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vllm_npu/models/qwen2_vl.py

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+#
+# 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.
+# Adapted from vllm/model_executor/models/qwen2_vl.py
+# This file is a part of the vllm-ascend project.
+
+from collections.abc import Iterable
+from functools import partial
+from typing import Callable, Optional, Set, Tuple, Type
+
+import torch
+import torch.nn as nn
+import torch_npu
+from einops import rearrange
+from transformers.models.qwen2_vl.configuration_qwen2_vl import \
+    Qwen2VLVisionConfig
+from vllm.config import VllmConfig
+from vllm.distributed import utils as dist_utils
+from vllm.model_executor.layers.activation import QuickGELU
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.qwen2_vl import (
+    Qwen2VisionAttention, Qwen2VisionBlock, Qwen2VisionPatchEmbed,
+    Qwen2VisionTransformer, Qwen2VLDummyInputsBuilder,
+    Qwen2VLForConditionalGeneration, Qwen2VLMultiModalProcessor,
+    Qwen2VLProcessingInfo)
+from vllm.model_executor.models.utils import maybe_prefix
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+from vllm_npu.utils import ACL_FORMAT_FRACTAL_ND, is_enable_nz
+
+MIN_PAD_SIZE = 64  # min_size to pad weight
+MAX_PAD_SIZE = 128  # max_size to pad weight
+
+
+class AscendQwen2VisionAttention(Qwen2VisionAttention):
+
+    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.cu_seqlens = None
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            projection_size, num_heads)
+        self.origin_hidden_size_per_attention_head = self.hidden_size_per_attention_head
+        if self.hidden_size_per_attention_head > MIN_PAD_SIZE and self.hidden_size_per_attention_head < MAX_PAD_SIZE:
+            self.hidden_size_per_attention_head = MAX_PAD_SIZE
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+    ) -> torch.Tensor:
+
+        self.cu_seqlens = cu_seqlens
+
+        # [s, b, c] --> [s, b, 3 * head * 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.empty_like(q)
+
+        # operator requires pta version >= 2.5.1
+        torch_npu._npu_flash_attention_unpad(
+            query=q,
+            key=k,
+            value=v,
+            seq_len=self.cu_seqlens,
+            scale_value=self.origin_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 AscendQwen2VisionBlock(Qwen2VisionBlock):
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float,
+        act_layer: Type[nn.Module] = QuickGELU,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(dim, num_heads, mlp_ratio, act_layer, norm_layer,
+                         quant_config, prefix)
+        self.attn = AscendQwen2VisionAttention(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 AscendQwen2VisionPatchEmbed(Qwen2VisionPatchEmbed):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.matmul(
+            self.proj.weight.data.view(self.embed_dim, -1).transpose(0, 1))
+        return x
+
+
+class AscendQwen2VisionTransformer(Qwen2VisionTransformer):
+
+    def __init__(
+        self,
+        vision_config: Qwen2VLVisionConfig,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        interleaved=False,
+    ) -> None:
+        super().__init__(vision_config, norm_eps, quant_config, prefix)
+
+        self.interleaved = interleaved
+        self.enable_pad = False
+        self.depth = vision_config.depth
+        self.hidden_size = vision_config.embed_dim
+        self.num_heads = vision_config.num_heads
+        self.patch_embed = AscendQwen2VisionPatchEmbed(
+            patch_size=vision_config.patch_size,
+            temporal_patch_size=vision_config.temporal_patch_size,
+            in_channels=vision_config.in_channels,
+            embed_dim=vision_config.embed_dim,
+        )
+
+        self.blocks = nn.ModuleList([
+            AscendQwen2VisionBlock(dim=self.embed_dim,
+                                   num_heads=self.num_heads,
+                                   mlp_ratio=vision_config.mlp_ratio,
+                                   norm_layer=partial(nn.LayerNorm,
+                                                      eps=norm_eps),
+                                   quant_config=quant_config,
+                                   prefix=f"{prefix}.blocks.{layer_idx}")
+            for layer_idx in range(vision_config.depth)
+        ])
+
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            self.hidden_size, self.num_heads)
+
+        if self.hidden_size_per_attention_head > MIN_PAD_SIZE and self.hidden_size_per_attention_head < MAX_PAD_SIZE:
+            self.enable_pad = True
+            self.origin_hidden_size_per_attention_head = self.hidden_size_per_attention_head
+            self.half_origin_hidden_size_per_attention_head = self.hidden_size_per_attention_head // 2
+            self.half_pad_hidden_size_per_attention_head = (
+                MAX_PAD_SIZE - self.hidden_size_per_attention_head) // 2
+            self.hidden_size_per_attention_head = MAX_PAD_SIZE
+
+    def cal_cos_sin(self, rotary_pos_emb):
+        cos = rotary_pos_emb.cos()  # [seqlen, rotary_dim / 2]
+        sin = rotary_pos_emb.sin()
+        if self.enable_pad:
+            cos = torch.nn.functional.pad(
+                cos, (0, self.half_pad_hidden_size_per_attention_head))
+            sin = torch.nn.functional.pad(
+                sin, (0, self.half_pad_hidden_size_per_attention_head))
+
+        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 pad_qkv_bias(self, bias):
+        first_half = bias.reshape(
+            -1, 3, self.origin_hidden_size_per_attention_head
+        )[:, :, :self.half_origin_hidden_size_per_attention_head]
+        second_half = bias.reshape(
+            -1, 3, self.origin_hidden_size_per_attention_head
+        )[:, :, self.half_origin_hidden_size_per_attention_head:]
+        first_half_padded = torch.nn.functional.pad(
+            first_half, (0, self.half_pad_hidden_size_per_attention_head))
+        second_half_padded = torch.nn.functional.pad(
+            second_half, (0, self.half_pad_hidden_size_per_attention_head))
+        bias_padded = torch.cat([first_half_padded, second_half_padded], dim=2)
+        bias_final = bias_padded.reshape(-1)
+        return bias_final
+
+    def pad_qkv_weight(self, data):
+        qkv_weight_first_half = data.reshape(
+            -1, 3, self.origin_hidden_size_per_attention_head, self.hidden_size
+        )[:, :, :self.half_origin_hidden_size_per_attention_head, :]
+        qkv_weight_second_half = data.reshape(
+            -1, 3, self.origin_hidden_size_per_attention_head, self.hidden_size
+        )[:, :, self.half_origin_hidden_size_per_attention_head:, :]
+
+        qkv_weight_first_half_padded = torch.nn.functional.pad(
+            qkv_weight_first_half,
+            (0, 0, 0, self.half_pad_hidden_size_per_attention_head))
+        qkv_weight_second_half_padded = torch.nn.functional.pad(
+            qkv_weight_second_half,
+            (0, 0, 0, self.half_pad_hidden_size_per_attention_head))
+        qkv_weight_padded = torch.cat(
+            [qkv_weight_first_half_padded, qkv_weight_second_half_padded],
+            dim=2)
+        qkv_weight_final = qkv_weight_padded.reshape(-1, self.hidden_size)
+
+        if is_enable_nz(qkv_weight_final.dtype):
+            qkv_weight_final_copy = torch.empty_like(qkv_weight_final).copy_(
+                qkv_weight_final)
+            qkv_weight_final_copy = torch_npu.npu_format_cast(
+                qkv_weight_final_copy, ACL_FORMAT_FRACTAL_ND)
+            return qkv_weight_final_copy
+
+        return qkv_weight_final
+
+    def pad_proj_weight(self, data):
+        out_weight = torch.nn.functional.pad(
+            data.reshape(self.hidden_size, -1,
+                         self.half_origin_hidden_size_per_attention_head),
+            (0, self.half_pad_hidden_size_per_attention_head, 0, 0)).reshape(
+                self.hidden_size, -1)
+
+        if is_enable_nz(out_weight.dtype):
+            out_weight_copy = torch.empty_like(out_weight).copy_(out_weight)
+            out_weight_copy = torch_npu.npu_format_cast(
+                out_weight_copy, ACL_FORMAT_FRACTAL_ND)
+            return out_weight_copy
+
+        return out_weight
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: Set[str] = set()
+
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+                if ("attn.proj.weight" in name) and self.enable_pad:
+                    param.data = self.pad_proj_weight(param.data)
+                if ("attn.qkv.weight" in name) and self.enable_pad:
+                    param.data = self.pad_qkv_weight(param.data)
+                if ("attn.qkv.bias" in name) and self.enable_pad:
+                    param.data = self.pad_qkv_bias(param.data)
+            loaded_params.add(name)
+        return loaded_params
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        grid_thw = torch.tensor(grid_thw, dtype=torch.int32)
+        # compute cu_seqlens and avoid cumsum to fit operator unpadFA
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2],
+                                             grid_thw[:,
+                                                      0]).cpu().to(torch.int32)
+
+        # patchify
+        x = x.to(device=self.device, dtype=self.dtype)
+        x = self.patch_embed(x)
+
+        # compute position embedding
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        cos, sin = self.cal_cos_sin(rotary_pos_emb)
+
+        x = x.unsqueeze(1)
+        for blk in self.blocks:
+            x = blk(x, cu_seqlens=cu_seqlens, cos=cos, sin=sin)
+
+        # adapter
+        x = self.merger(x)
+        return x
+
+
+@MULTIMODAL_REGISTRY.register_processor(Qwen2VLMultiModalProcessor,
+                                        info=Qwen2VLProcessingInfo,
+                                        dummy_inputs=Qwen2VLDummyInputsBuilder)
+class AscendQwen2VLForConditionalGeneration(Qwen2VLForConditionalGeneration):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        self.visual = AscendQwen2VisionTransformer(
+            self.config.vision_config,
+            norm_eps=getattr(self.config, "rms_norm_eps", 1e-6),
+            quant_config=vllm_config.quant_config,
+            prefix=maybe_prefix(prefix, "visual"),
+        )