Llama training with FP8

3.test_cases/XX.transformer-engine/.gitignore

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+checkpoints
+slurm-*.out

3.test_cases/XX.transformer-engine/0.transformer-engine.dockerfile

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+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: MIT-0
+
+FROM nvcr.io/nvidia/pytorch:24.04-py3
+ENV DEBIAN_FRONTEND=noninteractive
+
+# The three must-be-built packages.
+# Efa-installer>=1.29.1 required for nccl>=2.19.0 to avoid libfabric NCCL error.
+ENV EFA_INSTALLER_VERSION=1.30.0
+ENV AWS_OFI_NCCL_VERSION=1.8.1-aws
+ENV NCCL_TESTS_VERSION=master
+
+## Uncomment below when this Dockerfile builds a container image with efa-installer<1.29.1 and
+# nccl>=2.19.0. See https://github.com/aws-samples/awsome-distributed-training/tree/main/1.architectures/efa-cheatsheet.md
+#ENV FI_EFA_SET_CUDA_SYNC_MEMOPS=0
+
+RUN apt-get update -y
+RUN apt-get remove -y --allow-change-held-packages \
+                      libmlx5-1 ibverbs-utils libibverbs-dev libibverbs1
+
+# We noticed that since 23.09, we can't just delete the whole /opt/hpcx/, otherwise `import torch`
+# complains about missing libuc?.so.
+RUN rm -rf /opt/hpcx/ompi \
+    && rm -rf /usr/local/mpi \
+    && rm -rf /opt/hpcx/nccl_rdma_sharp_plugin \
+    && ldconfig
+ENV OPAL_PREFIX=
+RUN DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
+    git \
+    gcc \
+    vim \
+    kmod \
+    openssh-client \
+    openssh-server \
+    build-essential \
+    curl \
+    autoconf \
+    libtool \
+    gdb \
+    automake \
+    cmake \
+    apt-utils \
+    libhwloc-dev \
+    aptitude && \
+    DEBIAN_FRONTEND=noninteractive apt autoremove -y
+
+# EFA
+RUN apt-get update && \
+    cd /tmp && \
+    curl -O https://efa-installer.amazonaws.com/aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz  && \
+    tar -xf aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz && \
+    cd aws-efa-installer && \
+    # ONLY add `--skip-kmod`, `--no-verify` and `--skip-limit-conf` flags to container image.
+    # Those three flags must NOT be used on the host.
+    #
+    # Explanations:
+    # - to build EFA in the Dockerfile, we added --skip-kmod and --no-verify. Without these flags,
+    #   the Dockerfile will fail to build. If installing EFA on the host and not in a container,
+    #   please remove these flags.
+    # - The --skip-limit-conf can be retained in Dockerfile, but it's redundant as the host already
+    #   has these limits set by efa_installer.
+    ./efa_installer.sh -y -g -d --skip-kmod --no-verify --skip-limit-conf && \
+    ldconfig && \
+    rm -rf /tmp/aws-efa-installer /var/lib/apt/lists/*
+ENV LD_LIBRARY_PATH=/opt/amazon/efa/lib:$LD_LIBRARY_PATH
+ENV PATH=/opt/amazon/efa/bin:/opt/amazon/openmpi/bin:$PATH
+
+
+####################################################################################################
+# [CUSTOM_NCCL_OPTION_1] Uncomment below stanza to install another NCCL version using the official
+# binaries.
+#
+# NCCL EFA plugin (aws-ofi-nccl) depends on mpi, hence we must rebuild openmpi before building the
+# aws-ofi-ccnl.
+####################################################################################################
+#ENV NCCL_VERSION=2.19.3-1
+#RUN cd /opt && \
+#    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb && \
+#    dpkg -i cuda-keyring_1.0-1_all.deb && \
+#    apt update && \
+#    apt install -y libnccl2==${NCCL_VERSION} libnccl-dev==${NCCL_VERSION} && \
+#    echo NCCL_SOCKET_IFNAME=^docker0,lo >> /etc/nccl.conf
+
+
+####################################################################################################
+# [CUSTOM_NCCL_OPTION_2] Install NCCL from source to the same location as the built-in ones. The
+# benefits of installing to the same location as the built-in version are:
+#
+# 1. There's only ever a single libnccl version offered by this image, preventing application from
+#    mistakenly chooses a wrong version.
+# 2. No longer needing extra settings for LD_LIBRARY_PATH or LD_PRELOAD.
+#
+# NCCL EFA plugin (aws-ofi-nccl) depends on mpi, hence we must rebuild openmpi before building the
+# aws-ofi-ccnl.
+####################################################################################################
+ENV NCCL_VERSION=2.19.3-1
+RUN apt-get remove -y libnccl2 libnccl-dev \
+   && cd /tmp \
+   && git clone https://github.com/NVIDIA/nccl.git -b v${NCCL_VERSION} \
+   && cd nccl \
+   && make -j src.build BUILDDIR=/usr \
+   # Build for p4 & p5.
+   NVCC_GENCODE="-gencode=arch=compute_90,code=sm_90, -gencode=arch=compute_80,code=sm_80" \
+   && rm -rf /tmp/nccl \
+   && echo NCCL_SOCKET_IFNAME=^docker0,lo >> /etc/nccl.conf
+
+
+####################################################################################################
+# Rebuild OpenMPI with custom PMIX version. E.g., to match what host's Slurm is built with (see
+# /opt/pmix/ on host, or run pmix_info on host).
+#
+# May be needed on rare occassions when `srun --mpi=pmix --container-image=... <mpi_application>`
+# mysteriously crashes.
+#
+# NCCL EFA plugin (aws-ofi-nccl) depends on mpi, hence we must rebuild openmpi before building the
+# aws-ofi-ccnl.
+####################################################################################################
+ENV OPEN_MPI_PATH=/opt/amazon/openmpi
+
+# OpenMPI build script claims PMIX_VERSION, and complains if we use it.
+ENV CUSTOM_PMIX_VERSION=4.2.6
+RUN apt-get update && apt-get install -y libevent-dev \
+    && cd /tmp \
+    && wget https://github.com/openpmix/openpmix/releases/download/v${CUSTOM_PMIX_VERSION}/pmix-${CUSTOM_PMIX_VERSION}.tar.gz \
+    && tar -xzf pmix-${CUSTOM_PMIX_VERSION}.tar.gz \
+    && rm pmix-${CUSTOM_PMIX_VERSION}.tar.gz \
+    && cd pmix-${CUSTOM_PMIX_VERSION}/ \
+    && ./autogen.pl \
+    && ./configure --prefix=/opt/pmix \
+    && make -j \
+    && make install \
+    && echo /opt/pmix/lib > /etc/ld.so.conf.d/pmix.conf \
+    && ldconfig \
+    && cd / \
+    && rm -fr /tmp/pmix-${CUSTOM_PMIX_VERSION}/
+# To silence this runtime error message:
+# [p4de-st-p4de-2:110912] PMIX ERROR: ERROR in file gds_ds12_lock_pthread.c at line 168
+ENV PMIX_GDS_MODULE=^ds12 \
+    PMIX_MCA_gds=^ds12
+
+# Rebuild openmpi with DLC style (which it remarks as "without libfabric"), with the above pmix.
+ENV OMPI_VERSION=4.1.6
+RUN rm -fr ${OPEN_MPI_PATH} \
+ && mkdir /tmp/openmpi \
+ && cd /tmp/openmpi \
+ && wget --quiet https://download.open-mpi.org/release/open-mpi/v4.1/openmpi-${OMPI_VERSION}.tar.gz \
+ && tar zxf openmpi-${OMPI_VERSION}.tar.gz \
+ && rm openmpi-${OMPI_VERSION}.tar.gz \
+ && cd openmpi-${OMPI_VERSION} \
+ && ./configure --enable-orterun-prefix-by-default --prefix=$OPEN_MPI_PATH --with-cuda=${CUDA_HOME} --with-slurm --with-pmix=/opt/pmix \
+ && make -j $(nproc) all \
+ && make install \
+ && ldconfig \
+ && cd / \
+ && rm -rf /tmp/openmpi \
+ && ompi_info --parsable --all | grep mpi_built_with_cuda_support:value \
+ # Verify pmix from /opt/pmix/
+ && ldd /opt/amazon/openmpi/lib/openmpi/mca_pmix_ext3x.so | grep '/opt/pmix/lib/libpmix.so.* ' > /opt/amazon/openmpi-pmix.txt
+####################################################################################################
+
+
+# NCCL EFA Plugin
+RUN mkdir -p /tmp && \
+    cd /tmp && \
+    curl -LO https://github.com/aws/aws-ofi-nccl/archive/refs/tags/v${AWS_OFI_NCCL_VERSION}.tar.gz && \
+    tar -xzf /tmp/v${AWS_OFI_NCCL_VERSION}.tar.gz && \
+    rm /tmp/v${AWS_OFI_NCCL_VERSION}.tar.gz && \
+    mv aws-ofi-nccl-${AWS_OFI_NCCL_VERSION} aws-ofi-nccl && \
+    cd /tmp/aws-ofi-nccl && \
+    ./autogen.sh && \
+    ./configure --prefix=/opt/amazon/efa \
+        --with-libfabric=/opt/amazon/efa \
+        --with-cuda=/usr/local/cuda \
+        --enable-platform-aws \
+        --with-mpi=/opt/amazon/openmpi && \
+    make -j$(nproc) install && \
+    rm -rf /tmp/aws-ofi/nccl
+
+# Do this to minimize the ld path env vars that users need to define when running this image.
+RUN echo "/usr/local/lib"      >> /etc/ld.so.conf.d/local.conf && \
+    echo "/opt/amazon/openmpi/lib" >> /etc/ld.so.conf.d/efa.conf && \
+    ldconfig
+
+ENV OMPI_MCA_pml=^cm,ucx            \
+    OMPI_MCA_btl=tcp,self           \
+    OMPI_MCA_btl_tcp_if_exclude=lo,docker0 \
+    OPAL_PREFIX=/opt/amazon/openmpi \
+    # https://discuss.pytorch.org/t/nccl-network-is-unreachable-connection-refused-when-initializing-ddp/137352
+    # https://github.com/pytorch/pytorch/issues/68893
+    NCCL_SOCKET_IFNAME=^docker,lo
+
+ENV LD_LIBRARY_PATH="/usr/local/lib:/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"
+
+# NCCL-tests: always good to include this as a diagnostic tool.
+RUN git clone https://github.com/NVIDIA/nccl-tests.git /opt/nccl-tests \
+    && cd /opt/nccl-tests \
+    && git checkout ${NCCL_TESTS_VERSION} \
+    && make MPI=1 \
+    MPI_HOME=/opt/amazon/openmpi \
+    CUDA_HOME=/usr/local/cuda \
+    NVCC_GENCODE="-gencode=arch=compute_90,code=sm_90 -gencode=arch=compute_80,code=sm_80"
+
+
+####################################################################################################
+# Custom packages. Disable as you like. NOTE: always check `pip list` what's been installed. For
+# example, the base container comes pre-installed with Transformer Engine, flash attention, triton
+# (https://github.com/openai/triton/), etc.
+####################################################################################################
+# Install the xformers dependency from source, because pip install either breaks or try to pull
+# its own pt + cuda.
+#
+# Pre-requisite: build node has enough memory to compile xformers. More info on the stanza.
+RUN export TORCH_CUDA_ARCH_LIST="8.0;9.0+PTX" && \
+    # On p4de.24xlarge:
+    # - MAX_JOBS=16 => 145GB memory
+    # - MAX_JOBS=32 => 241GB memory
+    # - MAX_JOBS=48 => 243GB memory, 542.5s
+    #
+    # NOTE: must export MAX_JOBS. For some reason, `MAX_JOBS=16 pip install ...` doesn't seem to
+    #       work to prevent OOM.
+    export MAX_JOBS=32 && \
+    export NVCC_PREPEND_FLAGS="-t 32" && \
+    pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
+
+RUN pip install transformers datasets
+
+WORKDIR "/fsx"

3.test_cases/XX.transformer-engine/1.train_llama.sbatch

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+#!/bin/bash
+
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: MIT-0
+
+#SBATCH --nodes=2 # number of nodes to use
+#SBATCH --job-name=LlamaFP8 # name of your job
+#SBATCH --exclusive # job has exclusive use of the resource, no sharing
+
+set -ex;
+
+###########################
+###### User Variables #####
+###########################
+
+GPUS_PER_NODE=8 # 4 for G5.12x, 8 for P4/P5
+
+###########################
+## Environment Variables ##
+###########################
+
+## Plenty of EFA level variables
+## Comment out for non-efa instances (G4d, P3)
+## For G5.12x, Comment out RDMA and Fork safe
+## For G4dn and other G5, comment out all
+export FI_EFA_USE_DEVICE_RDMA=1 # use for p4d
+export FI_EFA_FORK_SAFE=1
+export FI_LOG_LEVEL=1
+export FI_PROVIDER=efa
+export NCCL_DEBUG=INFO
+## Switching SYNC_MEMOPS to zero can boost throughput with FSDP
+## Disables CU_POINTER_ATTRIBUTE_SYNC_MEMOPS
+## Reduces memory synchronizations
+## https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__UNIFIED.html
+export FI_EFA_SET_CUDA_SYNC_MEMOPS=0
+
+# default variables for Enroot
+: "${IMAGE:=$(pwd)/transformer-engine.sqsh}"
+: "${DATA_PATH:=/fsx}"
+: "${FSX_MOUNT:=$(pwd):$DATA_PATH}"
+
+declare -a ARGS=(
+    --container-image $IMAGE
+    --container-mounts $FSX_MOUNT
+)
+
+###########################
+####### Torch Dist  #######
+###########################
+
+declare -a TORCHRUN_ARGS=(
+    --nproc_per_node=$GPUS_PER_NODE
+    --nnodes=$SLURM_JOB_NUM_NODES
+    --rdzv_id=$SLURM_JOB_ID
+    --rdzv_backend=c10d
+    --rdzv_endpoint=$(hostname)
+)
+
+export TORCHRUN=torchrun
+export TRAIN_SCRIPT=./train.py
+
+############################
+# Llama 2 Training Params ##
+############################
+
+declare -a TRAINING_ARGS=(
+    --max_context_width=4096
+    --num_key_value_heads=32 # 7b: 32 13b: 40 70b: 8
+    --intermediate_size=11008 # 7b: 11008 13b: 13824 70b: 28672
+    --hidden_width=4096 # 7b: 4096 13b: 5120 70b: 8192
+    --num_layers=32 # 7b: 32 13b: 40 70b: 80
+    --num_heads=32 # 7b: 32 13b: 40 70b: 64
+    --model_type=llama_v2
+    --tokenizer="hf-internal-testing/llama-tokenizer"
+    --checkpoint_freq=5000
+    --validation_freq=100
+    --max_steps=5000
+    --checkpoint_dir=./checkpoints
+    --dataset='c4'
+    --dataset_config_name='en'
+    --resume_from_checkpoint=./checkpoints
+    --train_batch_size=1
+    --val_batch_size=1
+    --sharding_strategy="full" # https://pytorch.org/docs/stable/fsdp.html
+    --offload_activations=1
+    --fp8=1
+)
+
+AUTO_RESUME=""
+if [ -d "/opt/sagemaker_cluster" ]; then
+    echo "Detected Hyperpod cluster.. enabling --auto-resume=1"
+    AUTO_RESUME="--auto-resume=1"
+fi
+
+srun ${AUTO_RESUME} -l "${ARGS[@]}" torchrun "${TORCHRUN_ARGS[@]}" $TRAIN_SCRIPT "${TRAINING_ARGS[@]}"