xc-llm-ascend/vllm_ascend/patch/worker/patch_qwen3_5.py

#
# 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