[Feat] support basic pcp&dcp for qwen3next (#6091)
### What this PR does / why we need it?
This PR implements Context Parallelism (CP) support for the Qwen3-Next
model, including PCP (Parallel Context Parallelism) and DCP
(Dynamic/Data Context Parallelism).
- vLLM version: v0.15.0
- vLLM main:
f176443446
---------
Signed-off-by: SunnyLee219 <3294305115@qq.com>
Signed-off-by: Jingchun Gao <gaojingchun1@huawei.com>
Signed-off-by: 白永斌 <baiyongbin3@h-partners.com>
Signed-off-by: Bai Yongbin <845473182@qq.com>
Co-authored-by: SunnyLee219 <3294305115@qq.com>
Co-authored-by: Jingchun Gao <gaojingchun1@huawei.com>
Co-authored-by: 白永斌 <baiyongbin3@h-partners.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
This commit is contained in:
Bai Yongbin
@@ -12,15 +12,19 @@ import warnings
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import torch
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from einops import rearrange
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from vllm.distributed import get_pcp_group
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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.fla.ops.utils import SUPPRESS_LEVEL
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from .chunk_delta_h import chunk_gated_delta_rule_fwd_h
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from .chunk_delta_hupdate import chunk_gated_delta_rule_fwd_hupdate
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from .chunk_o import chunk_fwd_o
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from .chunk_o_update import chunk_fwd_o_update
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from .chunk_scaled_dot_kkt import chunk_scaled_dot_kkt_fwd
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from .cumsum import chunk_local_cumsum
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from .l2norm import l2norm_fwd
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from .solve_tril import solve_tril
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from .utils import input_guard
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from .utils import input_guard, prepare_final_chunk_indices
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from .wy_fast import recompute_w_u_fwd
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@@ -35,7 +39,15 @@ def chunk_gated_delta_rule_fwd(
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output_final_state: bool,
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cu_seqlens: torch.LongTensor | None = None,
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):
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g = chunk_local_cumsum(g, chunk_size=64, cu_seqlens=cu_seqlens)
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forward_context = get_forward_context()
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num_decodes = 0
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attn_metadata = forward_context.attn_metadata
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if attn_metadata is not None and isinstance(attn_metadata, dict):
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attn_metadata = next(iter(attn_metadata.values()), None)
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if attn_metadata is not None:
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num_decodes = attn_metadata.num_decodes
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chunk_size = 64
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g = chunk_local_cumsum(g, chunk_size=chunk_size, cu_seqlens=cu_seqlens)
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# obtain WY representation. u is actually the new v.
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A = chunk_scaled_dot_kkt_fwd(k=k, beta=beta, g_cumsum=g, cu_seqlens=cu_seqlens, output_dtype=torch.float32)
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A = solve_tril(A=A, cu_seqlens=cu_seqlens, output_dtype=k.dtype)
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@@ -56,6 +68,45 @@ def chunk_gated_delta_rule_fwd(
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output_final_state=output_final_state,
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cu_seqlens=cu_seqlens,
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)
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if get_pcp_group().world_size > 1:
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h_update = chunk_gated_delta_rule_fwd_hupdate(
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k=k,
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w=w,
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u=u,
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g=g,
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cu_seqlens=cu_seqlens,
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num_decodes=num_decodes,
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)
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all_final_state = get_pcp_group().all_gather(final_state.unsqueeze(0), 0)
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final_chunk_indices = prepare_final_chunk_indices(cu_seqlens, chunk_size)
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final_h_update = h_update[:, final_chunk_indices, :, :, :]
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all_final_h_update = get_pcp_group().all_gather(final_h_update, 0)
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updated_state = final_state.new_empty(get_pcp_group().world_size, *final_state.shape)
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updated_state[0, ...] = all_final_state[0]
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for i in range(1, get_pcp_group().world_size):
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updated_final_state = all_final_state[i] + torch.matmul(
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all_final_h_update[i, ...], updated_state[i - 1, ...]
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)
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updated_state[i, ...] = updated_final_state
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final_state = updated_state[-1, ...]
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if get_pcp_group().rank_in_group == 0:
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updated_h_state = torch.zeros_like(final_state)
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else:
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updated_h_state = updated_state[get_pcp_group().rank_in_group - 1, ...]
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h = chunk_fwd_o_update(
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q=q,
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v=v_new,
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h=h,
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h_update=h_update,
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updated_h_state=updated_h_state,
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cu_seqlens=cu_seqlens,
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)
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o = chunk_fwd_o(
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q=q,
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k=k,
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@@ -65,6 +116,7 @@ def chunk_gated_delta_rule_fwd(
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scale=scale,
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cu_seqlens=cu_seqlens,
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)
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if SUPPRESS_LEVEL < 3:
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return g, o, A, final_state, None, None, None
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elif SUPPRESS_LEVEL >= 3:
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@@ -90,7 +142,6 @@ class ChunkGatedDeltaRuleFunction(torch.autograd.Function):
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if use_qk_l2norm_in_kernel:
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q = l2norm_fwd(q)
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k = l2norm_fwd(k)
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g, o, A, final_state, w, h, v_new = chunk_gated_delta_rule_fwd(
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q=q,
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k=k,
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@@ -38,6 +38,7 @@ def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
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ht,
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cu_seqlens,
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chunk_offsets,
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h_update,
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T,
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H: tl.constexpr,
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Hg: tl.constexpr,
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@@ -72,6 +73,7 @@ def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
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b_h1_bv1 = tl.zeros([128, 64], dtype=tl.float32)
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b_h1_bv2 = tl.zeros([128, 64], dtype=tl.float32)
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# create b_hupd_bv1 and b_hupd_bv2
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v_start1 = 0
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v_start2 = 64
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@@ -204,6 +206,7 @@ def chunk_gated_delta_rule_fwd_h(
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assert K <= 256, "current kernel does not support head dimension larger than 256."
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h = k.new_empty(B, NT, H, K, V)
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h_update = k.new_empty(B, NT, H, K, K)
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final_state = k.new_empty(N, H, K, V, dtype=torch.float32) if output_final_state else None
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v_new = torch.empty_like(u) if save_new_value else None
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@@ -223,6 +226,7 @@ def chunk_gated_delta_rule_fwd_h(
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ht=final_state,
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cu_seqlens=cu_seqlens,
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chunk_offsets=chunk_offsets,
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h_update=h_update,
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T=T,
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H=H,
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Hg=Hg,
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211
vllm_ascend/ops/triton/fla/chunk_delta_hupdate.py
Normal file
211
vllm_ascend/ops/triton/fla/chunk_delta_hupdate.py
Normal file
@@ -0,0 +1,211 @@
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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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# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
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#
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# This file contains code copied from the flash-linear-attention project.
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# The original source code was licensed under the MIT license and included
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# the following copyright notice:
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# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
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# ruff: noqa: E501
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# mypy: ignore-errors
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import torch
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from vllm.triton_utils import tl, triton
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from .utils import prepare_chunk_indices, prepare_chunk_offsets, prepare_update_chunk_offsets, safe_exp
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_CONDITIONS = ("seq7168",)
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@triton.heuristics(
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{
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"USE_G": lambda args: args["g"] 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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)
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@triton.jit(do_not_specialize=["T"])
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def chunk_gated_delta_rule_fwd_kernel_hupdate_blockdim64(
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k,
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w,
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g,
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cu_seqlens,
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chunk_offsets,
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h_update,
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T,
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H: tl.constexpr,
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Hg: tl.constexpr,
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K: tl.constexpr,
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BT: tl.constexpr,
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USE_G: tl.constexpr,
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IS_VARLEN: tl.constexpr,
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):
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i_nh = tl.program_id(1)
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i_n, i_h = i_nh // H, i_nh % H
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T_max = 1 * T
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bos, eos = (
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tl.load(cu_seqlens + i_n).to(tl.int32),
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tl.load(cu_seqlens + i_n + 1).to(tl.int32),
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)
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T = eos - bos
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NT = tl.cdiv(T, BT)
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boh = tl.load(chunk_offsets + i_n).to(tl.int32)
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if IS_VARLEN:
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bos, eos = (
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tl.load(cu_seqlens + i_n).to(tl.int32),
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tl.load(cu_seqlens + i_n + 1).to(tl.int32),
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)
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T = eos - bos
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NT = tl.cdiv(T, BT)
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boh = tl.load(chunk_offsets + i_n).to(tl.int32)
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else:
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bos, eos = i_n * T, i_n * T + T
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NT = tl.cdiv(T, BT)
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boh = i_n * NT
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stride_k = Hg * K
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stride_w = H * K
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# create b_hupd_bv1 and b_hupd_bv2
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off_hupd_1_top = tl.arange(0, 64)[:, None]
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off_hupd_2_top = tl.arange(0, 64)[None, :]
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# main recurrence
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for i_t in range(NT):
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last_idx = min((i_t + 1) * BT, T) - 1
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b_g_last = tl.load(g + bos + i_h * T_max + last_idx)
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offs_t = i_t * BT + tl.arange(0, BT)
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mask_t = offs_t < T
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g_ptr = g + bos + i_h * T_max
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b_g = tl.load(g_ptr + offs_t, mask=mask_t, other=0.0)
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b_g = safe_exp(b_g_last - b_g)
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b_g_last = tl.exp(b_g_last)
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offs_t_wv = (i_t * BT + tl.arange(0, BT))[:, None]
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w_base = w + bos * H * K + i_h * K
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# get column-sliced w [BT, 64]
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offs_w_upd1 = tl.arange(0, 64)[None, :]
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mask_w_upd1 = (offs_t_wv < T) & (offs_w_upd1 < K)
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ptr_w_upd1 = w_base + offs_t_wv * stride_w + offs_w_upd1 * 1
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b_w_upd1 = tl.load(ptr_w_upd1, mask=mask_w_upd1, other=0.0).to(tl.float32)
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offs_w_upd2 = 64 + tl.arange(0, 64)[None, :]
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mask_w_upd2 = (offs_t_wv < T) & (offs_w_upd2 < K)
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ptr_w_upd2 = w_base + offs_t_wv * stride_w + offs_w_upd2 * 1
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b_w_upd2 = tl.load(ptr_w_upd2, mask=mask_w_upd2, other=0.0).to(tl.float32)
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k_base = k + bos * Hg * K + (i_h // (H // Hg)) * K
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# get row-sliced k [64, T]
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p_k_upd1 = tl.make_block_ptr(k_base, (K, T), (1, stride_k), (0, i_t * BT), (64, BT), (0, 1))
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b_k_upd1 = tl.load(p_k_upd1, boundary_check=(0, 1))
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p_k_upd2 = tl.make_block_ptr(k_base, (K, T), (1, stride_k), (64, i_t * BT), (64, BT), (0, 1))
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b_k_upd2 = tl.load(p_k_upd2, boundary_check=(0, 1))
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if USE_G:
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b_w_upd1 = b_w_upd1 * b_g[:, None]
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b_w_upd2 = b_w_upd2 * b_g[:, None]
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# compute [64, BT] @ [BT, 64]
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b_hupd_local_11 = (off_hupd_1_top == off_hupd_2_top).to(tl.float32)
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b_hupd_local_22 = (off_hupd_1_top == off_hupd_2_top).to(tl.float32)
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# fp32
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if USE_G:
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b_hupd_local_11 = b_hupd_local_11 * b_g_last
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b_hupd_local_22 = b_hupd_local_22 * b_g_last
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b_hupd_local_11 -= tl.dot(b_k_upd1, b_w_upd1.to(b_k_upd1.dtype))
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b_hupd_local_22 -= tl.dot(b_k_upd2, b_w_upd2.to(b_k_upd2.dtype))
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b_hupd_local_12 = -tl.dot(b_k_upd1, b_w_upd2.to(b_k_upd1.dtype)).to(tl.float32)
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b_hupd_local_21 = -tl.dot(b_k_upd2, b_w_upd1.to(b_k_upd2.dtype)).to(tl.float32)
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hupd_base = h_update + (boh + i_t + i_n) * H * K * K + i_h * K * K
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p_hupd_11 = tl.make_block_ptr(hupd_base, (K, K), (K, 1), (0, 0), (64, 64), (1, 0))
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b_hupd_11 = tl.load(p_hupd_11, boundary_check=(1, 0))
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p_hupd_21 = tl.make_block_ptr(hupd_base, (K, K), (K, 1), (64, 0), (64, 64), (1, 0))
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b_hupd_21 = tl.load(p_hupd_21, boundary_check=(1, 0))
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p_hupd_12 = tl.make_block_ptr(hupd_base, (K, K), (K, 1), (0, 64), (64, 64), (1, 0))
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b_hupd_12 = tl.load(p_hupd_12, boundary_check=(1, 0))
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p_hupd_22 = tl.make_block_ptr(hupd_base, (K, K), (K, 1), (64, 64), (64, 64), (1, 0))
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b_hupd_22 = tl.load(p_hupd_22, boundary_check=(1, 0))
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b_hupd11_new = tl.dot(b_hupd_local_11.to(b_hupd_11.dtype), b_hupd_11).to(tl.float32)
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b_hupd11_new += tl.dot(b_hupd_local_12.to(b_hupd_21.dtype), b_hupd_21)
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b_hupd21_new = tl.dot(b_hupd_local_21.to(b_hupd_11.dtype), b_hupd_11).to(tl.float32)
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b_hupd21_new += tl.dot(b_hupd_local_22.to(b_hupd_21.dtype), b_hupd_21)
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b_hupd12_new = tl.dot(b_hupd_local_11.to(b_hupd_12.dtype), b_hupd_12).to(tl.float32)
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b_hupd12_new += tl.dot(b_hupd_local_12.to(b_hupd_22.dtype), b_hupd_22)
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b_hupd22_new = tl.dot(b_hupd_local_21.to(b_hupd_12.dtype), b_hupd_12).to(tl.float32)
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b_hupd22_new += tl.dot(b_hupd_local_22.to(b_hupd_22.dtype), b_hupd_22)
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hupd_next = h_update + (boh + i_t + i_n + 1) * H * K * K + i_h * K * K
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p_hupd_11 = tl.make_block_ptr(hupd_next, (K, K), (K, 1), (0, 0), (64, 64), (1, 0))
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tl.store(p_hupd_11, b_hupd11_new.to(p_hupd_11.dtype.element_ty), boundary_check=(0, 1))
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p_hupd_21 = tl.make_block_ptr(hupd_next, (K, K), (K, 1), (64, 0), (64, 64), (1, 0))
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tl.store(p_hupd_21, b_hupd21_new.to(p_hupd_21.dtype.element_ty), boundary_check=(0, 1))
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p_hupd_12 = tl.make_block_ptr(hupd_next, (K, K), (K, 1), (0, 64), (64, 64), (1, 0))
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tl.store(p_hupd_12, b_hupd12_new.to(p_hupd_12.dtype.element_ty), boundary_check=(0, 1))
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p_hupd_22 = tl.make_block_ptr(hupd_next, (K, K), (K, 1), (64, 64), (64, 64), (1, 0))
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tl.store(p_hupd_22, b_hupd22_new.to(p_hupd_22.dtype.element_ty), boundary_check=(0, 1))
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def chunk_gated_delta_rule_fwd_hupdate(
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k: torch.Tensor,
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w: torch.Tensor,
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u: torch.Tensor,
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g: torch.Tensor | None = None,
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chunk_size: int = 64, # SY: remove this argument and force chunk size 64?
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cu_seqlens: torch.LongTensor | None = None,
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num_decodes: int = 0,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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# This kernel is slightly different from fla to support Q/K with different head numbers.
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# In fla, Q/K always have the same head number, so Hg is always equal to H.
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B, T, Hg, K, _ = *k.shape, u.shape[-1]
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H = u.shape[-2]
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BT = chunk_size
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chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) if cu_seqlens is not None else None
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# N: the actual number of sequences in the batch with either equal or variable lengths
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if cu_seqlens is None:
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N, NT, chunk_offsets = B, triton.cdiv(T, BT), None
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else:
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N, NT, chunk_offsets = (
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len(cu_seqlens) - 1,
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len(chunk_indices),
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prepare_chunk_offsets(cu_seqlens, BT),
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)
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assert K <= 256, "current kernel does not support head dimension larger than 256."
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h_update = k.new_empty(B, NT + N, H, K, K, dtype=torch.float32)
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update_indices = prepare_update_chunk_offsets(cu_seqlens, BT)[:-1]
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h_update[:, update_indices, :, :, :] = torch.eye(K, dtype=h_update.dtype, device=h_update.device)
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g = g.transpose(1, 2).contiguous()
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def grid(meta):
|
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return (1, N * H)
|
||||
|
||||
chunk_gated_delta_rule_fwd_kernel_hupdate_blockdim64[grid](
|
||||
k=k,
|
||||
w=w,
|
||||
g=g,
|
||||
cu_seqlens=cu_seqlens,
|
||||
chunk_offsets=chunk_offsets,
|
||||
h_update=h_update,
|
||||
T=T,
|
||||
H=H,
|
||||
Hg=Hg,
|
||||
K=K,
|
||||
BT=BT,
|
||||
num_warps=4,
|
||||
num_stages=2,
|
||||
)
|
||||
h_update[:, : num_decodes * 2, :, :, :] = torch.zeros((K, K), dtype=h_update.dtype, device=h_update.device)
|
||||
return h_update
|
||||
121
vllm_ascend/ops/triton/fla/chunk_o_update.py
Normal file
121
vllm_ascend/ops/triton/fla/chunk_o_update.py
Normal file
@@ -0,0 +1,121 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
|
||||
#
|
||||
# This file contains code copied from the flash-linear-attention project.
|
||||
# The original source code was licensed under the MIT license and included
|
||||
# the following copyright notice:
|
||||
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
|
||||
|
||||
# ruff: noqa: E501
|
||||
# mypy: ignore-errors
|
||||
|
||||
import torch
|
||||
from vllm.triton_utils import tl, triton
|
||||
|
||||
from .utils import prepare_chunk_offsets
|
||||
|
||||
|
||||
@triton.heuristics(
|
||||
{
|
||||
"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
|
||||
}
|
||||
)
|
||||
@triton.jit(do_not_specialize=["T"])
|
||||
def chunk_fwd_kernel_o_update(
|
||||
h,
|
||||
h_update,
|
||||
updated_h_state,
|
||||
cu_seqlens,
|
||||
chunk_offsets,
|
||||
T,
|
||||
H: tl.constexpr,
|
||||
Hg: tl.constexpr,
|
||||
K: tl.constexpr,
|
||||
V: tl.constexpr,
|
||||
BT: tl.constexpr,
|
||||
BK: tl.constexpr,
|
||||
BV: tl.constexpr,
|
||||
IS_VARLEN: tl.constexpr,
|
||||
):
|
||||
i_v, i_nh = tl.program_id(0), tl.program_id(1)
|
||||
i_n, i_h = i_nh // H, i_nh % H # splitting by the head of the req
|
||||
|
||||
if IS_VARLEN:
|
||||
bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
|
||||
T = eos - bos
|
||||
NT = tl.cdiv(T, BT)
|
||||
boh = tl.load(chunk_offsets + i_n).to(tl.int64)
|
||||
else:
|
||||
bos, eos = i_n * T, i_n * T + T
|
||||
NT = tl.cdiv(T, BT)
|
||||
boh = i_n * NT
|
||||
|
||||
# offset calculation
|
||||
updated_h_state += (i_n * H + i_h).to(tl.int64) * K * V
|
||||
|
||||
for i_t in range(NT):
|
||||
i_tg = boh + i_t
|
||||
h_base = h + (i_tg * H + i_h).to(tl.int64) * K * V
|
||||
hupd_base = h_update + ((i_tg + i_n) * H + i_h).to(tl.int64) * K * K
|
||||
|
||||
for i_k in range(tl.cdiv(K, BK)):
|
||||
p_h = tl.make_block_ptr(h_base, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
|
||||
p_hupd = tl.make_block_ptr(hupd_base, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BK), (1, 0))
|
||||
p_updated_h_state = tl.make_block_ptr(
|
||||
updated_h_state, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)
|
||||
)
|
||||
|
||||
# [BK, BV]
|
||||
b_h = tl.load(p_h, boundary_check=(0, 1))
|
||||
# [BK, BK]
|
||||
b_hupd = tl.load(p_hupd, boundary_check=(0, 1))
|
||||
# [BK, BV]
|
||||
b_updated_h_state = tl.load(p_updated_h_state, boundary_check=(0, 1))
|
||||
|
||||
b_h += tl.dot(b_hupd.to(tl.bfloat16), b_updated_h_state.to(tl.bfloat16))
|
||||
tl.store(p_h, b_h.to(p_h.dtype.element_ty), boundary_check=(0, 1))
|
||||
|
||||
|
||||
def chunk_fwd_o_update(
|
||||
q: torch.Tensor,
|
||||
v: torch.Tensor,
|
||||
h: torch.Tensor,
|
||||
h_update: torch.Tensor,
|
||||
updated_h_state: torch.Tensor,
|
||||
cu_seqlens: torch.LongTensor | None = None,
|
||||
chunk_size: int = 64,
|
||||
) -> torch.Tensor:
|
||||
B, T, Hg, K, V = *q.shape, v.shape[-1]
|
||||
H = v.shape[-2]
|
||||
BT = chunk_size
|
||||
|
||||
if cu_seqlens is None:
|
||||
N, chunk_offsets = B, None
|
||||
else:
|
||||
N, chunk_offsets = (
|
||||
len(cu_seqlens) - 1,
|
||||
prepare_chunk_offsets(cu_seqlens, BT),
|
||||
)
|
||||
|
||||
def grid(meta):
|
||||
return (triton.cdiv(V, meta["BV"]), N * H)
|
||||
|
||||
chunk_fwd_kernel_o_update[grid](
|
||||
h=h,
|
||||
h_update=h_update,
|
||||
updated_h_state=updated_h_state,
|
||||
cu_seqlens=cu_seqlens,
|
||||
chunk_offsets=chunk_offsets,
|
||||
T=T,
|
||||
H=H,
|
||||
Hg=Hg,
|
||||
K=K,
|
||||
V=V,
|
||||
BT=BT,
|
||||
BK=128,
|
||||
BV=128,
|
||||
num_warps=4,
|
||||
num_stages=2,
|
||||
)
|
||||
return h
|
||||
@@ -24,10 +24,19 @@ def prepare_chunk_indices(cu_seqlens: torch.LongTensor, chunk_size: int) -> torc
|
||||
return torch.stack([indices.eq(0).cumsum(0) - 1, indices], 1).to(cu_seqlens)
|
||||
|
||||
|
||||
def prepare_final_chunk_indices(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
|
||||
indices = triton.cdiv(prepare_lens(cu_seqlens), chunk_size) + 1
|
||||
return torch.cumsum(indices, 0) - 1
|
||||
|
||||
|
||||
def prepare_chunk_offsets(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
|
||||
return torch.cat([cu_seqlens.new_tensor([0]), triton.cdiv(prepare_lens(cu_seqlens), chunk_size)]).cumsum(-1)
|
||||
|
||||
|
||||
def prepare_update_chunk_offsets(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
|
||||
return torch.cat([cu_seqlens.new_tensor([0]), triton.cdiv(prepare_lens(cu_seqlens), chunk_size) + 1]).cumsum(-1)
|
||||
|
||||
|
||||
def input_guard(fn: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]:
|
||||
"""
|
||||
A decorator to make sure all input tensors are contiguous and set the device based on input tensors.
|
||||
|
||||
Reference in New Issue
Block a user