Add patch_qwen3_5 for triton ops fused_recurrent_gated_delta_rule

vllm_ascend/patch/__init__.py

@@ -332,3 +332,14 @@
 #       https://github.com/vllm-project/vllm/pull/36225
 #    Future Plan:
 #       Remove this patch when vLLM merges the PR.
+#
+# ** 17. File: worker/patch_qwen3_5.py**
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#   1. `vllm.model_executor.models.qwen3_5.Qwen3_5GatedDeltaNet._forward_core`
+#    Why:
+#       The class Qwen3_5GatedDeltaNet reuse the `_forward_core` method of Qwen3NextGatedDeltaNet,
+#       but the ascendC ops of Qwen3NextGatedDeltaNet do not support ssm_state with float32 format.
+#    How：
+#       patch Qwen3_5GatedDeltaNet._forward_core to use triton ops like `fused_recurrent_gated_delta_rule`.
+#    Future Plan:
+#       Remove this patch when all ops in _forward_core support both Qwen3_5 and Qwen3Next.

vllm_ascend/patch/worker/__init__.py

@@ -17,9 +17,14 @@
 
 from vllm.triton_utils import HAS_TRITON
 
+from vllm_ascend.utils import vllm_version_is
+
 if HAS_TRITON:
     import vllm_ascend.patch.worker.patch_triton
 
+if not vllm_version_is("v0.16.0"):
+    import vllm_ascend.patch.worker.patch_qwen3_5  # noqa
+
 # isort: off
 import vllm_ascend.patch.platform.patch_sched_yield  # noqa
 import vllm_ascend.patch.worker.patch_unquantized_gemm  # noqa

vllm_ascend/patch/worker/patch_qwen3_5.py

@@ -0,0 +1,257 @@
+#
+# 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.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
+
+
+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]
+        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
+                (
+                    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,
+                    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("", [])
+
+
+Qwen3_5GatedDeltaNet._forward_core = AscendQwen3_5GatedDeltaNet._forward_core

vllm_ascend/spec_decode/eagle_proposer.py

@@ -189,6 +189,8 @@ def load_model(self, model: nn.Module) -> None:
                 "Qwen2_5_VLForConditionalGeneration",
                 "Qwen3VLForConditionalGeneration",
                 "Qwen3VLMoeForConditionalGeneration",
+                "Qwen3_5ForConditionalGeneration",
+                "Qwen3_5MoeForConditionalGeneration",
             ]:
                 self.model.config.image_token_index = model.config.image_token_id
             elif self.get_model_name(model) == "PixtralForConditionalGeneration":