623 lines
22 KiB
Python
623 lines
22 KiB
Python
# 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 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.activations import ACT2FN
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from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm
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from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import (
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Qwen2_5_VLConfig,
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Qwen2_5_VLVisionConfig,
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)
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from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
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Qwen2_5_VisionPatchEmbed,
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Qwen2_5_VisionRotaryEmbedding,
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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.mm_utils import (
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MultiModalityDataPaddingPatternMultimodalTokens,
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general_mm_embed_routine,
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)
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from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
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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.utils import add_prefix
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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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prefix: str = "",
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):
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super().__init__()
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self.gate_proj = ColumnParallelLinear(
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in_features,
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hidden_features,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("gate_proj", prefix),
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)
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self.up_proj = ColumnParallelLinear(
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in_features,
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hidden_features,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("up_proj", prefix),
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)
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self.down_proj = RowParallelLinear(
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hidden_features,
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in_features,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("down_proj", prefix),
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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] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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num_dummy_heads: int = 0,
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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 is None:
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softmax_in_single_precision = False
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qkv_backend = None
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flatten_batch = True
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elif attn_implementation == "sdpa":
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softmax_in_single_precision = False
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qkv_backend = "sdpa"
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flatten_batch = True
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elif attn_implementation == "flash_attention_2":
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softmax_in_single_precision = False
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qkv_backend = "triton_attn"
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flatten_batch = True
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elif attn_implementation == "eager":
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softmax_in_single_precision = True
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qkv_backend = "sdpa"
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flatten_batch = True
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elif attn_implementation == "flash_attention_3":
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softmax_in_single_precision = False
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qkv_backend = "fa3"
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flatten_batch = True
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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=True,
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rotary_embed="normal",
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proj_bias=True,
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qkv_backend=qkv_backend,
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softmax_in_single_precision=softmax_in_single_precision,
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flatten_batch=flatten_batch,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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num_dummy_heads=num_dummy_heads,
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)
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self.mlp = Qwen2_5_VLMLP(
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dim,
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intermediate_dim,
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hidden_act=hidden_act,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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def forward(
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self,
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x: torch.Tensor,
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cu_seqlens: torch.Tensor,
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position_embeddings: 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,
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cu_seqlens=cu_seqlens,
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position_embeddings=position_embeddings,
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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_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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prefix: str = "",
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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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prefix=add_prefix("mlp.0", prefix),
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),
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nn.GELU(),
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RowParallelLinear(
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self.hidden_size,
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dim,
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bias=True,
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quant_config=quant_config,
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prefix=add_prefix("mlp.2", prefix),
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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_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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prefix: str = "",
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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_channels: int = vision_config.in_channels
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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_channels=in_channels,
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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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quant_config=quant_config,
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prefix=add_prefix(f"blocks.{i}", prefix),
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)
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for i 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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prefix=add_prefix("merger", prefix),
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)
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def get_window_index(self, grid_thw):
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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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window_index: list = []
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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.patch_embed.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 i in range(grid_thw.size(0)):
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t, h, w = grid_thw[i].tolist()
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hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
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hpos_ids = 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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hpos_ids = hpos_ids.permute(0, 2, 1, 3)
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hpos_ids = hpos_ids.flatten()
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wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
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wpos_ids = 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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wpos_ids = wpos_ids.permute(0, 2, 1, 3)
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wpos_ids = wpos_ids.flatten()
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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=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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emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
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position_embeddings = (emb.cos(), emb.sin())
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# compute cu_seqlens
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cu_seqlens = torch.cat(
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[
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torch.tensor([0], device=grid_thw.device),
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(grid_thw[:, 0] * grid_thw[:, 1] * grid_thw[:, 2]).cumsum(dim=0),
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]
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)
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cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
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|
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# transformers
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x = x.unsqueeze(1)
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for layer_num, blk in enumerate(self.blocks):
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if layer_num in self.fullatt_block_indexes:
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cu_seqlens_now = cu_seqlens
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else:
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cu_seqlens_now = cu_window_seqlens
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x = blk(
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x, cu_seqlens=cu_seqlens_now, position_embeddings=position_embeddings
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)
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# adapter
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x = self.merger(x)
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reverse_indices = torch.argsort(window_index)
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x = x[reverse_indices, :]
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return x
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cached_get_processor = lru_cache(get_processor)
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|
|
|
|
class Qwen2_5_VLForConditionalGeneration(nn.Module):
|
|
# BitandBytes specific attributes
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|
default_bitsandbytes_target_modules = [
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".gate_proj.",
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".down_proj.",
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".up_proj.",
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".q_proj.",
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".k_proj.",
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".v_proj.",
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".o_proj.",
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]
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bitsandbytes_stacked_params_mapping = {
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# shard_name, weight_name, index
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|
"q_proj": ("qkv_proj", 0),
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"k_proj": ("qkv_proj", 1),
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"v_proj": ("qkv_proj", 2),
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"gate_proj": ("gate_up_proj", 0),
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"up_proj": ("gate_up_proj", 1),
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}
|
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|
|
def __init__(
|
|
self,
|
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config: Qwen2_5_VLConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> 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_5-VL vision encoder currently supports BitsAndBytes 4-bit quantization.
|
|
# Other quantization methods (e.g., GPTQ, AWQ) are untested and may not be supported.
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("visual", prefix),
|
|
)
|
|
|
|
self.model = Qwen2Model(
|
|
config,
|
|
quant_config,
|
|
prefix=add_prefix("model", prefix),
|
|
)
|
|
|
|
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,
|
|
prefix=add_prefix("lm_head", prefix),
|
|
)
|
|
self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
|
|
|
|
self.logits_processor = LogitsProcessor(config)
|
|
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
|
|
|
|
def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
|
|
pattern = MultiModalityDataPaddingPatternMultimodalTokens()
|
|
return pattern.pad_input_tokens(input_ids, mm_inputs)
|
|
|
|
def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
|
|
# in qwen-vl, last dim is the same
|
|
pixel_values = torch.cat([item.feature for item in items], dim=0).type(
|
|
self.visual.dtype
|
|
)
|
|
image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
|
|
assert pixel_values.dim() == 2, pixel_values.dim()
|
|
assert image_grid_thw.dim() == 2, image_grid_thw.dim()
|
|
image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
|
|
return image_embeds
|
|
|
|
def get_video_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
|
|
# in qwen-vl, last dim is the same
|
|
pixel_values = torch.cat([item.feature for item in items], dim=0).type(
|
|
self.visual.dtype
|
|
)
|
|
video_grid_thw = torch.concat([item.video_grid_thw for item in items], dim=0)
|
|
assert pixel_values.dim() == 2, pixel_values.dim()
|
|
assert video_grid_thw.dim() == 2, video_grid_thw.dim()
|
|
video_embeds = self.visual(pixel_values, grid_thw=video_grid_thw)
|
|
return video_embeds
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
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 self.is_mrope_enabled:
|
|
positions = forward_batch.mrope_positions
|
|
|
|
if not (
|
|
forward_batch.forward_mode.is_decode()
|
|
or not forward_batch.contains_image_inputs()
|
|
):
|
|
if self.is_mrope_enabled:
|
|
assert positions.ndim == 2 and positions.size(0) == 3, (
|
|
"multimodal section rotary embedding requires "
|
|
f"(3, seq_len) positions, but got {positions.size()}"
|
|
)
|
|
|
|
hidden_states = general_mm_embed_routine(
|
|
input_ids=input_ids,
|
|
forward_batch=forward_batch,
|
|
language_model=self.model,
|
|
multimodal_model=self,
|
|
positions=positions,
|
|
)
|
|
|
|
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:
|
|
# 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]
|