[Perf] add packed recurrent fast path for decode (#36596)
Signed-off-by: hdj <1293066020@qq.com> Co-authored-by: Roger Wang <hey@rogerw.io>
This commit is contained in:
caozuoba
98
tests/kernels/test_fused_recurrent_packed_decode.py
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98
tests/kernels/test_fused_recurrent_packed_decode.py
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@@ -0,0 +1,98 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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import torch
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from vllm.model_executor.layers.fla.ops import (
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fused_recurrent_gated_delta_rule,
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fused_recurrent_gated_delta_rule_packed_decode,
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)
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@pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Need CUDA device")
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32])
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@pytest.mark.parametrize("strided_mixed_qkv", [False, True])
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def test_fused_recurrent_packed_decode_matches_reference(
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dtype: torch.dtype, strided_mixed_qkv: bool
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):
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torch.manual_seed(0)
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# Small but representative GDN config (Qwen3Next defaults are K=128, V=128).
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B = 32
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H = 4
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HV = 8 # grouped value attention: HV must be divisible by H
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K = 128
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V = 128
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qkv_dim = 2 * (H * K) + (HV * V)
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device = torch.device("cuda")
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if strided_mixed_qkv:
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# Simulate a packed view into a larger projection buffer:
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# mixed_qkv.stride(0) > mixed_qkv.shape[1]
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proj = torch.randn((B, qkv_dim + 64), device=device, dtype=dtype)
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mixed_qkv = proj[:, :qkv_dim]
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else:
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mixed_qkv = torch.randn((B, qkv_dim), device=device, dtype=dtype)
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a = torch.randn((B, HV), device=device, dtype=dtype)
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b = torch.randn((B, HV), device=device, dtype=dtype)
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A_log = torch.randn((HV,), device=device, dtype=dtype)
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dt_bias = torch.randn((HV,), device=device, dtype=dtype)
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# Continuous batching indices (include PAD_SLOT_ID=-1 cases).
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ssm_state_indices = torch.arange(B, device=device, dtype=torch.int32)
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ssm_state_indices[-3:] = -1
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state0 = torch.randn((B, HV, V, K), device=device, dtype=dtype)
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state_ref = state0.clone()
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state_packed = state0.clone()
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out_packed = torch.empty((B, 1, HV, V), device=device, dtype=dtype)
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# Reference path: materialize contiguous Q/K/V + explicit gating.
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q, k, v = torch.split(mixed_qkv, [H * K, H * K, HV * V], dim=-1)
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q = q.view(B, H, K).unsqueeze(1).contiguous()
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k = k.view(B, H, K).unsqueeze(1).contiguous()
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v = v.view(B, HV, V).unsqueeze(1).contiguous()
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x = a.float() + dt_bias.float()
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softplus_x = torch.where(
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x <= 20.0, torch.log1p(torch.exp(torch.clamp(x, max=20.0))), x
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)
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g = (-torch.exp(A_log.float()) * softplus_x).unsqueeze(1)
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beta = torch.sigmoid(b.float()).to(dtype).unsqueeze(1)
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out_ref, state_ref = fused_recurrent_gated_delta_rule(
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q=q,
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k=k,
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v=v,
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g=g,
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beta=beta,
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scale=K**-0.5,
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initial_state=state_ref,
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inplace_final_state=True,
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cu_seqlens=None,
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ssm_state_indices=ssm_state_indices,
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use_qk_l2norm_in_kernel=True,
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)
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# Packed path: fused gating + recurrent directly from packed mixed_qkv.
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fused_recurrent_gated_delta_rule_packed_decode(
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mixed_qkv=mixed_qkv,
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a=a,
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b=b,
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A_log=A_log,
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dt_bias=dt_bias,
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scale=K**-0.5,
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initial_state=state_packed,
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out=out_packed,
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ssm_state_indices=ssm_state_indices,
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use_qk_l2norm_in_kernel=True,
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)
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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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torch.testing.assert_close(out_packed, out_ref, rtol=rtol, atol=atol)
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torch.testing.assert_close(state_packed, state_ref, rtol=rtol, atol=atol)
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@@ -96,6 +96,7 @@ if TYPE_CHECKING:
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VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
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VLLM_SKIP_P2P_CHECK: bool = False
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VLLM_DISABLED_KERNELS: list[str] = []
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VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE: bool = True
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VLLM_DISABLE_PYNCCL: bool = False
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VLLM_USE_OINK_OPS: bool = False
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VLLM_ROCM_USE_AITER: bool = False
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@@ -899,6 +900,9 @@ environment_variables: dict[str, Callable[[], Any]] = {
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"VLLM_DISABLED_KERNELS": lambda: []
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if "VLLM_DISABLED_KERNELS" not in os.environ
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else os.environ["VLLM_DISABLED_KERNELS"].split(","),
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"VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE": lambda: bool(
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int(os.getenv("VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE", "1"))
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),
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# Disable pynccl (using torch.distributed instead)
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"VLLM_DISABLE_PYNCCL": lambda: (
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os.getenv("VLLM_DISABLE_PYNCCL", "False").lower() in ("true", "1")
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@@ -7,7 +7,10 @@
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# the following copyright notice:
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# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
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from .chunk import chunk_gated_delta_rule
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from .fused_recurrent import fused_recurrent_gated_delta_rule
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from .fused_recurrent import (
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fused_recurrent_gated_delta_rule,
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fused_recurrent_gated_delta_rule_packed_decode,
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)
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from .fused_sigmoid_gating import fused_sigmoid_gating_delta_rule_update
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from .layernorm_guard import RMSNormGated
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@@ -15,5 +18,6 @@ __all__ = [
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"RMSNormGated",
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"chunk_gated_delta_rule",
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"fused_recurrent_gated_delta_rule",
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"fused_recurrent_gated_delta_rule_packed_decode",
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"fused_sigmoid_gating_delta_rule_update",
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]
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@@ -252,6 +252,231 @@ def fused_recurrent_gated_delta_rule_fwd(
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return o, final_state
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@triton.jit
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def fused_recurrent_gated_delta_rule_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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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
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mask_v = o_v < V
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mask_h = mask_v[:, None] & mask_k[None, :]
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state_idx = tl.load(ssm_state_indices + i_n * stride_indices_seq).to(tl.int64)
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p_o = o + (i_n * HV + i_hv) * V + o_v
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if state_idx < 0:
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zero = tl.zeros([BV], dtype=tl.float32).to(p_o.dtype.element_ty)
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tl.store(p_o, zero, mask=mask_v)
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return
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p_h0 = h0 + state_idx * stride_init_state_token
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p_h0 = p_h0 + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
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b_h = tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)
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p_mixed = mixed_qkv + i_n * stride_mixed_qkv_tok
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q_off = i_h * K + o_k
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k_off = (H * K) + i_h * K + o_k
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v_off = (2 * H * K) + i_hv * V + o_v
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b_q = tl.load(p_mixed + q_off, mask=mask_k, other=0).to(tl.float32)
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b_k = tl.load(p_mixed + k_off, mask=mask_k, other=0).to(tl.float32)
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b_v = tl.load(p_mixed + v_off, mask=mask_v, other=0).to(tl.float32)
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if USE_QK_L2NORM_IN_KERNEL:
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b_q = b_q / tl.sqrt(tl.sum(b_q * b_q) + 1e-6)
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b_k = b_k / tl.sqrt(tl.sum(b_k * b_k) + 1e-6)
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b_q = b_q * scale
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a_val = tl.load(a + i_n * stride_a_tok + i_hv).to(tl.float32)
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b_val = tl.load(b + i_n * stride_b_tok + i_hv).to(tl.float32)
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A_log_val = tl.load(A_log + i_hv).to(tl.float32)
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dt_bias_val = tl.load(dt_bias + i_hv).to(tl.float32)
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x = a_val + dt_bias_val
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softplus_x = tl.where(x <= SOFTPLUS_THRESHOLD, tl.log(1.0 + tl.exp(x)), x)
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g_val = -tl.exp(A_log_val) * softplus_x
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beta_val = tl.sigmoid(b_val).to(b.dtype.element_ty).to(tl.float32)
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b_h *= exp(g_val)
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b_v -= tl.sum(b_h * b_k[None, :], 1)
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b_v *= beta_val
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b_h += b_v[:, None] * b_k[None, :]
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b_o = tl.sum(b_h * b_q[None, :], 1)
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tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v)
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p_ht = ht + state_idx * stride_final_state_token
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p_ht = p_ht + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
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tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
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def fused_recurrent_gated_delta_rule_packed_decode(
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mixed_qkv: torch.Tensor,
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a: torch.Tensor,
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b: torch.Tensor,
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A_log: torch.Tensor,
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dt_bias: torch.Tensor,
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scale: float,
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initial_state: torch.Tensor,
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out: torch.Tensor,
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ssm_state_indices: torch.Tensor,
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use_qk_l2norm_in_kernel: bool = False,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if mixed_qkv.ndim != 2:
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raise ValueError(
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f"`mixed_qkv` must be a 2D tensor (got ndim={mixed_qkv.ndim})."
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)
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if mixed_qkv.stride(-1) != 1:
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raise ValueError("`mixed_qkv` must be contiguous in the last dim.")
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if a.ndim != 2 or b.ndim != 2:
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raise ValueError(
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f"`a` and `b` must be 2D tensors (got a.ndim={a.ndim}, b.ndim={b.ndim})."
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)
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if a.stride(-1) != 1 or b.stride(-1) != 1:
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raise ValueError("`a`/`b` must be contiguous in the last dim.")
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if A_log.ndim != 1 or dt_bias.ndim != 1:
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raise ValueError("`A_log`/`dt_bias` must be 1D tensors.")
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if A_log.stride(0) != 1 or dt_bias.stride(0) != 1:
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raise ValueError("`A_log`/`dt_bias` must be contiguous.")
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if ssm_state_indices.ndim != 1:
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raise ValueError(
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f"`ssm_state_indices` must be 1D for packed decode (got ndim={ssm_state_indices.ndim})."
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)
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if not out.is_contiguous():
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raise ValueError("`out` must be contiguous.")
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dev = mixed_qkv.device
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if (
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a.device != dev
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or b.device != dev
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or A_log.device != dev
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or dt_bias.device != dev
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or initial_state.device != dev
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or out.device != dev
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or ssm_state_indices.device != dev
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):
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raise ValueError("All inputs must be on the same device.")
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B = mixed_qkv.shape[0]
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if a.shape[0] != B or b.shape[0] != B:
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raise ValueError(
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"Mismatched batch sizes: "
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f"mixed_qkv.shape[0]={B}, a.shape[0]={a.shape[0]}, b.shape[0]={b.shape[0]}."
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)
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if ssm_state_indices.shape[0] != B:
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raise ValueError(
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f"`ssm_state_indices` must have shape [B] (got {tuple(ssm_state_indices.shape)}; expected ({B},))."
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)
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if initial_state.ndim != 4:
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raise ValueError(
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f"`initial_state` must be a 4D tensor (got ndim={initial_state.ndim})."
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)
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if initial_state.stride(-1) != 1:
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raise ValueError("`initial_state` must be contiguous in the last dim.")
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HV, V, K = initial_state.shape[-3:]
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if a.shape[1] != HV or b.shape[1] != HV:
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raise ValueError(
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f"`a`/`b` must have shape [B, HV] with HV={HV} (got a.shape={tuple(a.shape)}, b.shape={tuple(b.shape)})."
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)
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if A_log.numel() != HV or dt_bias.numel() != HV:
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raise ValueError(
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f"`A_log` and `dt_bias` must have {HV} elements (got A_log.numel()={A_log.numel()}, dt_bias.numel()={dt_bias.numel()})."
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)
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if out.shape != (B, 1, HV, V):
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raise ValueError(
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f"`out` must have shape {(B, 1, HV, V)} (got out.shape={tuple(out.shape)})."
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)
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qkv_dim = mixed_qkv.shape[1]
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qk_dim = qkv_dim - HV * V
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if qk_dim <= 0 or qk_dim % 2 != 0:
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raise ValueError(
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f"Invalid packed `mixed_qkv` last dim={qkv_dim} for HV={HV}, V={V}."
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)
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q_dim = qk_dim // 2
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if q_dim % K != 0:
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raise ValueError(f"Invalid packed Q size {q_dim}: must be divisible by K={K}.")
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H = q_dim // K
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if H <= 0 or HV % H != 0:
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raise ValueError(
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f"Invalid head config inferred from mixed_qkv: H={H}, HV={HV}."
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)
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BK = triton.next_power_of_2(K)
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if triton.cdiv(K, BK) != 1:
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raise ValueError(
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f"Packed decode kernel only supports NK=1 (got K={K}, BK={BK})."
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)
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BV = min(triton.next_power_of_2(V), 32)
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num_stages = 3
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num_warps = 1
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stride_mixed_qkv_tok = mixed_qkv.stride(0)
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stride_a_tok = a.stride(0)
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stride_b_tok = b.stride(0)
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stride_init_state_token = initial_state.stride(0)
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stride_final_state_token = initial_state.stride(0)
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stride_indices_seq = ssm_state_indices.stride(0)
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NV = triton.cdiv(V, BV)
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grid = (NV, B * HV)
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fused_recurrent_gated_delta_rule_packed_decode_kernel[grid](
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mixed_qkv=mixed_qkv,
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a=a,
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b=b,
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A_log=A_log,
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dt_bias=dt_bias,
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o=out,
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h0=initial_state,
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ht=initial_state,
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ssm_state_indices=ssm_state_indices,
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scale=scale,
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stride_mixed_qkv_tok=stride_mixed_qkv_tok,
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stride_a_tok=stride_a_tok,
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stride_b_tok=stride_b_tok,
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stride_init_state_token=stride_init_state_token,
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stride_final_state_token=stride_final_state_token,
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stride_indices_seq=stride_indices_seq,
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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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BK=BK,
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BV=BV,
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SOFTPLUS_THRESHOLD=20.0,
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USE_QK_L2NORM_IN_KERNEL=use_qk_l2norm_in_kernel,
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num_warps=num_warps,
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num_stages=num_stages,
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)
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return out, initial_state
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class FusedRecurrentFunction(torch.autograd.Function):
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@staticmethod
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def forward(
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@@ -10,6 +10,7 @@ from einops import rearrange
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from torch import nn
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from transformers.activations import ACT2FN
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from vllm import envs
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (
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CacheConfig,
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@@ -34,6 +35,7 @@ from vllm.model_executor.layers.fla.ops import (
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chunk_gated_delta_rule as fla_chunk_gated_delta_rule,
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)
|
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from vllm.model_executor.layers.fla.ops import (
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fused_recurrent_gated_delta_rule_packed_decode,
|
||||
fused_sigmoid_gating_delta_rule_update,
|
||||
)
|
||||
from vllm.model_executor.layers.fla.ops.chunk import l2norm_fwd
|
||||
@@ -474,6 +476,9 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
|
||||
)
|
||||
|
||||
self.chunk_gated_delta_rule = ChunkGatedDeltaRule()
|
||||
self.enable_packed_recurrent_decode = (
|
||||
envs.VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE
|
||||
)
|
||||
|
||||
compilation_config = get_current_vllm_config().compilation_config
|
||||
if prefix in compilation_config.static_forward_context:
|
||||
@@ -747,9 +752,6 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
|
||||
a: torch.Tensor,
|
||||
core_attn_out: torch.Tensor,
|
||||
):
|
||||
"""
|
||||
Core attention computation (called by custom op).
|
||||
"""
|
||||
forward_context = get_forward_context()
|
||||
attn_metadata: AttentionMetadata = forward_context.attn_metadata
|
||||
|
||||
@@ -762,6 +764,22 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
|
||||
assert isinstance(attn_metadata, dict)
|
||||
attn_metadata = attn_metadata[self.prefix]
|
||||
assert isinstance(attn_metadata, GDNAttentionMetadata)
|
||||
|
||||
if (
|
||||
self.enable_packed_recurrent_decode
|
||||
and attn_metadata.spec_sequence_masks is None
|
||||
and attn_metadata.num_prefills == 0
|
||||
and attn_metadata.num_decodes > 0
|
||||
):
|
||||
return self._forward_core_decode_non_spec(
|
||||
mixed_qkv=mixed_qkv,
|
||||
b=b,
|
||||
a=a,
|
||||
core_attn_out=core_attn_out,
|
||||
attn_metadata=attn_metadata,
|
||||
virtual_engine=forward_context.virtual_engine,
|
||||
)
|
||||
|
||||
has_initial_state = attn_metadata.has_initial_state
|
||||
spec_query_start_loc = attn_metadata.spec_query_start_loc
|
||||
non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
|
||||
@@ -946,6 +964,55 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
|
||||
else:
|
||||
core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)
|
||||
|
||||
def _forward_core_decode_non_spec(
|
||||
self,
|
||||
mixed_qkv: torch.Tensor,
|
||||
b: torch.Tensor,
|
||||
a: torch.Tensor,
|
||||
core_attn_out: torch.Tensor,
|
||||
attn_metadata: GDNAttentionMetadata,
|
||||
virtual_engine: int,
|
||||
):
|
||||
"""
|
||||
Core attention computation with a packed non-spec decode fast path.
|
||||
"""
|
||||
non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor # noqa: E501
|
||||
self_kv_cache = self.kv_cache[virtual_engine]
|
||||
conv_state = self_kv_cache[0].transpose(-1, -2)
|
||||
ssm_state = self_kv_cache[1]
|
||||
num_actual_tokens = attn_metadata.num_actual_tokens
|
||||
|
||||
mixed_qkv = mixed_qkv[:num_actual_tokens]
|
||||
b = b[:num_actual_tokens]
|
||||
a = a[:num_actual_tokens]
|
||||
|
||||
conv_weights = self.conv1d.weight.view(
|
||||
self.conv1d.weight.size(0), self.conv1d.weight.size(2)
|
||||
)
|
||||
mixed_qkv_non_spec = causal_conv1d_update(
|
||||
mixed_qkv,
|
||||
conv_state,
|
||||
conv_weights,
|
||||
self.conv1d.bias,
|
||||
self.activation,
|
||||
conv_state_indices=non_spec_state_indices_tensor[:num_actual_tokens],
|
||||
validate_data=False,
|
||||
)
|
||||
out_buf = core_attn_out[:num_actual_tokens].unsqueeze(1)
|
||||
fused_recurrent_gated_delta_rule_packed_decode(
|
||||
mixed_qkv=mixed_qkv_non_spec,
|
||||
a=a,
|
||||
b=b,
|
||||
A_log=self.A_log,
|
||||
dt_bias=self.dt_bias,
|
||||
scale=self.head_k_dim**-0.5,
|
||||
initial_state=ssm_state,
|
||||
out=out_buf,
|
||||
ssm_state_indices=non_spec_state_indices_tensor[:num_actual_tokens],
|
||||
use_qk_l2norm_in_kernel=True,
|
||||
)
|
||||
return
|
||||
|
||||
|
||||
class Qwen3NextAttention(nn.Module):
|
||||
def __init__(
|
||||
|
||||
Reference in New Issue
Block a user