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wesslen/fireworks-colabtune-a100.ipynb

Created March 17, 2024 23:47
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fireworks-colabtune-a100.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/wesslen/9d3fa58d511bd562170d56162216355a/fireworks-colabtune-a100.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tmLUG6Yte4VC"
},
"source": [
"# Model tuning in colab"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NBQALK1uzPX5"
},
"source": [
"This notebook used A100, which is available under the Colab Pro account. Make sure to change your runtime type first."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T_Tbt2vYfIvB"
},
"source": [
"## Environment setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "w48IGhuPfOLd"
},
"source": [
"Make sure that you are connected to a GPU host."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-1SRQmvFezo1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5a7450cb-4df4-424e-8453-51b6c648ff3b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sun Mar 17 23:33:06 2024 \n",
"+---------------------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n",
"|-----------------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|=========================================+======================+======================|\n",
"| 0 NVIDIA A100-SXM4-40GB Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 29C P0 45W / 400W | 2MiB / 40960MiB | 0% Default |\n",
"| | | Disabled |\n",
"+-----------------------------------------+----------------------+----------------------+\n",
" \n",
"+---------------------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=======================================================================================|\n",
"| No running processes found |\n",
"+---------------------------------------------------------------------------------------+\n"
]
}
],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yn9T_kW3fZOg"
},
"source": [
"Check out the cook book code from github."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "usSJUnJve5Kp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6629a618-db98-4618-e8e2-ddda839e93e5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'cookbook'...\n",
"remote: Enumerating objects: 867, done.\u001b[K\n",
"remote: Counting objects: 100% (229/229), done.\u001b[K\n",
"remote: Compressing objects: 100% (112/112), done.\u001b[K\n",
"remote: Total 867 (delta 129), reused 197 (delta 114), pack-reused 638\u001b[K\n",
"Receiving objects: 100% (867/867), 754.03 KiB | 20.38 MiB/s, done.\n",
"Resolving deltas: 100% (466/466), done.\n"
]
}
],
"source": [
"!git clone https://github.com/fw-ai/cookbook.git"
]
},
{
"cell_type": "markdown",
"source": [
"**Manual update**: Need to comment out lines 50-51 in `cookbook/recipes/common/peft.py`:\n",
"\n",
"```\n",
" # load_in_4bit=load_in_4bit,\n",
" # load_in_8bit=load_in_8bit,\n",
"```\n",
"\n",
"If not, will get:\n",
"```\n",
"finetune/0 [0]:Error executing job with overrides: ['model=llama2-7b-chat-colab']\n",
"finetune/0 [0]:Traceback (most recent call last):\n",
"finetune/0 [0]: File \"/content/cookbook/recipes/tune/instruct_lora/finetune.py\", line 56, in _app\n",
"finetune/0 [0]: model = load_train_model(config)\n",
"finetune/0 [0]: File \"/content/cookbook/recipes/common/peft.py\", line 50, in load_train_model\n",
"finetune/0 [0]: model = base_model_class.from_pretrained(\n",
"finetune/0 [0]: File \"/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\", line 561, in from_pretrained\n",
"finetune/0 [0]: return model_class.from_pretrained(\n",
"finetune/0 [0]: File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\", line 2952, in from_pretrained\n",
"finetune/0 [0]: raise ValueError(\n",
"finetune/0 [0]:ValueError: You can't pass `load_in_4bit`or `load_in_8bit` as a kwarg when passing `quantization_config` argument at the same time.\n",
"```"
],
"metadata": {
"id": "I9apBUWwycKR"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "q92kngbbgBV6"
},
"source": [
"Install required dependencies. Added `wandb` and `flash-attn`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Dezn_0k3ffCr",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "af45129d-de50-4a58-acfe-06040dcab161"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting accelerate\n",
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"Building wheels for collected packages: fire, antlr4-python3-runtime, docstring-parser, flash-attn, langdetect, spacy-universal-sentence-encoder\n",
" Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for fire: filename=fire-0.6.0-py2.py3-none-any.whl size=117029 sha256=bf7c935fcbe5728309d5bc1f805ff47d895fa7c54e09de037ded96923ce63ce1\n",
" Stored in directory: /root/.cache/pip/wheels/d6/6d/5d/5b73fa0f46d01a793713f8859201361e9e581ced8c75e5c6a3\n",
" Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=e34e47ff6378d1e06f1584aa54786b69b63c56e54b4d778b12b5f9708099cfdc\n",
" Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
" Building wheel for docstring-parser (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for docstring-parser: filename=docstring_parser-0.8.1-py3-none-any.whl size=19661 sha256=0c5dce54a1bf9388cd09fdfa91a6a33e71060b6e00c086e74cd30a3e6a6d78de\n",
" Stored in directory: /root/.cache/pip/wheels/26/e4/54/64439f1d0c5d3721041ddc0f001e4b57756a394880a2af8981\n",
" Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for flash-attn: filename=flash_attn-2.5.6-cp310-cp310-linux_x86_64.whl size=120592258 sha256=d8cf54adda65f59820221d329d274e124972d7fdc05ab3b1130253c64eee6c8a\n",
" Stored in directory: /root/.cache/pip/wheels/a8/1c/88/b959d6818b98a46d61ba231683abb7523b89ac1a7ed1e0c206\n",
" Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993227 sha256=58bbd4c08a509a200b16ec777d1b53dc3640c944a8ab37e45fbd25a97dab211a\n",
" Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
" Building wheel for spacy-universal-sentence-encoder (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for spacy-universal-sentence-encoder: filename=spacy_universal_sentence_encoder-0.4.6-py3-none-any.whl size=16539 sha256=1a8c7c95d24b2e42160afa34b66de9609f1f2247bae618efb25b66163edbc118\n",
" Stored in directory: /root/.cache/pip/wheels/65/75/56/7fd780bb5e1f9c74625ccda3c03a1abeb27a2ade5a8946db33\n",
"Successfully built fire antlr4-python3-runtime docstring-parser flash-attn langdetect spacy-universal-sentence-encoder\n",
"Installing collected packages: texttable, ninja, brotli, asn1crypto, antlr4-python3-runtime, zstandard, xxhash, urllib3, types-PyYAML, tomlkit, smmap, setproctitle, rapidfuzz, pyzstd, pyppmd, pycryptodomex, pybcj, portalocker, platformdirs, packaging, orjson, ordered-set, omegaconf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, mypy-extensions, multivolumefile, mock, lz4, langdetect, jsonpointer, jmespath, inflate64, h11, fsspec, fire, einops, docstring-parser, docker-pycreds, diskcache, dill, colorama, backoff, aioitertools, typing-inspect, sentry-sdk, sacrebleu, pyformlang, py7zr, nvidia-cusparse-cu12, nvidia-cudnn-cu12, mypy, multiprocess, marshmallow, jsonpatch, jiwer, hydra-core, httpcore, gitdb, botocore, blobfile, tiktoken, pyre-extensions, nvidia-cusolver-cu12, langsmith, httpx, gptcache, GitPython, docker, dataclasses-json, aiobotocore, wandb, torchx, snowflake-connector-python, s3fs, openai, msal, langchain-core, datasets, langchain-text-splitters, langchain-community, guidance, flash-attn, bitsandbytes, accelerate, peft, langchain, spacy-universal-sentence-encoder, evals\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 2.0.7\n",
" Uninstalling urllib3-2.0.7:\n",
" Successfully uninstalled urllib3-2.0.7\n",
" Attempting uninstall: platformdirs\n",
" Found existing installation: platformdirs 4.2.0\n",
" Uninstalling platformdirs-4.2.0:\n",
" Successfully uninstalled platformdirs-4.2.0\n",
" Attempting uninstall: packaging\n",
" Found existing installation: packaging 24.0\n",
" Uninstalling packaging-24.0:\n",
" Successfully uninstalled packaging-24.0\n",
" Attempting uninstall: fsspec\n",
" Found existing installation: fsspec 2023.6.0\n",
" Uninstalling fsspec-2023.6.0:\n",
" Successfully uninstalled fsspec-2023.6.0\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"gcsfs 2023.6.0 requires fsspec==2023.6.0, but you have fsspec 2024.2.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed GitPython-3.1.42 accelerate-0.28.0 aiobotocore-2.12.1 aioitertools-0.11.0 antlr4-python3-runtime-4.9.3 asn1crypto-1.5.1 backoff-2.2.1 bitsandbytes-0.43.0 blobfile-2.1.1 botocore-1.34.51 brotli-1.1.0 colorama-0.4.6 dataclasses-json-0.6.4 datasets-2.18.0 dill-0.3.8 diskcache-5.6.3 docker-7.0.0 docker-pycreds-0.4.0 docstring-parser-0.8.1 einops-0.7.0 evals-2.0.0.post1 fire-0.6.0 flash-attn-2.5.6 fsspec-2024.2.0 gitdb-4.0.11 gptcache-0.1.43 guidance-0.1.10 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 hydra-core-1.3.2 inflate64-1.0.0 jiwer-3.0.3 jmespath-1.0.1 jsonpatch-1.33 jsonpointer-2.4 langchain-0.1.12 langchain-community-0.0.28 langchain-core-0.1.32 langchain-text-splitters-0.0.1 langdetect-1.0.9 langsmith-0.1.27 lz4-4.3.3 marshmallow-3.21.1 mock-5.1.0 msal-1.27.0 multiprocess-0.70.16 multivolumefile-0.2.3 mypy-1.9.0 mypy-extensions-1.0.0 ninja-1.11.1.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 omegaconf-2.3.0 openai-1.14.1 ordered-set-4.1.0 orjson-3.9.15 packaging-23.2 peft-0.9.0 platformdirs-3.11.0 portalocker-2.8.2 py7zr-0.21.0 pybcj-1.0.2 pycryptodomex-3.20.0 pyformlang-1.0.7 pyppmd-1.1.0 pyre-extensions-0.0.30 pyzstd-0.15.9 rapidfuzz-3.6.2 s3fs-2024.2.0 sacrebleu-2.4.1 sentry-sdk-1.42.0 setproctitle-1.3.3 smmap-5.0.1 snowflake-connector-python-3.7.1 spacy-universal-sentence-encoder-0.4.6 texttable-1.7.0 tiktoken-0.6.0 tomlkit-0.12.4 torchx-0.5.0 types-PyYAML-6.0.12.20240311 typing-inspect-0.9.0 urllib3-1.26.18 wandb-0.16.4 xxhash-3.4.1 zstandard-0.22.0\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"pydevd_plugins"
]
},
"id": "7e0d8aaa78024b9b8bb0d5fb36d00744"
}
},
"metadata": {}
}
],
"source": [
"!pip install \\\n",
" accelerate \\\n",
" bitsandbytes \\\n",
" datasets \\\n",
" evals \\\n",
" fire \\\n",
" guidance \\\n",
" huggingface_hub \\\n",
" hydra-core \\\n",
" ninja \\\n",
" packaging \\\n",
" peft \\\n",
" py7zr \\\n",
" s3fs \\\n",
" sentencepiece \\\n",
" torchx \\\n",
" transformers \\\n",
" zstandard \\\n",
" wandb \\\n",
" flash-attn --no-build-isolation"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0niRKJv-hKLq"
},
"source": [
"Log into huggingface. For that, you will need a huggingface token. You can find it by going to https://huggingface.co/settings/tokens\n",
"If you are running a recipe that uses llama2 models, don't forget to sign the license agreement on the model card."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yiA7dxI4gdhD",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9ed95fc4-ca5b-436e-e41d-a9c86057aed3"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
" _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
"\n",
" To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
"Token: \n",
"Add token as git credential? (Y/n) n\n",
"Token is valid (permission: write).\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
"Login successful\n"
]
}
],
"source": [
"!huggingface-cli login"
]
},
{
"cell_type": "markdown",
"source": [
"You will need permissions for LLaMa2: `llama2-7b-chat-colab`. You can request via HF/Meta website. It may take a few hours-days to get initial permissions."
],
"metadata": {
"id": "onvxgb2nzod2"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "dF84DfZ0L1b-"
},
"source": [
"## Fine tuning"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IBW5n1wegwdQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6aa56b03-27c7-42ce-8a3f-489ad26c8983"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34mtorchx\u001b[0m \u001b[2m2024-03-17 23:37:25 INFO \u001b[0m Tracker configurations: {}\n",
"\u001b[34mtorchx\u001b[0m \u001b[2m2024-03-17 23:37:25 INFO \u001b[0m Log directory not set in scheduler cfg. Creating a temporary log dir that will be deleted on exit. To preserve log directory set the `log_dir` cfg option\n",
"\u001b[34mtorchx\u001b[0m \u001b[2m2024-03-17 23:37:25 INFO \u001b[0m Log directory is: /tmp/torchx_bbmcnc96\n",
"local_cwd://torchx/finetune-xxh6pf7fmrg3kc\n",
"\u001b[34mtorchx\u001b[0m \u001b[2m2024-03-17 23:37:25 INFO \u001b[0m Waiting for the app to finish...\n",
"\u001b[32mfinetune/0\u001b[0m [0]:/usr/local/lib/python3.10/dist-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'summarize': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information\n",
"\u001b[32mfinetune/0\u001b[0m [0]: warnings.warn(msg, UserWarning)\n",
"\u001b[32mfinetune/0\u001b[0m [0]:config: version: '0.1'\n",
"\u001b[32mfinetune/0\u001b[0m [0]:working_dir: /mnt/text/model/llama2-7b-chat-colab/dialogsum-samsum/0.1\n",
"\u001b[32mfinetune/0\u001b[0m [0]:output_model_dir: /mnt/text/model/llama2-7b-chat-colab/dialogsum-samsum/0.1/final\n",
"\u001b[32mfinetune/0\u001b[0m [0]:wandb_key: null\n",
"\u001b[32mfinetune/0\u001b[0m [0]:wandb_project: null\n",
"\u001b[32mfinetune/0\u001b[0m [0]:model:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: name: llama2-7b-chat-colab\n",
"\u001b[32mfinetune/0\u001b[0m [0]: huggingface_model_name: meta-llama/Llama-2-7b-chat-hf\n",
"\u001b[32mfinetune/0\u001b[0m [0]: huggingface_model_revision: 01622a9\n",
"\u001b[32mfinetune/0\u001b[0m [0]: pad_token: <unk>\n",
"\u001b[32mfinetune/0\u001b[0m [0]: fireworks:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: base_model: accounts/fireworks/models/llama-v2-7b-chat\n",
"\u001b[32mfinetune/0\u001b[0m [0]: conversation_config:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: system: '[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: You are an assistant and you are tasked with writing text summaries. For each\n",
"\u001b[32mfinetune/0\u001b[0m [0]: input text, provide a summary. The summary should be concise, accurate and\n",
"\u001b[32mfinetune/0\u001b[0m [0]: truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: <</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: '\n",
"\u001b[32mfinetune/0\u001b[0m [0]: roles:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: - '[INST]'\n",
"\u001b[32mfinetune/0\u001b[0m [0]: - '[/INST]'\n",
"\u001b[32mfinetune/0\u001b[0m [0]: offset: 0\n",
"\u001b[32mfinetune/0\u001b[0m [0]: sep_style: LLAMA2\n",
"\u001b[32mfinetune/0\u001b[0m [0]: sep: ' '\n",
"\u001b[32mfinetune/0\u001b[0m [0]: sep2: ' </s><s>'\n",
"\u001b[32mfinetune/0\u001b[0m [0]: stop_token_ids:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: - 2\n",
"\u001b[32mfinetune/0\u001b[0m [0]: micro_batch_size: 2\n",
"\u001b[32mfinetune/0\u001b[0m [0]: batch_size: 32\n",
"\u001b[32mfinetune/0\u001b[0m [0]: epochs: 1\n",
"\u001b[32mfinetune/0\u001b[0m [0]: learning_rate: 2.0e-05\n",
"\u001b[32mfinetune/0\u001b[0m [0]: cutoff_len: 512\n",
"\u001b[32mfinetune/0\u001b[0m [0]: lora_r: 64\n",
"\u001b[32mfinetune/0\u001b[0m [0]: lora_alpha: 16\n",
"\u001b[32mfinetune/0\u001b[0m [0]: lora_dropout: 0.05\n",
"\u001b[32mfinetune/0\u001b[0m [0]: lora_target_modules:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: - q_proj\n",
"\u001b[32mfinetune/0\u001b[0m [0]: - k_proj\n",
"\u001b[32mfinetune/0\u001b[0m [0]: - v_proj\n",
"\u001b[32mfinetune/0\u001b[0m [0]: gradient_checkpointing: true\n",
"\u001b[32mfinetune/0\u001b[0m [0]: flash_attention: false\n",
"\u001b[32mfinetune/0\u001b[0m [0]: load_in_4bit: true\n",
"\u001b[32mfinetune/0\u001b[0m [0]: torch_dtype: float16\n",
"\u001b[32mfinetune/0\u001b[0m [0]: fp16: true\n",
"\u001b[32mfinetune/0\u001b[0m [0]: quantization_config:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: load_in_4bit: true\n",
"\u001b[32mfinetune/0\u001b[0m [0]: bnb_4bit_use_double_quant: true\n",
"\u001b[32mfinetune/0\u001b[0m [0]: bnb_4bit_quant_type: nf4\n",
"\u001b[32mfinetune/0\u001b[0m [0]: bnb_4bit_compute_dtype: float16\n",
"\u001b[32mfinetune/0\u001b[0m [0]:data:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: dataset:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: dialogsum:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: huggingface_name: knkarthick/dialogsum\n",
"\u001b[32mfinetune/0\u001b[0m [0]: huggingface_revision: '4195720'\n",
"\u001b[32mfinetune/0\u001b[0m [0]: max_samples: 10000\n",
"\u001b[32mfinetune/0\u001b[0m [0]: transform:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: class: recipes.common.batch_transform.StringTemplate\n",
"\u001b[32mfinetune/0\u001b[0m [0]: prompt_prefix: '[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: You are an assistant and you are tasked with writing text summaries. For\n",
"\u001b[32mfinetune/0\u001b[0m [0]: each input text, provide a summary. The summary should be concise, accurate\n",
"\u001b[32mfinetune/0\u001b[0m [0]: and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: <</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: '\n",
"\u001b[32mfinetune/0\u001b[0m [0]: task_prompt: '{dialogue}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: [/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: '\n",
"\u001b[32mfinetune/0\u001b[0m [0]: prompt_template: '[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: You are an assistant and you are tasked with writing text summaries. For\n",
"\u001b[32mfinetune/0\u001b[0m [0]: each input text, provide a summary. The summary should be concise, accurate\n",
"\u001b[32mfinetune/0\u001b[0m [0]: and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: <</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: {dialogue}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: [/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: '\n",
"\u001b[32mfinetune/0\u001b[0m [0]: completion_template: '{summary}'\n",
"\u001b[32mfinetune/0\u001b[0m [0]: samsum:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: huggingface_name: samsum\n",
"\u001b[32mfinetune/0\u001b[0m [0]: huggingface_revision: 500cefe\n",
"\u001b[32mfinetune/0\u001b[0m [0]: max_samples: 10000\n",
"\u001b[32mfinetune/0\u001b[0m [0]: transform:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: class: recipes.common.batch_transform.StringTemplate\n",
"\u001b[32mfinetune/0\u001b[0m [0]: prompt_prefix: '[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: You are an assistant and you are tasked with writing text summaries. For\n",
"\u001b[32mfinetune/0\u001b[0m [0]: each input text, provide a summary. The summary should be concise, accurate\n",
"\u001b[32mfinetune/0\u001b[0m [0]: and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: <</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: '\n",
"\u001b[32mfinetune/0\u001b[0m [0]: task_prompt: '{dialogue}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: [/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: '\n",
"\u001b[32mfinetune/0\u001b[0m [0]: prompt_template: '[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: You are an assistant and you are tasked with writing text summaries. For\n",
"\u001b[32mfinetune/0\u001b[0m [0]: each input text, provide a summary. The summary should be concise, accurate\n",
"\u001b[32mfinetune/0\u001b[0m [0]: and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: <</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: {dialogue}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: [/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: '\n",
"\u001b[32mfinetune/0\u001b[0m [0]: completion_template: '{summary}'\n",
"\u001b[32mfinetune/0\u001b[0m [0]: name: dialogsum-samsum\n",
"\u001b[32mfinetune/0\u001b[0m [0]: mask_prompt: false\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:tokenizer_config.json: 100%|██████████| 770/770 [00:00<00:00, 4.67MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:tokenizer.model: 100%|██████████| 500k/500k [00:00<00:00, 30.4MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:tokenizer.json: 100%|██████████| 1.84M/1.84M [00:00<00:00, 5.67MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:tokenizer.json: 100%|██████████| 1.84M/1.84M [00:00<00:00, 5.64MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:special_tokens_map.json: 0%| | 0.00/414 [00:00<?, ?B/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:special_tokens_map.json: 100%|██████████| 414/414 [00:00<00:00, 2.71MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading readme: 0%| | 0.00/4.56k [00:00<?, ?B/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading readme: 100%|██████████| 4.56k/4.56k [00:00<00:00, 24.1MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 0%| | 0.00/11.3M [00:00<?, ?B/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 37%|███▋ | 4.19M/11.3M [00:00<00:00, 24.2MB/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 100%|██████████| 11.3M/11.3M [00:00<00:00, 40.9MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 100%|██████████| 11.3M/11.3M [00:00<00:00, 37.0MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 0%| | 0.00/442k [00:00<?, ?B/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 100%|██████████| 442k/442k [00:00<00:00, 2.42MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 100%|██████████| 442k/442k [00:00<00:00, 2.41MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 0%| | 0.00/1.35M [00:00<?, ?B/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 100%|██████████| 1.35M/1.35M [00:00<00:00, 7.22MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Downloading data: 100%|██████████| 1.35M/1.35M [00:00<00:00, 7.20MB/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Generating train split: 0 examples [00:00, ? examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Generating train split: 10000 examples [00:00, 67877.01 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:loaded dataset dialogsum of size 12460\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Generating train split: 12460 examples [00:00, 68468.65 examples/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Generating validation split: 0 examples [00:00, ? examples/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Generating validation split: 500 examples [00:00, 48847.09 examples/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Generating test split: 0 examples [00:00, ? examples/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Generating test split: 1500 examples [00:00, 87348.58 examples/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:using max length 512\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 0%| | 0/12460 [00:00<?, ? examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 6%|▋ | 779/12460 [00:02<00:31, 376.06 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 19%|█▉ | 2337/12460 [00:02<00:08, 1198.30 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 25%|██▌ | 3116/12460 [00:02<00:05, 1578.24 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 44%|████▍ | 5453/12460 [00:02<00:02, 3492.64 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 63%|██████▎ | 7790/12460 [00:02<00:00, 5516.21 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 75%|███████▌ | 9347/12460 [00:02<00:00, 6449.24 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 88%|████████▊ | 10904/12460 [00:03<00:00, 7801.09 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 100%|██████████| 12460/12460 [00:03<00:00, 6956.47 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 100%|██████████| 12460/12460 [00:03<00:00, 3629.40 examples/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:truncated dataset dialogsum to size 10000\n",
"\u001b[32mfinetune/0\u001b[0m [0]:sample prompt 0 for dataset dialogsum:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:You are an assistant and you are tasked with writing text summaries. For each input text, provide a summary. The summary should be concise, accurate and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:<</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: How may I help you, miss?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: I want to change my hairstyle. What would you suggest?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: Would you like to have a perm?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: Do you think it will suit me?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: Oh, absolutely.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: Ok, I'll have it for a change.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2# wants to change her hairstyle. #Person1# suggests a perm and #Person2# agrees.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:sample prompt 1 for dataset dialogsum:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:You are an assistant and you are tasked with writing text summaries. For each input text, provide a summary. The summary should be concise, accurate and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:<</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: Hi, Bob. I heard about your accident but I didn't think it would be this bad.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: Well, thanks for making me feel better.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: I can hardly recognize you. Tell me what happened.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: I just got back from Africa where I had a terrible accident in a motorcycle race. I broke both my legs when my motor failed and was hit by another motorcycle. I was laid up in a hospital over there for three weeks.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: Three weeks, that's a long time. What did you do while you were in the hospital?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: Well, if you can believe it, I read all about motorcycle racing. I love racing even if it hurts.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: But I'm afraid you don't make it look very funny. You're lucky to be alive.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: That's for sure. I am lucky to be alive.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: How soon can you get well? Did the doctor tell you about it?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: They say I still need to stay in bed for two weeks or so.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: I think you'll get a bit fatter by then. By the way, is there anything I can do for you?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: No, thank you. Thank you for the beautiful flowers. It's very kind of you to come to see me.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: I'll be going then. Bye.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: Bye.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1# visits Bob in the hospital. Bob still needs to stay in bed. He had a terrible accident in a motorcycle race but still loves racing even if it hurts. #Person1# thinks he's lucky to be alive.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:sample prompt 2 for dataset dialogsum:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:You are an assistant and you are tasked with writing text summaries. For each input text, provide a summary. The summary should be concise, accurate and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:<</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: What's the problem, Nada? You look down in the dumps.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: I don't know. My life is a big mess. Everything is so complicated.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: come on, nothing can be that bad.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: but promise me, you'll keep it a secret.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: ok, I promise. So what's troubling you so much?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: I've fallen in love with my boss.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: really? Is he married?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: no, of course not. He is still single.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: then what's your problem?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: I try to keep it to myself. But there is a lot of gossip about us.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: oh, I see. Office romance tends to be the subject of gossip.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: worse still, he is trying to avoid me these days.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: office romance is very tricky.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person2#: it gives me a lot of pressure and I feel depressed.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:#Person1#: cheer up, Nada. You'll be fine.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Nada tells #Person1# she's upset because she has fallen in love with her boss which causes gossips and she finds her boss is trying to avoid her. #Person1# comforts Nada.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Map (num_proc=16): 0%| | 0/10000 [00:00<?, ? examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Map (num_proc=16): 0%| | 29/10000 [00:00<01:59, 83.51 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Map (num_proc=16): 2%|▏ | 155/10000 [00:00<00:26, 371.39 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Map (num_proc=16): 4%|▍ | 443/10000 [00:00<00:10, 947.96 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Map (num_proc=16): 7%|▋ | 670/10000 [00:00<00:07, 1167.32 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Map (num_proc=16): 12%|█▏ | 1231/10000 [00:00<00:03, 2266.35 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:Map (num_proc=16): 20%|█▉ | 1958/10000 [00:01<00:02, 2880.95 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:/usr/local/lib/python3.10/dist-packages/datasets/load.py:1461: FutureWarning: The repository for samsum contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/samsum\n",
"\u001b[32mfinetune/0\u001b[0m [0]:You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
"\u001b[32mfinetune/0\u001b[0m [0]: warnings.warn(\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:using max length 512\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 94%|█████████▍| 13812/14732 [00:03<00:00, 8101.29 examples/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Filter (num_proc=16): 100%|██████████| 14732/14732 [00:03<00:00, 3937.21 examples/s]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:truncated dataset samsum to size 10000\n",
"\u001b[32mfinetune/0\u001b[0m [0]:sample prompt 0 for dataset samsum:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:You are an assistant and you are tasked with writing text summaries. For each input text, provide a summary. The summary should be concise, accurate and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:<</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Caleb: hi, I am coming this evening!\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Nancy: I know! 🎉\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Caleb: How is the weather?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Nancy: Really ugly, raining all the time. But we'll have each other!\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Caleb: True!\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Caleb is coming to see Nancy this evening. It's raining all the time.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:sample prompt 1 for dataset samsum:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:You are an assistant and you are tasked with writing text summaries. For each input text, provide a summary. The summary should be concise, accurate and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:<</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Eleonor: ok gals where do we meet up next Sunday?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Patricia: I'm out, my mother in law has bday :C\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Tasha: oh fuck so you'll spend Sunday with Cercei then\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Betty: I want to meet up next to Palladium cinema\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Eleonor: ok so we'll meet up (minus Pat) at Jeff's bar\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Tasha: are u serious? bar? do you want to seduce a bald 40 yr old divorcee?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Patricia: lol\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Patricia: looks I'll be missing out a lot\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Betty: isn't Gloria a bald 40 yr old divorcee too? \n",
"\u001b[32mfinetune/0\u001b[0m [0]:Eleonor: hahahaha\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Patricia: LOL\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Tasha: <file_gif>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Betty: let's meet up in that French place, it's not cheap but c'mon it's SO worth it\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Tasha: agreed\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Eleonor: ok\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Patricia: if she dies before Sunday I'll come too\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Eleonor: lol\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Eleonor, Tasha and Betty agree to meet up in the French place next Sunday. Patricia can't come as the meeting collides with her mother-in-law's birthday.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:sample prompt 2 for dataset samsum:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[INST] <<SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:You are an assistant and you are tasked with writing text summaries. For each input text, provide a summary. The summary should be concise, accurate and truthful. Do not make up facts or answers.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:<</SYS>>\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Karen: Is Auntie's birthday this week or next?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:May: next\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Karen: Phew!\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Karen: My parents were getting nervous that they didn't buy her anything and may not make it this week\n",
"\u001b[32mfinetune/0\u001b[0m [0]:May: Hm... I haven't heard that she'll throw a party or anything\n",
"\u001b[32mfinetune/0\u001b[0m [0]:May: I'll ask her though\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Karen: If you may that'd be amazing. You know my parents, they need to know everything in advance\n",
"\u001b[32mfinetune/0\u001b[0m [0]:May: Ok, no problem. Are you going away this week?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Karen: They're probably going to the seaside for the weekend. I'm staying\n",
"\u001b[32mfinetune/0\u001b[0m [0]:May: Oh, so maybe I could join you?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Karen: Yeah, why not, bring wine, we could order some pizza\n",
"\u001b[32mfinetune/0\u001b[0m [0]:May: Cool. I was supposed to go out with my friends but they went down with a cold\n",
"\u001b[32mfinetune/0\u001b[0m [0]:May: Should I bring anything else?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Karen: No, I think we're pretty much set with netflix\n",
"\u001b[32mfinetune/0\u001b[0m [0]:May: when are your parents coming back?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Karen: Sunday, I think\n",
"\u001b[32mfinetune/0\u001b[0m [0]:May: Ok, can I come over on Saturday?\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Karen: Sure, you can come even on Friday. They're leaving straight after work\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:[/INST]\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Auntie's birthday is next week. May will ask if she's throwing a party. Karen's parents are going to the seaside for the weekend. May will join her on Friday. They'll have some wine, pizza and Netflix.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:The model was loaded with use_flash_attention_2=True, which is deprecated and may be removed in a future release. Please use `attn_implementation=\"flash_attention_2\"` instead.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]:Loading checkpoint shards: 50%|█████ | 1/2 [00:03<00:03, 3.55s/it]\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
"\u001b[32mfinetune/0\u001b[0m [0]: warnings.warn(\n",
"\u001b[32mfinetune/0\u001b[0m [0]:/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
"\u001b[32mfinetune/0\u001b[0m [0]: warnings.warn(\n",
"\u001b[32mfinetune/0\u001b[0m [0]:/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
"\u001b[32mfinetune/0\u001b[0m [0]: warnings.warn(\n",
"\u001b[32mfinetune/0\u001b[0m [0]:/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
"\u001b[32mfinetune/0\u001b[0m [0]: warnings.warn(\n",
"\u001b[32mfinetune/0\u001b[0m [0]:trainable params: 50,331,648 || all params: 6,788,747,264 || trainable%: 0.7413981702766185\n",
"\u001b[32mfinetune/0\u001b[0m [0]:per_device_train_batch_size: 2 gradient_accumulation_steps: 16\n",
"\u001b[32mfinetune/0\u001b[0m [0]:2024-03-17 23:38:52.345168: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"\u001b[32mfinetune/0\u001b[0m [0]:2024-03-17 23:38:52.345263: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"\u001b[32mfinetune/0\u001b[0m [0]:2024-03-17 23:38:52.347080: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"\u001b[32mfinetune/0\u001b[0m [0]:2024-03-17 23:38:53.438461: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"\u001b[32mfinetune/0\u001b[0m [0]:/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
"\u001b[32mfinetune/0\u001b[0m [0]: warnings.warn(\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: 0%| | 0/625 [00:00<?, ?it/s]\u001b[32mfinetune/0\u001b[0m [0]:/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
"\u001b[32mfinetune/0\u001b[0m [0]: warnings.warn(\n",
"\u001b[32mfinetune/0\u001b[0m [0]:The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in torch.float16.\n",
"\u001b[32mfinetune/0\u001b[0m [0]:{'loss': 3.2435, 'grad_norm': 0.2975690960884094, 'learning_rate': 2.0000000000000003e-06, 'epoch': 0.0}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: 0%| | 1/625 [00:30<5:17:36, 30.54s/it]\u001b[32mfinetune/0\u001b[0m [0]:{'loss': 3.1279, 'grad_norm': 0.29220321774482727, 'learning_rate': 4.000000000000001e-06, 'epoch': 0.0}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: 0%| | 3/625 [01:30<5:12:11, 30.12s/it]\u001b[32mfinetune/0\u001b[0m [0]:{'loss': 3.0367, 'grad_norm': 0.2809338867664337, 'learning_rate': 8.000000000000001e-06, 'epoch': 0.01}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: 1%| | 4/625 [02:01<5:14:21, 30.37s/it]\u001b[32mfinetune/0\u001b[0m [0]:{'loss': 3.1422, 'grad_norm': 0.3060510754585266, 'learning_rate': 1e-05, 'epoch': 0.01}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: 1%| | 6/625 [02:59<5:03:43, 29.44s/it]\u001b[32mfinetune/0\u001b[0m [0]:{'loss': 2.955, 'grad_norm': 0.28816351294517517, 'learning_rate': 1.4e-05, 'epoch': 0.01}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: 1%| | 7/625 [03:30<5:08:48, 29.98s/it]\u001b[32mfinetune/0\u001b[0m [0]:{'loss': 3.3336, 'grad_norm': 0.32736852765083313, 'learning_rate': 1.6000000000000003e-05, 'epoch': 0.01}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
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"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
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"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
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"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
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"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
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"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: 2%|▏ | 14/625 [06:55<4:57:43, 29.24s/it]\u001b[32mfinetune/0\u001b[0m [0]:{'loss': 3.2319, 'grad_norm': 0.3843749761581421, 'learning_rate': 1.983739837398374e-05, 'epoch': 0.02}\n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
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"\u001b[32mfinetune/0\u001b[0m [0]: \n",
"\u001b[32mfinetune/0\u001b[0m [0]:\n",
"\u001b[32mfinetune/0\u001b[0m [0]: 2%|▏ | 15/625 [07:24<4:58:28, 29.36s/it]\u001b[32mfinetune/0\u001b[0m [2024-03-17 23:46:32,325] torch.distributed.elastic.agent.server.api: [WARNING] Received Signals.SIGTERM death signal, shutting down workers\n",
"\u001b[32mfinetune/0\u001b[0m [2024-03-17 23:46:32,325] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 3300 closing signal SIGTERM\n",
"Traceback (most recent call last):\n",
" File \"/usr/local/bin/torchx\", line 8, in <module>\n",
" sys.exit(main())\n",
" File \"/usr/local/lib/python3.10/dist-packages/torchx/cli/main.py\", line 116, in main\n",
" run_main(get_sub_cmds(), argv)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torchx/cli/main.py\", line 112, in run_main\n",
" args.func(args)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torchx/cli/cmd_run.py\", line 248, in run\n",
" self._run(runner, args)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torchx/cli/cmd_run.py\", line 220, in _run\n",
" self._wait_and_exit(runner, app_handle, log=True)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torchx/cli/cmd_run.py\", line 255, in _wait_and_exit\n",
" status = runner.wait(app_handle, wait_interval=1)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torchx/runner/api.py\", line 472, in wait\n",
" time.sleep(wait_interval)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torchx/schedulers/local_scheduler.py\", line 80, in _terminate_process_handler\n",
" raise SignalException(f\"Process {os.getpid()} got signal: {sigval}\", sigval=sigval)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torchx/schedulers/local_scheduler.py\", line 65, in __init__\n",
" def __init__(self, msg: str, sigval: signal.Signals) -> None:\n",
" File \"/usr/local/lib/python3.10/dist-packages/torchx/schedulers/local_scheduler.py\", line 80, in _terminate_process_handler\n",
" raise SignalException(f\"Process {os.getpid()} got signal: {sigval}\", sigval=sigval)\n",
"torchx.schedulers.local_scheduler.SignalException: Process 3270 got signal: 2\n"
]
}
],
"source": [
"!PYTHONPATH=\"$PYTHONPATH:/content/cookbook\" torchx run -s local_cwd dist.ddp -j 1x1 --script /content/cookbook/recipes/tune/instruct_lora/finetune.py -- --config-name=summarize model=llama2-7b-chat-colab"
]
},
{
"cell_type": "markdown",
"source": [
"The process was stopped to avoid charges; estimated it would take about 5 hours using Colab A100."
],
"metadata": {
"id": "u5axPEyiz3xz"
}
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": [],
"machine_shape": "hm",
"gpuType": "A100",
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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