v0.10.1rc1

vllm_ascend/torchair/models/qwen2.py

@@ -0,0 +1,364 @@
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+
+from collections.abc import Iterable
+from typing import Any, List, Optional, Union
+
+import torch
+import torch.nn.functional as F
+import vllm
+import vllm.envs as envs
+from torch import nn
+from transformers import Qwen2Config
+from vllm.attention import AttentionMetadata, AttentionType
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group, tensor_model_parallel_all_gather,
+                              tensor_model_parallel_reduce_scatter)
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
+from vllm.model_executor.models.qwen2 import Qwen2Attention  # noqa: F401
+from vllm.model_executor.models.qwen2 import Qwen2ForCausalLM  # noqa: F401
+from vllm.model_executor.models.qwen2 import Qwen2MLP, Qwen2Model
+from vllm.model_executor.models.utils import (AutoWeightsLoader,
+                                              PPMissingLayer, maybe_prefix)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.attention.attention_v1 import AscendAttentionState
+
+
+def all_gather_and_maybe_unpad(
+    hidden_states: torch.Tensor,
+    pad_size: int,
+) -> torch.Tensor:
+    hidden_states = tensor_model_parallel_all_gather(hidden_states, 0)
+    if pad_size > 0:
+        return hidden_states[:-pad_size, :]
+    return hidden_states
+
+
+def maybe_pad_and_reduce_scatter(
+    hidden_states: torch.Tensor,
+    pad_size: int,
+) -> torch.Tensor:
+    if pad_size > 0:
+        hidden_states = F.pad(hidden_states, (0, 0, 0, pad_size))
+    hidden_states = tensor_model_parallel_reduce_scatter(hidden_states, 0)
+    return hidden_states
+
+
+class CustomQwen2Attention(Qwen2Attention):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position: int = 4096 * 32,
+        rope_theta: float = 10000,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        rope_scaling: Optional[tuple] = None,
+        prefix: str = "",
+        attn_type: str = AttentionType.DECODER,
+        dual_chunk_attention_config: Optional[dict[str, Any]] = None,
+    ) -> None:
+        super().__init__(
+            hidden_size=hidden_size,
+            num_heads=num_heads,
+            num_kv_heads=num_kv_heads,
+            max_position=max_position,
+            rope_theta=rope_theta,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            rope_scaling=rope_scaling,
+            prefix=prefix,
+            attn_type=attn_type,
+            dual_chunk_attention_config=dual_chunk_attention_config)
+        ascend_config = get_ascend_config()
+        self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
+
+    def forward(
+            self,
+            positions: torch.Tensor,
+            hidden_states: torch.Tensor,
+            kv_cache: Optional[torch.Tensor] = None,
+            attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.torchair_graph_enabled and attn_metadata is not None and attn_metadata.attn_state == AscendAttentionState.DecodeOnly:
+            q, k = self.rotary_emb(positions,
+                                   q,
+                                   k,
+                                   is_prefill=False,
+                                   is_qwen_torchair=True)
+            forward_kwargs = {}
+            if envs.VLLM_USE_V1:
+                output_shape = q.shape
+                output = torch.empty(output_shape,
+                                     dtype=q.dtype,
+                                     device=q.device)
+                forward_kwargs['output'] = output
+
+            attn_output = self.attn.impl.forward(self.attn,
+                                                 q,
+                                                 k,
+                                                 v,
+                                                 kv_cache=kv_cache,
+                                                 attn_metadata=attn_metadata,
+                                                 trace_flag=False,
+                                                 **forward_kwargs)
+            output, _ = self.o_proj(attn_output)
+            return output
+        else:
+            if type(self.rotary_emb) is RotaryEmbedding:
+                q, k = self.rotary_emb(positions, q, k, is_qwen_torchair=True)
+            else:
+                q, k = self.rotary_emb(positions, q, k)
+            attn_output = self.attn(q, k, v)
+            output, _ = self.o_proj(attn_output)
+            return output
+
+
+class CustomQwen2DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Qwen2Config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 1000000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        dual_chunk_attention_config = getattr(config,
+                                              "dual_chunk_attention_config",
+                                              None)
+
+        # By default, Qwen2 uses causal attention as it is a decoder-only model.
+        # You can override the HF config with `is_causal=False` to enable
+        # bidirectional attention, which is used in some embedding models
+        # (e.g. Alibaba-NLP/gte-Qwen2-7B-instruct)
+        if getattr(config, "is_causal", True):
+            attn_type = AttentionType.DECODER
+        else:
+            attn_type = AttentionType.ENCODER_ONLY
+
+        self.self_attn = CustomQwen2Attention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            rope_scaling=rope_scaling,
+            prefix=f"{prefix}.self_attn",
+            attn_type=attn_type,
+            dual_chunk_attention_config=dual_chunk_attention_config,
+        )
+        self.mlp = Qwen2MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        kv_cache: Optional[torch.Tensor] = None,
+        attn_metadata: Optional[AttentionMetadata] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+
+        hidden_states = self.self_attn(positions=positions,
+                                       hidden_states=hidden_states,
+                                       kv_cache=kv_cache,
+                                       attn_metadata=attn_metadata)
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
+        # otherwise (seq_len, ).
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+    })
+class CustomQwen2Model(Qwen2Model):
+
+    def __init__(
+            self,
+            *,
+            vllm_config: VllmConfig,
+            prefix: str = "",
+            decoder_layer_type: type[nn.Module] = CustomQwen2DecoderLayer):
+        super().__init__(vllm_config=vllm_config,
+                         prefix=prefix,
+                         decoder_layer_type=decoder_layer_type)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: Optional[List[torch.Tensor]] = None,
+        attn_metadata: Optional[AttentionMetadata] = None,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            kv_cache = kv_caches[i - self.start_layer] \
+                if kv_caches is not None else None
+            hidden_states, residual = layer(positions,
+                                            hidden_states,
+                                            residual,
+                                            kv_cache=kv_cache,
+                                            attn_metadata=attn_metadata)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class CustomQwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    # add `CustomQwen2Model` to init self.model
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.quant_config = quant_config
+        self.model = CustomQwen2Model(vllm_config=vllm_config,
+                                      prefix=maybe_prefix(prefix, "model"))
+
+        if get_pp_group().is_last_rank:
+            if config.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(config.vocab_size,
+                                              config.hidden_size,
+                                              quant_config=quant_config,
+                                              prefix=maybe_prefix(
+                                                  prefix, "lm_head"))
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: Optional[List[torch.Tensor]] = None,
+        attn_metadata: Optional[AttentionMetadata] = None,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   attn_metadata, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
+
+
+vllm.model_executor.models.qwen2.Qwen2ForCausalLM = CustomQwen2ForCausalLM

vllm_ascend/torchair/models/qwen3_moe.py

@@ -0,0 +1,537 @@
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+# Adapted from vllm/model_executor/models/qwen3_moe.py
+# This file is a part of the vllm-ascend project.
+from typing import Any, List, Optional, Union
+
+import torch
+import vllm.envs as envs
+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, CompilationLevel, 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)
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.fused_moe.layer import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.models.interfaces import (MixtureOfExperts,
+                                                   SupportsLoRA, SupportsPP)
+from vllm.model_executor.models.qwen3_moe import (Qwen3MoeAttention,
+                                                  Qwen3MoeDecoderLayer,
+                                                  Qwen3MoeForCausalLM,
+                                                  Qwen3MoeMLP, Qwen3MoeModel,
+                                                  Qwen3MoeSparseMoeBlock)
+from vllm.model_executor.models.utils import (
+    PPMissingLayer, extract_layer_index,
+    make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
+from vllm.sequence import IntermediateTensors
+
+from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.attention.attention_v1 import AscendAttentionState
+from vllm_ascend.ops.fused_moe import AscendFusedMoE
+from vllm_ascend.ops.sequence_parallel import (MetadataForPadding,
+                                               init_metadata_for_sp)
+
+
+class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        nn.Module.__init__(self)
+        self.tp_size = get_tensor_model_parallel_world_size()
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}.")
+
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.num_experts,
+            bias=False,
+            quant_config=None,
+            prefix=f"{prefix}.gate",
+        )
+
+        self.experts = AscendFusedMoE(
+            num_experts=config.num_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts",
+        )
+
+        self.top_k = config.num_experts_per_tok
+
+        self.dp_size = get_dp_group().world_size
+
+        self.tp_group = get_tp_group().device_group
+        self.tp_rank = get_tp_group().rank_in_group
+        self.ep_group = get_ep_group()
+
+        self.params_dtype = torch.get_default_dtype()
+
+    def forward(
+        self,
+        hidden_states,
+        attn_metadata=None,
+        _metadata_for_padding: Optional[MetadataForPadding] = None,
+    ):
+        if attn_metadata is None:
+            attn_metadata = get_forward_context().attn_metadata
+        # when profile runs, force experts to load balanced tokens
+        # to avoid high memory consumption on a single rank.
+        enable_force_load_balance = get_forward_context().in_profile_run
+        is_prefill = get_forward_context().with_prefill
+
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+
+        hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+            is_prefill=is_prefill,
+            top_k=self.top_k,
+            enable_force_load_balance=enable_force_load_balance,
+            shared_experts=None,
+            _metadata_for_padding=_metadata_for_padding,
+        )
+
+        return hidden_states
+
+
+class CustomQwen3MoeAttention(Qwen3MoeAttention):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        head_dim: Optional[int] = None,
+        rms_norm_eps: float = 1e-06,
+        qkv_bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        nn.Module.__init__(self)
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim or (hidden_size // self.total_num_heads)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(hidden_size,
+                                          self.head_dim,
+                                          self.total_num_heads,
+                                          self.total_num_kv_heads,
+                                          bias=qkv_bias,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.qkv_proj")
+
+        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
+                                        hidden_size,
+                                        bias=False,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.o_proj")
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+        self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+        self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+        ascend_config = get_ascend_config()
+        self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
+
+    @staticmethod
+    def normalize_qkv(qkv: torch.Tensor, q_size: int, kv_size: int,
+                      head_dim: int, q_norm, k_norm):
+        q, k, v = qkv.split([q_size, kv_size, kv_size], dim=-1)
+
+        q_by_head = q.view(*q.shape[:-1], q.shape[-1] // head_dim, head_dim)
+        q_by_head = q_norm(q_by_head)
+        q = q_by_head.view(q.shape)
+
+        k_by_head = k.view(*k.shape[:-1], k.shape[-1] // head_dim, head_dim)
+        k_by_head = k_norm(k_by_head)
+        k = k_by_head.view(k.shape)
+
+        return q, k, v
+
+    def forward(
+            self,
+            positions: torch.Tensor,
+            hidden_states: torch.Tensor,
+            kv_cache: Optional[torch.Tensor] = None,
+            attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = self.normalize_qkv(qkv, self.q_size, self.kv_size,
+                                     self.head_dim, self.q_norm, self.k_norm)
+
+        if (self.torchair_graph_enabled and attn_metadata is not None and
+                attn_metadata.attn_state == AscendAttentionState.DecodeOnly):
+            q, k = self.rotary_emb(positions,
+                                   q,
+                                   k,
+                                   is_prefill=False,
+                                   is_qwen_torchair=True)
+            forward_kwargs = {}
+            if envs.VLLM_USE_V1:
+                output_shape = q.shape
+                output = torch.empty(output_shape,
+                                     dtype=q.dtype,
+                                     device=q.device)
+                forward_kwargs['output'] = output
+
+            attn_output = self.attn.impl.forward(self.attn,
+                                                 q,
+                                                 k,
+                                                 v,
+                                                 kv_cache=kv_cache,
+                                                 attn_metadata=attn_metadata,
+                                                 trace_flag=False,
+                                                 **forward_kwargs)
+            output, _ = self.o_proj(attn_output)
+            return output
+        else:
+            q, k = self.rotary_emb(positions, q, k, is_qwen_torchair=True)
+            attn_output = self.attn(q, k, v)
+            output, _ = self.o_proj(attn_output)
+            return output
+
+
+class CustomQwen3MoeDecoderLayer(Qwen3MoeDecoderLayer):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        vllm_config: Optional[VllmConfig] = None,
+        prefix: str = "",
+    ) -> None:
+
+        nn.Module.__init__(self)
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = CustomQwen3MoeAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            rms_norm_eps=config.rms_norm_eps,
+            qkv_bias=getattr(config, 'attention_bias', False),
+            head_dim=getattr(config, 'head_dim', None),
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        # `mlp_only_layers` in the config.
+        layer_idx = extract_layer_index(prefix)
+        mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else
+                           config.mlp_only_layers)
+        self.use_aclgraph = (vllm_config is not None
+                             and vllm_config.compilation_config.level
+                             == CompilationLevel.PIECEWISE
+                             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):
+            if not self.use_aclgraph:
+                # FIXME: custom sparse moe block doesn't work with aclgraph.
+                self.mlp = CustomSparseMoeBlock(config=config,
+                                                quant_config=quant_config,
+                                                prefix=f"{prefix}.mlp")
+            else:
+                self.mlp = Qwen3MoeSparseMoeBlock(config=config,
+                                                  quant_config=quant_config,
+                                                  prefix=f"{prefix}.mlp")
+        else:
+            self.mlp = Qwen3MoeMLP(hidden_size=config.hidden_size,
+                                   intermediate_size=config.intermediate_size,
+                                   hidden_act=config.hidden_act,
+                                   quant_config=quant_config,
+                                   prefix=f"{prefix}.mlp")
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+        self.enable_sequence_parallelism = (
+            vllm_config.compilation_config.pass_config.
+            enable_sequence_parallelism if vllm_config is not None else False)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        kv_cache: Optional[torch.Tensor] = None,
+        attn_metadata: Optional[AttentionMetadata] = None,
+        _metadata_for_padding: Optional[MetadataForPadding] = None,
+    ) -> torch.Tensor:
+
+        # To prevent precision issues during the decoder phase when only prefilling enables SP
+        if not self.enable_sequence_parallelism:
+            self.self_attn.o_proj.reduce_results = True
+        else:
+            self.self_attn.o_proj.reduce_results = not _metadata_for_padding.not_dummy_and_is_prefill if _metadata_for_padding is not None else True
+
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
+                residual = _metadata_for_padding.padding_slice(residual)
+
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+
+            if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
+                hidden_states = _metadata_for_padding.allgather_unpadding_aligned(
+                    hidden_states)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            attn_metadata=attn_metadata,
+        )
+
+        if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
+            hidden_states = _metadata_for_padding.padding_aligned_reduce_scatter(
+                hidden_states)
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+
+        if not self.use_aclgraph:
+            hidden_states = self.mlp(
+                hidden_states, _metadata_for_padding=_metadata_for_padding)
+        else:
+            hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+@support_torch_compile
+class CustomQwen3MoeModel(Qwen3MoeModel):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        nn.Module.__init__(self)
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        parallel_config = vllm_config.parallel_config
+        self.num_redundant_experts = parallel_config.num_redundant_experts
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.config = config
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            prefix=f"{prefix}.embed_tokens")
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: CustomQwen3MoeDecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                vllm_config=vllm_config,
+                prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: Optional[List[torch.Tensor]] = None,
+        attn_metadata: Optional[AttentionMetadata] = None,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        _metadata_for_padding: Optional[MetadataForPadding] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+                kv_caches[i -
+                          self.start_layer] if kv_caches is not None else None,
+                attn_metadata,
+                _metadata_for_padding=_metadata_for_padding)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
+            hidden_states = _metadata_for_padding.allgather_unpadding_aligned(
+                hidden_states)
+
+        return hidden_states
+
+
+class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+        "experts":
+        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        nn.Module.__init__(self)
+        SupportsPP.__init__(self)
+        SupportsLoRA.__init__(self)
+        MixtureOfExperts.__init__(self)
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = CustomQwen3MoeModel(vllm_config=vllm_config,
+                                         prefix=maybe_prefix(prefix, "model"))
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(prefix, "lm_head"))
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+        self.enable_sequence_parallelism = vllm_config.compilation_config.pass_config.enable_sequence_parallelism
+        # Set MoE hyperparameters
+        self.expert_weights: list[torch.Tensor] = []
+
+        self.moe_layers: list[FusedMoE] = []
+        example_layer = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, Qwen3MoeDecoderLayer)
+            if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
+                example_layer = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_layer is None:
+            raise RuntimeError("No Qwen3MoE layer found in the model.layers.")
+
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_expert_groups = 1
+        self.num_shared_experts = 0
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: Optional[List[torch.Tensor]] = None,
+        attn_metadata: Optional[AttentionMetadata] = None,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        _metadata_for_padding = init_metadata_for_sp(
+            input_ids, self.enable_sequence_parallelism)
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   attn_metadata, intermediate_tensors,
+                                   inputs_embeds, _metadata_for_padding)
+        return hidden_states

vllm_ascend/torchair/models/torchair_deepseek_mtp.py

@@ -0,0 +1,218 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Adapted from vllm/model_executor/models/deepseek_mtp.py
+# Copyright 2023 The vLLM team.
+#
+# 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 typing import List, Optional
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+from vllm.attention.backends.abstract import AttentionMetadata
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.sampler import get_sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.models.deepseek_mtp import (
+    DeepSeekMTP, DeepSeekMultiTokenPredictor, DeepSeekMultiTokenPredictorLayer,
+    SharedHead)
+from vllm.model_executor.models.utils import maybe_prefix
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from vllm_ascend.torchair.models.torchair_deepseek_v2 import \
+    TorchairDeepseekV2DecoderLayer
+
+
+class TorchairDeepSeekShareHead(SharedHead):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = "") -> None:
+        nn.Module.__init__(self)
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.head = ParallelLMHead(config.vocab_size,
+                                   config.hidden_size,
+                                   quant_config=quant_config,
+                                   prefix=maybe_prefix(prefix, "head"))
+
+
+class TorchairDeepSeekMultiTokenPredictorLayer(DeepSeekMultiTokenPredictorLayer
+                                               ):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        prefix: str,
+        model_config: ModelConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        nn.Module.__init__(self)
+
+        self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.eh_proj = nn.Linear(config.hidden_size * 2,
+                                 config.hidden_size,
+                                 bias=False)
+        self.shared_head = TorchairDeepSeekShareHead(config=config,
+                                                     quant_config=quant_config,
+                                                     prefix=maybe_prefix(
+                                                         prefix,
+                                                         "shared_head"))
+        self.mtp_block = TorchairDeepseekV2DecoderLayer(
+            config, prefix, model_config, cache_config, quant_config)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        spec_step_index: int = 0,
+    ) -> torch.Tensor:
+        assert inputs_embeds is not None
+        # masking inputs at position 0, as not needed by MTP
+        inputs_embeds = torch.where((positions == 0).unsqueeze(-1),
+                                    torch.zeros_like(inputs_embeds),
+                                    inputs_embeds)
+        inputs_embeds = self.enorm(inputs_embeds)
+        previous_hidden_states = self.hnorm(previous_hidden_states)
+
+        hidden_states = self.eh_proj(
+            torch.cat([inputs_embeds, previous_hidden_states], dim=-1))
+
+        hidden_states, residual = self.mtp_block(positions=positions,
+                                                 hidden_states=hidden_states,
+                                                 kv_cache=kv_cache,
+                                                 attn_metadata=attn_metadata,
+                                                 residual=None)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class TorchairDeepSeekMultiTokenPredictor(DeepSeekMultiTokenPredictor):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        nn.Module.__init__(self)
+        config = vllm_config.model_config.hf_config
+        self.mtp_start_layer_idx = config.num_hidden_layers
+        self.num_mtp_layers = config.num_nextn_predict_layers
+        # to map the exact layer index from weights
+        self.layers = torch.nn.ModuleDict({
+            str(idx):
+            TorchairDeepSeekMultiTokenPredictorLayer(
+                config,
+                f"{prefix}.layers.{idx}",
+                model_config=vllm_config.model_config,
+                cache_config=vllm_config.cache_config,
+                quant_config=vllm_config.quant_config,
+            )
+            for idx in range(self.mtp_start_layer_idx,
+                             self.mtp_start_layer_idx + self.num_mtp_layers)
+        })
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+
+        # Note: torch._dynamo.exc.Unsupported: builtin: str
+        self.layers_list = [
+            self.layers[str(idx)]
+            for idx in range(self.mtp_start_layer_idx,
+                             self.mtp_start_layer_idx + self.num_mtp_layers)
+        ]
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        current_step_idx = (spec_step_idx % self.num_mtp_layers)
+        step_kv_cache = kv_caches[
+            current_step_idx] if kv_caches is not None else None
+        return self.layers_list[current_step_idx](
+            input_ids,
+            positions,
+            step_kv_cache,
+            attn_metadata,
+            previous_hidden_states,
+            inputs_embeds,
+            current_step_idx,
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        current_step_idx = (spec_step_idx % self.num_mtp_layers)
+        mtp_layer = self.layers_list[current_step_idx]
+        logits = self.logits_processor(mtp_layer.shared_head.head,
+                                       mtp_layer.shared_head(hidden_states),
+                                       sampling_metadata)
+        return logits
+
+
+class TorchairDeepSeekMTP(DeepSeekMTP):
+    # NOTE 1.The quantized MTP layer of deepseek on the NPU is not quantized;
+    # NOTE 2.The description file generated by the current msmodelslim tool does not have
+    # MTP layer info. Please manually add it and set the value to FLOAT.
+    packed_modules_mapping = {
+        "gate_up_proj": ["gate_proj", "up_proj"],
+        "experts":
+        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        nn.Module.__init__(self)
+        self.config = vllm_config.model_config.hf_config
+        self.model = TorchairDeepSeekMultiTokenPredictor(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model"))
+
+        self.sampler = get_sampler()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: Optional[List[torch.Tensor]] = None,
+        attn_metadata: Optional[AttentionMetadata] = None,
+        previous_hidden_states: Optional[torch.Tensor] = None,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   attn_metadata, previous_hidden_states,
+                                   inputs_embeds, spec_step_idx)
+        return hidden_states

vllm_ascend/torchair/models/torchair_deepseek_v3.py

@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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 vllm_ascend.torchair.models.torchair_deepseek_v2 import \
+    TorchairDeepseekV2ForCausalLM
+
+
+class TorchairDeepseekV3ForCausalLM(TorchairDeepseekV2ForCausalLM):
+    pass

vllm_ascend/torchair/ops/torchair_rotary_embedding.py

@@ -0,0 +1,372 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+
+import math
+from typing import Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+import torch_npu
+from vllm.model_executor.layers.rotary_embedding import (
+    DeepseekScalingRotaryEmbedding, RotaryEmbedding)
+
+from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.utils import enable_custom_op, is_310p
+
+
+def custom_rotary_embedding_enabled(query, neox_style, head_size):
+    return query.dtype == torch.float16 and neox_style and head_size % 32 == 0 and enable_custom_op(
+    )
+
+
+def rope_forward_oot(
+    self,
+    positions: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    offsets: Optional[torch.Tensor] = None,
+    is_neox_style_override: Optional[bool] = None,
+    is_qwen_torchair: Optional[bool] = False,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    if get_ascend_config(
+    ).torchair_graph_config.enabled and not is_qwen_torchair:
+        return self.forward_native(
+            positions,
+            query,
+            key,
+            offsets,
+        )
+
+    query_shape, key_shape = query.shape, key.shape
+    if self.cos_sin_cache.device != query.device:
+        self.cos_sin_cache = self.cos_sin_cache.to(query.device)
+    if self.cos_sin_cache.dtype != query.dtype:
+        self.cos_sin_cache = self.cos_sin_cache.to(query.dtype)
+    neox_style = self.is_neox_style
+    if is_neox_style_override is not None:
+        neox_style = is_neox_style_override
+    # adopt custom kernel path for rotary_embedding
+    if custom_rotary_embedding_enabled(query, neox_style,
+                                       self.head_size) and not is_310p():
+        query, key = torch.ops._C.rotary_embedding(
+            positions,
+            query,
+            key,
+            self.head_size,
+            self.cos_sin_cache,
+            neox_style,
+        )
+        return query.view(query_shape), key.view(key_shape)
+    if offsets is not None:
+        raise NotImplementedError(
+            "Batched rotary embedding is currently not supported on NPU.")
+    else:
+        # TODO: Remove the contiguous in the future.
+        query = query.contiguous().view(query.shape[0], -1)
+        key = key.contiguous().view(key.shape[0], -1)
+        torch_npu._npu_rotary_embedding(
+            positions,
+            query,
+            key,
+            self.head_size,
+            self.cos_sin_cache,
+            neox_style,
+        )
+    return query.view(query_shape), key.view(key_shape)
+
+
+def native_rope_deepseek_forward(self,
+                                 positions: torch.Tensor,
+                                 query: torch.Tensor,
+                                 key: torch.Tensor,
+                                 offsets: Optional[torch.Tensor] = None,
+                                 max_seq_len: Optional[int] = None):
+    if max_seq_len is not None and max_seq_len > self.max_seq_len:
+        _set_cos_sin_cache(self, max_seq_len, query.device, query.dtype)
+    if len(key.shape) == 2:
+        key = key[:, None, :]
+    # Note: we implement the non neox_style method with shuffle the last dim and neox style
+    # calculation method which is also more compute friendly to the ascend machine
+    # https://huggingface.co/deepseek-ai/DeepSeek-V3-0324/blob/main/modeling_deepseek.py
+    neox_style = True
+    if self.is_neox_style is False:
+        b, h_q, d = query.shape
+        query = query.view(b, h_q, d // 2, 2).transpose(3,
+                                                        2).reshape(b, h_q, d)
+        b, h_k, d = key.shape
+        key = key.view(b, h_k, d // 2, 2).transpose(3, 2).reshape(b, h_k, d)
+    q_pe, k_pe = rope_forward_oot(self, positions, query, key, offsets,
+                                  neox_style)
+    return q_pe, k_pe
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., :x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Inverse dim formula to find dim based on number of rotations
+def yarn_find_correction_dim(num_rotations,
+                             dim,
+                             base=10000,
+                             max_position_embeddings=2048):
+    # Note: use torch instead of math to solve MTP compilation error.
+    return (dim * torch.log(
+        torch.tensor(max_position_embeddings) /
+        (num_rotations * 2 * torch.pi))) / (2 * torch.log(torch.tensor(base)))
+
+
+def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
+
+
+# Find dim range bounds based on rotations
+def yarn_find_correction_range(low_rot,
+                               high_rot,
+                               dim,
+                               base=10000,
+                               max_position_embeddings=2048):
+    # Note: use torch instead of math to solve MTP compilation error.
+    low = torch.floor(
+        yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings))
+    high = torch.ceil(
+        yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings))
+    # Note: use torch instead of max/min to solve MTP compilation error.
+    return torch.clamp(low, min=0), torch.clamp(high, max=dim - 1)
+
+
+def yarn_linear_ramp_mask(min_value, max_value, dim):
+    # Note: The if conditional branch is not used here
+    # to solve MTP compilation error.
+    max_value += (min_value == max_value).float() * 0.001
+    linear_func = (torch.arange(dim, dtype=torch.float32) -
+                   min_value) / (max_value - min_value)
+    ramp_func = torch.clamp(linear_func, 0, 1)
+    return ramp_func
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids]
+    sin = sin[position_ids]
+    cos = cos[:, None, None, :]
+    sin = sin[:, None, None, :]
+
+    if len(q.shape) == 3:
+        q = q[:, :, None, :]
+    if len(k.shape) == 2:
+        k = k[:, None, None, :]
+    elif len(k.shape) == 3:
+        k = k[:, :, None, :]
+
+    b, h_q, s, d = q.shape
+    q = q.view(b, h_q, s, d // 2, 2).transpose(4, 3).reshape(b, h_q, s, d)
+
+    b, h_k, s, d = k.shape
+    k = k.view(b, h_k, s, d // 2, 2).transpose(4, 3).reshape(b, h_k, s, d)
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+
+    q_embed = q_embed.view(b, h_q, d)
+    k_embed = k_embed.view(b, h_k, d)
+
+    return q_embed, k_embed
+
+
+def _set_cos_sin_cache(self, seq_len, device, dtype):
+    self.max_seq_len_cached = seq_len
+    dim = self.rotary_dim
+
+    freq_extra = 1.0 / (self.base**(
+        torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
+    freq_inter = 1.0 / (self.scaling_factor * self.base**(
+        torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
+
+    low, high = yarn_find_correction_range(
+        self.beta_fast,
+        self.beta_slow,
+        dim,
+        self.base,
+        self.max_position_embeddings,
+    )
+    inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
+        device=device, dtype=torch.float32)
+    inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
+    self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    t = torch.arange(seq_len * self.scaling_factor,
+                     device=device,
+                     dtype=torch.float32)
+
+    freqs = torch.outer(t, inv_freq)
+    cos_cached = torch.cat([freqs, freqs], dim=-1).cos() * self.mscale
+    sin_cached = torch.cat([freqs, freqs], dim=-1).sin() * self.mscale
+    cos_cached = cos_cached.to(dtype)
+    sin_cached = sin_cached.to(dtype)
+    cache = torch.cat([freqs.cos() * self.mscale,
+                       freqs.sin() * self.mscale],
+                      dim=-1).to(dtype)
+    self.register_buffer("cos_sin_cache", cache, persistent=False)
+    self.register_buffer("cos_cached", cos_cached, persistent=False)
+    self.register_buffer("sin_cached", sin_cached, persistent=False)
+
+
+def __set_cos_sin_cache(self, seq_len, device, dtype):
+    inv_freq = 1.0 / (self.base**(torch.arange(
+        0, self.rotary_dim, 2, device=device, dtype=torch.float32) *
+                                  (1 / self.rotary_dim)))
+    self.register_buffer("inv_freq", inv_freq)
+
+    t = torch.arange(self.max_position_embeddings,
+                     device=self.inv_freq.device,
+                     dtype=torch.float32)
+    freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+
+    emb = torch.cat((freqs, freqs), dim=-1)
+    self.register_buffer("cos", emb.cos().to(dtype=dtype), persistent=False)
+    self.register_buffer("sin", emb.sin().to(dtype=dtype), persistent=False)
+    self.embed = F.embedding
+
+
+_original_re_init = RotaryEmbedding.__init__
+
+
+def qwen_rope_init_func(
+    self,
+    head_size: int,
+    rotary_dim: int,
+    max_position_embeddings: int,
+    base: float,
+    is_neox_style: bool,
+    dtype: torch.dtype,
+) -> None:
+    _original_re_init(self, head_size, rotary_dim, max_position_embeddings,
+                      base, is_neox_style, dtype)
+    if get_ascend_config().torchair_graph_config.enabled:
+        __set_cos_sin_cache(self,
+                            seq_len=max_position_embeddings,
+                            device="npu",
+                            dtype=dtype)
+
+
+def rope_forward(
+    self,
+    positions: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    offsets: Optional[torch.Tensor] = None,
+    is_neox_style_override: Optional[bool] = None,
+    max_seq_len: Optional[int] = None,
+    is_prefill: Optional[bool] = True,
+    is_qwen_torchair: Optional[bool] = False,
+):
+    if get_ascend_config().torchair_graph_config.enabled \
+            and is_qwen_torchair and not is_prefill:
+        if max_seq_len is not None and torch.gt(max_seq_len,
+                                                self.max_position_embeddings):
+            __set_cos_sin_cache(self,
+                                seq_len=max_seq_len,
+                                device=query.device,
+                                dtype=torch.float32)
+
+        # bsnd/bnsd
+        if positions is not None:
+            cos = self.embed(positions, self.cos)
+            sin = self.embed(positions, self.sin)
+            self.cos_embed = cos
+            self.sin_embed = sin
+        else:
+            cos = self.cos_embed
+            sin = self.sin_embed
+
+        query = query.view(*query.shape[:-1], -1, self.head_size).contiguous()
+        key = key.view(*key.shape[:-1], -1, self.head_size).contiguous()
+
+        cos = cos.unsqueeze(-2).unsqueeze(-2)
+        sin = sin.unsqueeze(-2).unsqueeze(-2)
+
+        query = query.unsqueeze(1)
+        key = key.unsqueeze(1)
+
+        q_embed, k_embed = torch_npu.npu_apply_rotary_pos_emb(
+            query, key, cos, sin)
+        return q_embed.flatten(-2), k_embed.flatten(-2)
+    else:
+        return rope_forward_oot(self, positions, query, key, offsets,
+                                is_neox_style_override,
+                                is_qwen_torchair)  # type: ignore
+
+
+def deepseek_rope_init_func(
+    self,
+    head_size: int,
+    rotary_dim: int,
+    max_position_embeddings: int,
+    base: int,
+    is_neox_style: bool,
+    scaling_factor: float,
+    dtype: torch.dtype,
+    *,
+    extrapolation_factor: float = 1,
+    attn_factor: float = 1,
+    beta_fast: int = 32,
+    beta_slow: int = 1,
+    mscale: float = 1,
+    mscale_all_dim: float = 0,
+) -> None:
+    self.scaling_factor = scaling_factor
+    self.extrapolation_factor = extrapolation_factor
+    self.attn_factor = attn_factor
+    self.beta_fast = beta_fast
+    self.beta_slow = beta_slow
+    # Get n-d magnitude scaling corrected for interpolation.
+    self.mscale = float(
+        yarn_get_mscale(self.scaling_factor, float(mscale)) /
+        yarn_get_mscale(self.scaling_factor, float(mscale_all_dim)) *
+        attn_factor)
+    super(DeepseekScalingRotaryEmbedding,
+          self).__init__(head_size, rotary_dim, max_position_embeddings, base,
+                         is_neox_style, dtype)
+    self.max_seq_len = max_position_embeddings
+    _set_cos_sin_cache(self,
+                       max_position_embeddings,
+                       dtype=dtype,
+                       device="npu")

vllm_ascend/torchair/quantization/torchair_quantizer.py

@@ -0,0 +1,29 @@
+from vllm_ascend.quantization.quantizer import VLLMAscendQuantizer
+from vllm_ascend.torchair.quantization.torchair_w4a8_dynamic import (
+    TorchairAscendW4A8DynamicFusedMoEMethod,
+    TorchairAscendW4A8DynamicLinearMethod)
+from vllm_ascend.torchair.quantization.torchair_w8a8_dynamic import (
+    TorchairAscendW8A8DynamicFusedMoEMethod,
+    TorchairAscendW8A8DynamicLinearMethod)
+
+
+class TorchairW8A8DYNAMICQuantizer(VLLMAscendQuantizer):
+
+    @staticmethod
+    def build_linear_method():
+        return TorchairAscendW8A8DynamicLinearMethod()
+
+    @staticmethod
+    def build_moe_method():
+        return TorchairAscendW8A8DynamicFusedMoEMethod()
+
+
+class TorchairW4A8DYNAMICQuantizer(VLLMAscendQuantizer):
+
+    @staticmethod
+    def build_linear_method():
+        return TorchairAscendW4A8DynamicLinearMethod()
+
+    @staticmethod
+    def build_moe_method():
+        return TorchairAscendW4A8DynamicFusedMoEMethod()

vllm_ascend/torchair/quantization/torchair_w4a8_dynamic.py

@@ -0,0 +1,439 @@
+#
+# 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 typing import Any, Callable, Dict, Optional
+
+import numpy as np
+import torch
+import torch_npu
+from vllm.config import get_current_vllm_config
+from vllm.distributed import get_ep_group
+from vllm.forward_context import get_forward_context
+
+from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.ascend_forward_context import FusedMoEState
+from vllm_ascend.distributed.parallel_state import get_mc2_group
+from vllm_ascend.torchair.ops.torchair_fused_moe import torchair_select_experts
+from vllm_ascend.torchair.quantization.torchair_w8a8_dynamic import (
+    torchair_fused_experts_with_all2all, torchair_fused_experts_with_mc2)
+from vllm_ascend.torchair.utils import npu_stream_switch, npu_wait_tensor
+
+
+class TorchairAscendW4A8DynamicLinearMethod:
+    """Linear method for Ascend W4A8_DYNAMIC
+    """
+
+    def __init__(self):
+        self.transpose_weight = True
+        try:
+            self.group_size = get_current_vllm_config(
+            ).quant_config.quant_description.get("group_size", 256)
+        except AttributeError:
+            self.group_size = 256
+
+    @staticmethod
+    def get_weight(input_size: int, output_size: int,
+                   params_dtype: torch.dtype) -> Dict[str, Any]:
+        params_dict = {
+            "weight": torch.empty(output_size, input_size, dtype=torch.int8)
+        }
+        return params_dict
+
+    @staticmethod
+    def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]:
+        return {}
+
+    @staticmethod
+    def get_perchannel_param(output_size: int,
+                             params_dtype: torch.dtype) -> Dict[str, Any]:
+        return {}
+
+    def get_pergroup_param(self, input_size: int, output_size: int,
+                           params_dtype: torch.dtype) -> Dict[str, Any]:
+        params_dict = {}
+        params_dict["weight_scale"] = torch.empty(output_size,
+                                                  1,
+                                                  dtype=params_dtype)
+        params_dict["weight_offset"] = torch.empty(output_size,
+                                                   1,
+                                                   dtype=params_dtype)
+        params_dict["weight_scale_second"] = torch.empty(output_size,
+                                                         input_size //
+                                                         self.group_size,
+                                                         dtype=params_dtype)
+        params_dict["weight_offset_second"] = torch.empty(output_size,
+                                                          input_size //
+                                                          self.group_size,
+                                                          dtype=params_dtype)
+        return params_dict
+
+    @staticmethod
+    def process_scale_second(weight: torch.Tensor, scale: torch.Tensor,
+                             per_group_scale: torch.Tensor):
+        k, n = weight.shape
+        group_num, n = per_group_scale.shape
+        weight_high = weight.to(torch.float32).reshape(
+            group_num, -1, n) * per_group_scale.reshape(group_num, 1, n)
+        weight_high = weight_high.reshape(k, n)
+        bias = 8 * (weight_high.to(torch.float32) * scale).sum(dim=0)
+        antiquant_scale = (scale * per_group_scale).reshape(group_num, n)
+        return antiquant_scale.npu(), bias
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+        tp_rank: Optional[int] = None,
+    ) -> torch.Tensor:
+        return torch_npu.npu_weight_quant_batchmatmul(
+            x,
+            layer.weight,
+            antiquant_scale=layer.weight_scale_second.to(x.dtype),
+            antiquant_group_size=self.group_size,
+        )
+
+    def process_weights_after_loading(self, layer: torch.nn.Module):
+        if self.transpose_weight:
+            layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
+        layer.weight_scale.data = layer.weight_scale.data.flatten().to(
+            torch.float32)
+        layer.weight_offset.data = layer.weight_offset.data.flatten()
+        layer.weight_scale_second.data, scale_bias = self.process_scale_second(
+            layer.weight.data,
+            layer.weight_scale.data,
+            layer.weight_scale_second.data.transpose(0, 1).contiguous(),
+        )
+        param = torch.nn.Parameter(scale_bias, requires_grad=False)
+        layer.register_parameter("weight_scale_bias", param)
+        layer.weight.data = torch_npu.npu_convert_weight_to_int4pack(
+            layer.weight.data.to(torch.int32))
+
+
+class TorchairAscendW4A8DynamicFusedMoEMethod:
+    """FusedMoe method for Ascend W4A8_DYNAMIC.
+    """
+
+    def __init__(self):
+        self.transpose_weight = True
+
+        self.ep_group = get_ep_group()
+
+        ascend_config = get_ascend_config()
+        self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
+
+        vllm_config = get_current_vllm_config()
+        self.group_size = vllm_config.quant_config.quant_description.get(
+            "group_size", 256)
+        quant_version = vllm_config.quant_config.quant_description.get(
+            "version", "0")
+        # NOTE: new quantize weights: 2 int4 pack into int8
+        self.new_quant_version = quant_version == "1.0.0"
+        self.tp_size = 1 if vllm_config.parallel_config.enable_expert_parallel else self.ep_group.world_size
+        if self.new_quant_version and self.tp_size > 16:
+            raise ValueError(
+                "The current weight does not support moe part tp>16.")
+
+        try:
+            device_group = get_mc2_group().device_group
+            # TODO: Try local_rank = ep_group.rank_in_group
+            local_rank = torch.distributed.get_rank(group=device_group)
+            backend = device_group._get_backend(torch.device("npu"))
+            self.moe_all_to_all_group_name = backend.get_hccl_comm_name(
+                local_rank)
+        except AttributeError:
+            self.moe_all_to_all_group_name = ""
+
+    def get_weight(self, num_experts: int,
+                   intermediate_size_per_partition: int, hidden_sizes: int,
+                   params_dtype: torch.dtype) -> Dict[str, Any]:
+        param_dict = {}
+        if self.new_quant_version:
+            w13_output_size = intermediate_size_per_partition
+            w2_output_size = hidden_sizes // 2
+        else:
+            w13_output_size = 2 * intermediate_size_per_partition
+            w2_output_size = hidden_sizes
+
+        param_dict["w13_weight"] = torch.empty(num_experts,
+                                               w13_output_size,
+                                               hidden_sizes,
+                                               dtype=torch.int8)
+        param_dict["w2_weight"] = torch.empty(num_experts,
+                                              w2_output_size,
+                                              intermediate_size_per_partition,
+                                              dtype=torch.int8)
+        return param_dict
+
+    def get_dynamic_quant_param(self, num_experts: int,
+                                intermediate_size_per_partition: int,
+                                hidden_sizes: int,
+                                params_dtype: torch.dtype) -> Dict[str, Any]:
+        param_dict = {}
+        param_dict["w13_weight_scale"] = torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            1,
+            dtype=params_dtype)
+
+        param_dict["w13_weight_offset"] = torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            1,
+            dtype=params_dtype)
+
+        param_dict["w13_weight_scale_second"] = torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            hidden_sizes // self.group_size,
+            dtype=params_dtype)
+
+        param_dict["w13_weight_offset_second"] = torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            hidden_sizes // self.group_size,
+            dtype=params_dtype)
+
+        param_dict["w2_weight_scale"] = torch.empty(num_experts,
+                                                    hidden_sizes,
+                                                    1,
+                                                    dtype=params_dtype)
+        param_dict["w2_weight_offset"] = torch.empty(num_experts,
+                                                     hidden_sizes,
+                                                     1,
+                                                     dtype=params_dtype)
+        param_dict["w2_weight_scale_second"] = torch.empty(
+            num_experts,
+            hidden_sizes,
+            intermediate_size_per_partition // self.group_size,
+            dtype=params_dtype)
+        param_dict["w2_weight_offset_second"] = torch.empty(
+            num_experts,
+            hidden_sizes,
+            intermediate_size_per_partition // self.group_size,
+            dtype=params_dtype)
+
+        if self.new_quant_version:
+            param_dict["w13_scale_bias"] = torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                1,
+                dtype=torch.float32)
+            param_dict["w2_scale_bias"] = torch.empty(num_experts,
+                                                      hidden_sizes,
+                                                      16 // self.tp_size,
+                                                      dtype=torch.float32)
+
+        return param_dict
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        is_prefill: bool = True,
+        enable_force_load_balance: bool = True,
+        log2phy: torch.Tensor = None,
+        global_redundant_expert_num: int = 0,
+        shared_experts: Optional[Any] = None,
+        quantized_x_for_share: Optional[Any] = None,
+        dynamic_scale_for_share: Optional[Any] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        assert router_logits.shape[
+            1] == global_num_experts, "Number of global experts mismatch"
+
+        if global_num_experts == 256:
+            topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
+                router_logits,
+                k=top_k,  # topk currently is 8
+                bias=e_score_correction_bias,
+                k_group=topk_group,  # fix: 4
+                group_count=num_expert_group,  # fix 8
+                group_select_mode=
+                1,  # 0: the maximum in the group; 1: topk2.sum(fix)
+                renorm=0,  # 0: softmax->topk(fix); 1: topk->softmax
+                norm_type=1,  # 0: softmax; 1: sigmoid(fix)
+                # out_flag=False, # todo new api; should the third output be output
+                # y2_flag=False, # old api; should the third output be output
+                routed_scaling_factor=1,
+                eps=float(1e-20))
+        else:
+            topk_weights, topk_ids = torchair_select_experts(
+                hidden_states=x,
+                router_logits=router_logits,
+                top_k=top_k,
+                use_grouped_topk=use_grouped_topk,
+                renormalize=renormalize,
+                topk_group=topk_group,
+                num_expert_group=num_expert_group,
+                custom_routing_function=custom_routing_function,
+                scoring_func=scoring_func,
+                e_score_correction_bias=e_score_correction_bias,
+            )
+
+        fused_moe_state = get_forward_context().fused_moe_state
+        shared_gate_up, shared_dequant_scale = None, None
+        if shared_experts is not None and fused_moe_state == FusedMoEState.MC2:
+            with npu_stream_switch("moe_secondary", 0):
+                npu_wait_tensor(quantized_x_for_share, router_logits)
+                share_up_out, _ = shared_experts.gate_up_proj(
+                    (quantized_x_for_share, dynamic_scale_for_share))
+                shared_gate_up, shared_dequant_scale = share_up_out[
+                    0], share_up_out[1]
+
+        # this is a naive implementation for experts load balance so as
+        # to avoid accumulating too much tokens on a single rank.
+        # currently it is only activated when doing profile runs.
+        if enable_force_load_balance:
+            topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
+
+        topk_weights = topk_weights.to(x.dtype)
+        if fused_moe_state == FusedMoEState.MC2:
+            return torchair_fused_experts_with_mc2(
+                hidden_states=x,
+                w1=layer.w13_weight,
+                w2=layer.w2_weight,
+                w1_scale=layer.w13_weight_scale_second,
+                w2_scale=layer.w2_weight_scale_second,
+                w1_scale_bias=layer.w13_scale_bias,
+                w2_scale_bias=layer.w2_scale_bias,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                top_k=top_k,
+                expert_map=expert_map,
+                moe_all_to_all_group_name=self.moe_all_to_all_group_name,
+                log2phy=log2phy,
+                global_redundant_expert_num=global_redundant_expert_num,
+                shared_experts=shared_experts,
+                is_torchair=self.torchair_graph_enabled,
+                quantized_x_for_share=shared_gate_up,
+                dynamic_scale_for_share=shared_dequant_scale,
+                mc2_mask=kwargs.get("mc2_mask", None))
+        else:
+            # The current implementation of deepseek moe splits hidden_states
+            # according to tp_size before they are feed into layers module.
+            # Therefore, all2all is needed no matter how dp/tp is set so as to
+            # dispatch/combine tokens.
+            return torchair_fused_experts_with_all2all(
+                hidden_states=x,
+                w1=layer.w13_weight,
+                w2=layer.w2_weight,
+                w1_scale=layer.w13_weight_scale_second,
+                w2_scale=layer.w2_weight_scale_second,
+                w1_scale_bias=layer.w13_scale_bias,
+                w2_scale_bias=layer.w2_scale_bias,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                top_k=top_k,
+                expert_map=expert_map,
+                ep_group=self.ep_group,
+                log2phy=log2phy,
+                global_redundant_expert_num=global_redundant_expert_num,
+            )
+
+    def process_scale(self, weight: torch.Tensor, scale, per_group_scale):
+        group_num, k, n = weight.shape
+        # the weight of the new version is reduced by half by pack n, so it needs to be restored
+        if self.new_quant_version:
+            n = n * 2
+        per_group_scale = per_group_scale.reshape(group_num, -1, n)
+        group_num, quantgroup_num, n = per_group_scale.shape
+        bias = None
+        if not self.new_quant_version:
+            weight_high = weight.to(torch.float32).reshape([group_num, quantgroup_num, -1, n]) * \
+                per_group_scale.reshape([group_num, quantgroup_num, 1, n])
+            weight_high = weight_high.reshape([group_num, k, n])
+            bias = 8 * (weight_high.to(torch.float32) * scale).sum(axis=1)
+        scale_fp32 = (scale * per_group_scale).to(torch.float16).to(
+            torch.float32)
+        scale_fp32_np = scale_fp32.cpu().numpy()
+        scale_fp32_np.dtype = np.uint32
+        sscale_uint64 = np.zeros((group_num, quantgroup_num, n * 2),
+                                 dtype=np.uint32)
+
+        sscale_uint64[..., ::2] = scale_fp32_np
+
+        sscale_uint64_buffer = np.frombuffer(sscale_uint64.tobytes(),
+                                             dtype=np.int64).copy()
+        sscale_uint64_tensor = torch.from_numpy(sscale_uint64_buffer).reshape(
+            group_num, quantgroup_num, n)
+        sscale_uint64_tensor = sscale_uint64_tensor.npu()
+        return sscale_uint64_tensor, bias
+
+    def update_bias(self, layer, w13_bias, w2_bias):
+        if self.new_quant_version:
+            layer.w13_scale_bias.data = layer.w13_scale_bias.data.transpose(
+                1, 2).contiguous().sum(axis=1)
+            layer.w2_scale_bias.data = layer.w2_scale_bias.data.transpose(
+                1, 2).contiguous().sum(axis=1)
+        else:
+            w13_scale_bias = torch.nn.Parameter(w13_bias, requires_grad=False)
+            layer.register_parameter("w13_scale_bias", w13_scale_bias)
+            w2_scale_bias = torch.nn.Parameter(w2_bias, requires_grad=False)
+            layer.register_parameter("w2_scale_bias", w2_scale_bias)
+
+    def pack_to_int32(self, weight: torch.Tensor):
+        if self.new_quant_version:
+            group_num, k, n = weight.shape
+            assert n % 4 == 0, "the last dim of weight needs to be divided by 4"
+            packed_n = n // 4
+            # pack 4 int8(int4*2) to int32, because in pytorch, we need to use int32 to represent int4
+            packed_weight = torch.from_numpy(
+                np.frombuffer(weight.cpu().numpy().tobytes(), dtype=np.int32))
+            return packed_weight.reshape(group_num, k, packed_n).npu()
+        else:
+            return torch_npu.npu_quantize(weight.to(torch.float32),
+                                          torch.tensor([1.]).npu(), None,
+                                          torch.quint4x2, -1, False)
+
+    def process_weights_after_loading(self, layer):
+        if self.transpose_weight:
+            layer.w13_weight.data = layer.w13_weight.data.transpose(
+                1, 2).contiguous()
+            layer.w2_weight.data = layer.w2_weight.data.transpose(
+                1, 2).contiguous()
+        layer.w13_weight_scale.data = layer.w13_weight_scale.data.transpose(
+            1, 2).contiguous()
+        layer.w2_weight_scale.data = layer.w2_weight_scale.data.transpose(
+            1, 2).contiguous()
+        layer.w13_weight_scale_second.data = layer.w13_weight_scale_second.data.transpose(
+            1, 2).contiguous()
+        layer.w2_weight_scale_second.data = layer.w2_weight_scale_second.data.transpose(
+            1, 2).contiguous()
+
+        layer.w13_weight_scale_second.data, w13_bias = self.process_scale(
+            layer.w13_weight, layer.w13_weight_scale.data,
+            layer.w13_weight_scale_second.data)
+        layer.w2_weight_scale_second.data, w2_bias = self.process_scale(
+            layer.w2_weight, layer.w2_weight_scale.data,
+            layer.w2_weight_scale_second.data)
+
+        self.update_bias(layer, w13_bias, w2_bias)
+
+        layer.w13_weight.data = self.pack_to_int32(layer.w13_weight.data)
+        layer.w2_weight.data = self.pack_to_int32(layer.w2_weight.data)

vllm_ascend/torchair/torchair_attention.py

@@ -0,0 +1,452 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Type
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch_npu
+from vllm.attention.backends.abstract import (AttentionImpl, AttentionLayer,
+                                              AttentionType)
+from vllm.attention.backends.utils import PAD_SLOT_ID
+from vllm.config import VllmConfig
+from vllm.utils import cdiv
+
+from vllm_ascend.attention.attention_v1 import (AscendAttentionBackend,
+                                                AscendAttentionMetadataBuilder,
+                                                AscendAttentionState,
+                                                AscendMetadata)
+from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
+from vllm_ascend.torchair.utils import TorchairCommonAttentionMetadata
+from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, aligned_16, is_310p,
+                               nd_to_nz_2d)
+
+
+class AscendAttentionTorchairBackend(AscendAttentionBackend):
+    accept_output_buffer: bool = True
+
+    @staticmethod
+    def get_name() -> str:
+        return "ASCEND_TORCHAIR"
+
+    @staticmethod
+    def get_impl_cls() -> Type["AscendAttentionTorchairBackendImpl"]:
+        return AscendAttentionTorchairBackendImpl
+
+    @staticmethod
+    def get_metadata_cls() -> Type["AscendTorchairMetadata"]:
+        return AscendTorchairMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["AscendAttentionTorchairMetadataBuilder"]:
+        return AscendAttentionTorchairMetadataBuilder
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[int, ...]:
+        return (2, num_blocks, block_size, num_kv_heads * head_size)
+
+    @staticmethod
+    def get_bsh_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[int, ...]:
+        return (2, num_blocks, block_size, num_kv_heads * head_size)
+
+
+@dataclass
+class AscendDecodeMetadata:
+    # Input positions for rotrary embeddings since for MLA the rotary
+    # position embeddings are applied inside the attention backend
+    input_positions: torch.Tensor
+    block_table: torch.Tensor
+    seq_lens: torch.Tensor
+    max_seq_lens: int
+    seq_lens_list: list[int]
+    attn_mask: Optional[torch.Tensor] = None
+
+
+@dataclass
+class AscendTorchairMetadata(AscendMetadata):
+
+    decode: Optional[AscendDecodeMetadata] = None
+
+
+class AscendAttentionTorchairMetadataBuilder(AscendAttentionMetadataBuilder):
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        device: torch.device,
+    ):
+        super().__init__(vllm_config, device)
+        self.max_num_blocks_per_req = cdiv(
+            self.model_config.max_model_len,
+            self.vllm_config.cache_config.block_size)
+        self.max_blocks = (self.model_config.max_model_len +
+                           self.vllm_config.cache_config.block_size -
+                           1) // self.vllm_config.cache_config.block_size
+
+    def _get_graph_runner_block_tables(
+            self, num_seqs: int, block_tables: torch.Tensor) -> torch.Tensor:
+        max_blocks = self.max_blocks
+
+        graph_block_tables = torch.zeros((num_seqs, max_blocks),
+                                         dtype=block_tables.dtype,
+                                         device=block_tables.device)
+
+        num_blocks = block_tables.size(1)
+        if num_blocks <= max_blocks:
+            graph_block_tables[:num_seqs, :
+                               num_blocks] = block_tables[:num_seqs, :
+                                                          num_blocks]
+        else:
+            graph_block_tables[:num_seqs, :
+                               max_blocks] = block_tables[:num_seqs, :
+                                                          max_blocks]
+
+        return graph_block_tables[:, :max_blocks]
+
+    def build_torchair_graph_dummy(
+        self, common_attn_metadata: TorchairCommonAttentionMetadata
+    ) -> AscendTorchairMetadata:
+        device = self.device
+        num_reqs = common_attn_metadata.num_reqs
+        block_table = torch.zeros((num_reqs, self.max_blocks),
+                                  dtype=torch.int32,
+                                  device=device)
+        block_table = self._get_graph_runner_block_tables(
+            num_reqs, block_table)
+        seq_lens = torch.ones(num_reqs, dtype=torch.int32, device=device)
+        input_positions = torch.zeros(num_reqs,
+                                      dtype=torch.int32,
+                                      device=device).long()
+        slot_mapping = torch.full((num_reqs, ),
+                                  PAD_SLOT_ID,
+                                  dtype=torch.int32,
+                                  device=device)
+        query_start_loc = torch.full((num_reqs, ),
+                                     -1,
+                                     dtype=torch.int32,
+                                     device=device)
+
+        decode_metadata = AscendDecodeMetadata(input_positions=input_positions,
+                                               block_table=block_table,
+                                               seq_lens=seq_lens,
+                                               seq_lens_list=seq_lens.tolist(),
+                                               max_seq_lens=1)
+
+        attn_metadata = AscendTorchairMetadata(
+            num_actual_tokens=common_attn_metadata.num_actual_tokens,
+            block_tables=block_table,
+            query_lens=0,
+            query_start_loc=query_start_loc,
+            seq_lens=seq_lens,
+            slot_mapping=slot_mapping,
+            attn_state=AscendAttentionState.DecodeOnly,
+            decode=decode_metadata)
+        return attn_metadata
+
+    def build(
+        self,
+        common_attn_metadata: AscendCommonAttentionMetadata,
+        model: nn.Module,
+    ):
+        num_reqs = common_attn_metadata.num_reqs
+        num_actual_tokens = common_attn_metadata.num_actual_tokens
+
+        block_table = common_attn_metadata.block_table_tensor
+        block_table[:num_reqs, :self.max_num_blocks_per_req] = (
+            block_table[:num_reqs])
+
+        seq_lens = common_attn_metadata.seq_lens_cpu[:num_reqs]
+        slot_mapping = common_attn_metadata.slot_mapping_cpu[:
+                                                             num_actual_tokens].to(
+                                                                 self.device,
+                                                                 non_blocking=
+                                                                 True)
+        attn_mask = common_attn_metadata.attn_mask
+
+        attn_state = common_attn_metadata.attn_state
+        if is_310p() and attn_state == AscendAttentionState.PrefillNoCache:
+            mask_nz = nd_to_nz_2d(attn_mask)
+            attn_mask = torch_npu.npu_format_cast(mask_nz.contiguous(), 29)
+
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[:
+                                                                       num_reqs
+                                                                       + 1]
+        query_start_loc = query_start_loc_cpu.to(self.device,
+                                                 non_blocking=True)
+        query_lens = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
+        input_positions = common_attn_metadata.positions[:
+                                                         num_actual_tokens].long(
+                                                         )
+
+        decode_metadata = None
+        graph_pad_size = common_attn_metadata.graph_pad_size
+        use_torchair_graph = graph_pad_size > -1
+        if common_attn_metadata.attn_state in [
+                AscendAttentionState.DecodeOnly,
+        ]:
+            max_seq_lens = seq_lens.max().item()
+            num_seqs = len(seq_lens)
+            if use_torchair_graph and common_attn_metadata.attn_state in [
+                    AscendAttentionState.DecodeOnly,
+            ]:
+                num_reqs_pad_size = 0
+                num_token_pad_size = 0
+                if graph_pad_size != 0:
+                    pad_value = 0
+                    num_token_pad_size = graph_pad_size - num_actual_tokens
+                    num_reqs_pad_size = (
+                        graph_pad_size //
+                        common_attn_metadata.decode_token_per_req - num_reqs)
+                pad_value = 1
+                padded_seq_lens = seq_lens.tolist() + [pad_value
+                                                       ] * num_reqs_pad_size
+
+                seq_lens = torch.from_numpy(
+                    np.array(padded_seq_lens).astype(np.int32))
+                padding = torch.full((num_token_pad_size, ),
+                                     PAD_SLOT_ID,
+                                     dtype=slot_mapping.dtype,
+                                     device=slot_mapping.device)
+                slot_mapping = torch.cat([slot_mapping, padding])
+                block_table_padding = torch.zeros(
+                    (num_reqs_pad_size, ) + block_table.shape[1:],
+                    dtype=block_table.dtype,
+                    device=block_table.device)
+                block_table = torch.cat([block_table, block_table_padding],
+                                        dim=0)
+                block_table = self._get_graph_runner_block_tables(
+                    num_seqs + num_reqs_pad_size, block_table)
+                padding_0 = torch.zeros(num_token_pad_size,
+                                        dtype=input_positions.dtype,
+                                        device=input_positions.device)
+                input_positions = torch.cat([input_positions, padding_0])
+
+            decode_metadata = AscendDecodeMetadata(
+                input_positions=input_positions,
+                block_table=block_table,
+                seq_lens=seq_lens,
+                seq_lens_list=seq_lens.tolist(),
+                max_seq_lens=max_seq_lens,
+                attn_mask=None)
+
+        attn_metadata = AscendTorchairMetadata(
+            decode=decode_metadata,
+            num_actual_tokens=num_actual_tokens,
+            block_tables=block_table,
+            query_start_loc=query_start_loc,
+            query_lens=query_lens,
+            seq_lens=seq_lens,
+            max_query_len=common_attn_metadata.max_query_len,
+            slot_mapping=slot_mapping,
+            attn_mask=attn_mask,
+            attn_state=attn_state,
+            enable_dbo_across_dp=common_attn_metadata.enable_dbo_across_dp)
+        return attn_metadata
+
+
+class AscendAttentionTorchairBackendImpl(AttentionImpl):
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: int,
+        alibi_slopes: Optional[List[float]],
+        sliding_window: Optional[int],
+        kv_cache_dtype: str,
+        logits_soft_cap: Optional[float],
+        attn_type: str,
+        kv_sharing_target_layer_name: Optional[str],
+        **kwargs,
+    ) -> None:
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
+        self.hidden_size = self.num_heads * self.head_size
+        self.kv_cache_dtype = kv_cache_dtype
+        self.sliding_window = sliding_window
+        if alibi_slopes is not None:
+            alibi_slopes = torch.tensor(alibi_slopes,
+                                        dtype=torch.float32,
+                                        device="npu")
+        self.alibi_slopes = alibi_slopes
+        self.attn_type = attn_type
+
+        assert self.num_heads % self.num_kv_heads == 0
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+        self.key_cache = None
+        self.value_cache = None
+        self.scale_tensor = torch.zeros((), device='npu', dtype=torch.int32)
+
+    def forward(
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AscendTorchairMetadata,
+        output: Optional[torch.Tensor] = None,
+        trace_flag: bool = False,
+    ) -> torch.Tensor:
+        """Forward pass with Ascend attention.
+        Args:
+            query: shape = [batch_size, seq_len, num_heads * head_size]
+            key: shape = [batch_size, seq_len, num_kv_heads * head_size]
+            value: shape = [batch_size, seq_len, num_kv_heads * head_size]
+            kv_cache: shape = [2, num_blocks, block_size,
+                               num_kv_heads, head_size]
+                      key_cache = [num_blocks, block_size,
+                                   num_kv_heads, head_size]
+                      value_cache = [num_blocks, block_size,
+                                     num_kv_heads, head_size]
+            attn_metadata: Metadata for attention.
+        Returns:
+            shape = [batch_size * seq_len, num_heads, head_size]
+        """
+        num_tokens = query.shape[0]
+        use_kv_cache_quant = (kv_cache is not None and len(kv_cache) > 0
+                              and kv_cache[0].numel() > 0
+                              and kv_cache[0].dtype == torch.int8)
+        if output is None:
+            output = torch.empty(num_tokens,
+                                 self.num_heads,
+                                 self.head_size,
+                                 dtype=query.dtype,
+                                 device=query.device)
+
+        if hasattr(layer, 'quant_method') and use_kv_cache_quant:
+            output = layer.quant_method.apply(layer, query, key, value,
+                                              kv_cache, attn_metadata,
+                                              self.attn_type, self.scale,
+                                              output)
+            return output.view(num_tokens, self.hidden_size)
+
+        if attn_metadata is None:
+            return output.view(num_tokens, self.hidden_size)
+
+        output = output.view(-1, self.num_heads, self.head_size)
+
+        assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
+        attn_type = self.attn_type
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "AscendAttentionTorchairBackendImpl")
+
+        if kv_cache is not None and kv_cache[0].numel() > 0:
+            key_cache, value_cache = kv_cache[0], kv_cache[1]
+            slots = attn_metadata.slot_mapping
+
+            block_size = self.scale_tensor + key_cache.shape[1]
+            slots_indices = slots.reshape(-1, 1)
+            block_indices = slots_indices // block_size
+            slots_indices = slots_indices % block_size
+            indices = torch.cat((block_indices, slots_indices), dim=1)
+            torch_npu.npu_scatter_nd_update_(key_cache, indices, key)
+            torch_npu.npu_scatter_nd_update_(value_cache, indices, value)
+
+        if attn_metadata.attn_state == AscendAttentionState.PrefillNoCache:
+            assert attn_metadata is not None
+            assert attn_metadata.attn_mask is not None
+            mask = attn_metadata.attn_mask
+
+            # View q k v to BSH.
+            query = query.view(-1, self.num_heads, self.head_size)
+            key = key.view(-1, self.num_kv_heads, self.head_size)
+            value = value.view(-1, self.num_kv_heads, self.head_size)
+
+            if is_310p():
+                # align q k v output tensors
+                query = aligned_16(query)
+                key = aligned_16(key)
+                value = aligned_16(value)
+                output = aligned_16(output)
+
+                # do reformat in case of broadcasted tensors
+                mask = mask.repeat(attn_metadata.seq_lens.size(0), 1, 1, 1)
+                mask = torch_npu.npu_format_cast(mask.contiguous(),
+                                                 ACL_FORMAT_FRACTAL_NZ)
+
+            torch_npu._npu_flash_attention(query=query,
+                                           key=key,
+                                           value=value,
+                                           mask=mask,
+                                           seq_len=attn_metadata.seq_lens,
+                                           scale_value=self.scale,
+                                           num_heads=self.num_heads,
+                                           num_kv_heads=self.num_kv_heads,
+                                           out=output)
+            output = output[:num_tokens, :, :]
+        elif attn_metadata.attn_state == AscendAttentionState.PrefillCacheHit:
+            assert attn_metadata is not None
+            assert attn_metadata.attn_mask is not None
+            compress_mask = attn_metadata.attn_mask
+            torch_npu._npu_flash_attention_qlens(
+                query=query,
+                key_cache=self.key_cache,
+                value_cache=self.value_cache,
+                block_table=attn_metadata.block_tables,
+                mask=compress_mask,
+                seq_len=attn_metadata.query_lens,
+                context_lens=attn_metadata.seq_lens,
+                num_kv_heads=self.num_kv_heads,
+                num_heads=self.num_heads,
+                scale_value=self.scale,
+                out=output)
+        elif attn_metadata.attn_state == AscendAttentionState.DecodeOnly:
+            decode_meta = attn_metadata.decode
+            assert decode_meta is not None
+            seq_lens = decode_meta.seq_lens_list
+            block_table = decode_meta.block_table
+            block_size = key_cache.shape[1]
+            query = query.view(num_tokens, 1,
+                               self.num_heads * self.head_size).contiguous()
+            output = torch_npu.npu_incre_flash_attention(
+                query,
+                key_cache,
+                value_cache,
+                num_key_value_heads=self.num_kv_heads,
+                num_heads=self.num_heads,
+                actual_seq_lengths=seq_lens,
+                scale_value=self.scale,
+                block_table=block_table,
+                input_layout='BSH',
+                block_size=block_size)
+        else:
+            raise NotImplementedError(
+                "Torchair graph mode with non-MLA attention backend is still experimental."
+                "v1 scheduler(chunked prefill) is not supported at this moment. Please"
+                "setting 'ascend_scheduler_config':{'enabled':true} in additional_config"
+                "to use ascend scheduler.")
+
+        return output.view(num_tokens, self.hidden_size)

vllm_ascend/torchair/torchair_model_runner.py

@@ -0,0 +1,446 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py
+# isort: skip_file
+
+import types
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch_npu
+import vllm.envs as envs_vllm
+from vllm.config import VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.distributed.parallel_state import get_dp_group
+from vllm.forward_context import get_forward_context
+from vllm.logger import logger
+
+import vllm_ascend.envs as envs_ascend
+from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.platform import NPUPlatform
+from vllm_ascend.torchair.utils import (
+    TORCHAIR_CACHE_DIR, TorchairCommonAttentionMetadata,
+    check_torchair_cache_exist, converting_weight_acl_format,
+    register_torchair_model, torchair_ops_patch,
+    torchair_quant_method_register, write_kv_cache_bytes_to_file)
+from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
+                               is_310p)
+from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
+
+
+class NPUTorchairModelRunner(NPUModelRunner):
+
+    def __init__(self, vllm_config: VllmConfig, device: torch.device):
+        super().__init__(vllm_config, device)
+        ascend_config = get_ascend_config()
+        self.new_kv_cache_bytes = -1
+        self.torchair_compiled_model = None  # type: ignore
+        self.torchair_compiled_models = {}  # type: ignore
+        self.use_cached_npu_graph = ascend_config.torchair_graph_config.use_cached_graph
+        self.use_cached_kv_cache_bytes = ascend_config.torchair_graph_config.use_cached_kv_cache_bytes
+        self.torchair_graph_batch_sizes = ascend_config.torchair_graph_config.graph_batch_sizes
+        if ascend_config.torchair_graph_config.graph_batch_sizes_init:
+            self.init_torchair_graph_batch_sizes()
+
+        self.check_torchair_graph_batch_sizes()
+
+        torch._dynamo.cache_size.config.cache_size_limit += len(
+            self.torchair_graph_batch_sizes)
+        torch._dynamo.config.capture_dynamic_output_shape_ops = True
+        torch._logging.set_logs(
+            recompiles=envs_ascend.VLLM_ASCEND_TRACE_RECOMPILES)
+
+        self._check_batch_sizes_consistency()
+        register_torchair_model()
+        torchair_ops_patch()
+        torchair_quant_method_register()
+
+    def _sync_metadata_across_dp(
+            self, num_tokens: int, with_prefill: bool, enable_dbo: bool
+    ) -> tuple[int, Optional[torch.Tensor], bool, bool]:
+        """Override from NPUModelRunner to pad num_tokens"""
+        if self.dp_size == 1:
+            if not with_prefill:
+                maybe_padded_num_tokens = self.select_torchair_padded_batch_size(
+                    num_tokens)
+                return maybe_padded_num_tokens, None, with_prefill, enable_dbo
+            return num_tokens, None, with_prefill, enable_dbo
+
+        num_tokens_across_dp = torch.zeros(self.dp_size + 2,
+                                           dtype=torch.int32,
+                                           device="npu")
+        num_tokens_across_dp[self.dp_rank] = num_tokens
+        num_tokens_across_dp[-2] = int(with_prefill)
+        num_tokens_across_dp[-1] = int(not enable_dbo)
+        dist.all_reduce(num_tokens_across_dp,
+                        group=get_dp_group().device_group)
+        with_prefill = bool(num_tokens_across_dp[-2])
+        enable_dbo = not bool(num_tokens_across_dp[-1])
+        num_tokens_across_dp = num_tokens_across_dp[:-2]
+
+        if not with_prefill:
+            max_num_token = num_tokens_across_dp.max().item()
+            maybe_padded_num_tokens = self.select_torchair_padded_batch_size(
+                max_num_token)
+            num_tokens_across_dp = torch.full((self.dp_size, ),
+                                              maybe_padded_num_tokens,
+                                              dtype=torch.int32,
+                                              device="npu")
+        else:
+            maybe_padded_num_tokens = num_tokens
+
+        return maybe_padded_num_tokens, num_tokens_across_dp, with_prefill, enable_dbo
+
+    def _build_attention_metadata(self, with_prefill, num_reqs, skip_attn):
+        # NOTE: If torchair graph mode and not with_prefill,
+        # we can't skip_attn, it will cause graph recompile.
+        if not with_prefill:
+            common_attn_metadata = TorchairCommonAttentionMetadata(
+                num_reqs=num_reqs,
+                num_actual_tokens=1,
+                actual_seq_lengths_q=self.actual_seq_lengths_q,
+                attn_mask=self.attn_mask,
+                spec_attn_mask=self.spec_attn_mask,
+                decode_token_per_req=self.decode_token_per_req,
+            )
+            attn_metadata = self.attn_metadata_builder.build_torchair_graph_dummy(
+                common_attn_metadata)
+        else:
+            attn_metadata = super()._build_attention_metadata(
+                with_prefill, num_reqs, skip_attn)
+        return attn_metadata
+
+    def _generate_dummy_run_hidden_states(self, with_prefill,
+                                          is_torchair_compile, input_ids,
+                                          positions, attn_metadata, num_tokens,
+                                          intermediate_tensors, inputs_embeds):
+
+        if not with_prefill:
+            # Only mark static while compiling
+            if is_torchair_compile:
+                torch._dynamo.mark_static(input_ids)
+                torch._dynamo.mark_static(positions)
+                torch._dynamo.mark_static(attn_metadata.decode.block_table)
+                torch._dynamo.mark_static(attn_metadata.decode.input_positions)
+                torch._dynamo.mark_static(get_forward_context().mc2_mask)
+                if hasattr(attn_metadata.decode, "sin"):
+                    torch._dynamo.mark_static(attn_metadata.decode.sin)
+                    torch._dynamo.mark_static(attn_metadata.decode.cos)
+                torch._dynamo.mark_static(attn_metadata.slot_mapping)
+                if self.speculative_config:
+                    torch._dynamo.mark_static(attn_metadata.decode.attn_mask)
+                for kv in self.kv_caches:
+                    assert isinstance(kv, tuple), "kv_cache must be a tuple"
+                    torch._dynamo.mark_static(kv[0])
+                    torch._dynamo.mark_static(kv[1])
+            if is_310p():
+                converting_weight_acl_format(self.model, ACL_FORMAT_FRACTAL_NZ)
+
+            compiled_model = self._get_torchair_lazy_compiled_model(num_tokens)
+            model_kwargs = {}
+            model_kwargs["kv_caches"] = self.kv_caches
+            model_kwargs["attn_metadata"] = attn_metadata
+            hidden_states = compiled_model(
+                input_ids=input_ids,
+                positions=positions,
+                intermediate_tensors=intermediate_tensors,
+                inputs_embeds=None,
+                **model_kwargs,
+            )
+        else:
+            if is_310p():
+                converting_weight_acl_format(self.model, ACL_FORMAT_FRACTAL_ND)
+            hidden_states = super()._generate_dummy_run_hidden_states(
+                with_prefill, is_torchair_compile, input_ids, positions,
+                attn_metadata, num_tokens, intermediate_tensors, inputs_embeds)
+        return hidden_states
+
+    def _convert_torch_format(self, kv_cache):
+        kv_cache = torch_npu.npu_format_cast(kv_cache, ACL_FORMAT_FRACTAL_ND)
+        return kv_cache
+
+    def _compile_torchair_graph(self, torchair_graph_batch_sizes) -> None:
+        # Trigger torchair graph capture for specific shapes.
+        # Capture the large shapes first so that the smaller shapes
+        # can reuse the memory pool allocated for the large shapes.
+        for idx, num_tokens in enumerate(reversed(torchair_graph_batch_sizes)):
+            for _ in range(self.vllm_config.compilation_config.
+                           cudagraph_num_of_warmups):
+                self._dummy_run(num_tokens, is_torchair_compile=True)
+            self._dummy_run(num_tokens, is_torchair_compile=True)
+            logger.info("Batchsize %d is compiled successfully: %d/%d.",
+                        num_tokens, idx + 1, len(torchair_graph_batch_sizes))
+
+    def _capture_model(self):
+        """Override from NPUModelRunner to use torchair graph capture."""
+        # TODO(NeverRaR): Calling graph_capture(device=self.device) in
+        # torchair graph capture can cause some issues, so now we just
+        # temporarily split the codepath for the two different graph patterns.
+        torchair_graph_batch_sizes = self.torchair_graph_batch_sizes
+        graph_num = len(torchair_graph_batch_sizes)
+
+        if self.use_cached_npu_graph and not check_torchair_cache_exist():
+            # If caching is enabled but does not exist (either
+            # use_cached_kv_cache_bytes is disabled or kv_cache_bytes are
+            # different), we will compile the model twice. The first time is
+            # used to generate the cache, and the second time is used to load the
+            # cache to skip the overhead caused by Dynamo guard mechanism.
+            logger.info(
+                "Cache compilation for torchair graph is enabled. Now we compile graph to genetate"
+                " torchair cache, this usually takes %.1f~%.1f mins.",
+                0.5 * graph_num, 1.5 * graph_num)
+            self._compile_torchair_graph(torchair_graph_batch_sizes)
+            NPUPlatform.synchronize()
+            # Note: We reset dynamo and reload the compiled torchair cached computation graph below
+            # that was compiled above. This operation reduces graph launch time by 2-4ms and avoids
+            # runtime errors caused by configuration mismatches in graph mode.
+            torch._dynamo.reset()
+            self.torchair_compiled_models.clear()
+        if self.use_cached_npu_graph:
+            logger.info(
+                "Loading torchair graph cache, this usually takes %.1f~%.1f mins.",
+                0.3 * graph_num, 0.5 * graph_num)
+            self._compile_torchair_graph(torchair_graph_batch_sizes)
+        else:
+            logger.info(
+                "Capturing torchair graph, this usually takes %.1f~%.1f mins.",
+                0.5 * graph_num, 1.5 * graph_num)
+            self._compile_torchair_graph(torchair_graph_batch_sizes)
+
+        if self.use_cached_kv_cache_bytes and self.new_kv_cache_bytes > 0:
+            write_kv_cache_bytes_to_file(torch.distributed.get_rank(),
+                                         self.new_kv_cache_bytes)
+
+    def _use_aclgraph(self) -> bool:
+        return False
+
+    def _check_batch_sizes_consistency(self) -> None:
+        if not dist.is_initialized():
+            return
+
+        local = torch.tensor(self.torchair_graph_batch_sizes,
+                             device="cpu",
+                             dtype=torch.int32)
+        gathered_graph_batch_size = local.clone()
+        dist.all_reduce(gathered_graph_batch_size,
+                        group=get_dp_group().cpu_group)
+        expected = local * self.dp_size
+
+        if not torch.equal(gathered_graph_batch_size, expected):
+            diff_idxs = (gathered_graph_batch_size != expected).nonzero(
+                as_tuple=False).flatten().tolist()
+            raise AssertionError(
+                f"[Graph BatchSize Mismatch] Found mismatches at indices {diff_idxs}.\n"
+                f"Local (rank {self.dp_rank}): {local.tolist()}\n"
+                f"Sum over ranks:     {gathered_graph_batch_size.tolist()}\n"
+                f"Expected if all equal: {[v * self.dp_size for v in local.tolist()]}"
+            )
+
+    def _update_graph_pad_size(self, with_prefill, graph_pad_size):
+        if not with_prefill:
+            self.graph_pad_size = graph_pad_size
+        else:
+            super()._update_graph_pad_size(with_prefill, graph_pad_size)
+
+    def _update_input_ids_and_positions(self, input_ids, positions,
+                                        num_input_tokens, with_prefill,
+                                        padded_num_tokens_across_dp):
+        """Override from NPUModelRunner to update input_ids and positions"""
+        input_ids, positions = super()._update_input_ids_and_positions(
+            input_ids, positions, num_input_tokens, with_prefill,
+            padded_num_tokens_across_dp)
+
+        if not with_prefill:
+            input_ids = self.input_ids[:padded_num_tokens_across_dp]
+            positions = self.positions[:padded_num_tokens_across_dp]
+        return input_ids, positions
+
+    def _generate_process_reqs_hidden_states(self, attn_metadata, with_prefill,
+                                             padded_num_tokens_across_dp,
+                                             input_ids, positions,
+                                             intermediate_tensors,
+                                             inputs_embeds):
+        model_kwargs = {
+            "kv_caches": self.kv_caches,
+            "attn_metadata": attn_metadata
+        }
+        if not with_prefill:
+            if is_310p():
+                converting_weight_acl_format(self.model, ACL_FORMAT_FRACTAL_NZ)
+            compiled_model = self._get_torchair_lazy_compiled_model(
+                padded_num_tokens_across_dp)
+            hidden_states = compiled_model(
+                input_ids=input_ids,
+                positions=positions,
+                intermediate_tensors=intermediate_tensors,
+                inputs_embeds=inputs_embeds,
+                **model_kwargs,
+            )
+        else:
+            assert self.model is not None
+            if is_310p():
+                converting_weight_acl_format(self.model, ACL_FORMAT_FRACTAL_ND)
+
+            hidden_states = self.model(
+                input_ids=input_ids,
+                positions=positions,
+                intermediate_tensors=intermediate_tensors,
+                inputs_embeds=inputs_embeds,
+                **model_kwargs,
+            )
+        return hidden_states
+
+    def _get_torchair_lazy_compiled_model(self, batch_size: int):
+        if batch_size < 0 or batch_size > self.torchair_graph_batch_sizes[-1]:
+            raise ValueError(
+                f"Bad graph batch size:{batch_size}! max_graph_batch_sizes:{self.torchair_graph_batch_sizes[-1]}"
+            )
+
+        compiled_model = self.torchair_compiled_models.get(
+            batch_size
+        ) if self.use_cached_npu_graph else self.torchair_compiled_model
+
+        if compiled_model:
+            return compiled_model
+
+        import torchair  # type: ignore
+        from torchair import patch_for_hcom  # type: ignore
+
+        patch_for_hcom()
+
+        if is_310p():
+            # on 300I Duo platform, we need to patch broadcast. however, this patch will be
+            # overwritten by patch_for_hcom in torchair. so we need to re-patch it here.
+            from vllm_ascend.patch.platform.patch_common.patch_distributed import \
+                communication_adaptation_310p
+            communication_adaptation_310p()
+
+        config = torchair.CompilerConfig()
+        if get_ascend_config().torchair_graph_config.mode:
+            config.mode = get_ascend_config().torchair_graph_config.mode
+        config.experimental_config.frozen_parameter = True
+        # enabling tiling_schedule_optimize on 300I Duo has some bugs, so we have to
+        # disable it on 300I Duo platform now.
+        config.experimental_config.tiling_schedule_optimize = not is_310p()
+        config.experimental_config.enable_view_optimize = \
+        get_ascend_config().torchair_graph_config.enable_view_optimize
+        torch.npu.set_compile_mode(jit_compile=False)
+        if not self.use_cached_npu_graph:
+            npu_backend = torchair.get_npu_backend(compiler_config=config)
+            self.torchair_compiled_model = torch.compile(
+                self.model,
+                dynamic=True,
+                fullgraph=envs_vllm.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
+                backend=npu_backend)
+            return self.torchair_compiled_model
+        else:
+            # Generate a new forward proxy code object to prevent the invalidation of
+            # compilation cache caused by dynamo retracing
+            forward_proxy_name = f"{self.model.__class__.__name__}_forward_with_batch_size_{batch_size}"
+            forward_fn = self.model.forward
+            code = forward_fn.__code__
+            # Mark code object with a new proxy name
+            modified_code = code.replace(co_name=forward_proxy_name, )
+
+            modified_func = types.FunctionType(modified_code,
+                                               forward_fn.__globals__,
+                                               name=forward_proxy_name,
+                                               argdefs=forward_fn.__defaults__)
+
+            self.model.__dict__[forward_proxy_name] = modified_func.__get__(
+                self.model, nn.Module)
+            self.torchair_compiled_models[
+                batch_size] = torchair.inference.cache_compile(
+                    self.model.__dict__[forward_proxy_name],
+                    dynamic=True,
+                    fullgraph=envs_vllm.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
+                    cache_dir=TORCHAIR_CACHE_DIR,
+                    config=config,
+                    ge_cache=False)
+            return self.torchair_compiled_models[batch_size]
+
+    def init_torchair_graph_batch_sizes(self):
+        start_graph_batch_size = 4
+        tp_size = get_tensor_model_parallel_world_size()
+
+        # NOTE: When use all2all | mc2, We need to slice the `num_tokens` dimension into `tp_size` blocks
+        start_graph_batch_size = max(start_graph_batch_size, tp_size)
+
+        while (start_graph_batch_size <= self.max_num_reqs):
+            self.torchair_graph_batch_sizes.append(start_graph_batch_size)
+            start_graph_batch_size *= 2
+
+    def select_torchair_padded_batch_size(self, batch_size: int):
+        for padded_batch_size in self.torchair_graph_batch_sizes:
+            if batch_size <= padded_batch_size:
+                # we treat batch_size as num of requests
+                return padded_batch_size
+        raise ValueError(
+            f"cur batch_size is invalid, torchair_graph_batch_sizes is "
+            f"{self.torchair_graph_batch_sizes}, but cur batch_size is {batch_size}."
+        )
+
+    def check_torchair_graph_batch_sizes(self):
+        # return graph_batch_sizes according to the max number of tokens
+        # first pad according to the number of requests
+        if len(self.torchair_graph_batch_sizes) == 0:
+            self.torchair_graph_batch_sizes = [1, self.max_num_reqs]
+        else:
+            self.torchair_graph_batch_sizes = sorted(
+                self.torchair_graph_batch_sizes)
+            while self.torchair_graph_batch_sizes[-1] > self.max_num_reqs:
+                self.torchair_graph_batch_sizes.pop()
+                if len(self.torchair_graph_batch_sizes) == 0:
+                    logger.warning(
+                        "torch_graph_batch_sizes is invalid, reset it to [1, max_num_seqs]"
+                    )
+                    self.torchair_graph_batch_sizes = [1, self.max_num_reqs]
+            if self.torchair_graph_batch_sizes[-1] < self.max_num_reqs:
+                self.torchair_graph_batch_sizes.append(self.max_num_reqs)
+
+        # padded max number tokens = max_num_req * decode_token_per_req
+        self.torchair_graph_batch_sizes = [
+            graph_batch_size * self.decode_token_per_req
+            for graph_batch_size in self.torchair_graph_batch_sizes
+        ]
+
+        # NOTE: when enable_expert_parallel, we need to check if `graph_batch_size` is divisible by `tp_size`
+        tp_size = self.parallel_config.tensor_parallel_size
+        if self.parallel_config.enable_expert_parallel:
+            new_graph_batch_sizes = []
+            for graph_batch_size in self.torchair_graph_batch_sizes:
+                cur_graph_batch_size = (graph_batch_size + tp_size -
+                                        1) // tp_size * tp_size
+                if cur_graph_batch_size not in new_graph_batch_sizes and \
+                    cur_graph_batch_size <= self.scheduler_config.max_num_batched_tokens:
+                    new_graph_batch_sizes.append(cur_graph_batch_size)
+                elif cur_graph_batch_size > self.scheduler_config.max_num_batched_tokens \
+                        and self.decode_token_per_req > 1:
+                    logger.warning(
+                        f"torchair_graph_batch_sizes {cur_graph_batch_size} is bigger than max_num_batched_tokens",
+                        f"{self.scheduler_config.max_num_batched_tokens} will skip this batch size."
+                    )
+            self.torchair_graph_batch_sizes = new_graph_batch_sizes
+
+    def _build_drafter_prepare_inputs_torchair_param(self):
+        return True
+
+    def get_dp_padding(self, num_tokens):
+        """Override from NPUModelRunner to get dp padding"""
+        return 0, None

vllm_ascend/torchair/torchair_worker.py

@@ -0,0 +1,63 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# 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.
+import torch
+from vllm.logger import logger
+
+import vllm_ascend.envs as envs_ascend
+from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.torchair.torchair_model_runner import NPUTorchairModelRunner
+from vllm_ascend.torchair.utils import (check_kv_cache_bytes_cache_exist,
+                                        delete_torchair_cache_file,
+                                        read_kv_cache_bytes_from_file)
+from vllm_ascend.worker.worker_v1 import NPUWorker
+
+
+class NPUTorchairWorker(NPUWorker):
+    """Torchair worker bases on NPUWorker. Only torchair specified code should be added in this class."""
+
+    def determine_available_memory(self) -> int:
+        """Override determine_available_memory to use cached torchair kv_cache_bytes."""
+
+        available_kv_cache_memory = super().determine_available_memory()
+
+        if get_ascend_config(
+        ).torchair_graph_config.use_cached_kv_cache_bytes and check_kv_cache_bytes_cache_exist(
+        ):
+            old_kv_cache_bytes = read_kv_cache_bytes_from_file(
+                torch.distributed.get_rank())
+            if 0 < old_kv_cache_bytes <= available_kv_cache_memory:
+                logger.info(
+                    f"Use cached torchair kv_cache_bytes: {old_kv_cache_bytes}"
+                )
+                self.model_runner.new_kv_cache_bytes = old_kv_cache_bytes
+                return old_kv_cache_bytes
+            else:
+                logger.info(
+                    "Cached torchair kv_cache_bytes is too big, invalidate old torchair_cache"
+                )
+                delete_torchair_cache_file()
+        bytes_floating_tolerance = 1024 * 1024 * envs_ascend.VLLM_ASCEND_KV_CACHE_MEGABYTES_FLOATING_TOLERANCE
+        available_kv_cache_memory -= bytes_floating_tolerance
+        logger.info(f"Use new kv_cache_bytes: {available_kv_cache_memory}")
+        self.model_runner.new_kv_cache_bytes = available_kv_cache_memory
+
+        return available_kv_cache_memory
+
+    def init_device(self):
+        """Override init_device to init torchair model runner"""
+        device = self._init_device()
+        # Init ModelRunner here, so that we have access to self.device.
+        self.model_runner = NPUTorchairModelRunner(self.vllm_config, device)

vllm_ascend/torchair/utils.py

@@ -0,0 +1,205 @@
+import fcntl
+import os
+import shutil
+from contextlib import contextmanager, nullcontext
+from dataclasses import dataclass
+
+import torch
+import torch_npu
+
+try:
+    # Recent release of torchair has moved these ops to `.scope`.
+    from torchair.scope import npu_stream_switch as _npu_stream_switch
+    from torchair.scope import npu_wait_tensor as _npu_wait_tensor
+except ImportError:
+    from torchair.ops import NpuStreamSwitch as _npu_stream_switch
+    from torchair.ops import npu_wait_tensor as _npu_wait_tensor
+
+KV_CACHE_BYTES_CACHE_PATH_NAME = ".kv_cache_bytes"
+KV_CACHE_BYTES_CACHE_FILE_NAME = "kv_cache_bytes"
+TORCHAIR_CACHE_PATH_NAME = ".torchair_cache"
+TORCHAIR_CACHE_DIR = os.path.join(
+    os.getenv('TORCHAIR_CACHE_HOME', os.getcwd()), TORCHAIR_CACHE_PATH_NAME)
+
+
+@dataclass
+class TorchairCommonAttentionMetadata:
+    """
+    Per-batch attention metadata, shared across layers and backends.
+    AttentionMetadataBuilder instances use it to construct per-layer metadata.
+    
+    For many of the tensors we keep both GPU and CPU versions.
+    """
+
+    num_reqs: int
+    """Number of requests"""
+
+    num_actual_tokens: int
+    """Total number of tokens in batch"""
+
+    decode_token_per_req: int
+
+    actual_seq_lengths_q: list[int]
+
+    attn_mask: torch.Tensor = None
+
+    spec_attn_mask: torch.Tensor = None
+
+    graph_pad_size: int = -1
+
+
+@contextmanager
+def _file_lock(file_descriptor, lock_type):
+    fcntl.flock(file_descriptor, lock_type)
+    try:
+        yield
+    finally:
+        fcntl.flock(file_descriptor, fcntl.LOCK_UN)
+
+
+def _get_torchair_current_work_dir(file_name=None):
+    if file_name is None:
+        return TORCHAIR_CACHE_DIR
+    return os.path.join(TORCHAIR_CACHE_DIR, file_name)
+
+
+def check_torchair_cache_exist():
+    res = False
+    torch_air_abs_path = _get_torchair_current_work_dir()
+    if os.path.exists(torch_air_abs_path):
+        file_list = os.listdir(torch_air_abs_path)
+        if len(file_list) != 0:
+            res = True
+    return res
+
+
+def check_kv_cache_bytes_cache_exist():
+    res = False
+    kv_cache_bytes_cache_abs_path = _get_torchair_current_work_dir(
+        KV_CACHE_BYTES_CACHE_PATH_NAME)
+    if os.path.exists(kv_cache_bytes_cache_abs_path):
+        file_list = os.listdir(kv_cache_bytes_cache_abs_path)
+        if len(file_list) != 0:
+            res = True
+    return res
+
+
+def read_kv_cache_bytes_from_file(rank) -> int:
+    kv_cache_bytes = -1
+    kv_cache_bytes_cache_abs_path = _get_torchair_current_work_dir(
+        KV_CACHE_BYTES_CACHE_PATH_NAME)
+    kv_cache_bytes_file = os.path.join(
+        kv_cache_bytes_cache_abs_path,
+        f"{rank}_{KV_CACHE_BYTES_CACHE_FILE_NAME}")
+    with open(kv_cache_bytes_file, "r", encoding="utf-8") as f:
+        with _file_lock(f, fcntl.LOCK_SH):
+            kv_cache_bytes = int(f.readline())
+    return kv_cache_bytes
+
+
+def write_kv_cache_bytes_to_file(rank, kv_cache_bytes):
+    kv_cache_bytes_cache_abs_path = _get_torchair_current_work_dir(
+        KV_CACHE_BYTES_CACHE_PATH_NAME)
+    os.makedirs(kv_cache_bytes_cache_abs_path, exist_ok=True)
+    kv_cache_bytes_file = os.path.join(
+        kv_cache_bytes_cache_abs_path,
+        f"{rank}_{KV_CACHE_BYTES_CACHE_FILE_NAME}")
+    with open(kv_cache_bytes_file, "w", encoding="utf-8") as f:
+        with _file_lock(f, fcntl.LOCK_EX):
+            f.write(f"{kv_cache_bytes}")
+
+
+def delete_torchair_cache_file():
+    torch_air_abs_path = _get_torchair_current_work_dir()
+    try:
+        shutil.rmtree(torch_air_abs_path)
+    except FileNotFoundError:
+        pass
+
+
+def npu_stream_switch(tag: str, priority: int, *, enabled: bool = True):
+    return _npu_stream_switch(tag, priority) if enabled else nullcontext()
+
+
+def npu_wait_tensor(self: torch.Tensor,
+                    dependency: torch.Tensor,
+                    *,
+                    enabled: bool = True):
+    return _npu_wait_tensor(self, dependency) if enabled else self
+
+
+def converting_weight_acl_format(model, format):
+    # currently, there are some operations which do not support ACL_FORMAT_FRACTAL_NZ
+    # in eager mode but support it in torchair graph mode. since ACL_FORMAT_FRACTAL_NZ
+    # is much more preferred than ACL_FORMAT_FRACTAL_ND on 300I Duo, we add this
+    # conversion when using torchair graph mode on 300I Duo platform.
+    # TODO: we will remove this conversion if npu_quant_grouped_matmul_dequant
+    # accepts weight format of ACL_FORMAT_FRACTAL_NZ in eager mode.
+    from vllm.model_executor.layers.fused_moe.layer import FusedMoE
+
+    for module in model.modules():
+        if isinstance(module, FusedMoE):
+            if torch_npu.get_npu_format(module.w13_weight.data) == format:
+                return
+            module.w13_weight.data = torch_npu.npu_format_cast(
+                module.w13_weight.data, format)
+            module.w2_weight.data = torch_npu.npu_format_cast(
+                module.w2_weight.data, format)
+
+
+def register_torchair_model():
+    from vllm import ModelRegistry
+
+    ModelRegistry.register_model(
+        "DeepSeekMTPModel",
+        "vllm_ascend.torchair.models.torchair_deepseek_mtp:TorchairDeepSeekMTP"
+    )
+
+    ModelRegistry.register_model(
+        "DeepseekV2ForCausalLM",
+        "vllm_ascend.torchair.models.torchair_deepseek_v2:TorchairDeepseekV2ForCausalLM"
+    )
+
+    ModelRegistry.register_model(
+        "DeepseekV3ForCausalLM",
+        "vllm_ascend.torchair.models.torchair_deepseek_v3:TorchairDeepseekV3ForCausalLM"
+    )
+
+    ModelRegistry.register_model(
+        "Qwen2ForCausalLM",
+        "vllm_ascend.torchair.models.qwen2:CustomQwen2ForCausalLM")
+
+    ModelRegistry.register_model(
+        "Qwen3MoeForCausalLM",
+        "vllm_ascend.torchair.models.qwen3_moe:CustomQwen3MoeForCausalLM")
+
+    ModelRegistry.register_model(
+        "PanguProMoEForCausalLM",
+        "vllm_ascend.torchair.models.torchair_pangu_moe:PanguProMoEForCausalLM"
+    )
+
+
+def torchair_quant_method_register():
+    from vllm_ascend.quantization.quantizer import \
+        SUPPORT_ASCEND_QUANTIZER_TYPE
+    from vllm_ascend.torchair.quantization.torchair_quantizer import (
+        TorchairW4A8DYNAMICQuantizer, TorchairW8A8DYNAMICQuantizer)
+
+    SUPPORT_ASCEND_QUANTIZER_TYPE[
+        "W8A8_DYNAMIC"] = TorchairW8A8DYNAMICQuantizer
+    SUPPORT_ASCEND_QUANTIZER_TYPE[
+        "W4A8_DYNAMIC"] = TorchairW4A8DYNAMICQuantizer
+
+
+def torchair_ops_patch():
+    from vllm_ascend.ops.rotary_embedding import (
+        AscendDeepseekScalingRotaryEmbedding, AscendRotaryEmbedding)
+    from vllm_ascend.torchair.ops.torchair_rotary_embedding import (
+        deepseek_rope_init_func, native_rope_deepseek_forward,
+        qwen_rope_init_func, rope_forward)
+
+    AscendRotaryEmbedding.__init__ = qwen_rope_init_func  # type: ignore[method-assign]
+    AscendRotaryEmbedding.forward_oot = rope_forward  # type: ignore[method-assign]
+
+    AscendDeepseekScalingRotaryEmbedding.__init__ = deepseek_rope_init_func  # type: ignore[method-assign]
+    AscendDeepseekScalingRotaryEmbedding.forward = native_rope_deepseek_forward  # type: ignore[method-assign]