From dbf44fe316ed29d45e9e747b9dd4dc628390ef54 Mon Sep 17 00:00:00 2001
From: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Date: Fri, 17 Feb 2023 10:49:11 +0100
Subject: [PATCH] [`core`] Some changes with `prepare_model_for_training` & few
 fixes (#105)

* changes

* apply to other notebooks
---
 .../Finetune_flan_t5_large_bnb_peft.ipynb     |    14 +-
 .../int8_training/Finetune_opt_bnb_peft.ipynb | 13959 ++++++++--------
 .../peft_bnb_whisper_large_v2_training.ipynb  |     4 +-
 src/peft/__init__.py                          |     2 +-
 src/peft/utils/__init__.py                    |     2 +-
 src/peft/utils/other.py                       |     2 +-
 6 files changed, 6992 insertions(+), 6991 deletions(-)

diff --git a/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb b/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb
index ec42414..f1452bd 100644
--- a/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb
+++ b/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb
@@ -11,6 +11,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "kBFPA3-aDT7H",
    "metadata": {
@@ -19,7 +20,9 @@
    "source": [
     "In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!\n",
     "\n",
-    "We will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset, that consists of pairs of text-labels to classify financial-related sentences, if they are either `positive`, `neutral` or `negative`."
+    "We will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset, that consists of pairs of text-labels to classify financial-related sentences, if they are either `positive`, `neutral` or `negative`.\n",
+    "\n",
+    "Note that you could use the same notebook to fine-tune `flan-t5-xl` as well, but you would need to shard the models first to avoid CPU RAM issues on Google Colab, check [these weights](https://huggingface.co/ybelkada/flan-t5-xl-sharded-bf16)."
    ]
   },
   {
@@ -317,13 +320,14 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "4o3ePxrjEDzv",
    "metadata": {
     "id": "4o3ePxrjEDzv"
    },
    "source": [
-    "Some pre-processing needs to be done before training such a model using `peft`, therefore let's import an utiliy function `prepare_model_for_training` that will: \n",
+    "Some pre-processing needs to be done before training such an int8 model using `peft`, therefore let's import an utiliy function `prepare_model_for_int8_training` that will: \n",
     "- Cast the layer norm in `float32` for stability purposes\n",
     "- Add a `forward_hook` to the input embedding layer to enable gradient computation of the input hidden states\n",
     "- Enable gradient checkpointing for more memory-efficient training\n",
@@ -339,9 +343,9 @@
    },
    "outputs": [],
    "source": [
-    "from peft import prepare_model_for_training\n",
+    "from peft import prepare_model_for_int8_training\n",
     "\n",
-    "model = prepare_model_for_training(model)"
+    "model = prepare_model_for_int8_training(model)"
    ]
   },
   {
@@ -1275,7 +1279,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.9.16"
   },
   "vscode": {
    "interpreter": {
diff --git a/examples/int8_training/Finetune_opt_bnb_peft.ipynb b/examples/int8_training/Finetune_opt_bnb_peft.ipynb
index 51b2951..a3b15be 100644
--- a/examples/int8_training/Finetune_opt_bnb_peft.ipynb
+++ b/examples/int8_training/Finetune_opt_bnb_peft.ipynb
@@ -2,27 +2,27 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {
+    "id": "WE5GJ6s7y0Xo"
+   },
    "source": [
     "## Fine-tune large models using 🤗 `peft` adapters, `transformers` & `bitsandbytes`\n",
     "\n",
     "In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in 8-bit.\n",
     "The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" (LoRA), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model. \n",
     "After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!"
-   ],
-   "metadata": {
-    "id": "WE5GJ6s7y0Xo"
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "id": "TfBzP8gWzkpv"
+   },
    "source": [
     "### Install requirements\n",
     "\n",
     "First, run the cells below to install the requirements:"
-   ],
-   "metadata": {
-    "id": "TfBzP8gWzkpv"
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -36,8 +36,8 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
       "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
@@ -65,14 +65,14 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "id": "FOtwYRI3zzXI"
+   },
    "source": [
     "### Model loading\n",
     "\n",
     "Here let's load the `opt-6.7b` model, its weights in half-precision (float16) are about 13GB on the Hub! If we load them in 8-bit we would require around 7GB of memory instead."
-   ],
-   "metadata": {
-    "id": "FOtwYRI3zzXI"
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -199,8 +199,8 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "\n",
       "===================================BUG REPORT===================================\n",
@@ -209,144 +209,144 @@
      ]
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4de260ffd8a440eb87eb900fc1bb1d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)lve/main/config.json:   0%|          | 0.00/651 [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "d4de260ffd8a440eb87eb900fc1bb1d3"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc2d5ffe254d425b939252ec46ec27cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)model.bin.index.json:   0%|          | 0.00/41.9k [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "fc2d5ffe254d425b939252ec46ec27cc"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6f712eadc4d49019b2bd355968cc155",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)00001-of-00002.bin\";:   0%|          | 0.00/9.96G [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "c6f712eadc4d49019b2bd355968cc155"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5aa74b9b30614172b07f88873cf89471",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)00002-of-00002.bin\";:   0%|          | 0.00/3.36G [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "5aa74b9b30614172b07f88873cf89471"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e73e5388182040a8937ccf1748171a87",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "e73e5388182040a8937ccf1748171a87"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a994beafbf3f4c20880a7bbe3898db36",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)neration_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "a994beafbf3f4c20880a7bbe3898db36"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e9391f6c89c4d08859ef3413edb19be",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)okenizer_config.json:   0%|          | 0.00/685 [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "1e9391f6c89c4d08859ef3413edb19be"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e6d5943bc374b388b93ed115e44b6a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "4e6d5943bc374b388b93ed115e44b6a5"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ca7684b79c5438fa06b047bd2b3283f",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "1ca7684b79c5438fa06b047bd2b3283f"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d46b5725c35142a89617e46c0e8d3679",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/441 [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "d46b5725c35142a89617e46c0e8d3679"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -368,41 +368,33 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "source": [
-    "### Post-processing on the model\n",
-    "\n",
-    "Finally, we need to apply some post-processing on the 8-bit model to enable training, let's freeze all our layers, and cast the layer-norm in `float32` for stability. We also cast the output of the last layer in `float32` for the same reasons."
-   ],
    "metadata": {
     "id": "9fTSZntA1iUG"
-   }
+   },
+   "source": [
+    "### Prepare model for training\n",
+    "\n",
+    "Some pre-processing needs to be done before training such an int8 model using `peft`, therefore let's import an utiliy function `prepare_model_for_int8_training` that will: \n",
+    "- Cast the layer norm in `float32` for stability purposes\n",
+    "- Add a `forward_hook` to the input embedding layer to enable gradient computation of the input hidden states\n",
+    "- Enable gradient checkpointing for more memory-efficient training\n",
+    "- Cast the output logits in `float32` for smoother sampling during the sampling procedure"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "for param in model.parameters():\n",
-    "    param.requires_grad = False  # freeze the model - train adapters later\n",
-    "    if param.ndim == 1:\n",
-    "        # cast the small parameters (e.g. layernorm) to fp32 for stability\n",
-    "        param.data = param.data.to(torch.float32)\n",
-    "\n",
-    "model.gradient_checkpointing_enable()  # reduce number of stored activations\n",
-    "model.model.decoder.project_in = lambda x: x.requires_grad_(True)\n",
-    "\n",
-    "\n",
-    "class CastOutputToFloat(nn.Sequential):\n",
-    "    def forward(self, x):\n",
-    "        return super().forward(x).to(torch.float32)\n",
-    "\n",
-    "\n",
-    "model.lm_head = CastOutputToFloat(model.lm_head)"
-   ],
+   "execution_count": 3,
    "metadata": {
     "id": "T-gy-LxM0yAi"
    },
-   "execution_count": 3,
-   "outputs": []
+   "outputs": [],
+   "source": [
+    "from peft import prepare_model_for_int8_training\n",
+    "\n",
+    "model = prepare_model_for_int8_training(model)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -450,8 +442,8 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "trainable params: 8388608 || all params: 6666862592 || trainable%: 0.12582542214183376\n"
      ]
@@ -479,10 +471,10 @@
   },
   {
    "cell_type": "markdown",
-   "source": [],
    "metadata": {
     "id": "b_RPiO7f3ClX"
-   }
+   },
+   "source": []
   },
   {
    "cell_type": "code",
@@ -576,127 +568,127 @@
    },
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f357166c6e5f43f39d0a287ca6d6f60e",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading readme:   0%|          | 0.00/5.55k [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "f357166c6e5f43f39d0a287ca6d6f60e"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "stream",
      "name": "stderr",
+     "output_type": "stream",
      "text": [
       "WARNING:datasets.builder:Using custom data configuration Abirate--english_quotes-6e72855d06356857\n"
      ]
     },
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Downloading and preparing dataset json/Abirate--english_quotes to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n"
      ]
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c81d20fe47ce4b7594427830d71504d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "c81d20fe47ce4b7594427830d71504d7"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae58b3f129644729aa2f890b1712fc6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading data:   0%|          | 0.00/647k [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "ae58b3f129644729aa2f890b1712fc6e"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8c29c1accf4479da91d393dd59ca82b",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "b8c29c1accf4479da91d393dd59ca82b"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "041944011c204558a4315c27ec5dabce",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Generating train split: 0 examples [00:00, ? examples/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "041944011c204558a4315c27ec5dabce"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"
      ]
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9225dd50bcea435289fa2eba64087076",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "9225dd50bcea435289fa2eba64087076"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a618f430e06645eb9c95bf16bb6ea59a",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "  0%|          | 0/3 [00:00<?, ?ba/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "a618f430e06645eb9c95bf16bb6ea59a"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "stream",
      "name": "stderr",
+     "output_type": "stream",
      "text": [
       "The model is loaded in 8-bit precision. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. Please check  the examples in https://github.com/huggingface/peft for more details.\n",
       "max_steps is given, it will override any value given in num_train_epochs\n",
@@ -717,11 +709,7 @@
      ]
     },
     {
-     "output_type": "display_data",
      "data": {
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ],
       "text/html": [
        "\n",
        "    <div>\n",
@@ -1343,9 +1331,13 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1376,20 +1368,16 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Share adapters on the 🤗 Hub"
-   ],
    "metadata": {
     "id": "Duak7T_B3VpJ"
-   }
+   },
+   "source": [
+    "## Share adapters on the 🤗 Hub"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ],
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1417,24 +1405,26 @@
     "id": "DpYr24pR8T_0",
     "outputId": "20186456-1bd4-4655-b2f2-8f24f9f37fcc"
    },
-   "execution_count": null,
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Token is valid.\n",
       "Your token has been saved to /root/.cache/huggingface/token\n",
       "Login successful\n"
      ]
     }
+   ],
+   "source": [
+    "from huggingface_hub import notebook_login\n",
+    "\n",
+    "notebook_login()"
    ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "model.push_to_hub(\"ybelkada/opt-6.7b-lora\", use_auth_token=True)"
-   ],
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1467,83 +1457,71 @@
     "id": "VxB6UV5XAvvP",
     "outputId": "c3b0133b-f5b1-4283-8367-f06524bea46c"
    },
-   "execution_count": null,
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stderr",
+     "output_type": "stream",
      "text": [
       "Uploading the following files to ybelkada/opt-6.7b-lora: adapter_config.json,adapter_model.bin\n"
      ]
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dbe077ed0c34e4eb1628418138ccbc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "3dbe077ed0c34e4eb1628418138ccbc6"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2693135b6954d35afb3120f3caf4000",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "adapter_model.bin:   0%|          | 0.00/33.6M [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "b2693135b6954d35afb3120f3caf4000"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "CommitInfo(commit_url='https://huggingface.co/ybelkada/opt-6.7b-lora/commit/6f240b184e666b54a51b3fe482e4711448e6c751', commit_message='Upload model', commit_description='', oid='6f240b184e666b54a51b3fe482e4711448e6c751', pr_url=None, pr_revision=None, pr_num=None)"
       ]
      },
+     "execution_count": 10,
      "metadata": {},
-     "execution_count": 10
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "model.push_to_hub(\"ybelkada/opt-6.7b-lora\", use_auth_token=True)"
    ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "id": "S65GcxNGA9kz"
+   },
    "source": [
     "## Load adapters from the Hub\n",
     "\n",
     "You can also directly load adapters from the Hub using the commands below:"
-   ],
-   "metadata": {
-    "id": "S65GcxNGA9kz"
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "import torch\n",
-    "from peft import PeftModel, PeftConfig\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "\n",
-    "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
-    "config = PeftConfig.from_pretrained(peft_model_id)\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map=\"auto\"\n",
-    ")\n",
-    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
-    "\n",
-    "# Load the Lora model\n",
-    "model = PeftModel.from_pretrained(model, peft_model_id)"
-   ],
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1576,11 +1554,10 @@
     "id": "hsD1VKqeA62Z",
     "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
    },
-   "execution_count": null,
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "\n",
       "===================================BUG REPORT===================================\n",
@@ -1595,8 +1572,8 @@
      ]
     },
     {
-     "output_type": "stream",
      "name": "stderr",
+     "output_type": "stream",
      "text": [
       "/usr/local/lib/python3.8/dist-packages/bitsandbytes/cuda_setup/main.py:134: UserWarning: /usr/lib64-nvidia did not contain libcudart.so as expected! Searching further paths...\n",
       "  warn(msg)\n",
@@ -1615,33 +1592,48 @@
      ]
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff2454cf69b346fea70070522cf93689",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "ff2454cf69b346fea70070522cf93689"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75913676a5df43fbbfe744b8882188df",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
        "Downloading (…)\"adapter_model.bin\";:   0%|          | 0.00/33.6M [00:00<?, ?B/s]"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "75913676a5df43fbbfe744b8882188df"
-      }
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     }
+   ],
+   "source": [
+    "import torch\n",
+    "from peft import PeftModel, PeftConfig\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "\n",
+    "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
+    "config = PeftConfig.from_pretrained(peft_model_id)\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map=\"auto\"\n",
+    ")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+    "\n",
+    "# Load the Lora model\n",
+    "model = PeftModel.from_pretrained(model, peft_model_id)"
    ]
   },
   {
@@ -1667,8 +1659,8 @@
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stderr",
+     "output_type": "stream",
      "text": [
       "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1359: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
       "  warnings.warn(\n",
@@ -1677,8 +1669,8 @@
      ]
     },
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "\n",
       "\n",
@@ -1698,12 +1690,12 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "As you can see by fine-tuning for few steps we have almost recovered the quote from Albert Einstein that is present in the [training data](https://huggingface.co/datasets/Abirate/english_quotes)."
-   ],
    "metadata": {
     "id": "HZL8ZbcJCHoy"
-   }
+   },
+   "source": [
+    "As you can see by fine-tuning for few steps we have almost recovered the quote from Albert Einstein that is present in the [training data](https://huggingface.co/datasets/Abirate/english_quotes)."
+   ]
   }
  ],
  "metadata": {
@@ -1714,7 +1706,7 @@
   },
   "gpuClass": "standard",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "fix-test",
    "language": "python",
    "name": "python3"
   },
@@ -1728,38 +1720,71 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "6c4e21ff5edce2fb2cfe7eb854551da92c6ec05cac2504057bb7aba62f43a5ec"
+   }
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       "placeholder": "​",
-      "style": "IPY_MODEL_a55954b8d7bf4057b0d7aa6a1cb9e91a",
-      "value": "<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co/settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. </center>"
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-      "disabled": false,
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-      "placeholder": "​",
-      "style": "IPY_MODEL_dab39ef354a84be3b37b6f151f9d9b9d",
-      "value": ""
+      "style": "IPY_MODEL_441a953149814848a24edc9398a2ef63",
+      "value": " 2/2 [01:04&lt;00:00, 29.33s/it]"
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+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "ff2454cf69b346fea70070522cf93689": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_87b36eb4ad3b4047a32c7d67a5aabc5e",
+       "IPY_MODEL_8bf9fd4bd28e4bc1b5895bc9315e727e",
+       "IPY_MODEL_01845549768a4db580b5555809e83342"
+      ],
+      "layout": "IPY_MODEL_a490025901df478f93be1f19c1be4b09"
+     }
     }
    }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb b/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb
index 41a60e6..0f5d5f7 100644
--- a/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb
+++ b/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb
@@ -1158,9 +1158,9 @@
    },
    "outputs": [],
    "source": [
-    "from peft import prepare_model_for_training\n",
+    "from peft import prepare_model_for_int8_training\n",
     "\n",
-    "model = prepare_model_for_training(model, output_embedding_layer_name=\"proj_out\")"
+    "model = prepare_model_for_int8_training(model, output_embedding_layer_name=\"proj_out\")"
    ]
   },
   {
diff --git a/src/peft/__init__.py b/src/peft/__init__.py
index b0784a4..3dd7acf 100644
--- a/src/peft/__init__.py
+++ b/src/peft/__init__.py
@@ -47,7 +47,7 @@ from .utils import (
     TaskType,
     bloom_model_postprocess_past_key_value,
     get_peft_model_state_dict,
-    prepare_model_for_training,
+    prepare_model_for_int8_training,
     set_peft_model_state_dict,
     shift_tokens_right,
 )
diff --git a/src/peft/utils/__init__.py b/src/peft/utils/__init__.py
index f466824..dd949c0 100644
--- a/src/peft/utils/__init__.py
+++ b/src/peft/utils/__init__.py
@@ -23,7 +23,7 @@ from .other import (
     TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,
     _set_trainable,
     bloom_model_postprocess_past_key_value,
-    prepare_model_for_training,
+    prepare_model_for_int8_training,
     shift_tokens_right,
     transpose,
 )
diff --git a/src/peft/utils/other.py b/src/peft/utils/other.py
index 82a4a45..3f4d2d5 100644
--- a/src/peft/utils/other.py
+++ b/src/peft/utils/other.py
@@ -30,7 +30,7 @@ def bloom_model_postprocess_past_key_value(past_key_values):
     return tuple(zip(keys, values))
 
 
-def prepare_model_for_training(model, output_embedding_layer_name="lm_head"):
+def prepare_model_for_int8_training(model, output_embedding_layer_name="lm_head"):
     r"""
     This method wrapps the entire protocol for preparing a model before running a training. This includes:
         1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm