Add patch_qwen3_5 for triton ops fused_recurrent_gated_delta_rule (#7109)
### What this PR does / why we need it? The ops `torch_npu.npu_recurrent_gated_delta_rule` currently does not support `ssm_state` inputs in float32 format, we temporarily retain the _forward_core implementation with triton for Qwen3_5 --------- Signed-off-by: pppeng <zepengliu912@qq.com> Signed-off-by: pppeng <60355449+ppppeng@users.noreply.github.com>
This commit is contained in:
pppeng
@@ -332,3 +332,14 @@
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# https://github.com/vllm-project/vllm/pull/36225
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# Future Plan:
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# Remove this patch when vLLM merges the PR.
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#
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# ** 17. File: worker/patch_qwen3_5.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.model_executor.models.qwen3_5.Qwen3_5GatedDeltaNet._forward_core`
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# Why:
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# The class Qwen3_5GatedDeltaNet reuse the `_forward_core` method of Qwen3NextGatedDeltaNet,
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# but the ascendC ops of Qwen3NextGatedDeltaNet do not support ssm_state with float32 format.
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# How：
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# patch Qwen3_5GatedDeltaNet._forward_core to use triton ops like `fused_recurrent_gated_delta_rule`.
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# Future Plan:
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# Remove this patch when all ops in _forward_core support both Qwen3_5 and Qwen3Next.
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@@ -17,9 +17,14 @@
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from vllm.triton_utils import HAS_TRITON
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from vllm_ascend.utils import vllm_version_is
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if HAS_TRITON:
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import vllm_ascend.patch.worker.patch_triton
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if not vllm_version_is("v0.16.0"):
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import vllm_ascend.patch.worker.patch_qwen3_5 # noqa
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# isort: off
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import vllm_ascend.patch.platform.patch_sched_yield # noqa
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import vllm_ascend.patch.worker.patch_unquantized_gemm # noqa
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257
vllm_ascend/patch/worker/patch_qwen3_5.py
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257
vllm_ascend/patch/worker/patch_qwen3_5.py
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@@ -0,0 +1,257 @@
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#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# from collections.abc import Iterable
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# mypy: ignore-errors
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import torch
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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.fla.ops import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
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from vllm.model_executor.layers.mamba.ops.causal_conv1d import causal_conv1d_update
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from vllm.model_executor.models.qwen3_5 import Qwen3_5GatedDeltaNet
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from vllm.v1.attention.backend import AttentionMetadata # type: ignore
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
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from vllm.v1.attention.backends.utils import PAD_SLOT_ID
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from vllm_ascend.attention.utils import maybe_save_kv_layer_to_connector
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from vllm_ascend.ops.triton.fla.sigmoid_gating import fused_sigmoid_gating_delta_rule_update
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from vllm_ascend.ops.triton.fused_gdn_gating import fused_gdn_gating_patch
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from vllm_ascend.utils import enable_sp
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class AscendQwen3_5GatedDeltaNet(Qwen3_5GatedDeltaNet):
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def _forward_core(
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self,
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mixed_qkv: torch.Tensor,
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b: torch.Tensor,
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a: torch.Tensor,
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core_attn_out: torch.Tensor,
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):
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# Core attention computation (called by custom op).
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# NOTE: The processing logic of Qwen3_5GatedDeltaNet is the same as Qwen3NextGatedDeltaNet.
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# However, because the ops `torch_npu.npu_recurrent_gated_delta_rule`
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# currently does not support `ssm_state` inputs in float32 format,
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# we temporarily retain the current _forward_core implementation.
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# Once the ops supports float32 `ssm_state`, this patch should be removed.
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forward_context = get_forward_context()
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attn_metadata: AttentionMetadata = forward_context.attn_metadata
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if attn_metadata is None:
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# V1 profile run
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return
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assert isinstance(attn_metadata, dict)
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attn_metadata = attn_metadata[self.prefix]
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assert isinstance(attn_metadata, GDNAttentionMetadata)
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has_initial_state = attn_metadata.has_initial_state
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spec_query_start_loc = attn_metadata.spec_query_start_loc
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non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
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spec_sequence_masks = attn_metadata.spec_sequence_masks
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spec_token_indx = attn_metadata.spec_token_indx
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non_spec_token_indx = attn_metadata.non_spec_token_indx
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spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor # noqa: E501
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non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor # noqa: E501
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self_kv_cache = self.kv_cache[forward_context.virtual_engine]
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conv_state = self_kv_cache[0].transpose(-1, -2)
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ssm_state = self_kv_cache[1]
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num_actual_tokens = attn_metadata.num_actual_tokens
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num_accepted_tokens = attn_metadata.num_accepted_tokens
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if not enable_sp():
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mixed_qkv = mixed_qkv[:num_actual_tokens]
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b = b[:num_actual_tokens]
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a = a[:num_actual_tokens]
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# 1. Convolution sequence transformation
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conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
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if spec_sequence_masks is not None:
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if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
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mixed_qkv_spec = mixed_qkv
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mixed_qkv_non_spec = None
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else:
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mixed_qkv_spec = mixed_qkv.index_select(0, spec_token_indx)
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mixed_qkv_non_spec = mixed_qkv.index_select(0, non_spec_token_indx)
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else:
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mixed_qkv_spec = None
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mixed_qkv_non_spec = mixed_qkv
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# 1.1: Process the multi-query part
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if spec_sequence_masks is not None:
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mixed_qkv_spec = causal_conv1d_update(
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mixed_qkv_spec,
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conv_state,
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conv_weights,
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self.conv1d.bias,
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self.activation,
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conv_state_indices=spec_state_indices_tensor[:, 0][: attn_metadata.num_spec_decodes],
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num_accepted_tokens=num_accepted_tokens,
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query_start_loc=spec_query_start_loc,
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max_query_len=spec_state_indices_tensor.size(-1),
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validate_data=False,
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)
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# 1.2: Process the remaining part
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if attn_metadata.num_prefills > 0:
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if mixed_qkv_non_spec is not None:
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conv_weights_T = conv_weights.transpose(0, 1)
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mixed_qkv_non_spec = torch.ops._C_ascend.causal_conv1d_fn(
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mixed_qkv_non_spec,
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conv_weights_T,
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self.conv1d.bias,
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activation=self.activation,
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conv_state=self_kv_cache[0],
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has_initial_state=has_initial_state,
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non_spec_state_indices_tensor=non_spec_state_indices_tensor,
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non_spec_query_start_loc=non_spec_query_start_loc,
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pad_slot_id=PAD_SLOT_ID,
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)
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elif attn_metadata.num_decodes > 0:
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mixed_qkv_non_spec = causal_conv1d_update(
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mixed_qkv_non_spec,
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conv_state,
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conv_weights,
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self.conv1d.bias,
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self.activation,
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conv_state_indices=non_spec_state_indices_tensor[: attn_metadata.num_actual_tokens],
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validate_data=True,
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)
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else:
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mixed_qkv_non_spec = None
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query_spec, key_spec, value_spec = self.rearrange_mixed_qkv(mixed_qkv_spec)
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query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(mixed_qkv_non_spec)
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if attn_metadata.num_prefills > 0 or spec_sequence_masks is not None:
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g, beta = fused_gdn_gating_patch(self.A_log, a, b, self.dt_bias)
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if spec_sequence_masks is not None:
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if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
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g_spec = g
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beta_spec = beta
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g_non_spec = None
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beta_non_spec = None
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else:
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g_spec = g.index_select(1, spec_token_indx)
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beta_spec = beta.index_select(1, spec_token_indx)
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g_non_spec = g.index_select(1, non_spec_token_indx)
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beta_non_spec = beta.index_select(1, non_spec_token_indx)
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else:
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g_spec = None
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beta_spec = None
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g_non_spec = g
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beta_non_spec = beta
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# 2. Recurrent attention
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# 2.1: Process the multi-query part
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if spec_sequence_masks is not None:
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core_attn_out_spec, last_recurrent_state = fused_recurrent_gated_delta_rule(
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q=query_spec,
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k=key_spec,
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v=value_spec,
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g=g_spec,
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beta=beta_spec,
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initial_state=ssm_state,
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inplace_final_state=True,
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cu_seqlens=spec_query_start_loc[: attn_metadata.num_spec_decodes + 1],
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ssm_state_indices=spec_state_indices_tensor,
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num_accepted_tokens=num_accepted_tokens,
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use_qk_l2norm_in_kernel=True,
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)
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else:
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core_attn_out_spec, last_recurrent_state = None, None
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# 2.2: Process the remaining part
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if attn_metadata.num_prefills > 0:
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initial_state = ssm_state[non_spec_state_indices_tensor].contiguous()
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initial_state[~has_initial_state, ...] = 0
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(
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core_attn_out_non_spec,
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last_recurrent_state,
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) = chunk_gated_delta_rule(
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q=query_non_spec,
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k=key_non_spec,
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v=value_non_spec,
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g=g_non_spec,
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beta=beta_non_spec,
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initial_state=initial_state,
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output_final_state=True,
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cu_seqlens=non_spec_query_start_loc,
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head_first=False,
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use_qk_l2norm_in_kernel=True,
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)
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# Init cache
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ssm_state[non_spec_state_indices_tensor] = last_recurrent_state.to(ssm_state.dtype)
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elif attn_metadata.num_decodes > 0:
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core_attn_out_non_spec, last_recurrent_state = fused_recurrent_gated_delta_rule(
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q=query_non_spec,
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k=key_non_spec,
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v=value_non_spec,
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g=g_non_spec,
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beta=beta_non_spec,
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initial_state=ssm_state,
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inplace_final_state=True,
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cu_seqlens=non_spec_query_start_loc[: attn_metadata.num_decodes + 1],
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ssm_state_indices=non_spec_state_indices_tensor,
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use_qk_l2norm_in_kernel=True,
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)
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else:
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core_attn_out_non_spec, last_recurrent_state = None, None
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elif attn_metadata.num_decodes > 0:
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core_attn_out_non_spec = fused_sigmoid_gating_delta_rule_update(
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A_log=self.A_log.contiguous(),
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dt_bias=self.dt_bias.contiguous(),
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q=query_non_spec.contiguous(),
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k=key_non_spec.contiguous(),
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v=value_non_spec.contiguous(),
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a=a.contiguous(),
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b=b.contiguous(),
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initial_state_source=ssm_state,
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initial_state_indices=non_spec_state_indices_tensor,
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cu_seqlens=non_spec_query_start_loc,
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use_qk_l2norm_in_kernel=True,
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softplus_beta=1.0,
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softplus_threshold=20.0,
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)
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# 3. Merge core attention output
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if spec_sequence_masks is not None and core_attn_out_non_spec is not None:
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merged_out = torch.empty(
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(1, num_actual_tokens, *core_attn_out_spec.shape[2:]),
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dtype=core_attn_out_non_spec.dtype,
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device=core_attn_out_non_spec.device,
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)
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merged_out.index_copy_(1, spec_token_indx, core_attn_out_spec)
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merged_out.index_copy_(1, non_spec_token_indx, core_attn_out_non_spec)
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if not enable_sp():
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core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)
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else:
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core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)[:num_actual_tokens]
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elif spec_sequence_masks is not None:
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if not enable_sp():
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core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)
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else:
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core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)[:num_actual_tokens]
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else:
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if not enable_sp():
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core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)
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else:
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core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)[:num_actual_tokens]
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maybe_save_kv_layer_to_connector("", [])
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Qwen3_5GatedDeltaNet._forward_core = AscendQwen3_5GatedDeltaNet._forward_core
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@@ -189,6 +189,8 @@ class AscendEagleProposer(EagleProposer):
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"Qwen2_5_VLForConditionalGeneration",
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"Qwen3VLForConditionalGeneration",
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"Qwen3VLMoeForConditionalGeneration",
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"Qwen3_5ForConditionalGeneration",
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"Qwen3_5MoeForConditionalGeneration",
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]:
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self.model.config.image_token_index = model.config.image_token_id
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elif self.get_model_name(model) == "PixtralForConditionalGeneration":
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