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docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_config_gpt_TEMPLATE.json

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+{
+  "train_batch_size" : CONFIG_BATCH_SIZE,
+  "train_micro_batch_size_per_gpu": CONFIG_MBSIZE,
+  "steps_per_print": LOG_INTERVAL,
+
+  "zero_optimization": {
+    "stage": ZERO_STAGE,
+    "elastic_checkpoint": true
+  },
+
+  "gradient_clipping": 1.0,
+  "prescale_gradients": PRESCALE_GRAD,
+
+  "fp16": {
+    "enabled": CONFIG_FP16_ENABLED,
+    "loss_scale": 0,
+    "loss_scale_window": 500,
+    "hysteresis": 2,
+    "min_loss_scale": 1,
+    "initial_scale_power": 11
+  },
+
+  "bf16": {
+    "enabled": CONFIG_BF16_ENABLED
+  },
+  "curriculum_learning": {
+    "enabled": CONFIG_CL_ENABLED,
+    "curriculum_type": "seqlen",
+    "min_difficulty": CONFIG_CL_MIN,
+    "max_difficulty": CONFIG_CL_MAX,
+    "schedule_type": "fixed_linear",
+    "schedule_config": {
+      "total_curriculum_step": CONFIG_CL_DURATION,
+      "difficulty_step": 8
+    }
+  },
+
+  "wall_clock_breakdown" : false
+}

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_config_gpt_Zero2_TEMPLATE.json

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+{
+  "train_batch_size" : CONFIG_BATCH_SIZE,
+  "train_micro_batch_size_per_gpu": CONFIG_MBSIZE,
+  "steps_per_print": LOG_INTERVAL,
+
+  "zero_optimization": {
+    "stage": 2
+  },
+
+  "gradient_clipping": 1.0,
+  "prescale_gradients": false,
+
+  "fp16": {
+    "enabled": CONFIG_FP16_ENABLED,
+    "loss_scale": 0,
+    "loss_scale_window": 500,
+    "hysteresis": 2,
+    "min_loss_scale": 1,
+    "initial_scale_power": 11
+  },
+
+  "bf16": {
+    "enabled": CONFIG_BF16_ENABLED
+  },
+  "curriculum_learning": {
+    "enabled": CONFIG_CL_ENABLED,
+    "curriculum_type": "seqlen",
+    "min_difficulty": CONFIG_CL_MIN,
+    "max_difficulty": CONFIG_CL_MAX,
+    "schedule_type": "fixed_linear",
+    "schedule_config": {
+      "total_curriculum_step": CONFIG_CL_DURATION,
+      "difficulty_step": 8
+    }
+  },
+
+  "wall_clock_breakdown" : false
+}

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_evalharness.sh

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+# This is an example zero-shot eval script. Please first read the readme_evalharness.md under the same directory.
+
+CHECKPOINT_PATH=/blob/users/conglli/project/gpt3_with_pile/checkpoint/gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-128-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-20728-token-45B/global_step81566/
+CONFIG_PATH=ds_config_gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-128-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-20728-token-45B.json
+RESULT_PATH=gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-128-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-20728-token-45B_global_step81566.log
+
+PP_SIZE=1
+TP_SIZE=1
+NO_PP="true"
+EP_PARALLEL_SIZE=1
+# Currently eval harness does not support data parallel
+# However, for MoE models it's possible to enable a "fake data parallel"
+# in order to load experts on multiple gpus. At the same time, it's not
+# real data parallel because we load the same data on all gpus.
+# On the other hand, it's better to use less number of gpus than training,
+# to reduce communication overhead.
+NUM_NODE=1
+NUM_GPU_PER_NODE=1
+
+TASKS="lambada"
+# WikiText-2, not used in GPT-3 paper but used in GPT-2 paper
+# TASKS="wikitext"
+# Tasks that appeared in GPT-3 paper (sorted based on the order in paper), plus WikiText-2.
+# TASKS="hellaswag,lambada,triviaqa,webqs,winogrande,piqa,arc_challenge,arc_easy,openbookqa,race,boolq,cb,copa,rte,wic,wsc,multirc,record,anli_r1,anli_r2,anli_r3,wikitext"
+# All tasks that confirmed to work, there are more tasks on https://github.com/EleutherAI/lm-evaluation-harness that we didn't test.
+# TASKS="hellaswag,lambada,triviaqa,webqs,winogrande,piqa,arc_challenge,arc_easy,openbookqa,race,boolq,cb,copa,rte,wic,wsc,multirc,record,anli_r1,anli_r2,anli_r3,wikitext,logiqa,mathqa,mc_taco,mrpc,prost,pubmedqa,qnli,qqp,sciq,sst,wnli"
+
+VOCAB_FILE=/data/Megatron-LM/data/gpt2-vocab.json
+MERGE_FILE=/data/Megatron-LM/data/gpt2-merges.txt
+
+export HF_DATASETS_OFFLINE=1
+
+# Dummy arguments to make megatron happy. No need to configure them.
+# The reason we don't need to configure them and many other arguments is
+# because the eval framework will read the arguments from checkpoint file.
+MEGATRON_REQUIRED_ARGS="\
+    --num-layers -1\
+    --hidden-size -1\
+    --num-attention-heads -1\
+    --seq-length -1 \
+    --max-position-embeddings -1
+"
+
+CMD="../../tasks/eval_harness/evaluate.py \
+    --load $CHECKPOINT_PATH\
+    --tensor-model-parallel-size $TP_SIZE \
+    --pipeline-model-parallel-size $PP_SIZE\
+    --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+    --vocab-file $VOCAB_FILE\
+    --merge-file $MERGE_FILE\
+    --micro-batch-size 12\
+    --no-load-optim \
+    --no-load-rng \
+    --inference \
+    --disable-moe-token-dropping \
+    --adaptive_seq_len\
+    --eval_fp32\
+    --task_list $TASKS\
+    --results_path $RESULT_PATH \
+    --deepspeed \
+    --deepspeed_config $CONFIG_PATH \
+    $MEGATRON_REQUIRED_ARGS\
+    "
+
+if [[ "${NO_PP}" = "true" ]]; then
+CMD="${CMD} \
+    --no-pipeline-parallel"
+fi
+
+LAUNCHER="deepspeed --num_nodes $NUM_NODE --num_gpus $NUM_GPU_PER_NODE"
+$LAUNCHER $CMD

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_1.3B_MoE128.sh

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+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+MODEL_SIZE=1.3
+NUM_LAYERS=24
+HIDDEN_SIZE=2048
+NUM_ATTN_HEADS=16
+GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_ITERS is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_ITERS.
+TRAIN_ITERS=$(( ${TRAIN_TOKENS} * 3 / ${GLOBAL_BATCH_SIZE} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=8
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 1 means dense model without MoE
+# EP_SIZE=1
+EP_SIZE=128
+
+if [[ $EP_SIZE -gt $NUM_GPUS ]]; then
+    EP_PARALLEL_SIZE=$NUM_GPUS
+else
+    EP_PARALLEL_SIZE=$EP_SIZE
+fi
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B MoE-128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## For 350M MoE-128 model we used LR=2.0e-4 and MIN_LR=2.0e-6, but they are not
+## heavily tuned.
+LR=1.2e-4
+MIN_LR=1.0e-6
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=10000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [[ $EP_SIZE -gt 1 ]]; then
+    NAME="${NAME}-ep-${EP_SIZE}-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+# USE_INTERNAL_DATA="true"
+USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    # BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    # DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    BASE_DATA_PATH=/data/Megatron-LM/data
+    DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+    MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+    # Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+    DATA_BLEND=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-iters ${TRAIN_ITERS} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [[ $EP_SIZE -gt 1 ]]; then
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+fi
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/0/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+if [[ $EP_SIZE -gt 1 ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_1.3B_PR-MoE64or128.sh

@@ -0,0 +1,341 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+MODEL_SIZE=1.3
+NUM_LAYERS=24
+HIDDEN_SIZE=2048
+NUM_ATTN_HEADS=16
+GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_ITERS is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_ITERS.
+TRAIN_ITERS=$(( ${TRAIN_TOKENS} * 3 / ${GLOBAL_BATCH_SIZE} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=8
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 128 means standard MoE
+# EP_SIZE=128
+EP_SIZE="64 64 64 64 64 64 64 64 64 64 128 128"
+
+
+EP_PARALLEL_SIZE=$NUM_GPUS
+
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B PR-MoE-64/128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## heavily tuned.
+LR=1.2e-4
+MIN_LR=1.0e-6
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=10000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+NAME="${NAME}-ep-pyramid-64+128-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+# USE_INTERNAL_DATA="true"
+USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+    MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+    # Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+    DATA_BLEND=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --mlp-type residual \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-iters ${TRAIN_ITERS} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_Zero2_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_1.3B_PR-MoE64or128_MoS.sh

@@ -0,0 +1,355 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+MODEL_SIZE=1.3
+NUM_LAYERS=24
+HIDDEN_SIZE=2048
+NUM_ATTN_HEADS=16
+GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_ITERS is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_ITERS.
+TRAIN_ITERS=$(( ${TRAIN_TOKENS} * 3 / ${GLOBAL_BATCH_SIZE} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=128
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 128 means standard MoE
+# EP_SIZE=128
+EP_SIZE="64 64 64 64 64 64 64 64 128 128"
+EP_SIZE_TEACHER="64 64 64 64 64 64 64 64 64 64 128 128"
+
+EP_PARALLEL_SIZE=$NUM_GPUS
+
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B PR-MoE-64/128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## heavily tuned.
+LR=1.2e-4
+MIN_LR=1.0e-6
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=10000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+NAME="${NAME}-ep-pyramid-64+128-mos-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+### Mixture-of-Students (MoS) configs
+KD_BETA_CE=1
+CHECKPOINT_PATH_STUDENT="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+CHECKPOINT_PATH_TEACHER="${OUTPUT_BASEPATH}/checkpoint/gpt-1.3B-lr-1.2e-4-minlr-1.0e-6-bs-512-gpus-128-mp-1-pp-1-ep-pyramid-64+128-mlc-0.01-cap-1.0-drop-true/"
+CHECKPOINT_PATH_SAVE="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+USE_INTERNAL_DATA="true"
+# USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    ## Placeholder, we plan to test a public dataset
+    VOCAB_PATH=""
+    MERGE_PATH=""
+    DATA_BLEND=""
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --mlp-type residual \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers 21 \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-iters ${TRAIN_ITERS} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH_STUDENT} \
+        --save ${CHECKPOINT_PATH_SAVE} \
+        --mos \
+        --kd-beta-ce ${KD_BETA_CE} \
+        --num-layers-teacher ${NUM_LAYERS} \
+        --num-experts-teacher ${EP_SIZE_TEACHER} \
+        --hidden-size-teacher ${HIDDEN_SIZE} \
+        --num-attention-heads-teacher ${NUM_ATTN_HEADS} \
+        --load-teacher ${CHECKPOINT_PATH_TEACHER} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_Zero2_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+# run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_1.3B_dense.sh

@@ -0,0 +1,350 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+MODEL_SIZE=1.3
+NUM_LAYERS=24
+HIDDEN_SIZE=2048
+NUM_ATTN_HEADS=16
+GLOBAL_BATCH_SIZE=512
+LR=2.0e-4
+MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+# LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=2
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=4
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 1 means dense model without MoE
+EP_SIZE=1
+# EP_SIZE=128
+
+if [[ $EP_SIZE -gt $NUM_GPUS ]]; then
+    EP_PARALLEL_SIZE=$NUM_GPUS
+else
+    EP_PARALLEL_SIZE=$EP_SIZE
+fi
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B MoE-128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## For 350M MoE-128 model we used LR=2.0e-4 and MIN_LR=2.0e-6, but they are not
+## heavily tuned.
+# LR=2.0e-4
+# MIN_LR=2e-06
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=1000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [[ $EP_SIZE -gt 1 ]]; then
+    NAME="${NAME}-ep-${EP_SIZE}-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+# USE_INTERNAL_DATA="true"
+USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    # BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    # DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    BASE_DATA_PATH=/data/Megatron-LM/data
+    DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+    MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+    # Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+    DATA_BLEND=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --rampup-batch-size 32 32 1953125 \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [[ $EP_SIZE -gt 1 ]]; then
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+fi
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/0/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+if [[ $EP_SIZE -gt 1 ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_1.3B_dense_cl.sh

@@ -0,0 +1,285 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+MODEL_SIZE=1.3
+NUM_LAYERS=24
+HIDDEN_SIZE=2048
+NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+MIN_LR=2.0e-5
+
+# Curriculum learning (CL) enables stable large-batch training
+GLOBAL_BATCH_SIZE=4096 # 8x
+LR=8.0e-4 # 4x
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+TRAIN_TOKENS=300000000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some samples,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=16
+
+## Model parallelism, 1 is no MP
+MP_SIZE=2
+
+## Pipeline parallelism. To disable PP, set PP_SIZE to 1 and NO_PP to true.
+PP_SIZE=1
+NO_PP="true"
+
+## ZeRO stage
+ZERO_STAGE=0
+
+## Total number of GPUs
+NUM_GPUS=128
+DP_SIZE=$(( ${NUM_GPUS} / ${PP_SIZE} / ${MP_SIZE} ))
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="true"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_STEP=$(( ${CL_TOKENS} * 1000000000 / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=1000
+
+## Standard deviation for weight initialization. Usually larger model needs
+## lower std. We used a heuristic equation of sqrt(1/3/HIDDEN_SIZE) from the
+## MT-NLG 530B work (https://arxiv.org/pdf/2201.11990.pdf)
+INIT_STD=0.013
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+LOG_OPTIMIZER_STATE="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt3-with-pile-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-zero-${ZERO_STAGE}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [ "${NO_PP}" = "true" ]; then
+    NAME="${NAME}-no_pp"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-startseqlen-${CL_START_SEQLEN}-step-${CL_STEP}-token-${CL_TOKENS}B"
+fi
+
+LOG_PATH="log/"
+TENSORBOARD_PATH="tensorboard/${NAME}_${host}_${current_time}"
+CHECKPOINT_PATH="/blob/users/conglli/project/gpt3_with_pile/checkpoint/${NAME}"
+mkdir -p ${LOG_PATH}
+mkdir -p ${TENSORBOARD_PATH}
+mkdir -p ${CHECKPOINT_PATH}
+
+VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+# Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+DATA_PATH=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_PATH}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [ "${LOG_OPTIMIZER_STATE}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --log-optimizer-states-to-tensorboard"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_${NAME}.json"
+if [[ $ZERO_STAGE -gt 0 ]]; then
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/false/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+else
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+fi
+
+deepspeed_options=" \
+            --deepspeed \
+            --deepspeed_config ${config_json} \
+            --zero-stage ${ZERO_STAGE} \
+            --pipeline-model-parallel-size ${PP_SIZE}"
+
+if [[ "${NO_PP}" = "true" ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${LOG_PATH}/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_125M_MoE64.sh

@@ -0,0 +1,373 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+MODEL_SIZE=0.125
+NUM_LAYERS=12
+HIDDEN_SIZE=768
+NUM_ATTN_HEADS=12
+GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+# MODEL_SIZE=1.3
+# NUM_LAYERS=24
+# HIDDEN_SIZE=2048
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_ITERS is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_ITERS.
+TRAIN_ITERS=$(( ${TRAIN_TOKENS} * 3 / ${GLOBAL_BATCH_SIZE} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=$(($(ds_ssh nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)-2))
+NUM_GPUS_PERNODE=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
+NUM_NODE=$(( ${NUM_GPUS} / ${NUM_GPUS_PERNODE} ))
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 1 means dense model without MoE
+# EP_SIZE=1
+EP_SIZE=64
+
+if [[ $EP_SIZE -gt $NUM_GPUS ]]; then
+    EP_PARALLEL_SIZE=$NUM_GPUS
+else
+    EP_PARALLEL_SIZE=$EP_SIZE
+fi
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B MoE-128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## For 350M MoE-128 model we used LR=2.0e-4 and MIN_LR=2.0e-6, but they are not
+## heavily tuned.
+LR=4.5e-4
+MIN_LR=4.5e-06
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=10000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [[ $EP_SIZE -gt 1 ]]; then
+    NAME="${NAME}-ep-${EP_SIZE}-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+# USE_INTERNAL_DATA="true"
+USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    # BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    # DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    BASE_DATA_PATH=/data/Megatron-LM/data
+    DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_PATH="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+    MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+    # Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+    # For cluster Azure-EastUS-V100-32GB-4, Lab-RR1-V100
+    DATA_PATH=/vc_data_blob/users/conglli/the_pile_public_merged_nopreprocessing/pile_text_document
+    # For cluster Azure-WestUS3-A100
+    # DATA_PATH=/blob/data/the_pile_public_merged_nopreprocessing/pile_text_document
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-iters ${TRAIN_ITERS} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [[ $EP_SIZE -gt 1 ]]; then
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+fi
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/0/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+if [[ $EP_SIZE -gt 1 ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+## When saving checkpoint to a storage with cache, their could be consistency
+## issue of the pointer to latest checkpoint. Here we find the correct pointer
+## and broadcast it to all nodes.
+ITERATION_FILE="$CHECKPOINT_PATH/latest_checkpointed_iteration.txt"
+ITERATION_FILE_2="$CHECKPOINT_PATH/latest"
+ITERATION=0
+for (( node = 0; node <= NUM_NODE-1; node++ ))
+do
+    if $(ssh -q worker-"$node" "test -f \"$ITERATION_FILE\""); then
+        LOCAL_ITERATION=$(ssh -q worker-"$node" cat $ITERATION_FILE)
+        ITERATION=$(( ${LOCAL_ITERATION} > ${ITERATION} ? ${LOCAL_ITERATION} :  ${ITERATION} ))
+    fi
+done
+if [[ $ITERATION -gt 0 ]]; then
+    ITERATION_2="global_step${ITERATION}"
+    ds_ssh "echo $ITERATION > $ITERATION_FILE"
+    ds_ssh "echo $ITERATION_2 > $ITERATION_FILE_2"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_125M_dense_cl.sh

@@ -0,0 +1,309 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+MODEL_SIZE=0.125
+NUM_LAYERS=12
+HIDDEN_SIZE=768
+NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+MIN_LR=6.0e-5
+
+# Curriculum learning (CL) enables stable large-batch training
+GLOBAL_BATCH_SIZE=2048 # 8x
+LR=2.4e-3 # 4x
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+# MODEL_SIZE=1.3
+# NUM_LAYERS=24
+# HIDDEN_SIZE=2048
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+TRAIN_TOKENS=300000000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some samples,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=16
+
+## Model parallelism, 1 is no MP
+MP_SIZE=1
+
+## Pipeline parallelism. To disable PP, set PP_SIZE to 1 and NO_PP to true.
+PP_SIZE=1
+NO_PP="true"
+
+## ZeRO stage
+ZERO_STAGE=0
+
+## Total number of GPUs
+NUM_GPUS=$(($(ds_ssh nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)-2))
+NUM_GPUS_PERNODE=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
+NUM_NODE=$(( ${NUM_GPUS} / ${NUM_GPUS_PERNODE} ))
+DP_SIZE=$(( ${NUM_GPUS} / ${PP_SIZE} / ${MP_SIZE} ))
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="true"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=72
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_STEP=$(( ${CL_TOKENS} * 1000000000 / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=1000
+
+## Standard deviation for weight initialization. Usually larger model needs
+## lower std. We used a heuristic equation of sqrt(1/3/HIDDEN_SIZE) from the
+## MT-NLG 530B work (https://arxiv.org/pdf/2201.11990.pdf)
+INIT_STD=0.02
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+LOG_OPTIMIZER_STATE="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt3-with-pile-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-zero-${ZERO_STAGE}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [ "${NO_PP}" = "true" ]; then
+    NAME="${NAME}-no_pp"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-startseqlen-${CL_START_SEQLEN}-step-${CL_STEP}-token-${CL_TOKENS}B"
+fi
+
+LOG_PATH="log/"
+TENSORBOARD_PATH="tensorboard/${NAME}_${host}_${current_time}"
+CHECKPOINT_PATH="/blob/users/conglli/project/gpt3_with_pile/checkpoint/${NAME}"
+mkdir -p ${LOG_PATH}
+mkdir -p ${TENSORBOARD_PATH}
+mkdir -p ${CHECKPOINT_PATH}
+
+VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+# Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+# For cluster Azure-EastUS-V100-32GB-4, Lab-RR1-V100
+DATA_PATH=/vc_data_blob/users/conglli/the_pile_public_merged_nopreprocessing/pile_text_document
+# For cluster Azure-WestUS3-A100
+# DATA_PATH=/blob/data/the_pile_public_merged_nopreprocessing/pile_text_document
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_PATH}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [ "${LOG_OPTIMIZER_STATE}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --log-optimizer-states-to-tensorboard"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_${NAME}.json"
+if [[ $ZERO_STAGE -gt 0 ]]; then
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/false/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+else
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+fi
+
+deepspeed_options=" \
+            --deepspeed \
+            --deepspeed_config ${config_json} \
+            --zero-stage ${ZERO_STAGE} \
+            --pipeline-model-parallel-size ${PP_SIZE}"
+
+if [[ "${NO_PP}" = "true" ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+## When saving checkpoint to a storage with cache, their could be consistency
+## issue of the pointer to latest checkpoint. Here we find the correct pointer
+## and broadcast it to all nodes.
+ITERATION_FILE="$CHECKPOINT_PATH/latest_checkpointed_iteration.txt"
+ITERATION_FILE_2="$CHECKPOINT_PATH/latest"
+ITERATION=0
+for (( node = 0; node <= NUM_NODE-1; node++ ))
+do
+    if $(ssh -q worker-"$node" "test -f \"$ITERATION_FILE\""); then
+        LOCAL_ITERATION=$(ssh -q worker-"$node" cat $ITERATION_FILE)
+        ITERATION=$(( ${LOCAL_ITERATION} > ${ITERATION} ? ${LOCAL_ITERATION} :  ${ITERATION} ))
+    fi
+done
+if [[ $ITERATION -gt 0 ]]; then
+    ITERATION_2="global_step${ITERATION}"
+    ds_ssh "echo $ITERATION > $ITERATION_FILE"
+    ds_ssh "echo $ITERATION_2 > $ITERATION_FILE_2"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${LOG_PATH}/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_350M_MoE128.sh

@@ -0,0 +1,349 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+MODEL_SIZE=0.35
+NUM_LAYERS=24
+HIDDEN_SIZE=1024
+NUM_ATTN_HEADS=16
+GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+# MODEL_SIZE=1.3
+# NUM_LAYERS=24
+# HIDDEN_SIZE=2048
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_ITERS is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_ITERS.
+TRAIN_ITERS=$(( ${TRAIN_TOKENS} * 3 / ${GLOBAL_BATCH_SIZE} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 1 means dense model without MoE
+# EP_SIZE=1
+EP_SIZE=128
+
+if [[ $EP_SIZE -gt $NUM_GPUS ]]; then
+    EP_PARALLEL_SIZE=$NUM_GPUS
+else
+    EP_PARALLEL_SIZE=$EP_SIZE
+fi
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B MoE-128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## For 350M MoE-128 model we used LR=2.0e-4 and MIN_LR=2.0e-6, but they are not
+## heavily tuned.
+LR=2.0e-4
+MIN_LR=2e-06
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=10000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [[ $EP_SIZE -gt 1 ]]; then
+    NAME="${NAME}-ep-${EP_SIZE}-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+# USE_INTERNAL_DATA="true"
+USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    # BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    # DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    BASE_DATA_PATH=/data/Megatron-LM/data
+    DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+    MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+    # Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+    DATA_BLEND=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-iters ${TRAIN_ITERS} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [[ $EP_SIZE -gt 1 ]]; then
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+fi
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/0/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+if [[ $EP_SIZE -gt 1 ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_350M_PR-MoE32or64.sh

@@ -0,0 +1,342 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+MODEL_SIZE=0.35
+NUM_LAYERS=24
+HIDDEN_SIZE=1024
+NUM_ATTN_HEADS=16
+GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+# MODEL_SIZE=1.3
+# NUM_LAYERS=24
+# HIDDEN_SIZE=2048
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_ITERS is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_ITERS.
+TRAIN_ITERS=$(( ${TRAIN_TOKENS} * 3 / ${GLOBAL_BATCH_SIZE} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 128 means standard MoE
+# EP_SIZE=128
+EP_SIZE="32 32 32 32 32 32 32 32 32 32 64 64"
+
+EP_PARALLEL_SIZE=$NUM_GPUS
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B PR-MoE-64/128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## For 350M PR-MoE-32/64 model we used LR=3.0e-4 and MIN_LR=1.0e-6, but they are not
+## heavily tuned.
+LR=3.0e-4
+MIN_LR=1.0e-06
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=10000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+NAME="${NAME}-ep-pyramid-32+64-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+# USE_INTERNAL_DATA="true"
+USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+    MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+    # Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+    DATA_BLEND=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --mlp-type residual \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-iters ${TRAIN_ITERS} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/0/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_350M_PR-MoE32or64_MoS.sh

@@ -0,0 +1,354 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+MODEL_SIZE=0.35
+NUM_LAYERS=24
+HIDDEN_SIZE=1024
+NUM_ATTN_HEADS=16
+GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+# MODEL_SIZE=1.3
+# NUM_LAYERS=24
+# HIDDEN_SIZE=2048
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_ITERS is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_ITERS.
+TRAIN_ITERS=$(( ${TRAIN_TOKENS} * 3 / ${GLOBAL_BATCH_SIZE} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 128 means standard MoE
+# EP_SIZE=128
+EP_SIZE="32 32 32 32 32 32 32 32 64 64"
+EP_SIZE_TEACHER="32 32 32 32 32 32 32 32 32 32 64 64"
+
+EP_PARALLEL_SIZE=$NUM_GPUS
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B PR-MoE-64/128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## For 350M PR-MoE-32/64 model we used LR=3.0e-4 and MIN_LR=1.0e-6, but they are not
+## heavily tuned.
+LR=3.0e-4
+MIN_LR=1.0e-06
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=10000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+NAME="${NAME}-ep-pyramid-32+64-mos-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+### Mixture-of-Students (MoS) configs
+KD_BETA_CE=1
+CHECKPOINT_PATH_STUDENT="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+CHECKPOINT_PATH_TEACHER="${OUTPUT_BASEPATH}/checkpoint/gpt-1.3B-lr-1.2e-4-minlr-1.0e-6-bs-512-gpus-128-mp-1-pp-1-ep-pyramid-64+128-mlc-0.01-cap-1.0-drop-true/"
+CHECKPOINT_PATH_SAVE="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+USE_INTERNAL_DATA="true"
+# USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    ## Placeholder, we plan to test a public dataset
+    VOCAB_PATH=""
+    MERGE_PATH=""
+    DATA_BLEND=""
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --mlp-type residual \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers 21 \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-iters ${TRAIN_ITERS} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH_STUDENT} \
+        --save ${CHECKPOINT_PATH_SAVE} \
+        --mos \
+        --kd-beta-ce ${KD_BETA_CE} \
+        --num-layers-teacher ${NUM_LAYERS} \
+        --num-experts-teacher ${EP_SIZE_TEACHER} \
+        --hidden-size-teacher ${HIDDEN_SIZE} \
+        --num-attention-heads-teacher ${NUM_ATTN_HEADS} \
+        --load-teacher ${CHECKPOINT_PATH_TEACHER} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_350M_dense.sh

@@ -0,0 +1,349 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+MODEL_SIZE=0.35
+NUM_LAYERS=24
+HIDDEN_SIZE=1024
+NUM_ATTN_HEADS=16
+GLOBAL_BATCH_SIZE=256
+LR=3.0e-4
+MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+# MODEL_SIZE=1.3
+# NUM_LAYERS=24
+# HIDDEN_SIZE=2048
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+# LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 1 means dense model without MoE
+EP_SIZE=1
+# EP_SIZE=128
+
+if [[ $EP_SIZE -gt $NUM_GPUS ]]; then
+    EP_PARALLEL_SIZE=$NUM_GPUS
+else
+    EP_PARALLEL_SIZE=$EP_SIZE
+fi
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B MoE-128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## For 350M MoE-128 model we used LR=2.0e-4 and MIN_LR=2.0e-6, but they are not
+## heavily tuned.
+# LR=2.0e-4
+# MIN_LR=2e-06
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=1000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [[ $EP_SIZE -gt 1 ]]; then
+    NAME="${NAME}-ep-${EP_SIZE}-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+# USE_INTERNAL_DATA="true"
+USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    # BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    # DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    BASE_DATA_PATH=/data/Megatron-LM/data
+    DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+    MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+    # Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+    DATA_BLEND=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [[ $EP_SIZE -gt 1 ]]; then
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+fi
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/0/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+if [[ $EP_SIZE -gt 1 ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/ds_pretrain_gpt_6.7B_dense.sh

@@ -0,0 +1,350 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+# MODEL_SIZE=1.3
+# NUM_LAYERS=24
+# HIDDEN_SIZE=2048
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+MODEL_SIZE=6.7
+NUM_LAYERS=32
+HIDDEN_SIZE=4096
+NUM_ATTN_HEADS=32
+GLOBAL_BATCH_SIZE=1024
+LR=1.2e-4
+MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+# LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=8
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 1 means dense model without MoE
+EP_SIZE=1
+# EP_SIZE=128
+
+if [[ $EP_SIZE -gt $NUM_GPUS ]]; then
+    EP_PARALLEL_SIZE=$NUM_GPUS
+else
+    EP_PARALLEL_SIZE=$EP_SIZE
+fi
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B MoE-128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## For 350M MoE-128 model we used LR=2.0e-4 and MIN_LR=2.0e-6, but they are not
+## heavily tuned.
+# LR=2.0e-4
+# MIN_LR=2e-06
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=1000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+# INIT_STD=0.014
+INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [[ $EP_SIZE -gt 1 ]]; then
+    NAME="${NAME}-ep-${EP_SIZE}-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+# USE_INTERNAL_DATA="true"
+USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    # BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    # DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    BASE_DATA_PATH=/data/Megatron-LM/data
+    DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+    MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+    # Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+    DATA_BLEND=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --rampup-batch-size 32 32 4882812 \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [[ $EP_SIZE -gt 1 ]]; then
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+fi
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/0/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+if [[ $EP_SIZE -gt 1 ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/MoE/readme_evalharness.md

@@ -0,0 +1,168 @@
+# How to run lm-eval on Megatron-DeepSpeed checkpoint using the original setup
+
+A great portion of this eval harness feature is inherited from https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/212, but with code/doc changes (e.g., to support case without pipeline parallelism and MoE models).
+
+This particular setup uses the normal deepspeed checkpoint and requires no conversion to Megatron-LM.
+
+## Prerequisites
+
+1. Install software
+
+On login console with external network
+
+Get lm-eval harness (https://github.com/EleutherAI/lm-evaluation-harness) and `best-download==0.0.7` needed to download some tasks.
+```
+(maybe need pip install --upgrade pip)
+pip install best-download==0.0.7
+pip install lm-eval
+(previously we used "pip install git+https://github.com/EleutherAI/lm-evaluation-harness" to install, but later found the command above has less dependency issues)
+```
+
+2. Pre-download needed datasets
+
+some symlinks due to lm-harness' issues with relative position of data
+```
+mkdir data
+cd ../../tasks/eval_harness/
+ln -s ../../examples/MoE/data/ data
+cd ../../examples/MoE/
+```
+<!-- Also make sure `data` is not on one of the limited paritions like WORKSF. -->
+
+Then install datasets for the tasks:
+```
+python ../../tasks/eval_harness/download.py --task_list hellaswag,lambada,triviaqa,webqs,winogrande,piqa,arc_challenge,arc_easy,openbookqa,race,boolq,cb,copa,rte,wic,wsc,multirc,record,anli_r1,anli_r2,anli_r3,wikitext,logiqa,mathqa,mc_taco,mrpc,prost,pubmedqa,qnli,qqp,sciq,sst,wnli
+```
+and make sure that `export HF_DATASETS_OFFLINE=1`
+
+<!-- If there are things like custom tokenizers, pre-download those too, e.g.:
+
+```
+python -c "from transformers import AutoTokenizer; AutoTokenizer.from_pretrained('bigscience/oscar_13_languages_alpha_weight')"
+```
+and make sure that `export TRANSFORMERS_OFFLINE=1` is in the script.
+You know there is a custom tokenizer if the training script had something like:
+
+```
+--tokenizer-type PretrainedFromHF \
+ --tokenizer-name-or-path bigscience/oscar_13_languages_alpha_weight \
+``` -->
+
+3. Prepare the script
+
+<!-- Prepare the run script, replace `variant` with a unique identifier for the current eval so that multiple evals could run in parallel and not all log into the same `results.json` file. so, e.g., `tr9c-1B3-swiglu`
+
+```
+cp examples/run_evalharness_deepspeed.slurm run_evalharness-variant.slurm
+```
+
+now edit `run_evalharness-variant.slurm`
+
+
+Note that the eval code knows to pull the original training args from the checkpoint, so we don't need to pass any of those. And we just need to setup the evaluation args. -->
+
+`ds_evalharness.sh` is the example script.
+
+1. Edit:
+
+```
+PP_SIZE=1
+TP_SIZE=1
+NO_PP="true"
+EP_PARALLEL_SIZE=1
+NUM_NODE=1
+NUM_GPU_PER_NODE=1
+```
+to match the eval topology. 
+
+Edit:
+```
+CHECKPOINT_PATH=
+CONFIG_PATH=
+RESULT_PATH=
+```
+to the checkpoint/ds config you want to use, and where to save the results.
+<!-- If the model fits into 1 gpu, then there is nothing to change.
+
+The eval script will automatically reshape the model if it was of a different topology. -->
+
+
+2. Adjust the following to fit the chosen GPU. As of last check for 1.3B model the settings are one of:
+```
+EVAL_MICRO_BATCH_SIZE=6  # 16GB GPU 1.3B model
+EVAL_MICRO_BATCH_SIZE=12 # 32GB GPU 1.3B model
+```
+
+If you get OOM lower it further.
+
+3. If not using the Deepspeed path, disable it by removing:
+
+```
+    --deepspeed \
+    --deepspeed_config ds_config.json \
+```
+
+If you didn't disable it and the program crashed on checkpoint loading unable to find some key, disable deepspeed as explained above.
+
+Note that for MoE models and for models without pipeline parallelism, currently they might not work for the case without deepspeed.
+
+<!-- ## Eval
+
+Currently it takes 2-3 hours to run on 32GB for 1.3B model, 6-7h for 16GB GPU, so a 20h slurm job should be enough.
+
+When ready, launch:
+```
+sbatch ./run_evalharness-variant.slurm
+```
+
+To monitor progress:
+```
+tail -f tail -f $VARIANT-eval-harness.log
+```
+where the variant is what you set `$VARIANT` to in the slurm script.
+
+The template is set up for 16GB gpu since they are easier to get by. If you change to 32GB, adjust:
+```
+#SBATCH --constraint=v100-32g
+...
+EVAL_MICRO_BATCH_SIZE=12 # 32GB GPU 1.3B model
+```
+
+
+Note that the original ETA at the start of the run can be 10x too longer than the actual outcome. For example it may suggest 18 hours but will complete in 2 hours.
+
+
+## Short eval
+
+if you just want to quickly test that everything can run to the end, edit `tasks/eval_harness/evaluate.py`,  e.g. to run only 10 batches:
+```
+- results = evaluator.evaluate(adaptor, task_dict, False, 0, None)
++ results = evaluator.evaluate(adaptor, task_dict, False, 0, 10)
+```
+
+(XXX: could be a cmd line option so that code won't need to be modified)
+
+
+## Import into spreadsheet
+
+https://docs.google.com/spreadsheets/d/1CI8Q9RCblLRzUOPJ6ViqBmo284-8ojluQ-CmaEuhuv0/edit?usp=sharing
+
+Note that the spreadsheet format is quite different, so use this script:
+```
+./tasks/eval_harness/report-to-csv.py results.json
+```
+to reformat the json results into csv while changing its shape to match the spreadsheet format
+
+Since some records might be missing or extraneous here is the best way to do it:
+
+1. copy the data from first 2 columns to some place under the main spreadsheet
+
+2. put the pointer to the 3rd column next to where the 2 first columns were copied.
+
+3. import `results.csv` using file-> import -> file ->
+
+Import location: Replace data at selected cell
+
+4. Now it should be easy to align the new records with the old ones - delete irrelevant records and Insert->Cells where data is missing until the first 2 columns match
+
+5. now create 2 cols in the main table on top and now it should be safe to Copy-n-Paste the 2-col data range, without the task/metrics columns into the newly created space. -->

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/azure/README.md

@@ -0,0 +1,27 @@
+## Recipes for experimentation on Azure
+
+The recipes have been tested on command line on a cluster setup using Azure VMs and VMSS as well as inside Docker based environments.
+
+To run any of the examples in this folder, please go to the base directory of Megatron-DeepSpeed and run as follows
+
+```bash examples/azure/run-benchmark-model.sh```
+
+### Pre-requisites
+
+To run the above script, you will need to either setup your own dataset and modify the scripts or use our helper scripts to download the publicly available Books dataset and GPT vocab files. Please use the following from the ```dataset``` folder
+
+```bash dataset/download_books.sh```
+
+```bash dataset/download_vocab.sh```
+
+### Run 175B and 1T models
+
+We have included two recipes for the 175B model and the 1T model. To train the model, we assume that the users will modify and tune hyperparameters and configurations by themselves. To facilitate initial training, we have made the recipes runnable with the Books dataset as follows.
+
+```bash examples/azure/run-175b.sh```
+
+```bash examples/azure/run-1t.sh```
+
+### Note about ZeRO stage 3 and CPU offload 
+
+By default, we have enabled ZeRO Stage 3 for both the recipes above. For the 1T model, we have also enabled the CPU-offload feature to save on memory and enable a larger batch size that offers better performance. 

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/azure/run-175b.sh

@@ -0,0 +1,142 @@
+#!/bin/bash
+set -ex
+
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+
+BASE_PATH=$PWD/dataset/
+DATA_PATH=${BASE_PATH}/BookCorpusDataset_text_document
+DS_CONFIG=ds_config.json
+
+# Hostfile path
+HF=/job/hostfile 
+
+# Disabling tensor/pipeline parallelism
+TP=1
+PP=1
+
+# HEADS ~= HIDDEN/128
+
+# Model: 175B
+NLAYERS=96
+HIDDEN=12288
+HEADS=96
+SEQ=1024
+
+
+MICRO_BATCH=4
+NODES=1
+GPN=8
+GLOBAL_BATCH=$(( ${GPN} * ${MICRO_BATCH} * ${NODES} ))
+
+# Initial power scale for loss
+SP=15
+
+# Uncomment/comment one of the following blocks.
+
+# For 1T model, start with microbatch=1, try to get 2 and 4.  If OOM w/ 4, use cpu-offloading
+
+# Set to cpu for offloading to cpu for larger models
+#OFFLOAD_DEVICE="cpu"
+#CPU_OPTIM=" --cpu-optimizer"
+
+# Set to none and empty string for no cpu offloading
+OFFLOAD_DEVICE="none"  
+CPU_OPTIM=" "
+
+ZERO_STAGE=3
+OUTPUT_DIR=ds_z_off-${OFFLOAD_DEVICE}_stage_${ZERO_STAGE}_nl${NLAYERS}_hs${HIDDEN}_mb${MICRO_BATCH}_seq${SEQ}_gb${GLOBAL_BATCH}_nodes${NODES}
+#OUTPUT_DIR=baseline_nl${NLAYERS}_hs${HIDDEN}_gb${GLOBAL_BATCH}_mb${MICRO_BATCH}
+mkdir -p $OUTPUT_DIR
+
+cat <<EOT > $DS_CONFIG
+{
+  "train_batch_size" : $GLOBAL_BATCH,
+  "train_micro_batch_size_per_gpu": $MICRO_BATCH,
+  "steps_per_print": 1,
+  "gradient_accumulation_steps": 1,
+  "zero_optimization": {
+    "stage": 3,
+    "stage3_max_live_parameters": 3e9,
+    "stage3_max_reuse_distance": 3e9,
+    "stage3_param_persistence_threshold": 1e5,
+    "stage3_prefetch_bucket_size": 5e7,
+    "contiguous_gradients": true,
+    "overlap_comm": true,
+    "reduce_bucket_size": 90000000,
+    "sub_group_size": 1e9,
+    "offload_optimizer": {
+      "device": "$OFFLOAD_DEVICE",
+      "buffer_count": 4,
+      "pipeline_read": false,
+      "pipeline_write": false,
+      "pin_memory": true
+    }
+  },
+  "gradient_clipping": 1.0,
+  "fp16": {
+    "enabled": true,
+    "initial_scale_power" : $SP,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "wall_clock_breakdown": true,
+  "zero_allow_untested_optimizer": false,
+  "aio": {
+    "block_size": 1048576,
+    "queue_depth": 16,
+    "single_submit": false,
+    "overlap_events": true,
+    "thread_count": 2
+  }
+}
+EOT
+
+export NCCL_DEBUG=warn 
+
+ds_args=" "
+ds_args=" --deepspeed ${ds_args}"
+ds_args=" --no-pipeline-parallel ${ds_args}" 
+ds_args=" --deepspeed_config=$DS_CONFIG ${ds_args}"
+ds_args=" --zero-stage=$ZERO_STAGE ${ds_args}"
+ds_args=" --deepspeed-activation-checkpointing ${ds_args}"
+
+
+
+deepspeed --force_multi --num_nodes=$NODES --hostfile $HF pretrain_gpt.py \
+    --tensor-model-parallel-size $TP \
+    --pipeline-model-parallel-size $PP \
+    --num-layers $NLAYERS \
+    --hidden-size $HIDDEN \
+    --num-attention-heads $HEADS \
+    --seq-length $SEQ \
+    --loss-scale $SP \
+    --max-position-embeddings $SEQ \
+    --micro-batch-size $MICRO_BATCH \
+    --global-batch-size $GLOBAL_BATCH \
+    --train-iters 1000 \
+    --lr 6.0e-5 \
+    --min-lr 6.0e-6 \
+    --lr-decay-style cosine \
+    --log-interval 1 \
+    --eval-iters 40 \
+    --eval-interval 1000 \
+    --data-path $DATA_PATH \
+    --vocab-file $BASE_PATH/gpt2-vocab.json \
+    --merge-file $BASE_PATH/gpt2-merges.txt \
+    --save-interval 1000 \
+    --split 98,2,0 \
+    --clip-grad 1.0 \
+    --weight-decay 0.1 \
+    --adam-beta1 0.9 \
+    --adam-beta2 0.95 \
+    --init-method-std 0.006 \
+    --fp16 \
+    --checkpoint-activations \
+    --tensorboard-dir $OUTPUT_DIR \
+    $CPU_OPTIM $ds_args \
+    --exit-interval 5000 | tee ${OUTPUT_DIR}/output.log

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/azure/run-1t.sh

@@ -0,0 +1,154 @@
+#!/bin/bash
+set -ex
+
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+
+BASE_PATH=$PWD/dataset/
+DATA_PATH=${BASE_PATH}/BookCorpusDataset_text_document
+DS_CONFIG=ds_config.json
+
+# Hostfile path
+HF=/job/hostfile
+
+# Disabling tensor/pipeline parallelism
+TP=1
+PP=1
+
+# HEADS ~= HIDDEN/128
+
+# Refer to Megatron-table in the README.md file for model sizes
+# Model: 310B
+#NLAYERS=96
+#HIDDEN=16384
+#HEADS=128
+#SEQ=2048
+
+# Model 530B
+#NLAYERS=105
+#HIDDEN=20480
+#HEADS=160
+#SEQ=2048
+
+# Model 1T
+NLAYERS=128
+HIDDEN=25600
+HEADS=160
+SEQ=1024
+
+MICRO_BATCH=1
+NODES=1
+GPN=8
+GLOBAL_BATCH=$(( ${GPN} * ${MICRO_BATCH} * ${NODES} ))
+
+# Initial power scale for loss
+SP=15
+
+# Uncomment/comment one of the following blocks.
+
+# For 1T model, start with microbatch=1, try to get 2 and 4.  If OOM w/ 4, use cpu-offloading
+
+# Set to cpu for offloading to cpu for larger models
+OFFLOAD_DEVICE="cpu"
+CPU_OPTIM=" --cpu-optimizer"
+
+# Set to none and empty string for no cpu offloading
+#OFFLOAD_DEVICE="none"  
+#CPU_OPTIM=" "
+
+ZERO_STAGE=3
+OUTPUT_DIR=ds_z_off-${OFFLOAD_DEVICE}_stage_${ZERO_STAGE}_nl${NLAYERS}_hs${HIDDEN}_mb${MICRO_BATCH}_seq${SEQ}_gb${GLOBAL_BATCH}_nodes${NODES}
+#OUTPUT_DIR=baseline_nl${NLAYERS}_hs${HIDDEN}_gb${GLOBAL_BATCH}_mb${MICRO_BATCH}
+mkdir -p $OUTPUT_DIR
+
+cat <<EOT > $DS_CONFIG
+{
+  "train_batch_size" : $GLOBAL_BATCH,
+  "train_micro_batch_size_per_gpu": $MICRO_BATCH,
+  "steps_per_print": 1,
+  "gradient_accumulation_steps": 1,
+  "zero_optimization": {
+    "stage": 3,
+    "stage3_max_live_parameters": 3e9,
+    "stage3_max_reuse_distance": 3e9,
+    "stage3_param_persistence_threshold": 1e5,
+    "stage3_prefetch_bucket_size": 5e7,
+    "contiguous_gradients": true,
+    "overlap_comm": true,
+    "reduce_bucket_size": 90000000,
+    "sub_group_size": 1e9,
+    "offload_optimizer": {
+      "device": "$OFFLOAD_DEVICE",
+      "buffer_count": 4,
+      "pipeline_read": false,
+      "pipeline_write": false,
+      "pin_memory": true
+    }
+  },
+  "gradient_clipping": 1.0,
+  "fp16": {
+    "enabled": true,
+    "initial_scale_power" : $SP,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "wall_clock_breakdown": true,
+  "zero_allow_untested_optimizer": false,
+  "aio": {
+    "block_size": 1048576,
+    "queue_depth": 16,
+    "single_submit": false,
+    "overlap_events": true,
+    "thread_count": 2
+  }
+}
+EOT
+
+export NCCL_DEBUG=warn 
+
+ds_args=" "
+ds_args=" --deepspeed ${ds_args}"
+ds_args=" --no-pipeline-parallel ${ds_args}" 
+ds_args=" --deepspeed_config=$DS_CONFIG ${ds_args}"
+ds_args=" --zero-stage=$ZERO_STAGE ${ds_args}"
+ds_args=" --deepspeed-activation-checkpointing ${ds_args}"
+
+
+
+deepspeed --force_multi --num_nodes=$NODES --hostfile $HF pretrain_gpt.py \
+    --tensor-model-parallel-size $TP \
+    --pipeline-model-parallel-size $PP \
+    --num-layers $NLAYERS \
+    --hidden-size $HIDDEN \
+    --num-attention-heads $HEADS \
+    --seq-length $SEQ \
+    --loss-scale $SP \
+    --max-position-embeddings $SEQ \
+    --micro-batch-size $MICRO_BATCH \
+    --global-batch-size $GLOBAL_BATCH \
+    --train-iters 1000 \
+    --lr 6.0e-5 \
+    --min-lr 6.0e-6 \
+    --lr-decay-style cosine \
+    --log-interval 1 \
+    --eval-iters 40 \
+    --eval-interval 1000 \
+    --data-path $DATA_PATH \
+    --vocab-file $BASE_PATH/gpt2-vocab.json \
+    --merge-file $BASE_PATH/gpt2-merges.txt \
+    --save-interval 1000 \
+    --split 98,2,0 \
+    --clip-grad 1.0 \
+    --weight-decay 0.1 \
+    --adam-beta1 0.9 \
+    --adam-beta2 0.95 \
+    --init-method-std 0.006 \
+    --fp16 \
+    --checkpoint-activations \
+    --tensorboard-dir $OUTPUT_DIR \
+    $CPU_OPTIM $ds_args \
+    --exit-interval 5000 | tee ${OUTPUT_DIR}/output.log

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/azure/run-benchmark-model.sh

@@ -0,0 +1,142 @@
+#!/bin/bash
+set -ex
+
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+
+BASE_PATH=$PWD/dataset/
+DATA_PATH=${BASE_PATH}/BookCorpusDataset_text_document
+DS_CONFIG=ds_config.json
+
+# Hostfile path
+HF=/job/hostfile 
+
+# Disabling tensor/pipeline parallelism
+TP=1
+PP=1
+
+# HEADS ~= HIDDEN/128
+
+# Model: Benchmark model
+NLAYERS=1
+HIDDEN=12288
+HEADS=96
+SEQ=1024
+
+
+MICRO_BATCH=4
+NODES=2
+GPN=8
+GLOBAL_BATCH=$(( ${GPN} * ${MICRO_BATCH} * ${NODES} ))
+
+# Initial power scale for loss
+SP=15
+
+# Uncomment/comment one of the following blocks.
+
+# For 1T model, start with microbatch=1, try to get 2 and 4.  If OOM w/ 4, use cpu-offloading
+
+# Set to cpu for offloading to cpu for larger models
+#OFFLOAD_DEVICE="cpu"
+#CPU_OPTIM=" --cpu-optimizer"
+
+# Set to none and empty string for no cpu offloading
+OFFLOAD_DEVICE="none"  
+CPU_OPTIM=" "
+
+ZERO_STAGE=3
+OUTPUT_DIR=ds_z_off-${OFFLOAD_DEVICE}_stage_${ZERO_STAGE}_nl${NLAYERS}_hs${HIDDEN}_mb${MICRO_BATCH}_seq${SEQ}_gb${GLOBAL_BATCH}_nodes${NODES}
+#OUTPUT_DIR=baseline_nl${NLAYERS}_hs${HIDDEN}_gb${GLOBAL_BATCH}_mb${MICRO_BATCH}
+mkdir -p $OUTPUT_DIR
+
+cat <<EOT > $DS_CONFIG
+{
+  "train_batch_size" : $GLOBAL_BATCH,
+  "train_micro_batch_size_per_gpu": $MICRO_BATCH,
+  "steps_per_print": 1,
+  "gradient_accumulation_steps": 1,
+  "zero_optimization": {
+    "stage": 3,
+    "stage3_max_live_parameters": 3e9,
+    "stage3_max_reuse_distance": 3e9,
+    "stage3_param_persistence_threshold": 1e5,
+    "stage3_prefetch_bucket_size": 5e7,
+    "contiguous_gradients": true,
+    "overlap_comm": true,
+    "reduce_bucket_size": 90000000,
+    "sub_group_size": 1e9,
+    "offload_optimizer": {
+      "device": "$OFFLOAD_DEVICE",
+      "buffer_count": 4,
+      "pipeline_read": false,
+      "pipeline_write": false,
+      "pin_memory": true
+    }
+  },
+  "gradient_clipping": 1.0,
+  "fp16": {
+    "enabled": true,
+    "initial_scale_power" : $SP,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "wall_clock_breakdown": true,
+  "zero_allow_untested_optimizer": false,
+  "aio": {
+    "block_size": 1048576,
+    "queue_depth": 16,
+    "single_submit": false,
+    "overlap_events": true,
+    "thread_count": 2
+  }
+}
+EOT
+
+export NCCL_DEBUG=warn 
+
+ds_args=" "
+ds_args=" --deepspeed ${ds_args}"
+ds_args=" --no-pipeline-parallel ${ds_args}" 
+ds_args=" --deepspeed_config=$DS_CONFIG ${ds_args}"
+ds_args=" --zero-stage=$ZERO_STAGE ${ds_args}"
+ds_args=" --deepspeed-activation-checkpointing ${ds_args}"
+
+
+
+deepspeed --force_multi --num_nodes=$NODES --hostfile $HF pretrain_gpt.py \
+    --tensor-model-parallel-size $TP \
+    --pipeline-model-parallel-size $PP \
+    --num-layers $NLAYERS \
+    --hidden-size $HIDDEN \
+    --num-attention-heads $HEADS \
+    --seq-length $SEQ \
+    --loss-scale $SP \
+    --max-position-embeddings $SEQ \
+    --micro-batch-size $MICRO_BATCH \
+    --global-batch-size $GLOBAL_BATCH \
+    --train-iters 50 \
+    --lr 6.0e-5 \
+    --min-lr 6.0e-6 \
+    --lr-decay-style cosine \
+    --log-interval 1 \
+    --eval-iters 40 \
+    --eval-interval 1000 \
+    --data-path $DATA_PATH \
+    --vocab-file $BASE_PATH/gpt2-vocab.json \
+    --merge-file $BASE_PATH/gpt2-merges.txt \
+    --save-interval 1000 \
+    --split 98,2,0 \
+    --clip-grad 1.0 \
+    --weight-decay 0.1 \
+    --adam-beta1 0.9 \
+    --adam-beta2 0.95 \
+    --init-method-std 0.006 \
+    --fp16 \
+    --checkpoint-activations \
+    --tensorboard-dir $OUTPUT_DIR \
+    $CPU_OPTIM $ds_args \
+    --exit-interval 5000 | tee ${OUTPUT_DIR}/output.log

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/azureml/Dockerfile.dockerfile

@@ -0,0 +1,14 @@
+FROM mcr.microsoft.com/azureml/aifx/stable-ubuntu2004-cu115-py38-torch1110
+
+USER root:root
+
+RUN pip install pybind11
+
+RUN pip install git+https://github.com/microsoft/DeepSpeed.git
+
+# add a100-topo.xml
+RUN mkdir -p /opt/microsoft/
+RUN wget -O /opt/microsoft/a100-topo.xml https://hpcbenchmarks.blob.core.windows.net/bookcorpus/data/a100-topo.xml
+
+# to use on A100, enable env var below in your job
+ENV NCCL_TOPO_FILE="/opt/microsoft/a100-topo.xml"

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/azureml/README.md

@@ -0,0 +1,14 @@
+## Megatron-DeepSpeed on AzureML
+Example script for running Megatron-DeepSpeed using Azure Machine Learning.
+
+------
+
+# Workspace Setup
+Setup an AML workspace. Refer to: [set-up doc](https://github.com/Azure/azureml-examples/tree/main/python-sdk#set-up).
+
+# Dataset Preparation
+Create AML Dataset. To run remote AML job, you need to provide AML FileDataset. 
+Refer to [prepare_dataset script](prepare_dataset.py) to upload .bin and .idx files to blob store and on how to create FileDataset.
+
+# Training
+Run Megatron-DeepSpeed on Azure ML. Refer to [aml_submit script](aml_submit.py).

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/compression/125M-Int8-test-64gpu-distilled-group48.sh

@@ -0,0 +1,253 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+MODEL_SIZE=0.125
+NUM_LAYERS=12
+HIDDEN_SIZE=768
+NUM_ATTN_HEADS=12
+GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+LR=6.0e-5
+MIN_LR=6.0e-5
+
+# Curriculum learning (CL) enables stable large-batch training
+# GLOBAL_BATCH_SIZE=16 # 8x
+# LR=6e-4 # 4x
+
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+# TRAIN_TOKENS=300000000000
+TRAIN_TOKENS=5250000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some samples,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+MP_SIZE=1
+
+## Pipeline parallelism. To disable PP, set PP_SIZE to 1 and NO_PP to true.
+PP_SIZE=1
+NO_PP="true"
+
+## ZeRO stage
+ZERO_STAGE=0
+
+## Total number of GPUs
+NUM_GPUS=$(($(ds_ssh nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)-2))
+NUM_GPUS_PERNODE=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
+NUM_NODE=$(( ${NUM_GPUS} / ${NUM_GPUS_PERNODE} ))
+DP_SIZE=$(( ${NUM_GPUS} / ${PP_SIZE} / ${MP_SIZE} ))
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=72
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_STEP=$(( ${CL_TOKENS} * 1000000000 / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=1000
+
+## Standard deviation for weight initialization. Usually larger model needs
+## lower std. We used a heuristic equation of sqrt(1/3/HIDDEN_SIZE) from the
+## MT-NLG 530B work (https://arxiv.org/pdf/2201.11990.pdf)
+INIT_STD=0.02
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+# ACTIVATION_CHECKPOINT="true"
+ACTIVATION_CHECKPOINT="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+LOG_OPTIMIZER_STATE="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="125M10L_Compression_Test_INT8_64gpu_lr6e-5_tokens5.25B_nocl"
+if [ "${NO_PP}" = "true" ]; then
+    NAME="${NAME}-no_pp"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-startseqlen-${CL_START_SEQLEN}-step-${CL_STEP}-token-${CL_TOKENS}B"
+fi
+
+LOG_PATH="log/"
+TENSORBOARD_PATH="tensorboard/${NAME}_${host}_${current_time}"
+CHECKPOINT_PATH="/blob/users/zheweiyao/compression_library/checkpoint/${NAME}"
+mkdir -p ${LOG_PATH}
+mkdir -p ${TENSORBOARD_PATH}
+mkdir -p ${CHECKPOINT_PATH}
+
+VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+# Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+# For cluster Azure-EastUS-V100-32GB-4, Lab-RR1-V100
+# DATA_PATH=/vc_data_blob/users/conglli/the_pile_public_merged_nopreprocessing/pile_text_document
+# For cluster Azure-WestUS3-A100
+DATA_PATH=/blob/data/the_pile_public_merged_nopreprocessing/pile_text_document
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers 10 \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load /blob/users/minjiaz/project/gpt3_distillation/checkpoint/gpt3-kd-staged-alpha1-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-32-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-27638-token-60B/ \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --no-load-lr-state \
+        --reset-iteration \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_PATH}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [ "${LOG_OPTIMIZER_STATE}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --log-optimizer-states-to-tensorboard"
+fi
+
+template_json="ds_config_gpt_TEMPLATE_compression.json"
+config_json="ds_config_${NAME}.json"
+if [[ $ZERO_STAGE -gt 0 ]]; then
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/false/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+else
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+fi
+
+deepspeed_options=" \
+            --deepspeed \
+            --deepspeed_config ${config_json} \
+            --zero-stage ${ZERO_STAGE} \
+            --pipeline-model-parallel-size ${PP_SIZE}"
+
+if [[ "${NO_PP}" = "true" ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+## When saving checkpoint to a storage with cache, their could be consistency
+## issue of the pointer to latest checkpoint. Here we find the correct pointer
+## and broadcast it to all nodes.
+ITERATION_FILE="$CHECKPOINT_PATH/latest_checkpointed_iteration.txt"
+ITERATION_FILE_2="$CHECKPOINT_PATH/latest"
+ITERATION=0
+for (( node = 0; node <= NUM_NODE-1; node++ ))
+do
+    if $(ssh -q worker-"$node" "test -f \"$ITERATION_FILE\""); then
+        LOCAL_ITERATION=$(ssh -q worker-"$node" cat $ITERATION_FILE)
+        ITERATION=$(( ${LOCAL_ITERATION} > ${ITERATION} ? ${LOCAL_ITERATION} :  ${ITERATION} ))
+    fi
+done
+if [[ $ITERATION -gt 0 ]]; then
+    ITERATION_2="global_step${ITERATION}"
+    ds_ssh "echo $ITERATION > $ITERATION_FILE"
+    ds_ssh "echo $ITERATION_2 > $ITERATION_FILE_2"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${LOG_PATH}/${NAME}.log"
+# run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options}"
+
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/compression/125M-L10-Int8-test-64gpu-distilled-group48.sh

@@ -0,0 +1,253 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+MODEL_SIZE=0.125
+NUM_LAYERS=12
+HIDDEN_SIZE=768
+NUM_ATTN_HEADS=12
+GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+LR=6.0e-5
+MIN_LR=6.0e-5
+
+# Curriculum learning (CL) enables stable large-batch training
+# GLOBAL_BATCH_SIZE=16 # 8x
+# LR=6e-4 # 4x
+
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+# TRAIN_TOKENS=300000000000
+TRAIN_TOKENS=5250000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some samples,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+MP_SIZE=1
+
+## Pipeline parallelism. To disable PP, set PP_SIZE to 1 and NO_PP to true.
+PP_SIZE=1
+NO_PP="true"
+
+## ZeRO stage
+ZERO_STAGE=0
+
+## Total number of GPUs
+NUM_GPUS=$(($(ds_ssh nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)-2))
+NUM_GPUS_PERNODE=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
+NUM_NODE=$(( ${NUM_GPUS} / ${NUM_GPUS_PERNODE} ))
+DP_SIZE=$(( ${NUM_GPUS} / ${PP_SIZE} / ${MP_SIZE} ))
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=72
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_STEP=$(( ${CL_TOKENS} * 1000000000 / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=1000
+
+## Standard deviation for weight initialization. Usually larger model needs
+## lower std. We used a heuristic equation of sqrt(1/3/HIDDEN_SIZE) from the
+## MT-NLG 530B work (https://arxiv.org/pdf/2201.11990.pdf)
+INIT_STD=0.02
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+# ACTIVATION_CHECKPOINT="true"
+ACTIVATION_CHECKPOINT="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+LOG_OPTIMIZER_STATE="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="125M10L_Compression_Test_INT8_64gpu_lr6e-5_tokens5.25B_nocl_alpha"
+if [ "${NO_PP}" = "true" ]; then
+    NAME="${NAME}-no_pp"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-startseqlen-${CL_START_SEQLEN}-step-${CL_STEP}-token-${CL_TOKENS}B"
+fi
+
+LOG_PATH="log/"
+TENSORBOARD_PATH="tensorboard/${NAME}_${host}_${current_time}"
+CHECKPOINT_PATH="/blob/users/minjiaz/compression_library/checkpoint/${NAME}"
+mkdir -p ${LOG_PATH}
+mkdir -p ${TENSORBOARD_PATH}
+mkdir -p ${CHECKPOINT_PATH}
+
+VOCAB_PATH=/blob/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+MERGE_PATH=/blob/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+# Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+# For cluster Azure-EastUS-V100-32GB-4, Lab-RR1-V100
+# DATA_PATH=/vc_data_blob/users/conglli/the_pile_public_merged_nopreprocessing/pile_text_document
+# For cluster Azure-WestUS3-A100
+DATA_PATH=/blob/data/the_pile_public_merged_nopreprocessing/pile_text_document
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers 10 \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load /blob/users/minjiaz/project/gpt3_distillation/checkpoint/gpt3-kd-staged-alpha1-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-32-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-27638-token-60B/ \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --no-load-lr-state \
+        --reset-iteration \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_PATH}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [ "${LOG_OPTIMIZER_STATE}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --log-optimizer-states-to-tensorboard"
+fi
+
+template_json="ds_config_gpt_TEMPLATE_compression.json"
+config_json="ds_config_${NAME}.json"
+if [[ $ZERO_STAGE -gt 0 ]]; then
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/false/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+else
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+fi
+
+deepspeed_options=" \
+            --deepspeed \
+            --deepspeed_config ${config_json} \
+            --zero-stage ${ZERO_STAGE} \
+            --pipeline-model-parallel-size ${PP_SIZE}"
+
+if [[ "${NO_PP}" = "true" ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+## When saving checkpoint to a storage with cache, their could be consistency
+## issue of the pointer to latest checkpoint. Here we find the correct pointer
+## and broadcast it to all nodes.
+ITERATION_FILE="$CHECKPOINT_PATH/latest_checkpointed_iteration.txt"
+ITERATION_FILE_2="$CHECKPOINT_PATH/latest"
+ITERATION=0
+for (( node = 0; node <= NUM_NODE-1; node++ ))
+do
+    if $(ssh -q worker-"$node" "test -f \"$ITERATION_FILE\""); then
+        LOCAL_ITERATION=$(ssh -q worker-"$node" cat $ITERATION_FILE)
+        ITERATION=$(( ${LOCAL_ITERATION} > ${ITERATION} ? ${LOCAL_ITERATION} :  ${ITERATION} ))
+    fi
+done
+if [[ $ITERATION -gt 0 ]]; then
+    ITERATION_2="global_step${ITERATION}"
+    ds_ssh "echo $ITERATION > $ITERATION_FILE"
+    ds_ssh "echo $ITERATION_2 > $ITERATION_FILE_2"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${LOG_PATH}/${NAME}.log"
+# run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options}"
+
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/compression/ds_config_gpt_TEMPLATE.json

@@ -0,0 +1,39 @@
+{
+  "train_batch_size" : CONFIG_BATCH_SIZE,
+  "train_micro_batch_size_per_gpu": CONFIG_MBSIZE,
+  "steps_per_print": LOG_INTERVAL,
+
+  "zero_optimization": {
+    "stage": ZERO_STAGE,
+    "elastic_checkpoint": true
+  },
+
+  "gradient_clipping": 1.0,
+  "prescale_gradients": PRESCALE_GRAD,
+
+  "fp16": {
+    "enabled": CONFIG_FP16_ENABLED,
+    "loss_scale": 0,
+    "loss_scale_window": 500,
+    "hysteresis": 2,
+    "min_loss_scale": 1,
+    "initial_scale_power": 11
+  },
+
+  "bf16": {
+    "enabled": CONFIG_BF16_ENABLED
+  },
+  "curriculum_learning": {
+    "enabled": CONFIG_CL_ENABLED,
+    "curriculum_type": "seqlen",
+    "min_difficulty": CONFIG_CL_MIN,
+    "max_difficulty": CONFIG_CL_MAX,
+    "schedule_type": "fixed_linear",
+    "schedule_config": {
+      "total_curriculum_step": CONFIG_CL_DURATION,
+      "difficulty_step": 8
+    }
+  },
+
+  "wall_clock_breakdown" : false
+}

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/compression/ds_config_gpt_TEMPLATE_compression.json

@@ -0,0 +1,87 @@
+{
+  "train_batch_size" : CONFIG_BATCH_SIZE,
+  "train_micro_batch_size_per_gpu": CONFIG_MBSIZE,
+  "steps_per_print": LOG_INTERVAL,
+
+  "zero_optimization": {
+    "stage": ZERO_STAGE,
+    "elastic_checkpoint": true
+  },
+
+  "gradient_clipping": 1.0,
+  "prescale_gradients": PRESCALE_GRAD,
+
+  "fp16": {
+    "enabled": CONFIG_FP16_ENABLED,
+    "loss_scale": 0,
+    "loss_scale_window": 500,
+    "hysteresis": 2,
+    "min_loss_scale": 1,
+    "initial_scale_power": 11
+  },
+
+  "bf16": {
+    "enabled": CONFIG_BF16_ENABLED
+  },
+  "curriculum_learning": {
+    "enabled": CONFIG_CL_ENABLED,
+    "curriculum_type": "seqlen",
+    "min_difficulty": CONFIG_CL_MIN,
+    "max_difficulty": CONFIG_CL_MAX,
+    "schedule_type": "fixed_linear",
+    "schedule_config": {
+      "total_curriculum_step": CONFIG_CL_DURATION,
+      "difficulty_step": 8
+    }
+  },
+
+  "wall_clock_breakdown" : false,
+
+  "compression_training": {
+    "weight_quantization": {
+      "shared_parameters":{
+        "enabled": true,
+        "quantizer_kernel": false,
+        "schedule_offset": 50,
+        "quantize_groups": 48,
+        "quantize_verbose": false,
+        "quantization_type": "symmetric",
+        "rounding": "nearest",
+        "fp16_mixed_quantize":{
+          "enabled": false,
+          "quantize_change_ratio": 0.001
+        }
+      },
+      "different_groups":{
+        "wq1": {
+          "params": {
+              "start_bits": 12, 
+              "target_bits": 4,
+              "quantization_period": 50
+          },
+          "modules": [
+            "encoder.layers"
+          ]
+        }
+      }
+    },
+    "activation_quantization": {
+      "shared_parameters":{
+        "enabled": true,
+        "quantization_type": "asymmetric",
+        "range_calibration": "static",
+        "schedule_offset": 50
+      },
+      "different_groups":{
+        "aq1": {
+          "params": { 
+              "bits": 8
+          },
+          "modules": [
+            "encoder.layers"
+          ]
+        }
+      }
+    }
+  }
+}

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/compression/ds_evalharness.sh

@@ -0,0 +1,74 @@
+# This is an example zero-shot eval script. Please first read the readme_evalharness.md under the same directory.
+
+# CHECKPOINT_PATH=/blob/users/minjiaz/compression_library/checkpoint/125M10L_Compression_Test_INT8_64gpu_lr6e-5_tokens5.25B_nocl_alpha-no_pp/global_step2000/
+# CHECKPOINT_PATH=/blob/users/conglli/project/gpt3_with_pile/checkpoint/gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-64-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-27638-token-60B/global_step71000/
+# CHECKPOINT_PATH=/blob/users/minjiaz/compression_library/checkpoint/125M12L_Compression_Test_INT8_64gpu_lr6e-5_tokens5.25B_nocl_alpha-no_pp/global_step5000/
+CHECKPOINT_PATH=/blob/users/minjiaz/project/gpt3_distillation/checkpoint/gpt3-kd-test2-alpha1-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-15-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-27638-token-60B/global_step71426/
+CONFIG_PATH=ds_config_gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus--1-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-27638-token-60B.json
+RESULT_PATH=gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-128-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-20728-token-45B_global_step81566.log
+
+PP_SIZE=1
+TP_SIZE=1
+NO_PP="true"
+EP_PARALLEL_SIZE=1
+# Currently eval harness does not support data parallel
+# However, for MoE models it's possible to enable a "fake data parallel"
+# in order to load experts on multiple gpus. At the same time, it's not
+# real data parallel because we load the same data on all gpus.
+# On the other hand, it's better to use less number of gpus than training,
+# to reduce communication overhead.
+NUM_NODE=1
+NUM_GPU_PER_NODE=1
+
+# TASKS="lambada"
+# WikiText-2, not used in GPT-3 paper but used in GPT-2 paper
+TASKS="lambada,wikitext"
+# Tasks that appeared in GPT-3 paper (sorted based on the order in paper), plus WikiText-2.
+# TASKS="hellaswag,lambada,triviaqa,webqs,winogrande,piqa,arc_challenge,arc_easy,openbookqa,race,boolq,cb,copa,rte,wic,wsc,multirc,record,anli_r1,anli_r2,anli_r3,wikitext"
+# All tasks that confirmed to work, there are more tasks on https://github.com/EleutherAI/lm-evaluation-harness that we didn't test.
+# TASKS="hellaswag,lambada,triviaqa,webqs,winogrande,piqa,arc_challenge,arc_easy,openbookqa,race,boolq,cb,copa,rte,wic,wsc,multirc,record,anli_r1,anli_r2,anli_r3,wikitext,logiqa,mathqa,mc_taco,mrpc,prost,pubmedqa,qnli,qqp,sciq,sst,wnli"
+
+VOCAB_FILE=/blob/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+MERGE_FILE=/blob/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+
+export HF_DATASETS_OFFLINE=1
+
+# Dummy arguments to make megatron happy. No need to configure them.
+# The reason we don't need to configure them and many other arguments is
+# because the eval framework will read the arguments from checkpoint file.
+MEGATRON_REQUIRED_ARGS="\
+    --num-layers -1\
+    --hidden-size -1\
+    --num-attention-heads -1\
+    --seq-length -1 \
+    --max-position-embeddings -1
+"
+
+CMD="../../tasks/eval_harness/evaluate.py \
+    --load $CHECKPOINT_PATH\
+    --tensor-model-parallel-size $TP_SIZE \
+    --pipeline-model-parallel-size $PP_SIZE\
+    --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+    --vocab-file $VOCAB_FILE\
+    --merge-file $MERGE_FILE\
+    --micro-batch-size 12\
+    --no-load-optim \
+    --no-load-rng \
+    --inference \
+    --disable-moe-token-dropping \
+    --adaptive_seq_len\
+    --eval_fp32\
+    --task_list $TASKS\
+    --results_path $RESULT_PATH \
+    --deepspeed \
+    --deepspeed_config $CONFIG_PATH \
+    $MEGATRON_REQUIRED_ARGS\
+    "
+
+if [[ "${NO_PP}" = "true" ]]; then
+CMD="${CMD} \
+    --no-pipeline-parallel"
+fi
+
+LAUNCHER="deepspeed --num_nodes $NUM_NODE --num_gpus $NUM_GPU_PER_NODE"
+$LAUNCHER $CMD

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/compression/ds_pretrain_gpt_1.3B_dense_cl_kd.sh

@@ -0,0 +1,322 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+MODEL_SIZE=1.3
+NUM_LAYERS=24
+HIDDEN_SIZE=2048
+NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+MIN_LR=2.0e-5
+
+# Curriculum learning (CL) enables stable large-batch training
+GLOBAL_BATCH_SIZE=4096 # 8x
+LR=8.0e-4 # 4x
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+TRAIN_TOKENS=300000000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some samples,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=16
+
+## Model parallelism, 1 is no MP
+MP_SIZE=2
+
+## Pipeline parallelism. To disable PP, set PP_SIZE to 1 and NO_PP to true.
+PP_SIZE=1
+NO_PP="true"
+
+## ZeRO stage
+ZERO_STAGE=0
+
+## Total number of GPUs
+NUM_GPUS=$(($(ds_ssh nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)-2))
+NUM_GPUS_PERNODE=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
+NUM_NODE=$(( ${NUM_GPUS} / ${NUM_GPUS_PERNODE} ))
+DP_SIZE=$(( ${NUM_GPUS} / ${PP_SIZE} / ${MP_SIZE} ))
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="true"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_STEP=$(( ${CL_TOKENS} * 1000000000 / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=10000
+
+## Standard deviation for weight initialization. Usually larger model needs
+## lower std. We used a heuristic equation of sqrt(1/3/HIDDEN_SIZE) from the
+## MT-NLG 530B work (https://arxiv.org/pdf/2201.11990.pdf)
+INIT_STD=0.013
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+LOG_OPTIMIZER_STATE="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt3-kd-with-pile-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-zero-${ZERO_STAGE}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [ "${NO_PP}" = "true" ]; then
+    NAME="${NAME}-no_pp"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-startseqlen-${CL_START_SEQLEN}-step-${CL_STEP}-token-${CL_TOKENS}B"
+fi
+
+LOG_PATH="log/"
+TENSORBOARD_PATH="tensorboard/${NAME}_${host}_${current_time}"
+CHECKPOINT_PATH="/blob/users/minjiaz/project/gpt3_distillation/checkpoint/${NAME}"
+mkdir -p ${LOG_PATH}
+mkdir -p ${TENSORBOARD_PATH}
+mkdir -p ${CHECKPOINT_PATH}
+
+### KD configs
+KD_BETA_CE=1
+CHECKPOINT_PATH_TEACHER="/blob/users/conglli/project/gpt3_with_pile/checkpoint/gpt3-with-pile-1.3B-lr-8.0e-4-minlr-2.0e-5-bs-4096-gpus-128-zero-0-mp-2-pp-1-no_pp-cl-startseqlen-80-step-13767-token-60B/"
+CHECKPOINT_PATH_SAVE="/blob/users/minjiaz/project/gpt3_distillation/checkpoint/${NAME}"
+
+mkdir -p ${CHECKPOINT_PATH_SAVE}
+
+VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+# Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+# DATA_PATH=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+# For cluster Azure-WestUS3-A100
+DATA_PATH=/blob/data/the_pile_public_merged_nopreprocessing/pile_text_document
+
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers 21 \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH_SAVE} \
+        --kd \
+        --kd-beta-ce ${KD_BETA_CE} \
+        --num-layers-teacher ${NUM_LAYERS} \
+        --hidden-size-teacher ${HIDDEN_SIZE} \
+        --num-attention-heads-teacher ${NUM_ATTN_HEADS} \
+        --load-teacher ${CHECKPOINT_PATH_TEACHER} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_PATH}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [ "${LOG_OPTIMIZER_STATE}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --log-optimizer-states-to-tensorboard"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_${NAME}.json"
+if [[ $ZERO_STAGE -gt 0 ]]; then
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/false/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+else
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+fi
+
+deepspeed_options=" \
+            --deepspeed \
+            --deepspeed_config ${config_json} \
+            --zero-stage ${ZERO_STAGE} \
+            --pipeline-model-parallel-size ${PP_SIZE}"
+
+if [[ "${NO_PP}" = "true" ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+## When saving checkpoint to a storage with cache, their could be consistency
+## issue of the pointer to latest checkpoint. Here we find the correct pointer
+## and broadcast it to all nodes.
+ITERATION_FILE="$CHECKPOINT_PATH/latest_checkpointed_iteration.txt"
+ITERATION_FILE_2="$CHECKPOINT_PATH/latest"
+ITERATION=0
+for (( node = 0; node <= NUM_NODE-1; node++ ))
+do
+    if $(ssh -q worker-"$node" "test -f \"$ITERATION_FILE\""); then
+        LOCAL_ITERATION=$(ssh -q worker-"$node" cat $ITERATION_FILE)
+        ITERATION=$(( ${LOCAL_ITERATION} > ${ITERATION} ? ${LOCAL_ITERATION} :  ${ITERATION} ))
+    fi
+done
+if [[ $ITERATION -gt 0 ]]; then
+    ITERATION_2="global_step${ITERATION}"
+    ds_ssh "echo $ITERATION > $ITERATION_FILE"
+    ds_ssh "echo $ITERATION_2 > $ITERATION_FILE_2"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${LOG_PATH}/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/compression/ds_pretrain_gpt_125M_dense_cl_kd.sh

@@ -0,0 +1,323 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+MODEL_SIZE=0.125
+NUM_LAYERS=12
+HIDDEN_SIZE=768
+NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+MIN_LR=6.0e-5
+
+# Curriculum learning (CL) enables stable large-batch training
+GLOBAL_BATCH_SIZE=2048 # 8x
+LR=2.4e-3 # 4x
+
+## GPT-3 Medium 350M
+# MODEL_SIZE=0.35
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1024
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=3.0e-4
+# MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+# MODEL_SIZE=1.3
+# NUM_LAYERS=24
+# HIDDEN_SIZE=2048
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+TRAIN_TOKENS=300000000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some samples,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=8
+
+## Model parallelism, 1 is no MP
+MP_SIZE=1
+
+## Pipeline parallelism. To disable PP, set PP_SIZE to 1 and NO_PP to true.
+PP_SIZE=1
+NO_PP="true"
+
+## ZeRO stage
+ZERO_STAGE=0
+
+## Total number of GPUs
+NUM_GPUS=$(($(ds_ssh nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)-2))
+NUM_GPUS_PERNODE=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
+NUM_NODE=$(( ${NUM_GPUS} / ${NUM_GPUS_PERNODE} ))
+DP_SIZE=$(( ${NUM_GPUS} / ${PP_SIZE} / ${MP_SIZE} ))
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="true"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=72
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_STEP=$(( ${CL_TOKENS} * 1000000000 / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=10000
+
+## Standard deviation for weight initialization. Usually larger model needs
+## lower std. We used a heuristic equation of sqrt(1/3/HIDDEN_SIZE) from the
+## MT-NLG 530B work (https://arxiv.org/pdf/2201.11990.pdf)
+INIT_STD=0.02
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+LOG_OPTIMIZER_STATE="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt3-kd-test1-alpha1-with-pile-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-zero-${ZERO_STAGE}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [ "${NO_PP}" = "true" ]; then
+    NAME="${NAME}-no_pp"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-startseqlen-${CL_START_SEQLEN}-step-${CL_STEP}-token-${CL_TOKENS}B"
+fi
+
+LOG_PATH="log/"
+TENSORBOARD_PATH="tensorboard/${NAME}_${host}_${current_time}"
+CHECKPOINT_PATH="/blob/users/minjiaz/project/gpt3_distillation/checkpoint/${NAME}"
+mkdir -p ${LOG_PATH}
+mkdir -p ${TENSORBOARD_PATH}
+mkdir -p ${CHECKPOINT_PATH}
+
+### KD configs
+KD_BETA_CE=1
+CHECKPOINT_PATH_TEACHER="/blob/users/conglli/project/gpt3_with_pile/checkpoint/gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-64-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-27638-token-60B/"
+CHECKPOINT_PATH_SAVE="/blob/users/minjiaz/project/gpt3_distillation/checkpoint/${NAME}"
+
+mkdir -p ${CHECKPOINT_PATH_SAVE}
+
+
+VOCAB_PATH=/blob/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+MERGE_PATH=/blob/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+# Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+# For cluster Azure-EastUS-V100-32GB-4, Lab-RR1-V100
+# DATA_PATH=/vc_data_blob/users/conglli/the_pile_public_merged_nopreprocessing/pile_text_document
+# For cluster Azure-WestUS3-A100
+DATA_PATH=/blob/data/the_pile_public_merged_nopreprocessing/pile_text_document
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_PATH} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers 10 \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH_SAVE} \
+        --kd \
+        --kd-beta-ce ${KD_BETA_CE} \
+        --num-layers-teacher ${NUM_LAYERS} \
+        --hidden-size-teacher ${HIDDEN_SIZE} \
+        --num-attention-heads-teacher ${NUM_ATTN_HEADS} \
+        --load-teacher ${CHECKPOINT_PATH_TEACHER} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_PATH}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [ "${LOG_OPTIMIZER_STATE}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --log-optimizer-states-to-tensorboard"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_${NAME}.json"
+if [[ $ZERO_STAGE -gt 0 ]]; then
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/false/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+else
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/${ZERO_STAGE}/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+      > ${config_json}
+fi
+
+deepspeed_options=" \
+            --deepspeed \
+            --deepspeed_config ${config_json} \
+            --zero-stage ${ZERO_STAGE} \
+            --pipeline-model-parallel-size ${PP_SIZE}"
+
+if [[ "${NO_PP}" = "true" ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+## When saving checkpoint to a storage with cache, their could be consistency
+## issue of the pointer to latest checkpoint. Here we find the correct pointer
+## and broadcast it to all nodes.
+ITERATION_FILE="$CHECKPOINT_PATH/latest_checkpointed_iteration.txt"
+ITERATION_FILE_2="$CHECKPOINT_PATH/latest"
+ITERATION=0
+for (( node = 0; node <= NUM_NODE-1; node++ ))
+do
+    if $(ssh -q worker-"$node" "test -f \"$ITERATION_FILE\""); then
+        LOCAL_ITERATION=$(ssh -q worker-"$node" cat $ITERATION_FILE)
+        ITERATION=$(( ${LOCAL_ITERATION} > ${ITERATION} ? ${LOCAL_ITERATION} :  ${ITERATION} ))
+    fi
+done
+if [[ $ITERATION -gt 0 ]]; then
+    ITERATION_2="global_step${ITERATION}"
+    ds_ssh "echo $ITERATION > $ITERATION_FILE"
+    ds_ssh "echo $ITERATION_2 > $ITERATION_FILE_2"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${LOG_PATH}/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/compression/ds_pretrain_gpt_350M_dense_kd.sh

@@ -0,0 +1,349 @@
+#!/bin/bash
+DIR=`pwd`
+###############################################################################
+### Main configs
+## GPT-3 models use 2K sequence length/context window
+SEQ_LEN=2048
+
+### The "GPT-3 XXX" below are configs from GPT-3 paper
+### https://arxiv.org/abs/2005.14165, choose based on
+### your desired model size or build your own configs
+
+## GPT-3 Small 125M
+# MODEL_SIZE=0.125
+# NUM_LAYERS=12
+# HIDDEN_SIZE=768
+# NUM_ATTN_HEADS=12
+# GLOBAL_BATCH_SIZE=256
+# LR=6.0e-4
+# MIN_LR=6.0e-5
+
+## GPT-3 Medium 350M
+MODEL_SIZE=0.35
+NUM_LAYERS=24
+HIDDEN_SIZE=1024
+NUM_ATTN_HEADS=16
+GLOBAL_BATCH_SIZE=256
+LR=3.0e-4
+MIN_LR=3.0e-5
+
+## GPT-3 Large 760M
+# MODEL_SIZE=0.76
+# NUM_LAYERS=24
+# HIDDEN_SIZE=1536
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=256
+# LR=2.5e-4
+# MIN_LR=2.5e-5
+
+## GPT-3 XL 1.3B
+# MODEL_SIZE=1.3
+# NUM_LAYERS=24
+# HIDDEN_SIZE=2048
+# NUM_ATTN_HEADS=16
+# GLOBAL_BATCH_SIZE=512
+# LR=2.0e-4
+# MIN_LR=2.0e-5
+
+## GPT-3 2.7B
+# MODEL_SIZE=2.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=2560
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=512
+# LR=1.6e-4
+# MIN_LR=1.6e-5
+
+## GPT-3 6.7B
+# MODEL_SIZE=6.7
+# NUM_LAYERS=32
+# HIDDEN_SIZE=4096
+# NUM_ATTN_HEADS=32
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.2e-4
+# MIN_LR=1.2e-5
+
+## GPT-3 13B
+# MODEL_SIZE=13
+# NUM_LAYERS=40
+# HIDDEN_SIZE=5120
+# NUM_ATTN_HEADS=40
+# GLOBAL_BATCH_SIZE=1024
+# LR=1.0e-4
+# MIN_LR=1.0e-5
+
+## GPT-3 175B
+# MODEL_SIZE=175
+# NUM_LAYERS=96
+# HIDDEN_SIZE=12288
+# NUM_ATTN_HEADS=96
+# GLOBAL_BATCH_SIZE=1536
+# LR=0.6e-4
+# MIN_LR=0.6e-5
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens
+## For MoE model, we found sometimes training a bit more to 330B tokens helps
+TRAIN_TOKENS=300000000000
+# TRAIN_TOKENS=330000000000
+
+## TRAIN_SAMPLES is another termination condition and also affect the number of 
+## data samples to be indexed. Since we want to reach the TRAIN_TOKENS
+## above, and techniques like curriculum learning has less token in some steps,
+## so we just set this config large enough to make sure we have enough
+## processed data and don't terminate by TRAIN_SAMPLES.
+TRAIN_SAMPLES=$(( ${TRAIN_TOKENS} * 3 / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+LR_DECAY_TOKENS=260000000000
+# LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+## Currently MoE models have divergence issue when MP > 1.
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 1 means dense model without MoE
+EP_SIZE=1
+# EP_SIZE=128
+
+if [[ $EP_SIZE -gt $NUM_GPUS ]]; then
+    EP_PARALLEL_SIZE=$NUM_GPUS
+else
+    EP_PARALLEL_SIZE=$EP_SIZE
+fi
+
+## Original GPT-3 model always set min LR at 10% of max LR. For MoE model, we
+## found that lower LR and min LR (than the base dense model) helps.
+## For 1.3B MoE-128 model we used LR=1.2e-4 and MIN_LR=1.0e-6.
+## For 350M MoE-128 model we used LR=2.0e-4 and MIN_LR=2.0e-6, but they are not
+## heavily tuned.
+# LR=2.0e-4
+# MIN_LR=2e-06
+
+## Coefficient for MoE loss. We find that 0.01 is a good value at least for
+## 1.3B MoE-128 model
+MLC=0.01
+
+## Below configs adjust the MoE expert token capacity limit during training and
+## eval. To completely disable capacity limit, set MOE_DROP_TOKEN to false.
+## Larger capacity factor or disabling capacity limit could improve training
+## convergence, but will also reduce training throughput.
+MOE_TRAIN_CAP_FACTOR=1.0
+MOE_EVAL_CAP_FACTOR=1.0
+MOE_MIN_CAP=4
+MOE_DROP_TOKEN="true"
+# MOE_DROP_TOKEN="false"
+###############################################################################
+### Curriculum learning (CL) configs
+## Enable/disable CL
+CL_ENABLED="false"
+## Consult the tutorial https://www.deepspeed.ai/tutorials/curriculum-learning/
+## for tuning the following configs
+CL_START_SEQLEN=80
+CL_AVG_SEQLEN=$(( (${CL_START_SEQLEN} + ${SEQ_LEN}) / 2 ))
+CL_TOKENS=60
+CL_TOKENS=$((${CL_TOKENS} * 1000000000))
+CL_STEP=$(( ${CL_TOKENS} / (${GLOBAL_BATCH_SIZE} * ${CL_AVG_SEQLEN}) ))
+###############################################################################
+### Misc configs
+LOG_INTERVAL=10
+EVAL_ITERS=10
+EVAL_INTERVAL=100
+SAVE_INTERVAL=1000
+
+## Standard deviation for weight initialization
+## We used 0.014 for 350M/1.3B dense/MoE models, and used 0.01 for 6.7B
+## dense model. Usually larger model needs lower std.
+INIT_STD=0.014
+# INIT_STD=0.01
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+ACTIVATION_CHECKPOINT="true"
+# ACTIVATION_CHECKPOINT="false"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+host="${HOSTNAME}"
+NAME="gpt-kd-${MODEL_SIZE}B-lr-${LR}-minlr-${MIN_LR}-bs-${GLOBAL_BATCH_SIZE}-gpus-${NUM_GPUS}-mp-${MP_SIZE}-pp-${PP_SIZE}"
+if [[ $EP_SIZE -gt 1 ]]; then
+    NAME="${NAME}-ep-${EP_SIZE}-mlc-${MLC}-cap-${MOE_TRAIN_CAP_FACTOR}-drop-${MOE_DROP_TOKEN}"
+fi
+if [ "${CL_ENABLED}" = "true" ]; then
+    NAME="${NAME}-cl-${CL_START_SEQLEN}-${CL_STEP}"
+fi
+
+OUTPUT_BASEPATH=$DIR/output
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/"
+mkdir -p "${OUTPUT_BASEPATH}/log/"
+TENSORBOARD_DIR="${OUTPUT_BASEPATH}/tensorboard/${NAME}_${host}_${current_time}"
+mkdir -p ${TENSORBOARD_DIR} 
+## Note that for MoE model with billion-scale base model, the checkpoint can be
+## as large as TB-scale which normal NFS cannot handle efficiently.
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/${NAME}"
+
+# USE_INTERNAL_DATA="true"
+USE_INTERNAL_DATA="false"
+
+if [ "${USE_INTERNAL_DATA}" = "true" ]; then
+    ## The internal data is only accessible within Microsoft
+    ## For cluster Azure-EastUS-V100-32GB-4, Azure-WestUS3-A100
+    # BASE_DATA_PATH=/vc_data/Megatron-LM/data
+    # DATA_HOME="/vc_data/pile-cc1-cc2-shuf"
+    ## For cluster Lab-RR1-V100
+    BASE_DATA_PATH=/data/Megatron-LM/data
+    DATA_HOME="/turing-ssd/users/conglli/data/pile-cc1-cc2-shuf"
+    ## For cluster Azure-CentralUS-A100
+    # BASE_DATA_PATH=/data/Megatron-LM/data
+    # DATA_HOME=/vc_data_1/users/amawa/blended
+
+    VOCAB_PATH=${BASE_DATA_PATH}/gpt2-vocab.json
+    MERGE_PATH=${BASE_DATA_PATH}/gpt2-merges.txt
+    ARX="${DATA_HOME}/ArXiv_ftfy_cleaned_id_shuf_text_document"
+    BC2="${DATA_HOME}/BookCorpus2_ftfy_cleaned_id_shuf_text_document"
+    B3="${DATA_HOME}/Books3_ftfy_cleaned_id_shuf_text_document"
+    CC2020="${DATA_HOME}/CC-2020-50_id_cleaned_shuf_text_document"
+    CC2021="${DATA_HOME}/CC-2021-04_id_cleaned_shuf_text_document"
+    GIT="${DATA_HOME}/Github_ftfy_id_shuf_text_document"
+    GUT="${DATA_HOME}/Gutenberg_PG-19_ftfy_cleaned_id_cleaned_shuf_text_document"
+    NIH="${DATA_HOME}/NIH_ExPorter_ftfy_id_shuf_text_document"
+    OWT2="${DATA_HOME}/OpenWebText2_ftfy_cleaned_id_shuf_text_document"
+    PCC="${DATA_HOME}/Pile-CC_id_cleaned_shuf_text_document"
+    PM="${DATA_HOME}/PubMed_Abstracts_ftfy_id_shuf_text_document"
+    RN="${DATA_HOME}/rn_dedup_shuf_cleaned_0.7_cleaned_shuf_text_document"
+    SE="${DATA_HOME}/StackExchange_ftfy_id_shuf_text_document"
+    ST="${DATA_HOME}/stories_dedup0.7_shuf_cleaned_shuf_text_document"
+    WIK="${DATA_HOME}/Wikipedia_en_ftfy_id_shuf_text_document"
+    DATA_BLEND="0.14336 ${B3} 0.08962 ${RN} 0.19336 ${OWT2} 0.05689 ${SE} \
+    0.00859 ${ST} 0.02897 ${PM} 0.04771 ${WIK} 0.00873 ${GUT} 0.01007 ${BC2} \
+    0.00208 ${NIH} 0.13017 ${CC2020} 0.09446 ${PCC} 0.15652 ${CC2021} \
+    0.01359 ${ARX} 0.01588 ${GIT}"
+else
+    VOCAB_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
+    MERGE_PATH=/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
+    # Public the Pile dataset, can be downloaded at https://mystic.the-eye.eu/public/AI/pile_neox/
+    DATA_BLEND=/data/the_pile_public_merged_nopreprocessing/pile_text_document
+fi
+###############################################################################
+data_options=" \
+         --vocab-file ${VOCAB_PATH} \
+         --merge-file ${MERGE_PATH} \
+         --data-path ${DATA_BLEND} \
+         --data-impl mmap"
+        
+megatron_options=" \
+        --override-lr-scheduler \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
+        --num-experts ${EP_SIZE} \
+        --moe-loss-coeff ${MLC} \
+        --moe-train-capacity-factor ${MOE_TRAIN_CAP_FACTOR} \
+        --moe-eval-capacity-factor ${MOE_EVAL_CAP_FACTOR} \
+        --moe-min-capacity ${MOE_MIN_CAP} \
+        --init-method-std ${INIT_STD} \
+        --lr-decay-tokens ${LR_DECAY_TOKENS} \
+        --lr-warmup-tokens ${WARMUP_TOKENS} \
+        --micro-batch-size ${BATCH_SIZE} \
+        --exit-duration-in-mins ${EXIT_DURATION} \
+        --global-batch-size ${GLOBAL_BATCH_SIZE} \
+        --num-layers ${NUM_LAYERS} \
+        --hidden-size ${HIDDEN_SIZE} \
+        --num-attention-heads ${NUM_ATTN_HEADS} \
+        --seq-length ${SEQ_LEN} \
+        --max-position-embeddings ${SEQ_LEN} \
+        --train-tokens ${TRAIN_TOKENS} \
+        --train-samples ${TRAIN_SAMPLES} \
+        --lr ${LR} \
+        --min-lr ${MIN_LR} \
+        --lr-decay-style cosine \
+        --split 98,2,0 \
+        --log-interval ${LOG_INTERVAL} \
+        --eval-interval ${EVAL_INTERVAL} \
+        --eval-iters ${EVAL_ITERS} \
+        --save-interval ${SAVE_INTERVAL} \
+        --weight-decay 0.1 \
+        --clip-grad 1.0 \
+        --hysteresis 2 \
+        --num-workers 0 \
+        --fp16 \
+        --load ${CHECKPOINT_PATH} \
+        --save ${CHECKPOINT_PATH} \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --tensorboard-dir ${TENSORBOARD_DIR}"
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+megatron_options="${megatron_options} \
+        --checkpoint-activations"
+fi
+
+if [[ $EP_SIZE -gt 1 ]]; then
+megatron_options="${megatron_options} \
+        --create-moe-param-group"
+fi
+
+if [ "${MOE_DROP_TOKEN}" = "false" ]; then
+megatron_options="${megatron_options} \
+        --disable-moe-token-dropping"
+fi
+
+template_json="ds_config_gpt_TEMPLATE.json"
+config_json="ds_config_gpt_${NAME}.json"
+sed "s/CONFIG_BATCH_SIZE/${GLOBAL_BATCH_SIZE}/" ${template_json} \
+    | sed "s/CONFIG_MBSIZE/${BATCH_SIZE}/" \
+    | sed "s/LOG_INTERVAL/${LOG_INTERVAL}/" \
+    | sed "s/ZERO_STAGE/0/" \
+    | sed "s/PRESCALE_GRAD/true/" \
+    | sed "s/CONFIG_FP16_ENABLED/true/" \
+    | sed "s/CONFIG_BF16_ENABLED/false/" \
+    | sed "s/CONFIG_CL_ENABLED/${CL_ENABLED}/" \
+    | sed "s/CONFIG_CL_MIN/${CL_START_SEQLEN}/" \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CL_STEP}/" \
+	  > ${config_json}
+
+deepspeed_options=" \
+		    --deepspeed \
+		    --deepspeed_config ${config_json} \
+		    --pipeline-model-parallel-size ${PP_SIZE}"
+
+# Currently MoE is not compatible with pipeline parallel
+if [[ $EP_SIZE -gt 1 ]]; then
+deepspeed_options="${deepspeed_options} \
+        --no-pipeline-parallel"
+fi
+
+if [ "${ACTIVATION_CHECKPOINT}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+        --deepspeed-activation-checkpointing"
+fi
+
+run_cmd="deepspeed ${DIR}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &> ${OUTPUT_BASEPATH}/log/${NAME}_${host}_${current_time}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+set +x

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/curriculum_learning/README.md

@@ -0,0 +1 @@
+This is an example of how to use DeepSpeed's curriculum learning (CL) feature which provides faster and more stable language model pre-training. Currently it is only integrated for GPT pre-training. Note that there are two curriculum learning examples in two different repos for Megatron-LM GPT-2 pre-training. Both of them have some unique features and limitations. See details in our [tutorial](https://www.deepspeed.ai/tutorials/curriculum-learning/). For technical details please refer to our [paper](https://arxiv.org/abs/2108.06084).

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/curriculum_learning/ds_train.sh

@@ -0,0 +1,37 @@
+# # baseline
+# CONFIG=baseline
+# TAG=baseline
+# MODEL_SIZE=1558
+# LR=1.5e-4
+# BSZ=512
+# SEQ_LEN=1024
+# MP_SIZE=1
+# SEED=1234
+# SAVE_INTERVAL=5000
+# NUM_ITER=600000
+# NUM_TOKEN=157286400000
+# LR_DECAY_TOKEN=157286400000
+# LR_WARMUP_ITER=3000
+# CONFIG_TEMPLATE=false
+# CURRICULUM_STEP=0
+# CURRICULUM_MIN=0
+
+# curriculum learning
+CONFIG=curriculum_fixed_linear
+MODEL_SIZE=1558
+LR=6e-4
+BSZ=4096
+SEQ_LEN=1024
+MP_SIZE=1
+SEED=1234
+SAVE_INTERVAL=1000
+NUM_ITER=75000
+NUM_TOKEN=157286400000
+LR_DECAY_TOKEN=157286400000
+LR_WARMUP_ITER=3000
+CONFIG_TEMPLATE=true
+CURRICULUM_STEP=45000
+CURRICULUM_MIN=64
+TAG="${CONFIG}_s${CURRICULUM_MIN}to${SEQ_LEN}_step${CURRICULUM_STEP}"
+
+bash ds_pretrain_gpt2.sh $CONFIG $TAG $MODEL_SIZE $LR $BSZ $SEQ_LEN $MP_SIZE $SEED $SAVE_INTERVAL $NUM_ITER $NUM_TOKEN $LR_DECAY_TOKEN $LR_WARMUP_ITER $CONFIG_TEMPLATE $CURRICULUM_STEP $CURRICULUM_MIN

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/curriculum_learning/ds_zero_stage_1_config_baseline.json

@@ -0,0 +1,26 @@
+{
+  "train_batch_size": 512,
+  "gradient_accumulation_steps": 1,
+  "steps_per_print": 1,
+  "zero_optimization": {
+    "stage": 1
+  },
+  "optimizer": {
+    "type": "Adam",
+    "params": {
+      "lr": 0.00015,
+      "max_grad_norm": 1.0,
+      "betas": [0.9, 0.95]
+    }
+  },
+  "gradient_clipping": 1.0,
+  "fp16": {
+    "enabled": true,
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "wall_clock_breakdown": false,
+  "zero_allow_untested_optimizer": false
+}

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/curriculum_learning/ds_zero_stage_1_config_curriculum_fixed_linear.json

@@ -0,0 +1,37 @@
+{
+  "train_batch_size": 512,
+  "gradient_accumulation_steps": 1,
+  "steps_per_print": 1,
+  "zero_optimization": {
+    "stage": 1
+  },
+  "optimizer": {
+    "type": "Adam",
+    "params": {
+      "lr": 0.00015,
+      "max_grad_norm": 1.0,
+      "betas": [0.9, 0.95]
+    }
+  },
+  "gradient_clipping": 1.0,
+  "fp16": {
+    "enabled": true,
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "wall_clock_breakdown": false,
+  "zero_allow_untested_optimizer": false,
+  "curriculum_learning": {
+    "enabled": true,
+    "curriculum_type": "seqlen",
+    "min_difficulty": CONFIG_CL_MIN,
+    "max_difficulty": CONFIG_CL_MAX,
+    "schedule_type": "fixed_linear",
+    "schedule_config": {
+      "total_curriculum_step": CONFIG_CL_DURATION,
+      "difficulty_step": 8
+    }
+  }
+}

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/evaluate_zeroshot_gpt.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+TASK="LAMBADA"
+
+VALID_DATA=<lambada path>
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+CHECKPOINT=checkpoints/gpt2_345m
+
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+               --task $TASK \
+               --valid-data $VALID_DATA \
+               --tokenizer-type GPT2BPETokenizer \
+               --strict-lambada \
+               --vocab-file $VOCAB_FILE \
+               --merge-file $MERGE_FILE \
+               --load $CHECKPOINT \
+               --tensor-model-parallel-size 1 \
+               --num-layers 24 \
+               --hidden-size 1024 \
+               --num-attention-heads 16 \
+               --batch-size 8 \
+               --checkpoint-activations \
+               --seq-length 1024 \
+               --max-position-embeddings 1024 \
+               --log-interval 10 \
+               --fp16 \
+               --no-load-optim \
+               --no-load-rng

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/generate_text.sh

@@ -0,0 +1,48 @@
+#!/bin/bash
+export TORCH_CUDA_ARCH_LIST=8.6+PTX
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+b=8
+mp=1
+experts=2
+nodes=1
+gpus=1
+
+
+use_tutel=""
+#use_tutel="--use-tutel"
+
+
+#ds_inference=""
+ds_inference="--ds-inference"
+
+launch_cmd="deepspeed --num_nodes $nodes --num_gpus $gpus"
+L=24
+H=2048
+A=16
+#experts1=${experts[$k]}
+program_cmd="tools/generate_samples_gpt.py \
+       --tensor-model-parallel-size $mp \
+       --num-layers $L \
+       --hidden-size $H \
+       --num-attention-heads $A \
+       --max-position-embeddings 1024 \
+       --tokenizer-type GPT2BPETokenizer \
+       --fp16 \
+       --num-experts ${experts} \
+       --mlp-type standard \
+       --micro-batch-size $b \
+       --seq-length 10 \
+       --out-seq-length 10 \
+       --temperature 1.0 \
+       --vocab-file $VOCAB_FILE \
+       --merge-file $MERGE_FILE \
+       --genfile unconditional_samples.json \
+       --top_p 0.9 \
+       --log-interval 1 \
+       --num-samples $((100*$b))
+       $use_tutel $ds_inference"
+
+echo $launch_cmd $program_cmd
+$launch_cmd $program_cmd

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/merge_mp_bert.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+TENSOR_MODEL_PARALLEL_SIZE=2
+
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m
+
+WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
+                                --model-type BERT \
+                                --tensor-model-parallel-size $TENSOR_MODEL_PARALLEL_SIZE \
+                                --tokenizer-type BertWordPieceLowerCase \
+                                --vocab-file $VOCAB_FILE \
+                                --num-layers 24 \
+                                --hidden-size 1024 \
+                                --num-attention-heads 16 \
+                                --seq-length 512 \
+                                --max-position-embeddings 512 \
+                                --load $CHECKPOINT_PATH

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/pretrain_bert_distributed.sh

@@ -0,0 +1,44 @@
+#!/bin/bash
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NNODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
+
+DATA_PATH=<Specify path and file prefix>_text_sentence
+CHECKPOINT_PATH=<Specify path>
+
+DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS \
+       pretrain_bert.py \
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --num-attention-heads 16 \
+       --micro-batch-size 4 \
+       --global-batch-size 32 \
+       --seq-length 512 \
+       --max-position-embeddings 512 \
+       --train-iters 1000000 \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+       --vocab-file bert-vocab.txt \
+       --data-impl mmap \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr 0.0001 \
+       --lr-decay-style linear \
+       --min-lr 1.0e-5 \
+       --lr-decay-iters 990000 \
+       --weight-decay 1e-2 \
+       --clip-grad 1.0 \
+       --lr-warmup-fraction .01 \
+       --log-interval 100 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --fp16

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/pretrain_bert_distributed_with_mp.sh

@@ -0,0 +1,46 @@
+#!/bin/bash
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NNODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
+
+DATA_PATH=<Specify path and file prefix>_text_sentence
+VOCAB_FILE=<Specify path to vocab.txt>
+CHECKPOINT_PATH=<Specify path>
+
+DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS \
+       pretrain_bert.py \
+       --tensor-model-parallel-size 2 \
+       --pipeline-model-parallel-size 2 \
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --num-attention-heads 16 \
+       --micro-batch-size 2 \
+       --global-batch-size 16 \
+       --max-position-embeddings 512 \
+       --train-iters 1000000 \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+       --vocab-file $VOCAB_FILE \
+       --data-impl mmap \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr 0.0001 \
+       --lr-decay-style linear \
+       --min-lr 1.0e-5 \
+       --lr-decay-iters 990000 \
+       --weight-decay 1e-2 \
+       --clip-grad 1.0 \
+       --lr-warmup-fraction .01 \
+       --log-interval 100 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --fp16

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/pretrain_gpt.sh

@@ -0,0 +1,41 @@
+#! /bin/bash
+
+# Runs the "345M" parameter model
+
+RANK=0
+WORLD_SIZE=1
+
+DATA_PATH=<Specify path and file prefix>_text_document
+CHECKPOINT_PATH=<Specify path>
+
+
+python pretrain_gpt.py \
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --num-attention-heads 16 \
+       --micro-batch-size 4 \
+       --global-batch-size 8 \
+       --seq-length 1024 \
+       --max-position-embeddings 1024 \
+       --train-iters 500000 \
+       --lr-decay-iters 320000 \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+       --vocab-file gpt2-vocab.json \
+       --merge-file gpt2-merges.txt \
+       --data-impl mmap \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr 0.00015 \
+       --min-lr 1.0e-5 \
+       --lr-decay-style cosine \
+       --weight-decay 1e-2 \
+       --clip-grad 1.0 \
+       --lr-warmup-fraction .01 \
+       --checkpoint-activations \
+       --log-interval 100 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --fp16

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/pretrain_gpt3_175B.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+
+
+#SBATCH <SLURM OPTIONS> --nodes=128 --exclusive --ntasks-per-node=8 --job-name=megatron_gpt3_175b
+
+
+DIR=`pwd`
+DATETIME=`date +'date_%y-%m-%d_time_%H-%M-%S'`
+mkdir -p $DIR/logs
+
+
+DATASET_1="<PATH TO THE FIRST DATASET>"
+DATASET_2="<PATH TO THE SECOND DATASET>"
+DATASET_3="<PATH TO THE THIRD DATASET>"
+DATASET="0.2 ${DATASET_1} 0.3 ${DATASET_2} 0.5 ${DATASET_3}"
+
+
+options=" \
+	--tensor-model-parallel-size 8 \
+	--pipeline-model-parallel-size 16 \
+        --num-layers 96 \
+        --hidden-size 12288 \
+        --num-attention-heads 96 \
+        --seq-length 2048 \
+        --max-position-embeddings 2048 \
+	--micro-batch-size 1 \
+	--global-batch-size 1536 \
+	--rampup-batch-size 16 16 5859375 \
+	--train-samples 146484375 \
+       	--lr-decay-samples 126953125 \
+        --lr-warmup-samples 183105 \
+        --lr 6.0e-5 \
+	--min-lr 6.0e-6 \
+        --lr-decay-style cosine \
+        --log-interval 10 \
+        --eval-iters 40 \
+        --eval-interval 1000 \
+	--data-path ${DATASET} \
+	--vocab-file <PATH TO gpt-vocab.json> \
+	--merge-file <PATH TO gpt-merges.txt> \
+	--save-interval 1000 \
+	--save <PATH TO CHECKPOINTS DIRECTORY> \
+	--load <PATH TO CHECKPOINTS DIRECTORY> \
+        --split 98,2,0 \
+        --clip-grad 1.0 \
+	--weight-decay 0.1 \
+	--adam-beta1 0.9 \
+	--adam-beta2 0.95 \
+	--init-method-std 0.006 \
+	--tensorboard-dir <TENSORBOARD DIRECTORY> \
+        --fp16 \
+	--checkpoint-activations "
+
+
+run_cmd="python -u ${DIR}/pretrain_gpt.py $@ ${options}"
+
+
+srun -l \
+     --container-image "nvcr.io/nvidia/pytorch:20.12-py3" \
+     --container-mounts "<DIRECTORIES TO MOUNT>" \
+     --output=$DIR/logs/%x_%j_$DATETIME.log sh -c "${run_cmd}"
+
+
+set +x
+

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/pretrain_gpt_distributed.sh

@@ -0,0 +1,48 @@
+#! /bin/bash
+
+# Runs the "345M" parameter model
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NNODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
+
+DATA_PATH=<Specify path and file prefix>_text_document
+CHECKPOINT_PATH=<Specify path>
+
+DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS \
+       pretrain_gpt.py \
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --num-attention-heads 16 \
+       --micro-batch-size 8 \
+       --global-batch-size 64 \
+       --seq-length 1024 \
+       --max-position-embeddings 1024 \
+       --train-iters 500000 \
+       --lr-decay-iters 320000 \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+       --vocab-file gpt2-vocab.json \
+       --merge-file gpt2-merges.txt \
+       --data-impl mmap \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr 0.00015 \
+       --lr-decay-style cosine \
+       --min-lr 1.0e-5 \
+       --weight-decay 1e-2 \
+       --clip-grad 1.0 \
+       --lr-warmup-fraction .01 \
+       --checkpoint-activations \
+       --log-interval 100 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --fp16

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/pretrain_gpt_distributed_with_mp.sh

@@ -0,0 +1,50 @@
+#! /bin/bash
+
+# Runs the "345M" parameter model
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NNODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
+
+DATA_PATH=<Specify path and file prefix>_text_document
+CHECKPOINT_PATH=<Specify path>
+
+DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS \
+       pretrain_gpt.py \
+       --tensor-model-parallel-size 2 \
+       --pipeline-model-parallel-size 2 \
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --num-attention-heads 16 \
+       --micro-batch-size 4 \
+       --global-batch-size 16 \
+       --seq-length 1024 \
+       --max-position-embeddings 1024 \
+       --train-iters 500000 \
+       --lr-decay-iters 320000 \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+       --vocab-file gpt2-vocab.json \
+       --merge-file gpt2-merges.txt \
+       --data-impl mmap \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr 0.00015 \
+       --lr-decay-style cosine \
+       --min-lr 1.0e-5 \
+       --weight-decay 1e-2 \
+       --clip-grad 1.0 \
+       --lr-warmup-fraction .01 \
+       --checkpoint-activations \
+       --log-interval 100 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --fp16

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/pretrain_ict.sh

@@ -0,0 +1,44 @@
+#! /bin/bash
+
+# Runs the "217M" parameter biencoder model for ICT retriever
+
+RANK=0
+WORLD_SIZE=1
+
+PRETRAINED_BERT_PATH=<Specify path of pretrained BERT model>
+TEXT_DATA_PATH=<Specify path and file prefix of the text data>
+TITLE_DATA_PATH=<Specify path and file prefix od the titles>
+CHECKPOINT_PATH=<Specify path>
+
+
+python pretrain_ict.py \
+        --num-layers 12 \
+        --hidden-size 768 \
+        --num-attention-heads 12 \
+        --tensor-model-parallel-size 1 \
+        --micro-batch-size 32 \
+        --seq-length 256 \
+        --max-position-embeddings 512 \
+        --train-iters 100000 \
+        --vocab-file bert-vocab.txt \
+        --tokenizer-type BertWordPieceLowerCase \
+        --DDP-impl torch \
+        --bert-load ${PRETRAINED_BERT_PATH} \
+        --log-interval 100 \
+        --eval-interval 1000 \
+        --eval-iters 10 \
+        --retriever-report-topk-accuracies 1 5 10 20 100 \
+        --retriever-score-scaling \
+        --load $CHECKPOINT_PATH \
+        --save $CHECKPOINT_PATH \
+        --data-path ${TEXT_DATA_PATH} \
+        --titles-data-path ${TITLE_DATA_PATH} \
+        --lr 0.0001 \
+        --lr-decay-style linear \
+        --weight-decay 1e-2 \
+        --clip-grad 1.0 \
+        --lr-warmup-fraction 0.01 \
+        --save-interval 4000 \
+        --exit-interval 8000 \
+        --query-in-block-prob 0.1 \
+        --fp16

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/pretrain_t5.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+
+RANK=0
+WORLD_SIZE=1
+DATA_PATH=<Specify path and file prefix>
+VOCAB_FILE=<Specify path to vocab.txt>
+CHECKPOINT_PATH=<Specify path>
+
+python pretrain_t5.py \
+       --num-layers 12 \
+       --hidden-size 768 \
+       --num-attention-heads 12 \
+       --kv-channels 64 \
+       --ffn-hidden-size 3072 \
+       --encoder-seq-length 512 \
+       --decoder-seq-length 128 \
+       --micro-batch-size 16 \
+       --global-batch-size 2048 \
+       --max-position-embeddings 512 \
+       --train-iters 1000000 \
+       --lr-decay-iters 1000000 \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+       --vocab-file $VOCAB_FILE \
+       --data-impl mmap \
+       --split 949,50,1 \
+       --lr 0.0001 \
+       --min-lr 0.00001 \
+       --lr-decay-style linear \
+       --lr-warmup-fraction .01 \
+       --weight-decay 1e-2 \
+       --clip-grad 1.0 \
+       --log-interval 100 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --fp16

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/examples/run_deepspeed_example.sh

@@ -0,0 +1,84 @@
+#!/bin/bash
+set -ex
+
+BASE_PATH=/vc_data/Megatron-LM/data
+DATA_PATH=${BASE_PATH}/indexed_datasets/megatron
+DS_CONFIG=ds_config.json
+
+TP=1
+PP=1
+NLAYERS=24
+HIDDEN=512
+
+GLOBAL_BATCH=64
+MICRO_BATCH=4
+
+ZERO_STAGE=2
+
+OUTPUT_DIR=ds_z${ZERO_STAGE}_nl${NLAYERS}_hs${HIDDEN}_gb${GLOBAL_BATCH}_mb${MICRO_BATCH}
+#OUTPUT_DIR=baseline_nl${NLAYERS}_hs${HIDDEN}_gb${GLOBAL_BATCH}_mb${MICRO_BATCH}
+mkdir -p $OUTPUT_DIR
+
+cat <<EOT > $DS_CONFIG
+{
+  "train_batch_size" : $GLOBAL_BATCH,
+  "train_micro_batch_size_per_gpu": $MICRO_BATCH,
+  "steps_per_print": 1,
+
+  "zero_optimization": {
+    "stage": $ZERO_STAGE
+  },
+
+  "fp16": {
+    "enabled": true,
+    "initial_scale_power": 12
+  },
+
+  "wall_clock_breakdown" : true
+}
+EOT
+
+export NCCL_DEBUG=warn 
+
+ds_args=""
+ds_args=" --deepspeed ${ds_args}"
+ds_args=" --no-pipeline-parallel ${ds_args}" 
+ds_args=" --deepspeed_config=$DS_CONFIG ${ds_args}"
+ds_args=" --zero-stage=$ZERO_STAGE ${ds_args}"
+ds_args=" --deepspeed-activation-checkpointing ${ds_args}"
+
+
+deepspeed pretrain_gpt.py \
+    --tensor-model-parallel-size $TP \
+    --pipeline-model-parallel-size $PP \
+    --num-layers $NLAYERS \
+    --hidden-size $HIDDEN \
+    --num-attention-heads 16 \
+    --seq-length 256 \
+    --loss-scale 12 \
+    --max-position-embeddings 1024 \
+    --micro-batch-size 4 \
+    --global-batch-size 1024 \
+    --train-iters 1000 \
+    --lr 6.0e-5 \
+    --min-lr 6.0e-6 \
+    --lr-decay-style cosine \
+    --log-interval 1 \
+    --eval-iters 40 \
+    --eval-interval 1000 \
+    --data-path $DATA_PATH \
+    --vocab-file $BASE_PATH/gpt2-vocab.json \
+    --merge-file $BASE_PATH/gpt2-merges.txt \
+    --save-interval 1000 \
+    --split 98,2,0 \
+    --clip-grad 1.0 \
+    --weight-decay 0.1 \
+    --adam-beta1 0.9 \
+    --adam-beta2 0.95 \
+    --init-method-std 0.006 \
+    --fp16 \
+    --checkpoint-activations \
+    --tensorboard-dir $OUTPUT_DIR \
+    $ds_args \
+    --exit-interval 5000 | tee ${OUTPUT_DIR}/output.log
+

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/tasks/data_utils.py

@@ -0,0 +1,118 @@
+# coding=utf-8
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" Tasks data utility."""
+
+import re
+import numpy as np
+
+
+def clean_text(text):
+    """Remove new lines and multiple spaces and adjust end of sentence dot."""
+
+    text = text.replace("\n", " ")
+    text = re.sub(r'\s+', ' ', text)
+    for _ in range(3):
+        text = text.replace(' . ', '. ')
+
+    return text
+
+
+def build_sample(ids, types, paddings, label, unique_id):
+    """Convert to numpy and return a sample consumed by the batch producer."""
+
+    ids_np = np.array(ids, dtype=np.int64)
+    types_np = np.array(types, dtype=np.int64)
+    paddings_np = np.array(paddings, dtype=np.int64)
+    sample = ({'text': ids_np,
+               'types': types_np,
+               'padding_mask': paddings_np,
+               'label': int(label),
+               'uid': int(unique_id)})
+
+    return sample
+
+
+def build_tokens_types_paddings_from_text(text_a, text_b,
+                                          tokenizer, max_seq_length):
+    """Build token types and paddings, trim if needed, and pad if needed."""
+
+    text_a_ids = tokenizer.tokenize(text_a)
+    text_b_ids = None
+    if text_b is not None:
+        text_b_ids = tokenizer.tokenize(text_b)
+
+    return build_tokens_types_paddings_from_ids(text_a_ids, text_b_ids,
+                                                max_seq_length, tokenizer.cls,
+                                                tokenizer.sep, tokenizer.pad)
+
+
+def build_tokens_types_paddings_from_ids(text_a_ids, text_b_ids, max_seq_length,
+                                         cls_id, sep_id, pad_id):
+    """Build token types and paddings, trim if needed, and pad if needed."""
+
+    ids = []
+    types = []
+    paddings = []
+
+    # [CLS].
+    ids.append(cls_id)
+    types.append(0)
+    paddings.append(1)
+
+    # A.
+    len_text_a = len(text_a_ids)
+    ids.extend(text_a_ids)
+    types.extend([0] * len_text_a)
+    paddings.extend([1] * len_text_a)
+
+    # [SEP].
+    ids.append(sep_id)
+    types.append(0)
+    paddings.append(1)
+
+    # B.
+    if text_b_ids is not None:
+        len_text_b = len(text_b_ids)
+        ids.extend(text_b_ids)
+        types.extend([1] * len_text_b)
+        paddings.extend([1] * len_text_b)
+
+    # Cap the size.
+    trimmed = False
+    if len(ids) >= max_seq_length:
+        max_seq_length_m1 = max_seq_length - 1
+        ids = ids[0:max_seq_length_m1]
+        types = types[0:max_seq_length_m1]
+        paddings = paddings[0:max_seq_length_m1]
+        trimmed = True
+
+    # [SEP].
+    if (text_b_ids is not None) or trimmed:
+        ids.append(sep_id)
+        if text_b_ids is None:
+            types.append(0)
+        else:
+            types.append(1)
+        paddings.append(1)
+
+    # Padding.
+    padding_length = max_seq_length - len(ids)
+    if padding_length > 0:
+        ids.extend([pad_id] * padding_length)
+        types.extend([pad_id] * padding_length)
+        paddings.extend([0] * padding_length)
+
+    return ids, types, paddings

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/tasks/detok.py

@@ -0,0 +1,73 @@
+import os
+import sys
+import torch
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
+                                             os.path.pardir)))
+
+from tqdm import tqdm
+from megatron import get_args
+from megatron import get_tokenizer
+from megatron.initialize import initialize_megatron
+from pretrain_gpt import train_valid_test_datasets_provider
+from megatron.training import build_train_valid_test_data_iterators
+
+
+def get_detok_args(parser):
+    group = parser.add_argument_group(title='detokenizer')
+    group.add_argument('--detokenizer_output',
+                       type=str,
+                       required=True,
+                       help='detokenizer output path')
+    return parser
+
+
+def process_split(split, dataset, out_path):
+    print(f'Processing {split}')
+    tokenizer = get_tokenizer()
+
+    full_text = []
+    for batch in tqdm(dataset, total=len(dataset)):
+        tokens = batch['text'].reshape(-1).tolist()
+        text = tokenizer.detokenize(tokens)
+        full_text.append(text)
+
+    out_name = os.path.join(out_path, f'{split}.text')
+    print(f'Writing to {out_name}')
+    with open(out_name, 'w') as f:
+        f.writelines(full_text)
+
+
+def main():
+
+    # below arguments are to force the full dataset according to the
+    # train/valid/test split based on args.split
+    forced_args = {
+        "micro_batch_size": 1,
+        "train_samples": None,
+        "train_iters": 1,
+        "eval_iters": 1,
+        "eval_interval": 2,
+        "use_seq_len_plus_one_tokens": False
+    }
+
+    initialize_megatron(extra_args_provider=get_detok_args, args_defaults=forced_args)
+    torch.distributed.barrier()
+
+    # after parsing, we have to force again the required args
+    args = get_args()
+    for name, value in forced_args.items():
+        setattr(args, name, value)
+
+    # create train/valid/test split based on args.split
+    args.iteration = 0
+    train_iter, valid_iter, test_iter = build_train_valid_test_data_iterators(
+        train_valid_test_datasets_provider)
+
+    os.makedirs(args.detokenizer_output, exist_ok=True)
+    process_split('test', test_iter._dataset, args.detokenizer_output)
+    process_split('valid', valid_iter._dataset, args.detokenizer_output)
+    process_split('train', train_iter._dataset, args.detokenizer_output)
+
+
+if __name__ == '__main__':
+    main()

docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/tasks/ensemble_classifier.py

@@ -0,0 +1,149 @@
+import os
+import argparse
+import collections
+
+import numpy as np
+import torch
+
+
+def process_files(args):
+    all_predictions = collections.OrderedDict()
+    all_labels = collections.OrderedDict()
+    all_uid = collections.OrderedDict()
+    for path in args.paths:
+        path = os.path.join(path, args.prediction_name)
+        try:
+            data = torch.load(path)
+            for dataset in data:
+                name, d = dataset
+                predictions, labels, uid = d
+                if name not in all_predictions:
+                    all_predictions[name] = np.array(predictions)
+                    if args.labels is None:
+                        args.labels = [i for i in range(all_predictions[name].shape[1])]
+                    if args.eval:
+                        all_labels[name] = np.array(labels)
+                    all_uid[name] = np.array(uid)
+                else:
+                    all_predictions[name] += np.array(predictions)
+                    assert np.allclose(all_uid[name], np.array(uid))
+        except Exception as e:
+            print(e)
+            continue
+    return all_predictions, all_labels, all_uid
+
+
+def get_threshold(all_predictions, all_labels, one_threshold=False):
+    if one_threshold:
+        all_predictons = {'combined': np.concatenate(list(all_predictions.values()))}
+        all_labels = {'combined': np.concatenate(list(all_predictions.labels()))}
+    out_thresh = []
+    for dataset in all_predictions:
+        preds = all_predictions[dataset]
+        labels = all_labels[dataset]
+        out_thresh.append(calc_threshold(preds, labels))
+    return out_thresh
+
+
+def calc_threshold(p, l):
+    trials = [(i) * (1. / 100.) for i in range(100)]
+    best_acc = float('-inf')
+    best_thresh = 0
+    for t in trials:
+        acc = ((apply_threshold(p, t).argmax(-1) == l).astype(float)).mean()
+        if acc > best_acc:
+            best_acc = acc
+            best_thresh = t
+    return best_thresh
+
+
+def apply_threshold(preds, t):
+    assert (np.allclose(preds.sum(-1), np.ones(preds.shape[0])))
+    prob = preds[:, -1]
+    thresholded = (prob >= t).astype(int)
+    preds = np.zeros_like(preds)
+    preds[np.arange(len(thresholded)), thresholded.reshape(-1)] = 1
+    return preds
+
+
+def threshold_predictions(all_predictions, threshold):
+    if len(threshold) != len(all_predictions):
+        threshold = [threshold[-1]] * (len(all_predictions) - len(threshold))
+    for i, dataset in enumerate(all_predictions):
+        thresh = threshold[i]
+        preds = all_predictions[dataset]
+        all_predictions[dataset] = apply_threshold(preds, thresh)
+    return all_predictions
+
+
+def postprocess_predictions(all_predictions, all_labels, args):
+    for d in all_predictions:
+        all_predictions[d] = all_predictions[d] / len(args.paths)
+
+    if args.calc_threshold:
+        args.threshold = get_threshold(all_predictions, all_labels, args.one_threshold)
+        print('threshold', args.threshold)
+
+    if args.threshold is not None:
+        all_predictions = threshold_predictions(all_predictions, args.threshold)
+
+    return all_predictions, all_labels
+
+
+def write_predictions(all_predictions, all_labels, all_uid, args):
+    all_correct = 0
+    count = 0
+    for dataset in all_predictions:
+        preds = all_predictions[dataset]
+        preds = np.argmax(preds, -1)
+        if args.eval:
+            correct = (preds == all_labels[dataset]).sum()
+            num = len(all_labels[dataset])
+            accuracy = correct / num
+            count += num
+            all_correct += correct
+            accuracy = (preds == all_labels[dataset]).mean()
+            print(accuracy)
+        if not os.path.exists(os.path.join(args.outdir, dataset)):
+            os.makedirs(os.path.join(args.outdir, dataset))
+        outpath = os.path.join(
+            args.outdir, dataset, os.path.splitext(
+                args.prediction_name)[0] + '.tsv')
+        with open(outpath, 'w') as f:
+            f.write('id\tlabel\n')
+            f.write('\n'.join(str(uid) + '\t' + str(args.labels[p])
+                              for uid, p in zip(all_uid[dataset], preds.tolist())))
+    if args.eval:
+        print(all_correct / count)
+
+
+def ensemble_predictions(args):
+    all_predictions, all_labels, all_uid = process_files(args)
+    all_predictions, all_labels = postprocess_predictions(all_predictions, all_labels, args)
+    write_predictions(all_predictions, all_labels, all_uid, args)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--paths', required=True, nargs='+',
+                        help='paths to checkpoint directories used in ensemble')
+    parser.add_argument('--eval', action='store_true',
+                        help='compute accuracy metrics against labels (dev set)')
+    parser.add_argument('--outdir',
+                        help='directory to place ensembled predictions in')
+    parser.add_argument('--prediction-name', default='test_predictions.pt',
+                        help='name of predictions in checkpoint directories')
+    parser.add_argument('--calc-threshold', action='store_true',
+                        help='calculate threshold classification')
+    parser.add_argument('--one-threshold', action='store_true',
+                        help='use on threshold for all subdatasets')
+    parser.add_argument('--threshold', nargs='+', default=None, type=float,
+                        help='user supplied threshold for classification')
+    parser.add_argument('--labels', nargs='+', default=None,
+                        help='whitespace separated list of label names')
+    args = parser.parse_args()
+    ensemble_predictions(args)
+
+
+if __name__ == '__main__':
+    main()