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https://git.datalinker.icu/vllm-project/vllm.git
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[Model][Perf] Use cos and sin cache in QwenVL (#28798)
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
This commit is contained in:
Canlin Guo
GitHub
parent
285eaa4285
commit
b9489f51e1
@ -83,6 +83,11 @@ class RotaryEmbeddingBase(CustomOp):
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):
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self.cos_sin_cache = self.cos_sin_cache.to(query.device, dtype=query.dtype)
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def get_cos_sin(self, seqlen: int) -> tuple[torch.Tensor, torch.Tensor]:
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cos_sin = self.cos_sin_cache[:seqlen]
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cos, sin = cos_sin.chunk(2, dim=-1)
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return cos, sin
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class RotaryEmbedding(RotaryEmbeddingBase):
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def __init__(
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@ -65,6 +65,7 @@ from vllm.model_executor.layers.linear import (
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RowParallelLinear,
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)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.multimodal import MULTIMODAL_REGISTRY
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@ -341,7 +342,8 @@ class Glm4vVisionAttention(nn.Module):
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self,
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x: torch.Tensor,
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cu_seqlens: torch.Tensor,
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rotary_pos_emb: torch.Tensor,
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rotary_pos_emb_cos: torch.Tensor,
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rotary_pos_emb_sin: torch.Tensor,
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max_seqlen: int | None = None, # Only used for Flash Attention
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seqlens: list[int] | None = None, # Only used for xFormers
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) -> torch.Tensor:
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@ -353,10 +355,12 @@ class Glm4vVisionAttention(nn.Module):
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batch_size = q.shape[1]
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q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v))
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if rotary_pos_emb is not None:
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if rotary_pos_emb_cos is not None and rotary_pos_emb_sin is not None:
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# [2 * b, s, heads, head_dim]
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qk_concat = torch.cat([q, k], dim=0)
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qk_rotated = apply_rotary_pos_emb_vision(qk_concat, rotary_pos_emb)
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qk_rotated = apply_rotary_pos_emb_vision(
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qk_concat, rotary_pos_emb_cos, rotary_pos_emb_sin
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)
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q, k = torch.chunk(qk_rotated, 2, dim=0)
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if self.is_flash_attn_backend:
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@ -454,14 +458,16 @@ class Glm4vVisionBlock(nn.Module):
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self,
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x: torch.Tensor,
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cu_seqlens: torch.Tensor,
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rotary_pos_emb: torch.Tensor,
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rotary_pos_emb_cos: torch.Tensor,
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rotary_pos_emb_sin: torch.Tensor,
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max_seqlen: int | None = None, # Only used for Flash Attention
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seqlens: list[int] | None = None, # Only used for xFormers
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) -> torch.Tensor:
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x_attn = self.attn(
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self.norm1(x),
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cu_seqlens=cu_seqlens,
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rotary_pos_emb=rotary_pos_emb,
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rotary_pos_emb_cos=rotary_pos_emb_cos,
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rotary_pos_emb_sin=rotary_pos_emb_sin,
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max_seqlen=max_seqlen,
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seqlens=seqlens,
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)
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@ -660,44 +666,6 @@ class Glm4vVisionEmbeddings(nn.Module):
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return embeddings
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class Glm4vVisionRotaryEmbedding(nn.Module):
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def __init__(self, dim: int, theta: float = 10000.0) -> None:
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super().__init__()
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self.dim = dim
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self.theta = theta
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inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self._seq_len_cached = 0
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self._freqs_cached = None
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def update_freqs_cache(self, seqlen: int) -> None:
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if seqlen > self._seq_len_cached:
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seqlen *= 2
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self._seq_len_cached = seqlen
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self.inv_freq = 1.0 / (
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self.theta
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** (
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torch.arange(
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0,
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self.dim,
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2,
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dtype=torch.float,
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device=self.inv_freq.device,
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)
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/ self.dim
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)
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)
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seq = torch.arange(
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seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
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)
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freqs = torch.outer(seq, self.inv_freq)
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self._freqs_cached = freqs
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def forward(self, seqlen: int) -> torch.Tensor:
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self.update_freqs_cache(seqlen)
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return self._freqs_cached[:seqlen]
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class Glm4vVisionTransformer(nn.Module):
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def __init__(
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self,
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@ -731,7 +699,13 @@ class Glm4vVisionTransformer(nn.Module):
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norm_layer = partial(RMSNorm, eps=norm_eps)
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head_dim = self.hidden_size // self.num_heads
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self.rotary_pos_emb = Glm4vVisionRotaryEmbedding(head_dim // 2)
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self.rotary_pos_emb = get_rope(
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head_size=head_dim,
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rotary_dim=head_dim // 2,
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max_position=8192,
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base=10000.0,
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is_neox_style=True,
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)
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self.blocks = nn.ModuleList(
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[
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Glm4vVisionBlock(
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@ -789,7 +763,9 @@ class Glm4vVisionTransformer(nn.Module):
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def device(self) -> torch.device:
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return self.patch_embed.proj.weight.device
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def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
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def rot_pos_emb(
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self, grid_thw: torch.Tensor
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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pos_ids = []
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for t, h, w in grid_thw:
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hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
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@ -817,9 +793,18 @@ class Glm4vVisionTransformer(nn.Module):
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pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
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pos_ids = torch.cat(pos_ids, dim=0)
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max_grid_size = grid_thw[:, 1:].max()
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rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
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rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
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return rotary_pos_emb, pos_ids
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# Use pre-computed cos_sin_cache from RotaryEmbedding
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cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
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cos_h = cos[pos_ids[:, 0]] # (num_tokens, rotary_dim // 2)
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cos_w = cos[pos_ids[:, 1]]
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sin_h = sin[pos_ids[:, 0]]
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sin_w = sin[pos_ids[:, 1]]
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cos_combined = torch.cat([cos_h, cos_w], dim=-1)
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sin_combined = torch.cat([sin_h, sin_w], dim=-1)
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return cos_combined, sin_combined, pos_ids
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def compute_attn_mask_seqlen(
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self,
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@ -848,7 +833,9 @@ class Glm4vVisionTransformer(nn.Module):
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x = self.post_conv_layernorm(x)
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# compute position embedding
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rotary_pos_emb, image_type_ids = self.rot_pos_emb(grid_thw)
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rotary_pos_emb_cos, rotary_pos_emb_sin, image_type_ids = self.rot_pos_emb(
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grid_thw
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)
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# compute cu_seqlens
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cu_seqlens = torch.repeat_interleave(
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grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
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@ -867,7 +854,8 @@ class Glm4vVisionTransformer(nn.Module):
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x = blk(
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x,
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cu_seqlens=cu_seqlens,
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rotary_pos_emb=rotary_pos_emb,
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rotary_pos_emb_cos=rotary_pos_emb_cos,
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rotary_pos_emb_sin=rotary_pos_emb_sin,
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max_seqlen=max_seqlen,
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seqlens=seqlens,
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)
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@ -64,6 +64,7 @@ from vllm.model_executor.layers.linear import (
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RowParallelLinear,
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)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.model_executor.models.vision import should_torch_compile_mm_vit
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@ -363,7 +364,8 @@ class Qwen2_5_VisionAttention(nn.Module):
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self,
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x: torch.Tensor,
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cu_seqlens: torch.Tensor,
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rotary_pos_emb: torch.Tensor,
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rotary_pos_emb_cos: torch.Tensor,
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rotary_pos_emb_sin: torch.Tensor,
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max_seqlen: torch.Tensor, # Only used for Flash Attention
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seqlens: torch.Tensor, # Only used for xFormers
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) -> torch.Tensor:
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@ -378,13 +380,15 @@ class Qwen2_5_VisionAttention(nn.Module):
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head=self.num_attention_heads_per_partition,
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)
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if rotary_pos_emb is not None:
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if rotary_pos_emb_cos is not None and rotary_pos_emb_sin is not None:
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qk, v = qkv[:, :, :2], qkv[:, :, 2]
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qk_reshaped = einops.rearrange(
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qk, "b s two head head_dim -> (two b) s head head_dim", two=2
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)
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qk_rotated = apply_rotary_pos_emb_vision(qk_reshaped, rotary_pos_emb)
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qk_rotated = apply_rotary_pos_emb_vision(
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qk_reshaped, cos=rotary_pos_emb_cos, sin=rotary_pos_emb_sin
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)
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qk_rotated = qk_rotated.view(
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2,
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batch_size,
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@ -434,7 +438,8 @@ class Qwen2_5_VisionAttention(nn.Module):
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dynamic_arg_dims={
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"x": 0,
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"cu_seqlens": 0,
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"rotary_pos_emb": 0,
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"rotary_pos_emb_cos": 0,
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"rotary_pos_emb_sin": 0,
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"seqlens": 0,
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},
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mark_unbacked_dims={"seqlens": 0},
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@ -485,14 +490,16 @@ class Qwen2_5_VisionBlock(nn.Module):
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self,
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x: torch.Tensor,
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cu_seqlens: torch.Tensor,
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rotary_pos_emb: torch.Tensor,
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rotary_pos_emb_cos: torch.Tensor,
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rotary_pos_emb_sin: torch.Tensor,
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max_seqlen: torch.Tensor, # Only used for Flash Attention
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seqlens: torch.Tensor, # Only used for xFormers
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) -> torch.Tensor:
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x_attn = self.attn(
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self.norm1(x),
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cu_seqlens=cu_seqlens,
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rotary_pos_emb=rotary_pos_emb,
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rotary_pos_emb_cos=rotary_pos_emb_cos,
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rotary_pos_emb_sin=rotary_pos_emb_sin,
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max_seqlen=max_seqlen,
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seqlens=seqlens,
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)
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@ -588,42 +595,6 @@ class Qwen2_5_VisionPatchMerger(nn.Module):
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return out
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class Qwen2_5_VisionRotaryEmbedding(nn.Module):
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def __init__(self, dim: int, theta: float = 10000.0) -> None:
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super().__init__()
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self.dim = dim
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self.theta = theta
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inv_freq = 1.0 / (
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theta ** (torch.arange(0, dim, 2, dtype=torch.float, device="cpu") / dim)
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)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self._seq_len_cached = 0
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self._freqs_cached = None
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def update_freqs_cache(self, seqlen: int) -> None:
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if seqlen > self._seq_len_cached:
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seqlen *= 2
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self._seq_len_cached = seqlen
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self.inv_freq = 1.0 / (
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self.theta
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** (
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torch.arange(
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0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device
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)
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/ self.dim
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)
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)
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seq = torch.arange(
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seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
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)
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freqs = torch.outer(seq, self.inv_freq)
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self._freqs_cached = freqs
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def forward(self, seqlen: int) -> torch.Tensor:
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self.update_freqs_cache(seqlen)
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return self._freqs_cached[:seqlen]
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class Qwen2_5_VisionTransformer(nn.Module):
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def __init__(
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self,
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@ -666,7 +637,13 @@ class Qwen2_5_VisionTransformer(nn.Module):
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norm_layer = partial(RMSNorm, eps=norm_eps)
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head_dim = self.hidden_size // self.num_heads
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self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
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self.rotary_pos_emb = get_rope(
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head_size=head_dim,
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rotary_dim=head_dim // 2,
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max_position=8192,
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base=10000.0,
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is_neox_style=True,
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)
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use_upstream_fa = False
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self.attn_backend = get_vit_attn_backend(
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@ -757,15 +734,30 @@ class Qwen2_5_VisionTransformer(nn.Module):
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)
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pos_ids = torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)
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max_size = max(h, w)
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rotary_pos_emb_full = self.rotary_pos_emb(max_size)
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rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
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rotary_pos_emb = rotary_pos_emb.reshape(
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rotary_pos_emb.shape[0] // self.spatial_merge_unit,
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# Use pre-computed cos_sin_cache from RotaryEmbedding
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cos, sin = self.rotary_pos_emb.get_cos_sin(max_size)
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cos_h = cos[pos_ids[:, 0]] # (num_tokens, rotary_dim // 2)
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cos_w = cos[pos_ids[:, 1]]
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sin_h = sin[pos_ids[:, 0]]
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sin_w = sin[pos_ids[:, 1]]
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cos_combined = torch.cat([cos_h, cos_w], dim=-1)
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sin_combined = torch.cat([sin_h, sin_w], dim=-1)
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cos_combined = cos_combined.reshape(
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cos_combined.shape[0] // self.spatial_merge_unit,
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self.spatial_merge_unit,
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-1,
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)
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sin_combined = sin_combined.reshape(
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sin_combined.shape[0] // self.spatial_merge_unit,
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self.spatial_merge_unit,
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-1,
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)
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return rotary_pos_emb
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return cos_combined, sin_combined
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def get_window_index_thw(self, grid_t, grid_h, grid_w):
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vit_merger_window_size = (
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@ -807,14 +799,19 @@ class Qwen2_5_VisionTransformer(nn.Module):
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@lru_cache(maxsize=1024) # noqa: B019
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def get_rope_by_thw(self, t, h, w):
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window_index_thw, cu_seqlens_window_thw = self.get_window_index_thw(t, h, w)
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rotary_pos_emb_thw = self.rotary_pos_emb_thw(t, h, w)
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rotary_pos_emb_thw = rotary_pos_emb_thw[window_index_thw, :, :]
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rotary_pos_emb_thw = rotary_pos_emb_thw.flatten(start_dim=0, end_dim=1)
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cos_thw, sin_thw = self.rotary_pos_emb_thw(t, h, w)
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cos_thw = cos_thw[window_index_thw, :, :]
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cos_thw = cos_thw.flatten(start_dim=0, end_dim=1)
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sin_thw = sin_thw[window_index_thw, :, :]
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sin_thw = sin_thw.flatten(start_dim=0, end_dim=1)
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cu_seqlens_thw = torch.repeat_interleave(
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torch.tensor([h * w], dtype=torch.int32), t
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)
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return (
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rotary_pos_emb_thw,
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cos_thw,
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sin_thw,
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window_index_thw,
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cu_seqlens_window_thw,
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cu_seqlens_thw,
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@ -849,7 +846,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
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) -> torch.Tensor:
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# patchify
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seq_len, _ = x.size()
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rotary_pos_emb = []
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rotary_pos_emb_cos = []
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rotary_pos_emb_sin = []
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window_index: list = []
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cu_window_seqlens: list = [torch.tensor([0], dtype=torch.int32)]
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cu_seqlens: list = []
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@ -865,7 +863,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
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llm_w = w // self.spatial_merge_size
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(
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rotary_pos_emb_thw,
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cos_thw,
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sin_thw,
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window_index_thw,
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cu_seqlens_window_thw,
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cu_seqlens_thw,
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@ -878,11 +877,13 @@ class Qwen2_5_VisionTransformer(nn.Module):
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cu_window_seqlens_last = cu_seqlens_window_thw[-1]
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cu_window_seqlens.append(cu_seqlens_window_thw)
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rotary_pos_emb.append(rotary_pos_emb_thw)
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rotary_pos_emb_cos.append(cos_thw)
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rotary_pos_emb_sin.append(sin_thw)
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cu_seqlens.append(cu_seqlens_thw)
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|
||||
rotary_pos_emb = torch.cat(rotary_pos_emb)
|
||||
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)
|
||||
@ -901,7 +902,12 @@ class Qwen2_5_VisionTransformer(nn.Module):
|
||||
|
||||
cu_seqlens = cu_seqlens.to(device=self.device, non_blocking=True)
|
||||
cu_window_seqlens = cu_window_seqlens.to(device=self.device, non_blocking=True)
|
||||
rotary_pos_emb = rotary_pos_emb.to(device=self.device, non_blocking=True)
|
||||
rotary_pos_emb_cos = rotary_pos_emb_cos.to(
|
||||
device=self.device, non_blocking=True
|
||||
)
|
||||
rotary_pos_emb_sin = rotary_pos_emb_sin.to(
|
||||
device=self.device, non_blocking=True
|
||||
)
|
||||
window_index = window_index.to(device=hidden_states.device, non_blocking=True)
|
||||
reverse_indices = reverse_indices.to(
|
||||
device=hidden_states.device, non_blocking=True
|
||||
@ -928,7 +934,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
|
||||
hidden_states = blk(
|
||||
hidden_states,
|
||||
cu_seqlens=cu_seqlens_now,
|
||||
rotary_pos_emb=rotary_pos_emb,
|
||||
rotary_pos_emb_cos=rotary_pos_emb_cos,
|
||||
rotary_pos_emb_sin=rotary_pos_emb_sin,
|
||||
max_seqlen=max_seqlen_now,
|
||||
seqlens=seqlens_now,
|
||||
)
|
||||
|
||||
@ -32,7 +32,7 @@ from typing import Annotated, Any, Literal, TypeAlias
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange, repeat
|
||||
from einops import rearrange
|
||||
from transformers import BatchFeature
|
||||
from transformers.models.qwen2_vl import Qwen2VLImageProcessor, Qwen2VLProcessor
|
||||
from transformers.models.qwen2_vl.configuration_qwen2_vl import (
|
||||
@ -59,7 +59,9 @@ from vllm.model_executor.layers.linear import (
|
||||
RowParallelLinear,
|
||||
)
|
||||
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.model_loader.weight_utils import default_weight_loader
|
||||
@ -275,47 +277,13 @@ class Qwen2VisionMLP(nn.Module):
|
||||
return x
|
||||
|
||||
|
||||
def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
|
||||
if not interleaved:
|
||||
x1, x2 = x.chunk(2, dim=-1)
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
else:
|
||||
x1, x2 = x[..., ::2], x[..., 1::2]
|
||||
return rearrange(
|
||||
torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
|
||||
)
|
||||
|
||||
|
||||
def apply_rotary_emb_torch(
|
||||
x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, interleaved: bool = False
|
||||
def apply_rotary_pos_emb_vision(
|
||||
t: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
x: (batch_size, seqlen, nheads, headdim)
|
||||
cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
|
||||
"""
|
||||
ro_dim = cos.shape[-1] * 2
|
||||
assert ro_dim <= x.shape[-1]
|
||||
cos = repeat(
|
||||
cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
|
||||
rotary_emb_function = dispatch_rotary_emb_function(
|
||||
default=partial(apply_rotary_emb_torch, is_neox_style=True)
|
||||
)
|
||||
sin = repeat(
|
||||
sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
|
||||
)
|
||||
return torch.cat(
|
||||
[
|
||||
x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin,
|
||||
x[..., ro_dim:],
|
||||
],
|
||||
dim=-1,
|
||||
)
|
||||
|
||||
|
||||
def apply_rotary_pos_emb_vision(t: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
|
||||
rotary_emb_function = dispatch_rotary_emb_function(default=apply_rotary_emb_torch)
|
||||
t_ = t.float()
|
||||
cos = freqs.cos()
|
||||
sin = freqs.sin()
|
||||
output = rotary_emb_function(t_, cos, sin).type_as(t)
|
||||
output = rotary_emb_function(t, cos, sin).type_as(t)
|
||||
return output
|
||||
|
||||
|
||||
@ -412,7 +380,8 @@ class Qwen2VisionAttention(nn.Module):
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
cu_seqlens: torch.Tensor,
|
||||
rotary_pos_emb: torch.Tensor,
|
||||
rotary_pos_emb_cos: torch.Tensor,
|
||||
rotary_pos_emb_sin: torch.Tensor,
|
||||
max_seqlen: int | None = None, # Only used for Flash Attention
|
||||
seqlens: list[int] | None = None, # Only used for xFormers
|
||||
) -> torch.Tensor:
|
||||
@ -424,11 +393,13 @@ class Qwen2VisionAttention(nn.Module):
|
||||
batch_size = q.shape[1]
|
||||
|
||||
q, k, v = (rearrange(x, "s b ... -> b s ...") for x in (q, k, v))
|
||||
if rotary_pos_emb is not None:
|
||||
# [2 * b, s, heads, head_dim]
|
||||
qk_concat = torch.cat([q, k], dim=0)
|
||||
qk_rotated = apply_rotary_pos_emb_vision(qk_concat, rotary_pos_emb)
|
||||
q, k = torch.chunk(qk_rotated, 2, dim=0)
|
||||
|
||||
# [2 * b, s, heads, head_dim]
|
||||
qk_concat = torch.cat([q, k], dim=0)
|
||||
qk_rotated = apply_rotary_pos_emb_vision(
|
||||
qk_concat, rotary_pos_emb_cos, rotary_pos_emb_sin
|
||||
)
|
||||
q, k = torch.chunk(qk_rotated, 2, dim=0)
|
||||
|
||||
if self.is_flash_attn_backend:
|
||||
q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
|
||||
@ -534,14 +505,16 @@ class Qwen2VisionBlock(nn.Module):
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
cu_seqlens: torch.Tensor,
|
||||
rotary_pos_emb: torch.Tensor,
|
||||
rotary_pos_emb_cos: torch.Tensor,
|
||||
rotary_pos_emb_sin: torch.Tensor,
|
||||
max_seqlen: int | None = None, # Only used for Flash Attention
|
||||
seqlens: list[int] | None = None, # Only used for xFormers
|
||||
) -> torch.Tensor:
|
||||
x = x + self.attn(
|
||||
self.norm1(x),
|
||||
cu_seqlens=cu_seqlens,
|
||||
rotary_pos_emb=rotary_pos_emb,
|
||||
rotary_pos_emb_cos=rotary_pos_emb_cos,
|
||||
rotary_pos_emb_sin=rotary_pos_emb_sin,
|
||||
max_seqlen=max_seqlen,
|
||||
seqlens=seqlens,
|
||||
)
|
||||
@ -628,40 +601,6 @@ class Qwen2VisionPatchMerger(nn.Module):
|
||||
return out
|
||||
|
||||
|
||||
class Qwen2VisionRotaryEmbedding(nn.Module):
|
||||
def __init__(self, dim: int, theta: float = 10000.0) -> None:
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.theta = theta
|
||||
inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
self._seq_len_cached = 0
|
||||
self._freqs_cached = None
|
||||
|
||||
def update_freqs_cache(self, seqlen: int) -> None:
|
||||
if seqlen > self._seq_len_cached:
|
||||
seqlen *= 2
|
||||
self._seq_len_cached = seqlen
|
||||
self.inv_freq = 1.0 / (
|
||||
self.theta
|
||||
** (
|
||||
torch.arange(
|
||||
0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device
|
||||
)
|
||||
/ self.dim
|
||||
)
|
||||
)
|
||||
seq = torch.arange(
|
||||
seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
|
||||
)
|
||||
freqs = torch.outer(seq, self.inv_freq)
|
||||
self._freqs_cached = freqs
|
||||
|
||||
def forward(self, seqlen: int) -> torch.Tensor:
|
||||
self.update_freqs_cache(seqlen)
|
||||
return self._freqs_cached[:seqlen]
|
||||
|
||||
|
||||
class Qwen2VisionTransformer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
@ -700,7 +639,13 @@ class Qwen2VisionTransformer(nn.Module):
|
||||
|
||||
norm_layer = partial(nn.LayerNorm, eps=norm_eps)
|
||||
head_dim = embed_dim // num_heads
|
||||
self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)
|
||||
self.rotary_pos_emb = get_rope(
|
||||
head_size=head_dim,
|
||||
rotary_dim=head_dim // 2,
|
||||
max_position=8192,
|
||||
base=10000.0,
|
||||
is_neox_style=True,
|
||||
)
|
||||
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
@ -744,7 +689,9 @@ class Qwen2VisionTransformer(nn.Module):
|
||||
def device(self) -> torch.device:
|
||||
return self.patch_embed.proj.weight.device
|
||||
|
||||
def rot_pos_emb(self, grid_thw: list[list[int]]) -> torch.Tensor:
|
||||
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:
|
||||
@ -773,9 +720,18 @@ class Qwen2VisionTransformer(nn.Module):
|
||||
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)
|
||||
rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
|
||||
rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
|
||||
return rotary_pos_emb
|
||||
|
||||
# Use pre-computed cos_sin_cache from RotaryEmbedding
|
||||
cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_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)
|
||||
return cos_combined, sin_combined
|
||||
|
||||
def compute_attn_mask_seqlen(
|
||||
self, cu_seqlens: torch.Tensor
|
||||
@ -806,7 +762,7 @@ class Qwen2VisionTransformer(nn.Module):
|
||||
grid_thw_list = grid_thw.tolist()
|
||||
|
||||
# compute position embedding
|
||||
rotary_pos_emb = self.rot_pos_emb(grid_thw_list)
|
||||
rotary_pos_emb_cos, rotary_pos_emb_sin = self.rot_pos_emb(grid_thw_list)
|
||||
|
||||
# compute cu_seqlens
|
||||
cu_seqlens = torch.repeat_interleave(
|
||||
@ -824,7 +780,8 @@ class Qwen2VisionTransformer(nn.Module):
|
||||
x = blk(
|
||||
x,
|
||||
cu_seqlens=cu_seqlens,
|
||||
rotary_pos_emb=rotary_pos_emb,
|
||||
rotary_pos_emb_cos=rotary_pos_emb_cos,
|
||||
rotary_pos_emb_sin=rotary_pos_emb_sin,
|
||||
max_seqlen=max_seqlen,
|
||||
seqlens=seqlens,
|
||||
)
|
||||
|
||||
@ -60,6 +60,7 @@ from vllm.model_executor.layers.linear import (
|
||||
)
|
||||
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
||||
from vllm.model_executor.layers.quantization import QuantizationConfig
|
||||
from vllm.model_executor.layers.rotary_embedding import get_rope
|
||||
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
|
||||
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
||||
from vllm.model_executor.models.qwen2_audio import Qwen2AudioProcessingInfo
|
||||
@ -90,7 +91,6 @@ from .qwen2_5_omni_thinker import (
|
||||
)
|
||||
from .qwen2_5_vl import (
|
||||
Qwen2_5_VisionAttention,
|
||||
Qwen2_5_VisionRotaryEmbedding,
|
||||
Qwen2_5_VLProcessingInfo,
|
||||
)
|
||||
from .qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
|
||||
@ -221,14 +221,16 @@ class Qwen3_VisionBlock(nn.Module):
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
cu_seqlens: torch.Tensor,
|
||||
rotary_pos_emb: torch.Tensor,
|
||||
rotary_pos_emb_cos: torch.Tensor,
|
||||
rotary_pos_emb_sin: torch.Tensor,
|
||||
max_seqlen: torch.Tensor, # Only used for Flash Attention
|
||||
seqlens: torch.Tensor, # Only used for xFormers
|
||||
) -> torch.Tensor:
|
||||
x = x + self.attn(
|
||||
self.norm1(x),
|
||||
cu_seqlens=cu_seqlens,
|
||||
rotary_pos_emb=rotary_pos_emb,
|
||||
rotary_pos_emb_cos=rotary_pos_emb_cos,
|
||||
rotary_pos_emb_sin=rotary_pos_emb_sin,
|
||||
max_seqlen=max_seqlen,
|
||||
seqlens=seqlens,
|
||||
)
|
||||
@ -332,7 +334,13 @@ class Qwen3Omni_VisionTransformer(nn.Module):
|
||||
|
||||
norm_layer = partial(nn.LayerNorm, eps=norm_eps)
|
||||
head_dim = self.hidden_size // self.num_heads
|
||||
self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
|
||||
self.rotary_pos_emb = get_rope(
|
||||
head_size=head_dim,
|
||||
rotary_dim=head_dim // 2,
|
||||
max_position=8192,
|
||||
base=10000.0,
|
||||
is_neox_style=True,
|
||||
)
|
||||
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
@ -416,9 +424,19 @@ class Qwen3Omni_VisionTransformer(nn.Module):
|
||||
pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
|
||||
pos_ids = torch.cat(pos_ids, dim=0)
|
||||
max_grid_size = grid_thw[:, 1:].max()
|
||||
rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
|
||||
rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
|
||||
return rotary_pos_emb
|
||||
|
||||
# Use pre-computed cos_sin_cache from RotaryEmbedding
|
||||
cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_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)
|
||||
|
||||
return cos_combined, sin_combined
|
||||
|
||||
def fast_pos_embed_interpolate(self, grid_thw: list[list[int]]) -> torch.Tensor:
|
||||
num_grid_per_side = self.num_grid_per_side
|
||||
@ -508,7 +526,7 @@ class Qwen3Omni_VisionTransformer(nn.Module):
|
||||
if self.apply_vit_abs_pos_embed:
|
||||
pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
|
||||
hidden_states = hidden_states + pos_embeds
|
||||
rotary_pos_emb = self.rot_pos_emb(grid_thw)
|
||||
rotary_pos_emb_cos, rotary_pos_emb_sin = self.rot_pos_emb(grid_thw)
|
||||
|
||||
cu_seqlens = torch.repeat_interleave(
|
||||
grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
|
||||
@ -519,7 +537,8 @@ class Qwen3Omni_VisionTransformer(nn.Module):
|
||||
cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
|
||||
|
||||
hidden_states = hidden_states.unsqueeze(1)
|
||||
rotary_pos_emb = rotary_pos_emb.to(hidden_states.device)
|
||||
rotary_pos_emb_cos = rotary_pos_emb_cos.to(hidden_states.device)
|
||||
rotary_pos_emb_sin = rotary_pos_emb_sin.to(hidden_states.device)
|
||||
max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
|
||||
|
||||
hidden_states_list = []
|
||||
@ -529,7 +548,8 @@ class Qwen3Omni_VisionTransformer(nn.Module):
|
||||
hidden_states = blk(
|
||||
hidden_states,
|
||||
cu_seqlens=cu_seqlens,
|
||||
rotary_pos_emb=rotary_pos_emb,
|
||||
rotary_pos_emb_cos=rotary_pos_emb_cos,
|
||||
rotary_pos_emb_sin=rotary_pos_emb_sin,
|
||||
max_seqlen=max_seqlen,
|
||||
seqlens=seqlens,
|
||||
)
|
||||
|
||||
@ -63,6 +63,7 @@ from vllm.model_executor.layers.linear import (
|
||||
)
|
||||
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
||||
from vllm.model_executor.layers.quantization import QuantizationConfig
|
||||
from vllm.model_executor.layers.rotary_embedding import get_rope
|
||||
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
|
||||
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
||||
from vllm.model_executor.models.module_mapping import MultiModelKeys
|
||||
@ -95,7 +96,6 @@ from .interfaces import (
|
||||
)
|
||||
from .qwen2_5_vl import (
|
||||
Qwen2_5_VisionAttention,
|
||||
Qwen2_5_VisionRotaryEmbedding,
|
||||
Qwen2_5_VLImageEmbeddingInputs,
|
||||
Qwen2_5_VLImageInputs,
|
||||
Qwen2_5_VLImagePixelInputs,
|
||||
@ -232,14 +232,16 @@ class Qwen3_VisionBlock(nn.Module):
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
cu_seqlens: torch.Tensor,
|
||||
rotary_pos_emb: torch.Tensor,
|
||||
rotary_pos_emb_cos: torch.Tensor,
|
||||
rotary_pos_emb_sin: torch.Tensor,
|
||||
max_seqlen: torch.Tensor, # Only used for Flash Attention
|
||||
seqlens: torch.Tensor, # Only used for xFormers
|
||||
) -> torch.Tensor:
|
||||
x = x + self.attn(
|
||||
self.norm1(x),
|
||||
cu_seqlens=cu_seqlens,
|
||||
rotary_pos_emb=rotary_pos_emb,
|
||||
rotary_pos_emb_cos=rotary_pos_emb_cos,
|
||||
rotary_pos_emb_sin=rotary_pos_emb_sin,
|
||||
max_seqlen=max_seqlen,
|
||||
seqlens=seqlens,
|
||||
)
|
||||
@ -339,7 +341,13 @@ class Qwen3_VisionTransformer(nn.Module):
|
||||
|
||||
norm_layer = partial(nn.LayerNorm, eps=norm_eps)
|
||||
head_dim = self.hidden_size // self.num_heads
|
||||
self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
|
||||
self.rotary_pos_emb = get_rope(
|
||||
head_size=head_dim,
|
||||
rotary_dim=head_dim // 2,
|
||||
max_position=8192,
|
||||
base=10000.0,
|
||||
is_neox_style=True,
|
||||
)
|
||||
|
||||
self.merger = Qwen3_VisionPatchMerger(
|
||||
d_model=vision_config.out_hidden_size,
|
||||
@ -452,9 +460,19 @@ class Qwen3_VisionTransformer(nn.Module):
|
||||
for t, h, w in grid_thw
|
||||
]
|
||||
pos_ids = torch.cat(pos_ids, dim=0)
|
||||
rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
|
||||
rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
|
||||
return rotary_pos_emb
|
||||
|
||||
# Use pre-computed cos_sin_cache from RotaryEmbedding
|
||||
cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_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)
|
||||
|
||||
return cos_combined, sin_combined
|
||||
|
||||
def fast_pos_embed_interpolate(self, grid_thw: list[list[int]]) -> torch.Tensor:
|
||||
num_grid_per_side = self.num_grid_per_side
|
||||
@ -547,8 +565,13 @@ class Qwen3_VisionTransformer(nn.Module):
|
||||
|
||||
pos_embeds = self.fast_pos_embed_interpolate(grid_thw_list)
|
||||
hidden_states = hidden_states + pos_embeds
|
||||
rotary_pos_emb = self.rot_pos_emb(grid_thw_list)
|
||||
rotary_pos_emb = rotary_pos_emb.to(hidden_states.device, non_blocking=True)
|
||||
rotary_pos_emb_cos, rotary_pos_emb_sin = self.rot_pos_emb(grid_thw_list)
|
||||
rotary_pos_emb_cos = rotary_pos_emb_cos.to(
|
||||
hidden_states.device, non_blocking=True
|
||||
)
|
||||
rotary_pos_emb_sin = rotary_pos_emb_sin.to(
|
||||
hidden_states.device, non_blocking=True
|
||||
)
|
||||
|
||||
cu_seqlens = torch.repeat_interleave(
|
||||
grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
|
||||
@ -564,7 +587,8 @@ class Qwen3_VisionTransformer(nn.Module):
|
||||
hidden_states = blk(
|
||||
hidden_states,
|
||||
cu_seqlens=cu_seqlens,
|
||||
rotary_pos_emb=rotary_pos_emb,
|
||||
rotary_pos_emb_cos=rotary_pos_emb_cos,
|
||||
rotary_pos_emb_sin=rotary_pos_emb_sin,
|
||||
max_seqlen=max_seqlen,
|
||||
seqlens=seqlens,
|
||||
)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user