617 lines
22 KiB
Python
617 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, TypedDict
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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 Qwen2VLConfig
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from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLVisionConfig
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from sglang.srt.hf_transformers_utils import get_processor
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from sglang.srt.layers.activation import QuickGELU
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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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MultiModalityDataPaddingPatternTokenPairs,
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general_mm_embed_routine,
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)
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from sglang.srt.managers.schedule_batch import 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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# === Vision Inputs === #
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class Qwen2VLImageInputs(TypedDict):
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pixel_values: torch.Tensor
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"""Shape:
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`(num_patches, num_channels * patch_size * patch_size)`
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"""
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image_grid_thw: torch.Tensor
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"""Shape: `(num_images, 3)`
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This should be in `(grid_t, grid_h, grid_w)` format.
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"""
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class Qwen2VLVideoInputs(TypedDict):
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pixel_values_videos: torch.Tensor
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"""Shape:
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`(num_patches,
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num_channels * temporal_patch_size * patch_size * patch_size)`
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"""
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video_grid_thw: torch.Tensor
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"""Shape: `(num_videos, 3)`
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This should be in `(grid_t, grid_h, grid_w)` format.
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"""
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# === Vision Encoder === #
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class Qwen2VisionMLP(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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act_layer: Type[nn.Module] = QuickGELU,
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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.fc1 = ColumnParallelLinear(
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in_features,
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hidden_features,
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quant_config=quant_config,
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prefix=add_prefix("fc1", prefix),
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)
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self.act = act_layer()
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self.fc2 = RowParallelLinear(
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hidden_features,
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in_features,
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quant_config=quant_config,
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prefix=add_prefix("fc2", prefix),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x_parallel, _ = self.fc1(x)
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x_parallel = self.act(x_parallel)
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x, _ = self.fc2(x_parallel)
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return x
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class Qwen2VisionBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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num_heads: int,
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mlp_ratio: float,
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act_layer: Type[nn.Module] = QuickGELU,
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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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prefix: str = "",
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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 = norm_layer(dim)
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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if attn_implementation == "sdpa":
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use_context_forward = False
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softmax_in_single_precision = False
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elif attn_implementation == "flash_attention_2":
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softmax_in_single_precision = False
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use_context_forward = True
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elif attn_implementation == "eager":
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softmax_in_single_precision = 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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softmax_in_single_precision=softmax_in_single_precision,
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flatten_batch=True,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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)
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self.mlp = Qwen2VisionMLP(
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dim,
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mlp_hidden_dim,
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act_layer=act_layer,
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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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x = x + self.mlp(self.norm2(x))
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return x
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class Qwen2VisionPatchEmbed(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 Qwen2VisionPatchMerger(nn.Module):
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def __init__(
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self,
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d_model: int,
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context_dim: int,
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norm_layer: Type[nn.Module] = None,
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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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if norm_layer is None:
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norm_layer = partial(nn.LayerNorm, eps=1e-6)
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self.ln_q = norm_layer(context_dim)
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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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d_model,
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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 Qwen2VisionRotaryEmbedding(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 Qwen2VisionTransformer(nn.Module):
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def __init__(
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self,
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vision_config: Qwen2VLVisionConfig,
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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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in_chans: int = vision_config.in_chans
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hidden_size: int = vision_config.hidden_size
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embed_dim: int = vision_config.embed_dim
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depth: int = vision_config.depth
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num_heads: int = vision_config.num_heads
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mlp_ratio: float = vision_config.mlp_ratio
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self.spatial_merge_size = spatial_merge_size
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self.patch_embed = Qwen2VisionPatchEmbed(
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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=embed_dim,
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)
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norm_layer = partial(nn.LayerNorm, eps=norm_eps)
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head_dim = embed_dim // num_heads
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self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)
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self.blocks = nn.ModuleList(
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[
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Qwen2VisionBlock(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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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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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 = Qwen2VisionPatchMerger(
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d_model=hidden_size,
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context_dim=embed_dim,
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norm_layer=norm_layer,
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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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@property
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def dtype(self) -> torch.dtype:
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return next(self.parameters()).dtype
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@property
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def device(self) -> torch.device:
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return self.blocks[0].mlp.fc2.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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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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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.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
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x = x.unsqueeze(1)
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for blk in self.blocks:
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x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings)
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# adapter
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x = self.merger(x)
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return x
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cached_get_processor = lru_cache(get_processor)
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class Qwen2VLForConditionalGeneration(nn.Module):
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def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]):
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processor = cached_get_processor(self.config._name_or_path)
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grid_t, grid_h, grid_w = image_grid_thw
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num_image_tokens = (
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grid_t
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* grid_h
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* grid_w
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// processor.image_processor.merge_size
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// processor.image_processor.merge_size
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)
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return num_image_tokens
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def __init__(
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self,
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config: Qwen2VLConfig,
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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.config = config
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self.visual = Qwen2VisionTransformer(
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config.vision_config,
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norm_eps=getattr(config, "rms_norm_eps", 1e-6),
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# NOTE: Qwen2-VL vision encoder does not support any
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# quantization method now.
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quant_config=None,
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prefix=add_prefix("visual", prefix),
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)
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self.model = Qwen2Model(
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config, quant_config, prefix=add_prefix("model", prefix)
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)
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if config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=add_prefix("lm_head", prefix),
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)
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self.logits_processor = LogitsProcessor(config)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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# Use grid_t * grid_w * grid_h to pad tokens for each image
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# add replaced padding by unique image hash
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def pad_input_ids(self, input_ids: List[int], multi_modal_inputs: MultimodalInputs):
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# Get all special token IDs
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im_start_id: int = multi_modal_inputs.im_start_id
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im_end_id: int = multi_modal_inputs.im_end_id
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media_token_pairs = [(im_start_id, im_end_id)]
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pattern = MultiModalityDataPaddingPatternTokenPairs(media_token_pairs)
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return pattern.pad_input_tokens(input_ids, multi_modal_inputs)
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def get_image_feature(self, image_input: MultimodalInputs) -> torch.Tensor:
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pixel_values = image_input.pixel_values.type(self.visual.dtype)
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image_embeds = self.visual(pixel_values, grid_thw=image_input.image_grid_thws)
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return image_embeds
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def _process_video_input(self, video_input: Qwen2VLVideoInputs) -> torch.Tensor:
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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 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-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
|
|
|
|
if not (
|
|
forward_batch.forward_mode.is_decode()
|
|
or not forward_batch.contains_image_inputs()
|
|
):
|
|
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()}"
|
|
)
|
|
|
|
inputs_embeds = general_mm_embed_routine(
|
|
input_ids=input_ids,
|
|
forward_batch=forward_batch,
|
|
embed_tokens=self.get_input_embeddings(),
|
|
mm_data_embedding_func=self.get_image_feature,
|
|
)
|
|
|
|
hidden_states = self.model(
|
|
input_ids=None,
|
|
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
|
|
if self.config.tie_word_embeddings and "lm_head.weight" in name:
|
|
continue
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
|
|
# 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.embed_dim
|
|
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.embed_dim
|
|
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 = Qwen2VLForConditionalGeneration
|