_size, HV, V, K, dtype=dtype, device=device) * 0.01
|
||||
).contiguous()
|
||||
cache_indices = torch.arange(B, device=device, dtype=torch.int32)
|
||||
return mixed_qkv, a, b, A_log, dt_bias, ssm_states, cache_indices
|
||||
|
||||
@staticmethod
|
||||
def _run_baseline(
|
||||
mixed_qkv, a, b, A_log, dt_bias, ssm_states, cache_indices, H, HV, K, V
|
||||
):
|
||||
B = mixed_qkv.shape[0]
|
||||
q_flat, k_flat, v_flat = torch.split(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)
|
||||
# The real backend passes query_start_loc = [0, 1, ..., B] so that
|
||||
# each of the B tokens becomes its own length-1 sequence with an
|
||||
# independent state; without this the kernel would share state.
|
||||
cu_seqlens = torch.arange(B + 1, device=mixed_qkv.device, dtype=torch.int32)
|
||||
return fused_sigmoid_gating_delta_rule_update(
|
||||
A_log=A_log,
|
||||
dt_bias=dt_bias,
|
||||
softplus_beta=1.0,
|
||||
softplus_threshold=20.0,
|
||||
q=q,
|
||||
k=k,
|
||||
v=v,
|
||||
a=a,
|
||||
b=b,
|
||||
initial_state_source=ssm_states,
|
||||
initial_state_indices=cache_indices,
|
||||
cu_seqlens=cu_seqlens,
|
||||
scale=K**-0.5,
|
||||
use_qk_l2norm_in_kernel=True,
|
||||
is_kda=True,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _run_packed(
|
||||
mixed_qkv, a, b, A_log, dt_bias, ssm_states, cache_indices, HV, K, V
|
||||
):
|
||||
B = mixed_qkv.shape[0]
|
||||
out = mixed_qkv.new_empty(B, 1, HV, V)
|
||||
fused_recurrent_kda_packed_decode(
|
||||
mixed_qkv=mixed_qkv,
|
||||
a=a,
|
||||
b=b,
|
||||
A_log=A_log,
|
||||
dt_bias=dt_bias,
|
||||
scale=K**-0.5,
|
||||
initial_state=ssm_states,
|
||||
out=out,
|
||||
ssm_state_indices=cache_indices,
|
||||
use_qk_l2norm_in_kernel=True,
|
||||
)
|
||||
return out.transpose(0, 1)
|
||||
|
||||
def _check(self, B, H, HV, K, V):
|
||||
device = get_device()
|
||||
dtype = torch.bfloat16
|
||||
pool_size = B + 4
|
||||
mixed_qkv, a, b, A_log, dt_bias, ssm_states, cache_indices = self._make_inputs(
|
||||
B, H, HV, K, V, pool_size, dtype, device
|
||||
)
|
||||
s_packed = ssm_states.clone()
|
||||
s_baseline = ssm_states.clone()
|
||||
|
||||
o_packed = self._run_packed(
|
||||
mixed_qkv, a, b, A_log, dt_bias, s_packed, cache_indices, HV, K, V
|
||||
)
|
||||
o_baseline = self._run_baseline(
|
||||
mixed_qkv, a, b, A_log, dt_bias, s_baseline, cache_indices, H, HV, K, V
|
||||
)
|
||||
|
||||
torch.testing.assert_close(
|
||||
o_packed.float(), o_baseline.float(), atol=2e-2, rtol=1e-2
|
||||
)
|
||||
torch.testing.assert_close(
|
||||
s_packed[cache_indices].float(),
|
||||
s_baseline[cache_indices].float(),
|
||||
atol=2e-2,
|
||||
rtol=1e-2,
|
||||
)
|
||||
|
||||
def test_b1(self):
|
||||
self._check(B=1, H=16, HV=16, K=128, V=128)
|
||||
|
||||
def test_b4(self):
|
||||
self._check(B=4, H=16, HV=16, K=128, V=128)
|
||||
|
||||
def test_b32(self):
|
||||
self._check(B=32, H=16, HV=16, K=128, V=128)
|
||||
|
||||
def test_b128(self):
|
||||
self._check(B=128, H=16, HV=16, K=128, V=128)
|
||||
|
||||
def test_asymmetric_heads(self):
|
||||
# Common KDA config with HV > H (grouped query).
|
||||
self._check(B=8, H=8, HV=16, K=128, V=128)
|
||||
|
||||
def test_pad_slot(self):
|
||||
"""Entries with state_idx == -1 must produce zero output and skip state writeback."""
|
||||
device = get_device()
|
||||
dtype = torch.bfloat16
|
||||
B, H, HV, K, V = 8, 16, 16, 128, 128
|
||||
pool_size = B + 4
|
||||
mixed_qkv, a, b, A_log, dt_bias, ssm_states, cache_indices = self._make_inputs(
|
||||
B, H, HV, K, V, pool_size, dtype, device
|
||||
)
|
||||
# Mark every other request as padded.
|
||||
cache_indices = cache_indices.clone()
|
||||
cache_indices[::2] = -1
|
||||
|
||||
s_packed = ssm_states.clone()
|
||||
s_baseline = ssm_states.clone()
|
||||
o_packed = self._run_packed(
|
||||
mixed_qkv, a, b, A_log, dt_bias, s_packed, cache_indices, HV, K, V
|
||||
)
|
||||
o_baseline = self._run_baseline(
|
||||
mixed_qkv, a, b, A_log, dt_bias, s_baseline, cache_indices, H, HV, K, V
|
||||
)
|
||||
torch.testing.assert_close(
|
||||
o_packed.float(), o_baseline.float(), atol=2e-2, rtol=1e-2
|
||||
)
|
||||
|
||||
def test_production_shapes_through_dispatcher(self):
|
||||
"""Go through ``TritonKDAKernel.packed_decode`` with the exact tensor
|
||||
shapes the KimiDeltaAttention model produces at decode time, so the
|
||||
a/b/A_log/dt_bias reshape-normalization is unit-tested (not only E2E).
|
||||
|
||||
Production decode shapes (see kimi_linear.py forward + __init__):
|
||||
- a (forget_gate): [B, HV*K] (2D, not unflattened in decode)
|
||||
- b (beta): [1, B, HV] (unsqueeze(0), pre-sigmoid)
|
||||
- A_log: [1, 1, HV, 1]
|
||||
- dt_bias: [HV*K]
|
||||
"""
|
||||
from sglang.srt.layers.attention.linear.kernels.kda_triton import (
|
||||
TritonKDAKernel,
|
||||
)
|
||||
|
||||
device = get_device()
|
||||
dtype = torch.bfloat16
|
||||
B, H, HV, K, V = 4, 16, 16, 128, 128
|
||||
pool_size = B + 4
|
||||
mixed_qkv, a, b, A_log, dt_bias, ssm_states, cache_indices = self._make_inputs(
|
||||
B, H, HV, K, V, pool_size, dtype, device
|
||||
)
|
||||
|
||||
# Reshape the flat reference tensors into the production layouts.
|
||||
b_prod = b.unsqueeze(0) # [B, HV] -> [1, B, HV]
|
||||
A_log_prod = A_log.view(1, 1, HV, 1) # [HV] -> [1, 1, HV, 1]
|
||||
|
||||
kernel = TritonKDAKernel()
|
||||
self.assertTrue(kernel.supports_packed_decode)
|
||||
|
||||
s_packed = ssm_states.clone()
|
||||
out = kernel.packed_decode(
|
||||
mixed_qkv,
|
||||
a,
|
||||
b_prod,
|
||||
A_log=A_log_prod,
|
||||
dt_bias=dt_bias,
|
||||
scale=K**-0.5,
|
||||
ssm_states=s_packed,
|
||||
cache_indices=cache_indices,
|
||||
num_v_heads=HV,
|
||||
head_v_dim=V,
|
||||
)
|
||||
|
||||
s_baseline = ssm_states.clone()
|
||||
o_baseline = self._run_baseline(
|
||||
mixed_qkv, a, b, A_log, dt_bias, s_baseline, cache_indices, H, HV, K, V
|
||||
)
|
||||
|
||||
# Dispatcher returns [1, B, HV, V], same layout as the baseline.
|
||||
torch.testing.assert_close(
|
||||
out.float(), o_baseline.float(), atol=2e-2, rtol=1e-2
|
||||
)
|
||||
torch.testing.assert_close(
|
||||
s_packed[cache_indices].float(),
|
||||
s_baseline[cache_indices].float(),
|
||||
atol=2e-2,
|
||||
rtol=1e-2,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
Reference in New Issue
Block a user