Release training code

csrc/layer_norm/README.md

@@ -2,6 +2,8 @@ This CUDA extension implements fused dropout + residual + LayerNorm, based on
 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 only supports a limited set of dimensions, see `csrc/layer_norm/ln_fwd_cuda_kernel.cu`.
+
 It has only been tested on A100s.
 
 ```sh

training/Dockerfile

@@ -0,0 +1,107 @@
+# Inspired by https://github.com/anibali/docker-pytorch/blob/master/dockerfiles/1.10.0-cuda11.3-ubuntu20.04/Dockerfile
+# ARG COMPAT=0
+ARG PERSONAL=0
+# FROM nvidia/cuda:11.3.1-devel-ubuntu20.04 as base-0
+FROM nvcr.io/nvidia/pytorch:22.11-py3 as base
+
+ENV HOST docker
+ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
+# https://serverfault.com/questions/683605/docker-container-time-timezone-will-not-reflect-changes
+ENV TZ America/Los_Angeles
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
+
+# git for installing dependencies
+# tzdata to set time zone
+# wget and unzip to download data
+# [2021-09-09] TD: zsh, stow, subversion, fasd are for setting up my personal environment.
+# [2021-12-07] TD: openmpi-bin for MPI (multi-node training)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    cmake \
+    curl \
+    ca-certificates \
+    sudo \
+    less \
+    htop \
+    git \
+    tzdata \
+    wget \
+    tmux \
+    zip \
+    unzip \
+    zsh stow subversion fasd \
+    && rm -rf /var/lib/apt/lists/*
+    # openmpi-bin \
+
+# Allow running runmpi as root
+# ENV OMPI_ALLOW_RUN_AS_ROOT=1 OMPI_ALLOW_RUN_AS_ROOT_CONFIRM=1
+
+# # Create a non-root user and switch to it
+# RUN adduser --disabled-password --gecos '' --shell /bin/bash user \
+#     && echo "user ALL=(ALL) NOPASSWD:ALL" > /etc/sudoers.d/90-user
+# USER user
+
+# All users can use /home/user as their home directory
+ENV HOME=/home/user
+RUN mkdir -p /home/user && chmod 777 /home/user
+WORKDIR /home/user
+
+# Set up personal environment
+# FROM base-${COMPAT} as env-0
+FROM base as env-0
+FROM env-0 as env-1
+# Use ONBUILD so that the dotfiles dir doesn't need to exist unless we're building a personal image
+# https://stackoverflow.com/questions/31528384/conditional-copy-add-in-dockerfile
+ONBUILD COPY dotfiles ./dotfiles
+ONBUILD RUN cd ~/dotfiles && stow bash zsh tmux && sudo chsh -s /usr/bin/zsh $(whoami)
+# nvcr pytorch image sets SHELL=/bin/bash
+ONBUILD ENV SHELL=/bin/zsh
+
+FROM env-${PERSONAL} as packages
+
+# Disable pip cache: https://stackoverflow.com/questions/45594707/what-is-pips-no-cache-dir-good-for
+ENV PIP_NO_CACHE_DIR=1
+
+# # apex and pytorch-fast-transformers take a while to compile so we install them first
+# TD [2022-04-28] apex is already installed. In case we need a newer commit:
+# RUN pip install --upgrade --force-reinstall --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_multihead_attn" --global-option="--fmha" --global-option="--fast_layer_norm" --global-option="--xentropy" git+https://github.com/NVIDIA/apex.git#egg=apex
+# TD [2021-10-28] pytorch-fast-transformers doesn't have a wheel compatible with CUDA 11.3 and Pytorch 1.10
+# So we install from source, and change compiler flag -arch=compute_60 -> -arch=compute_70 for V100
+# RUN pip install pytorch-fast-transformers==0.4.0
+# RUN pip install git+git://github.com/idiap/[email protected]  # doesn't work on V100
+RUN git clone https://github.com/idiap/fast-transformers \
+    && sed -i 's/\["-arch=compute_60"\]/\["-arch=compute_70"\]/' fast-transformers/setup.py \
+    && pip install fast-transformers/ \
+    && rm -rf fast-transformers
+
+# xgboost conflicts with deepspeed
+RUN pip uninstall -y xgboost && DS_BUILD_UTILS=1 DS_BUILD_FUSED_LAMB=1 pip install deepspeed==0.7.5
+
+# General packages that we don't care about the version
+# zstandard to extract the_pile dataset
+# psutil to get the number of cpu physical cores
+# twine to upload package to PyPI
+# ninja is broken for some reason, it returns error code 245
+RUN pip uninstall -y ninja && pip install ninja
+RUN pip install pytest matplotlib jupyter ipython ipdb gpustat scikit-learn spacy munch einops opt_einsum fvcore gsutil cmake pykeops zstandard psutil h5py twine \
+    && python -m spacy download en_core_web_sm
+# hydra
+RUN pip install hydra-core==1.2.0 hydra-colorlog==1.2.0 hydra-optuna-sweeper==1.2.0 pyrootutils rich
+# Core packages
+RUN pip install transformers==4.24.0 datasets==2.7.1 pytorch-lightning==1.7.7 triton==2.0.0.dev20221120 wandb==0.13.5 timm==0.6.12 torchmetrics==0.10.3
+
+# For MLPerf
+RUN pip install git+https://github.com/mlcommons/[email protected]
+
+# Install FlashAttention
+RUN pip install flash-attn==0.2.2
+
+# Install CUDA extensions for cross-entropy, fused dense, layer norm
+RUN git clone https://github.com/HazyResearch/flash-attention \
+    && cd flash-attention && git checkout v0.2.2 \
+    && cd csrc/fused_softmax && pip install . && cd ../../ \
+    && cd csrc/rotary && pip install . && cd ../../ \
+    && cd csrc/xentropy && pip install . && cd ../../ \
+    && cd csrc/layer_norm && pip install . && cd ../../ \
+    && cd csrc/fused_dense_lib && pip install . && cd ../../ \
+    && cd .. && rm -rf flash-attention

training/README.md

@@ -0,0 +1,133 @@
+Examples of how FlashAttention can be integrated into a model (e.g., GPT, ViT)
+and trained end-to-end.
+We also added optimized implementations of other layers (e.g., MLP, LayerNorm,
+cross-entropy loss, rotary embedding).
+
+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).
+
+We provide the following optimized components:
+
+- 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
+```
+
+- 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 .
+```
+- 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 .
+```
+- Fused rotary embedding:
+```sh
+cd ../csrc/rotary && pip install .
+```
+- 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 only supports a limited set of dimensions, see `csrc/layer_norm/ln_fwd_cuda_kernel.cu`.
+```sh
+cd ../csrc/layer_norm && pip install .
+```
+
+## Training
+
+Feel free to use the model in your training setup. We also provide here training
+scripts to train GPT2 on Openwebtext and GPT3 on The Pile as examples.
+
+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.
+
+### 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 steps.
+
+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 96-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
+python run.py experiment=owt/gpt2m-flash trainer.devices=8
+python run.py experiment=owt/gpt2l-flash trainer.devices=8
+python run.py experiment=owt/gpt2xl-flash trainer.devices=8
+```
+The default parameters are set for 8 x A100 80GB.
+
+To train with bf16 instead of fp16, add `trainer.precision=bf16`.
+To adjust device batch size to fit GPU memory (the global batch size stays the
+same, and gradient accumulation is calculated automatically), set `datamodule.batch_size=blah`.
+
+### 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
+python run.py experiment=pile/gpt3m-flash trainer.devices=8
+python run.py experiment=pile/gpt3l-flash trainer.devices=8
+python run.py experiment=pile/gpt3xl-flash trainer.devices=8
+```
+The default parameters are set for 8 x A100 80GB.
+
+## 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.

training/configs/callbacks/causality-monitor.yaml

@@ -0,0 +1,2 @@
+causality-monitor:
+  _target_: src.callbacks.causality_monitor.CausalityMonitor

training/configs/callbacks/default.yaml

@@ -0,0 +1,45 @@
+# rich_progress_bar:
+#   _target_: pytorch_lightning.callbacks.RichProgressBar
+
+rich_model_summary:
+  _target_: pytorch_lightning.callbacks.RichModelSummary
+
+model_checkpoint:
+  _target_: pytorch_lightning.callbacks.ModelCheckpoint
+  monitor: "val/acc" # name of the logged metric which determines when model is improving
+  mode: "max" # can be "max" or "min"
+  save_top_k: 1 # save k best models (determined by above metric)
+  save_last: True # additionaly always save model from last epoch
+  verbose: False
+  dirpath: ${oc.env:CHECKPOINT_DIR,checkpoints}/${oc.select:name,''}
+  filename: "epoch_{epoch:03d}"
+  auto_insert_metric_name: False
+
+early_stopping:
+  _target_: pytorch_lightning.callbacks.EarlyStopping
+  monitor: "val/acc" # name of the logged metric which determines when model is improving
+  mode: "max" # can be "max" or "min"
+  patience: 100 # how many epochs of not improving until training stops
+  min_delta: 0 # minimum change in the monitored metric needed to qualify as an improvement
+
+learning_rate_monitor:
+  _target_: pytorch_lightning.callbacks.LearningRateMonitor
+  logging_interval: step
+
+speed_monitor:
+  _target_: src.callbacks.speed_monitor.SpeedMonitor
+  intra_step_time: True
+  inter_step_time: True
+  epoch_time: True
+
+loss_scale_monitor:
+  _target_: src.callbacks.loss_scale_monitor.LossScaleMonitor
+
+params_log:
+  _target_: src.callbacks.params_log.ParamsLog
+  total_params_log: True
+  trainable_params_log: True
+  non_trainable_params_log: True
+
+gpu_affinity:
+  _target_: src.callbacks.gpu_affinity.GpuAffinity

training/configs/callbacks/ema.yaml

@@ -0,0 +1,4 @@
+ema:
+  _target_: src.callbacks.ema.EMACallback
+  decay: ???
+  use_num_updates: False

training/configs/callbacks/flop-count.yaml

@@ -0,0 +1,5 @@
+flop_count:
+  _target_: src.callbacks.flop_count.FlopCount
+  profilers: ['fvcore']
+  input_size: [3, 224, 224]
+  device: null

training/configs/callbacks/gpu-monitor.yaml

@@ -0,0 +1,11 @@
+defaults:
+  - default.yaml
+
+gpu_stats_monitor:
+  _target_: pytorch_lightning.callbacks.GPUStatsMonitor
+  # [2021-08-13] TD: I just want the intra_step_size but it'll error if I
+  # don't have memory_utilization and gpu_utilization.
+  # Maybe I should write a callback with just the intra_step_size.
+  memory_utilization: True
+  gpu_utilization: True
+  intra_step_time: True

training/configs/callbacks/model-summary.yaml

@@ -0,0 +1,2 @@
+model_summary:
+  _target_: pytorch_lightning.callbacks.RichModelSummary

training/configs/callbacks/norm-monitor.yaml

@@ -0,0 +1,2 @@
+norm_monitor:
+  _target_: src.callbacks.norm_monitor.NormMonitor

training/configs/callbacks/params-log.yaml

@@ -0,0 +1,5 @@
+params_log:
+  _target_: src.callbacks.params_log.ParamsLog
+  total_params_log: True
+  trainable_params_log: True
+  non_trainable_params_log: True

training/configs/callbacks/wandb.yaml

@@ -0,0 +1,26 @@
+defaults:
+  - default.yaml
+
+watch_model:
+  _target_: src.callbacks.wandb_callbacks.WatchModel
+  log: "all"
+  log_freq: 100
+
+upload_code_as_artifact:
+  _target_: src.callbacks.wandb_callbacks.UploadCodeAsArtifact
+  code_dir: ${work_dir}/src
+
+upload_ckpts_as_artifact:
+  _target_: src.callbacks.wandb_callbacks.UploadCheckpointsAsArtifact
+  ckpt_dir: "checkpoints/"
+  upload_best_only: True
+
+log_f1_precision_recall_heatmap:
+  _target_: src.callbacks.wandb_callbacks.LogF1PrecRecHeatmap
+
+log_confusion_matrix:
+  _target_: src.callbacks.wandb_callbacks.LogConfusionMatrix
+
+log_image_predictions:
+  _target_: src.callbacks.wandb_callbacks.LogImagePredictions
+  num_samples: 8