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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 PygmalionAI 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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+from array import array
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+from functools import lru_cache, partial
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+from typing import (Iterable, List, Mapping, Optional, Tuple, Type, TypedDict,
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+ Union)
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+
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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, repeat
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+from loguru import logger
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+from PIL import Image
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+from transformers.image_utils import (get_image_size,
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+ infer_channel_dimension_format,
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+ to_numpy_array)
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+from transformers.models.qwen2_vl.image_processing_qwen2_vl import (
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+ make_batched_images, make_batched_videos, smart_resize)
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+
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+import aphrodite.common.envs as envs
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+from aphrodite.attention import AttentionMetadata
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+from aphrodite.attention.selector import (_Backend, backend_name_to_enum,
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+ get_global_forced_attn_backend)
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+from aphrodite.common.config import CacheConfig, MultiModalConfig
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+from aphrodite.common.logger import log_once
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+from aphrodite.common.sequence import (APHRODITE_TOKEN_ID_ARRAY_TYPE,
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+ IntermediateTensors, SequenceData)
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+from aphrodite.distributed import parallel_state
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+from aphrodite.distributed import utils as dist_utils
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+from aphrodite.inputs import INPUT_REGISTRY, InputContext, LLMInputs
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+from aphrodite.modeling.layers.activation import QuickGELU
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+from aphrodite.modeling.layers.linear import (ColumnParallelLinear,
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+ RowParallelLinear)
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+from aphrodite.modeling.layers.logits_processor import LogitsProcessor
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+from aphrodite.modeling.layers.sampler import Sampler, SamplerOutput
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+from aphrodite.modeling.layers.vocab_parallel_embedding import ParallelLMHead
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+from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
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+from aphrodite.modeling.models.interfaces import SupportsMultiModal
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+from aphrodite.modeling.models.qwen2 import Qwen2Model
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+from aphrodite.modeling.sampling_metadata import SamplingMetadata
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+from aphrodite.multimodal import (MULTIMODAL_REGISTRY, MultiModalDataDict,
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+ MultiModalInputs)
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+from aphrodite.multimodal.base import MultiModalData
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+from aphrodite.multimodal.image import cached_get_image_processor
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+from aphrodite.platforms import current_platform
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+from aphrodite.quantization import QuantizationConfig
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+from aphrodite.transformers_utils.configs import (Qwen2VLConfig,
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+ Qwen2VLVisionConfig)
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+from aphrodite.transformers_utils.processor import get_processor
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+
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+
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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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+
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+ This should be in `(grid_t, grid_h, grid_w)` format.
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+ """
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+
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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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+
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+ This should be in `(grid_t, grid_h, grid_w)` format.
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+ """
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+
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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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+ ):
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+ super().__init__()
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+ self.fc1 = ColumnParallelLinear(
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+ in_features, hidden_features, quant_config=quant_config
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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, in_features, quant_config=quant_config
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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_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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+
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+
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+def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
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+ if not interleaved:
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+ x1, x2 = x.chunk(2, dim=-1)
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+ return torch.cat((-x2, x1), dim=-1)
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+ else:
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+ x1, x2 = x[..., ::2], x[..., 1::2]
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+ return rearrange(
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+ torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
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+ )
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+
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+
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+def apply_rotary_emb_torch(
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+ x: torch.Tensor,
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+ cos: torch.Tensor,
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+ sin: torch.Tensor,
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+ interleaved: bool = False,
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+) -> torch.Tensor:
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+ """
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+ x: (batch_size, seqlen, nheads, headdim)
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+ cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
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+ """
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+ ro_dim = cos.shape[-1] * 2
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+ assert ro_dim <= x.shape[-1]
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+ cos = repeat(
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+ cos,
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+ "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)",
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+ )
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+ sin = repeat(
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+ sin,
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+ "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)",
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+ )
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+ return torch.cat(
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+ [
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+ x[..., :ro_dim] * cos
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+ + rotate_half(x[..., :ro_dim], interleaved) * sin,
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+ x[..., ro_dim:],
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+ ],
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+ dim=-1,
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+ )
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+
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+
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+def apply_rotary_pos_emb_vision(
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+ t: torch.Tensor, freqs: torch.Tensor
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+) -> torch.Tensor:
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+ t_ = t.float()
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+ cos = freqs.cos()
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+ sin = freqs.sin()
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+ output = apply_rotary_emb_torch(t_, cos, sin).type_as(t)
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+ return output
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+
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+
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+class Qwen2VisionAttention(nn.Module):
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+ def __init__(
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+ self,
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+ embed_dim: Optional[int] = None,
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+ num_heads: Optional[int] = None,
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+ projection_size: Optional[int] = None,
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+ quant_config: Optional[QuantizationConfig] = None,
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+ ) -> None:
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+ super().__init__()
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+ # Per attention head and per partition values.
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+ world_size = parallel_state.get_tensor_model_parallel_world_size()
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+ self.hidden_size_per_attention_head = dist_utils.divide(
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+ projection_size, num_heads
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+ )
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+ self.num_attention_heads_per_partition = dist_utils.divide(
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+ num_heads, world_size
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+ )
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+ self.qkv = ColumnParallelLinear(
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+ input_size=embed_dim,
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+ output_size=3 * projection_size,
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+ quant_config=quant_config,
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+ )
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+ self.proj = RowParallelLinear(
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+ input_size=projection_size,
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+ output_size=embed_dim,
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+ quant_config=quant_config,
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+ )
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+ # Detect attention implementation.
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+ selected_backend: Optional[_Backend] = get_global_forced_attn_backend()
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+ if selected_backend is None:
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+ backend_by_env_var: Optional[str] = envs.APHRODITE_ATTENTION_BACKEND
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+ if backend_by_env_var is not None:
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+ selected_backend = backend_name_to_enum(backend_by_env_var)
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+ if selected_backend is None:
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+ # For Volta and Turing GPUs, use xformers instead.
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+ device_available = current_platform.get_device_capability()[0] >= 8
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+ if device_available:
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+ from transformers.utils import is_flash_attn_2_available
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+
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+ if is_flash_attn_2_available():
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+ self._use_flash_attn = True
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+ else:
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+ log_once(
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+ level="WARNING",
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+ message=
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+ "Current Qwen2-VL implementation has a bug with "
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+ "`aphrodite-flash-attn` inside vision module, so we use"
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+ " xformers backend instead. You can run `pip install "
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+ "flash-attn to use flash-attention backend."
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+ )
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+ self._use_flash_attn = False
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+ else:
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+ self._use_flash_attn = False
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+ else:
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+ if selected_backend == _Backend.FLASH_ATTN:
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+ self._use_flash_attn = True
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+ elif selected_backend == _Backend.XFORMERS:
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+ self._use_flash_attn = False
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+ else:
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+ raise RuntimeError(
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+ f"Qwen2-VL does not support {selected_backend} backend now."
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+ )
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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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+ rotary_pos_emb: torch.Tensor = None,
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+ ) -> torch.Tensor:
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+ # [s, b, c] --> [s, b, head * 3 * head_dim]
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+ x, _ = self.qkv(x)
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+ # [s, b, head * 3 * head_dim] --> [s, b, head, 3 * head_dim]
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+ new_x_shape = x.size()[:-1] + (
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+ self.num_attention_heads_per_partition,
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+ 3 * self.hidden_size_per_attention_head,
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+ )
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+ x = x.view(*new_x_shape)
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+ # [s, b, head, 3 * head_dim] --> 3 [s, b, head, head_dim]
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+ q, k, v = dist_utils.split_tensor_along_last_dim(x, 3)
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+ batch_size = q.shape[1]
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+ q, k, v = [
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+ rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v)
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+ ]
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+ if rotary_pos_emb is not None:
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+ q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
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+ k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
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+ if self._use_flash_attn:
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+ # from aphrodite_flash_attn.flash_attn_interface import (
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+ # flash_attn_varlen_func)
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+ from flash_attn import flash_attn_varlen_func
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+
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+ q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
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+ max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
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+ output = flash_attn_varlen_func(
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+ q,
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+ k,
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+ v,
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+ cu_seqlens_q=cu_seqlens,
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+ cu_seqlens_k=cu_seqlens,
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+ max_seqlen_q=max_seqlen,
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+ max_seqlen_k=max_seqlen,
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+ dropout_p=0,
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+ causal=False,
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+ )
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+ context_layer = rearrange(
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+ output, "(b s) ... -> b s ...", b=batch_size
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+ )
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+ else:
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+ from xformers import ops as xops
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+ from xformers.ops.fmha.attn_bias import BlockDiagonalMask
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+
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+ seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
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+ attn_bias = BlockDiagonalMask.from_seqlens(
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+ q_seqlen=seqlens, kv_seqlen=None
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+ )
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+ context_layer = xops.memory_efficient_attention_forward(
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+ q, k, v, attn_bias=attn_bias, p=0, scale=None
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+ )
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+ context_layer = rearrange(
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+ context_layer, "b s h d -> s b (h d)"
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+ ).contiguous()
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+ output, _ = self.proj(context_layer)
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+ return output
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+
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+
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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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+ 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 = 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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+ self.attn = Qwen2VisionAttention(
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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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+ quant_config=quant_config,
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+ )
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+ self.mlp = Qwen2VisionMLP(
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+ dim, mlp_hidden_dim, act_layer=act_layer, quant_config=quant_config
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+ )
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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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+ rotary_pos_emb: torch.Tensor,
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+ ) -> torch.Tensor:
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+ x = x + self.attn(
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+ self.norm1(x), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
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+ )
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+ x = x + self.mlp(self.norm2(x))
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+ return x
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+
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+
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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,
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+ embed_dim,
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+ kernel_size=kernel_size,
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+ stride=kernel_size,
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+ bias=False,
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+ )
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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(
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+ L, -1, self.temporal_patch_size, self.patch_size, self.patch_size
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+ )
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+ x = self.proj(x).view(L, self.embed_dim)
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+ return x
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+
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+
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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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|
+ ) -> None:
|
|
|
+ super().__init__()
|
|
|
+ self.hidden_size = context_dim * (spatial_merge_size**2)
|
|
|
+ if norm_layer is None:
|
|
|
+ norm_layer = partial(nn.LayerNorm, eps=1e-6)
|
|
|
+ self.ln_q = norm_layer(context_dim)
|
|
|
+ self.mlp = nn.ModuleList(
|
|
|
+ [
|
|
|
+ ColumnParallelLinear(
|
|
|
+ self.hidden_size,
|
|
|
+ self.hidden_size,
|
|
|
+ bias=True,
|
|
|
+ quant_config=quant_config,
|
|
|
+ ),
|
|
|
+ nn.GELU(),
|
|
|
+ RowParallelLinear(
|
|
|
+ self.hidden_size,
|
|
|
+ d_model,
|
|
|
+ bias=True,
|
|
|
+ quant_config=quant_config,
|
|
|
+ ),
|
|
|
+ ]
|
|
|
+ )
|
|
|
+
|
|
|
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
|
+ x = self.ln_q(x)
|
|
|
+ x = x.view(-1, self.hidden_size)
|
|
|
+ mlp_fc1, mlp_act, mlp_fc2 = self.mlp
|
|
|
+ x_parallel, _ = mlp_fc1(x)
|
|
|
+ x_parallel = mlp_act(x_parallel)
|
|
|
+ out, _ = mlp_fc2(x_parallel)
|
|
|
+ return out
|
|
|
+
|
|
|
+
|
|
|
+class Qwen2VisionRotaryEmbedding(nn.Module):
|
|
|
+ def __init__(self, dim: int, theta: float = 10000.0) -> None:
|
|
|
+ super().__init__()
|
|
|
+ self.dim = dim
|
|
|
+ self.theta = theta
|
|
|
+ inv_freq = 1.0 / (
|
|
|
+ theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)
|
|
|
+ )
|
|
|
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
|
+ self._seq_len_cached = 0
|
|
|
+ self._freqs_cached = None
|
|
|
+
|
|
|
+ def update_freqs_cache(self, seqlen: int) -> None:
|
|
|
+ if seqlen > self._seq_len_cached:
|
|
|
+ seqlen *= 2
|
|
|
+ self._seq_len_cached = seqlen
|
|
|
+ self.inv_freq = 1.0 / (
|
|
|
+ self.theta
|
|
|
+ ** (
|
|
|
+ torch.arange(
|
|
|
+ 0,
|
|
|
+ self.dim,
|
|
|
+ 2,
|
|
|
+ dtype=torch.float,
|
|
|
+ device=self.inv_freq.device,
|
|
|
+ )
|
|
|
+ / self.dim
|
|
|
+ )
|
|
|
+ )
|
|
|
+ seq = torch.arange(
|
|
|
+ seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
|
|
|
+ )
|
|
|
+ freqs = torch.outer(seq, self.inv_freq)
|
|
|
+ self._freqs_cached = freqs
|
|
|
+
|
|
|
+ def forward(self, seqlen: int) -> torch.Tensor:
|
|
|
+ self.update_freqs_cache(seqlen)
|
|
|
+ return self._freqs_cached[:seqlen]
|
|
|
+
|
|
|
+
|
|
|
+class Qwen2VisionTransformer(nn.Module):
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ vision_config: Qwen2VLVisionConfig,
|
|
|
+ norm_eps: float = 1e-6,
|
|
|
+ quant_config: Optional[QuantizationConfig] = None,
|
|
|
+ ) -> None:
|
|
|
+ super().__init__()
|
|
|
+ patch_size: int = vision_config.patch_size
|
|
|
+ temporal_patch_size: int = vision_config.temporal_patch_size
|
|
|
+ spatial_merge_size: int = vision_config.spatial_merge_size
|
|
|
+ in_chans: int = vision_config.in_chans
|
|
|
+ hidden_size: int = vision_config.hidden_size
|
|
|
+ embed_dim: int = vision_config.embed_dim
|
|
|
+ depth: int = vision_config.depth
|
|
|
+ num_heads: int = vision_config.num_heads
|
|
|
+ mlp_ratio: float = vision_config.mlp_ratio
|
|
|
+ self.spatial_merge_size = spatial_merge_size
|
|
|
+ self.patch_embed = Qwen2VisionPatchEmbed(
|
|
|
+ patch_size=patch_size,
|
|
|
+ temporal_patch_size=temporal_patch_size,
|
|
|
+ in_chans=in_chans,
|
|
|
+ embed_dim=embed_dim,
|
|
|
+ )
|
|
|
+ norm_layer = partial(nn.LayerNorm, eps=norm_eps)
|
|
|
+ head_dim = embed_dim // num_heads
|
|
|
+ self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)
|
|
|
+ self.blocks = nn.ModuleList(
|
|
|
+ [
|
|
|
+ Qwen2VisionBlock(
|
|
|
+ dim=embed_dim,
|
|
|
+ num_heads=num_heads,
|
|
|
+ mlp_ratio=mlp_ratio,
|
|
|
+ norm_layer=norm_layer,
|
|
|
+ quant_config=quant_config,
|
|
|
+ )
|
|
|
+ for _ in range(depth)
|
|
|
+ ]
|
|
|
+ )
|
|
|
+ self.merger = Qwen2VisionPatchMerger(
|
|
|
+ d_model=hidden_size,
|
|
|
+ context_dim=embed_dim,
|
|
|
+ norm_layer=norm_layer,
|
|
|
+ quant_config=quant_config,
|
|
|
+ )
|
|
|
+
|
|
|
+ @property
|
|
|
+ def dtype(self) -> torch.dtype:
|
|
|
+ return self.blocks[0].mlp.fc2.weight.dtype
|
|
|
+
|
|
|
+ @property
|
|
|
+ def device(self) -> torch.device:
|
|
|
+ return self.blocks[0].mlp.fc2.weight.device
|
|
|
+
|
|
|
+ def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
|
|
|
+ pos_ids = []
|
|
|
+ for t, h, w in grid_thw:
|
|
|
+ hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
|
|
|
+ wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
|
|
|
+ hpos_ids = (
|
|
|
+ hpos_ids.reshape(
|
|
|
+ h // self.spatial_merge_size,
|
|
|
+ self.spatial_merge_size,
|
|
|
+ w // self.spatial_merge_size,
|
|
|
+ self.spatial_merge_size,
|
|
|
+ )
|
|
|
+ .permute(0, 2, 1, 3)
|
|
|
+ .flatten()
|
|
|
+ )
|
|
|
+ wpos_ids = (
|
|
|
+ wpos_ids.reshape(
|
|
|
+ h // self.spatial_merge_size,
|
|
|
+ self.spatial_merge_size,
|
|
|
+ w // self.spatial_merge_size,
|
|
|
+ self.spatial_merge_size,
|
|
|
+ )
|
|
|
+ .permute(0, 2, 1, 3)
|
|
|
+ .flatten()
|
|
|
+ )
|
|
|
+ pos_ids.append(
|
|
|
+ torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)
|
|
|
+ )
|
|
|
+ pos_ids = torch.cat(pos_ids, dim=0)
|
|
|
+ max_grid_size = grid_thw[:, 1:].max()
|
|
|
+ rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
|
|
|
+ rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
|
|
|
+ return rotary_pos_emb
|
|
|
+
|
|
|
+ def forward(
|
|
|
+ self,
|
|
|
+ x: torch.Tensor,
|
|
|
+ grid_thw: torch.Tensor,
|
|
|
+ ) -> torch.Tensor:
|
|
|
+ # patchify
|
|
|
+ x = x.to(device=self.device, dtype=self.dtype)
|
|
|
+ x = self.patch_embed(x)
|
|
|
+ # compute position embedding
|
|
|
+ rotary_pos_emb = self.rot_pos_emb(grid_thw)
|
|
|
+ # compute cu_seqlens
|
|
|
+ cu_seqlens = torch.repeat_interleave(
|
|
|
+ grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
|
|
|
+ ).cumsum(dim=0, dtype=torch.int32)
|
|
|
+ cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
|
|
|
+ # transformers
|
|
|
+ x = x.unsqueeze(1)
|
|
|
+ for blk in self.blocks:
|
|
|
+ x = blk(x, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb)
|
|
|
+ # adapter
|
|
|
+ x = self.merger(x)
|
|
|
+ return x
|
|
|
+
|
|
|
+
|
|
|
+# === Vision input helpers === #
|
|
|
+cached_get_processor = lru_cache(get_processor)
|
|
|
+
|
|
|
+
|
|
|
+def mm_input_mapper_for_qwen2_vl(
|
|
|
+ ctx: InputContext,
|
|
|
+ data: MultiModalData[object],
|
|
|
+ data_type_key: str,
|
|
|
+) -> MultiModalInputs:
|
|
|
+ """Input mapper for Qwen2-VL."""
|
|
|
+ model_config = ctx.model_config
|
|
|
+ image_processor = cached_get_image_processor(
|
|
|
+ model_config.model, trust_remote_code=model_config.trust_remote_code
|
|
|
+ )
|
|
|
+ if image_processor is None:
|
|
|
+ raise RuntimeError(
|
|
|
+ "No HuggingFace processor is available "
|
|
|
+ "to process the image object"
|
|
|
+ )
|
|
|
+ images = None
|
|
|
+ videos = None
|
|
|
+ if data_type_key == "image":
|
|
|
+ images = data
|
|
|
+ else:
|
|
|
+ assert data_type_key == "video"
|
|
|
+ videos = data
|
|
|
+ try:
|
|
|
+ batch_data = image_processor.preprocess(
|
|
|
+ images=images, videos=videos, return_tensors="pt"
|
|
|
+ ).data
|
|
|
+ except Exception:
|
|
|
+ logger.error("Failed to process image (%s)", data)
|
|
|
+ raise
|
|
|
+ return MultiModalInputs(batch_data)
|
|
|
+
|
|
|
+
|
|
|
+image_input_mapper_for_qwen2_vl = partial(
|
|
|
+ mm_input_mapper_for_qwen2_vl, data_type_key="image"
|
|
|
+)
|
|
|
+video_input_mapper_for_qwen2_vl = partial(
|
|
|
+ mm_input_mapper_for_qwen2_vl, data_type_key="video"
|
|
|
+)
|
|
|
+
|
|
|
+
|
|
|
+def _get_vision_info(
|
|
|
+ image_processor,
|
|
|
+ height: int,
|
|
|
+ width: int,
|
|
|
+ min_pixels: int,
|
|
|
+ max_pixels: int,
|
|
|
+ do_resize: bool = True,
|
|
|
+ data_type_key: str = "image",
|
|
|
+ mm_count: int = 1,
|
|
|
+):
|
|
|
+ """Get information (resized height / width and number of vision tokens)
|
|
|
+ of input image / video frame."""
|
|
|
+ if do_resize:
|
|
|
+ resized_height, resized_width = smart_resize(
|
|
|
+ height=height,
|
|
|
+ width=width,
|
|
|
+ factor=image_processor.patch_size * image_processor.merge_size,
|
|
|
+ min_pixels=min_pixels,
|
|
|
+ max_pixels=max_pixels,
|
|
|
+ )
|
|
|
+ else:
|
|
|
+ resized_height, resized_width = height, width
|
|
|
+ if data_type_key == "image":
|
|
|
+ grid_t = mm_count
|
|
|
+ else:
|
|
|
+ assert data_type_key == "video"
|
|
|
+ grid_t = max(mm_count // image_processor.temporal_patch_size, 1)
|
|
|
+ grid_h = resized_height // image_processor.patch_size
|
|
|
+ grid_w = resized_width // image_processor.patch_size
|
|
|
+ vision_tokens = grid_t * grid_h * grid_w
|
|
|
+ llm_num_vision_tokens = (
|
|
|
+ vision_tokens
|
|
|
+ // image_processor.merge_size
|
|
|
+ // image_processor.merge_size
|
|
|
+ )
|
|
|
+ return resized_height, resized_width, llm_num_vision_tokens
|
|
|
+
|
|
|
+
|
|
|
+def _get_max_image_info(
|
|
|
+ image_processor,
|
|
|
+ data_type_key: str = "image",
|
|
|
+ mm_count: int = 1,
|
|
|
+):
|
|
|
+ return _get_vision_info(
|
|
|
+ image_processor,
|
|
|
+ height=9999999,
|
|
|
+ width=9999999,
|
|
|
+ # Limit min / max pixels.
|
|
|
+ min_pixels=max(image_processor.min_pixels, 28 * 28),
|
|
|
+ max_pixels=min(image_processor.max_pixels, 1280 * 28 * 28),
|
|
|
+ data_type_key=data_type_key,
|
|
|
+ mm_count=mm_count,
|
|
|
+ )
|
|
|
+
|
|
|
+
|
|
|
+def get_max_qwen2_vl_mm_tokens(ctx: InputContext, data_type_key: str) -> int:
|
|
|
+ image_processor = cached_get_image_processor(ctx.model_config.model)
|
|
|
+ (
|
|
|
+ max_resized_height,
|
|
|
+ max_resized_width,
|
|
|
+ max_llm_image_tokens,
|
|
|
+ ) = _get_max_image_info(
|
|
|
+ image_processor, data_type_key=data_type_key, mm_count=1
|
|
|
+ )
|
|
|
+ return max_llm_image_tokens
|
|
|
+
|
|
|
+
|
|
|
+get_max_qwen2_vl_image_tokens = partial(
|
|
|
+ get_max_qwen2_vl_mm_tokens, data_type_key="image"
|
|
|
+)
|
|
|
+get_max_qwen2_vl_video_tokens = partial(
|
|
|
+ get_max_qwen2_vl_mm_tokens, data_type_key="video"
|
|
|
+)
|
|
|
+
|
|
|
+
|
|
|
+def dummy_data_for_qwen2_vl(
|
|
|
+ ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int]
|
|
|
+) -> Tuple[SequenceData, Optional[MultiModalDataDict]]:
|
|
|
+ image_processor = cached_get_image_processor(ctx.model_config.model)
|
|
|
+ num_images = mm_counts["image"]
|
|
|
+ (
|
|
|
+ max_resized_height,
|
|
|
+ max_resized_width,
|
|
|
+ max_llm_image_tokens,
|
|
|
+ ) = _get_max_image_info(
|
|
|
+ image_processor, data_type_key="image", mm_count=num_images
|
|
|
+ )
|
|
|
+ if seq_len - max_llm_image_tokens - 2 < 0:
|
|
|
+ raise RuntimeError(
|
|
|
+ f"Qwen2-VL cannot process {num_images} images in a prompt, "
|
|
|
+ "please increase max_model_len or reduce image limit by "
|
|
|
+ "--limit-mm-per-prompt."
|
|
|
+ )
|
|
|
+ # Check video counts.
|
|
|
+ num_videos = mm_counts["video"]
|
|
|
+ (
|
|
|
+ max_resized_height,
|
|
|
+ max_resized_width,
|
|
|
+ max_llm_video_tokens,
|
|
|
+ ) = _get_max_image_info(
|
|
|
+ image_processor, data_type_key="video", mm_count=num_videos
|
|
|
+ )
|
|
|
+ if seq_len - max_llm_video_tokens - 2 < 0:
|
|
|
+ raise RuntimeError(
|
|
|
+ f"Qwen2-VL cannot process {num_images} videos in a prompt, "
|
|
|
+ "please increase max_model_len or reduce video limit by "
|
|
|
+ "--limit-mm-per-prompt."
|
|
|
+ )
|
|
|
+ hf_config = ctx.get_hf_config(Qwen2VLConfig)
|
|
|
+ token_ids = array(
|
|
|
+ APHRODITE_TOKEN_ID_ARRAY_TYPE, [hf_config.vision_start_token_id]
|
|
|
+ )
|
|
|
+ token_ids += (
|
|
|
+ array(APHRODITE_TOKEN_ID_ARRAY_TYPE, [hf_config.image_token_id])
|
|
|
+ * max_llm_image_tokens
|
|
|
+ )
|
|
|
+ token_ids += array(
|
|
|
+ APHRODITE_TOKEN_ID_ARRAY_TYPE, [hf_config.vision_end_token_id]
|
|
|
+ )
|
|
|
+ token_ids += array(APHRODITE_TOKEN_ID_ARRAY_TYPE, [0]) * (
|
|
|
+ seq_len - max_llm_image_tokens - 2
|
|
|
+ )
|
|
|
+ dummy_seqdata = SequenceData(token_ids)
|
|
|
+ dummy_image = Image.new(
|
|
|
+ "RGB", (max_resized_width, max_resized_height), color=0
|
|
|
+ )
|
|
|
+ return dummy_seqdata, {
|
|
|
+ "image": dummy_image if num_images == 1 else [dummy_image] * num_images
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+def _get_llm_num_vision_tokens(
|
|
|
+ mm_inputs: list,
|
|
|
+ data_type_key: str,
|
|
|
+ image_processor,
|
|
|
+):
|
|
|
+ """Get number of vision tokens of multimodal inputs.
|
|
|
+ This method is derived from `transformers.models.qwen2_vl.
|
|
|
+ image_processing_qwen2_vl.Qwen2VLImageProcessor._preprocess`.
|
|
|
+ """
|
|
|
+ image = to_numpy_array(mm_inputs[0])
|
|
|
+ input_data_format = infer_channel_dimension_format(image)
|
|
|
+ height, width = get_image_size(image, channel_dim=input_data_format)
|
|
|
+ _, _, llm_num_vision_tokens = _get_vision_info(
|
|
|
+ image_processor,
|
|
|
+ height=height,
|
|
|
+ width=width,
|
|
|
+ min_pixels=image_processor.min_pixels,
|
|
|
+ max_pixels=image_processor.max_pixels,
|
|
|
+ do_resize=image_processor.do_resize,
|
|
|
+ data_type_key=data_type_key,
|
|
|
+ mm_count=len(mm_inputs),
|
|
|
+ )
|
|
|
+ return llm_num_vision_tokens
|
|
|
+
|
|
|
+
|
|
|
+def input_processor_for_qwen2_vl(
|
|
|
+ ctx: InputContext, llm_inputs: LLMInputs
|
|
|
+) -> LLMInputs:
|
|
|
+ multi_modal_data = llm_inputs.get("multi_modal_data", None)
|
|
|
+ if multi_modal_data is None:
|
|
|
+ return llm_inputs
|
|
|
+ image_inputs = multi_modal_data.get("image", None)
|
|
|
+ video_inputs = multi_modal_data.get("video", None)
|
|
|
+ processor = cached_get_processor(ctx.model_config.model)
|
|
|
+ image_processor = processor.image_processor
|
|
|
+ hf_config = ctx.get_hf_config(Qwen2VLConfig)
|
|
|
+ # To avoid redundant processing of vision objects (resize, rescale, etc.),
|
|
|
+ # we extract code of calculating number of vision tokens from
|
|
|
+ # `transformers.models.qwen2_vl.processing_qwen2_vl.Qwen2VLProcessor`.
|
|
|
+ #
|
|
|
+ # The following code is equivalent to:
|
|
|
+ # prompt = llm_inputs["prompt"]
|
|
|
+ # inputs = processor(text=[prompt],
|
|
|
+ # images=image_inputs,
|
|
|
+ # videos=video_inputs,
|
|
|
+ # padding=True,
|
|
|
+ # return_tensors="pt")
|
|
|
+ # prompt_token_ids = inputs["input_ids"][0].tolist()
|
|
|
+ prompt_token_ids = llm_inputs.get("prompt_token_ids", None)
|
|
|
+ if prompt_token_ids is None:
|
|
|
+ prompt = llm_inputs["prompt"]
|
|
|
+ prompt_token_ids = processor.tokenizer(
|
|
|
+ prompt,
|
|
|
+ padding=True,
|
|
|
+ return_tensors=None,
|
|
|
+ )["input_ids"]
|
|
|
+ # Expand image pad tokens.
|
|
|
+ if image_inputs is not None:
|
|
|
+ image_indices = [
|
|
|
+ idx
|
|
|
+ for idx, token in enumerate(prompt_token_ids)
|
|
|
+ if token == hf_config.image_token_id
|
|
|
+ ]
|
|
|
+ image_inputs = make_batched_images(image_inputs)
|
|
|
+ assert len(image_indices) == len(image_inputs)
|
|
|
+ prompt_token_ids_with_image = []
|
|
|
+ for image_cnt, image in enumerate(image_inputs):
|
|
|
+ num_image_tokens = _get_llm_num_vision_tokens(
|
|
|
+ [image],
|
|
|
+ data_type_key="image",
|
|
|
+ image_processor=image_processor,
|
|
|
+ )
|
|
|
+ if image_cnt == 0:
|
|
|
+ non_image_tokens = prompt_token_ids[: image_indices[image_cnt]]
|
|
|
+ else:
|
|
|
+ non_image_tokens = prompt_token_ids[
|
|
|
+ image_indices[image_cnt - 1] + 1 : image_indices[image_cnt]
|
|
|
+ ]
|
|
|
+ prompt_token_ids_with_image.extend(non_image_tokens)
|
|
|
+ prompt_token_ids_with_image.extend(
|
|
|
+ hf_config.image_token_id for _ in range(num_image_tokens)
|
|
|
+ )
|
|
|
+ prompt_token_ids_with_image.extend(
|
|
|
+ prompt_token_ids[image_indices[-1] + 1 :]
|
|
|
+ )
|
|
|
+ prompt_token_ids = prompt_token_ids_with_image
|
|
|
+ # Expand video pad tokens.
|
|
|
+ if video_inputs is not None:
|
|
|
+ video_indices = [
|
|
|
+ idx
|
|
|
+ for idx, token in enumerate(prompt_token_ids)
|
|
|
+ if token == hf_config.video_token_id
|
|
|
+ ]
|
|
|
+ video_inputs = make_batched_videos(video_inputs)
|
|
|
+ assert len(video_indices) == len(video_inputs)
|
|
|
+ prompt_token_ids_with_video = []
|
|
|
+ for video_cnt, video in enumerate(video_inputs):
|
|
|
+ num_video_tokens = _get_llm_num_vision_tokens(
|
|
|
+ video,
|
|
|
+ data_type_key="video",
|
|
|
+ image_processor=image_processor,
|
|
|
+ )
|
|
|
+ if video_cnt == 0:
|
|
|
+ non_video_tokens = prompt_token_ids[: video_indices[video_cnt]]
|
|
|
+ else:
|
|
|
+ non_video_tokens = prompt_token_ids[
|
|
|
+ video_indices[video_cnt - 1] + 1 : video_indices[video_cnt]
|
|
|
+ ]
|
|
|
+ prompt_token_ids_with_video.extend(non_video_tokens)
|
|
|
+ prompt_token_ids_with_video.extend(
|
|
|
+ hf_config.video_token_id for _ in range(num_video_tokens)
|
|
|
+ )
|
|
|
+ prompt_token_ids_with_video.extend(
|
|
|
+ prompt_token_ids[video_indices[-1] + 1 :]
|
|
|
+ )
|
|
|
+ prompt_token_ids = prompt_token_ids_with_video
|
|
|
+ return LLMInputs(
|
|
|
+ prompt_token_ids=prompt_token_ids,
|
|
|
+ prompt=llm_inputs["prompt"],
|
|
|
+ multi_modal_data=multi_modal_data,
|
|
|
+ )
|
|
|
+
|
|
|
+
|
|
|
+@MULTIMODAL_REGISTRY.register_image_input_mapper(
|
|
|
+ image_input_mapper_for_qwen2_vl
|
|
|
+)
|
|
|
+@MULTIMODAL_REGISTRY.register_input_mapper(
|
|
|
+ "video", video_input_mapper_for_qwen2_vl
|
|
|
+)
|
|
|
+@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_qwen2_vl_image_tokens)
|
|
|
+@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
|
|
|
+ "video", get_max_qwen2_vl_video_tokens
|
|
|
+)
|
|
|
+@INPUT_REGISTRY.register_dummy_data(dummy_data_for_qwen2_vl)
|
|
|
+@INPUT_REGISTRY.register_input_processor(input_processor_for_qwen2_vl)
|
|
|
+class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal):
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ config: Qwen2VLConfig,
|
|
|
+ multimodal_config: MultiModalConfig,
|
|
|
+ cache_config: Optional[CacheConfig] = None,
|
|
|
+ quant_config: Optional[QuantizationConfig] = None,
|
|
|
+ ) -> None:
|
|
|
+ super().__init__()
|
|
|
+ assert (
|
|
|
+ not cache_config.enable_prefix_caching
|
|
|
+ ), "Qwen2-VL currently does not support prefix caching"
|
|
|
+ self.config = config
|
|
|
+ self.multimodal_config = multimodal_config
|
|
|
+ self.visual = Qwen2VisionTransformer(
|
|
|
+ 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, cache_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.vocab_size)
|
|
|
+ self.sampler = Sampler()
|
|
|
+
|
|
|
+ def _validate_and_reshape_mm_tensor(
|
|
|
+ self, mm_input: Union[torch.Tensor, List[torch.Tensor]], name: str
|
|
|
+ ) -> torch.Tensor:
|
|
|
+ if not isinstance(mm_input, (torch.Tensor, list)):
|
|
|
+ raise ValueError(
|
|
|
+ f"Incorrect type of {name}. " f"Got type: {type(mm_input)}"
|
|
|
+ )
|
|
|
+ if isinstance(mm_input, torch.Tensor):
|
|
|
+ if mm_input.ndim == 2:
|
|
|
+ return mm_input
|
|
|
+ if mm_input.ndim != 3:
|
|
|
+ raise ValueError(
|
|
|
+ f"{name} should be 2D or batched 3D tensor. "
|
|
|
+ f"Got ndim: {mm_input.ndim}"
|
|
|
+ )
|
|
|
+ return torch.concat(list(mm_input))
|
|
|
+ else:
|
|
|
+ return torch.concat(mm_input)
|
|
|
+
|
|
|
+ def _parse_and_validate_image_input(
|
|
|
+ self, **kwargs: object
|
|
|
+ ) -> Optional[Qwen2VLImageInputs]:
|
|
|
+ pixel_values = kwargs.pop("pixel_values", None)
|
|
|
+ image_grid_thw = kwargs.pop("image_grid_thw", None)
|
|
|
+ if pixel_values is None:
|
|
|
+ return None
|
|
|
+ pixel_values = self._validate_and_reshape_mm_tensor(
|
|
|
+ pixel_values, "image pixel values"
|
|
|
+ )
|
|
|
+ image_grid_thw = self._validate_and_reshape_mm_tensor(
|
|
|
+ image_grid_thw, "image grid_thw"
|
|
|
+ )
|
|
|
+ if not isinstance(pixel_values, (torch.Tensor, list)):
|
|
|
+ raise ValueError(
|
|
|
+ "Incorrect type of image pixel values. "
|
|
|
+ f"Got type: {type(pixel_values)}"
|
|
|
+ )
|
|
|
+ return Qwen2VLImageInputs(
|
|
|
+ pixel_values=pixel_values, image_grid_thw=image_grid_thw
|
|
|
+ )
|
|
|
+
|
|
|
+ def _parse_and_validate_video_input(
|
|
|
+ self, **kwargs: object
|
|
|
+ ) -> Optional[Qwen2VLVideoInputs]:
|
|
|
+ pixel_values_videos = kwargs.pop("pixel_values_videos", None)
|
|
|
+ video_grid_thw = kwargs.pop("video_grid_thw", None)
|
|
|
+ if pixel_values_videos is None:
|
|
|
+ return None
|
|
|
+ pixel_values_videos = self._validate_and_reshape_mm_tensor(
|
|
|
+ pixel_values_videos, "video pixel values"
|
|
|
+ )
|
|
|
+ video_grid_thw = self._validate_and_reshape_mm_tensor(
|
|
|
+ video_grid_thw, "video grid_thw"
|
|
|
+ )
|
|
|
+ return Qwen2VLVideoInputs(
|
|
|
+ pixel_values_videos=pixel_values_videos,
|
|
|
+ video_grid_thw=video_grid_thw,
|
|
|
+ )
|
|
|
+
|
|
|
+ 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 _merge_multimodal_embeddings(
|
|
|
+ self,
|
|
|
+ input_ids: torch.Tensor,
|
|
|
+ inputs_embeds: torch.Tensor,
|
|
|
+ multimodal_embeddings: torch.Tensor,
|
|
|
+ placeholder_token_id: int,
|
|
|
+ ) -> torch.Tensor:
|
|
|
+ mask = input_ids == placeholder_token_id
|
|
|
+ inputs_embeds[mask, :] = multimodal_embeddings
|
|
|
+ return inputs_embeds
|
|
|
+
|
|
|
+ def forward(
|
|
|
+ self,
|
|
|
+ input_ids: torch.Tensor,
|
|
|
+ positions: torch.Tensor,
|
|
|
+ kv_caches: List[torch.Tensor],
|
|
|
+ attn_metadata: AttentionMetadata,
|
|
|
+ intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
|
+ **kwargs: object,
|
|
|
+ ) -> SamplerOutput:
|
|
|
+ """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,).
|
|
|
+ pixel_values: Pixel values to be fed to a model.
|
|
|
+ `None` if no images are passed.
|
|
|
+ image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
|
|
|
+ `None` if no images are passed.
|
|
|
+ pixel_values_videos: Pixel values of videos to be fed to a model.
|
|
|
+ `None` if no videos are passed.
|
|
|
+ video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
|
|
|
+ `None` if no videos are passed.
|
|
|
+ """
|
|
|
+ image_input = self._parse_and_validate_image_input(**kwargs)
|
|
|
+ video_input = self._parse_and_validate_video_input(**kwargs)
|
|
|
+ if image_input is None and video_input is None:
|
|
|
+ inputs_embeds = None
|
|
|
+ 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()}"
|
|
|
+ )
|
|
|
+ inputs_embeds = self.model.embed_tokens(input_ids)
|
|
|
+ if image_input is not None:
|
|
|
+ image_embeds = self._process_image_input(image_input)
|
|
|
+ inputs_embeds = self._merge_multimodal_embeddings(
|
|
|
+ input_ids,
|
|
|
+ inputs_embeds,
|
|
|
+ image_embeds,
|
|
|
+ placeholder_token_id=self.config.image_token_id,
|
|
|
+ )
|
|
|
+ if video_input is not None:
|
|
|
+ video_embeds = self._process_video_input(video_input)
|
|
|
+ inputs_embeds = self._merge_multimodal_embeddings(
|
|
|
+ input_ids,
|
|
|
+ inputs_embeds,
|
|
|
+ video_embeds,
|
|
|
+ placeholder_token_id=self.config.video_token_id,
|
|
|
+ )
|
|
|
+ input_ids = None
|
|
|
+ hidden_states = self.model(
|
|
|
+ input_ids=input_ids,
|
|
|
+ positions=positions,
|
|
|
+ kv_caches=kv_caches,
|
|
|
+ attn_metadata=attn_metadata,
|
|
|
+ inputs_embeds=inputs_embeds,
|
|
|
+ )
|
|
|
+ return hidden_states
|
|
|
+
|
|
|
+ def compute_logits(
|
|
|
+ self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata
|
|
|
+ ) -> torch.Tensor:
|
|
|
+ logits = self.logits_processor(
|
|
|
+ self.lm_head, hidden_states, sampling_metadata
|
|
|
+ )
|
|
|
+ return logits
|
|
|
+
|
|
|
+ def sample(
|
|
|
+ self,
|
|
|
+ logits: torch.Tensor,
|
|
|
+ sampling_metadata: SamplingMetadata,
|
|
|
+ ) -> Optional[SamplerOutput]:
|
|
|
+ next_tokens = self.sampler(logits, sampling_metadata)
|
|
|
+ return next_tokens
|
|
|
+
|
|
|
+ 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)
|
|
|
+ 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)
|
|
|
+ try:
|
|
|
+ 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)
|
AlpinDale