106 lines
4.2 KiB
Python
106 lines
4.2 KiB
Python
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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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import torch
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from einops import rearrange
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from torch import nn
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from vllm.config import CUDAGraphMode
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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.mamba.abstract import MambaBase
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from vllm.model_executor.models.qwen3_next import Qwen3NextGatedDeltaNet
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from vllm.triton_utils import triton
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from vllm_ascend.ops.triton.fla.fused_qkvzba_split_reshape import \
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fused_qkvzba_split_reshape_cat
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class AscendQwen3Next_GatedDeltaNet(nn.Module, MambaBase):
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def forward(
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self,
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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):
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"""
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Forward pass with three parts:
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1. Input projection
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2. Core attention (custom op)
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3. Output projection
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"""
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num_tokens = hidden_states.size(0)
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# ============================================================
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# Part 1: Input Projection
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# ============================================================
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projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states)
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projected_states_ba, _ = self.in_proj_ba(hidden_states)
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forward_context = get_forward_context()
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is_cuda_graph = forward_context.cudagraph_runtime_mode != CUDAGraphMode.NONE
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# triton grid should be less than 66536
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divide_grid = projected_states_qkvz.shape[0] * triton.cdiv(
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self.num_k_heads, self.tp_size)
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if self.num_v_heads // self.num_k_heads in [1, 2, 4] and \
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is_cuda_graph and divide_grid < 65536:
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mixed_qkv, z, b, a = fused_qkvzba_split_reshape_cat(
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projected_states_qkvz,
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projected_states_ba,
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triton.cdiv(self.num_k_heads, self.tp_size),
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triton.cdiv(self.num_v_heads, self.tp_size),
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self.head_k_dim,
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self.head_v_dim,
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)
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else:
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query, key, value, z, b, a = self.fix_query_key_value_ordering(
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projected_states_qkvz, projected_states_ba)
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query, key, value = map(lambda x: rearrange(x, 'l p d -> l (p d)'),
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(query, key, value))
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mixed_qkv = torch.cat((query, key, value), dim=-1)
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# ============================================================
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# Part 2: Core Attention (Custom Op)
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# ============================================================
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# Note: we should not use torch.empty here like other attention backends,
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# see discussions in https://github.com/vllm-project/vllm/pull/28182
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core_attn_out = torch.zeros(
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(num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
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dtype=hidden_states.dtype,
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device=hidden_states.device,
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)
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torch.ops.vllm.gdn_attention_core(
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mixed_qkv,
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b,
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a,
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core_attn_out,
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self.prefix,
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)
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# ============================================================
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# Part 3: Output Projection
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# ============================================================
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z_shape_og = z.shape
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# Reshape input data into 2D tensor
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core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
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z = z.reshape(-1, z.shape[-1])
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core_attn_out = self.norm(core_attn_out, z)
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core_attn_out = core_attn_out.reshape(z_shape_og)
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core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
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output[:num_tokens], _ = self.out_proj(core_attn_out)
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Qwen3NextGatedDeltaNet.forward = AscendQwen3Next_GatedDeltaNet.forward
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