From fb15fec66224d828ed89174f23cc4f3b13b2e0ac Mon Sep 17 00:00:00 2001 From: Shanshan Shen <467638484@qq.com> Date: Thu, 4 Dec 2025 22:30:06 +0800 Subject: [PATCH] [MM][Patch] Remove patch for cos/sin cache (#4672) ### What this PR does / why we need it? Remove patch for https://github.com/vllm-project/vllm/pull/28798. - vLLM version: v0.12.0 Signed-off-by: shen-shanshan <467638484@qq.com> --- vllm_ascend/patch/worker/patch_qwen2_5_vl.py | 512 +------------------ vllm_ascend/patch/worker/patch_qwen3_vl.py | 160 +----- 2 files changed, 6 insertions(+), 666 deletions(-) diff --git a/vllm_ascend/patch/worker/patch_qwen2_5_vl.py b/vllm_ascend/patch/worker/patch_qwen2_5_vl.py index 062ecafe..1c2f356b 100644 --- a/vllm_ascend/patch/worker/patch_qwen2_5_vl.py +++ b/vllm_ascend/patch/worker/patch_qwen2_5_vl.py @@ -15,38 +15,16 @@ # limitations under the License. # -from functools import lru_cache, partial - import einops import torch import torch.nn as nn import torch.nn.functional as F import torch_npu -from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import \ - Qwen2_5_VLVisionConfig -from transformers.models.qwen2_vl.configuration_qwen2_vl import \ - Qwen2VLVisionConfig -from vllm.attention.backends.registry import AttentionBackendEnum -from vllm.attention.layer import maybe_get_vit_flash_attn_backend -from vllm.model_executor.layers.activation import get_act_and_mul_fn -from vllm.model_executor.layers.layernorm import RMSNorm -from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.rotary_embedding.common import ( - apply_rotary_emb_torch, dispatch_rotary_emb_function) from vllm.model_executor.models.qwen2_5_vl import ( - Qwen2_5_VisionAttention, Qwen2_5_VisionBlock, Qwen2_5_VisionPatchEmbed, - Qwen2_5_VisionPatchMerger, Qwen2_5_VisionTransformer, - Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLImageInputs, - Qwen2_5_VLVideoInputs) -from vllm.model_executor.models.qwen2_vl import (Qwen2VisionAttention, - Qwen2VisionBlock, - Qwen2VisionPatchEmbed, - Qwen2VisionPatchMerger, - Qwen2VisionTransformer) -from vllm.model_executor.models.utils import cast_overflow_tensors -from vllm.model_executor.models.vision import ( - get_vit_attn_backend, run_dp_sharded_mrope_vision_model) + Qwen2_5_VisionAttention, Qwen2_5_VLForConditionalGeneration, + Qwen2_5_VLImageInputs, Qwen2_5_VLVideoInputs) +from vllm.model_executor.models.qwen2_vl import Qwen2VisionAttention +from vllm.model_executor.models.vision import run_dp_sharded_mrope_vision_model import vllm_ascend.envs as envs_ascend from vllm_ascend.ascend_forward_context import set_ascend_forward_context @@ -130,468 +108,6 @@ class AscendQwen2_5_VisionAttention(nn.Module): return output -class AscendQwen2VisionBlock(nn.Module): - - def forward( - self, - x: torch.Tensor, - cu_seqlens: torch.Tensor, - rotary_pos_emb_cos: torch.Tensor, - rotary_pos_emb_sin: torch.Tensor, - max_seqlen: int | None = None, # Only used for Flash Attention - ) -> torch.Tensor: - x = x + self.attn( - self.norm1(x), - cu_seqlens=cu_seqlens, - rotary_pos_emb_cos=rotary_pos_emb_cos, - rotary_pos_emb_sin=rotary_pos_emb_sin, - max_seqlen=max_seqlen, - ) - x = x + self.mlp(self.norm2(x)) - return x - - -class AscendQwen2VisionTransformer(nn.Module): - - def __init__( - self, - vision_config: Qwen2VLVisionConfig, - norm_eps: float = 1e-6, - quant_config: QuantizationConfig | None = None, - prefix: str = "", - use_data_parallel: bool = False, - attn_backend_override: AttentionBackendEnum | None = None, - ) -> None: - nn.Module.__init__(self) - - patch_size = vision_config.patch_size - temporal_patch_size = vision_config.temporal_patch_size - spatial_merge_size = vision_config.spatial_merge_size - in_channels = vision_config.in_channels - hidden_size = vision_config.hidden_size - embed_dim = vision_config.embed_dim - depth = vision_config.depth - num_heads = vision_config.num_heads - mlp_ratio = vision_config.mlp_ratio - - self.use_data_parallel = use_data_parallel - self.out_hidden_size = vision_config.hidden_size - - self.spatial_merge_size = spatial_merge_size - self.num_heads = num_heads - self.embed_dim = embed_dim - - self.patch_embed = Qwen2VisionPatchEmbed( - patch_size=patch_size, - temporal_patch_size=temporal_patch_size, - in_channels=in_channels, - embed_dim=embed_dim, - ) - - norm_layer = partial(nn.LayerNorm, eps=norm_eps) - head_dim = embed_dim // num_heads - self.rotary_pos_emb = get_rope( - head_size=head_dim, - rotary_dim=head_dim // 2, - max_position=8192, - is_neox_style=True, - ) - - self.blocks = nn.ModuleList([ - Qwen2VisionBlock( - dim=embed_dim, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - norm_layer=norm_layer, - quant_config=quant_config, - prefix=f"{prefix}.blocks.{layer_idx}", - use_data_parallel=use_data_parallel, - attn_backend_override=attn_backend_override, - ) for layer_idx in range(depth) - ]) - self.merger = Qwen2VisionPatchMerger( - d_model=hidden_size, - context_dim=embed_dim, - norm_layer=norm_layer, - quant_config=quant_config, - prefix=f"{prefix}.merger", - use_data_parallel=use_data_parallel, - ) - self.attn_backend = get_vit_attn_backend( - head_size=head_dim, - dtype=torch.get_default_dtype(), - attn_backend_override=attn_backend_override, - ) - - def rot_pos_emb( - self, - grid_thw: list[list[int]]) -> tuple[torch.Tensor, torch.Tensor]: - pos_ids = [] - max_grid_size = 0 - for t, h, w in grid_thw: - hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) - wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) - hpos_ids = (hpos_ids.reshape( - h // self.spatial_merge_size, - self.spatial_merge_size, - w // self.spatial_merge_size, - self.spatial_merge_size, - ).permute(0, 2, 1, 3).flatten()) - wpos_ids = (wpos_ids.reshape( - h // self.spatial_merge_size, - self.spatial_merge_size, - w // self.spatial_merge_size, - self.spatial_merge_size, - ).permute(0, 2, 1, 3).flatten()) - pos_ids.append( - torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) - max_grid_size = max(max_grid_size, h, w) - pos_ids = torch.cat(pos_ids, dim=0) - - # Use pre-computed cos_sin_cache from RotaryEmbedding - cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size) - - # (num_tokens, rotary_dim // 2) - cos_h = cos[pos_ids[:, 0]] # type: ignore - cos_w = cos[pos_ids[:, 1]] # type: ignore - sin_h = sin[pos_ids[:, 0]] # type: ignore - sin_w = sin[pos_ids[:, 1]] # type: ignore - - cos_combined = torch.cat([cos_h, cos_w], dim=-1) - sin_combined = torch.cat([sin_h, sin_w], dim=-1) - return cos_combined, sin_combined - - def forward( - self, - x: torch.Tensor, - grid_thw: torch.Tensor | list[list[int]], - ) -> torch.Tensor: - # patchify - x = x.to(device=self.device, dtype=self.dtype) - x = self.patch_embed(x) - - if isinstance(grid_thw, list): - grid_thw_list = grid_thw - grid_thw = torch.tensor(grid_thw, dtype=torch.int32) - else: - grid_thw_list = grid_thw.tolist() - - # compute position embedding - rotary_pos_emb_cos, rotary_pos_emb_sin = self.rot_pos_emb( - grid_thw_list) - - # compute cu_seqlens - cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], - grid_thw[:, 0]).cumsum( - dim=0, dtype=torch.int32) - cu_seqlens = torch.cat([cu_seqlens.new_zeros(1), cu_seqlens]) - cu_seqlens = cu_seqlens.to(self.device, non_blocking=True) - - # transformers - x = x.unsqueeze(1) - - # pre-compute seqlens for attn mask to reduce cuMemcpy operations - max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens) - for blk in self.blocks: - x = blk( - x, - cu_seqlens=cu_seqlens, - rotary_pos_emb_cos=rotary_pos_emb_cos, - rotary_pos_emb_sin=rotary_pos_emb_sin, - max_seqlen=max_seqlen, - ) - - # adapter - x = self.merger(x) - - return x - - -class AscendQwen2_5_VisionBlock(nn.Module): - - def forward( - self, - x: torch.Tensor, - cu_seqlens: torch.Tensor, - rotary_pos_emb_cos: torch.Tensor, - rotary_pos_emb_sin: torch.Tensor, - max_seqlen: torch.Tensor, # Only used for Flash Attention - ) -> torch.Tensor: - x_attn = self.attn( - self.norm1(x), - cu_seqlens=cu_seqlens, - rotary_pos_emb_cos=rotary_pos_emb_cos, - rotary_pos_emb_sin=rotary_pos_emb_sin, - max_seqlen=max_seqlen, - ) - x_fused_norm, residual = self.norm2(x, residual=x_attn) - x = residual + self.mlp(x_fused_norm) - return x - - -class AscendQwen2_5_VisionTransformer(nn.Module): - - def __init__( - self, - vision_config: Qwen2_5_VLVisionConfig, - norm_eps: float = 1e-6, - quant_config: QuantizationConfig | None = None, - prefix: str = "", - use_data_parallel: bool = False, - attn_backend_override: AttentionBackendEnum | None = None, - ) -> None: - nn.Module.__init__(self) - - patch_size = vision_config.patch_size - temporal_patch_size = vision_config.temporal_patch_size - in_channels = vision_config.in_channels - depth = vision_config.depth - self.hidden_size = vision_config.hidden_size - self.num_heads = vision_config.num_heads - self.use_data_parallel = use_data_parallel - self.out_hidden_size = vision_config.out_hidden_size - - # args for get_window_index_thw - self.window_size = vision_config.window_size - self.patch_size = vision_config.patch_size - self.spatial_merge_size = vision_config.spatial_merge_size - self.fullatt_block_indexes = vision_config.fullatt_block_indexes - self.spatial_merge_unit = self.spatial_merge_size**2 - # TODO[@lucaskabela]: Investigate fixing this usage - # see https://github.com/vllm-project/vllm/issues/27044 - # DO NOT MOVE THIS IMPORT - from vllm.compilation.backends import set_model_tag - - with set_model_tag("Qwen2_5_VisionPatchEmbed"): - self.patch_embed = Qwen2_5_VisionPatchEmbed( - patch_size=patch_size, - temporal_patch_size=temporal_patch_size, - in_channels=in_channels, - hidden_size=self.hidden_size, - ) - - norm_layer = partial(RMSNorm, eps=norm_eps) - head_dim = self.hidden_size // self.num_heads - self.rotary_pos_emb = get_rope( - head_size=head_dim, - rotary_dim=head_dim // 2, - max_position=8192, - is_neox_style=True, - ) - - self.attn_backend = get_vit_attn_backend( - head_size=head_dim, - dtype=torch.get_default_dtype(), - attn_backend_override=attn_backend_override, - ) - - self.attn_backend, self.flash_attn_varlen_func = ( - maybe_get_vit_flash_attn_backend( - self.attn_backend, - attn_backend_override=attn_backend_override, - )) - - with set_model_tag("Qwen2_5_VisionBlock"): - self.blocks = nn.ModuleList([ - Qwen2_5_VisionBlock( - dim=self.hidden_size, - num_heads=self.num_heads, - mlp_hidden_dim=vision_config.intermediate_size, - act_fn=get_act_and_mul_fn(vision_config.hidden_act), - norm_layer=norm_layer, - quant_config=quant_config, - prefix=f"{prefix}.blocks.{layer_idx}", - use_data_parallel=use_data_parallel, - attn_backend=self.attn_backend, - attn_backend_override=attn_backend_override, - ) for layer_idx in range(depth) - ]) - - with set_model_tag("Qwen2_5_VisionPatchMerger"): - self.merger = Qwen2_5_VisionPatchMerger( - d_model=vision_config.out_hidden_size, - context_dim=self.hidden_size, - norm_layer=norm_layer, - spatial_merge_size=self.spatial_merge_size, - quant_config=quant_config, - prefix=f"{prefix}.merger", - use_data_parallel=use_data_parallel, - ) - - def rotary_pos_emb_thw(self, t, h, w): - hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) - wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) - hpos_ids = (hpos_ids.reshape( - h // self.spatial_merge_size, - self.spatial_merge_size, - w // self.spatial_merge_size, - self.spatial_merge_size, - ).permute(0, 2, 1, 3).flatten()) - wpos_ids = (wpos_ids.reshape( - h // self.spatial_merge_size, - self.spatial_merge_size, - w // self.spatial_merge_size, - self.spatial_merge_size, - ).permute(0, 2, 1, 3).flatten()) - pos_ids = torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1) - max_size = max(h, w) - - # Use pre-computed cos_sin_cache from RotaryEmbedding - cos, sin = self.rotary_pos_emb.get_cos_sin(max_size) - - cos_h = cos[pos_ids[:, 0]] # (num_tokens, rotary_dim // 2) - cos_w = cos[pos_ids[:, 1]] - sin_h = sin[pos_ids[:, 0]] - sin_w = sin[pos_ids[:, 1]] - - cos_combined = torch.cat([cos_h, cos_w], dim=-1) - sin_combined = torch.cat([sin_h, sin_w], dim=-1) - - cos_combined = cos_combined.reshape( - cos_combined.shape[0] // self.spatial_merge_unit, - self.spatial_merge_unit, - -1, - ) - sin_combined = sin_combined.reshape( - sin_combined.shape[0] // self.spatial_merge_unit, - self.spatial_merge_unit, - -1, - ) - - return cos_combined, sin_combined - - @lru_cache(maxsize=1024) # noqa: B019 - def get_rope_by_thw(self, t, h, w): - window_index_thw, cu_seqlens_window_thw = self.get_window_index_thw( - t, h, w) - cos_thw, sin_thw = self.rotary_pos_emb_thw(t, h, w) - - cos_thw = cos_thw[window_index_thw, :, :] - cos_thw = cos_thw.flatten(start_dim=0, end_dim=1) - sin_thw = sin_thw[window_index_thw, :, :] - sin_thw = sin_thw.flatten(start_dim=0, end_dim=1) - - cu_seqlens_thw = torch.repeat_interleave( - torch.tensor([h * w], dtype=torch.int32), t) - return ( - cos_thw, - sin_thw, - window_index_thw, - cu_seqlens_window_thw, - cu_seqlens_thw, - ) - - def forward( - self, - x: torch.Tensor, - grid_thw: list[list[int]], - ) -> torch.Tensor: - # patchify - seq_len, _ = x.size() - rotary_pos_emb_cos: list = [] - rotary_pos_emb_sin: list = [] - window_index: list = [] - cu_window_seqlens: list = [torch.tensor([0], dtype=torch.int32)] - cu_seqlens: list = [] - - hidden_states = x.to(device=self.device, dtype=self.dtype) - hidden_states = self.patch_embed(hidden_states) - - window_index_id = 0 - cu_window_seqlens_last = 0 - for t, h, w in grid_thw: - t, h, w = int(t), int(h), int(w) - llm_h = h // self.spatial_merge_size - llm_w = w // self.spatial_merge_size - - ( - cos_thw, - sin_thw, - window_index_thw, - cu_seqlens_window_thw, - cu_seqlens_thw, - ) = self.get_rope_by_thw(t, h, w) - - window_index.append(window_index_thw + window_index_id) - window_index_id += t * llm_h * llm_w - - cu_seqlens_window_thw = cu_seqlens_window_thw + cu_window_seqlens_last - cu_window_seqlens_last = cu_seqlens_window_thw[-1] - cu_window_seqlens.append(cu_seqlens_window_thw) - - rotary_pos_emb_cos.append(cos_thw) - rotary_pos_emb_sin.append(sin_thw) - - cu_seqlens.append(cu_seqlens_thw) - - rotary_pos_emb_cos = torch.cat(rotary_pos_emb_cos) - rotary_pos_emb_sin = torch.cat(rotary_pos_emb_sin) - window_index = torch.cat(window_index) - # compute reverse indices - reverse_indices = self.invert_permutation(window_index) - cu_window_seqlens = torch.cat(cu_window_seqlens) - cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) - cu_seqlens = torch.cat(cu_seqlens) - cu_seqlens = torch.cumsum(cu_seqlens, dim=0, dtype=torch.int32) - cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0) - - # transformers - # pre-compute seqlens for window/full attn to reduce cuMemcpy operations - max_seqlen_full = self.compute_attn_mask_seqlen(cu_seqlens) - max_seqlen_window = self.compute_attn_mask_seqlen(cu_window_seqlens) - - cu_seqlens = cu_seqlens.to( # type: ignore[attr-defined] - device=self.device, - non_blocking=True) - cu_window_seqlens = cu_window_seqlens.to( # type: ignore[attr-defined] - device=self.device, - non_blocking=True) - rotary_pos_emb_cos = rotary_pos_emb_cos.to( # type: ignore[attr-defined] - device=self.device, - non_blocking=True) - rotary_pos_emb_sin = rotary_pos_emb_sin.to( # type: ignore[attr-defined] - device=self.device, - non_blocking=True) - window_index = window_index.to( # type: ignore[attr-defined] - device=hidden_states.device, - non_blocking=True) - reverse_indices = reverse_indices.to(device=hidden_states.device, - non_blocking=True) - - hidden_states = hidden_states.reshape( - seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) - hidden_states = hidden_states[window_index, :, :] - hidden_states = hidden_states.reshape(seq_len, -1) - - hidden_states = hidden_states.unsqueeze(1) - - for layer_num, blk in enumerate(self.blocks): - if layer_num in self.fullatt_block_indexes: - cu_seqlens_now = cu_seqlens - max_seqlen_now = max_seqlen_full - else: - cu_seqlens_now = cu_window_seqlens - max_seqlen_now = max_seqlen_window - - hidden_states = blk( - hidden_states, - cu_seqlens=cu_seqlens_now, - rotary_pos_emb_cos=rotary_pos_emb_cos, - rotary_pos_emb_sin=rotary_pos_emb_sin, - max_seqlen=max_seqlen_now, - ) - - # For Qwen2.5-VL-3B, float16 will overflow at last block - # for long visual tokens sequences. - if hidden_states.dtype == torch.float16: - hidden_states = cast_overflow_tensors(hidden_states) - - # adapter - hidden_states = self.merger(hidden_states) - hidden_states = hidden_states[reverse_indices, :] - return hidden_states - - class AscendQwen2_5_VLForConditionalGeneration(nn.Module): def _process_image_input( @@ -650,14 +166,6 @@ class AscendQwen2_5_VLForConditionalGeneration(nn.Module): return video_embeds.split(sizes) -def _apply_rotary_pos_emb_vision(t: torch.Tensor, cos: torch.Tensor, - sin: torch.Tensor) -> torch.Tensor: - rotary_emb_function = dispatch_rotary_emb_function( - default=partial(apply_rotary_emb_torch, is_neox_style=True)) - output = rotary_emb_function(t, cos, sin).type_as(t) - return output - - # NOTE: This will be removed after MMEncoderAttention has been extract as a CustomOp in vllm. Qwen2VisionAttention.forward = AscendQwen2_5_VisionAttention.forward Qwen2_5_VisionAttention.forward = AscendQwen2_5_VisionAttention.forward @@ -665,15 +173,3 @@ Qwen2_5_VisionAttention.forward = AscendQwen2_5_VisionAttention.forward # NOTE: These will be removed after https://github.com/vllm-project/vllm/pull/29388 is merged. Qwen2_5_VLForConditionalGeneration._process_image_input = AscendQwen2_5_VLForConditionalGeneration._process_image_input Qwen2_5_VLForConditionalGeneration._process_video_input = AscendQwen2_5_VLForConditionalGeneration._process_video_input - -# NOTE: These will be removed after vllm-ascend is aligned with vllm latest main. -Qwen2VisionBlock.forward = AscendQwen2VisionBlock.forward -Qwen2VisionTransformer.__init__ = AscendQwen2VisionTransformer.__init__ -Qwen2VisionTransformer.rot_pos_emb = AscendQwen2VisionTransformer.rot_pos_emb -Qwen2VisionTransformer.forward = AscendQwen2VisionTransformer.forward -Qwen2_5_VisionBlock.forward = AscendQwen2_5_VisionBlock.forward -Qwen2_5_VisionTransformer.__init__ = AscendQwen2_5_VisionTransformer.__init__ -Qwen2_5_VisionTransformer.rotary_pos_emb_thw = AscendQwen2_5_VisionTransformer.rotary_pos_emb_thw -Qwen2_5_VisionTransformer.get_rope_by_thw = AscendQwen2_5_VisionTransformer.get_rope_by_thw -Qwen2_5_VisionTransformer.forward = AscendQwen2_5_VisionTransformer.forward -apply_rotary_pos_emb_vision = _apply_rotary_pos_emb_vision diff --git a/vllm_ascend/patch/worker/patch_qwen3_vl.py b/vllm_ascend/patch/worker/patch_qwen3_vl.py index 1fcf83f3..26d94850 100644 --- a/vllm_ascend/patch/worker/patch_qwen3_vl.py +++ b/vllm_ascend/patch/worker/patch_qwen3_vl.py @@ -15,167 +15,14 @@ # limitations under the License. # -from functools import partial - import numpy as np import torch import torch.nn as nn -from transformers.models.qwen3_vl.configuration_qwen3_vl import \ - Qwen3VLVisionConfig -from vllm.attention.backends.registry import AttentionBackendEnum -from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY -from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.models.qwen3_vl import (Qwen3_VisionBlock, - Qwen3_VisionPatchEmbed, - Qwen3_VisionPatchMerger, - Qwen3_VisionTransformer) -from vllm.model_executor.models.vision import get_vit_attn_backend - - -class AscendQwen3_VisionBlock(nn.Module): - - def forward( - self, - x: torch.Tensor, - cu_seqlens: torch.Tensor, - rotary_pos_emb_cos: torch.Tensor, - rotary_pos_emb_sin: torch.Tensor, - max_seqlen: torch.Tensor, # Only used for Flash Attention - ) -> torch.Tensor: - x = x + self.attn( - self.norm1(x), - cu_seqlens=cu_seqlens, - rotary_pos_emb_cos=rotary_pos_emb_cos, - rotary_pos_emb_sin=rotary_pos_emb_sin, - max_seqlen=max_seqlen, - ) - - x = x + self.mlp(self.norm2(x)) - return x +from vllm.model_executor.models.qwen3_vl import Qwen3_VisionTransformer class AscendQwen3_VisionTransformer(nn.Module): - def __init__( - self, - vision_config: Qwen3VLVisionConfig, - norm_eps: float = 1e-6, - quant_config: QuantizationConfig | None = None, - prefix: str = "", - use_data_parallel: bool = False, - attn_backend_override: AttentionBackendEnum | None = None, - ) -> None: - nn.Module.__init__(self) - - self.hidden_size = vision_config.hidden_size - self.num_heads = vision_config.num_heads - self.num_position_embeddings = vision_config.num_position_embeddings - self.patch_size = vision_config.patch_size - self.spatial_merge_size = vision_config.spatial_merge_size - self.spatial_merge_unit = self.spatial_merge_size**2 - self.temporal_patch_size = vision_config.temporal_patch_size - self.deepstack_visual_indexes = vision_config.deepstack_visual_indexes - self.use_data_parallel = use_data_parallel - self.num_grid_per_side = int(self.num_position_embeddings**0.5) - - # NOTE: This is used for creating empty tensor for all_gather for - # DP ViT. Here out_hidden_size is enlarged due to deepstack - self.out_hidden_size = vision_config.out_hidden_size * ( - 1 + len(self.deepstack_visual_indexes)) - - self.patch_embed = Qwen3_VisionPatchEmbed( - patch_size=self.patch_size, - temporal_patch_size=self.temporal_patch_size, - in_channels=vision_config.in_channels, - hidden_size=self.hidden_size, - ) - - self.pos_embed = nn.Embedding(self.num_position_embeddings, - self.hidden_size) - - norm_layer = partial(nn.LayerNorm, eps=norm_eps) - head_dim = self.hidden_size // self.num_heads - self.rotary_pos_emb = get_rope( - head_size=head_dim, - rotary_dim=head_dim // 2, - max_position=8192, - is_neox_style=True, - ) - - self.merger = Qwen3_VisionPatchMerger( - d_model=vision_config.out_hidden_size, - context_dim=self.hidden_size, - norm_layer=norm_layer, - spatial_merge_size=self.spatial_merge_size, - quant_config=quant_config, - prefix=f"{prefix}.merger", - use_data_parallel=use_data_parallel, - ) - - self.deepstack_merger_list = nn.ModuleList([ - Qwen3_VisionPatchMerger( - d_model=vision_config.out_hidden_size, - context_dim=self.hidden_size, - spatial_merge_size=self.spatial_merge_size, - use_postshuffle_norm=True, - norm_layer=norm_layer, - quant_config=quant_config, - prefix=f"{prefix}.deepstack_merger_list.{layer_idx}", - use_data_parallel=use_data_parallel, - ) for layer_idx in range(len(self.deepstack_visual_indexes)) - ]) - - self.attn_backend = get_vit_attn_backend( - head_size=head_dim, - dtype=torch.get_default_dtype(), - attn_backend_override=attn_backend_override, - ) - - if self.attn_backend not in { - AttentionBackendEnum.FLASH_ATTN, - AttentionBackendEnum.TORCH_SDPA, - AttentionBackendEnum.ROCM_AITER_FA, - }: - raise RuntimeError( - f"Qwen3-VL does not support {self.attn_backend} backend now.") - self.blocks = nn.ModuleList([ - Qwen3_VisionBlock( - 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}", - use_data_parallel=use_data_parallel, - attn_backend=self.attn_backend, - ) for layer_idx in range(vision_config.depth) - ]) - - def rot_pos_emb(self, grid_thw: list[list[int]]): - max_grid_size = max(max(h, w) for _, h, w in grid_thw) - pos_ids = [ - self.rot_pos_ids(h, w, self.spatial_merge_size) if t == 1 else - self.rot_pos_ids(h, w, self.spatial_merge_size).repeat(t, 1) - for t, h, w in grid_thw - ] - pos_ids = torch.cat(pos_ids, dim=0) - - # Use pre-computed cos_sin_cache from RotaryEmbedding - cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size) - - # (num_tokens, rotary_dim // 2) - cos_h = cos[pos_ids[:, 0]] # type: ignore - cos_w = cos[pos_ids[:, 1]] # type: ignore - sin_h = sin[pos_ids[:, 0]] # type: ignore - sin_w = sin[pos_ids[:, 1]] # type: ignore - - cos_combined = torch.cat([cos_h, cos_w], dim=-1) - sin_combined = torch.cat([sin_h, sin_w], dim=-1) - - return cos_combined, sin_combined - def forward( self, x: torch.Tensor, @@ -234,8 +81,5 @@ class AscendQwen3_VisionTransformer(nn.Module): return hidden_states -# NOTE: These will be removed after vllm-ascend is aligned with vllm latest main. -Qwen3_VisionBlock.forward = AscendQwen3_VisionBlock.forward -Qwen3_VisionTransformer.__init__ = AscendQwen3_VisionTransformer.__init__ -Qwen3_VisionTransformer.rot_pos_emb = AscendQwen3_VisionTransformer.rot_pos_emb +# NOTE: This will be removed after implementing multimodal_cpu_fields in vllm-ascend model_runner. Qwen3_VisionTransformer.forward = AscendQwen3_VisionTransformer.forward