c11a05c4e1
### What this PR does / why we need it?
Upgrade vllm commit to 0113 (11b6af5280d6d6dfb8953af16e67b25f819b3be9)
- Modify import paths due to the refactors
https://github.com/vllm-project/vllm/pull/31916
https://github.com/vllm-project/vllm/pull/32054
- Fix `TypeError: NPUOffloadingSpec.__init__() takes 2 positional
arguments but 3 were given` due to
https://github.com/vllm-project/vllm/pull/24498
- Skip the async-scheduling tests in
`tests/e2e/multicard/4-cards/long_sequence/test_mtp.py`, which are never
verified
https://github.com/vllm-project/vllm/pull/31998
- Skip some pooling tests, which are caused by
https://github.com/vllm-project/vllm/pull/32148
where vllm is also failed
https://buildkite.com/vllm/ci/builds/46705/steps/canvas?jid=019bb329-3834-4685-862b-1613b8e0f5d4
We will reopen those tests when main2main reachs
https://github.com/vllm-project/vllm/pull/32243
- Skip some cases in
`tests/e2e/multicard/4-cards/long_sequence/test_mtp.py`, which are
broken by
https://github.com/vllm-project/vllm/pull/32118
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: wjunLu <wjunlu217@gmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
351 lines
15 KiB
Python
351 lines
15 KiB
Python
#
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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.fla.ops import (
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chunk_gated_delta_rule, fused_recurrent_gated_delta_rule)
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from vllm.model_executor.layers.mamba.abstract import MambaBase
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from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
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causal_conv1d_fn, causal_conv1d_update)
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from vllm.model_executor.models.qwen3_next import (Qwen3NextGatedDeltaNet,
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fused_gdn_gating)
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from vllm.triton_utils import triton
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
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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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from vllm_ascend.ops.triton.fla.sigmoid_gating import \
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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 vllm_version_is
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# isort: off
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if vllm_version_is('0.13.0'):
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from vllm.attention.backends.abstract import AttentionMetadata # type: ignore
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else:
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from vllm.v1.attention.backend import AttentionMetadata # type: ignore
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# isort: on
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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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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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"""
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Core attention computation (called by custom op).
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"""
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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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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),
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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(
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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]
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[: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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mixed_qkv_non_spec_T = mixed_qkv_non_spec.transpose(0, 1)
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# - "cache_indices" updates the conv_state cache in positions
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# pointed to by "state_indices_tensor"
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mixed_qkv_non_spec = causal_conv1d_fn(
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mixed_qkv_non_spec_T,
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conv_weights,
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self.conv1d.bias,
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activation=self.activation,
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conv_states=conv_state,
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has_initial_state=has_initial_state,
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cache_indices=non_spec_state_indices_tensor,
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query_start_loc=non_spec_query_start_loc,
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metadata=attn_metadata,
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).transpose(0, 1)
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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=
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non_spec_state_indices_tensor[:attn_metadata.
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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(
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mixed_qkv_spec)
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query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(
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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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is_cuda_graph = forward_context.cudagraph_runtime_mode != CUDAGraphMode.NONE
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if (is_cuda_graph):
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g, beta = fused_gdn_gating_patch(self.A_log, a, b,
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self.dt_bias)
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else:
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g, beta = fused_gdn_gating(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.
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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[
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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[
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non_spec_state_indices_tensor] = last_recurrent_state.to(
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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 = (
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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.
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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,
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core_attn_out_non_spec)
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core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)
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elif spec_sequence_masks is not None:
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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_non_spec.squeeze(
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0)
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Qwen3NextGatedDeltaNet.forward = AscendQwen3Next_GatedDeltaNet.forward
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Qwen3NextGatedDeltaNet._forward_core = AscendQwen3Next_GatedDeltaNet._forward_core
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