diff --git "a/notebooks/SmolVLM2_Video_FT.ipynb" "b/notebooks/SmolVLM2_Video_FT.ipynb" deleted file mode 100644--- "a/notebooks/SmolVLM2_Video_FT.ipynb" +++ /dev/null @@ -1,2910 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nc0g2NLpUSGr" - }, - "source": [ - "# Fine-tune SmolVLM2 on Video Captioning\n", - "In this notebook we will fine-tune SmolVLM2-500M-Video-Instruct on Video Feedback dataset. It is ran on a Colab A100 for full fine-tuning, but you can squeeze it to L4 with QLoRA." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WIhA1lQ7j0kw", - "outputId": "928f2f4e-6cd8-452b-d621-605550fdd33c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m163.5/163.5 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n" - ] - } - ], - "source": [ - "%pip install -q accelerate datasets peft bitsandbytes tensorboard pyav num2words" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "FCYgmJtDRElR" - }, - "outputs": [], - "source": [ - "%pip install -q git+https://github.com/huggingface/transformers.git" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "XyJaqZZ3uYYl" - }, - "outputs": [], - "source": [ - "%pip install -q flash-attn --no-build-isolation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wAeMA0heVBjT" - }, - "source": [ - "We will push out model to Hub so we need to authenticate ourselves." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17, - "referenced_widgets": [ - "112da28d935543069e7a1a2abc22f9f4", - "0d22c009aa584ca1a71e32336a7985e0", - "ad17e30049cb4b5aa4046d94690f87d3", - "e77d3520a2d64f9a840652669c9a0ba1", - "1852745b0de44f4281cea0cbb3508459", - "166c19ec6d9f4455a56a0f146d1c0abc", - "f6362bc7b5b24dd592d35a76a1fbf26b", - "e99fbdfc8a22408a8c728a36c8744b24", - "0fee30c9bf2b4bdfad7a37261f92db64", - "4cd8babc92cc4aeba74d2147f28dee7d", - "a4fbf37fe0fe44cfbf72ca1e82af3467", - "be50e04c5629463eb18d029d045f25b3", - "5490c69c251144c4979e346c66ac1e53", - "44d0e1db5f664b3fb7c146c216566776", - "7af918a10ec745d7a3f4a883dbdc8b6a", - "4156b6897089446984196606ef0d3461", - "cf4b5a9cefe84fd9a4d120ab1da6f3f4", - "484155e67e36453c9d1ebd2ea1768eca", - "48bb89c434284b639f45b5929cf8d1a9", - "0ead4ab9bb7648c69352094bfbcb8800" - ] - }, - "id": "yKd5xtSGj7cm", - "outputId": "a6e841d8-f2d6-44a8-d44d-c0c244d95f9b" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "112da28d935543069e7a1a2abc22f9f4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value='
\")\n", - "]\n", - "\n", - "def collate_fn(examples):\n", - " instances = []\n", - " for example in examples:\n", - " prompt = example[\"text prompt\"]\n", - "\n", - " user_content = [{\"type\": \"text\", \"text\": \"Caption the video.\"}]\n", - " user_content.append({\"type\": \"video\", \"path\": example[\"video link\"]})\n", - "\n", - " messages = [\n", - " {\"role\": \"user\", \"content\": user_content},\n", - " {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": f\"{prompt}\"}]}\n", - " ]\n", - "\n", - " instance = processor.apply_chat_template(messages, add_generation_prompt=False,\n", - " tokenize=True, return_dict=True, return_tensors=\"pt\").to(\"cuda\").to(model.dtype)\n", - " instances.append(instance)\n", - "\n", - "\n", - " input_ids = pad_sequence(\n", - " [inst[\"input_ids\"].squeeze(0) for inst in instances],\n", - " batch_first=True,\n", - " padding_value=processor.tokenizer.pad_token_id\n", - " )\n", - " attention_mask = pad_sequence(\n", - " [inst[\"attention_mask\"].squeeze(0) for inst in instances],\n", - " batch_first=True,\n", - " padding_value=0\n", - " )\n", - " labels = pad_sequence(\n", - " [inst[\"input_ids\"].squeeze(0).clone() for inst in instances],\n", - " batch_first=True,\n", - " padding_value=-100\n", - " )\n", - "\n", - " labels[labels == image_token_id] = -100\n", - "\n", - " out = {\n", - " \"input_ids\": input_ids,\n", - " \"attention_mask\": attention_mask,\n", - " \"labels\": labels\n", - " }\n", - "\n", - "\n", - " # Step 1: figure out maximum frames, height, width across the batch\n", - " pvs = [inst[\"pixel_values\"].squeeze(0) for inst in instances if \"pixel_values\" in inst]\n", - " if pvs: # there is at least one non-None pixel_values\n", - " max_frames = max(pv.shape[0] for pv in pvs)\n", - " max_h = max(pv.shape[-2] for pv in pvs)\n", - " max_w = max(pv.shape[-1] for pv in pvs)\n", - " else:\n", - " max_h = max_w = processor.video_size['longest_edge']\n", - " max_frames = 1\n", - "\n", - " padded_pixel_values_list = []\n", - " for ex in instances:\n", - " pv = ex.get(\"pixel_values\", None).squeeze(0)\n", - "\n", - " if pv is None:\n", - " # text-only => fill pixel data + mask with zeros\n", - " shape_pv = (max_frames, 3, max_h, max_w)\n", - " padded_pv = torch.zeros(shape_pv, dtype=torch.float32)\n", - " else:\n", - " f, c, h, w = pv.shape\n", - " # Prepare final storage\n", - " padded_pv = torch.zeros(\n", - " (max_frames, c, max_h, max_w),\n", - " dtype=pv.dtype,\n", - " device=pv.device\n", - " )\n", - " padded_pv[:f, :, :h, :w] = pv\n", - " padded_pixel_values_list.append(padded_pv)\n", - "\n", - " out[\"pixel_values\"] = torch.stack(padded_pixel_values_list, dim=0)\n", - " return out" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kEYDjWpE3LD5" - }, - "source": [ - "## Training" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QvAs896cdwg8" - }, - "source": [ - "We can now initialize `Trainer` and initialize `TrainingArguments` to pass to `Trainer`.\n", - "\n", - "Some notes:\n", - "- If you use 8-bit QLoRA with the below setup it uses around 16.4 GB VRAM (beautiful, fits comfortably inside L4, Colab free tier)\n", - "- We use gradient accumulation to simulate a larger batch size.\n", - "- We also save up on memory from intermediate activations by using gradient checkpointing.\n", - "\n", - "**Disclaimer:**\n", - "The techniques here aren't free lunch. The latter two will add additional compute to the training, thus slow down a bit (for reference on two A100s with bsz of 16, we were able to train for 2 hrs 43 mins with the gradient accumulation steps of 4, disabling it reduced it with 2 hr 35 mins).\n", - "If you want to speed-up, you might play around, reduce to 4-bit precision and have a higher batch size. Note that 4-bit might result in model learning less." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "QNE2yWAYrAhD" - }, - "outputs": [], - "source": [ - "from transformers import TrainingArguments, Trainer\n", - "\n", - "model_name = model_id.split(\"/\")[-1]\n", - "\n", - "training_args = TrainingArguments(\n", - " num_train_epochs=1,\n", - " per_device_train_batch_size=2,\n", - " gradient_accumulation_steps=1,\n", - " warmup_steps=50,\n", - " learning_rate=1e-4,\n", - " weight_decay=0.01,\n", - " logging_steps=25,\n", - " save_strategy=\"steps\",\n", - " save_steps=250,\n", - " save_total_limit=1,\n", - " optim=\"adamw_hf\", # for 8-bit, keep paged_adamw_8bit, else adamw_hf\n", - " bf16=True,\n", - " output_dir=f\"./{model_name}-video-feedback\",\n", - " hub_model_id=f\"{model_name}-video-feedback\",\n", - " remove_unused_columns=False,\n", - " report_to=\"tensorboard\",\n", - " dataloader_pin_memory=False\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oBBSDpBhreJd" - }, - "outputs": [], - "source": [ - "trainer = Trainer(\n", - " model=model,\n", - " args=training_args,\n", - " data_collator=collate_fn,\n", - " train_dataset=train_ds,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "_QOCpw_-uYYo", - "outputId": "ad1fd1f6-41f9-4fa2-ae89-e75c9876cd65" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.11/dist-packages/transformers/optimization.py:640: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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5250.232200
5500.268100
5750.222400
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6500.257800
6750.241000
7000.229000
7250.236600
7500.220900
7750.271400
8000.259900
8250.243900
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Copy a token from your Hugging Face\ntokens page and paste it below.
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