From d54e0956bac4c8dc0ac8c07e0e113d8bd9c39971 Mon Sep 17 00:00:00 2001 From: tohrnii <100405913+tohrnii@users.noreply.github.com> Date: Wed, 31 Jan 2024 12:43:30 +0000 Subject: [PATCH] add support for mixtral --- examples/DPO_Mixtral_Unsloth_Example.ipynb | 8661 ++++++++++++++++++++ unsloth/models/loader.py | 4 +- unsloth/models/mixtral.py | 816 ++ 3 files changed, 9480 insertions(+), 1 deletion(-) create mode 100644 examples/DPO_Mixtral_Unsloth_Example.ipynb create mode 100644 unsloth/models/mixtral.py diff --git a/examples/DPO_Mixtral_Unsloth_Example.ipynb b/examples/DPO_Mixtral_Unsloth_Example.ipynb new file mode 100644 index 00000000..159ebf9f --- /dev/null +++ b/examples/DPO_Mixtral_Unsloth_Example.ipynb @@ -0,0 +1,8661 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "o6RxqIirisTj" + }, + "source": [ + "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + " \n", + " \n", + " Join our Discord if you need help!\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n", + "\n", + "You will learn how to do [DPO data prep](#Data), and how to [train via `DPOTrainer`](#Train).\n", + "To learn more about DPO, read TRL's [blog post](https://huggingface.co/blog/dpo-trl). We follow [Huggingface's Alignment Handbook](https://github.com/huggingface/alignment-handbook) to replicate [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2eSvM9zX_2d3" + }, + "outputs": [], + "source": [ + "%%capture\n", + "import torch\n", + "major_version, minor_version = torch.cuda.get_device_capability()\n", + "# if major_version >= 8:\n", + "# # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", + "# %pip install \"unsloth[colab_ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", + "# else:\n", + "# # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", + "# %pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n", + "# pass" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QYds3fcii6gC" + }, + "source": [ + "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n", + "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n", + "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", + "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", + "* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n", + "* DPO requires a model already trained by SFT on a similar dataset that is used for DPO. We use `HuggingFaceH4/mistral-7b-sft-beta` as the SFT model. Use this [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) first to train a SFT model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "E8-BWi7MzkRz", + "outputId": "0626186f-6e70-47b7-db59-b8e7591d0de4" + }, + "outputs": [], + "source": [ + "# One must patch the DPO Trainer first!\n", + "import sys\n", + "sys.path.append('/workspace/unsloth')\n", + "from unsloth import PatchDPOTrainer\n", + "from unsloth.models.mixtral import FastMixtralModel\n", + "PatchDPOTrainer()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 491, + "referenced_widgets": [ + "4c7a9e6327fc4b839f1e2eee705e1574", + "904fef1ac7404cc8a33e65ffe8938c8b", + "f7562580501845e9a67ab9a8b4a96ba0", + "96f9847627504ae7abe7461e4c0b3cff", + "5165aa69bef148af9717d90cc5a870b1", + "e6dfcf0456824556ad09fe7945674a14", + "80568bca0c604eaaab2933fc76da2bf0", + "079284acd5c6454eb03d8876a62e6f12", + "1d917d774780421fbdca680009a706ac", + "342cf2a9d6674b4d94fdbaf3b63b9b79", + "16ace0251b954bc08a33eaf3b2a47216", + "2cb8afe9c6e2441fa4e232146ecd08b4", + "bc745fa3da114007af35ea15dbd7ab7d", + "127ed424448840cea8b14fc9997b9cde", + "803f5904c00d4cde8729226139e3c258", + "4a273a69979b466dbf1c987552101fbf", + "68893d4db1f74557932d8b24e0f72820", + "3ccdc55cf6384695967eaf79325b1aef", + "859bc87dfda746819298e213e4e0b067", + "32478ec41d044773b70eba24e2ac0b43", + "5aeb33ba799149c1ad6e580a2a1c0a51", + "a2e31ba35dcf41c79c72dfd431da781f", + "f7c089469828416aa89ade0cfa5a04cc", + "3a52a2ec426b4fc2aaad23c7d8ce6d2b", + "c298081665e844539b8d366325dbdab5", + "65b1731e23364d4ea0bb292550d4a403", + "5f6fd4c19b34458aa8f2019a25bfd605", + "33dda465ac2f42fb8846171a974193ac", + "bf6bc9099d1a475884be6f15115f01ad", + "cdc411110d1b405ea12342b57fd8de30", + "84d445570b31490fb658b08f819dd6a7", + "ceb3830da80146da8fbe6423d25fd8a3", + "d58599afce36484c8508c16beba13e36", + "d43eea508a4d496e9460f01931659b52", + "b146c0bbd15644678198977d63edebb7", + "aa0323eef053447b9836931535984912", + "effec7acec3d4b5384bcf481e3263b6c", + "03ec8d28c0f34116b42e4e0dc65e3dd3", + "9fd5df9115cf4deb8a36daf4a654bfc7", + "cd6e07c3984a4e0e920d87c6ebd6ad1c", + "b0831169530b402cac31b1e60484fbb2", + "ccabf636e15e471cb82bb431b5ab4b5c", + "e4120afe895742848cf756fd58cb42f8", + "fe6eabd38a614113b36bafd1f8ce6920", + "9b12628a46c546aebe2c749085ba2e61", + "63d48b48f4ce449e85a55477df0b6967", + "068c416eaaed476eacffb8f2f343e08f", + "9fd3fe23f14b4403a18ec7d73b64dc6a", + "cf639313cba7442893fdf880d368616d", + "4bd0cf4d31604194a7761ea0345cc0a4", + "783ad9a7097e4fcdb768c8ecb1f21227", + "2557c3090a8d4760ae844fb518e1b555", + "b508872df72d45beb26a625c7499cc8b", + "cbc6fe3cc12a4d79ba08b67f23e5ce01", + "d0d340fa13a54bcf9f1130cc2fc09cc8", + "5d7fce052f8e4937adaaa670be8c1ae4", + "77939b191e82425da0a5908fdbca2a2d", + "30dc6079591b4908a57f5e285ebeda68", + "960bd84fc3fe4d8eb7e79ecba0f3edf6", + "bb7c7c1866af4641bc07d1ff0a5cd069", + "0dc6aefaa24043768d656f5a61d79545", + "d27056ab29e04c448fc62f9f18cbb82d", + "926319902fe84c5095e0b5bd4b3e8187", + "9f660e1b7b9c4ae7922d51353783acf9", + "82d00942a39e465c8a6f869ef97cc250", + "b9e2b829291247619a7b37c9f544d395", + "3f6ccae685e34d05be9fde5538dc48cf", + "3df8638084104cb7a0530a96ec6201af", + "06d1fd85a45541609d11ec7f0bd3b444", + "a9d05a8c3aaf4fe0a2f061c3eeb48b44", + "8095a44a62804d6d86c24d62b9e1a1c1", + "f61b85b63d5147db9f1040f30f4a3034", + "e152d696872242ab8e0bc00ce9b50142", + "623e912cfe404e69a64992fbb1c1bc47", + "69f9e4bf820047658699d55d84d682c0", + "a8306ce933304694aba188495743c2f4", + "1cd708f679de4d319af26797aecca94d" + ] + }, + "id": "QmUBVEnvCDJv", + "outputId": "f04a3ea4-0d0f-424d-8265-0fb99fd17ef3" + }, + "outputs": [], + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!\n", + "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", + "\n", + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"ybelkada/Mixtral-8x7B-Instruct-v0.1-bnb-4bit\", # Choose ANY! eg mistralai/Mistral-7B-Instruct-v0.2\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for name, module in model.named_modules():\n", + " print(\"------------------------------------------------\")\n", + " print(name, module)\n", + " print(\"------------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "AqkY_wHdKyOl" + }, + "outputs": [], + "source": [ + "#@title Alignment Handbook utils\n", + "import os\n", + "import re\n", + "from typing import List, Literal, Optional\n", + "\n", + "from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk\n", + "from datasets.builder import DatasetGenerationError\n", + "\n", + "\n", + "DEFAULT_CHAT_TEMPLATE = \"{% for message in messages %}\\n{% if message['role'] == 'user' %}\\n{{ '<|user|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'system' %}\\n{{ '<|system|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'assistant' %}\\n{{ '<|assistant|>\\n' + message['content'] + eos_token }}\\n{% endif %}\\n{% if loop.last and add_generation_prompt %}\\n{{ '<|assistant|>' }}\\n{% endif %}\\n{% endfor %}\"\n", + "\n", + "\n", + "def apply_chat_template(\n", + " example, tokenizer, task: Literal[\"sft\", \"generation\", \"rm\", \"dpo\"] = \"sft\", assistant_prefix=\"<|assistant|>\\n\"\n", + "):\n", + " def _strip_prefix(s, pattern):\n", + " # Use re.escape to escape any special characters in the pattern\n", + " return re.sub(f\"^{re.escape(pattern)}\", \"\", s)\n", + "\n", + " if task in [\"sft\", \"generation\"]:\n", + " messages = example[\"messages\"]\n", + " # We add an empty system message if there is none\n", + " if messages[0][\"role\"] != \"system\":\n", + " messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n", + " example[\"text\"] = tokenizer.apply_chat_template(\n", + " messages, tokenize=False, add_generation_prompt=True if task == \"generation\" else False\n", + " )\n", + " elif task == \"rm\":\n", + " if all(k in example.keys() for k in (\"chosen\", \"rejected\")):\n", + " chosen_messages = example[\"chosen\"]\n", + " rejected_messages = example[\"rejected\"]\n", + " # We add an empty system message if there is none\n", + " if chosen_messages[0][\"role\"] != \"system\":\n", + " chosen_messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n", + " if rejected_messages[0][\"role\"] != \"system\":\n", + " rejected_messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n", + " example[\"text_chosen\"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False)\n", + " example[\"text_rejected\"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False)\n", + " else:\n", + " raise ValueError(\n", + " f\"Could not format example as dialogue for `rm` task! Require `[chosen, rejected]` keys but found {list(example.keys())}\"\n", + " )\n", + " elif task == \"dpo\":\n", + " if all(k in example.keys() for k in (\"chosen\", \"rejected\")):\n", + " # Compared to reward modeling, we filter out the prompt, so the text is everything after the last assistant token\n", + " prompt_messages = [[msg for msg in example[\"chosen\"] if msg[\"role\"] == \"user\"][0]]\n", + " # Insert system message\n", + " if example[\"chosen\"][0][\"role\"] != \"system\":\n", + " prompt_messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n", + " else:\n", + " prompt_messages.insert(0, example[\"chosen\"][0])\n", + " # TODO: handle case where chosen/rejected also have system messages\n", + " chosen_messages = example[\"chosen\"][1:]\n", + " rejected_messages = example[\"rejected\"][1:]\n", + " example[\"text_chosen\"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False)\n", + " example[\"text_rejected\"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False)\n", + " example[\"text_prompt\"] = tokenizer.apply_chat_template(\n", + " prompt_messages, tokenize=False, add_generation_prompt=True\n", + " )\n", + " example[\"text_chosen\"] = _strip_prefix(example[\"text_chosen\"], assistant_prefix)\n", + " example[\"text_rejected\"] = _strip_prefix(example[\"text_rejected\"], assistant_prefix)\n", + " else:\n", + " raise ValueError(\n", + " f\"Could not format example as dialogue for `dpo` task! Require `[chosen, rejected]` keys but found {list(example.keys())}\"\n", + " )\n", + " else:\n", + " raise ValueError(\n", + " f\"Task {task} not supported, please ensure that the provided task is one of {['sft', 'generation', 'rm', 'dpo']}\"\n", + " )\n", + " return example\n", + "\n", + "\n", + "def get_datasets(\n", + " data_config: dict,\n", + " splits: List[str] = [\"train\", \"test\"],\n", + " shuffle: bool = True,\n", + ") -> DatasetDict:\n", + " \"\"\"\n", + " Loads one or more datasets with varying training set proportions.\n", + "\n", + " Args:\n", + " data_config (`DataArguments` or `dict`):\n", + " Dataset configuration and split proportions.\n", + " splits (`List[str]`, *optional*, defaults to `['train', 'test']`):\n", + " Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix.\n", + " shuffle (`bool`, *optional*, defaults to `True`):\n", + " Whether to shuffle the training and testing/validation data.\n", + "\n", + " Returns\n", + " [`DatasetDict`]: The dataset dictionary containing the loaded datasets.\n", + " \"\"\"\n", + "\n", + " if type(data_config) is dict:\n", + " # Structure of the input is:\n", + " # dataset_mixer = {\n", + " # \"dataset1\": 0.5,\n", + " # \"dataset1\": 0.3,\n", + " # \"dataset1\": 0.2,\n", + " # }\n", + " dataset_mixer = data_config\n", + " else:\n", + " raise ValueError(f\"Data config {data_config} not recognized.\")\n", + "\n", + " raw_datasets = mix_datasets(dataset_mixer, splits=splits, shuffle=shuffle)\n", + " return raw_datasets\n", + "\n", + "\n", + "def mix_datasets(dataset_mixer: dict, splits: Optional[List[str]] = None, shuffle=True) -> DatasetDict:\n", + " \"\"\"\n", + " Loads and mixes datasets according to proportions specified in `dataset_mixer`.\n", + "\n", + " Args:\n", + " dataset_mixer (`dict`):\n", + " Dictionary containing the dataset names and their training proportions. By default, all test proportions are 1.\n", + " splits (Optional[List[str]], *optional*, defaults to `None`):\n", + " Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix.\n", + " shuffle (`bool`, *optional*, defaults to `True`):\n", + " Whether to shuffle the training and testing/validation data.\n", + " \"\"\"\n", + " raw_datasets = DatasetDict()\n", + " raw_train_datasets = []\n", + " raw_val_datasets = []\n", + " fracs = []\n", + " for ds, frac in dataset_mixer.items():\n", + " fracs.append(frac)\n", + " for split in splits:\n", + " try:\n", + " # Try first if dataset on a Hub repo\n", + " dataset = load_dataset(ds, split=split)\n", + " except DatasetGenerationError:\n", + " # If not, check local dataset\n", + " dataset = load_from_disk(os.path.join(ds, split))\n", + "\n", + " if \"train\" in split:\n", + " raw_train_datasets.append(dataset)\n", + " elif \"test\" in split:\n", + " raw_val_datasets.append(dataset)\n", + " else:\n", + " raise ValueError(f\"Split type {split} not recognized as one of test or train.\")\n", + "\n", + " if any(frac < 0 for frac in fracs):\n", + " raise ValueError(\"Dataset fractions cannot be negative.\")\n", + "\n", + " if len(raw_train_datasets) > 0:\n", + " train_subsets = []\n", + " for dataset, frac in zip(raw_train_datasets, fracs):\n", + " train_subset = dataset.select(range(int(frac * len(dataset))))\n", + " train_subsets.append(train_subset)\n", + " if shuffle:\n", + " raw_datasets[\"train\"] = concatenate_datasets(train_subsets).shuffle(seed=42)\n", + " else:\n", + " raw_datasets[\"train\"] = concatenate_datasets(train_subsets)\n", + " # No subsampling for test datasets to enable fair comparison across models\n", + " if len(raw_val_datasets) > 0:\n", + " if shuffle:\n", + " raw_datasets[\"test\"] = concatenate_datasets(raw_val_datasets).shuffle(seed=42)\n", + " else:\n", + " raw_datasets[\"test\"] = concatenate_datasets(raw_val_datasets)\n", + "\n", + " if len(raw_datasets) == 0:\n", + " raise ValueError(\n", + " f\"Dataset {dataset_mixer} not recognized with split {split}. Check the dataset has been correctly formatted.\"\n", + " )\n", + "\n", + " return raw_datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EQ-Cp2V6kDcr" + }, + "source": [ + "\n", + "### Data Prep\n", + "We follow Huggingface's [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) and use the [Ultra Feedback dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), and sample 0.5% of it to speed things up. You can sample the full dataset for a full run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 553, + "referenced_widgets": [ + "5e875e8a442a41e8afef4ad494ef6897", + "7adc769f384346b3a5835c73542a2d62", + "fccd2f68b05d40c9b359133fbf383b1c", + "2a197f7c6c4746e1a7fded1e0539bf29", + "37a2eb8b91ed42a1be411ecb9079a082", + "833097685a694d1e9cd5aa016b6b7d73", + "4e48c220077d4546a51c02df9397a084", + "f6200f86a4084c5f978f62481100b7a3", + "530cd47150094e7fb925eb24ec071b5e", + "9f6197721b90446091bf8b97de656d4b", + "35edd118e5e54d19b87aa7e966e3c9fe", + "3b258a09c7e0462e9d9fefa6746ebb9f", + "ade70be3dc49443581a295e89faa65c6", + "ad03a2ff7f974fa3a0f1be2dee56c9a0", + "727d7d710957497c8bb91d52a04a00f3", + "347ae8c1baf54004953d0eb9325f39f8", + "6b419485aabe49c6bed15b5bf1316e43", + "9eabdceae6484ba7b543ed1fa4f65141", + "6cce73a8bdda4852968c807354a6d212", + "7953ace8fec34f358506307a3efb208d", + 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+ " fn_kwargs = {\"tokenizer\": tokenizer, \"task\": \"dpo\"},\n", + " num_proc = 12,\n", + " remove_columns = column_names,\n", + " desc = \"Formatting comparisons with prompt template\",\n", + ")\n", + "\n", + "# Replace column names with what TRL needs, text_chosen -> chosen and text_rejected -> rejected\n", + "for split in [\"train\", \"test\"]:\n", + " raw_datasets[split] = raw_datasets[split].rename_columns(\n", + " {\"text_prompt\": \"prompt\", \"text_chosen\": \"chosen\", \"text_rejected\": \"rejected\"}\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7AxUmeAGkjDd" + }, + "source": [ + "We shall print a random item from the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oF63zQqNlNJC", + "outputId": "5e4e2858-9ac2-4023-f554-13bfeec367a8" + }, + "outputs": [], + "source": [ + "import pprint\n", + "row = raw_datasets[\"train\"][8]\n", + "pprint.pprint(row[\"prompt\"])\n", + "pprint.pprint(row[\"chosen\"])\n", + "pprint.pprint(row[\"rejected\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "86wyNoeMj-Ph" + }, + "source": [ + "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6bZsfBuZDeCL", + "outputId": "677aef23-d549-44d3-aace-9efda302d1e8" + }, + "outputs": [], + "source": [ + "model = FastLanguageModel.get_peft_model(\n", + " model,\n", + " r = 64, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n", + " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", + " \"gate\", \"w1\", \"w2\", \"w3\"],\n", + " lora_alpha = 64,\n", + " lora_dropout = 0, # Currently only supports dropout = 0\n", + " bias = \"none\", # Currently only supports bias = \"none\"\n", + " use_gradient_checkpointing = True,\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-kyd_iyz7DUM" + }, + "source": [ + "\n", + "### Train the DPO model\n", + "Now let's use Huggingface TRL's `DPOTrainer`! More docs here: [TRL DPO docs](https://huggingface.co/docs/trl/dpo_trainer). We do 3 epochs on 0.5% of the dataset to speed things up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "v-2BFpDWzo1K" + }, + "outputs": [], + "source": [ + "# One must patch the DPO Trainer first!\n", + "from unsloth import PatchDPOTrainer\n", + "PatchDPOTrainer()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104, + "referenced_widgets": [ + "1410214ec6fd4e19a200b5e03464ceee", + "0ba04e31d310422f9efaef634afb1296", + "6ac7582965d341c383c806ae3a8b513a", + "971371cfb1bd447e94f9f2e38f9f50c8", + "3d4dbe5814d24b7dae1a7462a33e7f40", + "6b4743fee43b40638d8f2df36fa9fed9", + "77f706dc09eb43d7aa4522f8a0b36142", + "175c7bb9e8c94b8daa3287dd773b6837", + "a10418a202e14a96893e24267bc194c9", + "8138d9f175df4a28b2e58e9c852cabb8", + "43b39a8abcb94847bc21ec16fa373ed9" + ] + }, + "id": "QtoqUw80QDV0", + "outputId": "40764ccc-6502-4be1-fb4f-e386f5460147" + }, + "outputs": [], + "source": [ + "from transformers import TrainingArguments\n", + "from trl import DPOTrainer\n", + "\n", + "dpo_trainer = DPOTrainer(\n", + " model = model,\n", + " ref_model = None,\n", + " args = TrainingArguments(\n", + " per_device_train_batch_size = 2,\n", + " gradient_accumulation_steps = 4,\n", + " warmup_ratio = 0.1,\n", + " num_train_epochs = 3,\n", + " learning_rate = 5e-6,\n", + " fp16 = not torch.cuda.is_bf16_supported(),\n", + " bf16 = torch.cuda.is_bf16_supported(),\n", + " logging_steps = 1,\n", + " optim = \"adamw_8bit\",\n", + " weight_decay = 0.0,\n", + " lr_scheduler_type = \"linear\",\n", + " seed = 42,\n", + " output_dir = \"outputs\",\n", + " ),\n", + " beta = 0.1,\n", + " train_dataset = raw_datasets[\"train\"],\n", + " # eval_dataset = raw_datasets[\"test\"],\n", + " tokenizer = tokenizer,\n", + " max_length = 1024,\n", + " max_prompt_length = 512,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "EWGFqAo5Q2me", + "outputId": "1bcba65a-8253-4a49-a180-fde8e26ad4b5" + }, + "outputs": [], + "source": [ + "dpo_trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zt9CHJqO6p30" + }, + "source": [ + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Mistral 7b 2x faster [free Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)\n", + "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n", + "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n", + "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n", + "5. Llama 7b [free Kaggle](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp)\n", + "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n", + "\n", + "
\n", + " \n", + " \n", + " Support our work if you can! Thanks!\n", + "
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FastLlamaModel, logger from .mistral import FastMistralModel +from .mixtral import FastMixtralModel from transformers import AutoConfig from transformers import __version__ as transformers_version from peft import PeftConfig, PeftModel @@ -97,8 +98,9 @@ def from_pretrained( model_type = model_config.model_type - if model_type == "llama": dispatch_model = FastLlamaModel + if model_type == "llama": dispatch_model = FastLlamaModel elif model_type == "mistral": dispatch_model = FastMistralModel + elif model_type == "mixtral": dispatch_model = FastMixtralModel else: raise NotImplementedError( f"Unsloth: {model_name} not supported yet!\n"\ diff --git a/unsloth/models/mixtral.py b/unsloth/models/mixtral.py new file mode 100644 index 00000000..3bb905fe --- /dev/null +++ b/unsloth/models/mixtral.py @@ -0,0 +1,816 @@ +# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved. +# +# 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. + +from .mistral import * +from ._utils import __version__ +from peft import LoraConfig, TaskType, get_peft_model as _get_peft_model +from transformers.models.mixtral.modeling_mixtral import ( + MixtralAttention, + MixtralDecoderLayer, + MixtralSparseMoeBlock, + MixtralModel, + MixtralForCausalLM, + MoeModelOutputWithPast, + MoeCausalLMOutputWithPast, + load_balancing_loss_func +) + +# For Pytorch 2.1.1 +try: + from transformers.models.mixtral.modeling_mixtral import ( + MixtralSdpaAttention, + MixtralFlashAttention2, + ) +except: + MixtralSdpaAttention = MixtralAttention + MixtralFlashAttention2 = MixtralAttention +pass + +def MixtralDecoderLayer_fast_forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + output_router_logits: Optional[bool] = False, + use_cache: Optional[bool] = False, + *args, **kwargs, +) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + residual = hidden_states + hidden_states = fast_rms_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = fast_rms_layernorm(hidden_states) + hidden_states, router_logits = self.block_sparse_moe(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + if output_router_logits: + outputs += (router_logits,) + + return outputs +pass + +def MixtralModel_fast_forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_router_logits: Optional[bool] = None, + return_dict: Optional[bool] = None, +) -> Union[Tuple, MoeModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_router_logits = ( + output_router_logits if output_router_logits is not None else self.config.output_router_logits + ) + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + past_key_values_length = 0 + + # Fix out of bounds tokenization + if hasattr(self, "max_seq_length"): + if seq_length > self.max_seq_length: + logger.warning_once( + f"Unsloth: Input IDs of length {seq_length} > the model's max sequence length of {self.max_seq_length}.\n"\ + "We shall truncate it ourselves. It's imperative if you correct this issue first." + ) + if input_ids is not None: + input_ids = input_ids[:,:self.max_seq_length] + elif inputs_embeds is not None: + inputs_embeds = inputs_embeds[:,:self.max_seq_length,:] + pass + pass + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if use_cache: + past_key_values_length = past_key_values.get_usable_length(seq_length) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: + is_padding_right = attention_mask[:, -1].sum().item() != batch_size + if is_padding_right: + raise ValueError( + "You are attempting to perform batched generation with padding_side='right'" + " this may lead to unexpected behaviour for Flash Attention version of Mixtral. Make sure to " + " call `tokenizer.padding_side = 'left'` before tokenizing the input. " + ) + + if self._attn_implementation == "flash_attention_2": + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._attn_implementation == "sdpa" and not output_attentions: + # output_attentions=True can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask_for_sdpa + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + else: + # 4d mask is passed through the layers + from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_router_logits = () if output_router_logits else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + output_router_logits, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + output_router_logits=output_router_logits, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if output_router_logits: + all_router_logits += (layer_outputs[-1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, next_decoder_cache, all_hidden_states, all_self_attns, all_router_logits] + if v is not None + ) + return MoeModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + router_logits=all_router_logits, + ) +pass + +def MixtralForCausalLM_fast_forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_router_logits: Optional[bool] = None, + return_dict: Optional[bool] = None, +) -> Union[Tuple, MoeCausalLMOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_router_logits = ( + output_router_logits if output_router_logits is not None else self.config.output_router_logits + ) + + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + output_router_logits=output_router_logits, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = fast_cross_entropy_loss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + aux_loss = None + if output_router_logits: + aux_loss = load_balancing_loss_func( + outputs.router_logits if return_dict else outputs[-1], + self.num_experts, + self.num_experts_per_tok, + attention_mask, + ) + if labels is not None: + loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device + + if not return_dict: + output = (logits,) + outputs[1:] + if output_router_logits: + output = (aux_loss,) + output + return (loss,) + output if loss is not None else output + + return MoeCausalLMOutputWithPast( + loss=loss, + aux_loss=aux_loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + router_logits=outputs.router_logits, + ) +pass + +class FastMixtralModel: + @staticmethod + def pre_patch(): + MixtralAttention .forward = MistralAttention_fast_forward + MixtralSdpaAttention .forward = MistralAttention_fast_forward + MixtralFlashAttention2.forward = MistralAttention_fast_forward + MixtralDecoderLayer .forward = MixtralDecoderLayer_fast_forward + MixtralModel .forward = MixtralModel_fast_forward + MixtralForCausalLM .forward = MixtralForCausalLM_fast_forward + PeftModelForCausalLM .forward = PeftModelForCausalLM_fast_forward + return + pass + + @staticmethod + def from_pretrained( + model_name = "ybelkada/Mixtral-8x7B-Instruct-v0.1-bnb-4bit", + max_seq_length = 4096, + dtype = None, + load_in_4bit = True, + token = None, + device_map = "sequential", + rope_scaling = None, # Mixtral does not support RoPE scaling + fix_tokenizer = True, + **kwargs, + ): + # Mixtral does NOT support RoPE Scaling! + if rope_scaling is not None: + logger.warning_once("Unsloth: Mixtral models do not support RoPE scaling.") + pass + + SUPPORTS_BFLOAT16 = torch.cuda.is_bf16_supported() + gpu_stats = torch.cuda.get_device_properties(0) + max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3) + + statistics = \ + f"==((====))== Unsloth: Fast Mixtral patching release {__version__}\n"\ + f" \\\ /| GPU: {gpu_stats.name}. Max memory: {max_memory} GB. Platform = {platform_system}.\n"\ + f"O^O/ \_/ \\ Pytorch: {torch.__version__}. CUDA = {gpu_stats.major}.{gpu_stats.minor}. CUDA Toolkit = {torch.version.cuda}.\n"\ + f"\ / Bfloat16 = {str(SUPPORTS_BFLOAT16).upper()}. Xformers = {xformers_version}. FA = {HAS_FLASH_ATTENTION}.\n"\ + f' "-____-" Apache 2 free license: http://github.com/unslothai/unsloth' + print(statistics) + FastMixtralModel.pre_patch() + + if dtype is None: + dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16 + elif dtype == torch.bfloat16 and not SUPPORTS_BFLOAT16: + logger.warning_once("Device does not support bfloat16. Will change to float16.") + dtype = torch.float16 + + assert(dtype == torch.float16 or dtype == torch.bfloat16 or dtype == torch.float32) + + # Check max sequence length + model_config = AutoConfig.from_pretrained(model_name, token = token) + model_max_seq_length = model_config.max_position_embeddings + + # Mixtral does NOT support RoPE Scaling sadly so we have to error out. + if max_seq_length > model_max_seq_length: + raise RuntimeError( + "Unsloth: Unfortunately Mixtral type models do not support RoPE scaling!\n"\ + f"The maximum sequence length supported is {model_max_seq_length}.", + ) + pass + + bnb_config = None + if load_in_4bit: + bnb_config = BitsAndBytesConfig( + load_in_4bit = True, + bnb_4bit_use_double_quant = True, + bnb_4bit_quant_type = "nf4", + bnb_4bit_compute_dtype = dtype, + ) + + max_position_embeddings = max(max_seq_length, model_max_seq_length) + model = AutoModelForCausalLM.from_pretrained( + model_name, + device_map = device_map, + torch_dtype = dtype, + quantization_config = bnb_config, + token = token, + # rope_scaling = rope_scaling, + **kwargs, + ) + tokenizer = AutoTokenizer.from_pretrained( + model_name, + model_max_length = max_position_embeddings, + padding_side = "right", + token = token, + ) + + model, tokenizer = patch_tokenizer(model, tokenizer) + model = FastMixtralModel.post_patch(model) + + # Patch up QKV / O and MLP + for idx, layer in enumerate(model.model.layers): + layer.self_attn.apply_qkv = original_apply_qkv + layer.self_attn.apply_o = original_apply_o + pass + + # Save max_seq_length + max_position_embeddings = max(max_seq_length, model.config.max_position_embeddings) + model.max_seq_length = max_position_embeddings + internal_model = model + while hasattr(internal_model, "model"): + internal_model.max_seq_length = max_position_embeddings + internal_model = internal_model.model + pass + internal_model.max_seq_length = max_position_embeddings + + # We check the tokenizer first for errors + if fix_tokenizer: + tokenizer = check_tokenizer( + model = model, + tokenizer = tokenizer, + model_name = model_name, + model_max_length = max_position_embeddings, + padding_side = "right", + token = token, + ) + pass + patch_saving_functions(tokenizer) + + # Fix up config for transformers uploading PEFT + # Not necessary anymore since we require transformers>=4.37 + if False: + name = model.config._name_or_path + if name.startswith("unsloth/") and name.endswith("-bnb-4bit"): + name = name[:len(name) - len("-bnb-4bit")] + model.config.update({"_name_or_path" : name}) + pass + + # Log Unsloth version for future fastpaths for inference + model.config.update({"unsloth_version" : __version__}) + + # Add save modules + patch_saving_functions(model) + + return model, tokenizer + pass + + @staticmethod + def post_patch(model): + # Patch model + layers = model.model.layers + + # Torch.compile fails on embedding matrix?? + # Workaround randomnly fixes it for torch versions < 2.2 + model.model.embed_tokens = torch.nn.Embedding.from_pretrained(model.model.embed_tokens.weight) + model.config.update({"unsloth_version" : __version__}) + + # We also do this for the lm_head + lm_head = torch.nn.Linear(1, 1, bias = None) + del lm_head.weight + lm_head.weight = model.lm_head.weight + lm_head.in_features = lm_head.weight.shape[1] + lm_head.out_features = lm_head.weight.shape[0] + model.lm_head = lm_head + + # Also patch all dtypes - BnB seems to not allocate the correct type? + # BnB default dtype seems to be float16! + correct_dtype = lm_head.weight.dtype + + for name, module in model.named_modules(): + if isinstance(module, (Bnb_Linear4bit, Peft_Linear4bit)): + weight = module.weight + quant_state = weight.quant_state + + if type(quant_state) is list: + # BnB seems to have float16 as default! + module.weight.quant_state[2] = correct_dtype # Cast to correct dtype + else: + # https://github.com/TimDettmers/bitsandbytes/pull/763/files + quant_state.dtype = correct_dtype + pass + pass + pass + + # Clear deleted GPU items + import gc + for _ in range(3): + gc.collect() + torch.cuda.empty_cache() + return model + pass + + @staticmethod + def get_peft_model( + model, + r = 16, + target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", + "gate", "w1", "w2", "w3"], + lora_alpha = 16, + lora_dropout = 0, + bias = "none", + layers_to_transform = None, + layers_pattern = None, + use_gradient_checkpointing = True, + random_state = 3407, + max_seq_length = 2048, # not used anymore + use_rslora = False, + init_lora_weights = True, + loftq_config = {}, + **kwargs, + ): + transformers_set_seed(random_state) + + if isinstance(model, PeftModelForCausalLM): + raise TypeError( + "Unsloth: Your model already has LoRA adapters. No need to run this again!" + ) + pass + + import inspect + signature = str(inspect.signature(LoraConfig)) + SUPPORTS_LOFTQ = "loftq_config" in signature + SUPPORTS_RSLORA = "use_rslora" in signature + + assert(max_seq_length <= model.max_seq_length) + + if lora_dropout != 0: + logger.warning_once( + f"Unsloth: Dropout = 0 is supported for fast patching. You are using dropout = {lora_dropout}.\n"\ + f"Unsloth will patch all other layers, except LoRA matrices, causing a performance hit." + ) + pass + + if bias != "none": + logger.warning_once( + f"Unsloth: bias = `none` is supported for fast patching. You are using bias = {bias}.\n"\ + f"Unsloth will patch all other layers, except LoRA matrices, causing a performance hit." + ) + pass + + if not (type(init_lora_weights) is bool or \ + init_lora_weights == "gaussian" or init_lora_weights == "loftq"): + raise ValueError( + 'Unsloth: `init_lora_weights` must be either [True, False, "gaussian", "loftq"].' + ) + pass + + if init_lora_weights == "loftq": + + if not SUPPORTS_LOFTQ: + import peft + raise RuntimeError( + f"Unsloth: Your PEFT version of {peft.__version__} does not support LoftQ init.\n"\ + "Please install PEFT 0.7.2 or higher.\n"\ + "You can also install from source: `pip install git+https://github.com/huggingface/peft.git" + ) + pass + + if loftq_config == {}: + from peft import LoftQConfig + logger.warning_once( + f"Unsloth: init_lora_weights = `loftq` is set, but `loftq_config` is None.\n"\ + f"We shall use `loftq_config = LoftQConfig(loftq_bits = 4, loftq_iter = 1)`." + ) + loftq_config = LoftQConfig(loftq_bits = 4, loftq_iter = 1) + pass + + if hasattr(model.config, "quantization_config"): + raise ValueError( + "Unsloth: You are using `loftq` init, yet `load_in_4bit = True` was set.\n"\ + "Reload your model without any quantization by setting `load_in_4bit = False`." + ) + pass + pass + + assert(type(use_rslora) is bool) + if use_rslora: + if not SUPPORTS_RSLORA: + # We manually check for PEFT + import peft + raise RuntimeError( + f"Unsloth: Your PEFT version of {peft.__version__} does not support `use_rslora`.\n"\ + "Please install PEFT 0.7.2 or higher.\n"\ + "You can also install from source: `pip install git+https://github.com/huggingface/peft.git" + ) + pass + pass + + accepted_modules = frozenset(("q_proj", "k_proj", "v_proj", "o_proj", + "gate", "w1", "w2", "w3"),) + model.config.update({"unsloth_version" : __version__}) + for module in target_modules: + assert(module in accepted_modules) + pass + + # Get LoRA + arguments = dict( + r = r, + lora_alpha = lora_alpha, + target_modules = target_modules, + lora_dropout = lora_dropout, + bias = bias, + task_type = TaskType.CAUSAL_LM, + layers_to_transform = layers_to_transform, + init_lora_weights = init_lora_weights, + loftq_config = loftq_config, + use_rslora = use_rslora, + **kwargs, + ) + if not SUPPORTS_LOFTQ: del arguments["loftq_config"] + if not SUPPORTS_RSLORA: del arguments["use_rslora"] + + lora_config = LoraConfig(**arguments) + model = _get_peft_model(model, lora_config) + + model = FastMixtralModel.patch_peft_model(model, use_gradient_checkpointing) + return model + pass + + + @staticmethod + def patch_peft_model( + model, + use_gradient_checkpointing = True, + ): + if not isinstance(model, PeftModelForCausalLM): + raise TypeError( + "Unsloth: Your model needs to call `.get_peft_model` first!" + ) + pass + + model = prepare_model_for_kbit_training( + model, + use_gradient_checkpointing = use_gradient_checkpointing, + use_reentrant = True, + ) + + # Fix up config for transformers uploading PEFT + for active_adapter in model.peft_config.keys(): + # Not necessary since we requires transformers >= 4.37 + if False: + name = model.peft_config[active_adapter].base_model_name_or_path + if name.startswith("unsloth/") and name.endswith("-bnb-4bit"): + name = name[:len(name) - len("-bnb-4bit")] + model.peft_config[active_adapter].base_model_name_or_path = name + pass + # Add revision to enable future fast inference paths + model.peft_config[active_adapter].revision = f"unsloth" + pass + + # Do patching + n_mlp = 0 + n_qkv = 0 + n_o = 0 + import types + + active_adapter = model.active_adapters[0] if \ + hasattr(model, "active_adapters") else model.active_adapter + + # Get dropout and bias + lora_dropout = model.peft_config[active_adapter].lora_dropout + bias = model.peft_config[active_adapter].bias + + if lora_dropout == 0 and bias == "none": + for idx, layer in enumerate(model.model.model.layers): + + # # MLP patching + # gate_proj = layer.mlp.gate_proj + # up_proj = layer.mlp. up_proj + # down_proj = layer.mlp.down_proj + + # if hasattr(gate_proj, "lora_A") and \ + # hasattr( up_proj, "lora_A") and \ + # hasattr(down_proj, "lora_A") and \ + # (gate_proj.base_layer if hasattr(gate_proj, "base_layer") else gate_proj).bias is None and \ + # ( up_proj.base_layer if hasattr( up_proj, "base_layer") else up_proj).bias is None and \ + # (down_proj.base_layer if hasattr(down_proj, "base_layer") else down_proj).bias is None: + + # # https://stackoverflow.com/questions/50599045/python-replacing-a-function-within-a-class-of-a-module + # layer.mlp.forward = types.MethodType(apply_lora_mlp, layer.mlp) + # n_mlp += 1 + # else: + # logger.warning_once( + # "Unsloth cannot patch MLP layers with our manual autograd engine since either LoRA adapters\n"\ + # "are not enabled or a bias term (like in Qwen) is used." + # ) + # pass + + # QKV attention patching + q_proj = layer.self_attn.q_proj + k_proj = layer.self_attn.k_proj + v_proj = layer.self_attn.v_proj + if hasattr(q_proj, "lora_A") and \ + hasattr(k_proj, "lora_A") and \ + hasattr(v_proj, "lora_A") and \ + (q_proj.base_layer if hasattr(q_proj, "base_layer") else q_proj).bias is None and \ + (k_proj.base_layer if hasattr(k_proj, "base_layer") else k_proj).bias is None and \ + (v_proj.base_layer if hasattr(v_proj, "base_layer") else v_proj).bias is None: + + layer.self_attn.apply_qkv = apply_lora_qkv + n_qkv += 1 + else: + logger.warning_once( + "Unsloth cannot patch Attention layers with our manual autograd engine since either LoRA adapters\n"\ + "are not enabled or a bias term (like in Qwen) is used." + ) + pass + + # O attention patching + o_proj = layer.self_attn.o_proj + if hasattr(o_proj, "lora_A") and \ + (o_proj.base_layer if hasattr(o_proj, "base_layer") else o_proj).bias is None: + + layer.self_attn.apply_o = apply_lora_o + n_o += 1 + else: + logger.warning_once( + "Unsloth cannot patch O projection layer with our manual autograd engine since either LoRA adapters\n"\ + "are not enabled or a bias term (like in Qwen) is used." + ) + pass + pass + pass + + logger.warning_once( + f"Unsloth {__version__} patched {len(model.model.model.layers)} layers with "\ + f"{n_qkv} QKV layers, {n_o} O layers and {n_mlp} MLP layers.", + ) + patch_saving_functions(model) + + # Patch cross entropy loss labels + # Fixes https://github.com/unslothai/unsloth/issues/10 + max_seq_length = model.max_seq_length + extra_ignored_labels = torch.full((max_seq_length, 1), -100, device = "cuda") + model.model.extra_ignored_labels = extra_ignored_labels + internal_model = model + while hasattr(internal_model, "model"): + internal_model.max_seq_length = max_seq_length + internal_model = internal_model.model + pass + internal_model.max_seq_length = max_seq_length + return model + pass + + @staticmethod + def for_inference(model): + if not hasattr(model, "_original_forward"): + model._original_forward = model.forward + pass + model.forward = torch.inference_mode(model._original_forward) + + internal_model = model + internal_model.gradient_checkpointing = False + internal_model.training = False + + while hasattr(internal_model, "model"): + internal_model = internal_model.model + internal_model.gradient_checkpointing = False + internal_model.training = False + pass + pass + + + @staticmethod + def for_training(model, use_gradient_checkpointing = True): + if hasattr(model, "_original_forward"): + model.forward = model._original_forward + pass + + internal_model = model + internal_model.gradient_checkpointing = use_gradient_checkpointing + internal_model.training = True + + # Delete all fast inference loras + for param in model.parameters(): + if hasattr(param, "_fast_lora"): + del param._fast_lora + pass + + while hasattr(internal_model, "model"): + internal_model = internal_model.model + internal_model.gradient_checkpointing = use_gradient_checkpointing + internal_model.training = True + pass + pass +pass \ No newline at end of file