[Core] Cherry pick from 0.7.1 to keep the main code newest

.github/workflows/vllm_ascend_test.yaml

@@ -102,7 +102,7 @@ jobs:
         run: |
           pip install -e .
 
-      - name: Install torch-npu
+      - name: Install pta
         run: |
           mkdir pta
           cd pta

vllm_ascend/model_runner.py

@@ -53,7 +53,7 @@
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import IntermediateTensors, SequenceGroupMetadata
 from vllm.utils import (DeviceMemoryProfiler, PyObjectCache, flatten_2d_lists,
-                        is_pin_memory_available, make_tensor_with_pad)
+                        is_pin_memory_available)
 from vllm.worker.model_runner_base import (
     ModelRunnerBase, ModelRunnerInputBase, ModelRunnerInputBuilderBase,
     _add_attn_metadata_broadcastable_dict,
@@ -511,50 +511,21 @@ def build(self) -> ModelInputForNPU:
             for data in self.inter_data_list
         }
 
-        batch_size = len(input_tokens)
-
-        if self.inter_data_list[0].is_prompt:
-            input_tokens_tensor = make_tensor_with_pad(
-                input_tokens, 0, dtype=torch.int, device=self.runner.device)
-            input_tokens_tensor = torch.flatten(input_tokens_tensor)
-            if mrope_input_positions is not None:
-                mrope_input_positions_tensor = make_tensor_with_pad(
-                    mrope_input_positions,
-                    0,
-                    dtype=torch.int,
-                    device=self.runner.device)
-                input_positions_tensor = torch.tensor(
-                    mrope_input_positions_tensor,
-                    dtype=torch.long,
-                    device=self.runner.device)
-            else:
-                input_positions_tensor = make_tensor_with_pad(
-                    input_positions,
-                    0,
-                    dtype=torch.int,
-                    device=self.runner.device)
-                input_positions_tensor = torch.flatten(input_positions_tensor)
-
-            max_seq_len = max(seq_lens)
-            seq_lens = len(seq_lens) * [max_seq_len]
+        input_tokens_tensor = torch.tensor(flatten_2d_lists(input_tokens),
+                                           dtype=torch.long,
+                                           device=self.runner.device)
+        if mrope_input_positions is not None:
+            input_positions_tensor = torch.tensor(mrope_input_positions,
+                                                  dtype=torch.long,
+                                                  device=self.runner.device)
         else:
-            input_tokens_tensor = torch.tensor(flatten_2d_lists(input_tokens),
-                                               dtype=torch.long,
-                                               device=self.runner.device)
-            if mrope_input_positions is not None:
-                input_positions_tensor = torch.tensor(
-                    mrope_input_positions,
-                    dtype=torch.long,
-                    device=self.runner.device)
-            else:
-                input_positions_tensor = torch.tensor(
-                    flatten_2d_lists(input_positions),
-                    dtype=torch.long,
-                    device=self.runner.device)
+            input_positions_tensor = torch.tensor(
+                flatten_2d_lists(input_positions),
+                dtype=torch.long,
+                device=self.runner.device)
 
         # Attention metadata.
-        attn_metadata = self.attn_metadata_builder.build(
-            seq_lens, query_lens, -1, batch_size)
+        attn_metadata = self.attn_metadata_builder.build(seq_lens, query_lens)
 
         # Multi-modal data.
         multi_modal_kwargs_list = [
@@ -749,10 +720,14 @@ def _compute_multi_modal_input(self, inter_data: InterDataForSeqGroup,
                 mrope_input_positions, mrope_position_delta = \
                     MRotaryEmbedding.get_input_positions(
                         token_ids,
-                        hf_config,
                         image_grid_thw=image_grid_thw,
                         video_grid_thw=video_grid_thw,
-                        second_per_grid_ts=None,
+                        image_token_id=hf_config.image_token_id,
+                        video_token_id=hf_config.video_token_id,
+                        vision_start_token_id=hf_config.vision_start_token_id,
+                        vision_end_token_id=hf_config.vision_end_token_id,
+                        spatial_merge_size=hf_config.vision_config.
+                        spatial_merge_size,
                         context_len=inter_data.context_lens[seq_idx],
                         seq_len=inter_data.seq_lens[seq_idx],
                     )

vllm_ascend/ops/__init__.py

@@ -14,5 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-
+import vllm_ascend.ops.activation  # noqa
+import vllm_ascend.ops.fused_moe  # noqa
 import vllm_ascend.ops.layernorm  # noqa
+import vllm_ascend.ops.rotary_embedding  # noqa

vllm_ascend/ops/activation.py

@@ -0,0 +1,29 @@
+#
+# 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.
+#
+
+import torch
+from vllm.model_executor.layers.activation import SiluAndMul
+
+
+def silu_and_mul_forward_oot(self, x: torch.Tensor) -> torch.Tensor:
+    import torch_npu
+
+    out = torch_npu.npu_swiglu(x)
+    return out
+
+
+SiluAndMul.forward_oot = silu_and_mul_forward_oot

vllm_ascend/ops/fused_moe.py

@@ -0,0 +1,176 @@
+#
+# 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 Callable, Optional
+
+import torch
+import torch_npu
+from vllm.model_executor.layers.fused_moe.layer import \
+    UnquantizedFusedMoEMethod
+
+
+def group_topk(hidden_states: torch.Tensor,
+               gating_output: torch.Tensor,
+               topk: int,
+               renormalize: bool,
+               num_expert_group: Optional[int] = 0,
+               topk_group: Optional[int] = 0,
+               scoring_func: str = "softmax",
+               e_score_correction_bias: Optional[torch.Tensor] = None):
+
+    assert hidden_states.shape[0] == gating_output.shape[0], (
+        "Number of tokens mismatch")
+
+    if scoring_func == "softmax":
+        scores = torch.softmax(gating_output, dim=-1)
+    elif scoring_func == "sigmoid":
+        scores = gating_output.sigmoid()
+    else:
+        raise ValueError(f"Unsupported scoring function: {scoring_func}")
+
+    if e_score_correction_bias is not None:
+        # Store original scores before applying correction bias. We use biased
+        # scores for expert selection but original scores for routing weights
+        original_scores = scores
+        scores = scores + e_score_correction_bias.unsqueeze(0)
+
+    torch_npu.npu_group_topk(input=scores,
+                             out=scores,
+                             group_num=num_expert_group,
+                             k=topk_group)
+    if e_score_correction_bias is not None:
+        topk_ids = torch.topk(scores, k=topk, dim=-1, sorted=False)[1]
+        # Use original unbiased scores for the routing weights
+        topk_weights = original_scores.gather(1, topk_ids)
+    else:
+        topk_weights, topk_ids = torch.topk(scores,
+                                            k=topk,
+                                            dim=-1,
+                                            sorted=False)
+
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+
+
+def fused_experts(hidden_states: torch.Tensor, w1: torch.Tensor,
+                  w2: torch.Tensor, topk_weights: torch.Tensor,
+                  topk_ids: torch.Tensor, top_k: int):
+    # Check constraints.
+    assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
+    assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
+    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
+    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
+    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
+    assert hidden_states.dtype in [
+        torch.float32, torch.float16, torch.bfloat16
+    ]
+    ori_shape = hidden_states.shape
+    if len(ori_shape) == 3:
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+
+    num_tokens, _ = hidden_states.shape
+    E, N, _ = w1.shape
+
+    row_idx_len = num_tokens * top_k
+    row_idx = torch.arange(0,
+                           row_idx_len,
+                           dtype=torch.int32,
+                           device=topk_weights.device).view(top_k, -1).permute(
+                               1, 0).contiguous()
+    expanded_x, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
+        hidden_states,
+        row_idx=row_idx,
+        expert_idx=topk_ids,
+        active_num=num_tokens)
+
+    expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
+        expanded_expert_idx, E)
+    expert_tokens = expert_tokens.to(torch.int64)
+
+    w1 = w1.transpose(1, 2)
+    gate_up_out_list = torch_npu.npu_grouped_matmul(x=[expanded_x],
+                                                    weight=[w1],
+                                                    split_item=2,
+                                                    group_list_type=0,
+                                                    group_type=0,
+                                                    group_list=expert_tokens)
+
+    # TODO: Remove this in the future.
+    gate_up_out = torch.cat(gate_up_out_list, dim=0)
+    gate_up_out = torch_npu.npu_swiglu(gate_up_out)
+
+    w2 = w2.transpose(1, 2)
+    down_out_list = torch_npu.npu_grouped_matmul(x=[gate_up_out],
+                                                 weight=[w2],
+                                                 split_item=2,
+                                                 group_list_type=0,
+                                                 group_type=0,
+                                                 group_list=expert_tokens)
+
+    down_out_list = torch.cat(down_out_list, dim=0)
+    # TODO: Reorder device memory 2 times here, replace the current
+    # implementation here when suitable operators become available.
+    routing_weights = topk_weights.to(down_out_list.dtype)
+    hidden_states = torch_npu.npu_moe_finalize_routing(
+        down_out_list,
+        skip1=None,
+        skip2=None,
+        bias=None,
+        scales=routing_weights,
+        expanded_src_to_dst_row=expanded_row_idx,
+        export_for_source_row=topk_ids)
+    if len(ori_shape) == 3:
+        hidden_states = hidden_states.view(ori_shape)
+    return hidden_states
+
+
+def forward_oot(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        use_grouped_topk: bool,
+        top_k: int,
+        router_logits: torch.Tensor,
+        renormalize: bool,
+        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
+) -> torch.Tensor:
+
+    topk_weights, topk_ids = group_topk(
+        hidden_states=x,
+        gating_output=router_logits,
+        topk=top_k,
+        renormalize=renormalize,
+        num_expert_group=num_expert_group,
+        topk_group=topk_group,
+        scoring_func=scoring_func,
+        e_score_correction_bias=e_score_correction_bias)
+
+    return fused_experts(hidden_states=x,
+                         w1=layer.w13_weight,
+                         w2=layer.w2_weight,
+                         topk_weights=topk_weights,
+                         topk_ids=topk_ids,
+                         top_k=top_k)
+
+
+UnquantizedFusedMoEMethod.forward_oot = forward_oot

vllm_ascend/ops/rotary_embedding.py

@@ -0,0 +1,56 @@
+#
+# 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 Optional, Tuple
+
+import torch
+from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
+
+
+def rope_forward_oot(
+    self,
+    positions: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    offsets: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    import torch_npu
+
+    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)
+    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()
+        key = key.contiguous()
+        torch_npu.npu_rope(
+            positions,
+            query,
+            key,
+            self.head_size,
+            self.cos_sin_cache,
+            self.is_neox_style,
+        )
+
+    return query, key
+
+
+RotaryEmbedding.forward_oot = rope_forward_oot