Model: Support Qwen 2.5 vl (#3258)
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Mick
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python/sglang/srt/models/qwen2_5_vl.py
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python/sglang/srt/models/qwen2_5_vl.py
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# coding=utf-8
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# Adapted from
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# https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
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# Copyright 2024 The Qwen team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only Qwen2-VL model compatible with HuggingFace weights."""
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import logging
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from functools import lru_cache, partial
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from typing import Iterable, List, Optional, Tuple, Type
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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from transformers import AutoModel, Qwen2VLConfig
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from transformers.activations import ACT2FN
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from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm
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from sglang.srt.configs import Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig
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from sglang.srt.distributed import (
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from sglang.srt.hf_transformers_utils import get_processor
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from sglang.srt.layers.attention.vision import VisionAttention
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from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.pooler import Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
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from sglang.srt.managers.schedule_batch import ImageInputs
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.qwen2 import Qwen2Model
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from sglang.srt.models.qwen2_vl import Qwen2VLImageInputs, Qwen2VLVideoInputs
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logger = logging.getLogger(__name__)
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class Qwen2_5_VLMLP(nn.Module):
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def __init__(
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self,
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in_features: int,
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hidden_features: int = None,
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bias: bool = True,
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hidden_act="silu",
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.gate_proj = ColumnParallelLinear(
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in_features, hidden_features, bias=bias, quant_config=quant_config
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)
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self.up_proj = ColumnParallelLinear(
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in_features, hidden_features, bias=bias, quant_config=quant_config
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)
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self.down_proj = RowParallelLinear(
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hidden_features, in_features, bias=bias, quant_config=quant_config
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)
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self.act = ACT2FN[hidden_act]
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x_parallel_gate, _ = self.gate_proj(x)
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x_parallel_gate = self.act(x_parallel_gate)
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x_parallel_up, _ = self.up_proj(x)
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x_parallel = x_parallel_gate * x_parallel_up
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x, _ = self.down_proj(x_parallel)
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return x
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class Qwen2_5_VisionBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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intermediate_dim: int,
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num_heads: int,
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hidden_act="silu",
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norm_layer: Type[nn.Module] = None,
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attn_implementation: Optional[str] = "sdpa",
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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if norm_layer is None:
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norm_layer = partial(nn.LayerNorm, eps=1e-6)
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self.norm1 = Qwen2RMSNorm(dim, eps=1e-6)
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self.norm2 = Qwen2RMSNorm(dim, eps=1e-6)
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if attn_implementation == "sdpa":
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use_context_forward = False
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use_full_precision_softmax = False
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elif attn_implementation == "flash_attention_2":
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use_full_precision_softmax = False
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use_context_forward = True
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elif attn_implementation == "eager":
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use_full_precision_softmax = True
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use_context_forward = False
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self.attn = VisionAttention(
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embed_dim=dim,
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num_heads=num_heads,
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projection_size=dim,
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use_qkv_parallel=False,
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use_context_forward=use_context_forward,
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use_full_precision_softmax=use_full_precision_softmax,
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flatten_batch=True,
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quant_config=quant_config,
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)
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self.mlp = Qwen2_5_VLMLP(
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dim, intermediate_dim, hidden_act=hidden_act, quant_config=quant_config
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)
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def forward(
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self, x: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor
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) -> torch.Tensor:
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hidden_states = self.norm1(x)
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hidden_states = rearrange(hidden_states, "s b ... -> b s ...")
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attn = self.attn(
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hidden_states, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
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)
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attn = rearrange(attn, "b s ... -> s b ...")
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x = x + attn
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norm2 = self.norm2(x)
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mlp = self.mlp(norm2)
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x = x + mlp
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return x
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class Qwen2_5_VisionPatchEmbed(nn.Module):
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def __init__(
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self,
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patch_size: int = 14,
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temporal_patch_size: int = 2,
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in_chans: int = 3,
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embed_dim: int = 1152,
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) -> None:
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super().__init__()
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self.patch_size = patch_size
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self.temporal_patch_size = temporal_patch_size
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self.embed_dim = embed_dim
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kernel_size = [temporal_patch_size, patch_size, patch_size]
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self.proj = nn.Conv3d(
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in_chans, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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L, C = x.shape
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x = x.view(L, -1, self.temporal_patch_size, self.patch_size, self.patch_size)
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x = self.proj(x).view(L, self.embed_dim)
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return x
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class Qwen2_5_VisionPatchMerger(nn.Module):
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def __init__(
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self,
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dim: int,
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context_dim: int,
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spatial_merge_size: int = 2,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = context_dim * (spatial_merge_size**2)
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self.ln_q = Qwen2RMSNorm(context_dim, eps=1e-6)
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self.mlp = nn.ModuleList(
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[
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ColumnParallelLinear(
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self.hidden_size,
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self.hidden_size,
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bias=True,
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quant_config=quant_config,
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),
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nn.GELU(),
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RowParallelLinear(
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self.hidden_size, dim, bias=True, quant_config=quant_config
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),
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]
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.ln_q(x)
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x = x.view(-1, self.hidden_size)
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mlp_fc1, mlp_act, mlp_fc2 = self.mlp
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x_parallel, _ = mlp_fc1(x)
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x_parallel = mlp_act(x_parallel)
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out, _ = mlp_fc2(x_parallel)
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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 / (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, 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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vision_config: Qwen2_5_VLVisionConfig,
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norm_eps: float = 1e-6,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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patch_size: int = vision_config.patch_size
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temporal_patch_size: int = vision_config.temporal_patch_size
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spatial_merge_size: int = vision_config.spatial_merge_size
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self.spatial_merge_size = spatial_merge_size
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self.spatial_merge_unit: int = spatial_merge_size * spatial_merge_size
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in_chans: int = vision_config.in_chans
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hidden_size: int = vision_config.hidden_size
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depth: int = vision_config.depth
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num_heads: int = vision_config.num_heads
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self.fullatt_block_indexes = vision_config.fullatt_block_indexes
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self.window_size = vision_config.window_size
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self.patch_size = vision_config.patch_size
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mlp_hidden_size: int = vision_config.intermediate_size
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self.patch_embed = Qwen2_5_VisionPatchEmbed(
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patch_size=patch_size,
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temporal_patch_size=temporal_patch_size,
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in_chans=in_chans,
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embed_dim=hidden_size,
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)
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norm_layer = partial(nn.LayerNorm, eps=norm_eps)
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head_dim = hidden_size // num_heads
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self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
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self.blocks = nn.ModuleList(
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[
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Qwen2_5_VisionBlock(
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dim=hidden_size,
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intermediate_dim=mlp_hidden_size,
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num_heads=num_heads,
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hidden_act=vision_config.hidden_act,
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norm_layer=norm_layer,
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attn_implementation="sdpa",
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quant_config=quant_config,
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)
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for _ in range(depth)
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]
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)
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self.merger = Qwen2_5_VisionPatchMerger(
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dim=vision_config.out_hidden_size,
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context_dim=hidden_size,
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spatial_merge_size=spatial_merge_size,
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quant_config=quant_config,
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)
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def get_window_index(self, grid_thw):
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window_index: list = []
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cu_window_seqlens: list = [0]
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window_index_id = 0
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vit_merger_window_size = (
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self.window_size // self.spatial_merge_size // self.patch_size
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)
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for grid_t, grid_h, grid_w in grid_thw:
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llm_grid_h, llm_grid_w = (
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grid_h // self.spatial_merge_size,
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grid_w // self.spatial_merge_size,
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)
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index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(
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grid_t, llm_grid_h, llm_grid_w
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)
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pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
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pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
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num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
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num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
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index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
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index_padded = index_padded.reshape(
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grid_t,
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num_windows_h,
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vit_merger_window_size,
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num_windows_w,
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vit_merger_window_size,
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)
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index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
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grid_t,
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num_windows_h * num_windows_w,
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vit_merger_window_size,
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vit_merger_window_size,
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)
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seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
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index_padded = index_padded.reshape(-1)
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index_new = index_padded[index_padded != -100]
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window_index.append(index_new + window_index_id)
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cu_seqlens_tmp = (
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seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
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)
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cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
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window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
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window_index = torch.cat(window_index, dim=0)
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return window_index, cu_window_seqlens
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@property
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def dtype(self) -> torch.dtype:
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return self.blocks[0].mlp.gate_proj.weight.dtype
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@property
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def device(self) -> torch.device:
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return self.blocks[0].mlp.gate_proj.weight.device
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def rot_pos_emb(self, grid_thw: 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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wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
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hpos_ids = (
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hpos_ids.reshape(
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h // self.spatial_merge_size,
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self.spatial_merge_size,
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w // self.spatial_merge_size,
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self.spatial_merge_size,
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)
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.permute(0, 2, 1, 3)
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.flatten()
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)
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wpos_ids = (
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wpos_ids.reshape(
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h // self.spatial_merge_size,
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self.spatial_merge_size,
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w // self.spatial_merge_size,
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self.spatial_merge_size,
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)
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.permute(0, 2, 1, 3)
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.flatten()
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)
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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
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def forward(
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self,
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x: torch.Tensor,
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grid_thw: torch.Tensor,
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) -> torch.Tensor:
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# patchify
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x = x.to(device=self.device, dtype=self.dtype)
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x = self.patch_embed(x)
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# compute position embedding
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rotary_pos_emb = self.rot_pos_emb(grid_thw)
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window_index, cu_window_seqlens = self.get_window_index(grid_thw)
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cu_window_seqlens = torch.tensor(
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cu_window_seqlens,
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device=x.device,
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dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
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)
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cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
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seq_len, _ = x.size()
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x = x.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
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x = x[window_index, :, :]
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x = x.reshape(seq_len, -1)
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rotary_pos_emb = rotary_pos_emb.reshape(
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seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1
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)
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rotary_pos_emb = rotary_pos_emb[window_index, :, :]
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rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
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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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).cumsum(dim=0, dtype=torch.int32)
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cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
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# 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, rotary_pos_emb=rotary_pos_emb)
|
||||
|
||||
# adapter
|
||||
x = self.merger(x)
|
||||
|
||||
reverse_indices = torch.argsort(window_index)
|
||||
x = x[reverse_indices, :]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
cached_get_processor = lru_cache(get_processor)
|
||||
|
||||
|
||||
class Qwen2_5_VLForConditionalGeneration(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: Qwen2VLConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
self.visual = Qwen2_5_VisionTransformer(
|
||||
config.vision_config,
|
||||
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
|
||||
# NOTE: Qwen2-VL vision encoder does not support any
|
||||
# quantization method now.
|
||||
quant_config=None,
|
||||
)
|
||||
|
||||
self.model = Qwen2Model(config, quant_config)
|
||||
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head = self.model.embed_tokens
|
||||
else:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size, config.hidden_size, quant_config=quant_config
|
||||
)
|
||||
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
|
||||
|
||||
def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]):
|
||||
processor = cached_get_processor(self.config._name_or_path)
|
||||
grid_t, grid_h, grid_w = image_grid_thw
|
||||
num_image_tokens = (
|
||||
grid_t
|
||||
* grid_h
|
||||
* grid_w
|
||||
// processor.image_processor.merge_size
|
||||
// processor.image_processor.merge_size
|
||||
)
|
||||
return num_image_tokens
|
||||
|
||||
def pad_input_ids(self, input_ids: List[int], image_inputs: ImageInputs):
|
||||
new_input_ids = []
|
||||
last_idx = 0
|
||||
image_idx = -1
|
||||
image_inputs.image_offsets = []
|
||||
|
||||
# Get all special token IDs
|
||||
im_start_id = image_inputs.im_start_id
|
||||
im_end_id = image_inputs.im_end_id
|
||||
|
||||
# Find all start and end positions for both types
|
||||
start_indices = [i for i, x in enumerate(input_ids) if x == im_start_id]
|
||||
end_indices = [i for i, x in enumerate(input_ids) if x == im_end_id]
|
||||
|
||||
if len(start_indices) != len(end_indices):
|
||||
return input_ids
|
||||
# Process each region (both image and slice)
|
||||
for start_idx, end_idx in zip(start_indices, end_indices):
|
||||
# Add non-image tokens before this region
|
||||
new_input_ids.extend(input_ids[last_idx : start_idx + 1])
|
||||
|
||||
is_image_start = input_ids[start_idx] == im_start_id
|
||||
|
||||
if is_image_start:
|
||||
image_inputs.image_offsets += [start_idx]
|
||||
image_idx += 1
|
||||
|
||||
num_tokens = end_idx - start_idx - 1 # exclude start and end tokens
|
||||
|
||||
# Generate pad_ids
|
||||
pad_values = [image_inputs.pad_values[image_idx]]
|
||||
|
||||
pad_ids = pad_values * ((num_tokens + len(pad_values)) // len(pad_values))
|
||||
pad_ids = pad_ids[:num_tokens]
|
||||
|
||||
# Add pad_ids
|
||||
new_input_ids.extend(pad_ids)
|
||||
|
||||
# Update last_idx to after end token
|
||||
last_idx = end_idx
|
||||
|
||||
# Add remaining tokens after last region
|
||||
new_input_ids.extend(input_ids[last_idx:])
|
||||
assert len(input_ids) == len(new_input_ids)
|
||||
return new_input_ids
|
||||
|
||||
def _process_image_input(self, image_input: Qwen2VLImageInputs) -> torch.Tensor:
|
||||
pixel_values = image_input["pixel_values"].type(self.visual.dtype)
|
||||
image_embeds = self.visual(pixel_values, grid_thw=image_input["image_grid_thw"])
|
||||
return image_embeds
|
||||
|
||||
def _process_video_input(self, video_input: Qwen2VLVideoInputs) -> torch.Tensor:
|
||||
pixel_values_videos = video_input["pixel_values_videos"].type(self.visual.dtype)
|
||||
video_embeds = self.visual(
|
||||
pixel_values_videos, grid_thw=video_input["video_grid_thw"]
|
||||
)
|
||||
return video_embeds
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
get_embedding: bool = False,
|
||||
):
|
||||
"""Run forward pass for Qwen2_5-VL.
|
||||
|
||||
Args:
|
||||
input_ids: Flattened (concatenated) input_ids corresponding to a
|
||||
batch.
|
||||
positions: Flattened (concatenated) position ids corresponding to a
|
||||
batch.
|
||||
**NOTE**: If mrope is enabled (default setting for Qwen2-VL
|
||||
opensource models), the shape will be `(3, seq_len)`,
|
||||
otherwise it will be `(seq_len,).
|
||||
(Use input_metadata.mrope_positions to replace it)
|
||||
"""
|
||||
if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
|
||||
positions = forward_batch.mrope_positions
|
||||
|
||||
image_inputs = None
|
||||
if forward_batch.image_inputs is not None:
|
||||
image_inputs = [
|
||||
img for img in forward_batch.image_inputs if img is not None
|
||||
]
|
||||
|
||||
if (
|
||||
forward_batch.forward_mode.is_decode()
|
||||
or image_inputs is None
|
||||
or len(image_inputs) == 0
|
||||
):
|
||||
inputs_embeds = self.model.embed_tokens(input_ids)
|
||||
else:
|
||||
if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
|
||||
assert positions.ndim == 2 and positions.size(0) == 3, (
|
||||
"multimodal section rotary embedding requires "
|
||||
f"(3, seq_len) positions, but got {positions.size()}"
|
||||
)
|
||||
|
||||
# Clamp input ids. This is because the input_ids for the image tokens are
|
||||
# filled with the hash values of the image for the prefix matching in the radix attention.
|
||||
# There values are useless because their embeddings will be replaced by vision embeddings anyway.
|
||||
input_ids.clamp_(min=0, max=self.config.vocab_size - 1)
|
||||
# [B, s, hidden_size]
|
||||
inputs_embeds = self.model.embed_tokens(input_ids)
|
||||
extend_start_loc_cpu = forward_batch.extend_start_loc.cpu().numpy()
|
||||
prefix_lens_cpu = forward_batch.extend_prefix_lens_cpu
|
||||
for i, image in enumerate(forward_batch.image_inputs):
|
||||
if image is None:
|
||||
continue
|
||||
start_idx = extend_start_loc_cpu[i]
|
||||
prefix_len = prefix_lens_cpu[i]
|
||||
|
||||
pixel_values = image.pixel_values.clone().detach().requires_grad_(False)
|
||||
image_grid_thws = torch.tensor(
|
||||
np.array(image.image_grid_thws), device="cuda"
|
||||
)
|
||||
image_offsets = image.image_offsets
|
||||
image_input = Qwen2VLImageInputs(
|
||||
pixel_values=pixel_values, image_grid_thw=image_grid_thws
|
||||
)
|
||||
image_embeds = self._process_image_input(image_input)
|
||||
|
||||
image_embeds_offset = 0
|
||||
for idx, image_offset in enumerate(image_offsets):
|
||||
if image_offset < prefix_len:
|
||||
continue
|
||||
num_image_tokens = self.calculate_num_image_tokens(
|
||||
image_grid_thws[idx]
|
||||
)
|
||||
|
||||
left_idx = start_idx + (image_offset - prefix_len)
|
||||
right_idx = left_idx + num_image_tokens
|
||||
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
|
||||
hidden_size = image_embeds.shape[-1]
|
||||
|
||||
if hidden_size % tp_size != 0:
|
||||
padding_size = tp_size - (hidden_size % tp_size)
|
||||
image_embeds = F.pad(image_embeds, (0, padding_size))
|
||||
inputs_embeds = F.pad(inputs_embeds, (0, padding_size))
|
||||
|
||||
hidden_chunk_size = image_embeds.shape[-1] // tp_size
|
||||
rank = get_tensor_model_parallel_rank()
|
||||
start_dim = rank * hidden_chunk_size
|
||||
end_dim = (rank + 1) * hidden_chunk_size
|
||||
inputs_embeds[left_idx:right_idx, ..., start_dim:end_dim] = (
|
||||
image_embeds[
|
||||
image_embeds_offset : image_embeds_offset
|
||||
+ num_image_tokens,
|
||||
...,
|
||||
start_dim:end_dim,
|
||||
]
|
||||
)
|
||||
image_embeds_offset += num_image_tokens
|
||||
|
||||
input_ids = None
|
||||
hidden_states = self.model(
|
||||
input_ids=input_ids,
|
||||
positions=positions,
|
||||
forward_batch=forward_batch,
|
||||
input_embeds=inputs_embeds,
|
||||
)
|
||||
|
||||
if not get_embedding:
|
||||
return self.logits_processor(
|
||||
input_ids, hidden_states, self.lm_head, forward_batch
|
||||
)
|
||||
else:
|
||||
return self.pooler(hidden_states, forward_batch)
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
]
|
||||
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
if "visual" in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
if "visual" in name and "qkv.weight" in name:
|
||||
visual_num_heads = self.config.vision_config.num_heads
|
||||
visual_embed_dim = self.config.vision_config.hidden_size
|
||||
head_size = visual_embed_dim // visual_num_heads
|
||||
loaded_weight = loaded_weight.view(
|
||||
3, visual_num_heads, head_size, visual_embed_dim
|
||||
)
|
||||
loaded_weight = loaded_weight.transpose(0, 1)
|
||||
loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
|
||||
elif "visual" in name and "qkv.bias" in name:
|
||||
visual_num_heads = self.config.vision_config.num_heads
|
||||
visual_embed_dim = self.config.vision_config.hidden_size
|
||||
head_size = visual_embed_dim // visual_num_heads
|
||||
loaded_weight = loaded_weight.view(3, visual_num_heads, head_size)
|
||||
loaded_weight = loaded_weight.transpose(0, 1)
|
||||
loaded_weight = loaded_weight.reshape(-1)
|
||||
|
||||
if "visual" in name:
|
||||
# adapt to VisionAttention
|
||||
name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
|
||||
|
||||
try:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
except KeyError:
|
||||
print(params_dict.keys())
|
||||
raise
|
||||
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
EntryClass = [Qwen2_5_VLForConditionalGeneration]
|
||||
AutoModel.register(Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration)
|
||||
@@ -31,8 +31,9 @@ import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange
|
||||
from transformers import Qwen2VLConfig
|
||||
from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLVisionConfig
|
||||
|
||||
from sglang.srt.configs import Qwen2VLConfig, Qwen2VLVisionConfig
|
||||
from sglang.srt.hf_transformers_utils import get_processor
|
||||
from sglang.srt.layers.activation import QuickGELU
|
||||
from sglang.srt.layers.attention.vision import VisionAttention
|
||||
|
||||
Reference in New Issue
Block a user