[CherryPick] Add unpadded Qwen2.5-VL for verl scenario (#1095)

Add unpadded Qwen2.5-VL for verl scenario.

When using vllm-ascend for verl scenario, set `USE_OPTIMIZED_QWEN2_5_VL`
(default `1`) to `0` to use unpadded Qwen2.5-VL to avoid errors.

This is cherry-picked from 0.7.3-dev

Signed-off-by: shen-shanshan <467638484@qq.com>
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: Shanshan Shen <467638484@qq.com>

vllm_ascend/envs.py

@@ -128,6 +128,11 @@ env_variables: Dict[str, Callable[[], Any]] = {
     # enable `pin_memory` while creating a tensor using `torch.tensor`.
     "VLLM_ASCEND_ACL_OP_INIT_MODE":
     lambda: os.getenv("VLLM_ASCEND_ACL_OP_INIT_MODE", '1'),
+    # Some models are optimized by vllm ascend. While in some case, e.g. rlhf
+    # training, the optimized model may not be suitable. In this case, set this
+    # value to False to disable the optimized model.
+    "USE_OPTIMIZED_MODEL":
+    lambda: bool(int(os.getenv('USE_OPTIMIZED_MODEL', '1'))),
 }
 
 # end-env-vars-definition

vllm_ascend/models/__init__.py

@@ -20,10 +20,16 @@ def register_model():
         "Qwen2VLForConditionalGeneration",
         "vllm_ascend.models.qwen2_vl:AscendQwen2VLForConditionalGeneration")
 
-    ModelRegistry.register_model(
+    if envs.USE_OPTIMIZED_MODEL:
-        "Qwen2_5_VLForConditionalGeneration",
+        ModelRegistry.register_model(
-        "vllm_ascend.models.qwen2_5_vl:AscendQwen2_5_VLForConditionalGeneration"
+            "Qwen2_5_VLForConditionalGeneration",
-    )
+            "vllm_ascend.models.qwen2_5_vl:AscendQwen2_5_VLForConditionalGeneration"
+        )
+    else:
+        ModelRegistry.register_model(
+            "Qwen2_5_VLForConditionalGeneration",
+            "vllm_ascend.models.qwen2_5_vl_without_padding:AscendQwen2_5_VLForConditionalGeneration_Without_Padding"
+        )
 
     if envs.VLLM_ASCEND_ENABLE_DBO:
         ModelRegistry.register_model(

vllm_ascend/models/qwen2_5_vl_without_padding.py

@@ -0,0 +1,273 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Adapted from vllm/model_executor/models/qwen2_5_vl.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 functools import partial
+from typing import Callable, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch_npu
+from einops import rearrange
+from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import (
+    Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig)
+from vllm.config import VllmConfig
+from vllm.distributed import parallel_state
+from vllm.distributed import utils as dist_utils
+from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.models.qwen2_5_vl import (
+    Qwen2_5_VisionAttention, Qwen2_5_VisionBlock, Qwen2_5_VisionPatchEmbed,
+    Qwen2_5_VisionTransformer, Qwen2_5_VLDummyInputsBuilder,
+    Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLMultiModalProcessor,
+    Qwen2_5_VLProcessingInfo)
+from vllm.model_executor.models.utils import maybe_prefix
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+
+class AscendQwen2_5_VisionAttention_Without_Padding(Qwen2_5_VisionAttention):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        projection_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(
+            embed_dim,
+            num_heads,
+            projection_size,
+            quant_config,
+            prefix,
+        )
+        self.embed_dim = embed_dim
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            projection_size, num_heads)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+    ) -> torch.Tensor:
+        # [s, b, c] --> [s, b, head * 3 * head_dim]
+        x, _ = self.qkv(x)
+
+        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim]
+        q, k, v = self.split_qkv(x)
+        batch_size = q.shape[1]
+
+        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous()
+                   for x in (q, k, v))
+        q = torch_npu.npu_rotary_mul(q, cos, sin)
+        k = torch_npu.npu_rotary_mul(k, cos, sin)
+
+        q, k, v = [
+            rearrange(x, "b s h d -> (b s) h d").contiguous()
+            for x in (q, k, v)
+        ]
+
+        context_layer = torch.torch.empty_like(q)
+
+        # operator requires pta version >= 2.5.1.dev20250226
+        torch_npu._npu_flash_attention_unpad(
+            query=q,
+            key=k,
+            value=v,
+            seq_len=cu_seqlens,
+            scale_value=self.hidden_size_per_attention_head**-0.5,
+            num_heads=self.num_attention_heads_per_partition,
+            num_kv_heads=self.num_attention_heads_per_partition,
+            out=context_layer)
+
+        context_layer = rearrange(context_layer,
+                                  "(b s) h d -> s b (h d)",
+                                  b=batch_size).contiguous()
+
+        output, _ = self.proj(context_layer)
+        return output
+
+
+class AscendQwen2_5_VisionBlock_Without_Padding(Qwen2_5_VisionBlock):
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_hidden_dim: int,
+        act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(dim, num_heads, mlp_hidden_dim, act_fn, norm_layer,
+                         quant_config, prefix)
+        self.attn = AscendQwen2_5_VisionAttention_Without_Padding(
+            embed_dim=dim,
+            num_heads=num_heads,
+            projection_size=dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn")
+
+    def forward(self, x: torch.Tensor, cu_seqlens: torch.Tensor,
+                cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
+        x = x + self.attn(
+            self.norm1(x), cu_seqlens=cu_seqlens, cos=cos, sin=sin)
+
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class AscendQwen2_5_VisionPatchEmbed_Without_Padding(Qwen2_5_VisionPatchEmbed):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.matmul(
+            self.proj.weight.data.view(self.hidden_size, -1).transpose(0, 1))
+        return x
+
+
+class AscendQwen2_5_VisionTransformer_Without_Padding(Qwen2_5_VisionTransformer
+                                                      ):
+
+    def __init__(
+        self,
+        vision_config: Qwen2_5_VLVisionConfig,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        interleaved=False,
+    ) -> None:
+        super().__init__(vision_config, norm_eps, quant_config, prefix)
+        norm_layer = partial(RMSNorm, eps=norm_eps)
+        self.interleaved = interleaved
+        self.patch_embed = AscendQwen2_5_VisionPatchEmbed_Without_Padding(
+            patch_size=vision_config.patch_size,
+            temporal_patch_size=vision_config.temporal_patch_size,
+            in_channels=vision_config.in_channels,
+            hidden_size=self.hidden_size,
+        )
+        self.blocks = nn.ModuleList([
+            AscendQwen2_5_VisionBlock_Without_Padding(
+                dim=self.hidden_size,
+                num_heads=self.num_heads,
+                mlp_hidden_dim=vision_config.intermediate_size,
+                act_fn=_ACTIVATION_REGISTRY[vision_config.hidden_act],
+                norm_layer=norm_layer,
+                quant_config=quant_config,
+                prefix=f"{prefix}.blocks.{layer_idx}")
+            for layer_idx in range(vision_config.depth)
+        ])
+        self.tp_size = parallel_state.get_tensor_model_parallel_world_size()
+        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            self.hidden_size, self.num_heads)
+
+    def cal_cos_sin(self, rotary_pos_emb):
+        cos = rotary_pos_emb.cos()  # [seqlen, rotary_dim / 2]
+        sin = rotary_pos_emb.sin()
+
+        if not self.interleaved:
+            cos_new = torch.cat((cos, cos), dim=-1)
+            sin_new = torch.cat((sin, sin), dim=-1)
+        else:
+            cos_new = rearrange(torch.stack((cos, cos), dim=-1),
+                                "... d two -> ...(d two)",
+                                two=2)
+            sin_new = rearrange(torch.stack((sin, sin), dim=-1),
+                                "... d two -> ...(d two)",
+                                two=2)
+        cos_new = cos_new.reshape(1, -1, 1,
+                                  self.hidden_size_per_attention_head)
+        sin_new = sin_new.reshape(1, -1, 1,
+                                  self.hidden_size_per_attention_head)
+        return cos_new, sin_new
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        # compute cu_seqlens
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2],
+                                             grid_thw[:,
+                                                      0]).cpu().to(torch.int32)
+
+        # patchify
+        x = self.patch_embed(x)
+
+        # compute position embedding
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+        # windows attention
+        window_index, cu_window_seqlens = self.get_window_index(grid_thw)
+        cu_window_seqlens = torch.tensor(
+            cu_window_seqlens,
+            device=x.device,
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32)
+        cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
+        cu_window_seqlens = torch.diff(cu_window_seqlens).cpu().to(torch.int32)
+        seq_len, _ = x.size()
+        x = x.reshape(seq_len // self.spatial_merge_unit,
+                      self.spatial_merge_unit, -1)
+        x = x[window_index, :, :]
+        x = x.reshape(seq_len, -1)
+        rotary_pos_emb = rotary_pos_emb.reshape(
+            seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+        rotary_pos_emb = rotary_pos_emb[window_index, :, :]
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+
+        cos, sin = self.cal_cos_sin(rotary_pos_emb)
+
+        # transformers
+        x = x.unsqueeze(1)
+        for layer_num, blk in enumerate(self.blocks):
+            if layer_num in self.fullatt_block_indexes:
+                cu_seqlens_now = cu_seqlens
+            else:
+                cu_seqlens_now = cu_window_seqlens
+            x = blk(x, cu_seqlens=cu_seqlens_now, cos=cos, sin=sin)
+
+        # adapter
+        x = self.merger(x)
+        reverse_indices = torch.argsort(window_index)
+        x = x[reverse_indices, :]
+        return x
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen2_5_VLMultiModalProcessor,
+    info=Qwen2_5_VLProcessingInfo,
+    dummy_inputs=Qwen2_5_VLDummyInputsBuilder)
+class AscendQwen2_5_VLForConditionalGeneration_Without_Padding(
+        Qwen2_5_VLForConditionalGeneration):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        config: Qwen2_5_VLConfig = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.visual = AscendQwen2_5_VisionTransformer_Without_Padding(
+            vision_config=config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+            quant_config=self._maybe_ignore_quant_config(quant_config),
+            prefix=maybe_prefix(prefix, "visual"),
+        )