diff --git "a/Fine_tune_SmolVLM2_on_Video.ipynb" "b/Fine_tune_SmolVLM2_on_Video.ipynb" new file mode 100644--- /dev/null +++ "b/Fine_tune_SmolVLM2_on_Video.ipynb" @@ -0,0 +1,2909 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "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": [ + { + "output_type": "stream", + "name": "stdout", + "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", + "source": [ + "!pip install -q git+https://github.com/huggingface/transformers.git" + ], + "metadata": { + "id": "FCYgmJtDRElR" + }, + "execution_count": null, + "outputs": [] + }, + { + "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": [ + { + "output_type": "display_data", + "data": { + "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": { + "id": "_QOCpw_-uYYo", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "ad1fd1f6-41f9-4fa2-ae89-e75c9876cd65" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "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" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
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6750.241000
7000.229000
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7500.220900
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8000.259900
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Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.

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