188 lines
9.6 KiB
Markdown
188 lines
9.6 KiB
Markdown
# Dream
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> 📄 Paper: [Dream 7B: Diffusion Large Language Models](https://arxiv.org/abs/2508.15487) ｜ 💻 Code: [github.com/DreamLM/Dream](https://github.com/DreamLM/Dream)
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Resources and examples for training (finetuning & pretraining) and evaluating diffusion language models **Dream**.
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## Table of Contents
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- [Setup](#setup)
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- [Files overview](#files-overview)
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- [Training](#training)
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- [Inference](#inference)
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- [Evaluation](#evaluation)
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## Setup
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> [!IMPORTANT]
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> **Slurm users:** Update `scripts/train.slurm.sh` and `mkdir logps`: see [(optional) Slurm setup](/README.md/#optional-slurm-setup) for details.
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>
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## Files overview
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```
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# tools relevant with Dream
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dllm/pipelines/dream
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├── __init__.py # Package initialization
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├── models/
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│ ├── configuration_dream.py # Dream model configuration
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│ ├── generation_utils.py # Diffusion-based generation logic
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│ ├── modeling_dream.py # Core Dream model architecture
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│ └── tokenization_dream.py # Tokenizer implementation for Dream
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├── generator.py # Inference logic
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├── trainer.py # Training logic (pretraining and SFT)
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└── utils.py # Auxiliary utilities and helper functions
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# example entry points for training / inference / evaluation
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examples/dream
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├── chat.py # Interactive inference example
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├── eval.sh # Automatic evaluation script
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├── generate.py # Inference example
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├── pt.py # Pretraining example
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├── README.md # Documentation (you are here)
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└── sft.py # Supervised finetuning example
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```
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<!-- > [!NOTE]
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> We slightly modified [`modeling_dream.py`](/dllm/pipelines/dream/models/modeling_dream.py) so that the `model.forward()` supports 2-D attention masks. We recommend loading models with `dllm.utils.get_tokenizer`; otherwise `import dllm` before calling `AutoModel.from_pretrained` to ensure the correct models from `dllm` are used.
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>
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> We fixed bugs in `chat_template` and standardize `mask_token` through `dllm.utils.get_tokenizer`. If you use `AutoTokenizer`, keep in mind to set `chat_template` and `mask_token` appropriately yourselves. -->
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## Training
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### Finetuning
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For example, to SFT [`Dream-v0-Base-7B`](https://huggingface.co/Dream-org/Dream-v0-Base-7B) for instruction following on 8 GPUs, run:
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```shell
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accelerate launch \
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--config_file scripts/accelerate_configs/fsdp.yaml \
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examples/dream/sft.py \
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--model_name_or_path "Dream-org/Dream-v0-Base-7B" \
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--dataset_args "allenai/tulu-3-sft-mixture" \
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--output_dir "models/Dream-7B-SFT/tulu-3-sft-mixture" \
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--max_length 1024 \
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--num_train_epochs 4 \
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--learning_rate 2e-5
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```
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If you are using slurm and want to train across, for example, 2 nodes (16 GPUs total), run:
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```shell
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sbatch --nodes=2 --gres=gpu:8 scripts/train.slurm.sh \
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--accelerate_config "fsdp" \
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--script_path "examples/dream/sft.py" \
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--model_name_or_path "Dream-org/Dream-v0-Base-7B" \
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--dataset_args "allenai/tulu-3-sft-mixture" \
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--output_dir "models/Dream-7B-SFT/tulu-3-sft-mixture" \
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--max_length 1024 \
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--num_train_epochs 4 \
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--learning_rate 2e-5
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```
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<!-- **Reproducing [Dream-v0-Instruct-7B](https://huggingface.co/Dream-org/Dream-v0-Base-7B)**. We tried our best to reproduce Dream-v0-Instruct-7B by finetuning Dream-v0-Base-7B using our training pipeline on the public instruction-following dataset [allenai/tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture): -->
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#### Reproducing [`Dream-v0-Instruct-7B`](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B)
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We tried our best to reproduce [`Dream-v0-Instruct-7B`](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B) by finetuning [`Dream-v0-Base-7B`](https://huggingface.co/Dream-org/Dream-v0-Base-7B) using our training pipeline on the public instruction-following dataset [`allenai/tulu-3-sft-mixture`](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture):
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```shell
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# preprocessing SFT data (optional, but can avoid redundant preprocessing for multi-node training)
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PYTHONPATH=. python dllm/tools/preprocess_sft_dataset.py \
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--model_name_or_path "Dream-org/Dream-v0-Base-7B" \
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--sft_map_fn_path "examples.dream.sft.sft_map_fn" \
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--dataset_args "allenai/tulu-3-sft-mixture" \
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--output_dir "data/sft/dream/tulu-3-sft-mixture" \
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--num_proc 64
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# train on 24*8=192 A100s with FSDP, take about 8 hours
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sbatch --nodes=24 --gres=gpu:8 scripts/train.slurm.sh \
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--accelerate_config "fsdp" \
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--script_path "examples/dream/sft.py" \
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--model_name_or_path "Dream-org/Dream-v0-Base-7B" \
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--dataset_args "data/sft/dream/tulu-3-sft-mixture" \
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--load_preprocessed_data True \
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--output_dir "models/Dream-7B-SFT-tulu3-fsdp-bs4-len2048-ep5-lr1e-5" \
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--max_length 2048 \
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--truncation "right" \
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--group_by_length True \
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--num_train_epochs 5 \
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--learning_rate 1e-5 \
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--per_device_train_batch_size 2 \
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--gradient_accumulation_steps 2 \
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--per_device_eval_batch_size 2 \
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--eval_on_start False \
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--eval_steps 0.1 \
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--save_steps 0.05
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```
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<!-- [TODO] Training curves are on Wandb; checkpoints with evaluation results are available on Hugging Face. See the [Evaluation](#evaluation) section below for evaluation instructions. -->
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### Pretraining
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Pretrain on [`mlfoundations/dclm-baseline-1.0`](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0) from scratch using 192 GPUs (24x8) and FSDP:
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```shell
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sbatch --nodes=24 --gres=gpu:8 scripts/train.slurm.sh \
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--accelerate_config "fsdp" \
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--script_path "examples/dream/pt.py" \
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--model_name_or_path "Dream-org/Dream-v0-Base-7B" \
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--dataset_args "mlfoundations/dclm-baseline-1.0" \
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--output_dir "models/Dream-7B-PT/dclm-baseline-1.0" \
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--max_length 1024 \
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--max_steps 2000 \
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--learning_rate 3e-4
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```
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## Inference
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We support batch inference for standard generation and infilling:
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<!-- See [`examples/dream/generate.py`](/examples/dream/generate.py) for a full example: -->
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```shell
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python examples/dream/generate.py --model_name_or_path "Dream-org/Dream-v0-Instruct-7B"
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```
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We also support interactive multi-turn dialogue with visualization:
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```shell
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python examples/dream/chat.py --model_name_or_path "Dream-org/Dream-v0-Instruct-7B"
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```
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## Evaluation
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> Read [(optional) Evaluation setup](/README.md/#optional-evaluation-setup) before running evaluation.
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For example, to evaluate [`Dream-v0-Instruct-7B`](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B) on [`MMLU-Pro`](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro) using 4 GPUs, run:
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```shell
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# Use model_args to adjust the generation arguments for evalution.
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accelerate launch --num_processes 4 \
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dllm/pipelines/dream/eval.py \
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--tasks "mmlu_pro" \
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--model "dream" \
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--apply_chat_template \
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--num_fewshot 0 \
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--model_args "pretrained=Dream-org/Dream-v0-Instruct-7B,mc_num=1,max_new_tokens=128,max_length=128,steps=128,temperature=0.1,top_p=0.9,add_bos_token=true,escape_until=true"
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```
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To automatically evaluate [`Dream-v0-Base-7B`](https://huggingface.co/Dream-org/Dream-v0-Base-7B) and [`Dream-v0-Instruct-7B`](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B) on all benchmarks, run:
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```shell
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bash examples/dream/eval.sh --model_name_or_path "Dream-org/Dream-v0-Instruct-7B" --instruct True
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bash examples/dream/eval.sh --model_name_or_path "Dream-org/Dream-v0-Base-7B" --instruct False
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```
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### Evaluation results
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> Results (evaluated) are evaluated using our framework, while results (reported) come from the original paper. All evaluation settings follow the configurations in the [Dream](https://github.com/DreamLM/Dream) repository, with minor adjustments. Placeholder entries (“–”) indicate results not yet evaluated; full results will be released soon.
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| | MMLU | BBH | ARC‑C | ARC‑E | Hellaswag | WinoGrande | PIQA | GSM8K | Math | GPQA | HumanEval | MBPP | RACE | Countdown | Sudoku | Trip planning |
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|:----------------|:-------:|:-------:|:-----:|:-----:|:-----------:|:------------:|:----:|:-----:|:----:|:----:|:-----------:|:----:|:------:|:-----------:|:----:|:-----------:|
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| [`Dream-v0-Base-7B`](https://huggingface.co/Dream-org/Dream-v0-Base-7B) (reported) | 69.5 | 57.9 | 59.9 | 83.9 | 73.3 | 74.8 | 75.8 | 77.2 | 39.6 | 36.6 | 57.9 | 56.2 | 44.7 | 16.0 | 81.0 | 17.8 |
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| [`Dream-v0-Base-7B`](https://huggingface.co/Dream-org/Dream-v0-Base-7B) (evaluated) | – | – | 59.7 | 83.3 | 73.1 | 72.9 | 72.0 | 69.6 | – | 35.5 | 45.8 | – | 43.0 | – | – | – |
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<p align="center" style="color: #808080; font-size: 0.9em;">
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Table 1. Evaluation results of
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<a href="https://huggingface.co/Dream-org/Dream-v0-Base-7B" style="color: #808080; text-decoration: none;">
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<code>Dream-8B-Base</code>
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</a>.
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</p>
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| | MMLU | MMLU-Pro | GSM8K | Math | GPQA | HumanEval | MBPP | IFEval |
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|:----------------|:----:|:---------:|:-----:|:----:|:----:|:-----------:|:----:|:----:|
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| [`Dream-v0-Instruct-7B`](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B)(reported) | 67.0 | 43.3 | 81.0 | 39.2 | 33.0 | 55.5 | 58.8 | 62.5 |
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| [`Dream-v0-Instruct-7B`](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B)(evaluated) | – | 43.0 | 82.6 | 39.9 | 32.4 | 59.1 | – | 62.3 |
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<p align="center" style="color: #808080; font-size: 0.9em;">
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Table 2. Evaluation results of
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<a href="https://huggingface.co/Dream-org/Dream-v0-Instruct-7B" style="color: #808080; text-decoration: none;">
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<code>Dream-8B-Instruct</code>
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</a>.
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</p>
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