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"visibility": null, "width": null } }, "0c1041c79765478898ec13c4cc7a24cd": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "## Video RAG\n", "\n", "In this notebook, we'll do video RAG using OmniEmbed, the any-to-any embedding model and Qwen2.5-Omni, the any-to-any model that takes in images, videos, audios, text and outputs text.\n", "\n", "Alternatively, you could also do audio RAG or visual document RAG, this notebook serves as example to retrieve videos and do inference.\n", "\n", "Unfortunately due to how memory hungry Qwen models are, we run this notebook on A100. OmniEmbed model is based on 7B Qwen with video processing, perhaps at some point we get a 3B model and try running at L4." ], "metadata": { "id": "MXreCvaS9w1i" } }, { "cell_type": "markdown", "source": [ "## OmniEmbed Inference\n", "\n", "Here we initialize and write a simple inference function for OmniEmbed. We use multivent checkpoint because it's further trained on videos." ], "metadata": { "id": "S7XoN1bM-fqU" } }, { "cell_type": "code", "source": [ "!pip install -q qwen-omni-utils" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SNmX9V89HkvA", "outputId": "8e0081f7-cb38-4bc9-f56f-45a27fbb8854" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.7/39.7 MB\u001b[0m \u001b[31m62.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "from transformers import AutoProcessor, Qwen2_5OmniThinkerForConditionalGeneration\n", "from qwen_omni_utils import process_mm_info\n", "\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "processor = AutoProcessor.from_pretrained(\"Qwen/Qwen2.5-Omni-7B\")\n", "model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained(\n", " 'Tevatron/OmniEmbed-v0.1-multivent',\n", " torch_dtype=torch.bfloat16\n", ").to(device).eval()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 792, "referenced_widgets": [ "27833ddc0356430e90f632d358634fce", "a5fbc1e09cb34138aac4d420de0949ab", "def40b47a8d047cab5fdc28de75c68f0", "5fb6f70ed39d4a8a9ae6210086ae2f36", "cc5710793f4140ee9442e84f5578a3e4", "12d09bdcf85c429e85ee478a2874227d", "54785ba73d6043ca9ce1a0c628ef767b", "baac3c2850694790a50717795bdb3662", "28e9d0c8cacf437aaa5a214229b406c4", "12c8fd9b76b04c739f247119638097dc", "d5751ad7dbbf47baa40d46f5c3fcffe5", "c84e8101c68b4b6a8cfc8488cd2f777f", "7a48a646a6ed4429af2ec9c09cee8564", "682d594006e4462e8353332fda2a143e", "50345f870d7f4e92ad03cc4b6f7f205b", "407f566d7a3940dba85c3a4724915668", "67e3eeab193845fdb934eb6936e84aae", "a59a0728e66740daaf7b011815ca2f3c", "345e470e79a6456ea4e39b8eaa0de8b4", "b215fae0dcc84527bcb2b271d2ea694e", 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"58e4a168b7a34dc8b6cd05bb817b5ed2", "92fe56fc7bdb43f593524c6aa4839dbb", "c520199b82c647eb9049c7b73f596e9d", "14f0926a4277427da172d0ccfa85c393", "7515908727a14d7b9f6895b1f6bd85e1", "48188edad1c3431882aeb52dcb8469b2", "6f5d0502663745e881e30b782cd75827", "7423842637394aa88ea32d782bfa8e65", "8155f49df33f4902870371959ce122f0", "5e20c18368724564aa183eb7e1a85d53", "f72eb58597fd431e9a536686df465380", "9142b4f4e2ec4017949fd3da27cc7560", "8909c6b1d2954d39aa385bb5f2e25801", "e31b6c5bca9742e6aaab4e24c83e471c", "95d443a4cf094836b9c7e566d43d7945", "a38403a1bc4646f1a46510a5dc53b1dd", "c1aa7034b77744e5a0f23cbc0de33c95", "d0976add35eb4e13aa849893aab9894f", "daf7cfd0b0864bb7a2674de5261178b5", "4b418466db54467ca8269fe94cf53ac4", "61c8011391b2480d9b1e66647aa4476d", "2273b0902a514385828efb15794c8533", "06c823eca84c41ddbda95753383ec586", "8912475ada3a4cf2bec674e5745d8c56", "117cc43de77440b1b43205d8c0edc6d5", "165697c88bc445ad8143b57418a33460", "a4b57cbf66fd4467a52de4d8fc8dde37", "72c0cba0852c4b50b81a213e8837f901", "46b270512d3b46de802ef574c644ee5c", "9fc1e992ee234c5ba69cd4026e7437bd", "26f7b07bcc0f4f1bb91aac066547a3fa", "62609315bea24a7d9d6d6c992bb8c2df", "1fac87ab172c4b788ed2f39d8f565253", "ed348e76f57b4f1f8e4d7f12de302ff7", "a0263874260b42dcbf36c4293dc62f58", "74bf5f98f2fe45b7906065ba6cdde0c9", "50905c6911574c758565626114a248fa", "39b69adba5464ec38a765bbe0b4e23c0", "e9f0e85a93534923aa7a17043f99ec31", "d68b452b65e74a1b977bb7640a6f563b", "a2d0829a1dfc487f85dc32411002af59", "1d1fe7eaa3b842a282ee19e460def525", "26d091ffcf3646f7940f309a38de86b1", "f562bfb73d134563aab5d869c484dbed", "5978053bf77d4d938d33889d74706b29", "eb926d406c094ef5aec7be43b7ce2c73", "e98fbf10b77047c2a421c9beb0ba2ea3", "7717d82d23154f7a962e5060a24e80cc", "14e64e2b88674d06aa96d96d7cf4217e", "4395f1f5aada4499b3eab70cb39fc0ca", "64e3a7b0f5ef4299993beb20859214c5", "c5b14bb1a1e24dacb0f6b45a8dc0adee", "01807af4200f49379420d91a0e23161c", "f647794a9cf54eaeb2212379ac342285" ] }, "id": "ofPqrgrdHwrF", "outputId": "a95b9853-b9e5-47db-9d72-6757aee0e4bf" }, "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/667 [00:00\"\n", " audio_inputs, image_inputs, video_inputs = process_mm_info(message, use_audio_in_video=True)\n", "\n", " inputs = processor(\n", " text=texts,\n", " audio=audio_inputs,\n", " images=image_inputs,\n", " videos=video_inputs,\n", " return_tensors=\"pt\",\n", " padding=\"longest\",\n", " )\n", " for k in inputs:\n", " inputs[k] = inputs[k].to(device)\n", "\n", " cache_position = torch.arange(0, inputs['input_ids'].shape[1], device=device)\n", " inputs = model.prepare_inputs_for_generation(**inputs, use_cache=True, cache_position=cache_position)\n", " model_outputs = model(**inputs, return_dict=True, output_hidden_states=True)\n", "\n", " last_hidden_state = model_outputs.hidden_states[-1]\n", " reps = last_hidden_state[:, -1]\n", " reps = torch.nn.functional.normalize(reps, p=2, dim=-1)\n", " return reps\n" ] }, { "cell_type": "code", "source": [ "def template(message):\n", " if message.startswith(\"https://\"):\n", " message = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"video\",\n", " \"fps\": 0.5,\n", " \"video\": message},\n", " ],\n", " },\n", " ]\n", " else:\n", " message = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": message},\n", " ],\n", " },\n", " ]\n", " return message" ], "metadata": { "id": "TSZqHht1H7lM" }, "execution_count": 4, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Video Retrieval" ], "metadata": { "id": "pckMve8u835c" } }, { "cell_type": "markdown", "source": [ "Here we have a video of a ramen and video of a tofu recipe, we ask the model which video is the tofu recipe to retrieve the video. We then get most relevant video (tofu recipe) and pass it to the Qwen2.5-Omni for detailed generation." ], "metadata": { "id": "uGr_VjJ4VU7J" } }, { "cell_type": "code", "source": [ "text_query = template('Query: How to cook Mapo Tofu?')\n", "video_1 = template(\"https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/mapo_tofu.mp4\")\n", "video_2 = template(\"https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/ramen.mp4\")\n", "\n", "sim1 = torch.cosine_similarity(encode_message(text_query), encode_message(video_1))\n", "sim2 = torch.cosine_similarity(encode_message(text_query), encode_message(video_2))\n", "\n", "print(\"Similarities:\", sim1.item(), sim2.item())\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "p6Kcw5kk83e5", "outputId": "72004b3c-5b6e-4094-82e0-dc936cad6079" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:root:System prompt modified, audio output may not work as expected. Audio output mode only works when using default system prompt 'You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.'\n", "WARNING:root:System prompt modified, audio output may not work as expected. Audio output mode only works when using default system prompt 'You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.'\n", "/usr/local/lib/python3.11/dist-packages/librosa/core/audio.py:172: FutureWarning: librosa.core.audio.__audioread_load\n", "\tDeprecated as of librosa version 0.10.0.\n", "\tIt will be removed in librosa version 1.0.\n", " y, sr_native = __audioread_load(path, offset, duration, dtype)\n", "qwen-vl-utils using torchvision to read video.\n", "Unused or unrecognized kwargs: images.\n", "WARNING:root:System prompt modified, audio output may not work as expected. Audio output mode only works when using default system prompt 'You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.'\n", "WARNING:root:System prompt modified, audio output may not work as expected. Audio output mode only works when using default system prompt 'You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.'\n", "/usr/local/lib/python3.11/dist-packages/librosa/core/audio.py:172: FutureWarning: librosa.core.audio.__audioread_load\n", "\tDeprecated as of librosa version 0.10.0.\n", "\tIt will be removed in librosa version 1.0.\n", " y, sr_native = __audioread_load(path, offset, duration, dtype)\n", "Unused or unrecognized kwargs: images.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Similarities: 0.5078125 0.1962890625\n" ] } ] }, { "cell_type": "markdown", "source": [ "First video is more relevant, so we take that." ], "metadata": { "id": "j5h4x_s9Vs4d" } }, { "cell_type": "markdown", "source": [ "After we get our retrieval result we have to remove the model so we have some VRAM for Qwen2.5-Omni. #gpupoorlife" ], "metadata": { "id": "TgpVWQjcLCP8" } }, { "cell_type": "code", "source": [ "del model, processor" ], "metadata": { "id": "AhROPseFN5xn" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "import soundfile as sf\n", "from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor\n", "from qwen_omni_utils import process_mm_info\n", "\n", "model = Qwen2_5OmniForConditionalGeneration.from_pretrained(\"Qwen/Qwen2.5-Omni-7B\", torch_dtype=\"auto\", device_map=\"auto\")\n", "processor = Qwen2_5OmniProcessor.from_pretrained(\"Qwen/Qwen2.5-Omni-7B\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 581, "referenced_widgets": [ "6bf673c02a944a7fbf2c31af9cf5e0ee", "11cd18d682d54b61b1f98a79c8c1faeb", "775ede089b5a4c738bd289fb206baf20", "5a7bdac9d432414093d9e01ca099555e", "fa6a541f909244398c42e8a6dc836408", "bfd193b221d34a569841cbaaa89b06c9", "62f48b742397461f8d80cb728ec0b94a", "2898e31aa3bf4688b7800d395046917a", "171e09915f144af5914f07d3a0a0da45", 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