[Feature]: Support 310P device run qwen2.5/3 dense and qwen2.5vl models (#5776)

### What this PR does / why we need it?
Add basic 310p support. Only dense models work with eager mode now.

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: Tflowers-0129 <2906339855@qq.com>
Signed-off-by: Shaoxu Cheng <2906339855@qq.com>

CMakeLists.txt

@@ -62,14 +62,17 @@ set(VLLM_ASCEND_CUSTOM_OP
 )
 
 set(VLLM_ASCEND_CUSTOM_OP_EXCLUDE
-    ${KERNEL_FILES}/bgmv_expand.cpp
-    ${KERNEL_FILES}/bgmv_shrink.cpp
-    ${KERNEL_FILES}/sgmv_expand.cpp
-    ${KERNEL_FILES}/sgmv_shrink.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernels/bgmv_expand.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernels/bgmv_shrink.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernels/sgmv_expand.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernels/sgmv_shrink.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/csrc/mla_preprocess/op_kernel/mla_preprocess_kernel.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/csrc/batch_matmul_transpose/op_kernel/batch_matmul_transpose_kernel.cpp
 )
 
-if(SOC_VERSION STREQUAL "ASCEND310P3")
+if(SOC_VERSION STREQUAL "ascend310p3")
+    message(STATUS "310P hardware detected: disabling MLAPO operators")
+    message(STATUS "310P hardware detected: excluding batch_matmul_transpose operators")
     list(REMOVE_ITEM VLLM_ASCEND_CUSTOM_OP ${VLLM_ASCEND_CUSTOM_OP_EXCLUDE})
 endif()
 
@@ -79,7 +82,7 @@ ascendc_library(vllm_ascend_kernels SHARED
 
 message("TORCH_NPU_PATH is ${TORCH_NPU_PATH}")
 
-if(SOC_VERSION STREQUAL "ASCEND310P3")
+if(SOC_VERSION STREQUAL "ascend310p3")
     file(GLOB VLLM_ASCEND_SRC
     ${CMAKE_CURRENT_SOURCE_DIR}/csrc/*.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/csrc/aclnn_torch_adapter/*.cpp)

tests/e2e/310p/test_offline_inference_310p.py

@@ -36,7 +36,9 @@ def test_llm_models(dtype: str, max_tokens: int) -> None:
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-def test_multimodal_vl():
+@pytest.mark.skip(reason="310P: multimodal test skipped, offline is ok")
+@pytest.mark.parametrize("dtype", ["float16"])
+def test_multimodal_vl(dtype: str):
     image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
 
     img_questions = [
@@ -60,6 +62,7 @@ def test_multimodal_vl():
                         "max_pixels": 1280 * 28 * 28,
                         "fps": 1,
                     },
+                    dtype=dtype,
                     max_model_len=8192,
                     enforce_eager=True,
                     limit_mm_per_prompt={"image": 1}) as vllm_model:

tests/e2e/310p/test_offline_inference_parallel_310p.py

@@ -1,3 +1,20 @@
+#
+# 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.
+
 import pytest
 
 from tests.e2e.conftest import VllmRunner
@@ -5,7 +22,6 @@ from tests.e2e.conftest import VllmRunner
 
 @pytest.mark.parametrize("dtype", ["float16"])
 @pytest.mark.parametrize("max_tokens", [5])
-@pytest.skip("310p does not support parallel inference now. Fix me")
 def test_models(dtype: str, max_tokens: int) -> None:
     example_prompts = [
         "Hello, my name is",

vllm_ascend/_310p/attention/attention_mask.py

@@ -0,0 +1,98 @@
+#
+# 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 collections.abc import Callable
+from typing import Any
+
+import torch
+import torch_npu
+
+import vllm_ascend.attention.attention_mask as _base_mask
+from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, nd_to_nz_spec
+
+_BASE_BUILDER: Callable[[torch.device], Any] = _base_mask.AttentionMaskBuilder
+
+
+def _gen_causal_additive_mask_fp16(max_seq_len: int, device: torch.device) -> torch.Tensor:
+    tril = torch.ones((max_seq_len, max_seq_len), dtype=torch.bool, device=device).tril_()
+    upper = ~tril
+    m = torch.zeros((max_seq_len, max_seq_len), dtype=torch.float16, device=device)
+    m.masked_fill_(upper, float("-inf"))
+    return m
+
+
+def build_splitfuse_attn_mask_310p(attn_metadata, device, *, full_mask_cache=None, full_mask_cache_len=0):
+    qsl = attn_metadata.query_start_loc.detach().to("cpu", dtype=torch.int32)
+    qlens = qsl[1:] - qsl[:-1]
+
+    context_lens = attn_metadata.seq_lens.to(dtype=torch.int32)
+    L = int(context_lens.max().item())
+
+    q_list = qlens.tolist()
+    c_list = context_lens.detach().to("cpu", dtype=torch.int64).tolist()
+    pos_list = [p for ql, cl in zip(q_list, c_list) for p in range(cl - ql, cl)]
+    position = torch.tensor(pos_list, dtype=torch.long, device=device)
+
+    if full_mask_cache is None or full_mask_cache.device != device or full_mask_cache_len < L:
+        tril = torch.ones((L, L), dtype=torch.bool, device=device).tril_()
+        full = torch.zeros((L, L), dtype=torch.float16, device=device)
+        full.masked_fill_(~tril, float("-inf"))
+        full_mask_cache, full_mask_cache_len = full, L
+    else:
+        full = full_mask_cache[:L, :L].contiguous()
+
+    rows = full.index_select(0, position).contiguous()
+    mask = torch_npu.npu_format_cast(nd_to_nz_spec(rows).contiguous(), ACL_FORMAT_FRACTAL_NZ)
+    return mask, full_mask_cache, full_mask_cache_len
+
+
+class _AttentionMaskBuilder310P:
+    """
+    310P adapter:
+      - overrides fp16 causal additive mask generation (use -inf fp16)
+      - delegates all other behaviors to base AttentionMaskBuilder
+      - pooling runner_type is NOT supported on 310P (explicit)
+    """
+
+    def __init__(self, device: torch.device):
+        self._base = _BASE_BUILDER(device)
+
+        self._fp16_mask_cache: torch.Tensor | None = None
+        self._fp16_mask_cached_len: int = 0
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._base, name)
+
+    @property
+    def device(self) -> torch.device:
+        return self._base.device
+
+    def _get_fp16_mask(self, max_seq_len: int) -> torch.Tensor:
+        if self._fp16_mask_cache is None or max_seq_len > self._fp16_mask_cached_len:
+            self._fp16_mask_cache = _gen_causal_additive_mask_fp16(max_seq_len, self.device)
+            self._fp16_mask_cached_len = max_seq_len
+        assert self._fp16_mask_cache is not None
+        return self._fp16_mask_cache[:max_seq_len, :max_seq_len].contiguous()
+
+    def get_attention_mask(self, model_config) -> torch.Tensor:
+        if getattr(model_config, "runner_type", None) == "pooling":
+            raise NotImplementedError("310P does not support runner_type='pooling'")
+        return self._get_fp16_mask(2048)
+
+
+def AttentionMaskBuilder(device: torch.device) -> _AttentionMaskBuilder310P:
+    return _AttentionMaskBuilder310P(device)

vllm_ascend/_310p/attention/attention_v1.py

@@ -0,0 +1,172 @@
+#
+# 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 torch
+import torch_npu
+
+from vllm_ascend._310p.attention.attention_mask import AttentionMaskBuilder, build_splitfuse_attn_mask_310p
+from vllm_ascend._310p.attention.metadata_builder import AscendAttentionMetadataBuilder310P
+from vllm_ascend.attention.attention_v1 import AscendAttentionBackend as _BaseBackend
+from vllm_ascend.attention.attention_v1 import AscendAttentionBackendImpl as _BaseImpl
+from vllm_ascend.attention.attention_v1 import AscendAttentionMetadataBuilder, AscendAttentionState
+from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, aligned_16, nd_to_nz_2d
+
+
+class AscendAttentionBackend310(_BaseBackend):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.attn_mask_builder = AttentionMaskBuilder(self.device)
+
+    @staticmethod
+    def get_kv_cache_shape(num_blocks: int, block_size: int, num_kv_heads: int, head_size: int):
+        # Align to a multiple of 16, as required by the 310P device.
+        return (2, num_blocks, (num_kv_heads * head_size) // 16, block_size, 16)
+
+    @staticmethod
+    def get_impl_cls():
+        return AscendAttentionBackendImpl310
+
+    @staticmethod
+    def get_builder_cls() -> type["AscendAttentionMetadataBuilder"]:
+        return AscendAttentionMetadataBuilder310P
+
+
+class AscendAttentionBackendImpl310(_BaseImpl):
+    def forward_paged_attention(self, query, attn_metadata, output):
+        if attn_metadata.seq_lens.device != query.device:
+            attn_metadata.seq_lens = attn_metadata.seq_lens.to(device=query.device, non_blocking=True)
+        return super().forward_paged_attention(query, attn_metadata, output)
+
+    def _forward_prefill_310p_fallback(self, query, key, value, attn_metadata, output):
+        real_tokens = int(attn_metadata.seq_lens.sum().item())
+
+        query, key, value, output = (aligned_16(t) for t in (query, key, value, output))
+
+        seq_len = attn_metadata.seq_lens
+        if seq_len.dtype != torch.int32:
+            seq_len = seq_len.to(torch.int32)
+
+        aligned_tokens = int(query.shape[0])
+        delta = aligned_tokens - real_tokens
+        if delta:
+            seq_len = seq_len.clone()
+            seq_len[-1] += delta
+
+        mask = attn_metadata.attn_mask
+        if mask is not None and mask.dim() == 2:
+            max_len = int(seq_len.max().item())
+            aligned_len = ((max_len + 15) // 16) * 16
+
+            mask2d = mask[:aligned_len, :aligned_len].contiguous()
+            mask2d = mask2d.to(torch.float16)
+            mask_nz = nd_to_nz_2d(mask2d).contiguous()
+
+            bsz = int(seq_len.numel())
+            if bsz > 1:
+                mask_nz = mask_nz.repeat(bsz, 1, 1, 1).contiguous()
+
+            mask = torch_npu.npu_format_cast(mask_nz, ACL_FORMAT_FRACTAL_NZ)
+
+        torch_npu._npu_flash_attention(
+            query=query,
+            key=key,
+            value=value,
+            mask=mask,
+            seq_len=seq_len,
+            scale_value=self.scale,
+            num_heads=self.num_heads,
+            num_kv_heads=self.num_kv_heads,
+            out=output,
+        )
+
+        out_real = output[:real_tokens, :, :]
+        return out_real
+
+    def _forward_chunked_prefill_310p(self, query, attn_metadata, output):
+        assert attn_metadata is not None
+
+        if query.dtype == torch.float32:
+            query = query.to(torch.float16)
+
+        qsl_cpu = attn_metadata.query_start_loc.detach().to("cpu", dtype=torch.int32)
+        qlens = (qsl_cpu[1:] - qsl_cpu[:-1]).to(torch.int32)
+
+        context_lens = attn_metadata.seq_lens
+        if context_lens.dtype != torch.int32:
+            context_lens = context_lens.to(torch.int32)
+
+        block_table = attn_metadata.block_tables.detach()
+        if block_table.dtype != torch.int32:
+            block_table = block_table.to(torch.int32)
+
+        if not hasattr(self, "_sf_full_mask_cache"):
+            self._sf_full_mask_cache = None
+            self._sf_full_mask_cache_len = 0
+
+        mask, self._sf_full_mask_cache, self._sf_full_mask_cache_len = build_splitfuse_attn_mask_310p(
+            attn_metadata,
+            query.device,
+            full_mask_cache=self._sf_full_mask_cache,
+            full_mask_cache_len=int(self._sf_full_mask_cache_len),
+        )
+
+        if qlens.device.type != "cpu":
+            qlens = qlens.to("cpu")
+        if context_lens.device != query.device:
+            context_lens = context_lens.to(query.device, non_blocking=True)
+
+        torch_npu._npu_paged_attention_splitfuse(
+            query=query,
+            key_cache=self.key_cache,
+            value_cache=self.value_cache,
+            mask=mask,
+            block_table=block_table,
+            seq_len=qlens,
+            context_lens=context_lens,
+            num_kv_heads=self.num_kv_heads,
+            num_heads=self.num_heads,
+            scale_value=self.scale,
+            out=output,
+        )
+
+    def forward_impl(self, query, key, value, kv_cache, attn_metadata, output):
+        state = attn_metadata.attn_state
+
+        if state == AscendAttentionState.DecodeOnly:
+            return self.forward_paged_attention(query, attn_metadata, output)
+
+        if state == AscendAttentionState.PrefillNoCache:
+            num_tokens = query.shape[0]
+            q = query[:num_tokens]
+            k = key[:num_tokens]
+            v = value[:num_tokens]
+            out = self._forward_prefill_310p_fallback(q, k, v, attn_metadata, output)
+            output[:num_tokens] = out
+            return output
+
+        if state == AscendAttentionState.ChunkedPrefill:
+            self._forward_chunked_prefill_310p(query, attn_metadata, output)
+            return output
+
+        raise NotImplementedError(
+            f"{self.__class__.__name__}.forward_impl: 310P only supports "
+            f"{AscendAttentionState.DecodeOnly.name}, "
+            f"{AscendAttentionState.PrefillNoCache.name}, "
+            f"{AscendAttentionState.ChunkedPrefill.name}, "
+            f"got {state!r}."
+        )

vllm_ascend/_310p/attention/metadata_builder.py

@@ -0,0 +1,40 @@
+#
+# 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 __future__ import annotations
+
+from typing import Any
+
+import torch
+from vllm.config import VllmConfig
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+from vllm_ascend._310p.attention.attention_mask import AttentionMaskBuilder
+from vllm_ascend.attention.attention_v1 import AscendAttentionMetadataBuilder as _BaseBuilder
+
+
+class AscendAttentionMetadataBuilder310P(_BaseBuilder):
+    def __init__(
+        self,
+        kv_cache_spec: AttentionSpec,
+        layer_names: list[str],
+        vllm_config: VllmConfig,
+        device: torch.device,
+    ):
+        super().__init__(kv_cache_spec, layer_names, vllm_config, device)
+
+        self.attn_mask_builder: Any = AttentionMaskBuilder(self.device)

vllm_ascend/_310p/modelrunner_310p.py

@@ -0,0 +1,100 @@
+#
+# 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 __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch_npu
+from vllm.v1.kv_cache_interface import FullAttentionSpec, KVCacheConfig
+from vllm.v1.worker.utils import bind_kv_cache
+
+from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ
+from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
+
+
+class NPUModelRunner310(NPUModelRunner):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._acl_format = ACL_FORMAT_FRACTAL_NZ
+
+    def _initialize_kv_cache_tensors_310p(self, kv_cache_config: KVCacheConfig) -> dict[str, Any]:
+        if self.vllm_config.kv_transfer_config is not None:
+            raise ValueError("KV cache transfer is not supported for 310P.")
+
+        kv_cache_sizes: dict[str, int] = {}
+        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
+            assert len(kv_cache_tensor.shared_by) == 1, (
+                "KV cache tensor shared by multiple layers is not supported in 310P."
+            )
+            kv_cache_sizes[kv_cache_tensor.shared_by[0]] = kv_cache_tensor.size
+
+        kv_caches: dict[str, Any] = {}
+
+        for group in self._kv_cache_spec_attn_group_iterator():
+            kv_cache_spec = group.kv_cache_spec
+            attn_backend = group.backend
+
+            if not isinstance(kv_cache_spec, FullAttentionSpec):
+                raise ValueError("Unknown KV cache spec type.")
+
+            for layer_name in group.layer_names:
+                if layer_name in self.runner_only_attn_layers:
+                    continue
+
+                tensor_size = kv_cache_sizes[layer_name]
+                assert tensor_size % kv_cache_spec.page_size_bytes == 0
+                num_blocks = tensor_size // kv_cache_spec.page_size_bytes
+                assert num_blocks >= kv_cache_config.num_blocks
+
+                if hasattr(attn_backend, "get_supported_block_size") and self.use_hybrid_blocks:
+                    block_size = attn_backend.get_supported_block_size()[0]
+                    block_size_chunk = kv_cache_spec.block_size // block_size
+                    kv_cache_shape = attn_backend.get_kv_cache_shape(
+                        num_blocks * block_size_chunk,
+                        block_size,
+                        kv_cache_spec.num_kv_heads,
+                        kv_cache_spec.head_size,
+                    )
+                else:
+                    kv_cache_shape = attn_backend.get_kv_cache_shape(
+                        num_blocks,
+                        kv_cache_spec.block_size,
+                        kv_cache_spec.num_kv_heads,
+                        kv_cache_spec.head_size,
+                    )
+
+                dtype = kv_cache_spec.dtype
+
+                if "attn" in layer_name:
+                    k_tensor = torch.zeros(kv_cache_shape[1:], dtype=dtype, device=self.device)
+                    v_tensor = torch.zeros(kv_cache_shape[1:], dtype=dtype, device=self.device)
+                    k_cache = torch_npu.npu_format_cast(k_tensor, self._acl_format)
+                    v_cache = torch_npu.npu_format_cast(v_tensor, self._acl_format)
+                    kv_caches[layer_name] = (k_cache, v_cache)
+
+        bind_kv_cache(
+            kv_caches,
+            self.compilation_config.static_forward_context,
+            self.kv_caches,
+            1,  # 310p devices donnot support: hf_config.model_type == "longcat_flash"
+        )
+        return kv_caches
+
+    def initialize_kv_cache_tensors(self, kv_cache_config: KVCacheConfig) -> dict[str, Any]:
+        return self._initialize_kv_cache_tensors_310p(kv_cache_config)

vllm_ascend/_310p/ops/activation.py

@@ -0,0 +1,30 @@
+#
+# 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 torch
+import torch.nn.functional as F
+
+from vllm_ascend.ops.activation import AscendSiluAndMul as _Base
+
+
+class AscendSiluAndMul310(_Base):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        torch.ops.vllm.maybe_prefetch_mlp_down_proj(x)
+        h = x.shape[-1] // 2
+        out = (F.silu(x[..., :h].to(torch.float32)) * x[..., h:].to(torch.float32)).to(torch.float16)
+        torch.ops.vllm.maybe_wait_prefetch_done(out)
+        return out

vllm_ascend/_310p/ops/mm_encoder_attention.py

@@ -0,0 +1,104 @@
+#
+# 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 einops
+import torch
+import torch.nn.functional as F
+import torch_npu
+
+import vllm_ascend.envs as envs_ascend
+from vllm_ascend.ops.mm_encoder_attention import MAX_PAD_SIZE, MIN_PAD_SIZE
+from vllm_ascend.ops.mm_encoder_attention import AscendMMEncoderAttention as _Base
+
+
+class AscendMMEncoderAttention310(_Base):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward_oot(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        cu_seqlens: torch.Tensor | None = None,
+        max_seqlen: int | None = None,
+        **kwargs,
+    ):
+        bsz, q_len = query.size()[:2]
+        kv_len = key.size(1)
+
+        q, k, v = self.reshape_qkv_to_3d(query, key, value, bsz, q_len, kv_len)
+
+        enable_pad = envs_ascend.USE_OPTIMIZED_MODEL and self.head_size > MIN_PAD_SIZE and self.head_size < MAX_PAD_SIZE
+
+        origin_shape = q.shape[-1]
+        if enable_pad:
+            pad_len = MAX_PAD_SIZE - origin_shape
+            q = F.pad(q, (0, pad_len), mode="constant", value=0)
+            k = F.pad(k, (0, pad_len), mode="constant", value=0)
+            v = F.pad(v, (0, pad_len), mode="constant", value=0)
+
+        origin_dim = origin_shape
+        cur_dim = q.shape[-1]
+        pad16 = (16 - cur_dim % 16) % 16
+        if pad16:
+            q = F.pad(q, (0, pad16), mode="constant", value=0)
+            k = F.pad(k, (0, pad16), mode="constant", value=0)
+            v = F.pad(v, (0, pad16), mode="constant", value=0)
+
+        if cu_seqlens is None:
+            cu_seqlens = torch.arange(
+                0,
+                (bsz + 1) * q_len,
+                step=q_len,
+                dtype=torch.int32,
+                device=query.device,
+            )
+
+        total_q_tokens = bsz * q_len
+        context_flat = q.new_empty((total_q_tokens, self.num_heads, q.shape[-1]))
+
+        st = 0
+        seg_lens = torch.diff(cu_seqlens).to("cpu", dtype=torch.int64).tolist()
+        for seg_len in seg_lens:
+            seg_len = int(seg_len)
+            ed = st + seg_len
+
+            q_i = q[st:ed].unsqueeze(0)  # [1, S, H, D]
+            k_i = k[st:ed].unsqueeze(0)
+            v_i = v[st:ed].unsqueeze(0)
+
+            qs = int(q_i.shape[1])
+            kvs = int(k_i.shape[1])
+
+            out_i = torch_npu.npu_prompt_flash_attention(
+                q_i,
+                k_i,
+                v_i,
+                input_layout="BSND",
+                num_heads=self.num_heads,
+                num_key_value_heads=self.num_kv_heads,
+                scale_value=self.head_size**-0.5,
+                pre_tokens=qs,
+                next_tokens=kvs,
+            )
+            context_flat[st:ed] = out_i[0]
+            st = ed
+
+        context_flat = context_flat[..., :origin_dim]
+        context_layer = einops.rearrange(context_flat, "(b s) h d -> b s h d", b=bsz).contiguous()
+        return context_layer

vllm_ascend/_310p/ops/rotary_embedding.py

@@ -0,0 +1,23 @@
+#
+# 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 vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
+
+
+class AscendMRotaryEmbedding310(MRotaryEmbedding):
+    def forward_oot(self, positions, query, key):
+        return super().forward_oot(positions, query, key)

vllm_ascend/_310p/worker_310p.py

@@ -0,0 +1,37 @@
+#
+# 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 torch_npu
+from vllm.logger import logger
+
+from vllm_ascend._310p.modelrunner_310p import NPUModelRunner310
+from vllm_ascend.worker.worker import NPUWorker, init_workspace_manager
+
+
+class NPUWorker310(NPUWorker):
+    def init_device(self):
+        self.device = self._init_device()
+
+        torch_npu.npu.set_compile_mode(jit_compile=False)
+
+        init_workspace_manager(self.device, num_ubatches=1)
+
+        self.model_runner = NPUModelRunner310(self.vllm_config, self.device)
+
+    def _warm_up_atb(self):
+        # 310p device donot support torch_npu._npu_matmul_add_fp32 atb ops
+        logger.info("Skip warm-up atb ops for 310P device")

vllm_ascend/patch/platform/patch_distributed.py

@@ -23,7 +23,6 @@ from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
 
 
 class NullHandle:
-
     def __init__(self):
         pass
 
@@ -32,12 +31,12 @@ class NullHandle:
 
 
 def communication_adaptation_310p():
-
     def broadcast310p_wrapper(fn):
+        def broadcast310p(tensor, src=0, group=None, async_op=False, group_src=None):
+            root = group_src if group_src is not None else src
 
-        def broadcast310p(tensor, src, group=None, async_op=False):
-            if tensor.device == torch.device('cpu'):
-                return fn(tensor, src, group, async_op)
+            if tensor.device == torch.device("cpu"):
+                return fn(tensor, src=root, group=group, async_op=async_op)
             rank = torch.distributed.get_rank(group)
             world_size = torch.distributed.get_world_size(group)
             tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
@@ -51,13 +50,10 @@ def communication_adaptation_310p():
 
         return broadcast310p
 
-    torch.distributed.broadcast = broadcast310p_wrapper(
-        torch.distributed.broadcast)
-    torch.distributed.distributed_c10d.broadcast = broadcast310p_wrapper(
-        torch.distributed.distributed_c10d.broadcast)
+    torch.distributed.broadcast = broadcast310p_wrapper(torch.distributed.broadcast)
+    torch.distributed.distributed_c10d.broadcast = broadcast310p_wrapper(torch.distributed.distributed_c10d.broadcast)
 
     def all_reduce_wrapper_310p(fn):
-
         def all_reduce(
             tensor,
             op=torch.distributed.ReduceOp.SUM,
@@ -83,10 +79,10 @@ def communication_adaptation_310p():
 
         return all_reduce
 
-    torch.distributed.all_reduce = all_reduce_wrapper_310p(
-        torch.distributed.all_reduce)
+    torch.distributed.all_reduce = all_reduce_wrapper_310p(torch.distributed.all_reduce)
     torch.distributed.distributed_c10d.all_reduce = all_reduce_wrapper_310p(
-        torch.distributed.distributed_c10d.all_reduce)
+        torch.distributed.distributed_c10d.all_reduce
+    )
 
 
 if get_ascend_device_type() == AscendDeviceType._310P:

vllm_ascend/platform.py

@@ -48,6 +48,7 @@ from vllm_ascend.utils import (
     update_aclgraph_sizes,
     update_cudagraph_capture_sizes,
     update_default_aclgraph_sizes,
+    is_310p,
 )
 
 if TYPE_CHECKING:
@@ -322,7 +323,9 @@ class NPUPlatform(Platform):
         if parallel_config and parallel_config.worker_cls == "auto":
             # TODO: this is a tricky way to disable `use_sequence_parallel_moe` in vllm.
             parallel_config.all2all_backend = "flashinfer_all2allv"
-            if ascend_config.xlite_graph_config.enabled:
+            if is_310p():
+                parallel_config.worker_cls = "vllm_ascend._310p.worker_310p.NPUWorker310"
+            elif ascend_config.xlite_graph_config.enabled:
                 logger.info("openEuler Xlite enabled. See: https://atomgit.com/openeuler/GVirt/tree/master/xlite")
                 parallel_config.worker_cls = "vllm_ascend.xlite.xlite_worker.XliteWorker"
             else:
@@ -394,13 +397,27 @@ class NPUPlatform(Platform):
 
     @classmethod
     def get_attn_backend_cls(cls, selected_backend, attn_selector_config):
+        key = (attn_selector_config.use_mla, attn_selector_config.use_sparse)
+
         backend_map = {
             (True, False): "vllm_ascend.attention.mla_v1.AscendMLABackend",
             (False, False): "vllm_ascend.attention.attention_v1.AscendAttentionBackend",
             (True, True): "vllm_ascend.attention.sfa_v1.AscendSFABackend",
         }
+        backend_map_310 = {
+            (
+                False,
+                False,
+            ): "vllm_ascend._310p.attention.attention_v1.AscendAttentionBackend310",
+            # TODO If MLA/SFA is supported in the future, consider implementing the logic described in these comments.
+            # (True, False): "...AscendMLABackend310",
+            # (True, True):  "...AscendSFABackend310",
+        }
 
-        return backend_map[(attn_selector_config.use_mla, attn_selector_config.use_sparse)]
+        if is_310p():
+            return backend_map_310.get(key, backend_map_310[(False, False)])
+
+        return backend_map[key]
 
     @classmethod
     def get_punica_wrapper(cls) -> str:

vllm_ascend/utils.py

@@ -74,6 +74,10 @@ _GRAPH_PRINT_STREAM_LOCK = Lock()
 _HAS_ROPE = None
 
 
+def is_310p():
+    return get_ascend_device_type() == AscendDeviceType._310P
+
+
 def _print_callback_on_stream(*args):
     """Callback function to print arguments on the dedicated print stream."""
     global _GRAPH_PRINT_STREAM
@@ -713,6 +717,22 @@ def register_ascend_customop(vllm_config: VllmConfig | None = None):
         "ApplyRotaryEmb": AscendApplyRotaryEmb,
     }
 
+    # 310P: override selected ops with 310P implementations (keep minimal changes outside _310p)
+    if is_310p():
+        from vllm_ascend._310p.ops.activation import AscendSiluAndMul310
+        from vllm_ascend._310p.ops.mm_encoder_attention import AscendMMEncoderAttention310
+        from vllm_ascend._310p.ops.rotary_embedding import (
+            AscendMRotaryEmbedding310,
+        )
+
+        REGISTERED_ASCEND_OPS.update(
+            {
+                "SiluAndMul": AscendSiluAndMul310,
+                "MMEncoderAttention": AscendMMEncoderAttention310,
+                "MRotaryEmbedding": AscendMRotaryEmbedding310,
+            }
+        )
+
     for name, op_cls in REGISTERED_ASCEND_OPS.items():
         CustomOp.register_oot(_decorated_op_cls=op_cls, name=name)