| # Optimized Transformer implementation | |
| This repo contains examples of how FlashAttention can be integrated into a model | |
| (e.g., GPT, ViT) and trained end-to-end. We also provide optimized | |
| implementations of other layers (e.g., MLP, LayerNorm, cross-entropy loss, | |
| rotary embedding). Overall this speeds up training by 3-5x compared to the | |
| baseline implementation from Huggingface, reaching up to 189 TFLOPs/sec per A100, | |
| equivalent to 60.6\% model FLOPs utilization (we don't need any activation | |
| checkpointing). All without changing the model architecture (i.e., no | |
| approximation). | |
| Goals: | |
| - Performance: we optimize for model speed and memory, especially on 1-node | |
| (e.g., with 8 A100s). | |
| - Flexibility: we provide optimized building blocks (MLP, attention, LayerNorm), | |
| and the model code illustrates how these components can be put together. | |
| The training code also aims to be model- & task-agnostic. | |
| Non-goals (and other resources): | |
| - Support as many models as possible: Huggingface's | |
| [transformers](https://github.com/huggingface/transformers) and | |
| [timm](https://github.com/rwightman/pytorch-image-models/) are great for this. | |
| - Large-scale distributed training: our codebase has been used for multi-GPU and multi-node | |
| training for models up to 2.7B parameters. However, if you're looking for large-scale distributed | |
| training techniques (e.g., pipeline parallelism, tensor parallelism), | |
| check out [Megatron-LM](https://github.com/NVIDIA/Megatron-LM/) and | |
| [DeepSpeed](https://github.com/microsoft/deepspeed). | |
| - Inference: we currently focus on training (this might change in the future). | |
| If you want fast inference, take a look at | |
| [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). | |
| - Production: this codebase was written during several research projects to validate ideas | |
| on speeding up ML models. | |
| ## Model Components | |
| The GPT model is implemented | |
| [here](https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/models/gpt.py). | |
| And here's an example to construct the GPT3-1.3B model with rotary embedding: | |
| ```python | |
| from transformers.models.gpt2.configuration_gpt2 import GPT2Config | |
| from flash_attn.models.gpt import GPTLMHeadModel | |
| seqlen = 2048 | |
| hidden_dim = 2048 | |
| nheads = 16 | |
| n_layer = 24 | |
| rotary_emb_fraction = 0.5 | |
| config = GPT2Config(vocab_size=50257, n_positions=seqlen, n_embd=hidden_dim, | |
| n_layer=n_layer, n_head=nheads, | |
| scale_attn_by_inverse_layer_idx=True, | |
| rotary_emb_fraction=rotary_emb_fraction, | |
| use_flash_attn=True, fused_mlp=True, | |
| fused_bias_fc=True, fused_dropout_add_ln=True, | |
| pad_vocab_size_multiple=8) | |
| model = GPTLMHeadModel(config) | |
| ``` | |
| We provide the following optimized components: | |
| 1. FlashAttention: fast and memory-efficient exact attention. This makes | |
| attention much faster and saves a lot of activation memory. As a result we don't need | |
| to use any activation checkpointing. | |
| ```sh | |
| pip install flash-attn | |
| ``` | |
| 2. Fused matmul + bias (forward and backward), and fused matmul + bias + gelu | |
| (forward and backward), adapted from Apex's | |
| [FusedDense](https://github.com/NVIDIA/apex/tree/master/apex/fused_dense). We | |
| make it work for bfloat16. For best performance, you should use CUDA >= 11.8. CuBLAS versions before | |
| this doesn't have the best matmul + bias + gelu performance for bfloat16. | |
| ```sh | |
| cd ../csrc/fused_dense_lib && pip install . | |
| ``` | |
| 3. Optimized cross-entropy loss, adapted from Apex's | |
| [Xentropy](https://github.com/NVIDIA/apex/tree/master/apex/contrib/xentropy). We make it work for bfloat16 and support in-place backward to save memory. | |
| ```sh | |
| cd ../csrc/xentropy && pip install . | |
| ``` | |
| 4. Fused rotary embedding: | |
| ```sh | |
| cd ../csrc/rotary && pip install . | |
| ``` | |
| 5. Fused dropout + residual + LayerNorm, adapted from Apex's | |
| [FastLayerNorm](https://github.com/NVIDIA/apex/tree/master/apex/contrib/layer_norm). We add dropout and residual, and make it work for both pre-norm and post-norm architecture. | |
| This supports dimensions divisible by 8, up to 6144. | |
| ```sh | |
| cd ../csrc/layer_norm && pip install . | |
| ``` | |
| ## Training | |
| We also provide here training scripts to train GPT2 on Openwebtext and GPT3 on | |
| The Pile as examples. Feel free to use the model in your own training setup as | |
| well. | |
| We use [Hydra](https://hydra.cc/) for configuration, | |
| [Pytorch-Lightning](https://github.com/Lightning-AI/lightning) for training, and | |
| [Wandb](https://wandb.ai/) for logging. | |
| We use the template from `https://github.com/ashleve/lightning-hydra-template`. | |
| Please read the instructions there to understand the repo structure. | |
| ### Requirements | |
| Python 3.8+, Pytorch 1.12+, torchvision, einops, timm, hydra-core, | |
| hydra-colorlog, python-dotenv, rich, pytorch-lightning, triton, flash-attn. | |
| We recommend CUDA 11.8 (e.g., using the Nvidia's Pytorch Docker image from https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) | |
| We provide a Dockerfile that lists all the required packages. | |
| ### Dataset preparation | |
| Running the training command would automatically download the datasets | |
| (Openwebtext, Pile), tokenize with the GPT2 tokenizer, concatenate all the | |
| tokens, then save this cache to disk. Alternatively, you can also prepare the | |
| datasets as a separate step. | |
| The cached datasets are saved to `${DATA_DIR}/openwebtext` and | |
| `${DATA_DIR}/the_pile`. If `${DATA_DIR}` is not set, they will be saved to | |
| `./data/{openwebtext,the_pile}`. | |
| - Openwebtext: | |
| ```sh | |
| export PYTHONPATH=$PWD:$PYTHONPATH | |
| pytest -q -s tests/datamodules/test_language_modeling_hf.py -k "openwebtext" | |
| ``` | |
| This takes around 1h on a 64-core CPU. The processed dataset has size 17GB. | |
| - The Pile: | |
| ```sh | |
| export PYTHONPATH=$PWD:$PYTHONPATH | |
| pytest -q -s tests/datamodules/test_language_modeling_hf.py -k "pile" | |
| ``` | |
| This takes around 20h on a 64-core CPU. The processed dataset has size 699GB. | |
| ### GPT2 training on Openwebtext | |
| To train GPT2 on Openwebtext with 8 GPUs: | |
| ```sh | |
| python run.py experiment=owt/gpt2s-flash trainer.devices=8 # 125M | |
| python run.py experiment=owt/gpt2m-flash trainer.devices=8 # 355M | |
| python run.py experiment=owt/gpt2l-flash trainer.devices=8 # 760M | |
| python run.py experiment=owt/gpt2xl-flash trainer.devices=8 # 1.6B | |
| ``` | |
| The default parameters are set for 8 x A100 80GB. | |
| To train with bf16 instead of fp16, add `trainer.precision=bf16`. | |
| ### GPT3 training on The Pile | |
| To train GPT3 on The Pile with 8 GPUs: | |
| ```sh | |
| python run.py experiment=pile/gpt3s-flash trainer.devices=8 # 125M | |
| python run.py experiment=pile/gpt3m-flash trainer.devices=8 # 355M | |
| python run.py experiment=pile/gpt3l-flash trainer.devices=8 # 760M | |
| python run.py experiment=pile/gpt3xl-flash trainer.devices=8 # 1.3B | |
| python run.py experiment=pile/gpt3-2.7B-flash-hdim128 trainer.devices=8 # 2.7B | |
| ``` | |
| The default parameters are set for 8 x A100 80GB. We train with bf16 by default. | |
| To train with rotary embedding, run the experiments `pile/gpt3{s,m,l,xl}-flash-rotary`. | |
| ### Training options | |
| **Gradient accumulation**: to adjust device batch size to fit into GPU memory | |
| (the global batch size stays the same, and gradient accumulation is calculated | |
| automatically), set `datamodule.batch_size=blah`. | |
| **Multi-node**: to train on multiple nodes, add `trainer.num_nodes=blah`. | |
| **Speed benchmarking**: to print out iteration time, add `+callbacks.speed_monitor.verbose=True`. | |
| **Resumable training**: set a name to the run, and then set `resume=True` when | |
| you resume. Training will restart at exactly the same batch. | |
| ```sh | |
| python run.py experiment=pile/gpt3s-flash trainer.devices=8 name=pile-gpt3s-flash resume=True | |
| ``` | |
| ## Training speed | |
| We measure the wallclock training speed on one node with 8 x A100 80GB SXM4 80GB (400W) with NVLink. | |
| FLOPs are calculated using the formula from the [Megatron-LM | |
| paper](https://arxiv.org/abs/2104.04473) (Section 5.1), except we scale by 3/4 | |
| to get the model FLOPs (instead of hardware FLOPs with activation | |
| checkpointing). | |
| ### GPT2 (sequence length 1024) | |
|  | |
| The implementation in this repo (FlashAttention) is 3-4x faster than the | |
| baseline implementation from Huggingface. | |
| ### GPT3 (sequence length 2048) | |
|  | |
| The implementation in this repo (FlashAttention) is 3-5x faster than the | |
| baseline implementation from Huggingface. | |
| For the GPT3-2.7B model, we set head dimension to 128 (instead of 80) for better efficiency. | |
| We include here more details on the training speed with FlashAttention on 8 x | |
| A100 80GB. | |
| | Model | Batch size (tokens) | Through put (tokens/sec) | Hours / 1B tokens | | |
| | --------- | ------------------- | ------------------------ | ----------------- | | |
| | GPT3-125M | 0.5M | 1310k | 0.21 | | |
| | GPT3-355M | 0.5M | 503k | 0.55 | | |
| | GPT3-760M | 0.5M | 245k | 1.13 | | |
| | GPT3-1.3B | 1M | 169k | 1.64 | | |
| | GPT3-2.7B | 1M | 85k | 3.27 | | |
| As an example, this means that one can train a GPT3-1.3B model on 26B tokens | |
| (compute-optimal according to Chinchilla scaling) in about 43 hours on 8 x A100. | |
| ## Training quality | |
| We include here the loss curve for GPT2 on Openwebtext, trained for 200B tokens. | |
| For GPT2, the runs with FlashAttention yield the same loss curve as the runs | |
| with the baseline implementation from Huggingface for 125M and 355M models. For | |
| larger models the baseline implementation just takes too long. | |
|  | |
| We include here the loss curve for GPT3 on The Pile, trained for 400B tokens. | |
| The 125M, 355M, 760M models have batch size 512k tokens so this translates to | |
| 800k training steps, while the 1.3B and 2.7B models have batch size 1M tokens, | |
| which translates to 400k training steps. | |
|  | |