From 44e448ea25d9fc0082e5db515fe71ab895356fe7 Mon Sep 17 00:00:00 2001 From: Ankur Srivastava <101727556+awsankur@users.noreply.github.com> Date: Mon, 3 Jun 2024 12:41:06 -0700 Subject: [PATCH] Nsight (#343) * Added NCCL Nsight files Signed-off-by: Ankur Srivastava * Added NCCL Nsight files Signed-off-by: Ankur Srivastava * Updateed nccl and readme and fsdp Signed-off-by: Ankur Srivastava * Updated NCCL Signed-off-by: Ankur Srivastava * Added almost everything Signed-off-by: Ankur Srivastava * Removed token Signed-off-by: Ankur Srivastava --------- Signed-off-by: Ankur Srivastava --- .../5.nsight/2.generate_recipes.sh | 20 ++ .../5.nsight/EKS/Dockerfile.llama2-efa | 198 +++++++++++ .../5.nsight/EKS/custom_values.yaml | 35 ++ .../5.nsight/EKS/fsdp.yaml | 77 +++++ .../EKS/fsdp_eks_report_screenshot.png | Bin 0 -> 243392 bytes .../5.nsight/EKS/install-injector | 4 + .../5.nsight/EKS/label-namespace | 2 + .../5.nsight/EKS/move_report | 10 + .../5.nsight/EKS/uniinstall-injector | 17 + .../5.nsight/README.md | 327 +++++++++++++++++- .../fsdp-llama2/1.distributed-training.sbatch | 77 +++++ .../fsdp-llama2/fsdp_rep_screenshot.png | Bin 0 -> 556695 bytes .../5.nsight/fsdp-llama2/nsys-slurm-exec | 9 + .../5.nsight/fsdp-llama2/train.py | 274 +++++++++++++++ .../5.nsight/nccl/0.nsight_nccl.sbatch | 48 +++ .../5.nsight/nccl/NCCL_Scatter_Perf.png | Bin 0 -> 390650 bytes .../nccl/all_reduce_csv_screenshot.png | Bin 0 -> 55199 bytes .../5.nsight/nccl/all_reduce_sum.png | Bin 0 -> 45095 bytes .../5.nsight/nccl/plot_nccl.py | 39 +++ .../5.nsight/nemotron/1.nemotron.sbatch | 41 +++ .../5.nsight/nemotron/nemo.Dockerfile | 110 ++++++ .../nemotron/nemotron-15B-P5-report.png | Bin 0 -> 477449 bytes 22 files changed, 1269 insertions(+), 19 deletions(-) create mode 100644 4.validation_and_observability/5.nsight/2.generate_recipes.sh create mode 100644 4.validation_and_observability/5.nsight/EKS/Dockerfile.llama2-efa create mode 100644 4.validation_and_observability/5.nsight/EKS/custom_values.yaml create mode 100644 4.validation_and_observability/5.nsight/EKS/fsdp.yaml create mode 100644 4.validation_and_observability/5.nsight/EKS/fsdp_eks_report_screenshot.png create mode 100644 4.validation_and_observability/5.nsight/EKS/install-injector create mode 100644 4.validation_and_observability/5.nsight/EKS/label-namespace create mode 100644 4.validation_and_observability/5.nsight/EKS/move_report create mode 100644 4.validation_and_observability/5.nsight/EKS/uniinstall-injector create mode 100644 4.validation_and_observability/5.nsight/fsdp-llama2/1.distributed-training.sbatch create mode 100644 4.validation_and_observability/5.nsight/fsdp-llama2/fsdp_rep_screenshot.png create mode 100644 4.validation_and_observability/5.nsight/fsdp-llama2/nsys-slurm-exec create mode 100644 4.validation_and_observability/5.nsight/fsdp-llama2/train.py create mode 100644 4.validation_and_observability/5.nsight/nccl/0.nsight_nccl.sbatch create mode 100644 4.validation_and_observability/5.nsight/nccl/NCCL_Scatter_Perf.png create mode 100644 4.validation_and_observability/5.nsight/nccl/all_reduce_csv_screenshot.png create mode 100644 4.validation_and_observability/5.nsight/nccl/all_reduce_sum.png create mode 100644 4.validation_and_observability/5.nsight/nccl/plot_nccl.py create mode 100644 4.validation_and_observability/5.nsight/nemotron/1.nemotron.sbatch create mode 100644 4.validation_and_observability/5.nsight/nemotron/nemo.Dockerfile create mode 100644 4.validation_and_observability/5.nsight/nemotron/nemotron-15B-P5-report.png diff --git a/4.validation_and_observability/5.nsight/2.generate_recipes.sh b/4.validation_and_observability/5.nsight/2.generate_recipes.sh new file mode 100644 index 00000000..29ec86b7 --- /dev/null +++ b/4.validation_and_observability/5.nsight/2.generate_recipes.sh @@ -0,0 +1,20 @@ + +/fsx/nsight-efa/target-linux-x64/nsys recipe cuda_api_sum --input ${NSIGHT_REPORT_NAME}.nsys-rep + +/fsx/nsight-efa/target-linux-x64/nsys recipe cuda_api_sync --input ${NSIGHT_REPORT_NAME}.nsys-rep + +/fsx/nsight-efa/target-linux-x64/nsys recipe cuda_gpu_kern_pace --input ${NSIGHT_REPORT_NAME}.nsys-rep --name ncclDevKernel_ReduceScatter_Sum_f32_RING_LL + +/fsx/nsight-efa/target-linux-x64/nsys recipe cuda_gpu_kern_pace --input ${NSIGHT_REPORT_NAME}.nsys-rep --name ncclDevKernel_AllGather_RING_LL + +/fsx/nsight-efa/target-linux-x64/nsys recipe cuda_gpu_kern_sum --input ${NSIGHT_REPORT_NAME}.nsys-rep + +/fsx/nsight-efa/target-linux-x64/nsys recipe cuda_gpu_mem_size_sum --input ${NSIGHT_REPORT_NAME}.nsys-rep + +/fsx/nsight-efa/target-linux-x64/nsys recipe cuda_gpu_mem_time_sum --input ${NSIGHT_REPORT_NAME}.nsys-rep + +/fsx/nsight-efa/target-linux-x64/nsys recipe cuda_gpu_time_util_map --input ${NSIGHT_REPORT_NAME}.nsys-rep + +/fsx/nsight-efa/target-linux-x64/nsys recipe nccl_sum --input ${NSIGHT_REPORT_NAME}.nsys-rep + +/fsx/nsight-efa/target-linux-x64/nsys recipe nccl_gpu_time_util_map --input ${NSIGHT_REPORT_NAME}.nsys-rep \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/EKS/Dockerfile.llama2-efa b/4.validation_and_observability/5.nsight/EKS/Dockerfile.llama2-efa new file mode 100644 index 00000000..2dabed56 --- /dev/null +++ b/4.validation_and_observability/5.nsight/EKS/Dockerfile.llama2-efa @@ -0,0 +1,198 @@ +apiVersion: "kubeflow.org/v1" +kind: PyTorchJob +metadata: + name: fsdp +spec: + elasticPolicy: + rdzvBackend: etcd + rdzvHost: etcd + rdzvPort: 2379 + minReplicas: 1 + maxReplicas: 96 + maxRestarts: 100 + #metrics: + # - type: Resource + # resource: + # name: cpu + # target: + # type: Utilization + # averageUtilization: 80 + pytorchReplicaSpecs: + Worker: + replicas: 2 + restartPolicy: OnFailure + template: + metadata: + labels: + app: fsdp + nvidia-devtools-sidecar-injector: enabled + spec: + volumes: + - name: shmem + #emptyDir: + # medium: Memory + hostPath: + path: /dev/shm + #nodeSelector: + # node.kubernetes.io/instance-type: "p5.48xlarge" + containers: + - name: pytorch + image: 159553542841.dkr.ecr.us-west-2.amazonaws.com/fsdp:llama2-efa-main-02-13 + imagePullPolicy: Always + resources: + requests: + nvidia.com/gpu: + vpc.amazonaws.com/efa: 4 + limits: + nvidia.com/gpu: + vpc.amazonaws.com/efa: 4 + env: + # for P5 FI_* should be commented out + #- name: LOGLEVEL + # value: "DEBUG" + - name: FI_PROVIDER + value: efa + - name: FI_EFA_USE_DEVICE_RDMA + value: "1" + - name: FI_EFA_FORK_SAFE + value: "1" + - name: FI_LOG_LEVEL + value: "1" + - name: FI_EFA_ENABLE_SHM_TRANSFER + value: "1" + #- name: NCCL_DEBUG + # value: "INFO" + - name: NCCL_ASYNC_ERROR_HANDLING + value: "1" + #- name: NCCL_IGNORE_DISABLED_P2P + # value: "1" + - name: HF_TOKEN + value: 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 + command: + - bash + - -c + - "torchrun --nproc_per_node=8 --nnodes=2 examples/finetuning.py --num_epochs=1 --batch_size_training=3 --enable_fsdp --pure_bf16 --model_name meta-llama/Llama-2-7b-hf --output_dir ." + volumeMounts: + - name: shmem + mountPath: /dev/shm +root@cb9511473ccc:/eks/deployment/distributed-training/pytorch/pytorchjob/fsdp# cat Dockerfile.llama2-efa +FROM nvidia/cuda:12.2.2-devel-ubuntu22.04 + +ARG EFA_INSTALLER_VERSION=1.29.1 +ARG AWS_OFI_NCCL_VERSION=v1.7.3-aws +ARG NCCL_TESTS_VERSION=master +ARG NCCL_VERSION=2.18.5 + +RUN apt-get update -y +RUN apt-get remove -y --allow-change-held-packages \ + libmlx5-1 ibverbs-utils libibverbs-dev libibverbs1 libnccl2 libnccl-dev + +RUN rm -rf /opt/hpcx \ + && rm -rf /usr/local/mpi \ + && rm -f /etc/ld.so.conf.d/hpcx.conf \ + && 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 \ + python3-distutils \ + cmake \ + apt-utils \ + devscripts \ + debhelper \ + libsubunit-dev \ + check \ + pkg-config + +RUN mkdir -p /var/run/sshd +RUN sed -i 's/[ #]\(.*StrictHostKeyChecking \).*/ \1no/g' /etc/ssh/ssh_config && \ + echo " UserKnownHostsFile /dev/null" >> /etc/ssh/ssh_config && \ + sed -i 's/#\(StrictModes \).*/\1no/g' /etc/ssh/sshd_config +ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:/opt/amazon/openmpi/lib:/opt/nccl/build/lib:/opt/amazon/efa/lib:/opt/aws-ofi-nccl/install/lib:/usr/local/lib:$LD_LIBRARY_PATH +ENV PATH /opt/amazon/openmpi/bin/:/opt/amazon/efa/bin:/usr/bin:/usr/local/bin:$PATH + +RUN curl https://bootstrap.pypa.io/get-pip.py -o /tmp/get-pip.py \ + && python3 /tmp/get-pip.py \ + && pip3 install awscli pynvml + +################################################# +## Install NVIDIA GDRCopy +#RUN git clone https://github.com/NVIDIA/gdrcopy.git /opt/gdrcopy \ +# && cd /opt/gdrcopy \ +# && make lib_install install \ +# && cd /opt/gdrcopy/tests \ +# && make \ +# && mv copylat copybw sanity apiperf /usr/bin/ + +################################################# +## Install EFA installer +RUN cd $HOME \ + && curl -O https://efa-installer.amazonaws.com/aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz \ + && tar -xf $HOME/aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz \ + && cd aws-efa-installer \ + && ./efa_installer.sh -y -g -d --skip-kmod --skip-limit-conf --no-verify \ + && rm -rf $HOME/aws-efa-installer + +################################################### +## Install NCCL +RUN git clone https://github.com/NVIDIA/nccl -b v${NCCL_VERSION}-1 /opt/nccl \ + && cd /opt/nccl \ + && make -j $(nproc) src.build CUDA_HOME=/usr/local/cuda \ + NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_90,code=sm_90" + +################################################### +## Install AWS-OFI-NCCL plugin +RUN apt-get install libtool autoconf cmake nasm unzip pigz parallel nfs-common build-essential hwloc libhwloc-dev libjemalloc2 libnuma-dev numactl libjemalloc-dev preload htop iftop liblapack-dev libgfortran5 ipcalc wget curl devscripts debhelper check libsubunit-dev fakeroot pkg-config dkms -y +RUN export OPAL_PREFIX="" \ + && git clone https://github.com/aws/aws-ofi-nccl.git /opt/aws-ofi-nccl \ + && cd /opt/aws-ofi-nccl \ + && git checkout ${AWS_OFI_NCCL_VERSION} \ + && ./autogen.sh \ + && ./configure --prefix=/opt/aws-ofi-nccl/install \ + --with-libfabric=/opt/amazon/efa/ \ + --with-cuda=/usr/local/cuda \ + --with-nccl=/opt/nccl/build \ + --with-mpi=/opt/amazon/openmpi/ \ + && make -j $(nproc) && make install +################################################### +## Install fsdp + +RUN mkdir -p /workspace/ + +WORKDIR /workspace + +#RUN git clone -b flop_counter https://github.com/facebookresearch/llama-recipes.git +#RUN git clone -b flop_counter_gc https://github.com/facebookresearch/llama-recipes.git +RUN git clone https://github.com/facebookresearch/llama-recipes.git + +WORKDIR /workspace/llama-recipes + +RUN pip3 install -U pip setuptools + +RUN pip3 install fsspec==2023.1.0 +RUN pip3 install huggingface_hub==0.17.0 +RUN pip3 install -r requirements.txt + +RUN pip3 install -e . + +RUN pip3 install tabulate + +RUN pip3 install protobuf + +RUN pip3 install python-etcd + +#RUN pip3 uninstall -y torch +#RUN pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121 + +ENV PYTHONPATH="${PYTHONPATH}:/workspace/llama-recipes/src" \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/EKS/custom_values.yaml b/4.validation_and_observability/5.nsight/EKS/custom_values.yaml new file mode 100644 index 00000000..111f1eff --- /dev/null +++ b/4.validation_and_observability/5.nsight/EKS/custom_values.yaml @@ -0,0 +1,35 @@ +# If we dont specify the Nsight image, 2024.2 version is used by default. +# Will use 2024.4 version which is planned to be released by 5/24/2024 +devtoolBinariesImage: + image: ${REGISTRY}.dkr.ecr.${REGION}.amazonaws.com/nsight-systems-cli:2024.4.1-ubuntu22.04 + imagePullPolicy: Always + +# Assuming EKS cluster has a FSx for Lustre filesystem mounted on it. Nsight reports will be saved in /fsx_shared +profile: + volumes: + [ + { + "name": "nsys-output-volume", + "persistentVolumeClaim": { "claimName": "fsx-pvc" } + } + ] + volumeMounts: + [ + { + "name": "nsys-output-volume", + "mountPath": "/fsx_shared" + } + ] + + # CLI options: https://docs.nvidia.com/nsight-systems/UserGuide/index.html#cli-command-switches + # delay and duration values in secs + + # Use %{} to include environment variables in the Nsight report filename + + # The arguments for the Nsight Systems. The placeholders will be replaced with the actual values. + devtoolArgs: "profile --force-overwrite true --trace nvtx,cuda --delay 150 --duration 60 \ + -o /fsx_shared/fsdp/auto_{PROCESS_NAME}_%{POD_FULLNAME}_%{CONTAINER_NAME}_{TIMESTAMP}_{UID}.nsys-rep" + + injectionMatch: "^/usr/bin/python3 /usr/local/bin/torchrun.*$" + #injectionMatch: "^.*torchrun.*$" + diff --git a/4.validation_and_observability/5.nsight/EKS/fsdp.yaml b/4.validation_and_observability/5.nsight/EKS/fsdp.yaml new file mode 100644 index 00000000..6ce18c58 --- /dev/null +++ b/4.validation_and_observability/5.nsight/EKS/fsdp.yaml @@ -0,0 +1,77 @@ +apiVersion: "kubeflow.org/v1" +kind: PyTorchJob +metadata: + name: fsdp +spec: + elasticPolicy: + rdzvBackend: etcd + rdzvHost: etcd + rdzvPort: 2379 + minReplicas: 1 + maxReplicas: 96 + maxRestarts: 100 + #metrics: + # - type: Resource + # resource: + # name: cpu + # target: + # type: Utilization + # averageUtilization: 80 + pytorchReplicaSpecs: + Worker: + replicas: 2 + restartPolicy: OnFailure + template: + metadata: + labels: + app: fsdp + nvidia-devtools-sidecar-injector: enabled + spec: + volumes: + - name: shmem + #emptyDir: + # medium: Memory + hostPath: + path: /dev/shm + #nodeSelector: + # node.kubernetes.io/instance-type: "p5.48xlarge" + containers: + - name: pytorch + image: 159553542841.dkr.ecr.us-west-2.amazonaws.com/fsdp:llama2-efa-main-02-13 + imagePullPolicy: Always + resources: + requests: + nvidia.com/gpu: + vpc.amazonaws.com/efa: 4 + limits: + nvidia.com/gpu: + vpc.amazonaws.com/efa: 4 + env: + # for P5 FI_* should be commented out + #- name: LOGLEVEL + # value: "DEBUG" + - name: FI_PROVIDER + value: efa + - name: FI_EFA_USE_DEVICE_RDMA + value: "1" + - name: FI_EFA_FORK_SAFE + value: "1" + - name: FI_LOG_LEVEL + value: "1" + - name: FI_EFA_ENABLE_SHM_TRANSFER + value: "1" + #- name: NCCL_DEBUG + # value: "INFO" + - name: NCCL_ASYNC_ERROR_HANDLING + value: "1" + #- name: NCCL_IGNORE_DISABLED_P2P + # value: "1" + - name: HF_TOKEN + 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a/4.validation_and_observability/5.nsight/EKS/install-injector b/4.validation_and_observability/5.nsight/EKS/install-injector new file mode 100644 index 00000000..8fbd0f04 --- /dev/null +++ b/4.validation_and_observability/5.nsight/EKS/install-injector @@ -0,0 +1,4 @@ +#!/bin/bash -x + +helm install -f custom_values.yaml \ + devtools-sidecar-injector https://helm.ngc.nvidia.com/nvidia/devtools/charts/devtools-sidecar-injector-1.0.0.tgz \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/EKS/label-namespace b/4.validation_and_observability/5.nsight/EKS/label-namespace new file mode 100644 index 00000000..f2ce2a9b --- /dev/null +++ b/4.validation_and_observability/5.nsight/EKS/label-namespace @@ -0,0 +1,2 @@ +#!/bin/bash -x +kubectl label namespaces ${example-ns} nvidia-devtools-sidecar-injector=enabled --overwrite=true \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/EKS/move_report b/4.validation_and_observability/5.nsight/EKS/move_report new file mode 100644 index 00000000..4906fafd --- /dev/null +++ b/4.validation_and_observability/5.nsight/EKS/move_report @@ -0,0 +1,10 @@ +#!/bin/bash -x + +# kubectl cp -n : + +FILE=auto_python3_default_fsdp-worker-1_pytorch_1715996702335_5a061871.nsys-rep + +kubectl cp fsx-share-test:fsx_shared/fsdp/$FILE /eks/deployment/distributed-training/pytorch/pytorchjob/fsdp/$FILE + +aws s3 cp $FILE s3://${S3_BUCKET} + diff --git a/4.validation_and_observability/5.nsight/EKS/uniinstall-injector b/4.validation_and_observability/5.nsight/EKS/uniinstall-injector new file mode 100644 index 00000000..293d4b0d --- /dev/null +++ b/4.validation_and_observability/5.nsight/EKS/uniinstall-injector @@ -0,0 +1,17 @@ +#!/bin/bash -x + + +helm uninstall devtools-sidecar-injector + +kubectl delete namespace nvidia-devtools-sidecar-injector + +kubectl delete mutatingwebhookconfigurations sidecar-injector-webhook +kubectl delete mutatingwebhookconfiguration nvidia-devtools-sidecar-injector-webhook + +kubectl delete cm -n example-ns nvidia-devtools-sidecar-injector +kubectl delete cm -n example-ns nvidia-devtools-sidecar-injector-custom + +kubectl delete cm nvidia-devtools-sidecar-injector +kubectl delete cm nvidia-devtools-sidecar-injector-custom + +#kubectl get all --all-namespaces -l nvidia-devtools-sidecar-injector=enabled -o custom-columns=:.metadata.name,NS:.metadata.namespace,KIND:.kind --no-headers | while read name namespace > \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/README.md b/4.validation_and_observability/5.nsight/README.md index 202ad606..61afa81c 100644 --- a/4.validation_and_observability/5.nsight/README.md +++ b/4.validation_and_observability/5.nsight/README.md @@ -1,37 +1,326 @@ -# Profile Distributed Training Applications with Nsight +# Profile Distributed Training Applications with Nsight -[Nsight Systems](https://developer.nvidia.com/nsight-systems) is a statistical sampling profiler with tracing features. +[Nsight Systems](https://developer.nvidia.com/nsight-systems) is a system-wide performance analysis tool designed to profile and visualize multi-node CPU and GPU workloads such as distributed training and inference to identify the largest opportunities to optimize, and tune to scale efficiently across the cluster. It also enables researchers to add their own markers into their code to surface application-level metrics into the profiler and gain further observability. + +We will show how to profile and analyze: + +1. [NCCL Tests](https://github.com/aws-samples/awsome-distributed-training/tree/main/micro-benchmarks/nccl-tests/slurm) +2. [Distributed training run with NeMo](https://github.com/aws-samples/awsome-distributed-training/tree/main/3.test_cases/2.nemo-launcher) +3. [Distributed training run with FSDP](https://github.com/aws-samples/awsome-distributed-training/tree/main/3.test_cases/10.FSDP) +4. Setup Nsight on an EKS cluster # 0. Prerequisities -1. A slurm or Kubernetes cluster is created -2. The compute nodes have nsight installed -# 1. Installation -We have a Pcluster created with the Base Ubuntu AMI that has `nsight-systems-2023.2.3` pre-installed. We will use the `nsys` binary in `/usr/local/cuda/bin`. +1. A cluster created with P4de or P5 nodes with AWS ParallelCluster or EKS +2. Before profiling the above workloads, make sure you can run them on your cluster. +3. For EKS, we will be using a 2 node P4de cluster with EFA enabled and FSx for Lustre mounted on the cluster + +# 1. Export Environment Variables + +Export the following variables to setup the profiling: + +```bash +# Nsight Version +export Nsight_version=2024.3.1 +export Nsight_cli_installer=NsightSystems-linux-cli-public-2024.3.1.75-3419530.deb +export Nsight_download_url=https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2024_3/NsightSystems-linux-cli-public-2024.3.1.75-3419530.deb +export Nsight_Path=/fsx/nsight-efa +``` + +# 2. Installation + +If you created the cluster with DLAMI or are using the default ParallelCluster base image, Nsight comes pre-installed. You can check the version in the `/usr/local/cuda/` folder you should see `nsight-systems-202x.x.x` folder. ParallelCluster 3.8.0 has the version 2023.2 version pre-installed. + +To get the latest Nsight 2024.3 version from [here](https://developer.nvidia.com/nsight-systems/get-started). If you are installing it on a remote cluster, then the CLI version would suffice. To install it on a Ubuntu based OS node: + +```bash +# Download Nsight CLI +wget ${Nsight_download_url} + +# Install +sudo dpkg -i ${Nsight_cli_installer} + +# This would place the nsys binay at /opt/nvidia/nsight-systems-cli/2024.3.1/target-linux-x64/nsys +# Move to FSx filesystem +cp -r /opt/nvidia/nsight-systems-cli/${Nsight_version}/* ${Nsight_Path} +``` + +The `nsight-efa`folder will have the necessary dependencies for the `host` which is the head node in a Slurm cluster from which the user works and controls the profiling session and `target` which refers to the GPU on which profiling happens. This latest version also has the `nic_sampler` in `/nsight-efa/target-linux-x64/plugins/` which collects the EFA metrics. + +Note, the 2024.4 versionof Nsight will be released by 5/24/2024. + +# 3. Profiling NCCL tests +In this section we will show how to generate Nsight reports for NCCL tests. Follow the instructions [here](https://github.com/aws-samples/awsome-distributed-training/tree/main/4.validation_and_observability/0.nccl-tests) to setup NCCL tests and generate the Enroot image `nccl.sqsh`. The `0.nsight_nccl.sbatch` script shows an example on how to profile the NCCL run with Nsight and collect EFA metrics. Key differences between `0.nsight_nccl.sbatch` and [this](https://github.com/aws-samples/awsome-distributed-training/blob/main/4.validation_and_observability/0.nccl-tests/1.nccl-tests.sbatch) are: + +1. `/fsx` needs to be mounted to the container as this is where our Nsight binaries are located. +2. The `0.nsight_nccl.sbatch` script references the executable `nsys-slurm-exec` which is given below and should exist in `/fsx` + +```bash +#! /bin/bash -x + +NSYS_EXTRAS="" +if [ "$SLURM_LOCALID" == "0" ]; then +NSYS_EXTRAS="--enable efa_metrics" +fi + +/fsx/nsight-efa/target-linux-x64/nsys profile $NSYS_EXTRAS --sample none --delay \ + --force-overwrite true --output /report__job%q{SLURM_JOB_ID}_rank%q{SLURM_PROCID}_on_%q{HOSTNAME}.nsys-rep \ + "$@" +``` +The above executable needs the following: + +```bash +1. DELAY-PERIOD: Collection start delay in seconds. Typically the multi-node workload takes a few seconds before collection of relevant metrics start. Typically for distributed training applications delaying by ~30sec avoids having empty gaps in the timeline view of the Nsight report. For the NCCL test a delay of less than 5 seconds works. You can also specify --duration in seconds to collect metrics. + +2. PATH-TO-SAVE-REPORT: One report is generated per GPU. Provide a path to save all reports. +3. REPORT-NAME-TAG: Unique name tag to group all reports. Use %q{} to include environment variables in report names. +``` +Here, we are running the Nsight profile with 2 p4de nodes where each node has 4 EFA devices and 8 GPUs. The `nic sampler` metrics from all 4 EFA devices show up in every report so it is okay to collect these metrics only for 1 rank. + +Below is a screenshot of the generated Nsight report: + +


+
+ +Here there are the following things to note: + +• The RDMA read bytes per second shown in green are from the EFA NIC samplers. You can see there are 4 `rdma*` rows in the report, one corresponding to each of the EFA devices one 1 node. For a P5.48xlarge node, you will see 32 rows. +• This report is generated for the [Scatter Performance NCCL test](https://github.com/NVIDIA/nccl-tests/blob/2cbb968101e2bfc7d3a7f0f1826c0189355de6fe/src/scatter.cu#L34), which essentially calls the ncclSendRecv kernels again and again which is why ncclDevKernel_SendRecv takes 99.3% utilization among all kernels. +• You can right click on any row to see the meta-data over time in the Events View which shows start times, durations and other meta-data for each kernel + +> [!TIP] +> The *.qdstrm files are temporarily generated first using the nsys binaries in `.../target-linux-x64` while the `*.nsys-rep` report file is generated using the `/host-linux-x64/QdstrmImporter` binary. If for some reason, only `*.qdstrm` files are generated, use the above importer like below to generate a `*.nsys-rep report` +```bash +/host-linux-x64/QdstrmImporter –input-file .qdstrm +``` + + +## 3.1 NCCL All Reduce Test + +Following the steps above, you can generate a similar result for NCCL All Reduce Test also see NCCL test output in the logs. Here we will visualize the spread in NCCL All Reduce communication for 1GB and 2GB message sizes. To do so you can: +1. Run NCCL test and generate report. Save the result for 1GB and 2GB message sizes. +2. Right click on `all_reduce_perf > NCCL` row to show in Events View. This Events View shows NCCL Kernel API calls on the CPU. You can see the NCCL Message Size for each call. Note row numbers where NCCL Message Sizes change. +3. Right click on `ncclDevKernel_AllReduce_Sum_f32_TREE_LL(ncclDevComm *, unsigned long, ncclWork *)` row and show in Events View. This Events View shows NCCL Kernel calls executed on the GPU, it start time and duration. Copy paste the entire table in a csv. +4. You should see 1-on-1 correlation between 3 and 4. Meaning for each NCCL call on the CPU there is a call executed on the GPU. Or in other words, the number of rows in Events View from 3 and 4 should exactly be the same. +5. Add NCCL Message Sizes from Step 3 to csv from Step 4. Save the csv as `all_reduce.csv` which should look like below: + +

+
+ +You can generate the plot below using the python script `/nccl/plot_nccl.py` + +

+
+ +# 4. Multi-node training with Slurm and Pyxis + +In this section, we will show how to generate Nsight reports for a distributed training run with containers on Slurm. We will use the [NeMo launcher](https://github.com/NVIDIA/NeMo-Framework-Launcher/tree/main) to train the [Nemotron-15b](https://github.com/NVIDIA/NeMo-Framework-Launcher/blob/main/launcher_scripts/conf/training/nemotron/nemotron_15b.yaml) model on mock data. To setup NeMo launcher on Parallelcluster please refer to this [README](https://github.com/aws-samples/awsome-distributed-training/tree/main/3.test_cases/2.nemo-launcher). You can specify [nsys_profile.enabled:True](https://github.com/NVIDIA/NeMo-Framework-Launcher/blob/main/launcher_scripts/conf/training/nemotron/nemotron_15b.yaml#L162) in the model config and launch the training run. + +You can either generate the Nsight profile either by turning on the nsys_profile flag or if you want to generate the nsys report with a different Nsight version, then you can use the run script `/nemotron/1.nemotron.sbatch` + + +Below is a screenshot of the generated Nsight report: + +

+
+ +# 5. Working with the report + +1. The Nsight Systems GUI offers to view the following. You can see these options by clicking on the Timeline View menu button. +a. Output and error logfiles from the training run +b. Analysis summary that gives a summary of the profiling session. +c. Timeline view of the report +d. Diagnostics summary view +2. You can right click and pin any row at the top. This helps in analyzing multiple rows simultaneously. +3. You can view start and execution times of any kernel by viewing them in the Events view. +4. From the Events View, you can zoom to that specific kernel event by right clicking. This provides an easy way to look into kernel events preceding and following a specific kernel even if their durations are in nanoseconds. +5. You can export the report in different formats such as sqllite and others as well for custom analysis. -# 2. Multi-node training with Slurm and Pyxis +# 6. Nsight Recipes -We will use the [BioNemo](https://github.com/aws-samples/awsome-distributed-training/tree/main/3.test_cases/14.bionemo) test case. +Once the report is generated, we can generate [recipes](https://docs.nvidia.com/nsight-systems/UserGuide/index.html#available-multi-report-recipes) to analyze the data in the report. We provide the script ` 2.generate_recipes.sh` which will generate multiple recipes for the report and upload to S3. Each recipe run will summarize the relevant data from the report and provide python scripts and jupyter notebooks to analyze the data. -Follow the BioNemo use case to make sure you can run the example. Then to profile the training run, execute +Next, we will show what kind of analysis can be generated from the recipes. + +To install requirements to generate recipes: ```bash -sbatch 0.nsys_bionemo.slurm +pip3 install -r ${Nsight_Path}/target-linux-x64/python/packages/nsys_recipe/requirements/common.txt +pip3 install -r ${Nsight_Path}/target-linux-x64/python/packages/nsys_recipe/requirements/dask.txt ``` +With Nsight 2024.3, the following recipes are available: + +```bash +The following built-in recipes are available: -You can modify the srun command: + cuda_api_sum -- CUDA API Summary + cuda_api_sync -- CUDA Synchronization APIs + cuda_gpu_kern_pace -- CUDA GPU Kernel Pacing + cuda_gpu_kern_sum -- CUDA GPU Kernel Summary + cuda_gpu_mem_size_sum -- CUDA GPU MemOps Summary (by Size) + cuda_gpu_mem_time_sum -- CUDA GPU MemOps Summary (by Time) + cuda_gpu_time_util_map -- CUDA GPU Time Utilization Heatmap + cuda_memcpy_async -- CUDA Async Memcpy with Pageable Memory + cuda_memcpy_sync -- CUDA Synchronous Memcpy + cuda_memset_sync -- CUDA Synchronous Memset + diff -- Statistics Diff + dx12_mem_ops -- DX12 Memory Operations + gpu_gaps -- GPU Gaps + gpu_metric_util_map -- GPU Metric Utilization Heatmap + gpu_time_util -- GPU Time Utilization + mpi_gpu_time_util_map -- MPI and GPU Time Utilization Heatmap + mpi_sum -- MPI Summary + nccl_gpu_overlap_trace -- NCCL GPU Overlap Trace + nccl_gpu_proj_sum -- NCCL GPU Projection Summary + nccl_gpu_time_util_map -- NCCL GPU Time Utilization Heatmap + nccl_sum -- NCCL Summary + network_traffic_map -- Network Devices Traffic Heatmap + nvtx_gpu_proj_pace -- NVTX GPU Projection Pacing + nvtx_gpu_proj_sum -- NVTX GPU Projection Summary + nvtx_gpu_proj_trace -- NVTX GPU Projection Trace + nvtx_pace -- NVTX Pacing + nvtx_sum -- NVTX Range Summary + osrt_sum -- OS Runtime Summary + ucx_gpu_time_util_map -- UCX and GPU Time Utilization Heatmap +``` + +Please see the `2.generate_recipes.sh` to generate multiple recipes from a given report as below: ```bash -srun -l "${ARGS[@]}" /usr/local/cuda/bin/nsys profile --output /fsx/nsys_profiles/ --stats true -- +# Do not include nsys-rep extension +export NSIGHT_REPORT_NAME= +./2.generate_recipes.sh ``` -# 3. Sample stats +# 7. Distributed training run with FSDP + +Next we will show how to profile the [10.FSDP](https://github.com/aws-samples/awsome-distributed-training/tree/main/3.test_cases/10.FSDP) test case. We will show explicitly how to generate a profile for specific training steps rather than providing `--delay` and `--duration` parameters. To this end, we provide the relevant files in the `fsdp-llama2` folder. To profile speciific training steps: + +1. Add `nsys_start_step` and `nsys_end_step` as input arguments to your train.py +2. Add the following in the training loop to start collecting data from Cuda and OSRT traces: +```python +if batch_idx == args.nsys_start_step and global_rank == 0: + logger.info("====== Start nsys profiling ======") + torch.cuda.cudart().cudaProfilerStart() +``` +3. Add to stop collection: +```python +if batch_idx == args.nsys_end_step and global_rank == 0: + logger.info("====== Stop nsys profiling ======") + torch.cuda.cudart().cudaProfilerStop() +``` +4. Add `--capture-range=cudaProfilerApi --capture-range-end=stop` to the `nsys profile ...` command. + +Below is a screenshot of the generated Nsight report: + +

+
+ +# 8. Nsight on EKS + +We will use the [Nvidia Devtools Sidecar Injector](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/devtools/helm-charts/devtools-sidecar-injector) to profile containerized applications. + +Pull the Nsight docker image - This step will not be needed once the 2024.4 version is released. + +```bash +docker pull nvcr.io/nvstaging/devtools/nsight-systems-cli:2024.4.1-ubuntu22.04 + +# Push image to ECR +``` + +## 8.1 Make changes to the `custom_values.yaml` + +```bash +# If we dont specify the Nsight image, 2024.2 version is used by default. +# Will use 2024.4 version which is planned to be released by 5/24/2024 +devtoolBinariesImage: + image: ${REGISTRY}.dkr.ecr.${REGION}.amazonaws.com/nsight-systems-cli:2024.4.1-ubuntu22.04 + imagePullPolicy: Always + +# Assuming EKS cluster has a FSx for Lustre filesystem mounted on it. Nsight reports will be saved in /fsx_shared +profile: + volumes: + [ + { + "name": "nsys-output-volume", + "persistentVolumeClaim": { "claimName": "fsx-pvc" } + } + ] + volumeMounts: + [ + { + "name": "nsys-output-volume", + "mountPath": "/fsx_shared" + } + ] + + # CLI options: https://docs.nvidia.com/nsight-systems/UserGuide/index.html#cli-command-switches + # delay and duration values in secs + + # Use %{} to include environment variables in the Nsight report filename + + # The arguments for the Nsight Systems. The placeholders will be replaced with the actual values. + devtoolArgs: "profile --force-overwrite true --trace nvtx,cuda --delay 150 --duration 60 \ + -o /fsx_shared/fsdp/auto_{PROCESS_NAME}_%{POD_FULLNAME}_%{CONTAINER_NAME}_{TIMESTAMP}_{UID}.nsys-rep" + + injectionMatch: "^/usr/bin/python3 /usr/local/bin/torchrun.*$" + #injectionMatch: "^.*torchrun.*$" +``` + +## 8.2 Install injector + +Install helm chart for the sidecar injector as below: +```bash +helm install -f custom_values.yaml \ + devtools-sidecar-injector https://helm.ngc.nvidia.com/nvidia/devtools/charts/devtools-sidecar-injector-1.0.0.tgz +``` + +## 8.3 Add label to training job manifest + +Add the following label: + +```bash +pytorchReplicaSpecs: + Worker: + replicas: 2 + restartPolicy: OnFailure + template: + metadata: + labels: + app: fsdp + nvidia-devtools-sidecar-injector: enabled +``` + +## 8.4 Run Training job + +Run the training job as: + +```bash +kubectl apply -f fsdp.yaml +``` +The report will get saved to `/fsx_shared` + +Below is a screenshot of the generated Nsight report: + +

+
+ +## 8.5 Uninstall injector + +To uninstall injector: + +```bash +helm uninstall devtools-sidecar-injector + +kubectl delete namespace nvidia-devtools-sidecar-injector + +kubectl delete mutatingwebhookconfigurations sidecar-injector-webhook +kubectl delete mutatingwebhookconfiguration nvidia-devtools-sidecar-injector-webhook -Example slurm output file provided: `slurm-esm1nv-train-102.out` +kubectl delete cm -n example-ns nvidia-devtools-sidecar-injector +kubectl delete cm -n example-ns nvidia-devtools-sidecar-injector-custom -1. 'cuda_api_sum' stats report -2. 'cuda_gpu_mem_time_sum' stats report -3. 'osrt_sum' stats report -4. 'cuda_gpu_kern_sum' stats report -5. 'nvtx_sum' stats report \ No newline at end of file +kubectl delete cm nvidia-devtools-sidecar-injector +kubectl delete cm nvidia-devtools-sidecar-injector-custom +``` \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/fsdp-llama2/1.distributed-training.sbatch b/4.validation_and_observability/5.nsight/fsdp-llama2/1.distributed-training.sbatch new file mode 100644 index 00000000..78ff85ab --- /dev/null +++ b/4.validation_and_observability/5.nsight/fsdp-llama2/1.distributed-training.sbatch @@ -0,0 +1,77 @@ +#!/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=FSDP # 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 + +########################### +####### 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) \ +) + +source /home/ubuntu/.bashrc +source activate fsdp + +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 + --llama_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=500 \ + --max_steps 80 \ + --checkpoint_dir=./checkpoints \ + --dataset='c4' \ + --dataset_config_name='en' \ + --resume_from_checkpoint=./checkpoints \ + --train_batch_size=1 \ + --val_batch_size=1 \ + --sharding_strategy="hybrid" \ + --epochs=1 + --nsys_start_step=70 + --nsys_end_step=75 +) + +srun -l 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/dev/null +++ b/4.validation_and_observability/5.nsight/fsdp-llama2/nsys-slurm-exec @@ -0,0 +1,9 @@ +#! /bin/bash -x + +NSYS_EXTRAS="" +if [ "$SLURM_LOCALID" == "0" ]; then +NSYS_EXTRAS="--enable nic_sampler,-mode:counters,-struct:true,-efa:true" +fi + +/fsx/nsight-efa/target-linux-x64/nsys profile $NSYS_EXTRAS --sample none -t cuda,nvtx --capture-range=cudaProfilerApi --capture-range-end=stop --force-overwrite true --output /fsx/nsight-reports/fsdp/report_fsdp_efa_job%q{SLURM_JOB_ID}_rank%q{SLURM_PROCID}_on_%q{HOSTNAME}.nsys-rep \ + "$@" \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/fsdp-llama2/train.py b/4.validation_and_observability/5.nsight/fsdp-llama2/train.py new file mode 100644 index 00000000..0150a9e4 --- /dev/null +++ b/4.validation_and_observability/5.nsight/fsdp-llama2/train.py @@ -0,0 +1,274 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +import datetime +import functools +import math +import re +import time + +import numpy as np +import torch +from torch import optim +import torch.distributed as dist +import torch.utils.data + +import transformers +from transformers import AutoModelForCausalLM, AutoTokenizer +from datasets import load_dataset + +from torch.distributed.fsdp import FullyShardedDataParallel as FSDP +from torch.distributed.fsdp import MixedPrecision +from torch.distributed.fsdp import ShardingStrategy +from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy +from torch.utils.data import DataLoader + +from model_utils.concat_dataset import ConcatTokensDataset +from model_utils.train_utils import (get_model_config, + compute_num_params, + get_transformer_layer, + get_sharding_strategy, + get_backward_fetch_policy, + apply_activation_checkpoint, + get_param_groups_by_weight_decay, + get_logger, + get_learning_rate_scheduler, + create_streaming_dataloader) +from model_utils.checkpoint import save_checkpoint, load_checkpoint +from model_utils.arguments import parse_args + +logger = get_logger() + +def eval_model(model, dataloader, num_batches): + """Eval step.""" + model = model.eval() + n_batches = 0 + loss = 0.0 + + with torch.no_grad(): + for batch_idx, input_data in enumerate(dataloader): + if batch_idx >= num_batches: + break + + loss += model(input_ids=input_data, attention_mask=None, labels=input_data)["loss"] + n_batches += 1 + + if n_batches > 0: + detached_loss = loss.detach() + torch.distributed.all_reduce(detached_loss) + loss = detached_loss.item() / dist.get_world_size() + loss /= n_batches + ppl = math.exp(loss) + else: + loss = -1.0 + ppl = -1.0 + + return loss, ppl + +def train( + model, + optimizer, + train_dataloader, + val_dataloader, + lr_scheduler, + model_config, + num_params, + args, + global_rank, + world_size, + total_steps=0, + start_batch_index=0 + ): + model.train() + for index in range(args.epochs): + for batch_idx, input_data in enumerate(train_dataloader): + if batch_idx < start_batch_index: + continue + + if batch_idx == args.nsys_start_step and global_rank == 0: + logger.info("====== Start nsys profiling ======") + torch.cuda.cudart().cudaProfilerStart() + + + optimizer.zero_grad(set_to_none=True) + step_start = time.time() + loss = model(input_ids=input_data, attention_mask=None, labels=input_data)["loss"] + loss.backward() + model.clip_grad_norm_(args.grad_clip) + optimizer.step() + lr_scheduler.step() + total_steps += 1 + loss_metric = loss.item() + step_time = time.time() - step_start + sample_processed = input_data.shape[0] * world_size + throughput = sample_processed / step_time + loss_scalar = loss.item() + current_lr = lr_scheduler.get_lr() + if global_rank==0 and batch_idx%args.logging_freq==0: + logger.info( + "Epoch %d Batch %d Steps %d Loss: %.5f, Speed: %.2f samples/sec, lr: %.6f", # pylint: disable=line-too-long + index, + batch_idx, + total_steps, + loss_scalar, + throughput, + current_lr, + ) + if args.validation_freq and not total_steps % args.validation_freq: + val_loss, val_ppl = eval_model( + model, val_dataloader, args.validation_batches + ) + model = model.train() + if global_rank == 0: + logger.info( + "Batch %d Validation loss: %s", + batch_idx, + val_loss, + ) + if args.checkpoint_dir and not total_steps % args.checkpoint_freq: + user_content = { + "cli_args": args.__dict__, + "num_params": num_params, + "total_steps": total_steps, + "model_config": model_config, + "start_batch_index": batch_idx + 1, + } + sub_dir = f"{args.model_type}-{total_steps}steps" + + save_checkpoint( + model, + optimizer, + lr_scheduler, + user_content, + args.checkpoint_dir, + sub_dir, + ) + if total_steps >= args.max_steps: + break + + if batch_idx == args.nsys_end_step and global_rank == 0: + logger.info("====== Stop nsys profiling ======") + torch.cuda.cudart().cudaProfilerStop() + + +def main(args): + dist.init_process_group() + global_rank = dist.get_rank() + device = global_rank % torch.cuda.device_count() + world_size = dist.get_world_size() + + if args.bf16: + dtype = torch.bfloat16 + else: + dtype = torch.get_default_dtype() + + model_config = get_model_config(args) + if global_rank == 0: + logger.info( + "Creating Model" + ) + model = AutoModelForCausalLM.from_config(model_config) + + num_params = compute_num_params(model) + if global_rank == 0: + logger.info( + "Created model with total parameters: %d (%.2f B)", num_params, num_params * 1e-9 + ) + transformer_layer = get_transformer_layer(args.model_type) + + gpt_auto_wrap_policy = functools.partial( + transformer_auto_wrap_policy, + transformer_layer_cls={ + transformer_layer, + }, + ) + + torch.cuda.set_device(device) + mixed_precision_policy = MixedPrecision( + param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype + ) + + if args.sharding_strategy=="full": + sharding_strategy = ShardingStrategy.FULL_SHARD + elif args.sharding_strategy=="hybrid": + sharding_strategy = ShardingStrategy.HYBRID_SHARD + else: + raise NotImplementedError("Available sharding strategies are full and hybrid") + + model = FSDP( + model, + auto_wrap_policy=gpt_auto_wrap_policy, + mixed_precision=mixed_precision_policy, + limit_all_gathers=args.limit_all_gathers, + device_id=torch.cuda.current_device(), + use_orig_params=False, + sharding_strategy=sharding_strategy, + ) + + if global_rank == 0: + logger.info("Wrapped model with FSDP") + + if args.activation_checkpointing > 0: + apply_activation_checkpoint(args, model=model) + + if args.offload_activations > 0: + from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import offload_wrapper + + model = offload_wrapper(model) + + param_groups = get_param_groups_by_weight_decay(model) + + optimizer = optim.AdamW( + param_groups, betas=(args.beta1, args.beta2), lr=args.lr, weight_decay=args.weight_decay + ) + + if global_rank == 0: + logger.info("Created optimizer") + + lr_scheduler = get_learning_rate_scheduler(optimizer, args) + + if args.resume_from_checkpoint: + ( + model, + optimizer, + lr_scheduler, + total_steps, + start_batch_index, + ) = load_checkpoint(model, + optimizer, + lr_scheduler, + args.resume_from_checkpoint, + args.model_type, + device) + else: + total_steps = 0 + start_batch_index = 0 + + train_dataloader = create_streaming_dataloader(args.dataset, + args.tokenizer, + name=args.dataset_config_name, + batch_size=args.train_batch_size, + split='train') + + val_dataloader = create_streaming_dataloader(args.dataset, + args.tokenizer, + name=args.dataset_config_name, + batch_size=args.train_batch_size, + split='validation') + + train(model, + optimizer, + train_dataloader, + val_dataloader, + lr_scheduler, + model_config, + num_params, + args, + global_rank, + world_size, + total_steps, + start_batch_index) + +if __name__ == "__main__": + args, _ = parse_args() + main(args) \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/nccl/0.nsight_nccl.sbatch b/4.validation_and_observability/5.nsight/nccl/0.nsight_nccl.sbatch new file mode 100644 index 00000000..1fdd2546 --- /dev/null +++ b/4.validation_and_observability/5.nsight/nccl/0.nsight_nccl.sbatch @@ -0,0 +1,48 @@ +#!/bin/bash + +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +#SBATCH -N 2 # number of nodes to use, 24 p4d(e) = 192 A100 GPUs +#SBATCH --job-name=megatron_gpt # name of your job +#SBATCH --ntasks-per-node 8 # Number of GPU per node +#SBATCH --gres=gpu:8 # number of GPU we reserve +#SBATCH --exclusive +#SBATCH --wait-all-nodes=1 + +### Disable hyperthreading by setting the tasks per core to 1 +#SBATCH --ntasks-per-core=1 + +########################### +###### User Variables ##### +########################### + +# default variables for Enroot +: "${APPS_PATH:=/apps}" +: "${NCCL_TESTS_PATH:=/opt/nccl-tests/build}" +: "${DATA_PATH:=/fsx}" +: "${FSX_MOUNT:=$DATA_PATH:$DATA_PATH}" + +: "${IMAGE:=$APPS_PATH/nccl.sqsh}" + +## Plenty of EFA level variables +export FI_EFA_USE_DEVICE_RDMA=1 # use for p4d +export FI_PROVIDER=efa +export FI_EFA_FORK_SAFE=1 +export FI_LOG_LEVEL=1 +export FI_PROVIDER=efa # change to eth if you want to use ENA for comparisons +export FI_EFA_ENABLE_SHM_TRANSFER=1 +# https://discuss.pytorch.org/t/nccl-network-is-unreachable-connection-refused-when-initializing-ddp/137352 +# https://github.com/pytorch/pytorch/issues/68893 +#export NCCL_SOCKET_IFNAME=ens +export NCCL_ASYNC_ERROR_HANDLING=1 +export NCCL_DEBUG=INFO + +declare -a ARGS=( + --container-image $IMAGE + --container-mount-home + --container-mounts $FSX_MOUNT + --no-container-remap-root +) + +srun -l "${ARGS[@]}" --mpi=pmix /fsx/nccl-slurm-exec $NCCL_TESTS_PATH/scatter_perf -b 8 -e 2G -f 2 -g 1 -c 1 -n 100 \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/nccl/NCCL_Scatter_Perf.png b/4.validation_and_observability/5.nsight/nccl/NCCL_Scatter_Perf.png new file mode 100644 index 0000000000000000000000000000000000000000..1c1e74c9e5c541368ccb20a631f2386530b3cee3 GIT binary patch literal 390650 zcmeFZbyQW|yEY6+w^Aw+Vh|EacO!yqLfC{fg7l_KN=iTkEJ7Moy1NmiL{cO-o0Mh~ z5}W3m%ir@m=Zy26N1ylo;~n34$LAQpcGp^St~u}fy07cHSGc;WA{p^zVk|5yGNpU+ 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b/4.validation_and_observability/5.nsight/nccl/plot_nccl.py new file mode 100644 index 00000000..ddf1aac1 --- /dev/null +++ b/4.validation_and_observability/5.nsight/nccl/plot_nccl.py @@ -0,0 +1,39 @@ +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns + +# Read CSV +all_reduce_df = pd.read_csv('./all_reduce.csv') + +# Convert duration to float +all_reduce_df[['duration_ms','unit']] = all_reduce_df['Duration'].str.split(' ',expand = True) +all_reduce_df['duration_ms']=all_reduce_df['duration_ms'].astype('float') + +# Filter by Message Size +one_df_1GB = all_reduce_df.loc[all_reduce_df['Message_Size']=='1,073,741,824',] +one_df_2GB = all_reduce_df.loc[all_reduce_df['Message_Size']=='2,147,483,648',] + +NCCL_Test_allreduce_1GB = 53.361 +NCCL_Test_allreduce_2GB = 92.866 + +fig, (ax1, ax2) = plt.subplots(2, 1) +ax1.hist(one_df_1GB['duration_ms'], density=True, bins=100) # density=False would make counts +ax1.set_ylabel('Probability') +ax1.set_xlabel('Duration ms') +ax1.set_xlim([20, 220]) +ax1.axvline(NCCL_Test_allreduce_1GB, color='k', linestyle='dashed', linewidth=1) +ax1.set_title('All Reduce:Sum Message Size = 1GB') +ax1.legend(['NCCL Test Reported Time', 'NCCL Test Iterations']) + +ax2.hist(one_df_2GB['duration_ms'], density=True, bins=100) # density=False would make counts +ax2.set_ylabel('Probability') +ax2.set_xlabel('Duration ms') +ax2.set_xlim([20, 220]) +ax2.axvline(NCCL_Test_allreduce_2GB, color='k', linestyle='dashed', linewidth=1) +ax2.set_title('All Reduce:Sum Message Size = 2GB') +ax2.legend(['NCCL Test Reported Time', 'NCCL Test Iterations']) + +plt.tight_layout() + +plt.savefig('all_reduce_sum.png') \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/nemotron/1.nemotron.sbatch b/4.validation_and_observability/5.nsight/nemotron/1.nemotron.sbatch new file mode 100644 index 00000000..4b0f4adf --- /dev/null +++ b/4.validation_and_observability/5.nsight/nemotron/1.nemotron.sbatch @@ -0,0 +1,41 @@ +#!/bin/bash + +# Parameters +#SBATCH --dependency=singleton +#SBATCH --error=/fsx/nemotron/nsight-awsankur/log-nemotron4--15B-16g_%j.err +#SBATCH --exclusive +#SBATCH --gpus-per-node=8 +#SBATCH --job-name=nemo--15B-16g +#SBATCH --mem=0 +#SBATCH --nodes=2 +#SBATCH --ntasks-per-node=8 +#SBATCH --output=/fsx/nemotron/nsight-awsankur/log-nemotron4--15B-16g_%j.out +#SBATCH --time=1:00:00 + +# setup +export NCCL_DEBUG=INFO +export TRANSFORMERS_OFFLINE=0 +export TORCH_NCCL_AVOID_RECORD_STREAMS=1 +export NCCL_NVLS_ENABLE=1 +export NVTE_DP_AMAX_REDUCE_INTERVAL=0 +export NVTE_ASYNC_AMAX_REDUCTION=1 +export NVTE_FUSED_ATTN=0 +export FI_PROVIDER=efa +export FI_EFA_USE_DEVICE_RDMA=1 +export FI_EFA_FORK_SAFE=1 +export NCCL_IB_DISABLE=1 +export NCCL_IBEXT_DISABLE=1 +export NVTE_FWD_LAYERNORM_SM_MARGIN=16 +export NVTE_BWD_LAYERNORM_SM_MARGIN=16 +export OFI_NCCL_ROUND_ROBIN_THRESHOLD=262144 +export OFI_NCCL_GDR_FLUSH_DISABLE=1 + +# command 1 +srun --container-image /fsx/nemotron/nemotron-nccl-efa.sqsh --container-mounts /home/ubuntu:/home/ubuntu,/fsx:/fsx --no-container-mount-home --mpi=pmix bash -c " + cd /opt/NeMo; + git rev-parse HEAD; + export PYTHONPATH=/opt/NeMo:\${PYTHONPATH}; + CUDA_DEVICE_MAX_CONNECTIONS=1 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 nsys profile -s none -t nvtx,cuda -o /fsx/nemotron/nsight-awsankur/results/nemotron4--15B-16g/profile_logs/profile_\${SLURM_JOB_ID}_node\${SLURM_NODEID}_rank\${SLURM_PROCID} --force-overwrite true --capture-range=cudaProfilerApi --capture-range-end=stop python3 -u /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \ + --config-path=/fsx/nemotron/nsight-awsankur \ + --config-name=nemotron4--15B-16g_hydra.yaml \ + model.gc_interval=100" \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/nemotron/nemo.Dockerfile b/4.validation_and_observability/5.nsight/nemotron/nemo.Dockerfile new file mode 100644 index 00000000..a478bf35 --- /dev/null +++ b/4.validation_and_observability/5.nsight/nemotron/nemo.Dockerfile @@ -0,0 +1,110 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +# DOCKER_BUILDKIT=1 docker build --progress plain -t aws-nemo-megatron:latest . + +#FROM nvcr.io/ea-bignlp/ga-participants/nemofw-training:23.11 + +FROM nvcr.io/nvidia/nemo:24.01.framework + +ENV DEBIAN_FRONTEND=noninteractive +ARG EFA_INSTALLER_VERSION=1.30.0 +ARG AWS_OFI_NCCL_VERSION=v1.8.1-aws +ARG NCCL_VERSION=2.20.3 + +ARG GDRCOPY_VERSION=2.4.1 + +RUN apt-get update -y +RUN apt-get remove -y --allow-change-held-packages \ + libmlx5-1 ibverbs-utils libibverbs-dev libibverbs1 libnccl2 libnccl-dev + +# Remove existing MPI +# RUN rm -rf /opt/hpcx \ +# && rm -rf /usr/local/mpi \ +# && rm -f /etc/ld.so.conf.d/hpcx.conf \ +# && 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 \ + python3-distutils \ + cmake \ + apt-utils \ + devscripts \ + debhelper \ + libsubunit-dev \ + check \ + pkg-config + +RUN mkdir -p /var/run/sshd +RUN sed -i 's/[ #]\(.*StrictHostKeyChecking \).*/ \1no/g' /etc/ssh/ssh_config && \ + echo " UserKnownHostsFile /dev/null" >> /etc/ssh/ssh_config && \ + sed -i 's/#\(StrictModes \).*/\1no/g' /etc/ssh/sshd_config + +ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:/opt/amazon/openmpi/lib:/opt/nccl/build/lib:/opt/amazon/efa/lib:/opt/aws-ofi-nccl/install/lib:/usr/local/lib:$LD_LIBRARY_PATH + + +RUN curl https://bootstrap.pypa.io/get-pip.py -o /tmp/get-pip.py \ + && python3 /tmp/get-pip.py \ + && pip3 install awscli pynvml + +################################################# +## Install NVIDIA GDRCopy +RUN git clone -b v${GDRCOPY_VERSION} https://github.com/NVIDIA/gdrcopy.git /tmp/gdrcopy \ + && cd /tmp/gdrcopy \ + && make lib_install install + +################################################# +## Install EFA installer +RUN cd $HOME \ + && curl -O https://efa-installer.amazonaws.com/aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz \ + && tar -xf $HOME/aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz \ + && cd aws-efa-installer \ + && ./efa_installer.sh -y -g -d --skip-kmod --skip-limit-conf --no-verify \ + && rm -rf $HOME/aws-efa-installer + +ENV PATH /opt/amazon/openmpi/bin/:/opt/amazon/efa/bin:/usr/bin:/usr/local/bin:$PATH + +################################################### +## Install NCCL +RUN git clone -b v${NCCL_VERSION}-1 https://github.com/NVIDIA/nccl.git /opt/nccl \ + && cd /opt/nccl \ + && make -j src.build CUDA_HOME=/usr/local/cuda \ + NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_90,code=sm_90" + +################################################### +## Install AWS-OFI-NCCL plugin +RUN apt-get install libtool autoconf cmake nasm unzip pigz parallel nfs-common build-essential hwloc libhwloc-dev libjemalloc2 libnuma-dev numactl libjemalloc-dev preload htop iftop liblapack-dev libgfortran5 ipcalc wget curl devscripts debhelper check libsubunit-dev fakeroot pkg-config dkms -y +RUN export OPAL_PREFIX="" \ + && git clone -b ${AWS_OFI_NCCL_VERSION} https://github.com/aws/aws-ofi-nccl.git /opt/aws-ofi-nccl \ + && cd /opt/aws-ofi-nccl \ + && ./autogen.sh \ + && ./configure --prefix=/opt/aws-ofi-nccl/install \ + --with-mpi=/opt/amazon/openmpi \ + --with-libfabric=/opt/amazon/efa \ + --with-cuda=/usr/local/cuda \ + --enable-platform-aws \ + && make && make install + + +ENV OMPI_MCA_pml=^cm,ucx \ + OMPI_MCA_btl=tcp,self \ + OMPI_MCA_btl_tcp_if_exclude=lo,docker0,veth_def_agent\ + OPAL_PREFIX=/opt/amazon/openmpi \ + NCCL_SOCKET_IFNAME=^docker,lo +ENV PMIX_MCA_gds=hash + +RUN rm -rf /var/lib/apt/lists/* +ENV LD_PRELOAD /opt/nccl/build/lib/libnccl.so +ENV LD_LIBRARY_PATH /opt/hpcx/ucx/lib:$LD_LIBRARY_PATH \ No newline at end of file diff --git a/4.validation_and_observability/5.nsight/nemotron/nemotron-15B-P5-report.png 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