├── .gitignore
├── LICENSE
├── README.md
├── chat
├── README.md
├── config.py
├── config.yaml
├── deepspeed_z3_config_bf16.json
├── dialogues.py
├── generate.py
├── requirements.txt
├── train.py
└── utils.py
├── finetune
├── finetune.py
└── merge_peft_adapters.py
└── requirements.txt
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
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12 | develop-eggs/
13 | dist/
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27 | MANIFEST
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35 | # Installer logs
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76 | target/
77 |
78 | # Jupyter Notebook
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80 |
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82 | profile_default/
83 | ipython_config.py
84 |
85 | # pyenv
86 | # For a library or package, you might want to ignore these files since the code is
87 | # intended to run in multiple environments; otherwise, check them in:
88 | # .python-version
89 |
90 | # pipenv
91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
94 | # install all needed dependencies.
95 | #Pipfile.lock
96 |
97 | # poetry
98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
99 | # This is especially recommended for binary packages to ensure reproducibility, and is more
100 | # commonly ignored for libraries.
101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
102 | #poetry.lock
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106 | #pdm.lock
107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
108 | # in version control.
109 | # https://pdm.fming.dev/#use-with-ide
110 | .pdm.toml
111 |
112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
113 | __pypackages__/
114 |
115 | # Celery stuff
116 | celerybeat-schedule
117 | celerybeat.pid
118 |
119 | # SageMath parsed files
120 | *.sage.py
121 |
122 | # Environments
123 | .env
124 | .venv
125 | env/
126 | venv/
127 | ENV/
128 | env.bak/
129 | venv.bak/
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132 | .spyderproject
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134 |
135 | # Rope project settings
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137 |
138 | # mkdocs documentation
139 | /site
140 |
141 | # mypy
142 | .mypy_cache/
143 | .dmypy.json
144 | dmypy.json
145 |
146 | # Pyre type checker
147 | .pyre/
148 |
149 | # pytype static type analyzer
150 | .pytype/
151 |
152 | # Cython debug symbols
153 | cython_debug/
154 |
155 | # PyCharm
156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
158 | # and can be added to the global gitignore or merged into this file. For a more nuclear
159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder.
160 | #.idea/
161 |
162 | data/
163 | wandb/
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/README.md:
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1 | # 💫 StarCoder
2 |
3 | [Paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) | [Model](https://huggingface.co/bigcode/starcoder) | [Playground](https://huggingface.co/spaces/bigcode/bigcode-playground) | [VSCode](https://marketplace.visualstudio.com/items?itemName=HuggingFace.huggingface-vscode) | [Chat](https://huggingface.co/spaces/HuggingFaceH4/starchat-playground)
4 |
5 | # What is this about?
6 | 💫 StarCoder is a language model (LM) trained on source code and natural language text. Its training data incorporates more that 80 different programming languages as well as text extracted from GitHub issues and commits and from notebooks. This repository showcases how we get an overview of this LM's capabilities.
7 |
8 | # News
9 |
10 | * **May 9, 2023:** We've fine-tuned StarCoder to act as a helpful coding assistant 💬! Check out the `chat/` directory for the training code and play with the model [here](https://huggingface.co/spaces/HuggingFaceH4/starchat-playground).
11 |
12 | # Disclaimer
13 |
14 | Before you can use the model go to `hf.co/bigcode/starcoder` and accept the agreement. And make sure you are logged into the Hugging Face hub with:
15 | ```bash
16 | huggingface-cli login
17 | ```
18 |
19 | # Table of Contents
20 | 1. [Quickstart](#quickstart)
21 | - [Installation](#installation)
22 | - [Code generation with StarCoder](#code-generation)
23 | - [Text-generation-inference code](#text-generation-inference)
24 | 2. [Fine-tuning](#fine-tuning)
25 | - [Step by step installation with conda](#step-by-step-installation-with-conda)
26 | - [Datasets](#datasets)
27 | - [Stack Exchange](#stack-exchange-se)
28 | - [Merging PEFT adapter layers](#merging-peft-adapter-layers)
29 | 3. [Evaluation](#evaluation)
30 | 4. [Inference hardware requirements](#inference-hardware-requirements)
31 |
32 | # Quickstart
33 | StarCoder was trained on GitHub code, thus it can be used to perform code generation. More precisely, the model can complete the implementation of a function or infer the following characters in a line of code. This can be done with the help of the 🤗's [transformers](https://github.com/huggingface/transformers) library.
34 |
35 | ## Installation
36 | First, we have to install all the libraries listed in `requirements.txt`
37 | ```bash
38 | pip install -r requirements.txt
39 | ```
40 | ## Code generation
41 | The code generation pipeline is as follows
42 |
43 | ```python
44 | from transformers import AutoModelForCausalLM, AutoTokenizer
45 |
46 | checkpoint = "bigcode/starcoder"
47 | device = "cuda" # for GPU usage or "cpu" for CPU usage
48 |
49 | tokenizer = AutoTokenizer.from_pretrained(checkpoint)
50 | # to save memory consider using fp16 or bf16 by specifying torch_dtype=torch.float16 for example
51 | model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
52 |
53 | inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
54 | outputs = model.generate(inputs)
55 | # clean_up_tokenization_spaces=False prevents a tokenizer edge case which can result in spaces being removed around punctuation
56 | print(tokenizer.decode(outputs[0], clean_up_tokenization_spaces=False))
57 | ```
58 | or
59 | ```python
60 | from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
61 | checkpoint = "bigcode/starcoder"
62 |
63 | model = AutoModelForCausalLM.from_pretrained(checkpoint)
64 | tokenizer = AutoTokenizer.from_pretrained(checkpoint)
65 |
66 | pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
67 | print( pipe("def hello():") )
68 | ```
69 | For hardware requirements, check the section [Inference hardware requirements](#inference-hardware-requirements).
70 |
71 | ## Text-generation-inference
72 |
73 | ```bash
74 | docker run -p 8080:80 -v $PWD/data:/data -e HUGGING_FACE_HUB_TOKEN=<YOUR BIGCODE ENABLED TOKEN> -d ghcr.io/huggingface/text-generation-inference:latest --model-id bigcode/starcoder --max-total-tokens 8192
75 | ```
76 | For more details, see [here](https://github.com/huggingface/text-generation-inference).
77 |
78 | # Fine-tuning
79 |
80 | Here, we showcase how we can fine-tune this LM on a specific downstream task.
81 |
82 | ## Step by step installation with conda
83 |
84 | Create a new conda environment and activate it
85 | ```bash
86 | conda create -n env
87 | conda activate env
88 | ```
89 | Install the `pytorch` version compatible with your version of cuda [here](https://pytorch.org/get-started/previous-versions/), for example the following command works with cuda 11.6
90 | ```bash
91 | conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
92 | ```
93 | Install `transformers` and `peft`
94 | ```bash
95 | conda install -c huggingface transformers
96 | pip install git+https://github.com/huggingface/peft.git
97 | ```
98 | Note that you can install the latest stable version of transformers by using
99 |
100 | ```bash
101 | pip install git+https://github.com/huggingface/transformers
102 | ```
103 |
104 | Install `datasets`, `accelerate` and `huggingface_hub`
105 |
106 | ```bash
107 | conda install -c huggingface -c conda-forge datasets
108 | conda install -c conda-forge accelerate
109 | conda install -c conda-forge huggingface_hub
110 | ```
111 |
112 | Finally, install `bitsandbytes` and `wandb`
113 | ```bash
114 | pip install bitsandbytes
115 | pip install wandb
116 | ```
117 | To get the full list of arguments with descriptions you can run the following command on any script:
118 | ```
119 | python scripts/some_script.py --help
120 | ```
121 | Before you run any of the scripts make sure you are logged in and can push to the hub:
122 | ```bash
123 | huggingface-cli login
124 | ```
125 | Make sure you are logged in `wandb`:
126 | ```bash
127 | wandb login
128 | ```
129 | Now that everything is done, you can clone the repository and get into the corresponding directory.
130 |
131 | ## Datasets
132 | 💫 StarCoder can be fine-tuned to achieve multiple downstream tasks. Our interest here is to fine-tune StarCoder in order to make it follow instructions. [Instruction fine-tuning](https://arxiv.org/pdf/2109.01652.pdf) has gained a lot of attention recently as it proposes a simple framework that teaches language models to align their outputs with human needs. That procedure requires the availability of quality instruction datasets, which contain multiple `instruction - answer` pairs. Unfortunately such datasets are not ubiquitous but thanks to Hugging Face 🤗's [datasets](https://github.com/huggingface/datasets) library we can have access to some good proxies. To fine-tune cheaply and efficiently, we use Hugging Face 🤗's [PEFT](https://github.com/huggingface/peft) as well as Tim Dettmers' [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
133 |
134 |
135 | ### Stack Exchange SE
136 | [Stack Exchange](https://en.wikipedia.org/wiki/Stack_Exchange) is a well-known network of Q&A websites on topics in diverse fields. It is a place where a user can ask a question and obtain answers from other users. Those answers are scored and ranked based on their quality. [Stack exchange instruction](https://huggingface.co/datasets/ArmelR/stack-exchange-instruction) is a dataset that was obtained by scrapping the site in order to build a collection of Q&A pairs. A language model can then be fine-tuned on that dataset to make it elicit strong and diverse question-answering skills.
137 |
138 | To execute the fine-tuning script run the following command:
139 | ```bash
140 | python finetune/finetune.py \
141 | --model_path="bigcode/starcoder"\
142 | --dataset_name="ArmelR/stack-exchange-instruction"\
143 | --subset="data/finetune"\
144 | --split="train"\
145 | --size_valid_set 10000\
146 | --streaming\
147 | --seq_length 2048\
148 | --max_steps 1000\
149 | --batch_size 1\
150 | --input_column_name="question"\
151 | --output_column_name="response"\
152 | --gradient_accumulation_steps 16\
153 | --learning_rate 1e-4\
154 | --lr_scheduler_type="cosine"\
155 | --num_warmup_steps 100\
156 | --weight_decay 0.05\
157 | --output_dir="./checkpoints" \
158 | ```
159 | The size of the SE dataset is better manageable when using streaming. We also have to precise the split of the dataset that is used. For more details, check the [dataset's page](https://huggingface.co/datasets/ArmelR/stack-exchange-instruction) on 🤗. Similarly we can modify the command to account for the availability of GPUs
160 |
161 | ```bash
162 | python -m torch.distributed.launch \
163 | --nproc_per_node number_of_gpus finetune/finetune.py \
164 | --model_path="bigcode/starcoder"\
165 | --dataset_name="ArmelR/stack-exchange-instruction"\
166 | --subset="data/finetune"\
167 | --split="train"\
168 | --size_valid_set 10000\
169 | --streaming \
170 | --seq_length 2048\
171 | --max_steps 1000\
172 | --batch_size 1\
173 | --input_column_name="question"\
174 | --output_column_name="response"\
175 | --gradient_accumulation_steps 16\
176 | --learning_rate 1e-4\
177 | --lr_scheduler_type="cosine"\
178 | --num_warmup_steps 100\
179 | --weight_decay 0.05\
180 | --output_dir="./checkpoints" \
181 | ```
182 | ## Merging PEFT adapter layers
183 | If you train a model with PEFT, you'll need to merge the adapter layers with the base model if you want to run inference / evaluation. To do so, run:
184 | ```bash
185 | python finetune/merge_peft_adapters.py --base_model_name_or_path model_to_merge --peft_model_path model_checkpoint
186 |
187 | # Push merged model to the Hub
188 | python finetune/merge_peft_adapters.py --base_model_name_or_path model_to_merge --peft_model_path model_checkpoint --push_to_hub
189 | ```
190 | For example
191 |
192 | ```bash
193 | python finetune/merge_peft_adapters.py --model_name_or_path bigcode/starcoder --peft_model_path checkpoints/checkpoint-1000 --push_to_hub
194 | ```
195 |
196 | # Evaluation
197 | To evaluate StarCoder and its derivatives, you can use the [BigCode-Evaluation-Harness](https://github.com/bigcode-project/bigcode-evaluation-harness) for evaluating Code LLMs.
198 |
199 | # Inference hardware requirements
200 | In FP32 the model requires more than 60GB of RAM, you can load it in FP16 or BF16 in ~30GB, or in 8bit under 20GB of RAM with
201 | ```python
202 | # make sure you have accelerate and bitsandbytes installed
203 | from transformers import AutoModelForCausalLM, AutoTokenizer
204 |
205 | tokenizer = AutoTokenizer.from_pretrained("bigcode/starcoder")
206 | # for fp16 replace with `load_in_8bit=True` with `torch_dtype=torch.float16`
207 | model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder", device_map="auto", load_in_8bit=True)
208 | print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
209 | ````
210 | ```
211 | Memory footprint: 15939.61 MB
212 | ```
213 | You can also try [starcoder.cpp](https://github.com/bigcode-project/starcoder.cpp), a C++ implementation with [ggml](https://github.com/ggerganov/ggml) library.
214 |
--------------------------------------------------------------------------------
/chat/README.md:
--------------------------------------------------------------------------------
1 | # Fine-tuning StarCoder for chat-based applications
2 |
3 | This is a fully-working example to fine-tune `StarCoder` on a corpus of multi-turn dialogues and thus create a coding assistant that is chatty and helpful. The resulting model is quite good at generating code for plots and other programming tasks. For example, given the prompt:
4 |
5 | ```
6 | Draw me a map of the world using geopandas. Make it so that only Germany and Spain are colored red.
7 | ```
8 |
9 | the model will produce:
10 |
11 | ```python
12 | import pandas as pd
13 | from matplotlib import pyplot as plt
14 | import geopandas as gpd
15 | from shapely.geometry import Point, Polygon
16 | %matplotlib inline
17 | # Load data from URL or local file
18 | url = 'https://raw.githubusercontent.com/datasets/geo-countries/master/data/countries.geojson'
19 | df = gpd.read_file(url)
20 |
21 | # Create plot
22 | fig, ax = plt.subplots()
23 | ax.set_aspect('equal')
24 | world = gpd.GeoDataFrame({'name': ['World'],
25 | 'geometry': [Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])]})
26 | germany = df[df['ADMIN'] == "Germany"]
27 | spain = df[df['ADMIN'] == "Spain"]
28 | other = df[(df['ADMIN']!= "Germany") & (df['ADMIN']!= "Spain")]
29 | world.plot(color='lightgrey', edgecolor='white', ax=ax)
30 | germany.plot(color="red", ax=ax)
31 | spain.plot(color="red", ax=ax)
32 | other.plot(color="skyblue", ax=ax)
33 | plt.title("European Countries")
34 | plt.show()
35 | ```
36 |
37 | Check out our [blog post](https://huggingface.co/blog/starchat-alpha) for more details.
38 |
39 | ## Getting started
40 |
41 | To run the `train.py` script, first create a Python virtual environment using e.g. Conda:
42 |
43 | ```shell
44 | conda create -n chat python=3.10 && conda activate chat
45 | ```
46 |
47 | Next, install PyTorch v1.13.1. Since this is hardware-dependent, we direct you to the [PyTorch Installation Page](https://pytorch.org/get-started/previous-versions/#v1131) for this step. Next, install the rest of the project dependencies:
48 |
49 | ```shell
50 | pip install -r requirements.txt
51 | ```
52 |
53 | You'll also need to be logged into both your Hugging Face account. To do so, run:
54 |
55 | ```shell
56 | huggingface-cli login
57 | ```
58 |
59 | Finally, install Git LFS with:
60 |
61 | ```shell
62 | sudo apt-get install git-lfs
63 | ```
64 |
65 | ## Prepare your dataset
66 |
67 | For training and inference, we use _dialogue templates_ to format each message in a conversation. For example, a typical dialogue between a human user and AI assistant takes the form:
68 |
69 | ```json
70 | {
71 | "messages": [
72 | {
73 | "content": "Is it possible to imagine a society without law?",
74 | "role": "user"},
75 | {
76 | "content": "It is difficult to imagine a society that is able to be maintained without any semblance of Law.",
77 | "role": "assistant",
78 | },
79 | {
80 | "content": "It seems like you consider the absence of law equal to the absence of anything that could guide the behaviour of the individual.",
81 | "role": "user",
82 | },
83 | {
84 | "content": "You are correct that there are other factors that can guide behavior in a society and play a role in shaping individuals' behavior and interactions with each other. However, even in societies where these factors are present, laws still serve an important role in maintaining social order and resolving conflicts.",
85 | "role": "assistant",
86 | }
87 | ]
88 | }
89 | ```
90 |
91 | Make sure you convert your dataset according to this schema, in particular you need to include a `messages` column like the above. You can adjust the model, dataset, and hyperparamters in the `config.yaml` file.
92 |
93 | ## Launch training
94 |
95 | We use DeepSpeed ZeRO-3 to shard the model and optimizer across 8 x A100 (80GB) GPUs. To fine-tune run:
96 |
97 | ```
98 | TRANSFORMERS_VERBOSITY=info torchrun --nproc_per_node=8 train.py config.yaml --deepspeed=deepspeed_z3_config_bf16.json
99 | ```
100 |
101 | By default, this will save the model checkpoint in the `data/` directory and also push it to the Hugging Face Hub.
102 |
103 |
104 | ## Generate samples
105 |
106 | To generate a few coding examples from your model, run:
107 |
108 | ```shell
109 | python generate.py --model_id path/to/your/model
110 | ```
111 |
112 |
--------------------------------------------------------------------------------
/chat/config.py:
--------------------------------------------------------------------------------
1 | # coding=utf-8
2 | # Copyright 2023 The HuggingFace Team. All rights reserved.
3 | #
4 | # Licensed under the Apache License, Version 2.0 (the "License");
5 | # you may not use this file except in compliance with the License.
6 | # You may obtain a copy of the License at
7 | #
8 | # http://www.apache.org/licenses/LICENSE-2.0
9 | #
10 | # Unless required by applicable law or agreed to in writing, software
11 | # distributed under the License is distributed on an "AS IS" BASIS,
12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 | # See the License for the specific language governing permissions and
14 | # limitations under the License.
15 | from dataclasses import dataclass, field
16 | from typing import List, Optional
17 |
18 | import transformers
19 | from transformers import MODEL_FOR_CAUSAL_LM_MAPPING
20 |
21 | MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
22 | MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
23 |
24 |
25 | @dataclass
26 | class ModelArguments:
27 | """
28 | Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
29 | """
30 |
31 | model_name_or_path: Optional[str] = field(
32 | default=None,
33 | metadata={
34 | "help": (
35 | "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
36 | )
37 | },
38 | )
39 | model_revision: str = field(
40 | default="main",
41 | metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
42 | )
43 | torch_dtype: Optional[str] = field(
44 | default=None,
45 | metadata={
46 | "help": (
47 | "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
48 | "dtype will be automatically derived from the model's weights."
49 | ),
50 | "choices": ["auto", "bfloat16", "float16", "float32"],
51 | },
52 | )
53 | use_fast_tokenizer: bool = field(
54 | default=True,
55 | metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
56 | )
57 |
58 |
59 | @dataclass
60 | class DataArguments:
61 | """
62 | Arguments pertaining to what data we are going to input our model for training and eval.
63 | """
64 |
65 | dataset_name: Optional[str] = field(
66 | default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
67 | )
68 | max_train_samples: Optional[int] = field(
69 | default=None,
70 | metadata={
71 | "help": (
72 | "For debugging purposes or quicker training, truncate the number of training examples to this "
73 | "value if set."
74 | )
75 | },
76 | )
77 | max_eval_samples: Optional[int] = field(
78 | default=None,
79 | metadata={
80 | "help": (
81 | "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
82 | "value if set."
83 | )
84 | },
85 | )
86 | block_size: Optional[int] = field(
87 | default=None,
88 | metadata={
89 | "help": (
90 | "Optional input sequence length after tokenization. "
91 | "The training dataset will be truncated in block of this size for training. "
92 | "Default to the model max input length for single sentence inputs (take into account special tokens)."
93 | )
94 | },
95 | )
96 | overwrite_cache: bool = field(
97 | default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
98 | )
99 | preprocessing_num_workers: Optional[int] = field(
100 | default=None,
101 | metadata={"help": "The number of processes to use for the preprocessing."},
102 | )
103 | dialogue_template: Optional[str] = field(
104 | default="no_system",
105 | metadata={
106 | "help": "The name of the dialogue template to use for conditioning the model. See h4.training.dialogues for choices."
107 | },
108 | )
109 |
110 |
111 | @dataclass
112 | class TrainingArguments(transformers.TrainingArguments):
113 | """
114 | Arguments related to the training process itself. For all parameters, see: https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments
115 | """
116 |
117 | logging_first_step: Optional[bool] = field(
118 | default=True,
119 | metadata={"help": ("Whether to log and evaluate the first global_step or not.")},
120 | )
121 | optim: Optional[str] = field(default="adamw_torch")
122 |
--------------------------------------------------------------------------------
/chat/config.yaml:
--------------------------------------------------------------------------------
1 | # Model arguments
2 | model_name_or_path: bigcode/starcoderbase
3 |
4 | # Data training arguments
5 | block_size: 1024
6 | dataset_name: HuggingFaceH4/oasst1_en
7 | dialogue_template: no_system
8 | preprocessing_num_workers: 12
9 |
10 | # Training arguments with sensible defaults
11 | # Add other options from here: https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments
12 | bf16: true # Gives ~2x speed up in training time, but disable if you start seeing NaNs
13 | do_eval: true
14 | do_train: true
15 | evaluation_strategy: epoch # One of ["no", "steps", "epoch"]
16 | gradient_accumulation_steps: 8
17 | gradient_checkpointing: true
18 | hub_model_id: lewtun/starchat-alpha
19 | hub_private_repo: true
20 | hub_strategy: every_save
21 | learning_rate: 2.0e-05
22 | log_level: passive
23 | logging_steps: 8
24 | logging_strategy: steps
25 | lr_scheduler_type: cosine
26 | max_steps: -1
27 | num_train_epochs: 3
28 | output_dir: data/starchat-alpha
29 | overwrite_output_dir: true
30 | per_device_eval_batch_size: 4
31 | per_device_train_batch_size: 4
32 | push_to_hub: true
33 | remove_unused_columns: true
34 | report_to:
35 | - tensorboard
36 | save_steps: 500
37 | save_strategy: steps
38 | save_total_limit: null
39 | seed: 42
40 | tf32: true
41 | warmup_ratio: 0.03
42 | weight_decay: 0.
--------------------------------------------------------------------------------
/chat/deepspeed_z3_config_bf16.json:
--------------------------------------------------------------------------------
1 | {
2 | "bf16": {
3 | "enabled": "auto"
4 | },
5 | "optimizer": {
6 | "type": "AdamW",
7 | "params": {
8 | "lr": "auto",
9 | "betas": "auto",
10 | "eps": "auto",
11 | "weight_decay": "auto"
12 | }
13 | },
14 | "scheduler": {
15 | "type": "WarmupLR",
16 | "params": {
17 | "warmup_min_lr": "auto",
18 | "warmup_max_lr": "auto",
19 | "warmup_num_steps": "auto"
20 | }
21 | },
22 | "zero_optimization": {
23 | "stage": 3,
24 | "overlap_comm": true,
25 | "contiguous_gradients": true,
26 | "sub_group_size": 1e9,
27 | "reduce_bucket_size": "auto",
28 | "stage3_prefetch_bucket_size": "auto",
29 | "stage3_param_persistence_threshold": "auto",
30 | "stage3_max_live_parameters": 1e9,
31 | "stage3_max_reuse_distance": 1e9,
32 | "stage3_gather_16bit_weights_on_model_save": true
33 | },
34 | "gradient_accumulation_steps": "auto",
35 | "gradient_clipping": "auto",
36 | "steps_per_print": 2000,
37 | "train_batch_size": "auto",
38 | "train_micro_batch_size_per_gpu": "auto",
39 | "wall_clock_breakdown": false
40 | }
--------------------------------------------------------------------------------
/chat/dialogues.py:
--------------------------------------------------------------------------------
1 | # coding=utf-8
2 | # Copyright 2023 The HuggingFace Team. All rights reserved.
3 | #
4 | # Licensed under the Apache License, Version 2.0 (the "License");
5 | # you may not use this file except in compliance with the License.
6 | # You may obtain a copy of the License at
7 | #
8 | # http://www.apache.org/licenses/LICENSE-2.0
9 | #
10 | # Unless required by applicable law or agreed to in writing, software
11 | # distributed under the License is distributed on an "AS IS" BASIS,
12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 | # See the License for the specific language governing permissions and
14 | # limitations under the License.
15 |
16 | import json
17 | import os
18 | from dataclasses import asdict, dataclass
19 | from pathlib import Path
20 | from typing import Any, Dict, List, Optional, Type, TypeVar, Union
21 |
22 | from huggingface_hub import ModelHubMixin, hf_hub_download
23 |
24 | # Generic variable that is either ModelHubMixin or a subclass thereof
25 | T = TypeVar("T", bound="ModelHubMixin")
26 |
27 | TEMPLATE_FILENAME = "dialogue_template.json"
28 | IGNORE_INDEX = -100
29 |
30 |
31 | @dataclass
32 | class DialogueTemplate(ModelHubMixin):
33 | """Converts all turns of a dialogue between a user and assistant to a standardized format.
34 |
35 | Adapted from OpenAI's ChatML (https://github.com/openai/openai-python/blob/main/chatml.md) and Vicuna (https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py)
36 | """
37 |
38 | system: str
39 | messages: List[Dict[str, str]] = None
40 | system_token: str = "<|system|>"
41 | user_token: str = "<|user|>"
42 | assistant_token: str = "<|assistant|>"
43 | end_token: str = "<|end|>"
44 |
45 | def get_training_prompt(self) -> str:
46 | prompt = self.system_token + "\n" + self.system + self.end_token + "\n"
47 | if self.messages is None:
48 | raise ValueError("Dialogue template must have at least one message.")
49 | for message in self.messages:
50 | if message["role"] == "user":
51 | prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n"
52 | else:
53 | prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n"
54 | return prompt
55 |
56 | def get_inference_prompt(self) -> str:
57 | prompt = self.system_token + "\n" + self.system + self.end_token + "\n"
58 | if self.messages is None:
59 | raise ValueError("Dialogue template must have at least one message.")
60 | for message in self.messages:
61 | if message["role"] == "user":
62 | prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n"
63 | else:
64 | prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n"
65 | prompt += self.assistant_token
66 | return prompt
67 |
68 | def get_dialogue(self):
69 | """Helper function to format the messages as an easy-to-read dialogue."""
70 | prompt = ""
71 | if self.messages is None:
72 | raise ValueError("Dialogue template must have at least one message.")
73 | for message in self.messages:
74 | if message["role"] == "user":
75 | prompt += "\n\nHuman: " + message["content"]
76 | else:
77 | prompt += "\n\nAssistant: " + message["content"]
78 | return prompt
79 |
80 | def get_special_tokens(self) -> List[str]:
81 | return [self.system_token, self.user_token, self.assistant_token, self.end_token]
82 |
83 | def copy(self):
84 | return DialogueTemplate(
85 | system=self.system,
86 | messages=self.messages,
87 | system_token=self.system_token,
88 | user_token=self.user_token,
89 | assistant_token=self.assistant_token,
90 | end_token=self.end_token,
91 | )
92 |
93 | def to_dict(self) -> Dict[str, Any]:
94 | return {k: v for k, v in asdict(self).items()}
95 |
96 | @classmethod
97 | def from_dict(cls, data):
98 | return DialogueTemplate(
99 | system=data["system"] if "system" in data else "",
100 | messages=data["messages"] if "messages" in data else None,
101 | system_token=data["system_token"] if "system_token" in data else "<|system|>",
102 | user_token=data["user_token"] if "user_token" in data else "<|user|>",
103 | assistant_token=data["assistant_token"] if "assistant_token" in data else "<|assistant|>",
104 | end_token=data["end_token"] if "end_token" in data else "<|end|>",
105 | )
106 |
107 | def _save_pretrained(self, save_directory: Union[str, Path]) -> None:
108 | save_directory = Path(save_directory)
109 | save_directory.mkdir(exist_ok=True)
110 | with open(save_directory / "dialogue_template.json", "w") as f:
111 | json.dump(self.to_dict(), f, indent=2)
112 |
113 | @classmethod
114 | def _from_pretrained(
115 | cls: Type[T],
116 | *,
117 | model_id: str,
118 | revision: Optional[str],
119 | cache_dir: Optional[Union[str, Path]],
120 | force_download: bool,
121 | proxies: Optional[Dict],
122 | resume_download: bool,
123 | local_files_only: bool,
124 | token: Optional[Union[str, bool]],
125 | **model_kwargs,
126 | ) -> T:
127 | """Loads the dialogue template from a local directory or the Huggingface Hub.
128 |
129 | Args:
130 | model_id (`str`):
131 | ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`).
132 | revision (`str`, *optional*):
133 | Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the
134 | latest commit on `main` branch.
135 | force_download (`bool`, *optional*, defaults to `False`):
136 | Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
137 | the existing cache.
138 | resume_download (`bool`, *optional*, defaults to `False`):
139 | Whether to delete incompletely received files. Will attempt to resume the download if such a file exists.
140 | proxies (`Dict[str, str]`, *optional*):
141 | A dictionary of proxy servers to use by protocol or endpoint (e.g., `{'http': 'foo.bar:3128',
142 | 'http://hostname': 'foo.bar:4012'}`).
143 | token (`str` or `bool`, *optional*):
144 | The token to use as HTTP bearer authorization for remote files. By default, it will use the token
145 | cached when running `huggingface-cli login`.
146 | cache_dir (`str`, `Path`, *optional*):
147 | Path to the folder where cached files are stored.
148 | local_files_only (`bool`, *optional*, defaults to `False`):
149 | If `True`, avoid downloading the file and return the path to the local cached file if it exists.
150 | model_kwargs:
151 | Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method.
152 | """
153 | if os.path.isdir(model_id): # Can either be a local directory
154 | print("Loading dialogue template from local directory")
155 | template_file = os.path.join(model_id, TEMPLATE_FILENAME)
156 | else: # Or a template on the Hub
157 | template_file = hf_hub_download( # Download from the hub, passing same input args
158 | repo_id=model_id,
159 | filename=TEMPLATE_FILENAME,
160 | revision=revision,
161 | cache_dir=cache_dir,
162 | force_download=force_download,
163 | proxies=proxies,
164 | resume_download=resume_download,
165 | token=token,
166 | local_files_only=local_files_only,
167 | )
168 |
169 | # Load template
170 | with open(template_file, "r") as f:
171 | data = json.load(f)
172 | return cls.from_dict(data=data)
173 |
174 |
175 | # A shortened version of the system message in Anthropic's HHH prompt: https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt
176 | default_template = DialogueTemplate(
177 | system="Below is a dialogue between a human user and an AI assistant. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed.",
178 | )
179 |
180 | # OpenAI and OpenAssistant train on few to no system messages.
181 | # TODO: consider defining this as the `default` template
182 | no_system_template = DialogueTemplate(
183 | system="",
184 | )
185 |
186 | alpaca_template = DialogueTemplate(
187 | system="Below is an instruction that describes a task. Write a response that appropriately completes the request.",
188 | user_token="### Instruction:",
189 | assistant_token="### Response:",
190 | )
191 |
192 | SUPPORTED_DIALOGUE_TEMPLATES = {
193 | "default": default_template,
194 | "no_system": no_system_template,
195 | "alpaca": alpaca_template,
196 | }
197 |
198 |
199 | def get_dialogue_template(template: str) -> DialogueTemplate:
200 | if template not in SUPPORTED_DIALOGUE_TEMPLATES.keys():
201 | raise ValueError(f"Template {template} is not supported!")
202 | return SUPPORTED_DIALOGUE_TEMPLATES[template].copy()
203 |
204 |
205 | def prepare_dialogue(example, dialogue_template, is_train=True):
206 | """Format example to single- or multi-turn dialogue."""
207 | # TODO: make this simpler by just ensuring every dataset has a messages column
208 | if "messages" in example.keys() and example["messages"] is not None:
209 | dialogue_template.messages = example["messages"]
210 | elif all(k in example.keys() for k in ("prompt", "completion")):
211 | # Construct single-turn dialogue from prompt and completion
212 | dialogue_template.messages = [
213 | {"role": "user", "content": example["prompt"]},
214 | {"role": "assistant", "content": example["completion"]},
215 | ]
216 | elif "prompt" in example.keys():
217 | # Construct single-turn dialogue from prompt (inference only)
218 | dialogue_template.messages = [
219 | {"role": "user", "content": example["prompt"]},
220 | ]
221 | else:
222 | raise ValueError(
223 | f"Could not format example as dialogue! Require either `messages` or `[prompt, completion]` or `[prompt]` keys but found {list(example.keys())}"
224 | )
225 | if is_train:
226 | example["text"] = dialogue_template.get_training_prompt()
227 | else:
228 | example["text"] = dialogue_template.get_inference_prompt()
229 | return example
230 |
231 |
232 | def mask_user_labels(tokenizer, dialogue_template, labels):
233 | """Masks the user turns of a dialogue from the loss"""
234 | user_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.user_token)
235 | assistant_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.assistant_token)
236 | for idx, label_id in enumerate(labels):
237 | if label_id == user_token_id:
238 | current_idx = idx
239 | while labels[current_idx] != assistant_token_id and current_idx < len(labels):
240 | labels[current_idx] = IGNORE_INDEX
241 | current_idx += 1
242 |
--------------------------------------------------------------------------------
/chat/generate.py:
--------------------------------------------------------------------------------
1 | # coding=utf-8
2 | # Copyright 2023 The BigCode and HuggingFace teams. All rights reserved.
3 | #
4 | # Licensed under the Apache License, Version 2.0 (the "License");
5 | # you may not use this file except in compliance with the License.
6 | # You may obtain a copy of the License at
7 | #
8 | # http://www.apache.org/licenses/LICENSE-2.0
9 | #
10 | # Unless required by applicable law or agreed to in writing, software
11 | # distributed under the License is distributed on an "AS IS" BASIS,
12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 | # See the License for the specific language governing permissions and
14 | # limitations under the License.
15 | #
16 | """A simple script to quickly check the model outputs of a generative model"""
17 | import argparse
18 |
19 | import torch
20 | from dialogues import DialogueTemplate, get_dialogue_template
21 | from transformers import (AutoModelForCausalLM, AutoTokenizer,
22 | GenerationConfig, set_seed)
23 |
24 |
25 | def main():
26 | parser = argparse.ArgumentParser()
27 | parser.add_argument(
28 | "--model_id",
29 | type=str,
30 | help="Name of model to generate samples with",
31 | )
32 | parser.add_argument(
33 | "--revision",
34 | type=str,
35 | default=None,
36 | help="The model repo's revision to use",
37 | )
38 | parser.add_argument(
39 | "--system_prompt", type=str, default=None, help="Overrides the dialogue template's system prompt"
40 | )
41 | args = parser.parse_args()
42 |
43 | # Set seed for reproducibility
44 | set_seed(42)
45 |
46 | prompts = [
47 | [
48 | {
49 | "role": "user",
50 | "content": "Develop a C++ program that reads a text file line by line and counts the number of occurrences of a specific word in the file.",
51 | }
52 | ],
53 | [
54 | {
55 | "role": "user",
56 | "content": "Implement a Python function to find the longest common subsequence of two input strings using dynamic programming.",
57 | }
58 | ],
59 | [{"role": "user", "content": "Implement a regular expression in Python to validate an email address."}],
60 | [
61 | {
62 | "role": "user",
63 | "content": "Write a program to find the nth Fibonacci number using dynamic programming.",
64 | }
65 | ],
66 | [
67 | {
68 | "role": "user",
69 | "content": "Implement a binary search algorithm to find a specific element in a sorted array.",
70 | }
71 | ],
72 | [{"role": "user", "content": "Implement a queue data structure using two stacks in Python."}],
73 | [
74 | {
75 | "role": "user",
76 | "content": "Implement a program to find the common elements in two arrays without using any extra data structures.",
77 | }
78 | ],
79 | ]
80 |
81 | try:
82 | dialogue_template = DialogueTemplate.from_pretrained(args.model_id, revision=args.revision)
83 | except Exception:
84 | print("No dialogue template found in model repo. Defaulting to the `no_system` template.")
85 | dialogue_template = get_dialogue_template("no_system")
86 |
87 | if args.system_prompt is not None:
88 | dialogue_template.system = args.system_prompt
89 | formatted_prompts = []
90 | for prompt in prompts:
91 | dialogue_template.messages = [prompt] if isinstance(prompt, dict) else prompt
92 | formatted_prompts.append(dialogue_template.get_inference_prompt())
93 |
94 | print("=== SAMPLE PROMPT ===")
95 | print(formatted_prompts[0])
96 | print("=====================")
97 |
98 | device = "cuda" if torch.cuda.is_available() else "cpu"
99 | tokenizer = AutoTokenizer.from_pretrained(args.model_id, revision=args.revision)
100 | print(f"Special tokens: {tokenizer.special_tokens_map}")
101 | print(f"EOS token ID for generation: {tokenizer.convert_tokens_to_ids(dialogue_template.end_token)}")
102 | generation_config = GenerationConfig(
103 | temperature=0.2,
104 | top_k=50,
105 | top_p=0.95,
106 | repetition_penalty=1.2,
107 | do_sample=True,
108 | pad_token_id=tokenizer.eos_token_id,
109 | eos_token_id=tokenizer.convert_tokens_to_ids(dialogue_template.end_token),
110 | min_new_tokens=32,
111 | max_new_tokens=256,
112 | )
113 | model = AutoModelForCausalLM.from_pretrained(
114 | args.model_id, revision=args.revision, load_in_8bit=True, device_map="auto", torch_dtype=torch.float16
115 | )
116 | outputs = ""
117 | for idx, prompt in enumerate(formatted_prompts):
118 | batch = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False).to(device)
119 | generated_ids = model.generate(**batch, generation_config=generation_config)
120 | generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=False).lstrip()
121 | outputs += generated_text + "\n\n"
122 | print(f"=== EXAMPLE {idx} ===")
123 | print()
124 | print(generated_text)
125 | print()
126 | print("======================")
127 | print()
128 |
129 | raw_model_name = args.model_id.split("/")[-1]
130 | model_name = f"{raw_model_name}"
131 | if args.revision is not None:
132 | model_name += f"-{args.revision}"
133 |
134 | with open(f"data/samples-{model_name}.txt", "w", encoding="utf-8") as f:
135 | f.write(outputs)
136 |
137 |
138 | if __name__ == "__main__":
139 | main()
140 |
--------------------------------------------------------------------------------
/chat/requirements.txt:
--------------------------------------------------------------------------------
1 | transformers>=4.28.1
2 | tokenizers>=0.13.3
3 | deepspeed==0.9.1
4 | datasets>=2.12.0
5 | accelerate>=0.18.0
6 | tensorboard
--------------------------------------------------------------------------------
/chat/train.py:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env python
2 | # coding=utf-8
3 | # Copyright 2023 The BigCode & HuggingFace Inc. teams. All rights reserved.
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the "License");
6 | # you may not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # http://www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an "AS IS" BASIS,
13 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 | """
17 | Script to instruction fine-tune causal language models on a Hub dataset
18 |
19 | Adapted from huggingface/transformers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py
20 | """
21 |
22 | import logging
23 | import math
24 | import os
25 | import random
26 | import sys
27 | from itertools import chain
28 |
29 | import datasets
30 | import torch
31 | import transformers
32 | from config import DataArguments, ModelArguments, TrainingArguments
33 | from datasets import load_dataset
34 | from dialogues import get_dialogue_template, mask_user_labels, prepare_dialogue
35 | from transformers import (AutoModelForCausalLM, AutoTokenizer, Trainer,
36 | default_data_collator, set_seed)
37 | from transformers.testing_utils import CaptureLogger
38 | from transformers.trainer_utils import get_last_checkpoint
39 | from utils import StarChatArgumentParser, hf_login
40 |
41 | logger = logging.getLogger(__name__)
42 |
43 |
44 | def main():
45 | parser = StarChatArgumentParser((ModelArguments, DataArguments, TrainingArguments))
46 | if len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
47 | # If we pass only one argument to the script and it's the path to a YAML file,
48 | # let's parse it to get our arguments.
49 | model_args, data_args, training_args = parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
50 | # parse command line args and yaml file
51 | elif len(sys.argv) > 2 and sys.argv[1].endswith(".yaml"):
52 | model_args, data_args, training_args = parser.parse_yaml_and_args(os.path.abspath(sys.argv[1]), sys.argv[2:])
53 | # parse command line args only
54 | else:
55 | model_args, data_args, training_args = parser.parse_args_into_dataclasses()
56 |
57 | # Set seed for reproducibility
58 | set_seed(training_args.seed)
59 |
60 | ###############
61 | # Setup logging
62 | ###############
63 | logging.basicConfig(
64 | format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
65 | datefmt="%Y-%m-%d %H:%M:%S",
66 | handlers=[logging.StreamHandler(sys.stdout)],
67 | )
68 | log_level = training_args.get_process_log_level()
69 | logger.setLevel(log_level)
70 | datasets.utils.logging.set_verbosity(log_level)
71 | transformers.utils.logging.set_verbosity(log_level)
72 | transformers.utils.logging.enable_default_handler()
73 | transformers.utils.logging.enable_explicit_format()
74 |
75 | # Log on each process a small summary
76 | logger.warning(
77 | f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
78 | + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
79 | )
80 | logger.info(f"Model parameters {model_args}")
81 | logger.info(f"Data parameters {data_args}")
82 | logger.info(f"Training/evaluation parameters {training_args}")
83 |
84 | # Login to HuggingFace Hub if needed
85 | hf_login()
86 |
87 | ###########################
88 | # Detecting last checkpoint
89 | ###########################
90 | last_checkpoint = None
91 | if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
92 | last_checkpoint = get_last_checkpoint(training_args.output_dir)
93 | if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
94 | raise ValueError(
95 | f"Output directory ({training_args.output_dir}) already exists and is not empty. "
96 | "Use --overwrite_output_dir to overcome."
97 | )
98 | elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
99 | logger.info(
100 | f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
101 | "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
102 | )
103 |
104 | ###############
105 | # Load datasets
106 | ###############
107 | raw_datasets = load_dataset(data_args.dataset_name)
108 | logger.info(
109 | f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
110 | )
111 | with training_args.main_process_first(desc="Log a few random samples from the raw training set"):
112 | for index in random.sample(range(len(raw_datasets["train"])), 3):
113 | logger.info(f"Sample {index} of the raw training set:\n\n{raw_datasets['train'][index]['messages']}")
114 |
115 | #########################
116 | # Apply dialogue template
117 | #########################
118 | dialogue_template = get_dialogue_template(data_args.dialogue_template)
119 | logger.info(f"System prompt for dialogue template: {dialogue_template.system}")
120 | raw_datasets = raw_datasets.map(prepare_dialogue, fn_kwargs={"dialogue_template": dialogue_template})
121 |
122 | #####################################
123 | # Load tokenizer and process datasets
124 | #####################################
125 | tokenizer = AutoTokenizer.from_pretrained(
126 | model_args.model_name_or_path,
127 | revision=model_args.model_revision,
128 | )
129 |
130 | # Note that we must call `add_tokens` before adding any special tokens
131 | dialogue_tokens = dialogue_template.get_special_tokens()
132 | num_added_tokens = tokenizer.add_special_tokens({"additional_special_tokens": dialogue_tokens})
133 | logger.info(f"Added {num_added_tokens} new tokens: {dialogue_tokens}")
134 |
135 | if training_args.do_train:
136 | column_names = list(raw_datasets["train"].features)
137 | else:
138 | column_names = list(raw_datasets["test"].features)
139 | text_column_name = "text" if "text" in column_names else column_names[0]
140 |
141 | with training_args.main_process_first(desc="Log a few random samples from the training set"):
142 | for index in random.sample(range(len(raw_datasets["train"])), 3):
143 | logger.info(f"Sample {index} of the raw training set:\n\n{raw_datasets['train'][index]['text']}")
144 |
145 | # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
146 | tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
147 |
148 | def tokenize_function(examples):
149 | with CaptureLogger(tok_logger) as cl:
150 | output = tokenizer(examples[text_column_name], return_token_type_ids=False)
151 | # clm input could be much much longer than block_size
152 | if "Token indices sequence length is longer than the" in cl.out:
153 | tok_logger.warning(
154 | "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
155 | " before being passed to the model."
156 | )
157 | return output
158 |
159 | with training_args.main_process_first(desc="dataset map tokenization"):
160 | tokenized_datasets = raw_datasets.map(
161 | tokenize_function,
162 | batched=True,
163 | num_proc=data_args.preprocessing_num_workers,
164 | remove_columns=column_names,
165 | load_from_cache_file=not data_args.overwrite_cache,
166 | desc="Running tokenizer on dataset",
167 | )
168 |
169 | ##############################
170 | # Concatenate and chunk corpus
171 | ##############################
172 | if data_args.block_size is None:
173 | block_size = tokenizer.model_max_length
174 | if block_size > 1024:
175 | logger.warning(
176 | "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
177 | " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
178 | " override this default with `--block_size xxx`."
179 | )
180 | block_size = 1024
181 | else:
182 | if data_args.block_size > tokenizer.model_max_length:
183 | logger.warning(
184 | f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
185 | f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
186 | )
187 | block_size = min(data_args.block_size, tokenizer.model_max_length)
188 |
189 | # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
190 | def group_texts(examples):
191 | # Concatenate all texts.
192 | concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
193 | total_length = len(concatenated_examples[list(examples.keys())[0]])
194 | # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
195 | # customize this part to your needs.
196 | if total_length >= block_size:
197 | total_length = (total_length // block_size) * block_size
198 | # Split by chunks of max_len.
199 | result = {
200 | k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
201 | for k, t in concatenated_examples.items()
202 | }
203 | labels = result["input_ids"].copy()
204 | mask_user_labels(tokenizer, dialogue_template, labels)
205 | result["labels"] = labels
206 | return result
207 |
208 | # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
209 | # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
210 | # to preprocess.
211 | with training_args.main_process_first(desc="grouping texts together"):
212 | lm_datasets = tokenized_datasets.map(
213 | group_texts,
214 | batched=True,
215 | num_proc=data_args.preprocessing_num_workers,
216 | load_from_cache_file=not data_args.overwrite_cache,
217 | desc=f"Grouping texts in chunks of {block_size}",
218 | )
219 |
220 | if training_args.do_train:
221 | if "train" not in tokenized_datasets:
222 | raise ValueError("--do_train requires a train dataset")
223 | train_dataset = lm_datasets["train"]
224 | if data_args.max_train_samples is not None:
225 | max_train_samples = min(len(train_dataset), data_args.max_train_samples)
226 | train_dataset = train_dataset.select(range(max_train_samples))
227 |
228 | if training_args.do_eval:
229 | if "test" not in tokenized_datasets:
230 | raise ValueError("--do_eval requires a validation dataset")
231 | eval_dataset = lm_datasets["test"]
232 | if data_args.max_eval_samples is not None:
233 | max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
234 | eval_dataset = eval_dataset.select(range(max_eval_samples))
235 |
236 | #######################
237 | # Load pretrained model
238 | #######################
239 | logger.info("*** Load pretrained model ***")
240 | torch_dtype = (
241 | model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
242 | )
243 | model = AutoModelForCausalLM.from_pretrained(
244 | model_args.model_name_or_path,
245 | revision=model_args.model_revision,
246 | torch_dtype=torch_dtype,
247 | use_cache=False if training_args.gradient_checkpointing else True,
248 | )
249 | model.resize_token_embeddings(len(tokenizer))
250 |
251 | ########################
252 | # Initialize the Trainer
253 | ########################
254 | trainer = Trainer(
255 | model=model,
256 | args=training_args,
257 | train_dataset=train_dataset if training_args.do_train else None,
258 | eval_dataset=eval_dataset if training_args.do_eval else None,
259 | tokenizer=tokenizer,
260 | # Data collator defaults to DataCollatorWithPadding, so we change it
261 | # since we've already chunked our corpus
262 | data_collator=default_data_collator,
263 | )
264 |
265 | ###############
266 | # Training loop
267 | ###############
268 | if training_args.do_train:
269 | logger.info("*** Train ***")
270 | checkpoint = None
271 | if training_args.resume_from_checkpoint is not None:
272 | checkpoint = training_args.resume_from_checkpoint
273 | elif last_checkpoint is not None:
274 | checkpoint = last_checkpoint
275 | train_result = trainer.train(resume_from_checkpoint=checkpoint)
276 |
277 | metrics = train_result.metrics
278 |
279 | max_train_samples = (
280 | data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
281 | )
282 | metrics["train_samples"] = min(max_train_samples, len(train_dataset))
283 |
284 | trainer.log_metrics("train", metrics)
285 | trainer.save_metrics("train", metrics)
286 | trainer.save_state()
287 |
288 | ##########
289 | # Evaluate
290 | ##########
291 | if training_args.do_eval:
292 | logger.info("*** Evaluate ***")
293 |
294 | metrics = trainer.evaluate()
295 |
296 | max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
297 | metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
298 | try:
299 | perplexity = math.exp(metrics["eval_loss"])
300 | except OverflowError:
301 | perplexity = float("inf")
302 | metrics["perplexity"] = perplexity
303 |
304 | trainer.log_metrics("eval", metrics)
305 | trainer.save_metrics("eval", metrics)
306 |
307 | #################################
308 | # Create model card & push to Hub
309 | #################################
310 | kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
311 | if data_args.dataset_name is not None:
312 | kwargs["dataset_tags"] = data_args.dataset_name
313 | if data_args.dataset_config_name is not None:
314 | kwargs["dataset_args"] = data_args.dataset_config_name
315 | kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
316 | else:
317 | kwargs["dataset"] = data_args.dataset_name
318 | kwargs["dataset_args"] = "default"
319 |
320 | # Store dialogue template so we can load it at deployment time
321 | dialogue_template.save_pretrained(training_args.output_dir)
322 |
323 | if training_args.push_to_hub:
324 | trainer.push_to_hub(**kwargs)
325 | else:
326 | trainer.save_model(training_args.output_dir)
327 | trainer.create_model_card(**kwargs)
328 |
329 | with training_args.main_process_first(desc="Generate a sample from the model"):
330 | inputs = tokenizer(
331 | "<|system|>\n<|end|>\n<|user|>\nHow many helicopters can a human eat in one sitting?<|end|>\n<|assistant|>",
332 | return_tensors="pt",
333 | return_token_type_ids=False,
334 | ).to(training_args.device)
335 | outputs = model.generate(
336 | **inputs,
337 | max_new_tokens=256,
338 | pad_token_id=tokenizer.eos_token_id,
339 | eos_token_id=tokenizer.convert_tokens_to_ids(dialogue_template.end_token),
340 | )
341 | logger.info(f"=== SAMPLE OUTPUT ==\n\n{tokenizer.decode(outputs[0], skip_special_tokens=False)}")
342 |
343 |
344 | if __name__ == "__main__":
345 | main()
346 |
--------------------------------------------------------------------------------
/chat/utils.py:
--------------------------------------------------------------------------------
1 | # coding=utf-8
2 | # Copyright 2023 The HuggingFace Team. All rights reserved.
3 | #
4 | # Licensed under the Apache License, Version 2.0 (the "License");
5 | # you may not use this file except in compliance with the License.
6 | # You may obtain a copy of the License at
7 | #
8 | # http://www.apache.org/licenses/LICENSE-2.0
9 | #
10 | # Unless required by applicable law or agreed to in writing, software
11 | # distributed under the License is distributed on an "AS IS" BASIS,
12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 | # See the License for the specific language governing permissions and
14 | # limitations under the License.
15 |
16 | import dataclasses
17 | import os
18 | from dataclasses import dataclass
19 | from typing import List, Optional
20 |
21 | from huggingface_hub import login
22 | from transformers import HfArgumentParser
23 |
24 |
25 | class StarChatArgumentParser(HfArgumentParser):
26 | def parse_yaml_and_args(self, yaml_arg: str, other_args: Optional[List[str]] = None) -> List[dataclass]:
27 | arg_list = self.parse_yaml_file(os.path.abspath(yaml_arg))
28 |
29 | outputs = []
30 | # strip other args list into dict of key-value pairs
31 | other_args = {arg.split("=")[0].strip("-"): arg.split("=")[1] for arg in other_args}
32 | used_args = {}
33 |
34 | # overwrite the default/loaded value with the value provided to the command line
35 | # adapted from https://github.com/huggingface/transformers/blob/d0b5002378daabf62769159add3e7d66d3f83c3b/src/transformers/hf_argparser.py#L327
36 | for data_yaml, data_class in zip(arg_list, self.dataclass_types):
37 | keys = {f.name for f in dataclasses.fields(data_yaml) if f.init}
38 | inputs = {k: v for k, v in vars(data_yaml).items() if k in keys}
39 | for arg, val in other_args.items():
40 | # add only if in keys
41 | if arg in keys:
42 | base_type = data_yaml.__dataclass_fields__[arg].type
43 | inputs[arg] = val
44 |
45 | # cast type for ints, floats, and bools (default to strings)
46 | if base_type in [int, float, bool]:
47 | inputs[arg] = base_type(val)
48 |
49 | # add to used-args so we can check if double add
50 | if arg not in used_args:
51 | used_args[arg] = val
52 | else:
53 | raise ValueError(f"Duplicate argument provided: {arg}, may cause unexpected behavior")
54 |
55 | obj = data_class(**inputs)
56 | outputs.append(obj)
57 |
58 | return outputs
59 |
60 |
61 | def hf_login():
62 | """Login to HuggingFace Hub if HF_TOKEN is defined in the environment"""
63 | hf_token = os.getenv("HF_TOKEN")
64 | if hf_token is not None:
65 | login(token=hf_token)
66 |
--------------------------------------------------------------------------------
/finetune/finetune.py:
--------------------------------------------------------------------------------
1 | import argparse
2 | import os
3 |
4 | import torch
5 | from accelerate import Accelerator
6 | from datasets import load_dataset
7 | from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, set_peft_model_state_dict
8 | from torch.utils.data import IterableDataset
9 | from tqdm import tqdm
10 | from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments, logging, set_seed
11 | from transformers import TrainerCallback, TrainingArguments, TrainerState, TrainerControl
12 | from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
13 |
14 | """
15 | Fine-Tune StarCoder on Code Alpaca/SE
16 | """
17 |
18 | class SavePeftModelCallback(TrainerCallback):
19 | def on_save(
20 | self,
21 | args: TrainingArguments,
22 | state: TrainerState,
23 | control: TrainerControl,
24 | **kwargs,
25 | ):
26 | checkpoint_folder = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
27 |
28 | kwargs["model"].save_pretrained(checkpoint_folder)
29 |
30 | pytorch_model_path = os.path.join(checkpoint_folder, "pytorch_model.bin")
31 | torch.save({}, pytorch_model_path)
32 | return control
33 |
34 |
35 | class LoadBestPeftModelCallback(TrainerCallback):
36 | def on_train_end(
37 | self,
38 | args: TrainingArguments,
39 | state: TrainerState,
40 | control: TrainerControl,
41 | **kwargs,
42 | ):
43 | print(f"Loading best peft model from {state.best_model_checkpoint} (score: {state.best_metric}).")
44 | best_model_path = os.path.join(state.best_model_checkpoint, "adapter_model.bin")
45 | adapters_weights = torch.load(best_model_path)
46 | model = kwargs["model"]
47 | set_peft_model_state_dict(model, adapters_weights)
48 | return control
49 |
50 |
51 | def get_args():
52 | parser = argparse.ArgumentParser()
53 | parser.add_argument("--model_path", type=str, default="bigcode/large-model")
54 | parser.add_argument("--dataset_name", type=str, default="HuggingFaceH4/CodeAlpaca_20K")
55 | parser.add_argument("--subset", type=str)
56 | parser.add_argument("--split", type=str)
57 | parser.add_argument("--size_valid_set", type=int, default=10000)
58 | parser.add_argument("--streaming", action="store_true")
59 | parser.add_argument("--shuffle_buffer", type=int, default=5000)
60 |
61 | parser.add_argument("--input_column_name", type=str, default="prompt")
62 | parser.add_argument("--output_column_name", type=str, default="completion")
63 |
64 | parser.add_argument("--seq_length", type=int, default=2048)
65 | parser.add_argument("--max_steps", type=int, default=10000)
66 | parser.add_argument("--batch_size", type=int, default=1)
67 | parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
68 | parser.add_argument("--eos_token_id", type=int, default=49152)
69 |
70 | parser.add_argument("--lora_r", type=int, default=16)
71 | parser.add_argument("--lora_alpha", type=int, default=32)
72 | parser.add_argument("--lora_dropout", type=float, default=0.05)
73 |
74 | parser.add_argument("--learning_rate", type=float, default=5e-6)
75 | parser.add_argument("--lr_scheduler_type", type=str, default="cosine")
76 | parser.add_argument("--num_warmup_steps", type=int, default=100)
77 | parser.add_argument("--weight_decay", type=float, default=0.05)
78 |
79 | parser.add_argument("--local_rank", type=int, default=0)
80 | parser.add_argument("--no_fp16", action="store_false")
81 | parser.add_argument("--bf16", action="store_true", default=True)
82 | parser.add_argument("--no_gradient_checkpointing", action="store_false", default=False)
83 | parser.add_argument("--seed", type=int, default=0)
84 | parser.add_argument("--num_workers", type=int, default=None)
85 | parser.add_argument("--output_dir", type=str, default="./checkpoints")
86 | parser.add_argument("--log_freq", default=100, type=int)
87 | parser.add_argument("--eval_freq", default=100, type=int)
88 | parser.add_argument("--save_freq", default=1000, type=int)
89 |
90 | return parser.parse_args()
91 |
92 |
93 | def chars_token_ratio(dataset, tokenizer, input_column_name="prompt", output_column_name="completion", nb_examples=400):
94 | """
95 | Estimate the average number of characters per token in the dataset.
96 | """
97 | total_characters, total_tokens = 0, 0
98 | for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):
99 | text = prepare_sample_text(example, input_column_name, output_column_name)
100 | total_characters += len(text)
101 | if tokenizer.is_fast:
102 | total_tokens += len(tokenizer(text).tokens())
103 | else:
104 | total_tokens += len(tokenizer.tokenize(text))
105 |
106 | return total_characters / total_tokens
107 |
108 |
109 | def print_trainable_parameters(model):
110 | """
111 | Prints the number of trainable parameters in the model.
112 | """
113 | trainable_params = 0
114 | all_param = 0
115 | for _, param in model.named_parameters():
116 | all_param += param.numel()
117 | if param.requires_grad:
118 | trainable_params += param.numel()
119 | print(
120 | f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
121 | )
122 |
123 |
124 | def prepare_sample_text(example, input_column_name="prompt", output_column_name="completion"):
125 | """Prepare the text from a sample of the dataset."""
126 | text = f"Question: {example[input_column_name]}\n\nAnswer: {example[output_column_name]}"
127 | return text
128 |
129 |
130 | class ConstantLengthDataset(IterableDataset):
131 | """
132 | Iterable dataset that returns constant length chunks of tokens from stream of text files.
133 | Args:
134 | tokenizer (Tokenizer): The processor used for proccessing the data.
135 | dataset (dataset.Dataset): Dataset with text files.
136 | infinite (bool): If True the iterator is reset after dataset reaches end else stops.
137 | seq_length (int): Length of token sequences to return.
138 | num_of_sequences (int): Number of token sequences to keep in buffer.
139 | chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
140 | """
141 |
142 | def __init__(
143 | self,
144 | tokenizer,
145 | dataset,
146 | infinite=False,
147 | seq_length=1024,
148 | num_of_sequences=1024,
149 | chars_per_token=3.6,
150 | input_column_name="prompt",
151 | output_column_name="completion"
152 | ):
153 | self.tokenizer = tokenizer
154 | self.concat_token_id = tokenizer.eos_token_id if tokenizer.eos_token_id is not None else args.eos_token_id
155 | self.dataset = dataset
156 | self.seq_length = seq_length
157 | self.infinite = infinite
158 | self.current_size = 0
159 | self.max_buffer_size = seq_length * chars_per_token * num_of_sequences
160 | self.input_column_name = input_column_name
161 | self.output_column_name = output_column_name
162 |
163 | def __iter__(self):
164 | iterator = iter(self.dataset)
165 | more_examples = True
166 | while more_examples:
167 | buffer, buffer_len = [], 0
168 | while True:
169 | if buffer_len >= self.max_buffer_size:
170 | break
171 | try:
172 | buffer.append(prepare_sample_text(next(iterator), self.input_column_name, self.output_column_name))
173 | buffer_len += len(buffer[-1])
174 | except StopIteration:
175 | if self.infinite:
176 | iterator = iter(self.dataset)
177 | else:
178 | more_examples = False
179 | break
180 | tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
181 | all_token_ids = []
182 | for tokenized_input in tokenized_inputs:
183 | all_token_ids.extend(tokenized_input + [self.concat_token_id])
184 | for i in range(0, len(all_token_ids), self.seq_length):
185 | input_ids = all_token_ids[i : i + self.seq_length]
186 | if len(input_ids) == self.seq_length:
187 | self.current_size += 1
188 | yield {
189 | "input_ids": torch.LongTensor(input_ids),
190 | "labels": torch.LongTensor(input_ids),
191 | }
192 |
193 |
194 | def create_datasets(tokenizer, args):
195 | dataset = load_dataset(
196 | args.dataset_name,
197 | data_dir=args.subset,
198 | split=args.split,
199 | use_auth_token=True,
200 | num_proc=args.num_workers if not args.streaming else None,
201 | streaming=args.streaming,
202 | )
203 | if args.streaming:
204 | print("Loading the dataset in streaming mode")
205 | valid_data = dataset.take(args.size_valid_set)
206 | train_data = dataset.skip(args.size_valid_set)
207 | train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed)
208 | else:
209 | train_data = dataset["train"]
210 | valid_data = dataset["test"]
211 | print(f"Size of the train set: {len(train_data)}. Size of the validation set: {len(valid_data)}")
212 |
213 | chars_per_token = chars_token_ratio(train_data, tokenizer, args.input_column_name, args.output_column_name)
214 | print(f"The character to token ratio of the dataset is: {chars_per_token:.2f}")
215 |
216 | train_dataset = ConstantLengthDataset(
217 | tokenizer,
218 | train_data,
219 | infinite=True,
220 | seq_length=args.seq_length,
221 | chars_per_token=chars_per_token,
222 | input_column_name=args.input_column_name,
223 | output_column_name=args.output_column_name
224 | )
225 | valid_dataset = ConstantLengthDataset(
226 | tokenizer,
227 | valid_data,
228 | infinite=False,
229 | seq_length=args.seq_length,
230 | chars_per_token=chars_per_token,
231 | input_column_name=args.input_column_name,
232 | output_column_name=args.output_column_name
233 | )
234 | return train_dataset, valid_dataset
235 |
236 |
237 | def run_training(args, train_data, val_data):
238 | print("Loading the model")
239 | # disable caching mechanism when using gradient checkpointing
240 | model = AutoModelForCausalLM.from_pretrained(
241 | args.model_path,
242 | use_auth_token=True,
243 | use_cache=not args.no_gradient_checkpointing,
244 | load_in_8bit=True,
245 | device_map={"": Accelerator().process_index},
246 | )
247 | model = prepare_model_for_int8_training(model)
248 |
249 | lora_config = LoraConfig(
250 | r=args.lora_r,
251 | lora_alpha=args.lora_alpha,
252 | lora_dropout=args.lora_dropout,
253 | bias="none",
254 | task_type="CAUSAL_LM",
255 | target_modules = ["c_proj", "c_attn", "q_attn"]
256 | )
257 |
258 | model = get_peft_model(model, lora_config)
259 |
260 | print_trainable_parameters(model)
261 |
262 | train_data.start_iteration = 0
263 |
264 | print("Starting main loop")
265 |
266 | training_args = TrainingArguments(
267 | output_dir=args.output_dir,
268 | dataloader_drop_last=True,
269 | evaluation_strategy="steps",
270 | save_strategy="steps",
271 | load_best_model_at_end=True,
272 | max_steps=args.max_steps,
273 | eval_steps=args.eval_freq,
274 | save_steps=args.save_freq,
275 | logging_steps=args.log_freq,
276 | per_device_train_batch_size=args.batch_size,
277 | per_device_eval_batch_size=args.batch_size,
278 | learning_rate=args.learning_rate,
279 | lr_scheduler_type=args.lr_scheduler_type,
280 | warmup_steps=args.num_warmup_steps,
281 | gradient_accumulation_steps=args.gradient_accumulation_steps,
282 | gradient_checkpointing=not args.no_gradient_checkpointing,
283 | fp16=not args.no_fp16,
284 | bf16=args.bf16,
285 | weight_decay=args.weight_decay,
286 | run_name="StarCoder-finetuned",
287 | report_to="wandb",
288 | ddp_find_unused_parameters=False,
289 | )
290 |
291 | trainer = Trainer(model=model, args=training_args, train_dataset=train_data, eval_dataset=val_data, callbacks=[SavePeftModelCallback, LoadBestPeftModelCallback])
292 |
293 | print("Training...")
294 | trainer.train()
295 |
296 | print("Saving last checkpoint of the model")
297 | model.save_pretrained(os.path.join(args.output_dir, "final_checkpoint/"))
298 |
299 |
300 | def main(args):
301 | tokenizer = AutoTokenizer.from_pretrained(args.model_path, use_auth_token=True)
302 | train_dataset, eval_dataset = create_datasets(tokenizer, args)
303 | run_training(args, train_dataset, eval_dataset)
304 |
305 |
306 | if __name__ == "__main__":
307 | args = get_args()
308 |
309 | set_seed(args.seed)
310 | os.makedirs(args.output_dir, exist_ok=True)
311 |
312 | logging.set_verbosity_error()
313 |
314 | main(args)
315 |
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/finetune/merge_peft_adapters.py:
--------------------------------------------------------------------------------
1 | from transformers import AutoModelForCausalLM, AutoTokenizer
2 | from peft import PeftModel
3 | import torch
4 |
5 | import os
6 | import argparse
7 |
8 | def get_args():
9 | parser = argparse.ArgumentParser()
10 | parser.add_argument("--base_model_name_or_path", type=str, default="bigcode/large-model")
11 | parser.add_argument("--peft_model_path", type=str, default="/")
12 | parser.add_argument("--push_to_hub", action="store_true", default=True)
13 |
14 | return parser.parse_args()
15 |
16 | def main():
17 | args = get_args()
18 |
19 | base_model = AutoModelForCausalLM.from_pretrained(
20 | args.base_model_name_or_path,
21 | return_dict=True,
22 | torch_dtype=torch.float16
23 | )
24 |
25 | model = PeftModel.from_pretrained(base_model, args.peft_model_path)
26 | model = model.merge_and_unload()
27 |
28 | tokenizer = AutoTokenizer.from_pretrained(args.base_model_name_or_path)
29 |
30 | if args.push_to_hub:
31 | print(f"Saving to hub ...")
32 | model.push_to_hub(f"{args.base_model_name_or_path}-merged", use_temp_dir=False, private=True)
33 | tokenizer.push_to_hub(f"{args.base_model_name_or_path}-merged", use_temp_dir=False, private=True)
34 | else:
35 | model.save_pretrained(f"{args.base_model_name_or_path}-merged")
36 | tokenizer.save_pretrained(f"{args.base_model_name_or_path}-merged")
37 | print(f"Model saved to {args.base_model_name_or_path}-merged")
38 |
39 | if __name__ == "__main__" :
40 | main()
41 |
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/requirements.txt:
--------------------------------------------------------------------------------
1 | tqdm==4.65.0
2 | transformers==4.28.1
3 | datasets==2.11.0
4 | huggingface-hub==0.13.4
5 | accelerate==0.18.0
6 |
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