From 32fb5ae6231c3bd8246186677fddfd0e1c3cb7e7 Mon Sep 17 00:00:00 2001 From: Junpu Fan Date: Fri, 11 Oct 2024 14:28:18 -0700 Subject: [PATCH] Remove AWS Pytorch channel in examples (#453) * remove AWS Pytorch channel * removing reference from example 16 17 20 * update conda env for example 16 * adding llama2 name to script * add workaround for 17.SM-modelparallelv2 * fix conda install cmd * fix identifies issues in example 17 --- 3.test_cases/10.FSDP/0.create_conda_env.sh | 10 +---- ...h => 1.distributed-training-llama2.sbatch} | 6 +-- 3.test_cases/10.FSDP/README.md | 22 +++------ .../slurm/0.create-conda-env.sh | 4 +- 3.test_cases/17.SM-modelparallelv2/README.md | 10 ----- .../bin/aws-ofi-nccl-1.7.4-aws_0.tar.bz2 | Bin 0 -> 87372 bytes .../bin/hwloc-2.9.2-h2bc3f7f_0.tar.bz2 | Bin 0 -> 3155032 bytes .../17.SM-modelparallelv2/setup_conda_env.sh | 42 +++++++----------- 3.test_cases/20.FSDP-Mamba/0.create_conda.sh | 5 --- 9 files changed, 25 insertions(+), 74 deletions(-) rename 3.test_cases/10.FSDP/{1.distributed-training.sbatch => 1.distributed-training-llama2.sbatch} (93%) create mode 100644 3.test_cases/17.SM-modelparallelv2/bin/aws-ofi-nccl-1.7.4-aws_0.tar.bz2 create mode 100644 3.test_cases/17.SM-modelparallelv2/bin/hwloc-2.9.2-h2bc3f7f_0.tar.bz2 diff --git a/3.test_cases/10.FSDP/0.create_conda_env.sh b/3.test_cases/10.FSDP/0.create_conda_env.sh index 83433a3d..25496d12 100755 --- a/3.test_cases/10.FSDP/0.create_conda_env.sh +++ b/3.test_cases/10.FSDP/0.create_conda_env.sh @@ -14,15 +14,7 @@ conda create -y -p ./pt_fsdp python=3.11 source activate ./pt_fsdp/ -# Set true to install AWS Pytorch. see https://aws-pytorch-doc.com/ -use_aws_pytorch=true - -if $use_aws_pytorch; then - conda install -y pytorch=2.3.0 pytorch-cuda=12.1 aws-ofi-nccl torchvision torchaudio transformers datasets fsspec=2023.9.2 --strict-channel-priority --override-channels -c https://aws-ml-conda.s3.us-west-2.amazonaws.com -c nvidia -c conda-forge -else - conda install -y pytorch=2.4.1 torchvision torchaudio transformers datasets fsspec=2023.9.2 pytorch-cuda=12.1 -c pytorch -c nvidia - conda install -y aws-ofi-nccl=1.9.1 -c https://aws-ml-conda.s3.us-west-2.amazonaws.com -c conda-forge -fi +conda install -y pytorch=2.4.1 torchvision torchaudio transformers datasets fsspec=2023.9.2 pytorch-cuda=12.1 -c pytorch -c nvidia # Create checkpoint dir mkdir checkpoints diff --git a/3.test_cases/10.FSDP/1.distributed-training.sbatch b/3.test_cases/10.FSDP/1.distributed-training-llama2.sbatch similarity index 93% rename from 3.test_cases/10.FSDP/1.distributed-training.sbatch rename to 3.test_cases/10.FSDP/1.distributed-training-llama2.sbatch index bf350fa3..1992d526 100755 --- a/3.test_cases/10.FSDP/1.distributed-training.sbatch +++ b/3.test_cases/10.FSDP/1.distributed-training-llama2.sbatch @@ -20,12 +20,8 @@ GPUS_PER_NODE=8 # 4 for G5.12x, 8 for P4/P5 ########################### ## 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_LOG_LEVEL=warn export FI_PROVIDER=efa # export FI_EFA_USE_HUGE_PAGE=0 # Set to 0 when you see os.fork() causes OSError: Cannot allocate memory. Disabling huge page causes minor performance hit. export NCCL_DEBUG=INFO diff --git a/3.test_cases/10.FSDP/README.md b/3.test_cases/10.FSDP/README.md index 0c30554a..4712e6d3 100644 --- a/3.test_cases/10.FSDP/README.md +++ b/3.test_cases/10.FSDP/README.md @@ -36,24 +36,12 @@ If you'd like to instead use your own dataset, you can do so by [formatting it a ## 3. Launch Training -- The script to launch a Llama 2 Slurm batch training job can be found in `1.distributed_training.sbatch`. -- The script to launch a Mixtral training can be found in `2.distributed_training_mixtral.sbatch`. +- The script to launch a Llama 2 Slurm batch training job can be found in `1.distributed-training-llama2.sbatch`. +- The script to launch a Mixtral training can be found in `2.distributed-training-mixtral.sbatch`. - Th script to launch Mistral Mathstral training can be foudn in `3.distributed-training-mistral-mathstral.sbatch`. - You can adjust the number of training nodes by modifying `#SBATCH --nodes=4` to match the size of your cluster. -If you are using a non-RDMA enable instance, such as g5.12xlarge, comment out lines 21-22. These instances have EFA between nodes, but do not have the GPU direct RDMA access of P4d and P5 instances. - -``` -## Plenty of EFA level variables -## Comment out for non-efa instances (G5, G4d, P3) -# 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 -``` - -If you are using non-EFA enabled instances, such as G4dn, or single GPU g5 nodes, comment out all EFA environment variables on lines 21-25. +If you are using non-EFA enabled instances, such as G4dn, or single GPU g5 nodes, comment out all EFA environment variables on lines 24-25. Also, under `User Variables` make sure to adjust `GPUS_PER_NODE` to match the number of GPUs on your instance type (8 for P4d(e)/P5, 4 for G5.12xlarge, 1 for G5.xlarge). @@ -127,9 +115,9 @@ declare -a TRAINING_ARGS=( To launch your training for Llama 2, run ``` -sbatch 1.distributed_training.sbatch +sbatch 1.distributed-training-llama2.sbatch ``` -Similarly for Mixtral 8x7B and Mathstral, launch run `sbatch` with the `2.distributed_training_mixtral.sbatch` and the `3.distributed-training-mistral-mathstral.sbatch` files respectively. +Similarly for Mixtral 8x7B and Mathstral, launch run `sbatch` with the `2.distributed-training-mixtral.sbatch` and the `3.distributed-training-mistral-mathstral.sbatch` files respectively. You'll find a new file in the FSDP directory of the form `slurm-[job-number].out`. This will be continuously updated with your training logs. Don't be worried if you see a long stream of NCCL logs (we prefer to use `NCCL_DEBUG=INFO` for verbose logging). After about a minute, you should see your model training, with an output similar to below for Llama2 : diff --git a/3.test_cases/16.pytorch-cpu-ddp/slurm/0.create-conda-env.sh b/3.test_cases/16.pytorch-cpu-ddp/slurm/0.create-conda-env.sh index a25c4337..280990b1 100644 --- a/3.test_cases/16.pytorch-cpu-ddp/slurm/0.create-conda-env.sh +++ b/3.test_cases/16.pytorch-cpu-ddp/slurm/0.create-conda-env.sh @@ -10,10 +10,8 @@ chmod +x Miniconda3-latest-Linux-x86_64.sh source ./miniconda3/bin/activate -conda create -y -p ./pt_cpu python=3.10 --strict-channel-priority --override-channels -c https://aws-ml-conda.s3.us-west-2.amazonaws.com -c nvidia -c conda-forge +conda create -y -p ./pt_cpu python=3.10 pytorch=2.0.1 -c pytorch -c nvidia -c conda-forge source activate ./pt_cpu/ -conda install -y pytorch=2.0.1 --strict-channel-priority --override-channels -c https://aws-ml-conda.s3.us-west-2.amazonaws.com -c nvidia -c conda-forge - rm Miniconda3-latest-Linux-x86_64.sh* diff --git a/3.test_cases/17.SM-modelparallelv2/README.md b/3.test_cases/17.SM-modelparallelv2/README.md index d6b8951c..38fa42db 100644 --- a/3.test_cases/17.SM-modelparallelv2/README.md +++ b/3.test_cases/17.SM-modelparallelv2/README.md @@ -49,16 +49,6 @@ bash setup_conda_env.sh These scripts need to be put in a shared file system that can be accessed by all nodes, such as [FSx for Lustre](https://docs.aws.amazon.com/fsx/latest/LustreGuide/what-is.html). We also recommend setting all paths for input data and checkpoints as shared directories using FSx for Lustre. -### cuDNN Download for cuda11.8 and cuda12.1 -We recommend that you install cuDNN for your desired cuda version using from the [NVIDIA Developer page](https://developer.nvidia.com/cudnn). Click on the link and: -1. Make a developer account. -2. Click on "Download cuDNN Library". -3. Agree to the terms. -4. Download the Local Installer for Linux x86_64 (Tar) for cuda11 or cuda12 (we will use version 8.9.5 in the example going forward). -4. 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