6cdf2ec87b
来源:
1. fla-org/flash-linear-attention (5538 stars)
→ upstream_ref/fla/ops/gated_delta_rule/naive.py (正确的纯 PyTorch GDN)
→ upstream_ref/fla/ops/gated_delta_rule/chunk.py (Triton chunk kernel)
→ upstream_ref/fla/layers/gated_deltanet.py (层集成)
2. vllm-project/vllm main (88717 stars)
→ upstream_ref/vllm_gdn/gdn/qwen_gdn_linear_attn.py (1751行, Qwen3.5 原生 GDN)
→ upstream_ref/vllm_gdn/ops/causal_conv1d.py (1289行, 正确的 Conv1d)
→ upstream_ref/vllm_gdn/third_party/ops/ (FLA Triton ops vendored)
→ upstream_ref/vllm_gdn/models/qwen3_5.py (vllm 最新 Qwen3.5 模型)
3. Deep-Spark/xllm (BI-V100 硬件厂商)
→ upstream_ref/xllm_latest/core/layers/npu_torch/qwen3_gated_delta_net_base.cpp (576行)
→ upstream_ref/xllm_latest/core/kernels/npu/npu_causal_conv1d.cpp
→ upstream_ref/xllm_latest/core/kernels/npu/npu_recurrent_gated_delta_rule.cpp
目的: 修复 corex_gdn.py Conv1d groups 接口不匹配问题
错误: conv1d_weight shape (2560,1,4) 被当成 (num_k_heads,1,4) 索引
conv_dim = key_dim*2 + value_dim = 10240, TP=4 后 2560
FLA naive.py 和 vllm qwen_gdn_linear_attn.py 有正确的实现可直接对接
345 lines
9.2 KiB
Python
345 lines
9.2 KiB
Python
# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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# For a list of all contributors, visit:
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# https://github.com/fla-org/flash-linear-attention/graphs/contributors
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import torch
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import torch.nn.functional as F
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import triton
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import triton.language as tl
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from fla.ops.backends import dispatch
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from fla.ops.utils.cache import fla_cache_autotune
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from fla.ops.utils.index import prepare_chunk_indices
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from fla.ops.utils.op import exp
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from fla.ops.utils.softplus import softplus
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from fla.utils import autocast_custom_bwd, autocast_custom_fwd, autotune_cache_kwargs, input_guard
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def naive_gdn_gate(
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g: torch.Tensor,
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A_log: torch.Tensor,
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dt_bias: torch.Tensor | None = None,
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output_dtype: torch.dtype = torch.float32,
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) -> torch.Tensor:
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"""
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Torch reference implementation for GDN gate computation.
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Computes: ``g = -A_log.exp() * softplus(g + dt_bias)``
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Args:
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g (torch.Tensor):
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Input tensor of shape `[..., HV]`.
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A_log (torch.Tensor):
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Decay parameter tensor with `HV` elements.
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dt_bias (torch.Tensor | None):
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Optional bias tensor added to `g` before activation, shape `[HV]`.
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Returns:
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Output tensor of shape `[..., HV]`.
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"""
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g = g.float()
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if dt_bias is not None:
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g = g + dt_bias.float()
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return (-A_log.float().exp() * F.softplus(g)).to(output_dtype)
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@triton.heuristics({
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'HAS_BIAS': lambda args: args['dt_bias'] is not None,
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'HAS_SCALE': lambda args: args['scale'] is not None,
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'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
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})
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@fla_cache_autotune(
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configs=[
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triton.Config({}, num_warps=num_warps)
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for num_warps in [1, 2, 4, 8]
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],
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key=['H', 'BT', 'IS_VARLEN', 'REVERSE'],
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**autotune_cache_kwargs,
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)
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@triton.jit(do_not_specialize=['T'])
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def gdn_gate_chunk_cumsum_scalar_kernel(
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g,
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A_log,
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dt_bias,
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o,
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scale,
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cu_seqlens,
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chunk_indices,
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T,
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H: tl.constexpr,
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BT: tl.constexpr,
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REVERSE: tl.constexpr,
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HAS_BIAS: tl.constexpr,
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HAS_SCALE: tl.constexpr,
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IS_VARLEN: tl.constexpr,
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):
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i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64)
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i_b, i_h = i_bh // H, i_bh % H
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if IS_VARLEN:
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i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64)
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bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64)
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T = eos - bos
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else:
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bos, eos = i_b * T, i_b * T + T
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o_t = i_t * BT + tl.arange(0, BT)
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m_t = o_t < T
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p_g = g + bos * H + i_h + o_t * H
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p_o = o + bos * H + i_h + o_t * H
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b_g = tl.load(p_g, mask=m_t, other=0.0).to(tl.float32)
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if HAS_BIAS:
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b_g = b_g + tl.load(dt_bias + i_h).to(tl.float32)
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b_A = tl.load(A_log + i_h).to(tl.float32)
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b_gate = -exp(b_A) * softplus(b_g)
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b_o = tl.cumsum(b_gate, axis=0)
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if REVERSE:
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b_z = tl.sum(b_gate, axis=0)
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b_o = -b_o + b_z[None] + b_gate
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if HAS_SCALE:
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b_o *= scale
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tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_t)
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@triton.heuristics({
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'HAS_BIAS': lambda args: args['dt_bias'] is not None,
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})
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@fla_cache_autotune(
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configs=[
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triton.Config({}, num_warps=num_warps)
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for num_warps in [1, 2, 4, 8]
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],
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key=['H', 'BT'],
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**autotune_cache_kwargs,
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)
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@triton.jit(do_not_specialize=['T'])
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def gdn_gate_bwd_kernel(
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g,
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A_log,
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dt_bias,
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dyg,
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dg,
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dA,
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T,
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H: tl.constexpr,
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BT: tl.constexpr,
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HAS_BIAS: tl.constexpr,
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):
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i_t, i_h = tl.program_id(0).to(tl.int64), tl.program_id(1)
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b_A = tl.load(A_log + i_h).to(tl.float32)
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o_t = i_t * BT + tl.arange(0, BT)
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m_t = o_t < T
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p_g = g + i_h + o_t * H
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p_dg = dg + i_h + o_t * H
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p_dyg = dyg + i_h + o_t * H
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b_g = tl.load(p_g, mask=m_t, other=0.0).to(tl.float32)
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b_dyg = tl.load(p_dyg, mask=m_t, other=0.0).to(tl.float32)
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if HAS_BIAS:
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b_g = b_g + tl.load(dt_bias + i_h).to(tl.float32)
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# gate = -exp(A_log) * softplus(g + bias)
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# d(gate)/d(g) = -exp(A_log) * sigmoid(g + bias) (softplus' = sigmoid)
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# d(gate)/d(A_log) = -exp(A_log) * softplus(g + bias) = gate
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b_neg_expA = -exp(b_A)
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b_yg = b_neg_expA * softplus(b_g)
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b_dg = b_neg_expA * (b_dyg * tl.sigmoid(b_g))
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b_dA = tl.sum(b_dyg * b_yg, 0)
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tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), mask=m_t)
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tl.store(dA + i_t * H + i_h, b_dA)
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@input_guard
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@dispatch('gated_delta_rule')
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def gdn_gate_chunk_cumsum(
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g: torch.Tensor,
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A_log: torch.Tensor,
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chunk_size: int,
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scale: float = None,
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dt_bias: torch.Tensor | None = None,
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cu_seqlens: torch.LongTensor | None = None,
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chunk_indices: torch.LongTensor | None = None,
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output_dtype: torch.dtype | None = torch.float,
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) -> torch.Tensor:
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B, T, H = g.shape
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BT = chunk_size
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if chunk_indices is None and cu_seqlens is not None:
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chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
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NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
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o = torch.empty_like(g, dtype=output_dtype or g.dtype)
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gdn_gate_chunk_cumsum_scalar_kernel[(NT, B * H)](
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g=g,
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A_log=A_log,
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dt_bias=dt_bias,
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o=o,
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scale=scale,
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cu_seqlens=cu_seqlens,
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chunk_indices=chunk_indices,
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T=T,
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H=H,
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BT=BT,
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REVERSE=False,
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)
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return o
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@dispatch('gated_delta_rule')
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def gdn_gate_bwd(
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g: torch.Tensor,
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A_log: torch.Tensor,
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dt_bias: torch.Tensor | None,
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dyg: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
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H = g.shape[-1]
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T = g.numel() // H
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BT = 32
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NT = triton.cdiv(T, BT)
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dg = torch.empty_like(g, dtype=torch.float32)
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dA = A_log.new_empty(NT, H, dtype=torch.float32)
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gdn_gate_bwd_kernel[(NT, H)](
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g=g,
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A_log=A_log,
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dt_bias=dt_bias,
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dyg=dyg,
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dg=dg,
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dA=dA,
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T=T,
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H=H,
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BT=BT,
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)
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dg = dg.view_as(g).type_as(g)
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dA = dA.sum(0).view_as(A_log).type_as(A_log)
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dbias = dg.view(-1, H).sum(0).to(dt_bias) if dt_bias is not None else None
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return dg, dA, dbias
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@triton.heuristics({
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'HAS_BIAS': lambda args: args['dt_bias'] is not None,
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})
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@fla_cache_autotune(
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configs=[
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triton.Config({'BT': BT}, num_warps=num_warps, num_stages=num_stages)
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for BT in [32, 64, 128]
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for num_warps in [1, 2, 4, 8]
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for num_stages in [2, 3]
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],
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key=['H'],
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**autotune_cache_kwargs,
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)
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@triton.jit(do_not_specialize=['T'])
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def gdn_gate_fwd_kernel(
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g,
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A_log,
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dt_bias,
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yg,
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T,
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H: tl.constexpr,
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BT: tl.constexpr,
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HAS_BIAS: tl.constexpr,
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):
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i_t, i_h = tl.program_id(0).to(tl.int64), tl.program_id(1)
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b_A = tl.load(A_log + i_h).to(tl.float32)
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o_t = i_t * BT + tl.arange(0, BT)
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m_t = o_t < T
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p_g = g + i_h + o_t * H
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p_yg = yg + i_h + o_t * H
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b_g = tl.load(p_g, mask=m_t, other=0.0).to(tl.float32)
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if HAS_BIAS:
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b_g = b_g + tl.load(dt_bias + i_h).to(tl.float32)
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b_yg = -exp(b_A) * softplus(b_g)
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tl.store(p_yg, b_yg.to(p_yg.dtype.element_ty), mask=m_t)
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@dispatch('gated_delta_rule')
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def gdn_gate_fwd(
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g: torch.Tensor,
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A_log: torch.Tensor,
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dt_bias: torch.Tensor | None = None,
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output_dtype: torch.dtype = torch.float32,
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) -> torch.Tensor:
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H = g.shape[-1]
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T = g.numel() // H
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yg = torch.empty_like(g, dtype=output_dtype)
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def grid(meta):
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return (triton.cdiv(T, meta['BT']), H)
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gdn_gate_fwd_kernel[grid](
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g=g,
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A_log=A_log,
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dt_bias=dt_bias,
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yg=yg,
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T=T,
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H=H,
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)
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return yg
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class GDNGateFunction(torch.autograd.Function):
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@staticmethod
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@input_guard
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@autocast_custom_fwd
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def forward(
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ctx,
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g: torch.Tensor,
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A_log: torch.Tensor,
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dt_bias: torch.Tensor | None = None,
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output_dtype: torch.dtype = torch.float32,
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) -> torch.Tensor:
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yg = gdn_gate_fwd(g=g, A_log=A_log, dt_bias=dt_bias, output_dtype=output_dtype)
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ctx.save_for_backward(g, A_log, dt_bias)
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return yg
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@staticmethod
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@input_guard
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@autocast_custom_bwd
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def backward(ctx, dyg: torch.Tensor):
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g, A_log, dt_bias = ctx.saved_tensors
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dg, dA, dbias = gdn_gate_bwd(g=g, A_log=A_log, dt_bias=dt_bias, dyg=dyg)
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return dg, dA, dbias, None
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@torch.compiler.disable
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def fused_gdn_gate(
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g: torch.Tensor,
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A_log: torch.Tensor,
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dt_bias: torch.Tensor | None = None,
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output_dtype: torch.dtype = torch.float32,
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) -> torch.Tensor:
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r"""
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Fused GDN gate computation with autograd support.
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Computes: ``g = -A_log.exp() * softplus(g + dt_bias)``
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Args:
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g (torch.Tensor):
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Input tensor of shape `[..., HV]`.
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A_log (torch.Tensor):
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Decay parameter tensor with `HV` elements.
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dt_bias (torch.Tensor | None):
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Optional bias tensor added to `g` before activation, shape `[HV]`.
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output_dtype (torch.dtype):
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The dtype of the output tensor. Default: `torch.float32`.
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Returns:
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Output tensor of shape `[..., HV]`.
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"""
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return GDNGateFunction.apply(g, A_log, dt_bias, output_dtype)
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