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xc-llm-ascend/vllm_ascend/patch/worker/patch_qwen3_5.py
wangbj127 e6ba5a88f7 [v0.18.0][BugFix] Fix Qwen3.5 MoE FC1 error under high concurrency when dp>1 (#8395) 
### What this PR does / why we need it?
GDN Attention uses FIA's query_start_loc (padded), which may cause
conv1d update errors under high concurrency when dp > 1, and this PR is
to make GDN use its own query_start_loc (unpadded).

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
- vLLM version: v0.18.0

Signed-off-by: Wangbingjie <wangbj1207@126.com>
2026-04-20 10:26:19 +08:00

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#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# from collections.abc import Iterable
# mypy: ignore-errors
import torch
from einops import rearrange
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.fla.ops import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from vllm.model_executor.layers.mamba.ops.causal_conv1d import causal_conv1d_update
from vllm.model_executor.models.qwen3_5 import Qwen3_5DecoderLayer, Qwen3_5GatedDeltaNet
from vllm.model_executor.models.qwen3_next import Qwen3NextAttention
from vllm.v1.attention.backend import AttentionMetadata # type: ignore
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
from vllm.v1.attention.backends.utils import PAD_SLOT_ID
from vllm_ascend.ascend_forward_context import _EXTRA_CTX
from vllm_ascend.attention.utils import maybe_save_kv_layer_to_connector
from vllm_ascend.ops.triton.fla.sigmoid_gating import fused_sigmoid_gating_delta_rule_update
from vllm_ascend.ops.triton.fused_gdn_gating import fused_gdn_gating_patch
from vllm_ascend.utils import vllm_version_is
def to_int64_tuple(t):
t = t.to(torch.int64)
if t.dim() == 0:
return (t.item(),)
return tuple(t.tolist())
class AscendQwen3_5GatedDeltaNet(Qwen3_5GatedDeltaNet):
def forward(
self,
hidden_states: torch.Tensor,
output: torch.Tensor,
):
"""
Forward pass with three parts:
1. Input projection
2. Core attention (custom op)
3. Output projection
"""
# ============================================================
# Part 1: Input Projection
# ============================================================
mixed_qkvz, _ = self.in_proj_qkvz(hidden_states)
num_tokens = mixed_qkvz.size(0)
qkv_size = (self.key_dim * 2 + self.value_dim) // self.tp_size
z_size = self.value_dim // self.tp_size
mixed_qkv, z = mixed_qkvz.split([qkv_size, z_size], dim=-1)
z = z.reshape(z.size(0), -1, self.head_v_dim)
ba, _ = self.in_proj_ba(hidden_states)
b, a = ba.chunk(2, dim=-1)
b = b.contiguous()
a = a.contiguous()
# ============================================================
# Part 2: Core Attention (Custom Op)
# ============================================================
# Note: we should not use torch.empty here like other attention backends,
# see discussions in https://github.com/vllm-project/vllm/pull/28182
core_attn_out = torch.zeros(
(num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
dtype=hidden_states.dtype,
device=hidden_states.device,
)
torch.ops.vllm.gdn_attention_core(
mixed_qkv,
b,
a,
core_attn_out,
self.prefix,
)
# ============================================================
# Part 3: Output Projection
# ============================================================
z_shape_og = z.shape
# Reshape input data into 2D tensor
core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
z = z.reshape(-1, z.shape[-1])
core_attn_out = self.norm(core_attn_out, z)
core_attn_out = core_attn_out.reshape(z_shape_og)
core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
o_out, _ = self.out_proj(core_attn_out)
actual_num_tokens = o_out.shape[0]
output[:actual_num_tokens] = o_out
def _forward_core(
self,
mixed_qkv: torch.Tensor,
b: torch.Tensor,
a: torch.Tensor,
core_attn_out: torch.Tensor,
):
# Core attention computation (called by custom op).
# NOTE: The processing logic of Qwen3_5GatedDeltaNet is the same as Qwen3NextGatedDeltaNet.
# However, because the ops `torch_npu.npu_recurrent_gated_delta_rule`
# currently does not support `ssm_state` inputs in float32 format,
# we temporarily retain the current _forward_core implementation.
# Once the ops supports float32 `ssm_state`, this patch should be removed.
forward_context = get_forward_context()
attn_metadata: AttentionMetadata = forward_context.attn_metadata
if attn_metadata is None:
# V1 profile run
return
assert isinstance(attn_metadata, dict)
attn_metadata = attn_metadata[self.prefix]
assert isinstance(attn_metadata, GDNAttentionMetadata)
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
spec_sequence_masks = attn_metadata.spec_sequence_masks
spec_token_indx = attn_metadata.spec_token_indx
non_spec_token_indx = attn_metadata.non_spec_token_indx
spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor # noqa: E501
non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor # noqa: E501
self_kv_cache = self.kv_cache[forward_context.virtual_engine if vllm_version_is("0.18.0") else 0]
conv_state = self_kv_cache[0].transpose(-1, -2)
ssm_state = self_kv_cache[1]
num_actual_tokens = attn_metadata.num_actual_tokens
num_accepted_tokens = attn_metadata.num_accepted_tokens
mixed_qkv = mixed_qkv[:num_actual_tokens]
b = b[:num_actual_tokens]
a = a[:num_actual_tokens]
# 1. Convolution sequence transformation
conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
if spec_sequence_masks is not None:
if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
mixed_qkv_spec = mixed_qkv
mixed_qkv_non_spec = None
else:
mixed_qkv_spec = mixed_qkv.index_select(0, spec_token_indx)
mixed_qkv_non_spec = mixed_qkv.index_select(0, non_spec_token_indx)
else:
mixed_qkv_spec = None
mixed_qkv_non_spec = mixed_qkv
# 1.1: Process the multi-query part
if spec_sequence_masks is not None:
mixed_qkv_spec = causal_conv1d_update(
mixed_qkv_spec,
conv_state,
conv_weights,
self.conv1d.bias,
self.activation,
conv_state_indices=spec_state_indices_tensor[:, 0][: attn_metadata.num_spec_decodes],
num_accepted_tokens=num_accepted_tokens,
query_start_loc=spec_query_start_loc,
max_query_len=spec_state_indices_tensor.size(-1),
validate_data=False,
)
# 1.2: Process the remaining part
if attn_metadata.num_prefills > 0:
if mixed_qkv_non_spec is not None:
conv_weights_T = conv_weights.transpose(0, 1)
activation_num = 1 if self.activation else 0
mixed_qkv_non_spec = torch.ops._C_ascend.npu_causal_conv1d_custom(
mixed_qkv_non_spec,
conv_weights_T,
conv_state=self_kv_cache[0],
bias_opt=self.conv1d.bias,
query_start_loc_opt=to_int64_tuple(non_spec_query_start_loc),
cache_indices_opt=to_int64_tuple(non_spec_state_indices_tensor),
initial_state_mode_opt=to_int64_tuple(has_initial_state),
num_accepted_tokens_opt=[],
activation_mode=activation_num,
pad_slot_id=PAD_SLOT_ID,
run_mode=0,
)
elif attn_metadata.num_decodes > 0:
mixed_qkv_non_spec = causal_conv1d_update(
mixed_qkv_non_spec,
conv_state,
conv_weights,
self.conv1d.bias,
self.activation,
conv_state_indices=non_spec_state_indices_tensor[: attn_metadata.num_actual_tokens],
validate_data=True,
)
else:
mixed_qkv_non_spec = None
query_spec, key_spec, value_spec = self.rearrange_mixed_qkv(mixed_qkv_spec)
query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(mixed_qkv_non_spec)
if attn_metadata.num_prefills > 0 or spec_sequence_masks is not None:
g, beta = fused_gdn_gating_patch(self.A_log, a, b, self.dt_bias)
if spec_sequence_masks is not None:
if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
g_spec = g
beta_spec = beta
g_non_spec = None
beta_non_spec = None
else:
g_spec = g.index_select(1, spec_token_indx)
beta_spec = beta.index_select(1, spec_token_indx)
g_non_spec = g.index_select(1, non_spec_token_indx)
beta_non_spec = beta.index_select(1, non_spec_token_indx)
else:
g_spec = None
beta_spec = None
g_non_spec = g
beta_non_spec = beta
# 2. Recurrent attention
# 2.1: Process the multi-query part
if spec_sequence_masks is not None:
core_attn_out_spec, last_recurrent_state = fused_recurrent_gated_delta_rule(
q=query_spec,
k=key_spec,
v=value_spec,
g=g_spec,
beta=beta_spec,
initial_state=ssm_state,
inplace_final_state=True,
cu_seqlens=spec_query_start_loc[: attn_metadata.num_spec_decodes + 1],
ssm_state_indices=spec_state_indices_tensor,
num_accepted_tokens=num_accepted_tokens,
use_qk_l2norm_in_kernel=True,
)
else:
core_attn_out_spec, last_recurrent_state = None, None
# 2.2: Process the remaining part
if attn_metadata.num_prefills > 0:
initial_state = ssm_state[non_spec_state_indices_tensor].contiguous()
initial_state[~has_initial_state, ...] = 0
non_spec_chunked_prefill_meta = getattr(
attn_metadata,
"non_spec_chunked_prefill_meta",
None,
)
(
core_attn_out_non_spec,
last_recurrent_state,
) = chunk_gated_delta_rule(
q=query_non_spec,
k=key_non_spec,
v=value_non_spec,
g=g_non_spec,
beta=beta_non_spec,
initial_state=initial_state,
output_final_state=True,
cu_seqlens=non_spec_query_start_loc,
prebuilt_meta=non_spec_chunked_prefill_meta,
head_first=False,
use_qk_l2norm_in_kernel=True,
)
# Init cache
ssm_state[non_spec_state_indices_tensor] = last_recurrent_state.to(ssm_state.dtype)
elif attn_metadata.num_decodes > 0:
core_attn_out_non_spec, last_recurrent_state = fused_recurrent_gated_delta_rule(
q=query_non_spec,
k=key_non_spec,
v=value_non_spec,
g=g_non_spec,
beta=beta_non_spec,
initial_state=ssm_state,
inplace_final_state=True,
cu_seqlens=non_spec_query_start_loc[: attn_metadata.num_decodes + 1],
ssm_state_indices=non_spec_state_indices_tensor,
use_qk_l2norm_in_kernel=True,
)
else:
core_attn_out_non_spec, last_recurrent_state = None, None
elif attn_metadata.num_decodes > 0:
core_attn_out_non_spec = fused_sigmoid_gating_delta_rule_update(
A_log=self.A_log.contiguous(),
dt_bias=self.dt_bias.contiguous(),
q=query_non_spec.contiguous(),
k=key_non_spec.contiguous(),
v=value_non_spec.contiguous(),
a=a.contiguous(),
b=b.contiguous(),
initial_state_source=ssm_state,
initial_state_indices=non_spec_state_indices_tensor,
cu_seqlens=non_spec_query_start_loc,
use_qk_l2norm_in_kernel=True,
softplus_beta=1.0,
softplus_threshold=20.0,
)
# 3. Merge core attention output
if spec_sequence_masks is not None and core_attn_out_non_spec is not None:
merged_out = torch.empty(
(1, num_actual_tokens, *core_attn_out_spec.shape[2:]),
dtype=core_attn_out_non_spec.dtype,
device=core_attn_out_non_spec.device,
)
merged_out.index_copy_(1, spec_token_indx, core_attn_out_spec)
merged_out.index_copy_(1, non_spec_token_indx, core_attn_out_non_spec)
core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)
elif spec_sequence_masks is not None:
core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)
else:
core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)
maybe_save_kv_layer_to_connector("", [])
class AscendQwen3NextAttention(Qwen3NextAttention):
def forward(self, positions: torch.Tensor, output: torch.Tensor, hidden_states: torch.Tensor):
qkv, _ = self.qkv_proj(hidden_states)
if "qwen3_5" in self.config.model_type:
cos_sin = self.rotary_emb.cos_sin_cache[positions]
if cos_sin.device != qkv.device:
cos_sin = cos_sin.to(qkv.device)
if cos_sin.dtype != qkv.dtype:
cos_sin = cos_sin.to(qkv.dtype)
q, k, v, gate = torch.ops.vllm.triton_split_qkv_rmsnorm_mrope(
qkv=qkv,
q_weight=1.0 + self.q_norm.weight,
k_weight=1.0 + self.k_norm.weight,
cos_sin=cos_sin,
num_q_heads=self.num_heads,
num_kv_heads=self.num_kv_heads,
head_size=self.head_dim,
eps=self.config.rms_norm_eps,
mrope_section=self.rotary_emb.mrope_section,
is_interleaved=self.rotary_emb.mrope_interleaved,
rope_dim=self.rotary_emb.rotary_dim,
has_gate=self.attn_output_gate,
)
else:
if self.attn_output_gate:
q_gate, k, v = qkv.split([self.q_size * 2, self.kv_size, self.kv_size], dim=-1)
orig_shape = q_gate.shape[:-1]
q_gate = q_gate.view(*orig_shape, self.num_heads, -1)
q, gate = torch.chunk(q_gate, 2, dim=-1)
q = q.reshape(*orig_shape, -1)
gate = gate.reshape(*orig_shape, -1)
else:
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q = self.q_norm(q.view(-1, self.num_heads, self.head_dim)).view(-1, self.num_heads * self.head_dim)
k = self.k_norm(k.view(-1, self.num_kv_heads, self.head_dim)).view(-1, self.num_kv_heads * self.head_dim)
q, k = self.rotary_emb(positions, q, k)
attn_output = self.attn(q, k, v)
if self.attn_output_gate:
gate = torch.sigmoid(gate)
attn_output = attn_output * gate
output[:], _ = self.o_proj(attn_output)
class AscendQwen3_5DecoderLayer(Qwen3_5DecoderLayer):
def forward(
self,
hidden_states: torch.Tensor,
residual: torch.Tensor | None,
positions: torch.Tensor = None,
**kwargs: object,
):
if residual is None:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(hidden_states, residual)
if self.layer_idx == 0 and _EXTRA_CTX.flash_comm_v1_enabled:
tp_size = get_tensor_model_parallel_world_size()
n_out = (hidden_states.shape[0] + tp_size - 1) // tp_size
hidden_dim = hidden_states.shape[-1]
self_attention_output = torch.empty(
(n_out, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
)
else:
self_attention_output = torch.empty_like(hidden_states)
if self.layer_type == "linear_attention":
self.linear_attn(
hidden_states=hidden_states,
output=self_attention_output,
)
elif self.layer_type == "full_attention":
self.self_attn(
hidden_states=hidden_states,
output=self_attention_output,
positions=positions,
)
else:
raise ValueError("Invalid layer_type")
hidden_states = self_attention_output
if self.layer_scale:
if len(hidden_states.shape) == 2:
hidden_states = hidden_states * (self.attn_layer_scale.to(hidden_states.dtype)[0] + 1)
else:
hidden_states = hidden_states * (self.attn_layer_scale.to(hidden_states.dtype) + 1)
# Fully Connected
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
hidden_states = self.mlp(hidden_states)
if self.layer_scale:
if len(hidden_states.shape) == 2:
hidden_states = hidden_states * (self.ffn_layer_scale.to(hidden_states.dtype)[0] + 1)
else:
assert len(hidden_states.shape) == len(self.ffn_layer_scale.shape), (
f"shape must be the same {len(hidden_states.shape)}, {len(self.ffn_layer_scale.shape)}"
)
hidden_states = hidden_states * (self.ffn_layer_scale.to(hidden_states.dtype) + 1)
return hidden_states, residual
Qwen3_5GatedDeltaNet.forward = AscendQwen3_5GatedDeltaNet.forward
Qwen3_5GatedDeltaNet._forward_core = AscendQwen3_5GatedDeltaNet._forward_core
Qwen3NextAttention.forward = AscendQwen3NextAttention.forward
Qwen3_5DecoderLayer.forward = AscendQwen3_5DecoderLayer.forward
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