Initial commit

examples/academic_paper_scripts/sc21/CONFIG.sh

+#!/bin/bash
+
+
+# SLURM options.
+export SLURM_PARTITION=<slurm partition, used to feed -p option in slurm>
+export SLURM_ACCOUNT=<slurm account, used to feed -A option in slurm>
+
+
+# Source code.
+export MEGATRON_CODE_DIR=<megatron source code directory>
+
+
+# This variable is used to mount the relevant part of the filesystem
+# inside the docker container. Note that the `MEGATRON_CODE_DIR` and the
+# launch directory already get mounted; this variable should be used to
+# mount the directories that contain the data and tokenizer files.
+export DOCKER_MOUNT_DIR=<megatron dataset and bpe tokenizer vocab path>
+
+
+# Data and tokenizer files.
+MEGATRON_DATA=<path to megatron processed data>
+BPE_VOCAB_FILE=<path to bpe vocab file>
+BPE_MERGE_FILE=<path to bpe merges file>
+
+
+# Megatron input parameters.
+# `MEGATRON_EXTRA_PARAMS` can be used to provide any extra parameters
+# that are not listed here. 
+export MEGATRON_PARAMS=" ${MEGATRON_EXTRA_PARAMS} \
+	--tensor-model-parallel-size ${TP} \
+	--pipeline-model-parallel-size ${PP} \
+	--micro-batch-size ${MBS} \
+	--global-batch-size ${GBS} \
+        --num-layers ${NLS} \
+        --hidden-size ${HS} \
+        --num-attention-heads ${NAH} \
+	--DDP-impl ${DDP} \
+	--data-path ${MEGATRON_DATA} \
+	--vocab-file ${BPE_VOCAB_FILE} \
+	--merge-file ${BPE_MERGE_FILE} \
+        --log-interval 5 \
+        --seq-length 2048 \
+        --max-position-embeddings 2048 \
+        --train-iters 500 \
+        --lr-decay-iters 320 \
+        --lr 0.0001 \
+	--min-lr 0.00001 \
+        --lr-decay-style cosine \
+        --lr-warmup-fraction 0.01 \
+        --split 969,30,1 \
+        --eval-iters 100 \
+        --eval-interval 1000 \
+        --clip-grad 1.0 \
+        --fp16 \
+	--loss-scale 8192 "
+
+

examples/academic_paper_scripts/sc21/README.md

+# Reproducing Figures in SC21 Paper
+
+
+This directory contains some of the scripts that were used to produce the
+results in the [Megatron paper](https://arxiv.org/pdf/2104.04473.pdf) that is
+to appear at [SuperComputing 2021](https://sc21.supercomputing.org/). These
+scripts use [Slurm](https://slurm.schedmd.com/documentation.html) with the
+[pyxis plugin](https://github.com/NVIDIA/pyxis), but can be modified for other
+schedulers as well.
+
+
+## Git commit
+
+To replicate these results use Megatron-LM commit: 6985e58938d40ad91ac07b0fddcfad8132e1447e
+
+
+## Setup
+
+All the cluster-dependent variables are in [`CONFIG.sh`](./CONFIG.sh). Please
+update the unspecified values (in angle brackets `<...>`) before launching any
+scripts.
+
+
+
+## Scripts
+
+Below is a list of scripts that can be used to reproduce various figures in our
+[paper](https://arxiv.org/pdf/2104.04473.pdf):
+
+* [run_table_1.sh](./run_table_1.sh): Table 1 showing weak-scaling throughput
+for GPT models ranging from 1 billion to 1 trillion parameters.
+* [run_figure_11.sh](./run_figure_11.sh): Figure 11 showing the weak-scaling
+performance of pipeline parallelism.
+* [run_figure_12.sh](./run_figure_12.sh): Figure 12 showing the effect of
+the interleaved schedule on a 175B GPT model.
+* [run_figure_13.sh](./run_figure_13.sh): Figure 13 showing the effect of
+different degrees of pipeline and tensor model parallelism on a model with
+162.2 billion parameters.
+* [run_figure_14.sh](./run_figure_14.sh): Figure 14 showing the effect of
+different degrees of data and pipeline model parallelism on a model with
+5.9 billion parameters.
+* [run_figure_15.sh](./run_figure_15.sh): Figure 15 showing the effect of
+different degrees of data and tensor model parallelism on a model with
+5.9 billion parameters.
+* [run_figure_16.sh](./run_figure_16.sh): Figure 16 showing the effect of
+microbatch size.
+* [run_figure_17.sh](./run_figure_17.sh): Figure 17 showing the effect of
+activation recomputation.
+* [run_figure_18.sh](./run_figure_18.sh): Figure 18 showing the effect of
+the scatter-gather communication optimization.

examples/academic_paper_scripts/sc21/SBATCH.sh

+#!/bin/bash
+
+
+sbatch -p ${SLURM_PARTITION} \
+       -A ${SLURM_ACCOUNT} \
+       --job-name=${JOB_NAME} \
+       --nodes=${NNODES} \
+       --export=MEGATRON_CODE_DIR,MEGATRON_PARAMS,DOCKER_MOUNT_DIR SRUN.sh
+
+exit 0
+
+
+

examples/academic_paper_scripts/sc21/SRUN.sh

+#!/bin/bash
+
+#SBATCH -t 0:30:00 --exclusive --mem=0 --overcommit --ntasks-per-node=8
+
+
+THIS_DIR=`pwd`
+DATETIME=`date +'date_%y-%m-%d_time_%H-%M-%S'`
+mkdir -p ${THIS_DIR}/logs
+
+
+CMD="python -u ${MEGATRON_CODE_DIR}/pretrain_gpt.py ${MEGATRON_PARAMS}"
+
+
+srun -l \
+     --container-image "nvcr.io#nvidia/pytorch:20.12-py3" \
+     --container-mounts "${THIS_DIR}:${THIS_DIR},${MEGATRON_CODE_DIR}:${MEGATRON_CODE_DIR},${DOCKER_MOUNT_DIR}:${DOCKER_MOUNT_DIR}" \
+     --output=${THIS_DIR}/logs/%x_%j_$DATETIME.log sh -c "${CMD}"
+

examples/academic_paper_scripts/sc21/run_figure_11.sh

+#!/bin/bash
+
+# ================================
+# Choose the case to run.
+# ================================
+
+# Pipeline-parallel size options = [1, 2, 4, 8].
+PP=1
+
+# Batch size (global batch size) options = [8, 128].
+GBS=8
+
+
+
+
+
+# Set pipeline-parallel size options.
+NLS=$((3*PP))
+NNODES=${PP}
+
+
+# Other params.
+TP=8
+MBS=1
+HS=20480
+NAH=128
+DDP=local
+MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+
+
+# Name of the job.
+export JOB_NAME=results_figure_11_pipeline_parallel_size_${PP}_batch_size_${GBS}
+
+
+# Import the configs.
+. `pwd`/CONFIG.sh
+
+
+# Submit the job.
+. `pwd`/SBATCH.sh
+
+
+exit 0
+
+
+

examples/academic_paper_scripts/sc21/run_figure_12.sh

+#!/bin/bash
+
+# ================================
+# Choose the case to run.
+# ================================
+
+# Interleaved schedule options = [YES, NO].
+INTERLEAVED=YES
+
+# Batch size (global batch size) options = [12, 24, 36, ..., 60].
+GBS=12
+
+
+
+
+
+# Set interleaved schedule options.
+if [ ${INTERLEAVED} == "YES" ]; then
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 2 "
+elif [ ${INTERLEAVED} == "NO" ]; then
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+else
+    echo "Invalid configuration"
+    exit 1
+fi
+
+
+# Other params.
+TP=8
+PP=12
+MBS=1
+NLS=96
+HS=12288
+NAH=96
+DDP=local
+NNODES=12
+
+
+# Name of the job.
+export JOB_NAME=results_figure_12_interleaved_${INTERLEAVED}_batch_size_${GBS}
+
+
+# Import the configs.
+. `pwd`/CONFIG.sh
+
+
+# Submit the job.
+. `pwd`/SBATCH.sh
+
+
+exit 0
+
+
+

examples/academic_paper_scripts/sc21/run_figure_13.sh

+#!/bin/bash
+
+# ================================
+# Choose the case to run.
+# ================================
+
+# Pipeline-parallel size options = [2, 4, 8, 16, 32].
+PP=2
+
+# Batch size (global batch size) options = [32, 128].
+GBS=32
+
+
+
+
+
+# Set pipeline-parallel and tensor-parallel size options.
+TP=$((64/PP))
+
+
+# Other params.
+MBS=1
+NLS=32
+HS=20480
+NAH=128
+DDP=local
+MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+NNODES=8
+
+
+# Name of the job.
+export JOB_NAME=results_figure_13_pipeline_parallel_size_${PP}_tensor_parallel_size_${TP}_batch_size_${GBS}
+
+
+# Import the configs.
+. `pwd`/CONFIG.sh
+
+
+# Submit the job.
+. `pwd`/SBATCH.sh
+
+
+exit 0
+
+
+

examples/academic_paper_scripts/sc21/run_figure_14.sh

+#!/bin/bash
+
+# ================================
+# Choose the case to run.
+# ================================
+
+# Pipeline-parallel size options = [2, 4, 8, 16, 32].
+PP=2
+
+# Batch size (global batch size) options = [32, 512].
+GBS=32
+
+
+
+
+
+# Set pipeline-parallel and data-parallel size options.
+DP=$((64/PP))
+
+
+# Other params.
+TP=1
+MBS=1
+NLS=32
+HS=3840
+NAH=32
+DDP=local
+MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+NNODES=8
+
+
+# Name of the job.
+export JOB_NAME=results_figure_14_pipeline_parallel_size_${PP}_data_parallel_size_${DP}_batch_size_${GBS}
+
+
+# Import the configs.
+. `pwd`/CONFIG.sh
+
+
+# Submit the job.
+. `pwd`/SBATCH.sh
+
+
+exit 0
+
+
+

examples/academic_paper_scripts/sc21/run_figure_15.sh

+#!/bin/bash
+
+# ================================
+# Choose the case to run.
+# ================================
+
+# Tensor-parallel size options = [2, 4, 8, 16, 32].
+TP=2
+
+# Batch size (global batch size) options = [32, 128, 512].
+GBS=32
+
+
+
+
+
+# Set tensor-parallel and data-parallel size options.
+DP=$((64/TP))
+
+
+# Other params.
+PP=1
+MBS=1
+NLS=32
+HS=3840
+NAH=32
+DDP=local
+MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+NNODES=8
+
+
+# Name of the job.
+export JOB_NAME=results_figure_15_tensor_parallel_size_${TP}_data_parallel_size_${DP}_batch_size_${GBS}
+
+
+# Import the configs.
+. `pwd`/CONFIG.sh
+
+
+# Submit the job.
+. `pwd`/SBATCH.sh
+
+
+exit 0
+
+
+

examples/academic_paper_scripts/sc21/run_figure_16.sh

+#!/bin/bash
+
+# ================================
+# Choose the case to run.
+# ================================
+
+# Microbatch size options = [1, 2, 4, 8].
+MBS=1
+
+# Batch size (global batch size) options = [128, 512].
+GBS=128
+
+
+
+
+
+# Other params.
+TP=8
+PP=8
+NLS=32
+HS=15360
+NAH=128
+DDP=local
+MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+NNODES=8
+
+
+# Name of the job.
+export JOB_NAME=results_figure_16_microbatch_size_${MBS}_batch_size_${GBS}
+
+
+# Import the configs.
+. `pwd`/CONFIG.sh
+
+
+# Submit the job.
+. `pwd`/SBATCH.sh
+
+
+exit 0
+
+
+

examples/academic_paper_scripts/sc21/run_figure_17.sh

+#!/bin/bash
+
+# ================================
+# Choose the case to run.
+# ================================
+
+# Activation recomputation options = [YES, NO].
+ACTIVATION_RECOMPUTATION=YES
+
+# Batch size (global batch size) options = [1, 2, 4, ..., 256].
+GBS=1
+
+
+
+
+
+# Set activation recomputation.
+if [ ${ACTIVATION_RECOMPUTATION} == "YES" ]; then
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+elif [ ${ACTIVATION_RECOMPUTATION} == "NO" ]; then
+    MEGATRON_EXTRA_PARAMS=""
+else
+    echo "Invalid configuration"
+    exit 1
+fi
+
+
+# Other params.
+TP=8
+PP=16
+MBS=1
+NLS=80
+HS=12288
+NAH=96
+DDP=local
+NNODES=16
+
+
+# Name of the job.
+export JOB_NAME=results_figure_17_activation_recomputation_${ACTIVATION_RECOMPUTATION}_batch_size_${GBS}
+
+
+# Import the configs.
+. `pwd`/CONFIG.sh
+
+
+# Submit the job.
+. `pwd`/SBATCH.sh
+
+
+exit 0
+
+
+

examples/academic_paper_scripts/sc21/run_figure_18.sh

+#!/bin/bash
+
+# ================================
+# Choose the case to run.
+# ================================
+
+# Scatter-gather communication optimization options = [YES, NO].
+SCATTER_GATHER=YES
+
+# Batch size (global batch size) options = [12, 24, 36, ..., 60].
+GBS=12
+
+
+
+
+
+# Set scatter-gather communication optimization options.
+if [ ${SCATTER_GATHER} == "YES" ]; then
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 2 "
+elif [ ${SCATTER_GATHER} == "NO" ]; then
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 2 --no-scatter-gather-tensors-in-pipeline "
+else
+    echo "Invalid configuration"
+    exit 1
+fi
+
+
+# Other params.
+TP=8
+PP=12
+MBS=1
+NLS=96
+HS=12288
+NAH=96
+DDP=local
+NNODES=12
+
+
+# Name of the job.
+export JOB_NAME=results_figure_18_scatter_gather_${SCATTER_GATHER}_batch_size_${GBS}
+
+
+# Import the configs.
+. `pwd`/CONFIG.sh
+
+
+# Submit the job.
+. `pwd`/SBATCH.sh
+
+
+exit 0
+
+
+

examples/academic_paper_scripts/sc21/run_table_1.sh

+#!/bin/bash
+
+# ================================
+# Choose the case to run.
+# ================================
+# model size options = [1.7B, 3.6B, 7.5B, 18B, 39B, 76B, 145B, 310B, 530B, 1T]
+MODEL_SIZE=1.7B
+
+
+
+
+
+
+if [ ${MODEL_SIZE} == "1.7B" ]; then
+    TP=1
+    PP=1
+    MBS=16
+    GBS=512
+    NLS=24
+    HS=2304
+    NAH=24
+    DDP=torch
+    NNODES=4
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+elif [ ${MODEL_SIZE} == "3.6B" ]; then
+    TP=2
+    PP=1
+    MBS=16
+    GBS=512
+    NLS=30
+    HS=3072
+    NAH=32
+    DDP=torch
+    NNODES=8
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+elif [ ${MODEL_SIZE} == "7.5B" ]; then
+    TP=4
+    PP=1
+    MBS=16
+    GBS=512
+    NLS=36
+    HS=4096
+    NAH=32
+    DDP=torch
+    NNODES=16
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+elif [ ${MODEL_SIZE} == "18B" ]; then
+    TP=8
+    PP=1
+    MBS=8
+    GBS=1024
+    NLS=40
+    HS=6144
+    NAH=48
+    DDP=torch
+    NNODES=32
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+elif [ ${MODEL_SIZE} == "39B" ]; then
+    TP=8
+    PP=2
+    MBS=4
+    GBS=1536
+    NLS=48
+    HS=8192
+    NAH=64
+    DDP=local
+    NNODES=64
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+elif [ ${MODEL_SIZE} == "76B" ]; then
+    TP=8
+    PP=4
+    MBS=2
+    GBS=1792
+    NLS=60
+    HS=10240
+    NAH=80
+    DDP=local
+    NNODES=128
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 5"
+elif [ ${MODEL_SIZE} == "145B" ]; then
+    TP=8
+    PP=8
+    MBS=2
+    GBS=2304
+    NLS=80
+    HS=12288
+    NAH=96
+    DDP=local
+    NNODES=192
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 5 "
+elif [ ${MODEL_SIZE} == "310B" ]; then
+    TP=8
+    PP=16
+    MBS=1
+    GBS=2160
+    NLS=96
+    HS=16384
+    NAH=128
+    DDP=local
+    NNODES=240
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 3 "
+elif [ ${MODEL_SIZE} == "530B" ]; then
+    TP=8
+    PP=35
+    MBS=1
+    GBS=2520
+    NLS=105
+    HS=20480
+    NAH=128
+    DDP=local
+    NNODES=315
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 1 "
+elif [ ${MODEL_SIZE} == "1T" ]; then
+    TP=8
+    PP=64
+    MBS=1
+    GBS=3072
+    NLS=128
+    HS=25600
+    NAH=160
+    DDP=local
+    NNODES=384
+    MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
+else
+    echo "Invalid configuration"
+    exit 1
+fi
+
+
+# Name of the job
+export JOB_NAME=results_table_1_model_size_${MODEL_SIZE}
+
+
+# Import the configs.
+. `pwd`/CONFIG.sh
+
+
+# Submit the job.
+. `pwd`/SBATCH.sh
+
+
+exit 0
+
+
+

examples/bert/README.md

+# BERT MODEL
+
+## Table of contents
+- [1. Training Setup](#1-training-setup)
+- [2. Configurations](#2-configurations)
+
+## 1. Training setup
+<a id="markdown-training-setup" name="training-setup"></a>
+
+To run the model using a docker container run it as follows
+```
+PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.01-py3
+CHECKPOINT_PATH="" #<Specify path>
+TENSORBOARD_LOGS_PATH=""#<Specify path>
+VOCAB_FILE="" #<Specify path to file>//bert-vocab.txt
+DATA_PATH="" #<Specify path and file prefix>_text_document
+
+docker run \
+  --gpus=all \
+  --ipc=host \
+  --workdir /workspace/megatron-lm \
+  -v /path/to/data:/path/to/data \
+  -v /path/to/megatron-lm:/workspace/megatron-lm \
+  megatron-lm nvcr.io/nvidia/pytorch:24.01-py3 \
+  bash examples/bert/train_bert_340m_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $DATA_PATH "
+
+```
+NOTE: Depending on the environment you are running it the above command might like slightly different.
+
+
+## 2. Configurations
+<a id="markdown-configurations" name="configurations"></a>
+The example in this folder shows you how to run 340m large model. There are other configs you could run as well
+
+### 4B
+```
+       --num-layers 48 \
+       --hidden-size 2560 \
+       --num-attention-heads 32 \
+       --tensor-model-parallel-size 1 \
+       --pipeline-model-parallel-size 1 \
+
+```
+
+### 20B
+```
+       --num-layers 48 \
+       --hidden-size 6144 \
+       --num-attention-heads 96 \
+       --tensor-model-parallel-size 4 \
+       --pipeline-model-parallel-size 4 \
+
+```
\ No newline at end of file

examples/bert/train_bert_340m_distributed.sh

+#!/bin/bash
+
+# Runs the "340M" parameter model (Bert - Large)
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NUM_NODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
+
+CHECKPOINT_PATH=$1 #<Specify path>
+TENSORBOARD_LOGS_PATH=$2 #<Specify path>
+VOCAB_FILE=$3 #<Specify path to file>/bert-vocab.json
+DATA_PATH=$4 #<Specify path and file prefix>_text_document
+
+DISTRIBUTED_ARGS=(
+    --nproc_per_node $GPUS_PER_NODE 
+    --nnodes $NUM_NODES 
+    --master_addr $MASTER_ADDR 
+    --master_port $MASTER_PORT
+)
+
+BERT_MODEL_ARGS=(
+    --num-layers 24 
+    --hidden-size 1024 
+    --num-attention-heads 16 
+    --seq-length 512 
+    --max-position-embeddings 512 
+)
+
+TRAINING_ARGS=(
+    --micro-batch-size 4 
+    --global-batch-size 32 
+    --train-iters 1000000 
+    --weight-decay 1e-2 
+    --clip-grad 1.0 
+    --fp16
+    --lr 0.0001
+    --lr-decay-iters 990000 
+    --lr-decay-style linear 
+    --min-lr 1.0e-5 
+    --weight-decay 1e-2 
+    --lr-warmup-fraction .01 
+    --clip-grad 1.0 
+)
+
+MODEL_PARALLEL_ARGS=(
+	--tensor-model-parallel-size 8 
+	--pipeline-model-parallel-size 16 
+)
+
+DATA_ARGS=(
+    --data-path $DATA_PATH 
+    --vocab-file $VOCAB_FILE 
+    --split 949,50,1
+)
+
+EVAL_AND_LOGGING_ARGS=(
+    --log-interval 100
+    --save-interval 10000 
+    --eval-interval 1000 
+    --save $CHECKPOINT_PATH 
+    --load $CHECKPOINT_PATH 
+    --eval-iters 10
+    --tensorboard-dir $TENSORBOARD_LOGS_PATH 
+)
+
+torchrun ${DISTRIBUTED_ARGS[@]} pretrain_bert.py \
+    ${BERT_MODEL_ARGS[@]} \
+    ${TRAINING_ARGS[@]} \
+    ${MODEL_PARALLEL_ARGS[@]} \
+    ${DATA_ARGS[@]} \
+    ${EVAL_AND_LOGGING_ARGS[@]}

examples/gpt3/README.md

+# GPT3 MODEL
+
+## Table of contents
+- [1. Training Setup](#1-training-setup)
+- [2. Configurations](#2-configurations)
+- [3. Training Results](#3-training-results)
+
+## 1. Training setup
+<a id="markdown-training-setup" name="training-setup"></a>
+
+To run the model using a docker container run it as follows
+```
+PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.01-py3
+CHECKPOINT_PATH="" #<Specify path>
+TENSORBOARD_LOGS_PATH=""#<Specify path>
+VOCAB_FILE="" #<Specify path to file>/gpt2-vocab.json
+MERGE_FILE="" #<Specify path to file>/gpt2-merges.txt
+DATA_PATH="" #<Specify path and file prefix>_text_document
+
+docker run \
+  --gpus=all \
+  --ipc=host \
+  --workdir /workspace/megatron-lm \
+  -v /path/to/data:/path/to/data \
+  -v /path/to/megatron-lm:/workspace/megatron-lm \
+  megatron-lm nvcr.io/nvidia/pytorch:24.01-py3 \
+  bash examples/gpt3/train_gpt3_175b_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $MERGE_FILE $DATA_PATH "
+
+```
+NOTE: Depending on the environment you are running it the above command might like slightly different.
+
+
+## 2. Configurations
+<a id="markdown-configurations" name="configurations"></a>
+The example in this folder shows you how to run 175B model. There are other configs you could run as well
+
+### 345M
+```
+       --num-layers 12 \
+       --hidden-size 512 \
+       --num-attention-heads 8 \
+       --seq-length 1024 \
+       --tensor-model-parallel-size 1 \
+       --pipeline-model-parallel-size 1 \
+
+```
+
+### 857M
+```
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --num-attention-heads 16 \
+       --seq-length 2048 \
+       --tensor-model-parallel-size 1 \
+       --pipeline-model-parallel-size 1 \
+
+```

examples/gpt3/gpt_config.yaml

+# WARNING: Yaml configs is currently an experimental feature
+language_model:
+  # model architecture
+  num_layers: 24
+  hidden_size: 1024
+  num_attention_heads: 16
+  num_query_groups: null
+
+  ffn_hidden_size: null
+  kv_channels: null
+  hidden_dropout: 0.0
+  attention_dropout: 0.0
+  fp32_residual_connection: False
+
+  apply_residual_connection_post_layernorm: False
+  layernorm_epsilon: 1.e-5
+  layernorm_zero_centered_gamma: True
+  add_bias_linear: False
+  bias_activation_fusion: False
+  add_qkv_bias: False
+  gated_linear_unit: False
+  activation_func: swiglu
+  num_moe_experts: null
+  rotary_interleaved: False
+  window_size: null
+
+  # initialization
+  init_method: null
+  init_method_std: 0.02
+  output_layer_init_method: null
+
+  # mixed-precision
+  apply_query_key_layer_scaling: False
+  attention_softmax_in_fp32: False
+
+  # fusion
+  bias_swiglu_fusion: True
+  masked_softmax_fusion: True
+  persist_layer_norm: False
+  memory_efficient_layer_norm: False
+  bias_dropout_fusion: True
+  apply_rope_fusion: True
+
+  # activation recomputation
+  recompute_granularity: null
+  recompute_method: null
+  recompute_num_layers: null
+  distribute_saved_activations: null
+
+  # fp8 related
+  fp8: null
+  fp8_margin: 0
+  fp8_interval: 1
+  fp8_amax_history_len: 1
+  fp8_amax_compute_algo: "most_recent"
+  fp8_wgrad: True
+
+  # miscellaneous
+  clone_scatter_output_in_embedding: True
+
+  normalization: "LayerNorm"  # alt value supported by TE: "RMSNorm"
+
+  # MoE related
+  moe_router_load_balancing_type: "aux_loss"
+  moe_router_topk: 2
+  moe_grouped_gemm: False
+  moe_aux_loss_coeff: 0  # 1e-2 would be a good start value for load balance loss.
+  moe_z_loss_coeff: null  # 1e-3 would be a good start value for z-loss
+  moe_input_jitter_eps: null
+  moe_token_dropping: False
+
+model_parallel:
+  # Model parallelism
+  tensor_model_parallel_size: 1
+  context_parallel_size: 1
+  pipeline_model_parallel_size: 1
+  virtual_pipeline_model_parallel_size: null
+  sequence_parallel: True
+  expert_model_parallel_size: 1
+
+  # Initialization
+  perform_initialization: True
+  use_cpu_initialization: null
+
+  # Training
+  fp16: False
+  bf16: True
+  params_dtype: null # Set from above arguments for core
+  timers: null
+
+  # Optimizations
+  gradient_accumulation_fusion: True
+  async_tensor_model_parallel_allreduce: True
+  tp_comm_overlap: False
+
+  # Debug Options
+  tp_comm_split_ag: True
+  tp_comm_atomic_ag: True
+  tp_comm_split_rs: True
+  tp_comm_atomic_rs: True
+  tp_comm_bulk_wgrad: True
+  tp_comm_bulk_dgrad: True
+
+  # Parallelism
+  finalize_model_grads_func: null
+
+  # Pipeline Parallel
+  pipeline_dtype: null
+  grad_scale_func: null
+  enable_autocast: False
+  autocast_dtype: null
+  variable_seq_lengths: False
+  num_microbatches_with_partial_activation_checkpoints: null
+  overlap_p2p_comm: False
+  batch_p2p_comm: True
+  batch_p2p_sync: True
+  use_ring_exchange_p2p: False
+  deallocate_pipeline_outputs: False
+  no_sync_func: null
+  grad_sync_func: null
+  param_sync_func: null
+  pipeline_model_parallel_split_rank: null
+
+  # CPU Offloading
+  cpu_offloading: False
+  cpu_offloading_num_layers: 0
+  _cpu_offloading_context: null
+  cpu_offloading_weights: False
+  cpu_offloading_activations: True
+
+  # Timing
+  barrier_with_L1_time: True
+
+# training:
+use_legacy_models: False
+spec: null
+micro_batch_size: 2
+global_batch_size: 128
+rampup_batch_size: [32, 32, 65324160] 
+check_for_nan_in_loss_and_grad: True
+num_layers_per_virtual_pipeline_stage: null
+
+encoder_num_layers: null
+decoder_num_layers: null
+rotary_seq_len_interpolation_factor: null
+add_position_embedding: False
+make_vocab_size_divisible_by: 128
+group_query_attention: False
+
+
+exit_signal_handler: False
+exit_duration_in_mins: null
+exit_interval: null
+
+untie_embeddings_and_output_weights: True
+position_embedding_type: rope
+rotary_percent: 0.5
+openai_gelu: False
+squared_relu: False
+swiglu: True
+onnx_safe: null
+bert_binary_head: True
+max_position_embeddings: 4096
+
+transformer_impl: local
+use_flash_attn: False
+seed: 1234
+data_parallel_random_init: False
+
+# Optimizer
+optimizer: adam
+lr: 2.5e-4
+lr_decay_style: cosine
+lr_decay_iters: null
+lr_decay_samples: 255126953
+lr_warmup_fraction: null
+lr_warmup_iters: 0
+lr_warmup_samples: 81381
+lr_warmup_init: 0.0
+min_lr: 2.5e-5
+weight_decay: 0.1
+start_weight_decay: null
+end_weight_decay: null
+weight_decay_incr_style: constant
+clip_grad: 1.0
+adam_beta1: 0.9
+adam_beta2: 0.95
+adam_eps: 1.e-08
+sgd_momentum: 0.9
+override_opt_param_scheduler: False
+use_checkpoint_opt_param_scheduler: False
+
+# checkpointing arguments
+save: null
+save_interval: 20000
+no_save_optim: null
+no_save_rng: null
+load: null
+no_load_optim: null
+no_load_rng: null
+finetune: False
+use_checkpoint_args: False
+exit_on_missing_checkpoint: False
+
+# loss arguments
+loss_scale: null
+initial_loss_scale: 4294967296
+min_loss_scale: 1.0
+loss_scale_window: 1000 
+hysteresis: 2
+accumulate_allreduce_grads_in_fp32: False
+fp16_lm_cross_entropy: False
+
+# distributed arguments
+distributed_backend: nccl
+distributed_timeout_minutes: 10
+overlap_grad_reduce: False
+delay_grad_reduce: True
+overlap_param_gather: False
+delay_param_gather: False
+scatter_gather_tensors_in_pipeline: True
+local_rank: null
+lazy_mpu_init: null
+empty_unused_memory_level: 0
+standalone_embedding_stage: False
+use_distributed_optimizer: False
+nccl_communicator_config_path: null
+
+train_iters: null
+eval_iters: 32
+eval_interval: 2000
+skip_train: False
+
+adlr_autoresume: False
+adlr_autoresume_interval: 1000
+
+# garbage collection
+manual_gc: False
+manual_gc_interval: 0
+manual_gc_eval: True
+
+tp_comm_overlap_cfg: null
+
+#data
+data_path: null
+split: '99,1,0'
+train_data_path: null
+valid_data_path: null
+test_data_path: null
+data_cache_path: null
+mock_data: False
+vocab_size: null
+vocab_file: null
+merge_file: null
+vocab_extra_ids: 0
+seq_length: 4096
+encoder_seq_length: null
+decoder_seq_length: null
+retriever_seq_length: 256
+sample_rate: 1.0
+mask_prob: 0.15
+short_seq_prob: 0.1
+num_workers: 2
+tokenizer_type: GPTSentencePieceTokenizer
+tokenizer_model: null
+reset_position_ids: False
+reset_attention_mask: False
+eod_mask_loss: False
+train_samples: 268554688
+dataloader_type: null
+
+#profile:
+profile: False
+profile_ranks: [0]
+profile_step_end: 12
+profile_step_start: 10
+
+#logging:
+log_params_norm: True
+log_num_zeros_in_grad: True
+log_throughput: False
+log_progress: False
+timing_log_level: 0
+timing_log_option: minmax
+tensorboard_log_interval: 1
+tensorboard_queue_size: 1000
+log_timers_to_tensorboard: False
+log_batch_size_to_tensorboard: False
+log_learning_rate_to_tensorboard: True
+log_learning_rate_to_tensorboard: True
+log_validation_ppl_to_tensorboard: False
+log_memory_to_tensorboard: False
+log_world_size_to_tensorboard: False
+log_loss_scale_to_tensorboard: True
+wandb_project: ''
+wandb_exp_name: ''
+wandb_save_dir: ''
+enable_one_logger: False
+one_logger_project: e2e-tracking
+one_logger_entity: hwinf_dcm
+one_logger_run_name: null
+log_interval: 100
+tensorboard_dir: null

examples/gpt3/train_gpt3_175b_distributed.sh

+#!/bin/bash
+
+# Runs the "175B" parameter model
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NUM_NODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
+
+CHECKPOINT_PATH=$1 #<Specify path>
+TENSORBOARD_LOGS_PATH=$2 #<Specify path>
+VOCAB_FILE=$3 #<Specify path to file>/gpt2-vocab.json
+MERGE_FILE=$4 #<Specify path to file>/gpt2-merges.txt
+DATA_PATH=$5 #<Specify path and file prefix>_text_document
+
+DISTRIBUTED_ARGS=(
+    --nproc_per_node $GPUS_PER_NODE 
+    --nnodes $NUM_NODES 
+    --master_addr $MASTER_ADDR 
+    --master_port $MASTER_PORT
+)
+
+GPT_MODEL_ARGS=(
+    --num-layers 96 
+    --hidden-size 12288 
+    --num-attention-heads 96 
+    --seq-length 2048 
+    --max-position-embeddings 2048 
+)
+
+TRAINING_ARGS=(
+    --micro-batch-size 1 
+    --global-batch-size 1536 
+    --rampup-batch-size 16 16 5859375 
+    --train-iters 500000 
+    --weight-decay 0.1 
+    --adam-beta1 0.9 
+    --adam-beta2 0.95 
+    --init-method-std 0.006 
+    --clip-grad 1.0 
+    --fp16
+    --lr 6.0e-5 
+    --lr-decay-style cosine 
+    --min-lr 6.0e-6
+    --lr-warmup-fraction .001 
+    --lr-decay-iters 430000 
+)
+
+MODEL_PARALLEL_ARGS=(
+	--tensor-model-parallel-size 8 
+	--pipeline-model-parallel-size 16 
+)
+
+DATA_ARGS=(
+    --data-path $DATA_PATH 
+    --vocab-file $VOCAB_FILE 
+    --merge-file $MERGE_FILE 
+    --split 949,50,1
+)
+
+EVAL_AND_LOGGING_ARGS=(
+    --log-interval 100
+    --save-interval 10000 
+    --eval-interval 1000 
+    --save $CHECKPOINT_PATH 
+    --load $CHECKPOINT_PATH 
+    --eval-iters 10
+    --tensorboard-dir $TENSORBOARD_LOGS_PATH 
+)
+
+torchrun ${DISTRIBUTED_ARGS[@]} pretrain_gpt.py \
+    ${GPT_MODEL_ARGS[@]} \
+    ${TRAINING_ARGS[@]} \
+    ${MODEL_PARALLEL_ARGS[@]} \
+    ${DATA_ARGS[@]} \
+    ${EVAL_AND_LOGGING_ARGS[@]}

examples/gpt3/train_gpt3_345M_distributed.sh

+#!/bin/bash
+
+# Runs the "175B" parameter model
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+GPUS_PER_NODE=1 #8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NUM_NODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
+
+CHECKPOINT_PATH=./tmp #$1 #<Specify path>
+TENSORBOARD_LOGS_PATH=./tmp  #$2 #<Specify path>
+#VOCAB_FILE=$3 #<Specify path to file>/gpt2-vocab.json
+#MERGE_FILE=$4 #<Specify path to file>/gpt2-merges.txt
+DATA_PATH="/root/megatron-llama/dataset/my-llama_text_document" #<Specify path and file prefix>_text_document
+TOKENIZER_PATH="/root/megatron-llama/tokenizer.model"
+
+DISTRIBUTED_ARGS=(
+    --nproc_per_node $GPUS_PER_NODE 
+    --nnodes $NUM_NODES 
+    --master_addr $MASTER_ADDR 
+    --master_port $MASTER_PORT
+)
+
+GPT_MODEL_ARGS=(
+    --num-layers 12 
+    --hidden-size 512 
+    --num-attention-heads 8
+    --seq-length 2048 
+    --max-position-embeddings 2048 
+)
+
+TRAINING_ARGS=(
+    --transformer-impl local
+    --use-legacy-models 
+    --micro-batch-size 1 
+    --global-batch-size 60 
+    --train-iters 50 
+    --weight-decay 0.1 
+    --adam-beta1 0.9 
+    --adam-beta2 0.95 
+    --init-method-std 0.006 
+    --clip-grad 1.0 
+    --fp16
+    --lr 6.0e-5 
+    --lr-decay-style cosine 
+    --min-lr 6.0e-6
+    --lr-warmup-fraction .001 
+    --lr-decay-iters 20 
+)
+
+MODEL_PARALLEL_ARGS=(
+	--tensor-model-parallel-size 1 
+	--pipeline-model-parallel-size 1 
+)
+
+DATA_ARGS=(
+    --data-path $DATA_PATH 
+    --split 949,50,1
+    --untie-embeddings-and-output-weights 
+     --position-embedding-type rope
+    --tokenizer-model $TOKENIZER_PATH 
+    --tokenizer-type GPTSentencePieceTokenizer
+)
+
+EVAL_AND_LOGGING_ARGS=(
+    --log-interval 1
+    --save-interval 10000 
+    --eval-interval 1000 
+    --save $CHECKPOINT_PATH 
+    --load $CHECKPOINT_PATH 
+    --eval-iters 10
+    --tensorboard-dir $TENSORBOARD_LOGS_PATH 
+)
+
+torchrun ${DISTRIBUTED_ARGS[@]} pretrain_gpt.py \
+    ${GPT_MODEL_ARGS[@]} \
+    ${TRAINING_ARGS[@]} \
+    ${MODEL_PARALLEL_ARGS[@]} \
+    ${DATA_ARGS[@]} \
+    ${EVAL_AND_LOGGING_ARGS[@]}

examples/inference/modelopt/README.md

+# Megatron Model Optimization and Deployment
+
+## Installation
+We recommend that users follow TensorRT-LLM's official installation guide to build it from source
+and proceed with a containerized environment (`docker.io/tensorrt_llm/release:latest`):
+
+```sh
+git clone https://github.com/NVIDIA/TensorRT-LLM.git
+cd TensorRT-LLM
+git checkout v0.10.0
+make -C docker release_build
+```
+
+> **TROUBLE SHOOTING:** rather than copying each folder separately in `docker/Dockerfile.multi`,
+> you may need to copy the entire dir as `COPY ./ /src/tensorrt_llm` since a `git submodule` is
+> called later which requires `.git` to continue.
+
+Once the container is built, install `nvidia-modelopt` and additional dependencies for sharded checkpoint support:
+```sh
+pip install "nvidia-modelopt[all]~=0.13.0" --extra-index-url https://pypi.nvidia.com
+pip install zarr tensorstore==0.1.45
+```
+TensorRT-LLM quantization functionalities are currently packaged in `nvidia-modelopt`.
+You can find more documentation about `nvidia-modelopt` [here](https://nvidia.github.io/TensorRT-Model-Optimizer/).
+
+## Support Matrix
+
+The following matrix shows the current support for the PTQ + TensorRT-LLM export flow.
+
+| model                       | fp16 | int8_sq | fp8 | int4_awq |
+|-----------------------------|------|---------| ----| -------- |
+| nextllm-2b                  | x    | x       |   x |          |
+| nemotron3-8b                | x    |         |   x |          |
+| nemotron3-15b               | x    |         |   x |          |
+| llama2-text-7b              | x    | x       |   x |      TP2 |
+| llama2-chat-70b             | x    | x       |   x |      TP4 |
+
+Our PTQ + TensorRT-LLM flow has native support on MCore `GPTModel` with a mixed layer spec (native ParallelLinear
+and Transformer-Engine Norm (`TENorm`). Note that this is not the default mcore gpt spec. You can still load the
+following checkpoint formats with some remedy:
+
+| GPTModel                          | sharded |                        remedy arguments     |
+|-----------------------------------|---------|---------------------------------------------|
+| megatron.legacy.model             |         | `--export-legacy-megatron` |
+| TE-Fused (default mcore gpt spec) |         | `--export-te-mcore-model`       |
+| TE-Fused (default mcore gpt spec) |       x |                                             |
+
+> **TROUBLE SHOOTING:** If you are trying to load an unpacked `.nemo` sharded checkpoint, then typically you will
+> need to adding `additional_sharded_prefix="model."` to `modelopt_load_checkpoint()` since NeMo has an additional
+> `model.` wrapper on top of the `GPTModel`.
+
+> **NOTE:** flag `--export-legacy-megatron` may not work on all legacy checkpoint versions.
+
+## Examples
+
+> **NOTE:** we only provide a simple text generation script to test the generated TensorRT-LLM engines. For
+> a production-level API server or enterprise support, see [NeMo](https://github.com/NVIDIA/NeMo) and TensorRT-LLM's
+> backend for [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server).
+
+### nemotron3-8B FP8 Quantization and TensorRT-LLM Deployment
+First download the nemotron checkpoint from https://huggingface.co/nvidia/nemotron-3-8b-base-4k, extract the
+sharded checkpoint from the `.nemo` tarbal and fix the tokenizer file name.
+
+> **NOTE:** The following cloning method uses `ssh`, and assume you have registered the `ssh-key` in Hugging Face.
+> If you are want to clone with `https`, then `git clone https://huggingface.co/nvidia/nemotron-3-8b-base-4k` with an access token.
+
+```sh
+git lfs install
+git clone git@hf.co:nvidia/nemotron-3-8b-base-4k
+cd nemotron-3-8b-base-4k
+tar -xvf Nemotron-3-8B-Base-4k.nemo
+mv 586f3f51a9cf43bc9369bd53fa08868c_a934dc7c3e1e46a6838bb63379916563_3feba89c944047c19d5a1d0c07a85c32_mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model tokenizer.model
+cd ..
+```
+
+Now launch the PTQ + TensorRT-LLM export script,
+```sh
+bash examples/inference/ptq_trtllm_nemotron3_8b ./nemotron-3-8b-base-4k None
+```
+By default, `cnn_dailymail` is used for calibration. The `GPTModel` will have quantizers for simulating the
+quantization effect. The checkpoint will be saved optionally (with quantizers as additional states) and can
+be restored for further evaluation. TensorRT-LLM checkpoint and engine are exported to `/tmp/trtllm_ckpt` and
+built in `/tmp/trtllm_engine` by default.
+
+The script expects `${CHECKPOINT_DIR}` (`./nemotron-3-8b-base-4k`) to have the following structure:
+```
+├── model_weights
+│   ├── common.pt
+│   ...
+│
+├── model_config.yaml
+├── mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model
+```
+
+> **NOTE:** The script is using `TP=8`. Change `$TP` in the script if your checkpoint has a different tensor
+> model parallelism.
+
+> **KNOWN ISSUES:** The `mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model` in the checkpoint is for
+> Megatron-LM's `GPTSentencePiece` tokenizer.
+> For TensorRT-LLM, we are trying to load this tokenizer as a Hugging Face `T5Tokenizer` by changing
+> some special tokens, `encode`, and `batch_decode`. As a result, the tokenizer behavior in TensorRT-LLM engine may
+> not match exactly.
+
+### llama2-text-7b INT8 SmoothQuant and TensorRT-LLM Deployment
+> **NOTE:** Due to the LICENSE issue, we do not provide a MCore checkpoint to download. Users can follow
+> the instruction in `docs/llama2.md` to convert the checkpoint to megatron legacy `GPTModel` format and
+> use `--export-legacy-megatron` flag which will remap the checkpoint to the MCore `GPTModel` spec
+> that we support.
+
+```sh
+bash examples/inference/ptq_trtllm_llama_7b.sh ${CHECKPOINT_DIR}
+```
+
+The script expect `${CHECKPOINT_DIR}` to have the following structure:
+```
+├── hf
+│   ├── tokenizer.config
+│   ├── tokenizer.model
+│   ...
+│
+├── iter_0000001
+│   ├── mp_rank_00
+│   ...
+│
+├── latest_checkpointed_iteration.txt
+```
+In short, other than the converted llama megatron checkpoint, also put the Hugging Face checkpoint inside as
+the source of the tokenizer.