Drop 0.11.0 support (#4377)

There is a lot hack code for v0.11.0, which makes the code hard to
upgrade to newer vLLM version. Since v0.11.0 will release soon. Let's
drop v0.11.0 support first. Then we'll upgrade to v0.11.2 soon.


- vLLM version: v0.11.0
- vLLM main:
2918c1b49c

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/torchair/models/qwen3_moe.py

@@ -23,7 +23,7 @@ from torch import nn
 from transformers import PretrainedConfig
 from vllm.attention import Attention, AttentionMetadata
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, CompilationMode, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import (get_dp_group, get_ep_group,
                                              get_tp_group)
@@ -55,12 +55,6 @@ from vllm_ascend.attention.attention_v1 import AscendAttentionState
 from vllm_ascend.torchair.ops.sequence_parallel import (MetadataForPadding,
                                                         init_metadata_for_sp)
 from vllm_ascend.torchair.ops.torchair_fused_moe import TorchairAscendFusedMoE
-from vllm_ascend.utils import vllm_version_is
-
-if vllm_version_is("0.11.0"):
-    from vllm.config import CompilationLevel
-else:
-    from vllm.config import CompilationMode
 
 
 class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock):
@@ -299,16 +293,10 @@ class CustomQwen3MoeDecoderLayer(Qwen3MoeDecoderLayer):
         layer_idx = extract_layer_index(prefix)
         mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else
                            config.mlp_only_layers)
-        if vllm_version_is("0.11.0"):
-            self.use_aclgraph = (vllm_config is not None
-                                 and vllm_config.compilation_config.level
-                                 == CompilationLevel.PIECEWISE and
-                                 not vllm_config.model_config.enforce_eager)
-        else:
-            self.use_aclgraph = (vllm_config is not None
-                                 and vllm_config.compilation_config.mode
-                                 == CompilationMode.VLLM_COMPILE and
-                                 not vllm_config.model_config.enforce_eager)
+        self.use_aclgraph = (vllm_config is not None
+                             and vllm_config.compilation_config.mode
+                             == CompilationMode.VLLM_COMPILE
+                             and not vllm_config.model_config.enforce_eager)
         if (layer_idx not in mlp_only_layers) and (
                 config.num_experts > 0 and
             (layer_idx + 1) % config.decoder_sparse_step == 0):

vllm_ascend/torchair/models/torchair_deepseek_v2.py

@@ -32,6 +32,7 @@ import torch_npu
 from torch import nn
 from transformers import PretrainedConfig
 from vllm.attention import AttentionMetadata
+from vllm.attention.layer import MLAAttention
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
@@ -74,12 +75,7 @@ from vllm_ascend.quantization.quant_config import AscendLinearMethod
 from vllm_ascend.torchair.ops.torchair_fused_moe import TorchairAscendFusedMoE
 from vllm_ascend.torchair.quantization.torchair_w8a8_dynamic import \
     TorchairAscendW8A8DynamicLinearMethod
-from vllm_ascend.utils import dispose_tensor, oproj_tp_enable, vllm_version_is
-
-if vllm_version_is("0.11.0"):
-    from vllm.attention import Attention
-else:
-    from vllm.attention.layer import MLAAttention
+from vllm_ascend.utils import dispose_tensor, oproj_tp_enable
 
 
 class Indexer(nn.Module):
@@ -616,67 +612,31 @@ class TorchairDeepseekV2MLAAttention(DeepseekV2MLAAttention):
         #     k_c.size(1) + k_pe.size(1) == kv_cache.size(2)
         # i.e.
         #     kv_lora_rank + qk_rope_head_dim == head_size
-        if vllm_version_is("0.11.0"):
-            self.mla_attn = Attention(
-                num_heads=self.num_local_heads,
-                head_size=self.kv_lora_rank + self.qk_rope_head_dim,
-                scale=self.scaling,
-                num_kv_heads=1,
-                cache_config=cache_config,
-                quant_config=quant_config,
-                prefix=f"{prefix}.attn",
-                use_mla=True,
-                use_sparse=False,
-                indexer=None,
-                # SFA Args
-                q_lora_rank=self.q_lora_rank,
-                kv_lora_rank=self.kv_lora_rank,
-                qk_nope_head_dim=self.qk_nope_head_dim,
-                qk_rope_head_dim=self.qk_rope_head_dim,
-                qk_head_dim=self.qk_head_dim,
-                v_head_dim=self.v_head_dim,
-                rotary_emb=self.rotary_emb,
-                q_a_proj=self.q_a_proj
-                if self.q_lora_rank is not None else None,
-                q_a_layernorm=self.q_a_layernorm
-                if self.q_lora_rank is not None else None,
-                q_proj=self.q_proj
-                if self.q_lora_rank is None else self.q_b_proj,
-                kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
-                kv_a_layernorm=self.kv_a_layernorm,
-                kv_b_proj=self.kv_b_proj,
-                o_proj=self.o_proj,
-                decoder_layer=decoder_layer,
-            )
-        else:
-            self.mla_attn = MLAAttention(
-                num_heads=self.num_local_heads,
-                scale=self.scaling,
-                qk_nope_head_dim=self.qk_nope_head_dim,
-                qk_rope_head_dim=self.qk_rope_head_dim,
-                v_head_dim=self.v_head_dim,
-                q_lora_rank=self.q_lora_rank,
-                kv_lora_rank=self.kv_lora_rank,
-                cache_config=cache_config,
-                quant_config=quant_config,
-                prefix=f"{prefix}.attn",
-                use_sparse=False,
-                indexer=None,
-                # MLA Args
-                rotary_emb=self.rotary_emb,
-                q_a_proj=self.q_a_proj
-                if self.q_lora_rank is not None else None,
-                q_a_layernorm=self.q_a_layernorm
-                if self.q_lora_rank is not None else None,
-                q_proj=self.q_proj
-                if self.q_lora_rank is None else self.q_b_proj,
-                q_b_proj=self.q_b_proj
-                if self.q_lora_rank is not None else None,
-                kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
-                kv_a_layernorm=self.kv_a_layernorm,
-                kv_b_proj=self.kv_b_proj,
-                o_proj=self.o_proj,
-            )
+        self.mla_attn = MLAAttention(
+            num_heads=self.num_local_heads,
+            scale=self.scaling,
+            qk_nope_head_dim=self.qk_nope_head_dim,
+            qk_rope_head_dim=self.qk_rope_head_dim,
+            v_head_dim=self.v_head_dim,
+            q_lora_rank=self.q_lora_rank,
+            kv_lora_rank=self.kv_lora_rank,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+            use_sparse=False,
+            indexer=None,
+            # MLA Args
+            rotary_emb=self.rotary_emb,
+            q_a_proj=self.q_a_proj if self.q_lora_rank is not None else None,
+            q_a_layernorm=self.q_a_layernorm
+            if self.q_lora_rank is not None else None,
+            q_proj=self.q_proj if self.q_lora_rank is None else self.q_b_proj,
+            q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None,
+            kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
+            kv_a_layernorm=self.kv_a_layernorm,
+            kv_b_proj=self.kv_b_proj,
+            o_proj=self.o_proj,
+        )
 
     def forward(
             self,
@@ -882,66 +842,30 @@ class TorchairDeepseekV2SFAAttention(DeepseekV2MLAAttention):
             index_topk=self.index_topk,
             prefix=f"{prefix}.indexer",
         )
-
-        if vllm_version_is("0.11.0"):
-            self.sfa_attn = Attention(
-                num_heads=self.num_local_heads,
-                head_size=self.kv_lora_rank + self.qk_rope_head_dim,
-                scale=self.scaling,
-                num_kv_heads=1,
-                cache_config=cache_config,
-                quant_config=quant_config,
-                prefix=f"{prefix}.attn",
-                use_mla=True,
-                use_sparse=True,
-                indexer=self.indexer,
-                # SFA Args
-                q_lora_rank=self.q_lora_rank,
-                kv_lora_rank=self.kv_lora_rank,
-                qk_nope_head_dim=self.qk_nope_head_dim,
-                qk_rope_head_dim=self.qk_rope_head_dim,
-                qk_head_dim=self.qk_head_dim,
-                v_head_dim=self.v_head_dim,
-                rotary_emb=self.rotary_emb,
-                q_a_proj=self.q_a_proj
-                if self.q_lora_rank is not None else None,
-                q_a_layernorm=self.q_a_layernorm
-                if self.q_lora_rank is not None else None,
-                q_proj=self.q_proj
-                if self.q_lora_rank is None else self.q_b_proj,
-                kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
-                kv_a_layernorm=self.kv_a_layernorm,
-                kv_b_proj=self.kv_b_proj,
-                o_proj=self.o_proj,
-                decoder_layer=decoder_layer,
-            )
-        else:
-            self.sfa_attn = MLAAttention(
-                num_heads=self.num_local_heads,
-                scale=self.scaling,
-                qk_nope_head_dim=self.qk_nope_head_dim,
-                qk_rope_head_dim=self.qk_rope_head_dim,
-                v_head_dim=self.v_head_dim,
-                q_lora_rank=self.q_lora_rank,
-                kv_lora_rank=self.kv_lora_rank,
-                cache_config=cache_config,
-                quant_config=quant_config,
-                prefix=f"{prefix}.attn",
-                use_sparse=True,
-                indexer=self.indexer,
-                # MLA Args
-                rotary_emb=self.rotary_emb,
-                q_a_proj=self.q_a_proj
-                if self.q_lora_rank is not None else None,
-                q_a_layernorm=self.q_a_layernorm
-                if self.q_lora_rank is not None else None,
-                q_proj=self.q_proj
-                if self.q_lora_rank is None else self.q_b_proj,
-                kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
-                kv_a_layernorm=self.kv_a_layernorm,
-                kv_b_proj=self.kv_b_proj,
-                o_proj=self.o_proj,
-            )
+        self.sfa_attn = MLAAttention(
+            num_heads=self.num_local_heads,
+            scale=self.scaling,
+            qk_nope_head_dim=self.qk_nope_head_dim,
+            qk_rope_head_dim=self.qk_rope_head_dim,
+            v_head_dim=self.v_head_dim,
+            q_lora_rank=self.q_lora_rank,
+            kv_lora_rank=self.kv_lora_rank,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+            use_sparse=True,
+            indexer=self.indexer,
+            # MLA Args
+            rotary_emb=self.rotary_emb,
+            q_a_proj=self.q_a_proj if self.q_lora_rank is not None else None,
+            q_a_layernorm=self.q_a_layernorm
+            if self.q_lora_rank is not None else None,
+            q_proj=self.q_proj if self.q_lora_rank is None else self.q_b_proj,
+            kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
+            kv_a_layernorm=self.kv_a_layernorm,
+            kv_b_proj=self.kv_b_proj,
+            o_proj=self.o_proj,
+        )
 
     def forward(
             self,