xc-llm-ascend/vllm_ascend/patch/worker/patch_qwen3_next.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

import torch
from einops import rearrange
from torch import nn
from vllm.config import CUDAGraphMode
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.abstract import MambaBase
from vllm.model_executor.layers.mamba.ops.causal_conv1d import causal_conv1d_fn, causal_conv1d_update
from vllm.model_executor.models.qwen3_next import Qwen3NextGatedDeltaNet
from vllm.triton_utils import triton
from vllm.v1.attention.backend import AttentionMetadata  # type: ignore
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata

from vllm_ascend.ops.triton.fla.fused_qkvzba_split_reshape import fused_qkvzba_split_reshape_cat
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


class AscendQwen3Next_GatedDeltaNet(nn.Module, MambaBase):
    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
        """
        num_tokens = hidden_states.size(0)

        # ============================================================
        # Part 1: Input Projection
        # ============================================================
        projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states)
        projected_states_ba, _ = self.in_proj_ba(hidden_states)
        forward_context = get_forward_context()
        is_cuda_graph = forward_context.cudagraph_runtime_mode != CUDAGraphMode.NONE
        # triton grid should be less than 66536
        divide_grid = projected_states_qkvz.shape[0] * triton.cdiv(self.num_k_heads, self.tp_size)
        if self.num_v_heads // self.num_k_heads in [1, 2, 4] and is_cuda_graph and divide_grid < 65536:
            mixed_qkv, z, b, a = fused_qkvzba_split_reshape_cat(
                projected_states_qkvz,
                projected_states_ba,
                triton.cdiv(self.num_k_heads, self.tp_size),
                triton.cdiv(self.num_v_heads, self.tp_size),
                self.head_k_dim,
                self.head_v_dim,
            )
        else:
            query, key, value, z, b, a = self.fix_query_key_value_ordering(projected_states_qkvz, projected_states_ba)
            query, key, value = map(lambda x: rearrange(x, "l p d -> l (p d)"), (query, key, value))
            mixed_qkv = torch.cat((query, key, value), dim=-1)

        # ============================================================
        # 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)")
        output[:num_tokens], _ = self.out_proj(core_attn_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).
        """
        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

        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:
                mixed_qkv_non_spec_T = mixed_qkv_non_spec.transpose(0, 1)
                # - "cache_indices" updates the conv_state cache in positions
                #   pointed to by "state_indices_tensor"
                mixed_qkv_non_spec = causal_conv1d_fn(
                    mixed_qkv_non_spec_T,
                    conv_weights,
                    self.conv1d.bias,
                    activation=self.activation,
                    conv_states=conv_state,
                    has_initial_state=has_initial_state,
                    cache_indices=non_spec_state_indices_tensor,
                    query_start_loc=non_spec_query_start_loc,
                    metadata=attn_metadata,
                ).transpose(0, 1)
        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)
            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)


Qwen3NextGatedDeltaNet.forward = AscendQwen3Next_GatedDeltaNet.forward
Qwen3NextGatedDeltaNet._forward_core = AscendQwen3Next_GatedDeltaNet._forward_core