92 lines
3.2 KiB
Python
92 lines
3.2 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import logging
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
import torch
|
|
from transformers import AutoModel
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class SupportedModels:
|
|
"""Supported multimodal model identifiers"""
|
|
|
|
LLAVA_1_5_7B = "llava-hf/llava-1.5-7b-hf"
|
|
QWEN_2_5_VL_7B = "Qwen/Qwen2.5-VL-7B-Instruct"
|
|
LLAVA_NEXT_VIDEO_7B = "llava-hf/LLaVA-NeXT-Video-7B-hf"
|
|
QWEN_2_AUDIO_7B = "Qwen/Qwen2-Audio-7B-Instruct"
|
|
|
|
|
|
def load_vision_model(model_id: str) -> torch.nn.Module:
|
|
"""
|
|
Load a vision model from a HuggingFace model ID.
|
|
"""
|
|
model = AutoModel.from_pretrained(
|
|
model_id, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True
|
|
)
|
|
return model
|
|
|
|
|
|
def construct_mm_data(
|
|
model: str,
|
|
embeddings_dtype: torch.dtype,
|
|
image_embeds: Optional[torch.Tensor] = None,
|
|
video_numpy: Optional[Any] = None,
|
|
image_grid_thw: Optional[List[Any]] = None,
|
|
audio_embeds: Optional[torch.Tensor] = None,
|
|
) -> Dict[str, torch.Tensor | Dict[str, Any]]:
|
|
"""Construct multimodal data for a vLLM request for models that require additional parameters alongside the embeddings"""
|
|
if model == SupportedModels.QWEN_2_AUDIO_7B:
|
|
audio_embeds = audio_embeds.to(torch.bfloat16)
|
|
assert audio_embeds.ndim == 2, "Audio embeddings must be 2D"
|
|
return {"audio": [audio_embeds]}
|
|
# Handle video models
|
|
if model == SupportedModels.LLAVA_NEXT_VIDEO_7B:
|
|
if video_numpy is None:
|
|
raise ValueError("No video frames provided.")
|
|
return {"video": video_numpy}
|
|
|
|
# Handle image models - validate image embeddings first
|
|
if image_embeds is None:
|
|
raise ValueError("No image embeddings provided.")
|
|
|
|
image_embeds = image_embeds.to(embeddings_dtype)
|
|
|
|
# Model-specific image handling
|
|
if model == SupportedModels.QWEN_2_5_VL_7B:
|
|
return _construct_qwen_image_data(image_embeds, image_grid_thw)
|
|
else:
|
|
# Default image handling for other models (e.g., LLAVA_1_5_7B)
|
|
return {"image": image_embeds}
|
|
|
|
|
|
def _construct_qwen_image_data(
|
|
image_embeds: torch.Tensor, image_grid_thw: Optional[List[Any]]
|
|
) -> Dict[str, Dict[str, torch.Tensor]]:
|
|
"""Construct image data specifically for Qwen models."""
|
|
if image_grid_thw is None or len(image_grid_thw) == 0:
|
|
raise ValueError("No image grid provided for Qwen model.")
|
|
|
|
grid_thw_tensor = torch.tensor(image_grid_thw)
|
|
|
|
return {
|
|
"image": {
|
|
"image_embeds": image_embeds.squeeze(0),
|
|
"image_grid_thw": grid_thw_tensor,
|
|
}
|
|
}
|