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xc-llm-ascend/vllm_ascend/patch/worker/patch_qwen3_5.py
ZhuQi-seu e942b62d74 [features]support split qkv rmsnorm rmope for qwen3.5 (#7368) 
### What this PR does / why we need it?
Qwen3.5 full attention supports enabling the split_qkv_rmsnorm_mrope
fusion operator.

### How was this patch tested?
vLLM version: v0.16.0
vLLM-Ascend main: https://github.com/vllm-project/vllm-ascend/pull/6730
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: ZhuQi-seu <zhuqi12@huawei.com>
2026-03-23 23:58:12 +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 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_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.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 enable_sp, vllm_version_is
class AscendQwen3_5GatedDeltaNet(Qwen3_5GatedDeltaNet):
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
if not enable_sp():
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)
mixed_qkv_non_spec = torch.ops._C_ascend.causal_conv1d_fn(
mixed_qkv_non_spec,
conv_weights_T,
self.conv1d.bias,
activation=self.activation,
conv_state=self_kv_cache[0],
has_initial_state=has_initial_state,
non_spec_state_indices_tensor=non_spec_state_indices_tensor,
non_spec_query_start_loc=non_spec_query_start_loc,
pad_slot_id=PAD_SLOT_ID,
)
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)
if not enable_sp():
core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)
else:
core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)[:num_actual_tokens]
elif spec_sequence_masks is not None:
if not enable_sp():
core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)
else:
core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)[:num_actual_tokens]
else:
if not enable_sp():
core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)
else:
core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)[:num_actual_tokens]
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)
Qwen3_5GatedDeltaNet._forward_core = AscendQwen3_5GatedDeltaNet._forward_core
Qwen3NextAttention.forward = AscendQwen3NextAttention.forward
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