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docker/intel_code/llama13b/Megatron-DeepSpeed/dataset/README.md

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+# Run the scripts below to setup dataset 
+
+bash download_books.sh
+
+bash download_vocab.sh

docker/intel_code/llama13b/Megatron-DeepSpeed/dataset/download_books.sh

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+wget https://the-eye.eu/public/AI/pile_neox/data/BookCorpusDataset_text_document.bin
+wget https://the-eye.eu/public/AI/pile_neox/data/BookCorpusDataset_text_document.idx

docker/intel_code/llama13b/Megatron-DeepSpeed/dataset/download_ckpt.sh

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+mkdir -p checkpoints/gpt2_345m
+
+cd checkpoints/gpt2_345m
+wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O megatron_lm_345m_v0.0.zip
+unzip megatron_lm_345m_v0.0.zip
+rm megatron_lm_345m_v0.0.zip
+cd ../..
+

docker/intel_code/llama13b/Megatron-DeepSpeed/dataset/download_vocab.sh

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+wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json
+wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/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
+  },
+
+  "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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/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 \
+    --tokenizer-type GPT2BPETokenizer \
+    --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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/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
+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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/MoE/ds_pretrain_gpt_1.3B_PR-MoE64or128.sh

@@ -0,0 +1,340 @@
+#!/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} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=8
+
+## Model parallelism, 1 is no MP
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 128 means standard MoE
+# EP_SIZE=128
+EP_SIZE="64 64 64 64 64 64 64 64 64 64 128 128"
+
+
+EP_PARALLEL_SIZE=$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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/MoE/ds_pretrain_gpt_1.3B_PR-MoE64or128_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} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=128
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 128 means standard MoE
+# EP_SIZE=128
+EP_SIZE="64 64 64 64 64 64 64 64 128 128"
+EP_SIZE_TEACHER="64 64 64 64 64 64 64 64 64 64 128 128"
+
+EP_PARALLEL_SIZE=$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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/MoE/ds_pretrain_gpt_1.3B_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=2
+
+## Model parallelism, 1 is no MP
+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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/MoE/ds_pretrain_gpt_125M_MoE64.sh

@@ -0,0 +1,372 @@
+#!/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} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=$(($(ds_ssh nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)-2))
+NUM_GPUS_PERNODE=$(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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/MoE/ds_pretrain_gpt_350M_MoE128.sh

@@ -0,0 +1,348 @@
+#!/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
+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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/MoE/ds_pretrain_gpt_350M_PR-MoE32or64.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} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 128 means standard MoE
+# EP_SIZE=128
+EP_SIZE="32 32 32 32 32 32 32 32 32 32 64 64"
+
+EP_PARALLEL_SIZE=$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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/MoE/ds_pretrain_gpt_350M_PR-MoE32or64_MoS.sh

@@ -0,0 +1,353 @@
+#!/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} / ${SEQ_LEN} ))
+
+## Another termination condition in minutes. Set it large enough to avoid
+## undesired early termination.
+EXIT_DURATION=30000000
+###############################################################################
+### LR configs
+## LR warmup and decay duration, this token-based config is preferable since
+## no need to readjust when the batch size/seqlen is changed.
+## Original GPT-3 paper uses 375M warmup tokens and 260B decay tokens.
+## For MoE model, we found that setting the decay token to 300B helps.
+WARMUP_TOKENS=375000000
+# LR_DECAY_TOKENS=260000000000
+LR_DECAY_TOKENS=300000000000
+###############################################################################
+### Parallelism configs
+## Micro batch size per GPU
+## Make sure that BATCH_SIZE <= GLOBAL_BATCH_SIZE*PP_SIZE*MP_SIZE/NUM_GPUS
+BATCH_SIZE=4
+
+## Model parallelism, 1 is no MP
+MP_SIZE=1
+
+## Pipeline parallelism
+## Currently we don't support PP for MoE. To disable PP, set PP_SIZE
+## to 1 and use the "--no-pipeline-parallel" arg.
+PP_SIZE=1
+NUM_GPUS=64
+###############################################################################
+### MoE configs
+## Number of experts. EP_SIZE 128 means standard MoE
+# EP_SIZE=128
+EP_SIZE="32 32 32 32 32 32 32 32 64 64"
+EP_SIZE_TEACHER="32 32 32 32 32 32 32 32 32 32 64 64"
+
+EP_PARALLEL_SIZE=$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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/MoE/ds_pretrain_gpt_350M_dense.sh

@@ -0,0 +1,348 @@
+#!/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
+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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/MoE/ds_pretrain_gpt_6.7B_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
+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-opt_param-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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/README.md

@@ -0,0 +1,33 @@
+# Megatron-DeepSpeed Recipes and Scripts
+
+This folder includes various example scripts with DeepSpeed technologies integrated. Below we describe each sub-folder, sorted by last update date.
+
+## Sync with NVIDIA/Megatron-LM (last updated: Jul 2023)
+The ```rebase``` folder includes details about the recent sync with the NVIDIA/Megatron-LM repo (where this repo is forked from). It includes example scripts we used to test after the sync, together with a README documentation about what were tested.
+
+## Data Efficiency (last updated: Feb 2023)
+
+The ```data_efficiency``` folder includes GPT-3 and BERT pretraining examples for DeepSpeed Data Efficiency Library, together with examples of zero-shot evaluation for GPT models and GLUE finetuning for BERT models. Please refer to the detailed tutorials in data_efficiency/README.MD. Currently this folder includes the newest example scripts for GPT/BERT pretraining/eval/finetuning, both with and without DeepSpeed Data Efficiency Library techniques.
+
+## BERT example (last updated: Dec 2022)
+
+The ```bert_with_pile``` folder includes examples about BERT-style model pre-training (using the public Pile data or user's own data) with DeepSpeed integration. Please refer to the readme in the folder for tutorial.
+
+## Azure (last updated: Nov 2022)
+
+We strongly recommend to start with AzureML recipe in the ```azureml``` folder.
+
+If you have a custom infrastructure (e.g. HPC clusters) or Azure VM and VMSS based environments, please refer to the bash scripts in the ```azure``` folder.
+
+## Model Compression (last updated: Aug 2022)
+
+The ```compression``` folder includes examples about layer reduction for task-agnostic compression. Please refer to [this tutorial](https://www.deepspeed.ai/tutorials/model-compression/#11-layer-reduction) about the DeepSpeed Model Compression Library. These recipes are for GPT-style NLG models.
+
+## MoE (last updated: Jun 2022)
+
+Please see the ```MoE``` folder for different training recipes and scripts for Mixture-of-expert based models and dense models. These recipes are for GPT-style NLG models, and currently this is the only folder with MoE training examples.
+
+## Curriculum Learning (last updated: Oct 2021)
+
+Curriculum learning recipes are in the ```curriculum_learning``` folder. Please refer to the detailed tutorials linked inside. These recipes are for GPT-style NLG models.
+Note that the DeepSpeed Data Efficiency Library above includes a more general curriculum learning support. This legacy curriculum learning feature is still compatible, but we recommend using the DeepSpeed Data Efficiency Library above. However, the newer DeepSpeed Data Efficiency Library currently is not compatible with pipeline parallelism. So if you have to use pipeline parallelism, you would need to use this legacy curriculum learning version.

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/curriculum_learning/ds_pretrain_gpt2.sh

@@ -0,0 +1,150 @@
+#! /bin/bash
+
+CONFIG=$1
+TAG=$2
+MODEL_SIZE=$3
+LR=$4
+TOTAL_BATCHSIZE=$5
+SEQ_LEN=$6
+MP_SIZE=$7
+SEED=$8
+SAVE_INTERVAL=$9
+NUM_ITER=${10}
+NUM_TOKEN=${11}
+LR_DECAY_TOKEN=${12}
+LR_WARMUP_ITER=${13}
+CONFIG_TEMPLATE=${14}
+CURRICULUM_STEP=${15}
+CURRICULUM_MIN=${16}
+
+# 12-layer, 768-hidden, 12-heads, 117M parameters
+# 24-layer, 1024-hidden, 16-heads, 345M parameters
+# 36-layer, 1280-hidden, 20-heads, 774M parameters
+# 48-layer, 1600-hidden, 25-heads, 1558M parameters
+if [[ $MODEL_SIZE -eq 117 ]]; then
+        NUM_LAYERS=12
+        HIDDEN_SIZE=768
+        NUM_ATTN_HEADS=12
+elif [[ $MODEL_SIZE -eq 345 ]]; then
+        NUM_LAYERS=24
+        HIDDEN_SIZE=1024
+        NUM_ATTN_HEADS=16
+elif [[ $MODEL_SIZE -eq 774 ]]; then
+        NUM_LAYERS=36
+        HIDDEN_SIZE=1280
+        NUM_ATTN_HEADS=20
+elif [[ $MODEL_SIZE -eq 1558 ]]; then
+        NUM_LAYERS=48
+        HIDDEN_SIZE=1600
+        NUM_ATTN_HEADS=25
+else
+        echo "Model size not supported."
+        exit 1
+fi
+
+# Pipeline parallelism. 1 means no pipelines.
+PP_SIZE=1
+
+# Change for multinode config
+NUM_WORKERS=16
+NUM_GPUS_PER_WORKER=8
+NUM_GPUS=$(( ${NUM_WORKERS} * ${NUM_GPUS_PER_WORKER} ))
+if [[ $PP_SIZE -gt 0 ]]; then
+    DP_SIZE=$(( ${NUM_GPUS} / (${PP_SIZE} * ${MP_SIZE}) ))
+else
+    DP_SIZE=$(( ${NUM_GPUS} / ${MP_SIZE} ))
+fi
+# Batch size per gpu, here we assume grad accumulation step 1
+# you can reduce this if gpu OOM
+BATCHSIZE=$((TOTAL_BATCHSIZE/DP_SIZE))
+
+DATA_PATH=/vc_data/Megatron-LM/data/indexed_datasets/megatron
+VOCAB_PATH=/vc_data/Megatron-LM/data/gpt2-vocab.json
+MERGE_PATH=/vc_data/Megatron-LM/data/gpt2-merges.txt
+
+#ZeRO Configs
+stage=1
+
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+script_path=$(realpath $0)
+script_dir=$(dirname $script_path)
+host="${HOSTNAME}"
+
+if [ "${CONFIG_TEMPLATE}" = "true" ]; then
+template_json="$script_dir/ds_zero_stage_${stage}_config_${CONFIG}.json"
+config_json="$script_dir/ds_zero_stage_${stage}_config_${CONFIG}_min${CURRICULUM_MIN}_max${SEQ_LEN}_step${CURRICULUM_STEP}.json"
+sed "s/CONFIG_CL_MIN/${CURRICULUM_MIN}/" ${template_json} \
+    | sed "s/CONFIG_CL_MAX/${SEQ_LEN}/" \
+    | sed "s/CONFIG_CL_DURATION/${CURRICULUM_STEP}/" \
+	  > ${config_json}
+else
+config_json="$script_dir/ds_zero_stage_${stage}_config_${CONFIG}.json"
+fi
+
+JOB_NAME="gpt2_${MODEL_SIZE}M_bsz${TOTAL_BATCHSIZE}_seq${SEQ_LEN}_lr${LR}_warmup${LR_WARMUP_ITER}_decay${LR_DECAY_TOKEN}_seed${SEED}_${TAG}_stage${stage}_n${NUM_WORKERS}_g${NUM_GPUS_PER_WORKER}_mp${MP_SIZE}"
+LOG_NAME="${JOB_NAME}_${host}_${current_time}"
+
+OUTPUT_BASEPATH="/vc_data_blob/users/conglli"
+mkdir -p "${OUTPUT_BASEPATH}/tensorboard/curriculum/"
+mkdir -p "${OUTPUT_BASEPATH}/checkpoint/curriculum/"
+mkdir -p "${OUTPUT_BASEPATH}/log/curriculum/"
+LOGDIR="${OUTPUT_BASEPATH}/tensorboard/curriculum/${LOG_NAME}"
+CHECKPOINT_PATH="${OUTPUT_BASEPATH}/checkpoint/curriculum/${JOB_NAME}"
+
+gpt_options=" \
+        --tensor-model-parallel-size ${MP_SIZE} \
+        --num-layers $NUM_LAYERS \
+        --hidden-size $HIDDEN_SIZE \
+        --num-attention-heads $NUM_ATTN_HEADS \
+        --seq-length $SEQ_LEN \
+        --max-position-embeddings $SEQ_LEN \
+        --micro-batch-size $BATCHSIZE \
+        --global-batch-size ${TOTAL_BATCHSIZE} \
+        --train-iters $NUM_ITER \
+        --train-tokens $NUM_TOKEN \
+        --lr-decay-tokens $LR_DECAY_TOKEN \
+        --save $CHECKPOINT_PATH \
+        --load $CHECKPOINT_PATH \
+        --data-path $DATA_PATH \
+        --vocab-file $VOCAB_PATH \
+        --merge-file $MERGE_PATH \
+        --data-impl mmap \
+        --split 949,50,1 \
+        --distributed-backend nccl \
+        --override-opt_param-scheduler \
+        --lr $LR \
+        --lr-decay-style cosine \
+        --min-lr 1.0e-5 \
+        --weight-decay 1e-2 \
+        --clip-grad 1.0 \
+        --lr-warmup-iters $LR_WARMUP_ITER \
+        --checkpoint-activations \
+        --log-interval 100 \
+        --save-interval $SAVE_INTERVAL \
+        --eval-interval 100 \
+        --eval-iters 10 \
+        --fp16 \
+        --seed $SEED \
+        --tensorboard-queue-size 1 \
+        --log-timers-to-tensorboard \
+        --log-batch-size-to-tensorboard \
+        --log-validation-ppl-to-tensorboard \
+        --no-masked-softmax-fusion \
+        --tensorboard-dir ${LOGDIR}
+"
+
+deepspeed_options=" \
+        --deepspeed \
+        --deepspeed_config ${config_json} \
+        --zero-stage ${stage} \
+        --pipeline-model-parallel-size ${PP_SIZE} \
+        --deepspeed-activation-checkpointing
+"
+
+full_options="${gpt_options} ${deepspeed_options}"
+
+run_cmd="deepspeed --num_nodes ${NUM_WORKERS} --num_gpus ${NUM_GPUS_PER_WORKER}  ../../pretrain_gpt.py ${full_options} &>> ${OUTPUT_BASEPATH}/log/curriculum/${JOB_NAME}.log"
+echo ${run_cmd}
+eval ${run_cmd}
+
+set +x

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/curriculum_learning/ds_pretrain_gpt_1.3B_rope_slw.sh

@@ -0,0 +1,347 @@
+#!/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
+
+## init_std is 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)
+
+## We changed min_lr to a lower number (1.0e-6), which we found is able to
+## provide better zero-shot eval results.
+
+## 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=1.0e-6
+# init_std=0.02
+
+## 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=1.0e-6
+# init_std=0.018
+
+## 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=1.0e-6
+# init_std=0.015
+
+## 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=1.0e-6
+init_std=0.013
+
+## 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=1.0e-6
+# init_std=0.011
+
+## 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=1.0e-6
+# init_std=0.009
+
+## 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-6
+# init_std=0.008
+
+## 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=1.0e-6
+# init_std=0.005
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens.
+train_tokens_in_billion=300
+train_tokens=$((${train_tokens_in_billion} * 1000000000))
+
+## 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 data efficiency techniques may change num tokens 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=$(( 300 * 1000000000 * 2 / ${seq_len} ))
+
+## Another wall-clock time termination condition in minutes. Set it large
+## enough to avoid undesired early termination.
+exit_duration=30000000
+###############################################################################
+### lr configs
+## lr warmup and decay duration.
+## Original GPT-3 paper uses 375M warmup tokens and 260B cosine decay tokens.
+## Here we increase the warmup tokens to 3B since when batch size warmup is not
+## used, there are more tokens per step. Thus we need to increase warmup tokens
+## to make sure there are enough warmup steps, which is important for training
+## stability.
+lr_warmup_tokens_in_million=3000
+lr_warmup_tokens=$((${lr_warmup_tokens_in_million} * 1000000))
+## Here we changed the LR decay tokens to align with total train tokens, since
+## related works (e.g., https://arxiv.org/abs/2203.15556) find that setting the
+## learning rate schedule to match the number of training tokens results in the
+## best final model quality 
+lr_decay_tokens_in_billion=${train_tokens_in_billion}
+lr_decay_tokens=$((${lr_decay_tokens_in_billion} * 1000000000))
+lr_decay_style="cosine"
+###############################################################################
+### Parallelism configs
+## Model parallelism, 1 is no MP
+mp_size=4
+
+## Pipeline parallelism. To disable PP, set pp_size to 1 and no_pp to true.
+## Note that currently both curriculum learning and random-LTD are NOT
+## compatible with pipeline parallelism.
+pp_size=8
+no_pp="false"
+
+## ZeRO-based data parallelism, stage=0 will disable ZeRO
+zero_stage=1
+
+## Total number of GPUs. ds_ssh is from DeepSpeed library.
+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} / ${num_gpus_pernode} ))
+
+## Data parallel size.
+dp_size=$(( ${num_gpus} / ${pp_size} / ${mp_size} ))
+
+## Micro batch size per GPU
+## Make sure that batch_size <= global_batch_size*pp_size*mp_size/num_gpus
+## Reduce it manually if GPU OOM
+# batch_size=$(( ${global_batch_size} / ${dp_size} ))
+batch_size=2
+###############################################################################
+### curriculum learning (sequence length warmup) configs
+# The "divided by 3" means we use 1/3 of baseline's total steps for sequence length warmup.
+# This is not always the best config, but usually a reasonable choice to start with.
+cl_step=$(( ${lr_warmup_tokens} / 3 / ${global_batch_size} / ${seq_len} ))
+# Starting sequence length during sequence length warmup. If the train/validation loss is
+# unstable at the beginning of training, need to increase this but also need to keep as multiples
+# of 8 in order to enable Tensor Core acceleration.
+cl_min=64
+###############################################################################
+### Misc configs
+log_interval=10
+eval_iters=10
+eval_interval=100
+# num_save controls how frequent to save checkpoint. num_save=20 means that a
+# checkpoint will be saved every 5% of training. For longer training you would
+# want larger num_save to save more frequently, and vice versa.
+num_save=100
+estimated_train_iter=$((${train_tokens} / ${seq_len} / ${global_batch_size}))
+# save_interval=$((${estimated_train_iter} / ${num_save}))
+save_interval=100
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+activation_checkpoint="true"
+# activation_checkpoint="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+log_optimizer_state="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d_%H.%M.%S")
+host="${HOSTNAME}"
+seed=1234
+num_workers=0
+
+## Public the Pile dataset, can be downloaded at
+## https://mystic.the-eye.eu/public/AI/pile_neox/ or 
+## https://the-eye.eu/public/AI/pile_neox/ Change data_home to where you
+## store the pile_text_document.bin and pile_text_document.idx.
+data_home="/vc_data_blob/users/conglli/the_pile_public_merged_nopreprocessing"
+data_path="${data_home}/pile_text_document"
+
+vocab_path="gpt2-vocab.json"
+if [ ! -f "$vocab_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json
+fi
+merge_path="gpt2-merges.txt"
+if [ ! -f "$merge_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt
+fi
+
+prescale_grad="true"
+jobname="gpt_${model_size}B_tok${train_tokens_in_billion}B"
+jobname="${jobname}_lr${lr}_min${min_lr}_w${lr_warmup_tokens_in_million}M_d${lr_decay_tokens_in_billion}B_${lr_decay_style}"
+jobname="${jobname}_gbs${global_batch_size}_mbs${batch_size}_g${num_gpus}"
+if [[ $zero_stage -gt 0 ]]; then
+    jobname="${jobname}_z${zero_stage}"
+    prescale_grad="false"
+fi
+if [[ $mp_size -gt 1 ]]; then
+    jobname="${jobname}_mp${mp_size}"
+fi
+if [ "${no_pp}" = "false" ]; then
+    jobname="${jobname}_pp${pp_size}"
+fi
+jobname="${jobname}_seed${seed}_rebase_rope0.25"
+jobname="${jobname}_cl_step${cl_step}_cl_min${cl_min}"
+
+username=$(whoami)
+output_home="/blob/users/${username}/project/data_efficient_gpt"
+log_path="${output_home}/log/"
+checkpoint_path="${output_home}/checkpoint/${jobname}"
+## Microsoft internal constraint: because tensorboard is logged by last rank,
+## it's better to put the path in NFS instead of Blob.
+tensorboard_dir="/vc_data/users/${username}/project/data_efficient_gpt/tensorboard/"
+tensorboard_path="${tensorboard_dir}${jobname}_${host}_${current_time}"
+mkdir -p ${log_path}
+mkdir -p ${checkpoint_path}
+mkdir -p ${tensorboard_path}
+###############################################################################
+data_options=" \
+    --vocab-file ${vocab_path} \
+    --merge-file ${merge_path} \
+    --data-path ${data_path} \
+    --data-impl mmap"
+
+## If CL is used, make sure to set "--split" the same as what you used during
+## offline data analysis&indexing.
+megatron_options=" \
+    --override-opt_param-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 ${lr_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 ${lr_decay_style} \
+    --split 949,50,1 \
+    --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 ${num_workers} \
+    --fp16 \
+    --seed ${seed} \
+    --load ${checkpoint_path} \
+    --save ${checkpoint_path} \
+    --no-async-tensor-model-parallel-allreduce \
+    --use-rotary-position-embeddings \
+    --rotary-percent 0.25 \
+    --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="${megatron_options} \
+    --log-optimizer-states-to-tensorboard"
+fi
+
+config_json="ds_config_gbs${global_batch_size}_mbs${batch_size}_log${log_interval}_zero${zero_stage}_cl_step${cl_step}_cl_min${cl_min}.json"
+template_json="ds_config_gpt_slw_TEMPLATE.json"
+sed "s/GBSIZE/${global_batch_size}/" ${template_json} \
+    | sed "s/MBSIZE/${batch_size}/" \
+    | sed "s/LOG_INTERVAL/${log_interval}/" \
+    | sed "s/ZERO_STAGE/${zero_stage}/" \
+    | sed "s/PRESCALE_GRAD/${prescale_grad}/" \
+    | sed "s/CONFIG_CL_MIN/${cl_min}/" \
+    | sed "s/CONFIG_CL_MAX/${seq_len}/" \
+    | sed "s/CONFIG_CL_DURATION/${cl_step}/" \
+      > ${config_json}
+
+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
+
+deepspeed ${dir}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &>> ${log_path}/${jobname}_${host}_${current_time}.log

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/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/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/data_efficiency/analyze_data.py

@@ -0,0 +1,239 @@
+# 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.
+
+'''
+Copyright 2022 The Microsoft DeepSpeed Team
+'''
+
+import os
+import time
+import sys
+import math
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
+                                             os.path.pardir,os.path.pardir)))
+from datetime import datetime
+import numpy as np
+import torch
+
+from deepspeed.runtime.data_pipeline.data_sampling.data_analyzer \
+    import DataAnalyzer
+from deepspeed.runtime.data_pipeline.data_sampling.indexed_dataset \
+    import MMapIndexedDataset
+
+from megatron import get_args
+from megatron import print_rank_0
+from megatron.initialize import initialize_megatron
+
+def get_tasks_args(parser):
+    """Provide extra arguments required for data analyzing."""
+    group = parser.add_argument_group(title='data_analyzing')
+
+    group.add_argument('--analyzing-task', type=str, required=True,
+                       default=None,
+                       choices=['map',
+                                'reduce'],
+                       help='What type of analyzing task to perform.')
+    group.add_argument('--analyzing-data-type', type=str, required=True,
+                       default=None,
+                       choices=['BERT',
+                                'GPT'],
+                       help='What type of data.')
+    group.add_argument('--analyzing-metric', type=str, nargs='+', default=[],
+                       help='What kinds of metrics to analyze.')
+    group.add_argument('--analyzing-num-workers', type=int, default=1,
+                       help='Number of workers. Each worker could be a single CPU node.')
+    group.add_argument('--analyzing-worker-id', type=int, default=0,
+                       help='Worker id of current node.')
+    group.add_argument('--analyzing-num-threads', type=int, default=1,
+                       help='Number of threads for each worker.')
+    group.add_argument('--analyzing-num-threads-reduce', type=int, default=1,
+                       help='Number of threads for each worker.')
+    group.add_argument('--analyzing-specific-threads', type=int, nargs='+', default=[],
+                       help='Which specific threads to run. Helpful when there are specific thread failed in previous run.')
+    return parser
+
+def train_valid_test_datasets_provider_gpt():
+    """Build train, valid, and test datasets."""
+    args = get_args()
+
+    print_rank_0('> building train, validation, and test datasets '
+                 'for GPT ...')
+    from megatron.data.gpt_dataset import build_train_valid_test_datasets
+    train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
+        data_prefix=args.data_path,
+        data_impl=args.data_impl,
+        splits_string=args.split,
+        train_valid_test_num_samples=[1,1,1], # Just dummy numbers since we assume args.train_data_exact_num_epochs will override them
+        seq_length=args.seq_length,
+        seed=args.seed,
+        skip_warmup=(not args.mmap_warmup))
+    print_rank_0("> finished creating GPT datasets ...")
+
+    return train_ds, valid_ds, test_ds
+
+def train_valid_test_datasets_provider_bert():
+    """Build train, valid, and test datasets."""
+    args = get_args()
+
+    print_rank_0('> building train, validation, and test datasets '
+                 'for BERT ...')
+    from megatron.data.dataset_utils import build_train_valid_test_datasets
+    train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
+        data_prefix=args.data_path,
+        data_impl=args.data_impl,
+        splits_string=args.split,
+        train_valid_test_num_samples=[1,1,1], # Just dummy numbers since we assume args.train_data_exact_num_epochs will override them
+        max_seq_length=args.seq_length,
+        masked_lm_prob=args.mask_prob,
+        short_seq_prob=args.short_seq_prob,
+        seed=args.seed,
+        skip_warmup=(not args.mmap_warmup),
+        binary_head=args.bert_binary_head)
+    print_rank_0("> finished creating BERT datasets ...")
+
+    return train_ds, valid_ds, test_ds
+
+def metric_seqlen(data):
+    metric = torch.count_nonzero(data['padding_mask'], dim=1)
+    return metric
+
+def metric_total_vocab_freq(data):
+    args = get_args()
+    if args.analyzing_data_type == 'BERT':
+        frequency = torch.bincount(data['text'].view(-1),
+            minlength=args.padded_vocab_size+1,
+            weights=data['padding_mask'].view(-1))
+    elif args.analyzing_data_type == 'GPT':
+        frequency = torch.bincount(data['text'].view(-1),
+            minlength=args.padded_vocab_size+1)
+    return frequency
+
+def metric_vocab_rarity(data):
+    args = get_args()
+    if args.analyzing_data_type == 'BERT':
+        rarity = torch.sum(data['padding_mask'] * \
+            args.total_vocab_freq[data['text']], dim=1).to(torch.long)
+    elif args.analyzing_data_type == 'GPT':
+        rarity = []
+        # Do one by one to avoid too high memory consumption
+        for row in range(data['text'].size()[0]):
+            rarity.append(int(torch.sum(args.total_vocab_freq[data['text'][row]]).item()))
+        rarity = torch.tensor(rarity, dtype=torch.long)
+    print(f"rarity min {min(rarity)}, max {max(rarity)}, len {len(rarity)}, avg {sum(rarity)/len(rarity)}")
+    return rarity
+
+def metric_seqlen_vocab_rarity(data):
+    args = get_args()
+    metric = torch.count_nonzero(data['padding_mask'], dim=1).to(torch.long) * args.seqlen_coeff
+    metric += torch.sum(data['padding_mask'] * \
+        args.total_vocab_freq[data['text']], dim=1).to(torch.long)
+    print(f"metric min {min(metric)}, max {max(metric)}, len {len(metric)}, avg {sum(metric)/len(metric)}")
+    return metric
+
+def get_metric_function(metric_name):
+    if metric_name == 'seqlen':
+        return metric_seqlen
+    if metric_name == 'total_vocab_freq':
+        return metric_total_vocab_freq
+    if metric_name == 'vocab_rarity':
+        return metric_vocab_rarity
+    if metric_name == 'seqlen_vocab_rarity':
+        return metric_seqlen_vocab_rarity
+
+def get_metric_type(metric_name):
+    if metric_name == 'seqlen':
+        return 'single_value_per_sample'
+    if metric_name == 'total_vocab_freq':
+        return 'accumulate_value_over_samples'
+    if metric_name == 'vocab_rarity':
+        return 'single_value_per_sample'
+    if metric_name == 'seqlen_vocab_rarity':
+        return 'single_value_per_sample'
+
+def run_map():
+    args = get_args()
+    if args.analyzing_data_type == 'BERT':
+        args.mask_prob = 0 # When analyzing data, we don't want any mask.
+        train_ds, _, _ = train_valid_test_datasets_provider_bert()
+    elif args.analyzing_data_type == 'GPT':
+        train_ds, _, _ = train_valid_test_datasets_provider_gpt()
+        assert 'seqlen' not in args.analyzing_metric, 'GPT data has fixed seqlen, thus unnecessary to analyze seqlen metric.'
+        assert 'seqlen_vocab_rarity' not in args.analyzing_metric, 'GPT data has fixed seqlen, thus unnecessary to analyze seqlen metric.'
+    if 'vocab_rarity' in args.analyzing_metric or 'seqlen_vocab_rarity' in args.analyzing_metric:
+        total_vocab_freq_fname = f"{args.save}/total_vocab_freq/total_vocab_freq_metric_value"
+        assert os.path.isfile(f"{total_vocab_freq_fname}.bin") and os.path.isfile(f"{total_vocab_freq_fname}.idx"), "To analyze vocab rarity, first need to analyze the total vocab freq."
+        total_vocab_freq = MMapIndexedDataset(total_vocab_freq_fname, skip_warmup=True)
+        total_vocab_freq = np.copy(total_vocab_freq[0])
+        total_vocab_freq[total_vocab_freq == 0] = 1 # Avoid log(0) error
+        total_vocab_freq = np.log(total_vocab_freq/sum(total_vocab_freq)) * -1
+        args.total_vocab_freq = torch.tensor(total_vocab_freq, dtype=torch.double)
+        if 'seqlen_vocab_rarity' in args.analyzing_metric:
+            # Use large coeff to make seqlen dominates vocab_rarity
+            max_possible_rarity = args.seq_length * torch.max(args.total_vocab_freq).item()
+            args.seqlen_coeff = 10 ** (math.ceil(math.log(max_possible_rarity, 10)) + 1)
+            print(f"Metric seqlen_vocab_rarity: using {args.seqlen_coeff} as coefficient for seqlen.")
+    metric_functions = [get_metric_function(x) for x in args.analyzing_metric]
+    metric_types = [get_metric_type(x) for x in args.analyzing_metric]
+    # For metric_dtypes we int64 by default since it could be hard to estimate
+    # the appropriate dtype before the mapping analysis. During reduce where
+    # we merge the analysis results, the DataAnalyzer will automatically choose
+    # the dtype of merged result file as the smallest one that meet the range
+    # requirement.
+    metric_dtypes = [np.int64 for x in args.analyzing_metric]
+    start = time.time()
+    data_analyzer = DataAnalyzer(train_ds,
+        num_workers=args.analyzing_num_workers,
+        worker_id=args.analyzing_worker_id,
+        num_threads=args.analyzing_num_threads,
+        specific_threads=args.analyzing_specific_threads,
+        batch_size=args.global_batch_size, metric_names=args.analyzing_metric,
+        metric_functions=metric_functions, metric_types=metric_types,
+        metric_dtypes=metric_dtypes, save_path=args.save)
+    data_analyzer.run_map()
+    duration = (time.time() - start) / 3600.0
+    print(f"map job finished in {duration} hr.")
+
+def run_reduce():
+    args = get_args()
+    if args.analyzing_data_type == 'BERT':
+        args.mask_prob = 0 # When analyzing data, we don't want any mask.
+        train_ds, _, _ = train_valid_test_datasets_provider_bert()
+    elif args.analyzing_data_type == 'GPT':
+        train_ds, _, _ = train_valid_test_datasets_provider_gpt()
+    metric_functions = [get_metric_function(x) for x in args.analyzing_metric]
+    metric_types = [get_metric_type(x) for x in args.analyzing_metric]
+    metric_dtypes = [np.int64 for x in args.analyzing_metric]
+    start = time.time()
+    data_analyzer = DataAnalyzer(train_ds,
+        num_workers=args.analyzing_num_workers,
+        num_threads=args.analyzing_num_threads,
+        num_threads_reduce=args.analyzing_num_threads_reduce,
+        batch_size=args.global_batch_size, metric_names=args.analyzing_metric,
+        metric_functions=metric_functions, metric_types=metric_types,
+        metric_dtypes=metric_dtypes, save_path=args.save)
+    data_analyzer.run_reduce()
+    duration = (time.time() - start) / 3600.0
+    print(f"reduce job finished in {duration} hr.")
+
+if __name__ == "__main__":
+    initialize_megatron(extra_args_provider=get_tasks_args, allow_no_cuda=True)
+    args = get_args()
+    if args.analyzing_task == 'map':
+        run_map()
+    elif args.analyzing_task == 'reduce':
+        run_reduce()
+    else:
+        raise NotImplementedError('Task {} is not implemented.'.format(
+            args.analyzing_task))

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/data_efficiency/bert/ds_analyze_bert_data_map.sh

@@ -0,0 +1,67 @@
+#!/bin/bash
+
+num_workers=1 # Num nodes to run the map job
+num_threads=40 # Num threads on each node. Set this based on #CPU cores
+
+# If different data epochs have slightly different data samples (e.g., due
+# to randomness), then you need to specify large enough num_epochs that cover
+# whole pretraining. If different data epochs are the same, set num_epochs to
+# 1 to only index 1 epoch, and during pretraining DeepSpeed data efficiency
+# library will automatically handle reshuffling when reaching another epoch.
+num_epochs=5
+
+# Which node is this node (start with 0 and end with num_workers-1). This
+# script only launch the map job on 1 worker node, since we don't expect
+# running on many nodes and workers don't need any communication. But you
+# can modify this script to add a MPI/torch distributed launcher.
+worker_id=$1
+save_path="/blob/users/conglli/data/analysis_pile_bert_${num_epochs}epoch/"
+
+metric='total_vocab_freq'
+# metric='vocab_rarity' # this requires the result of total_vocab_freq
+# metric='seqlen_vocab_rarity' # this requires the result of total_vocab_freq
+# metric='seqlen'
+
+seq_len=512
+batch_size=10000
+
+jobname="bert-pile-analyzing-${metric}-${num_epochs}epoch-map-worker${worker_id}"
+## Public the Pile dataset, see prepare_pile_data.py in the same directory
+## about how to download and preprocess the data.
+## Change data_home to your own training data path.
+# data_home="/vc_data_blob/users/conglli/the_pile_bert"
+data_home="/blob/data/the_pile_bert"
+data_path="${data_home}/pile_bert_train_text_sentence"
+
+vocab_path="bert-large-uncased-vocab.txt"
+if [ ! -f "$vocab_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt
+fi
+
+# Make sure the "--split" is the same as what you will use for pre-training.
+options=" \
+    --analyzing-task map \
+    --analyzing-data-type BERT \
+    --analyzing-metric ${metric} \
+    --analyzing-num-workers ${num_workers} \
+    --analyzing-worker-id ${worker_id} \
+    --analyzing-num-threads ${num_threads} \
+    --vocab-file ${vocab_path} \
+    --data-path ${data_path} \
+    --data-impl mmap \
+    --tokenizer-type BertWordPieceLowerCase \
+    --micro-batch-size ${batch_size} \
+    --global-batch-size ${batch_size} \
+    --seq-length ${seq_len} \
+    --max-position-embeddings ${seq_len} \
+    --num-layers 1 \
+    --hidden-size 1 \
+    --num-attention-heads 1 \
+    --split 949,50,1 \
+    --distributed-backend gloo \
+    --train-data-exact-num-epochs ${num_epochs} \
+    --return-data-index \
+    --save-interval 1 \
+    --save ${save_path}"
+
+python ../analyze_data.py ${options} &> ${jobname}.log

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/data_efficiency/bert/ds_analyze_bert_data_reduce.sh

@@ -0,0 +1,66 @@
+#!/bin/bash
+
+# Set these 2 to the same as what you used during map job. We need these 2
+# configs to know how many map job result files do we have.
+num_workers=1
+num_threads=40
+# Reduce job only has 1 worker but can accelerate by multithreading.
+num_threads_reduce=40
+
+# If different data epochs have slightly different data samples (e.g., due
+# to randomness), then you need to specify large enough num_epochs that cover
+# whole pretraining. If different data epochs are the same, set num_epochs to
+# 1 to only index 1 epoch, and during pretraining DeepSpeed data efficiency
+# library will automatically handle reshuffling when reaching another epoch.
+num_epochs=5
+
+save_path="/blob/users/conglli/data/analysis_pile_bert_${num_epochs}epoch/"
+
+metric='total_vocab_freq'
+# metric='vocab_rarity' # this requires the result of total_vocab_freq
+# metric='seqlen_vocab_rarity' # this requires the result of total_vocab_freq
+# metric='seqlen'
+
+seq_len=512
+batch_size=10000
+
+jobname="bert-pile-analyzing-${metric}-${num_epochs}epoch-reduce"
+## Public the Pile dataset, see prepare_pile_data.py in the same directory
+## about how to download and preprocess the data.
+## Change data_home to your own training data path.
+# data_home="/vc_data_blob/users/conglli/the_pile_bert"
+data_home="/blob/data/the_pile_bert"
+data_path="${data_home}/pile_bert_train_text_sentence"
+
+vocab_path="bert-large-uncased-vocab.txt"
+if [ ! -f "$vocab_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt
+fi
+
+# Make sure the "--split" is the same as what you will use for pre-training.
+options=" \
+    --analyzing-task reduce \
+    --analyzing-data-type BERT \
+    --analyzing-metric ${metric} \
+    --analyzing-num-workers ${num_workers} \
+    --analyzing-num-threads ${num_threads} \
+    --analyzing-num-threads-reduce ${num_threads_reduce} \
+    --vocab-file ${vocab_path} \
+    --data-path ${data_path} \
+    --data-impl mmap \
+    --tokenizer-type BertWordPieceLowerCase \
+    --micro-batch-size ${batch_size} \
+    --global-batch-size ${batch_size} \
+    --seq-length ${seq_len} \
+    --max-position-embeddings ${seq_len} \
+    --num-layers 1 \
+    --hidden-size 1 \
+    --num-attention-heads 1 \
+    --split 949,50,1 \
+    --distributed-backend gloo \
+    --train-data-exact-num-epochs ${num_epochs} \
+    --return-data-index \
+    --save-interval 1 \
+    --save ${save_path}"
+
+python ../analyze_data.py ${options} &> ${jobname}.log

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/data_efficiency/bert/finetune/ds_finetune_bert_mnli.sh

@@ -0,0 +1,150 @@
+seed=1234
+pretrained_checkpoint="/blob/users/conglli/project/bert_with_pile/checkpoint/bert-pile-0.336B-iters-2M-lr-1e-4-min-1e-5-wmup-10000-dcy-2M-sty-linear-gbs-1024-mbs-16-gpu-64-zero-0-mp-1-pp-1-nopp"
+
+###############################################################################
+### Main configs
+### The main configs are from Megatron-LM paper
+### https://arxiv.org/abs/1909.08053. Choose based on your desired model size
+### or build your own configs.
+seq_len=512
+
+## From Table 6 in https://arxiv.org/abs/1909.08053.
+task="MNLI"
+global_batch_size=128
+lr=1e-5
+epochs=10
+
+train_data="/blob/data/GlueData/MNLI/train.tsv"
+valid_data="/blob/data/GlueData/MNLI/dev_matched.tsv \
+            /blob/data/GlueData/MNLI/dev_mismatched.tsv"
+
+## Adjust based on number of GPUs.
+batch_size=16
+
+## BERT 110M (same config as original BERT-Base model)
+## This config is not included in Megatron-LM paper
+# model_size=0.11
+# num_layers=12
+# hidden_size=768
+# num_attn_heads=12
+
+## BERT 336M (same config as original BERT-Large model)
+model_size=0.336
+num_layers=24
+hidden_size=1024
+num_attn_heads=16
+
+## BERT 1.3B
+# model_size=1.3
+# num_layers=24
+# hidden_size=2048
+# num_attn_heads=32
+
+## BERT 3.9B
+# model_size=3.9
+# num_layers=48
+# hidden_size=2560
+# num_attn_heads=40
+###############################################################################
+### Parallelism configs
+## Model parallelism, 1 is no MP
+mp_size=1
+
+## Pipeline parallelism. To disable PP, set pp_size to 1 and no_pp to true.
+## Currently pipeline parallelism is not supported for BERT model: DeepSpeed's
+## pipeline parallelism is only integrated with the GPT case, and currently
+## DeepSpeed is not integrated with Megatron's own pipeline parallelism.
+pp_size=1
+no_pp="true"
+
+## ZeRO stage
+zero_stage=0
+###############################################################################
+### Misc configs
+log_interval=10
+eval_iters=50
+eval_interval=100
+save_interval=500000
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+# activation_checkpoint="true"
+activation_checkpoint="false"
+###############################################################################
+vocab_file="bert-large-uncased-vocab.txt"
+if [ ! -f "$vocab_file" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt
+fi
+
+jobname="${task}-bsz${global_batch_size}-lr${lr}-epochs${epochs}-seed${seed}"
+checkpoint_path="${pretrained_checkpoint}-finetune/${jobname}"
+mkdir -p ${checkpoint_path}
+
+template_json="ds_config_bert_TEMPLATE.json"
+config_json="ds_config_bert_bsz${global_batch_size}_mbsz${batch_size}_log${log_interval}_zero${zero_stage}.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/" \
+      > ${config_json}
+else
+sed "s/CONFIG_BATCH_SIZE/${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/" \
+      > ${config_json}
+fi
+
+options=" \
+    --finetune \
+    --deepspeed \
+    --deepspeed_config ${config_json} \
+    --zero-stage ${zero_stage} \
+    --task ${task} \
+    --seed ${seed} \
+    --train-data ${train_data} \
+    --valid-data ${valid_data} \
+    --tokenizer-type BertWordPieceLowerCase \
+    --vocab-file ${vocab_file} \
+    --epochs ${epochs} \
+    --pretrained-checkpoint ${pretrained_checkpoint} \
+    --tensor-model-parallel-size ${mp_size} \
+    --pipeline-model-parallel-size ${pp_size} \
+    --num-layers ${num_layers} \
+    --hidden-size ${hidden_size} \
+    --num-attention-heads ${num_attn_heads} \
+    --global-batch-size ${global_batch_size} \
+    --micro-batch-size ${batch_size} \
+    --lr ${lr} \
+    --lr-decay-style linear \
+    --lr-warmup-fraction 0.065 \
+    --seq-length ${seq_len} \
+    --max-position-embeddings ${seq_len} \
+    --save-interval ${save_interval} \
+    --save ${checkpoint_path} \
+    --log-interval ${log_interval} \
+    --eval-interval ${eval_interval} \
+    --eval-iters ${eval_iters} \
+    --weight-decay 1.0e-1 \
+    --fp16"
+
+if [ "${activation_checkpoint}" = "true" ]; then
+options="${options} \
+    --checkpoint-activations \
+    --deepspeed-activation-checkpointing"
+fi
+
+if [[ "${no_pp}" = "true" ]]; then
+options="${options} \
+    --no-pipeline-parallel"
+fi
+
+# After the fine-tuning finishes, you can find the dev set accuracy numbers by
+# "grep -e "overall:" -e "metrics for" ${checkpoint_path}/output.log"
+deepspeed ../../../../tasks/main.py ${options} &> ${checkpoint_path}/output.log

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/data_efficiency/bert/finetune/ds_finetune_bert_qqp.sh

@@ -0,0 +1,158 @@
+seed=1234
+pretrained_checkpoint="/blob/users/conglli/project/bert_with_pile/checkpoint/bert-pile-0.336B-iters-2M-lr-1e-4-min-1e-5-wmup-10000-dcy-2M-sty-linear-gbs-1024-mbs-16-gpu-64-zero-0-mp-1-pp-1-nopp"
+
+###############################################################################
+### Main configs
+### The main configs are from Megatron-LM paper
+### https://arxiv.org/abs/1909.08053. Choose based on your desired model size
+### or build your own configs.
+seq_len=512
+
+## From Table 6 in https://arxiv.org/abs/1909.08053.
+task="QQP"
+
+train_data="/blob/data/GlueData/QQP/train.tsv"
+valid_data="/blob/data/GlueData/QQP/dev.tsv"
+
+## Adjust based on number of GPUs.
+batch_size=16
+
+## BERT 110M (same config as original BERT-Base model)
+## This config is not included in Megatron-LM paper
+# model_size=0.11
+# num_layers=12
+# hidden_size=768
+# num_attn_heads=12
+# global_batch_size=128
+# lr=5e-5
+# epochs=12
+
+## BERT 336M (same config as original BERT-Large model)
+model_size=0.336
+num_layers=24
+hidden_size=1024
+num_attn_heads=16
+global_batch_size=128
+lr=5e-5
+epochs=12
+
+## BERT 1.3B
+# model_size=1.3
+# num_layers=24
+# hidden_size=2048
+# num_attn_heads=32
+# global_batch_size=128
+# lr=3e-5
+# epochs=12
+
+## BERT 3.9B
+# model_size=3.9
+# num_layers=48
+# hidden_size=2560
+# num_attn_heads=40
+# global_batch_size=256
+# lr=4e-5
+# epochs=12
+###############################################################################
+### Parallelism configs
+## Model parallelism, 1 is no MP
+mp_size=1
+
+## Pipeline parallelism. To disable PP, set pp_size to 1 and no_pp to true.
+## Currently pipeline parallelism is not supported for BERT model: DeepSpeed's
+## pipeline parallelism is only integrated with the GPT case, and currently
+## DeepSpeed is not integrated with Megatron's own pipeline parallelism.
+pp_size=1
+no_pp="true"
+
+## ZeRO stage
+zero_stage=0
+###############################################################################
+### Misc configs
+log_interval=10
+eval_iters=50
+eval_interval=100
+save_interval=500000
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+# activation_checkpoint="true"
+activation_checkpoint="false"
+###############################################################################
+vocab_file="bert-large-uncased-vocab.txt"
+if [ ! -f "$vocab_file" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt
+fi
+
+jobname="${task}-bsz${global_batch_size}-lr${lr}-epochs${epochs}-seed${seed}"
+checkpoint_path="${pretrained_checkpoint}-finetune/${jobname}"
+mkdir -p ${checkpoint_path}
+
+template_json="ds_config_bert_TEMPLATE.json"
+config_json="ds_config_bert_bsz${global_batch_size}_mbsz${batch_size}_log${log_interval}_zero${zero_stage}.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/" \
+      > ${config_json}
+else
+sed "s/CONFIG_BATCH_SIZE/${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/" \
+      > ${config_json}
+fi
+
+options=" \
+    --finetune \
+    --deepspeed \
+    --deepspeed_config ${config_json} \
+    --zero-stage ${zero_stage} \
+    --task ${task} \
+    --seed ${seed} \
+    --train-data ${train_data} \
+    --valid-data ${valid_data} \
+    --tokenizer-type BertWordPieceLowerCase \
+    --vocab-file ${vocab_file} \
+    --epochs ${epochs} \
+    --pretrained-checkpoint ${pretrained_checkpoint} \
+    --tensor-model-parallel-size ${mp_size} \
+    --pipeline-model-parallel-size ${pp_size} \
+    --num-layers ${num_layers} \
+    --hidden-size ${hidden_size} \
+    --num-attention-heads ${num_attn_heads} \
+    --global-batch-size ${global_batch_size} \
+    --micro-batch-size ${batch_size} \
+    --lr ${lr} \
+    --lr-decay-style linear \
+    --lr-warmup-fraction 0.065 \
+    --seq-length ${seq_len} \
+    --max-position-embeddings ${seq_len} \
+    --save-interval ${save_interval} \
+    --save ${checkpoint_path} \
+    --log-interval ${log_interval} \
+    --eval-interval ${eval_interval} \
+    --eval-iters ${eval_iters} \
+    --weight-decay 1.0e-1 \
+    --fp16"
+
+if [ "${activation_checkpoint}" = "true" ]; then
+options="${options} \
+    --checkpoint-activations \
+    --deepspeed-activation-checkpointing"
+fi
+
+if [[ "${no_pp}" = "true" ]]; then
+options="${options} \
+    --no-pipeline-parallel"
+fi
+
+# After the fine-tuning finishes, you can find the dev set accuracy numbers by
+# "grep -e "overall:" -e "metrics for" ${checkpoint_path}/output.log"
+deepspeed ../../../../tasks/main.py ${options} &> ${checkpoint_path}/output.log

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/data_efficiency/bert/pretrain/ds_config_bert_1clmetric_TEMPLATE.json

@@ -0,0 +1,73 @@
+{
+  "train_batch_size": GBSIZE,
+  "train_micro_batch_size_per_gpu": MBSIZE,
+  "steps_per_print": LOG_INTERVAL,
+
+  "zero_optimization": {
+    "stage": ZERO_STAGE
+  },
+
+  "gradient_clipping": 1.0,
+  "prescale_gradients": PRESCALE_GRAD,
+
+  "fp16": {
+    "enabled": true,
+    "loss_scale": 0,
+    "loss_scale_window": 500,
+    "hysteresis": 2,
+    "min_loss_scale": 1,
+    "initial_scale_power": 11
+  },
+
+  "wall_clock_breakdown" : false,
+  "dataloader_drop_last": true,
+  "data_efficiency": {
+    "enabled": true,
+    "seed": DATA_EFFICIENCY_SEED,
+    "data_routing": {
+      "enabled": LTD_ENABLED,
+      "random_ltd":{
+        "enabled": LTD_ENABLED,
+        "total_layer_num": 24,
+        "random_ltd_layer_num": 22,
+        "random_ltd_layer_id": [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22],
+        "model_mask_name": "attention_mask",
+        "model_type": "encoder",
+        "hidden_state_order": "seq_batch_dim",
+        "random_ltd_schedule": {
+          "min_value": LTD_MIN,
+          "max_value": LTD_MAX,
+          "schedule_type":"fixed_linear",
+          "schedule_config": {
+            "require_steps": LTD_STEP,
+            "seq_per_step": 16
+          }
+        }
+      } 
+    },
+    "data_sampling": {
+      "enabled": CL_ENABLED,
+      "num_workers": DATA_SAMPLING_NUM_WORKERS,
+      "curriculum_learning": {
+        "enabled": CL_ENABLED,
+        "data_cluster_path": "CL_CLUSTER_PATH",
+        "curriculum_metrics": {
+          "CL_1st_METRIC_NAME": {
+            "index_to_sample_path": "CL_1st_SAMPLE_PATH",
+            "index_to_metric_path": "CL_1st_METRIC_PATH",
+            "difficulty_type": "CL_1st_DIFF_TYPE",
+            "clustering_type": "CL_1st_CLUSTER_TYPE",
+            "min_difficulty": CL_1st_MIN,
+            "max_difficulty": CL_1st_MAX,
+            "schedule_type": "fixed_root",
+            "schedule_config": {
+              "total_curriculum_step": CL_1st_TOTAL_STEP,
+              "difficulty_step": CL_1st_DIFF_STEP,
+              "root_degree": CL_1st_ROOT
+            }
+          }
+        }
+      }
+    }
+  }
+}

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/data_efficiency/bert/pretrain/ds_config_bert_2clmetrics_TEMPLATE.json

@@ -0,0 +1,87 @@
+{
+  "train_batch_size": GBSIZE,
+  "train_micro_batch_size_per_gpu": MBSIZE,
+  "steps_per_print": LOG_INTERVAL,
+
+  "zero_optimization": {
+    "stage": ZERO_STAGE
+  },
+
+  "gradient_clipping": 1.0,
+  "prescale_gradients": PRESCALE_GRAD,
+
+  "fp16": {
+    "enabled": true,
+    "loss_scale": 0,
+    "loss_scale_window": 500,
+    "hysteresis": 2,
+    "min_loss_scale": 1,
+    "initial_scale_power": 11
+  },
+
+  "wall_clock_breakdown" : false,
+  "dataloader_drop_last": true,
+  "data_efficiency": {
+    "enabled": true,
+    "seed": DATA_EFFICIENCY_SEED,
+    "data_routing": {
+      "enabled": LTD_ENABLED,
+      "random_ltd":{
+        "enabled": LTD_ENABLED,
+        "total_layer_num": 24,
+        "random_ltd_layer_num": 22,
+        "random_ltd_layer_id": [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22],
+        "model_mask_name": "attention_mask",
+        "model_type": "encoder",
+        "hidden_state_order": "seq_batch_dim",
+        "random_ltd_schedule": {
+          "min_value": LTD_MIN,
+          "max_value": LTD_MAX,
+          "schedule_type":"fixed_linear",
+          "schedule_config": {
+            "require_steps": LTD_STEP,
+            "seq_per_step": 16
+          }
+        }
+      } 
+    },
+    "data_sampling": {
+      "enabled": CL_ENABLED,
+      "num_workers": DATA_SAMPLING_NUM_WORKERS,
+      "curriculum_learning": {
+        "enabled": CL_ENABLED,
+        "data_cluster_path": "CL_CLUSTER_PATH",
+        "curriculum_metrics": {
+          "CL_1st_METRIC_NAME": {
+            "index_to_sample_path": "CL_1st_SAMPLE_PATH",
+            "index_to_metric_path": "CL_1st_METRIC_PATH",
+            "difficulty_type": "CL_1st_DIFF_TYPE",
+            "clustering_type": "CL_1st_CLUSTER_TYPE",
+            "min_difficulty": CL_1st_MIN,
+            "max_difficulty": CL_1st_MAX,
+            "schedule_type": "fixed_root",
+            "schedule_config": {
+              "total_curriculum_step": CL_1st_TOTAL_STEP,
+              "difficulty_step": CL_1st_DIFF_STEP,
+              "root_degree": CL_1st_ROOT
+            }
+          },
+          "CL_2nd_METRIC_NAME": {
+            "index_to_sample_path": "CL_2nd_SAMPLE_PATH",
+            "index_to_metric_path": "CL_2nd_METRIC_PATH",
+            "difficulty_type": "CL_2nd_DIFF_TYPE",
+            "clustering_type": "CL_2nd_CLUSTER_TYPE",
+            "min_difficulty": CL_2nd_MIN,
+            "max_difficulty": CL_2nd_MAX,
+            "schedule_type": "fixed_root",
+            "schedule_config": {
+              "total_curriculum_step": CL_2nd_TOTAL_STEP,
+              "difficulty_step": CL_2nd_DIFF_STEP,
+              "root_degree": CL_2nd_ROOT
+            }
+          }
+        }
+      }
+    }
+  }
+}

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/data_efficiency/bert/pretrain/ds_pretrain_bert_336M_run.sh

@@ -0,0 +1,363 @@
+###############################################################################
+### Each block below is one pretraining setup. Uncomment one block to try.
+###############################################################################
+### Baseline cases, mostly based on Megatron-LM's BERT-Large hyperparameters,
+### but with some changes (different LR schedule).
+## Baseline 1049B tokens (100%):
+# lr=1e-4
+# train_iters_in_million=2
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million}
+###############################################################################
+## Baseline 703B tokens (67%):
+# lr=1.5e-4
+# train_iters_in_million=134e-2
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million}
+###############################################################################
+## Baseline 524B tokens (50%):
+# lr=2e-4
+# train_iters_in_million=1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million}
+###############################################################################
+### Curriculum learning (CL) + Random layerwise token dropping (random-LTD).
+### DeepSpeed Data Efficiency's composed solution.
+### BERT pretraining.
+## CL+random-LTD 1049B tokens (100%):
+# lr=1e-4
+# train_iters_in_million=2
+# ltd_enabled="true"
+# ltd_start=128
+# ltd_step_in_million=2
+# dropout=1e-1
+# cl_enabled="true"
+# cl_num_metric=2
+# cl_1st_metric="voc"
+# cl_1st_index_to_sample_path="/vc_data/users/conglli/code/data_efficiency/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_sample_percentile_merged"
+# cl_1st_index_to_metric_path="/vc_data/users/conglli/code/data_efficiency/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_metric"
+# cl_1st_difficulty_type="percentile"
+# cl_1st_clustering_type="schedule_based"
+# cl_1st_min=5
+# cl_1st_max=100
+# cl_1st_total_step_in_million=96e-2
+# cl_1st_difficulty_step=1
+# cl_1st_root=2
+# cl_2nd_metric="seqlen_truncate"
+# cl_2nd_index_to_sample_path="dummy"
+# cl_2nd_index_to_metric_path="dummy"
+# cl_2nd_difficulty_type="value"
+# cl_2nd_clustering_type="single_cluster"
+# cl_2nd_min=128
+# cl_2nd_max=512
+# cl_2nd_total_step_in_million=96e-2
+# cl_2nd_difficulty_step=8
+# cl_2nd_root=1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout} \
+#     ${cl_enabled} ${cl_num_metric} ${cl_1st_metric} \
+#     ${cl_1st_index_to_sample_path} ${cl_1st_index_to_metric_path} \
+#     ${cl_1st_difficulty_type} ${cl_1st_clustering_type} ${cl_1st_min} \
+#     ${cl_1st_max} ${cl_1st_total_step_in_million} ${cl_1st_difficulty_step} \
+#     ${cl_1st_root} ${cl_2nd_metric} ${cl_2nd_index_to_sample_path} \
+#     ${cl_2nd_index_to_metric_path} ${cl_2nd_difficulty_type} \
+#     ${cl_2nd_clustering_type} ${cl_2nd_min} ${cl_2nd_max} \
+#     ${cl_2nd_total_step_in_million} ${cl_2nd_difficulty_step} ${cl_2nd_root}
+###############################################################################
+## CL+random-LTD 524B tokens (50%):
+# lr=2e-4
+# train_iters_in_million=1
+# ltd_enabled="true"
+# ltd_start=128
+# ltd_step_in_million=1
+# dropout=1e-1
+# cl_enabled="true"
+# cl_num_metric=2
+# cl_1st_metric="voc"
+# cl_1st_index_to_sample_path="/vc_data/users/conglli/code/data_efficiency/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_sample_percentile_merged"
+# cl_1st_index_to_metric_path="/vc_data/users/conglli/code/data_efficiency/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_metric"
+# cl_1st_difficulty_type="percentile"
+# cl_1st_clustering_type="schedule_based"
+# cl_1st_min=5
+# cl_1st_max=100
+# cl_1st_total_step_in_million=48e-2
+# cl_1st_difficulty_step=1
+# cl_1st_root=2
+# cl_2nd_metric="seqlen_truncate"
+# cl_2nd_index_to_sample_path="dummy"
+# cl_2nd_index_to_metric_path="dummy"
+# cl_2nd_difficulty_type="value"
+# cl_2nd_clustering_type="single_cluster"
+# cl_2nd_min=128
+# cl_2nd_max=512
+# cl_2nd_total_step_in_million=48e-2
+# cl_2nd_difficulty_step=8
+# cl_2nd_root=1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout} \
+#     ${cl_enabled} ${cl_num_metric} ${cl_1st_metric} \
+#     ${cl_1st_index_to_sample_path} ${cl_1st_index_to_metric_path} \
+#     ${cl_1st_difficulty_type} ${cl_1st_clustering_type} ${cl_1st_min} \
+#     ${cl_1st_max} ${cl_1st_total_step_in_million} ${cl_1st_difficulty_step} \
+#     ${cl_1st_root} ${cl_2nd_metric} ${cl_2nd_index_to_sample_path} \
+#     ${cl_2nd_index_to_metric_path} ${cl_2nd_difficulty_type} \
+#     ${cl_2nd_clustering_type} ${cl_2nd_min} ${cl_2nd_max} \
+#     ${cl_2nd_total_step_in_million} ${cl_2nd_difficulty_step} ${cl_2nd_root}
+###############################################################################
+### Random layerwise token dropping (random-LTD).
+## random-LTD 1049B tokens (100%):
+# lr=1e-4
+# train_iters_in_million=2
+# ltd_enabled="true"
+# ltd_start=128
+# ltd_step_in_million=2
+# dropout=1e-1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout}
+###############################################################################
+## random-LTD 703B tokens (67%):
+# lr=1.5e-4
+# train_iters_in_million=134e-2
+# ltd_enabled="true"
+# ltd_start=128
+# ltd_step_in_million=134e-2
+# dropout=1e-1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout}
+###############################################################################
+## random-LTD 524B tokens (50%):
+# lr=2e-4
+# train_iters_in_million=1
+# ltd_enabled="true"
+# ltd_start=128
+# ltd_step_in_million=1
+# dropout=1e-1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout}
+###############################################################################
+### Curriculum learning (CL).
+## CL vocab rarity + seqlen truncation 524B tokens (50%):
+# lr=2e-4
+# train_iters_in_million=1
+# ltd_enabled="false"
+# ltd_start=512
+# ltd_step_in_million=1
+# dropout=1e-1
+# cl_enabled="true"
+# cl_num_metric=2
+# cl_1st_metric="voc"
+# cl_1st_index_to_sample_path="/vc_data/users/conglli/code/data_efficiency/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_sample_percentile_merged"
+# cl_1st_index_to_metric_path="/vc_data/users/conglli/code/data_efficiency/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_metric"
+# cl_1st_difficulty_type="percentile"
+# cl_1st_clustering_type="schedule_based"
+# cl_1st_min=5
+# cl_1st_max=100
+# cl_1st_total_step_in_million=48e-2
+# cl_1st_difficulty_step=1
+# cl_1st_root=2
+# cl_2nd_metric="seqlen_truncate"
+# cl_2nd_index_to_sample_path="dummy"
+# cl_2nd_index_to_metric_path="dummy"
+# cl_2nd_difficulty_type="value"
+# cl_2nd_clustering_type="single_cluster"
+# cl_2nd_min=128
+# cl_2nd_max=512
+# cl_2nd_total_step_in_million=48e-2
+# cl_2nd_difficulty_step=8
+# cl_2nd_root=1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout} \
+#     ${cl_enabled} ${cl_num_metric} ${cl_1st_metric} \
+#     ${cl_1st_index_to_sample_path} ${cl_1st_index_to_metric_path} \
+#     ${cl_1st_difficulty_type} ${cl_1st_clustering_type} ${cl_1st_min} \
+#     ${cl_1st_max} ${cl_1st_total_step_in_million} ${cl_1st_difficulty_step} \
+#     ${cl_1st_root} ${cl_2nd_metric} ${cl_2nd_index_to_sample_path} \
+#     ${cl_2nd_index_to_metric_path} ${cl_2nd_difficulty_type} \
+#     ${cl_2nd_clustering_type} ${cl_2nd_min} ${cl_2nd_max} \
+#     ${cl_2nd_total_step_in_million} ${cl_2nd_difficulty_step} ${cl_2nd_root}
+###############################################################################
+## CL vocab rarity + seqlen truncation 703B tokens (67%):
+# lr=1.5e-4
+# train_iters_in_million=134e-2
+# ltd_enabled="false"
+# ltd_start=512
+# ltd_step_in_million=1
+# dropout=1e-1
+# cl_enabled="true"
+# cl_num_metric=2
+# cl_1st_metric="voc"
+# cl_1st_index_to_sample_path="/vc_data/users/conglli/code/data_efficiency/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_sample_percentile_merged"
+# cl_1st_index_to_metric_path="/vc_data/users/conglli/code/data_efficiency/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_metric"
+# cl_1st_difficulty_type="percentile"
+# cl_1st_clustering_type="schedule_based"
+# cl_1st_min=5
+# cl_1st_max=100
+# cl_1st_total_step_in_million=64e-2
+# cl_1st_difficulty_step=1
+# cl_1st_root=2
+# cl_2nd_metric="seqlen_truncate"
+# cl_2nd_index_to_sample_path="dummy"
+# cl_2nd_index_to_metric_path="dummy"
+# cl_2nd_difficulty_type="value"
+# cl_2nd_clustering_type="single_cluster"
+# cl_2nd_min=128
+# cl_2nd_max=512
+# cl_2nd_total_step_in_million=64e-2
+# cl_2nd_difficulty_step=8
+# cl_2nd_root=1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout} \
+#     ${cl_enabled} ${cl_num_metric} ${cl_1st_metric} \
+#     ${cl_1st_index_to_sample_path} ${cl_1st_index_to_metric_path} \
+#     ${cl_1st_difficulty_type} ${cl_1st_clustering_type} ${cl_1st_min} \
+#     ${cl_1st_max} ${cl_1st_total_step_in_million} ${cl_1st_difficulty_step} \
+#     ${cl_1st_root} ${cl_2nd_metric} ${cl_2nd_index_to_sample_path} \
+#     ${cl_2nd_index_to_metric_path} ${cl_2nd_difficulty_type} \
+#     ${cl_2nd_clustering_type} ${cl_2nd_min} ${cl_2nd_max} \
+#     ${cl_2nd_total_step_in_million} ${cl_2nd_difficulty_step} ${cl_2nd_root}
+###############################################################################
+## CL vocab rarity + seqlen truncation 1049B tokens (100%):
+# lr=1e-4
+# train_iters_in_million=2
+# ltd_enabled="false"
+# ltd_start=512
+# ltd_step_in_million=1
+# dropout=1e-1
+# cl_enabled="true"
+# cl_num_metric=2
+# cl_1st_metric="voc"
+# cl_1st_index_to_sample_path="/blob/users/conglli/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_sample"
+# cl_1st_index_to_metric_path="/blob/users/conglli/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_metric"
+# cl_1st_difficulty_type="percentile"
+# cl_1st_clustering_type="schedule_based"
+# cl_1st_min=5
+# cl_1st_max=100
+# cl_1st_total_step_in_million=96e-2
+# cl_1st_difficulty_step=1
+# cl_1st_root=2
+# cl_2nd_metric="seqlen_truncate"
+# cl_2nd_index_to_sample_path="dummy"
+# cl_2nd_index_to_metric_path="dummy"
+# cl_2nd_difficulty_type="value"
+# cl_2nd_clustering_type="single_cluster"
+# cl_2nd_min=128
+# cl_2nd_max=512
+# cl_2nd_total_step_in_million=96e-2
+# cl_2nd_difficulty_step=8
+# cl_2nd_root=1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout} \
+#     ${cl_enabled} ${cl_num_metric} ${cl_1st_metric} \
+#     ${cl_1st_index_to_sample_path} ${cl_1st_index_to_metric_path} \
+#     ${cl_1st_difficulty_type} ${cl_1st_clustering_type} ${cl_1st_min} \
+#     ${cl_1st_max} ${cl_1st_total_step_in_million} ${cl_1st_difficulty_step} \
+#     ${cl_1st_root} ${cl_2nd_metric} ${cl_2nd_index_to_sample_path} \
+#     ${cl_2nd_index_to_metric_path} ${cl_2nd_difficulty_type} \
+#     ${cl_2nd_clustering_type} ${cl_2nd_min} ${cl_2nd_max} \
+#     ${cl_2nd_total_step_in_million} ${cl_2nd_difficulty_step} ${cl_2nd_root}
+###############################################################################
+## CL vocab rarity + seqlen reorder 1049B tokens (100%):
+# lr=1e-4
+# train_iters_in_million=2
+# ltd_enabled="false"
+# ltd_start=512
+# ltd_step_in_million=1
+# dropout=1e-1
+# cl_enabled="true"
+# cl_num_metric=1
+# cl_1st_metric="seqlenvocabrarity"
+# cl_1st_index_to_sample_path="/blob/users/conglli/data/analysis_pile_bert_5epoch/seqlen_vocab_rarity/seqlen_vocab_rarity_index_to_sample_percentile_merged"
+# cl_1st_index_to_metric_path="/blob/users/conglli/data/analysis_pile_bert_5epoch/seqlen_vocab_rarity/seqlen_vocab_rarity_index_to_metric"
+# cl_1st_difficulty_type="percentile"
+# cl_1st_clustering_type="schedule_based"
+# cl_1st_min=5
+# cl_1st_max=100
+# cl_1st_total_step_in_million=96e-2
+# cl_1st_difficulty_step=1
+# cl_1st_root=2
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout} \
+#     ${cl_enabled} ${cl_num_metric} ${cl_1st_metric} \
+#     ${cl_1st_index_to_sample_path} ${cl_1st_index_to_metric_path} \
+#     ${cl_1st_difficulty_type} ${cl_1st_clustering_type} ${cl_1st_min} \
+#     ${cl_1st_max} ${cl_1st_total_step_in_million} ${cl_1st_difficulty_step} \
+#     ${cl_1st_root}
+###############################################################################
+## CL vocab rarity 1049B tokens (100%):
+# lr=1e-4
+# train_iters_in_million=2
+# ltd_enabled="false"
+# ltd_start=512
+# ltd_step_in_million=1
+# dropout=1e-1
+# cl_enabled="true"
+# cl_num_metric=1
+# cl_1st_metric="voc"
+# cl_1st_index_to_sample_path="/blob/users/conglli/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_sample"
+# cl_1st_index_to_metric_path="/blob/users/conglli/data/analysis_pile_bert_5epoch/vocab_rarity/vocab_rarity_index_to_metric"
+# cl_1st_difficulty_type="percentile"
+# cl_1st_clustering_type="schedule_based"
+# cl_1st_min=5
+# cl_1st_max=100
+# cl_1st_total_step_in_million=96e-2
+# cl_1st_difficulty_step=1
+# cl_1st_root=2
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout} \
+#     ${cl_enabled} ${cl_num_metric} ${cl_1st_metric} \
+#     ${cl_1st_index_to_sample_path} ${cl_1st_index_to_metric_path} \
+#     ${cl_1st_difficulty_type} ${cl_1st_clustering_type} ${cl_1st_min} \
+#     ${cl_1st_max} ${cl_1st_total_step_in_million} ${cl_1st_difficulty_step} \
+#     ${cl_1st_root}
+###############################################################################
+## CL seqlen truncation 1049B tokens (100%):
+# lr=1e-4
+# train_iters_in_million=2
+# ltd_enabled="false"
+# ltd_start=512
+# ltd_step_in_million=1
+# dropout=1e-1
+# cl_enabled="true"
+# cl_num_metric=1
+# cl_1st_metric="seqlen_truncate"
+# cl_1st_index_to_sample_path="dummy"
+# cl_1st_index_to_metric_path="dummy"
+# cl_1st_difficulty_type="value"
+# cl_1st_clustering_type="single_cluster"
+# cl_1st_min=128
+# cl_1st_max=512
+# cl_1st_total_step_in_million=96e-2
+# cl_1st_difficulty_step=8
+# cl_1st_root=1
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout} \
+#     ${cl_enabled} ${cl_num_metric} ${cl_1st_metric} \
+#     ${cl_1st_index_to_sample_path} ${cl_1st_index_to_metric_path} \
+#     ${cl_1st_difficulty_type} ${cl_1st_clustering_type} ${cl_1st_min} \
+#     ${cl_1st_max} ${cl_1st_total_step_in_million} ${cl_1st_difficulty_step} \
+#     ${cl_1st_root}
+###############################################################################
+## CL seqlen reorder 1049B tokens (100%):
+# lr=1e-4
+# train_iters_in_million=2
+# ltd_enabled="false"
+# ltd_start=512
+# ltd_step_in_million=1
+# dropout=1e-1
+# cl_enabled="true"
+# cl_num_metric=1
+# cl_1st_metric="seqlen"
+# cl_1st_index_to_sample_path="/blob/users/conglli/data/analysis_pile_bert_5epoch/seqlen/seqlen_index_to_sample_percentile_merged"
+# cl_1st_index_to_metric_path="/blob/users/conglli/data/analysis_pile_bert_5epoch/seqlen/seqlen_index_to_metric"
+# cl_1st_difficulty_type="percentile"
+# cl_1st_clustering_type="single_cluster"
+# cl_1st_min=5
+# cl_1st_max=100
+# cl_1st_total_step_in_million=96e-2
+# cl_1st_difficulty_step=8
+# cl_1st_root=2
+# bash ds_pretrain_bert_336M_base_script.sh ${lr} ${train_iters_in_million} \
+#     ${ltd_enabled} ${ltd_start} ${ltd_step_in_million} ${dropout} \
+#     ${cl_enabled} ${cl_num_metric} ${cl_1st_metric} \
+#     ${cl_1st_index_to_sample_path} ${cl_1st_index_to_metric_path} \
+#     ${cl_1st_difficulty_type} ${cl_1st_clustering_type} ${cl_1st_min} \
+#     ${cl_1st_max} ${cl_1st_total_step_in_million} ${cl_1st_difficulty_step} \
+#     ${cl_1st_root}
+###############################################################################

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/generate_text.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+export TORCH_CUDA_ARCH_LIST=8.6+PTX
+CHECKPOINT_PATH=dataset/checkpoints/gpt2_345m
+VOCAB_FILE=dataset/gpt2-vocab.json
+MERGE_FILE=dataset/gpt2-merges.txt
+b=8
+mp=1
+experts=1
+nodes=1
+gpus=1
+
+
+use_tutel=""
+#use_tutel="--use-tutel"
+
+
+ds_inference=""
+#ds_inference="--ds-inference"
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+launch_cmd="deepspeed --num_nodes $nodes --num_gpus $gpus"
+L=24
+H=1024
+A=16
+#experts1=${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 1024 \
+       --out-seq-length 1024 \
+       --temperature 1.0 \
+       --vocab-file $VOCAB_FILE \
+       --merge-file $MERGE_FILE \
+       --genfile unconditional_samples.json \
+       --top_p 0.9 \
+       --log-interval 1 \
+       --num-samples 0 \
+       --load $CHECKPOINT_PATH \
+       $use_tutel $ds_inference"
+
+echo $launch_cmd $program_cmd
+$launch_cmd $program_cmd

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/pretrain_llama2_distributed.sh

@@ -0,0 +1,135 @@
+#!/bin/bash
+# This example script is contributed by external user https://github.com/nrailgun
+set -ex
+
+######################################
+# Change the below configurations here
+BASE_PATH=./tmp
+DS_CONFIG=${BASE_PATH}/deepspeed.json
+DATASET_1="./tmp/data/bookcorpus_train_1m_text_sentence"
+DATASET="1 ${DATASET_1}"
+CHECKPOINT_PATH=./tmp
+TOKENIZER_PATH=./tmp/tokenizer.model # offical llama tokenizer.model
+
+TP=2
+PP=2
+ZERO_STAGE=0
+
+GPUS_PER_NODE=8
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NNODES=1
+NODE_RANK=0
+
+HIDDEN_SIZE=2048 # e.g. llama-13b: 5120
+FFN_HIDDEN_SIZE=5504 # e.g. llama-13b: 13824
+NUM_LAYERS=24 # e.g. llama-13b: 40
+NUM_HEADS=16 # e.g. llama-13b: 40
+SEQ_LENGTH=2048
+NUM_KV_HEADS=4 # llama2 70B uses GQA
+
+MICRO_BATCH_SIZE=4
+GLOBAL_BATCH_SIZE=32 # e.g. llama: 4M tokens
+TRAIN_STEPS=250000 # e.g. llama: 1T tokens / 4M tokens_per_batch = 250000 steps
+LR=3e-4
+MIN_LR=3e-5
+LR_WARMUP_STEPS=2000
+WEIGHT_DECAY=0.1
+GRAD_CLIP=1
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+# activation_checkpoint="true"
+activation_checkpoint="false"
+
+# Below configuration required for llama model as per llama paper
+# --no-query-key-layer-scaling \
+# --attention-dropout 0 \
+# --hidden-dropout 0 \
+# --use-rotary-position-embeddings \
+# --untie-embeddings-and-output-weights \
+# --swiglu \
+# --normalization rmsnorm \
+# --disable-bias-linear \
+######################################
+
+
+
+cat <<EOT > $DS_CONFIG
+{
+  "train_batch_size" : $GLOBAL_BATCH_SIZE,
+  "train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE,
+  "steps_per_print": 1,
+  "zero_optimization": {
+    "stage": $ZERO_STAGE
+  },
+  "bf16": {
+    "enabled": true
+  }
+}
+EOT
+
+ds_args=""
+ds_args=" --deepspeed ${ds_args}"
+ds_args=" --deepspeed_config=$DS_CONFIG ${ds_args}"
+ds_args=" --zero-stage=$ZERO_STAGE ${ds_args}"
+
+if [ "${activation_checkpoint}" = "true" ]; then
+  ds_args="--deepspeed-activation-checkpointing ${ds_args}"
+
+  ## old argument for recomputing the transformer layer
+  # ds_args="--checkpoint-activations ${ds_args}"
+
+  ## new argument for recomputing the transformer layer
+  ds_args="--recompute-granularity full --recompute-method uniform ${ds_args}"
+  ## new argument for recomputing only the attention layer
+  # ds_args="--recompute-granularity selective ${ds_args}"
+fi
+
+
+DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
+
+torchrun $DISTRIBUTED_ARGS \
+       pretrain_gpt.py \
+       --tensor-model-parallel-size $TP \
+       --pipeline-model-parallel-size $PP \
+       --num-layers $NUM_LAYERS \
+       --hidden-size $HIDDEN_SIZE \
+       --ffn-hidden-size $FFN_HIDDEN_SIZE \
+       --num-attention-heads $NUM_HEADS \
+       --micro-batch-size $MICRO_BATCH_SIZE \
+       --global-batch-size $GLOBAL_BATCH_SIZE \
+       --seq-length $SEQ_LENGTH \
+       --max-position-embeddings $SEQ_LENGTH \
+       --train-iters $TRAIN_STEPS \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATASET \
+       --data-impl mmap \
+       --tokenizer-type GPTSentencePieceTokenizer \
+       --tokenizer-model $TOKENIZER_PATH \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr $LR \
+       --lr-decay-style cosine \
+       --min-lr $MIN_LR \
+       --weight-decay $WEIGHT_DECAY \
+       --clip-grad $GRAD_CLIP \
+       --lr-warmup-iters $LR_WARMUP_STEPS \
+       --optimizer adam \
+       --adam-beta1 0.9 \
+       --adam-beta2 0.95 \
+       --log-interval 1 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --bf16 \
+       --no-query-key-layer-scaling \
+       --attention-dropout 0 \
+       --hidden-dropout 0 \
+       --use-rotary-position-embeddings \
+       --untie-embeddings-and-output-weights \
+       --swiglu \
+       --normalization rmsnorm \
+       --disable-bias-linear \
+       --num-key-value-heads $NUM_KV_HEADS \
+       $ds_args

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/pretrain_llama_distributed.sh

@@ -0,0 +1,132 @@
+#!/bin/bash
+# This example script is contributed by external user https://github.com/LydiaXiaohongLi
+set -ex
+
+######################################
+# Change the below configurations here
+BASE_PATH=./tmp
+DS_CONFIG=${BASE_PATH}/deepspeed.json
+DATASET_1="./tmp/data/bookcorpus_train_1m_text_sentence"
+DATASET="1 ${DATASET_1}"
+CHECKPOINT_PATH=./tmp
+TOKENIZER_PATH=./tmp/tokenizer.model # offical llama tokenizer.model
+
+TP=2
+PP=2
+ZERO_STAGE=0
+
+GPUS_PER_NODE=8
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NNODES=1
+NODE_RANK=0
+
+HIDDEN_SIZE=2048 # e.g. llama-13b: 5120
+FFN_HIDDEN_SIZE=5504 # e.g. llama-13b: 13824
+NUM_LAYERS=24 # e.g. llama-13b: 40
+NUM_HEADS=16 # e.g. llama-13b: 40
+SEQ_LENGTH=2048
+
+MICRO_BATCH_SIZE=4
+GLOBAL_BATCH_SIZE=32 # e.g. llama: 4M tokens
+TRAIN_STEPS=250000 # e.g. llama: 1T tokens / 4M tokens_per_batch = 250000 steps
+LR=3e-4
+MIN_LR=3e-5
+LR_WARMUP_STEPS=2000
+WEIGHT_DECAY=0.1
+GRAD_CLIP=1
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+# activation_checkpoint="true"
+activation_checkpoint="false"
+
+# Below configuration required for llama model as per llama paper
+# --no-query-key-layer-scaling \
+# --attention-dropout 0 \
+# --hidden-dropout 0 \
+# --use-rotary-position-embeddings \
+# --untie-embeddings-and-output-weights \
+# --swiglu \
+# --normalization rmsnorm \
+# --disable-bias-linear \
+######################################
+
+
+
+cat <<EOT > $DS_CONFIG
+{
+  "train_batch_size" : $GLOBAL_BATCH_SIZE,
+  "train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE,
+  "steps_per_print": 1,
+  "zero_optimization": {
+    "stage": $ZERO_STAGE
+  },
+  "bf16": {
+    "enabled": true
+  }
+}
+EOT
+
+ds_args=""
+ds_args=" --deepspeed ${ds_args}"
+ds_args=" --deepspeed_config=$DS_CONFIG ${ds_args}"
+ds_args=" --zero-stage=$ZERO_STAGE ${ds_args}"
+
+if [ "${activation_checkpoint}" = "true" ]; then
+  ds_args="--deepspeed-activation-checkpointing ${ds_args}"
+
+  ## old argument for recomputing the transformer layer
+  # ds_args="--checkpoint-activations ${ds_args}"
+
+  ## new argument for recomputing the transformer layer
+  ds_args="--recompute-granularity full --recompute-method uniform ${ds_args}"
+  ## new argument for recomputing only the attention layer
+  # ds_args="--recompute-granularity selective ${ds_args}"
+fi
+
+DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
+
+torchrun $DISTRIBUTED_ARGS \
+       pretrain_gpt.py \
+       --tensor-model-parallel-size $TP \
+       --pipeline-model-parallel-size $PP \
+       --num-layers $NUM_LAYERS \
+       --hidden-size $HIDDEN_SIZE \
+       --ffn-hidden-size $FFN_HIDDEN_SIZE \
+       --num-attention-heads $NUM_HEADS \
+       --micro-batch-size $MICRO_BATCH_SIZE \
+       --global-batch-size $GLOBAL_BATCH_SIZE \
+       --seq-length $SEQ_LENGTH \
+       --max-position-embeddings $SEQ_LENGTH \
+       --train-iters $TRAIN_STEPS \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATASET \
+       --data-impl mmap \
+       --tokenizer-type GPTSentencePieceTokenizer \
+       --tokenizer-model $TOKENIZER_PATH \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr $LR \
+       --lr-decay-style cosine \
+       --min-lr $MIN_LR \
+       --weight-decay $WEIGHT_DECAY \
+       --clip-grad $GRAD_CLIP \
+       --lr-warmup-iters $LR_WARMUP_STEPS \
+       --optimizer adam \
+       --adam-beta1 0.9 \
+       --adam-beta2 0.95 \
+       --log-interval 1 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --bf16 \
+       --no-query-key-layer-scaling \
+       --attention-dropout 0 \
+       --hidden-dropout 0 \
+       --use-rotary-position-embeddings \
+       --untie-embeddings-and-output-weights \
+       --swiglu \
+       --normalization rmsnorm \
+       --disable-bias-linear \
+       $ds_args

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/rebase/ds_config_gpt_TEMPLATE.json

@@ -0,0 +1,23 @@
+{
+  "train_batch_size": GBSIZE,
+  "train_micro_batch_size_per_gpu": MBSIZE,
+  "steps_per_print": LOG_INTERVAL,
+
+  "zero_optimization": {
+    "stage": ZERO_STAGE
+  },
+
+  "gradient_clipping": 1.0,
+  "prescale_gradients": PRESCALE_GRAD,
+
+  "fp16": {
+    "enabled": true,
+    "loss_scale": 0,
+    "loss_scale_window": 500,
+    "hysteresis": 2,
+    "min_loss_scale": 1,
+    "initial_scale_power": 11
+  },
+
+  "wall_clock_breakdown" : false
+}

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/rebase/ds_pretrain_gpt_1.3B_megatron_checkpointing.sh

@@ -0,0 +1,345 @@
+#!/bin/bash
+###############################################################################
+###############################################################################
+###############################################################################
+## WARNING: This script is only for evaluating Megatron-LM's activation
+## checkpointing. We do not recommend using it for actual training because
+## you are not able to use any DeepSpeed technologies.
+###############################################################################
+###############################################################################
+###############################################################################
+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
+
+## init_std is 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)
+
+## We changed min_lr to a lower number (1.0e-6), which we found is able to
+## provide better zero-shot eval results.
+
+## 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=1.0e-6
+# init_std=0.02
+
+## 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=1.0e-6
+# init_std=0.018
+
+## 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=1.0e-6
+# init_std=0.015
+
+## 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=1.0e-6
+init_std=0.013
+
+## 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=1.0e-6
+# init_std=0.011
+
+## 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=1.0e-6
+# init_std=0.009
+
+## 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-6
+# init_std=0.008
+
+## 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=1.0e-6
+# init_std=0.005
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens.
+train_tokens_in_billion=300
+train_tokens=$((${train_tokens_in_billion} * 1000000000))
+
+## 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 data efficiency techniques may change num tokens 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=$(( 300 * 1000000000 * 2 / ${seq_len} ))
+
+## Another wall-clock time termination condition in minutes. Set it large
+## enough to avoid undesired early termination.
+exit_duration=30000000
+###############################################################################
+### lr configs
+## lr warmup and decay duration.
+## Original GPT-3 paper uses 375M warmup tokens and 260B cosine decay tokens.
+## Here we increase the warmup tokens to 3B since when batch size warmup is not
+## used, there are more tokens per step. Thus we need to increase warmup tokens
+## to make sure there are enough warmup steps, which is important for training
+## stability.
+lr_warmup_tokens_in_million=3000
+lr_warmup_tokens=$((${lr_warmup_tokens_in_million} * 1000000))
+## Here we changed the LR decay tokens to align with total train tokens, since
+## related works (e.g., https://arxiv.org/abs/2203.15556) find that setting the
+## learning rate schedule to match the number of training tokens results in the
+## best final model quality 
+lr_decay_tokens_in_billion=${train_tokens_in_billion}
+lr_decay_tokens=$((${lr_decay_tokens_in_billion} * 1000000000))
+lr_decay_style="cosine"
+###############################################################################
+### Parallelism configs
+## Model parallelism, 1 is no MP
+mp_size=2
+
+## Pipeline parallelism. To disable PP, set pp_size to 1 and no_pp to true.
+## Note that currently both curriculum learning and random-LTD are NOT
+## compatible with pipeline parallelism.
+pp_size=1
+no_pp="true"
+
+## ZeRO-based data parallelism, stage=0 will disable ZeRO
+zero_stage=0
+
+## Total number of GPUs. ds_ssh is from DeepSpeed library.
+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} / ${num_gpus_pernode} ))
+
+## Data parallel size.
+dp_size=$(( ${num_gpus} / ${pp_size} / ${mp_size} ))
+
+## Micro batch size per GPU
+## Make sure that batch_size <= global_batch_size*pp_size*mp_size/num_gpus
+## Reduce it manually if GPU OOM
+# batch_size=$(( ${global_batch_size} / ${dp_size} ))
+batch_size=2
+###############################################################################
+### Misc configs
+log_interval=10
+eval_iters=10
+eval_interval=100
+# num_save controls how frequent to save checkpoint. num_save=20 means that a
+# checkpoint will be saved every 5% of training. For longer training you would
+# want larger num_save to save more frequently, and vice versa.
+num_save=100
+estimated_train_iter=$((${train_tokens} / ${seq_len} / ${global_batch_size}))
+# save_interval=$((${estimated_train_iter} / ${num_save}))
+save_interval=100
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+activation_checkpoint="true"
+# activation_checkpoint="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+log_optimizer_state="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d_%H.%M.%S")
+host="${HOSTNAME}"
+seed=1234
+num_workers=0
+
+## Public the Pile dataset, can be downloaded at
+## https://mystic.the-eye.eu/public/AI/pile_neox/ or 
+## https://the-eye.eu/public/AI/pile_neox/ Change data_home to where you
+## store the pile_text_document.bin and pile_text_document.idx.
+data_home="/vc_data_blob/users/conglli/the_pile_public_merged_nopreprocessing"
+data_path="${data_home}/pile_text_document"
+
+vocab_path="gpt2-vocab.json"
+if [ ! -f "$vocab_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json
+fi
+merge_path="gpt2-merges.txt"
+if [ ! -f "$merge_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt
+fi
+
+prescale_grad="true"
+jobname="gpt_${model_size}B_tok${train_tokens_in_billion}B"
+jobname="${jobname}_lr${lr}_min${min_lr}_w${lr_warmup_tokens_in_million}M_d${lr_decay_tokens_in_billion}B_${lr_decay_style}"
+jobname="${jobname}_gbs${global_batch_size}_mbs${batch_size}_g${num_gpus}"
+if [[ $zero_stage -gt 0 ]]; then
+    jobname="${jobname}_z${zero_stage}"
+    prescale_grad="false"
+fi
+if [[ $mp_size -gt 1 ]]; then
+    jobname="${jobname}_mp${mp_size}"
+fi
+if [ "${no_pp}" = "false" ]; then
+    jobname="${jobname}_pp${pp_size}"
+fi
+jobname="${jobname}_seed${seed}_rebase_megatron_checkpointing"
+
+username=$(whoami)
+output_home="/blob/users/${username}/project/data_efficient_gpt"
+log_path="${output_home}/log/"
+checkpoint_path="${output_home}/checkpoint/${jobname}"
+## Microsoft internal constraint: because tensorboard is logged by last rank,
+## it's better to put the path in NFS instead of Blob.
+tensorboard_dir="/vc_data/users/${username}/project/data_efficient_gpt/tensorboard/"
+tensorboard_path="${tensorboard_dir}${jobname}_${host}_${current_time}"
+mkdir -p ${log_path}
+mkdir -p ${checkpoint_path}
+mkdir -p ${tensorboard_path}
+###############################################################################
+data_options=" \
+    --vocab-file ${vocab_path} \
+    --merge-file ${merge_path} \
+    --data-path ${data_path} \
+    --data-impl mmap"
+
+## If CL is used, make sure to set "--split" the same as what you used during
+## offline data analysis&indexing.
+megatron_options=" \
+    --override-opt_param-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 ${lr_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 ${lr_decay_style} \
+    --split 949,50,1 \
+    --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 ${num_workers} \
+    --fp16 \
+    --seed ${seed} \
+    --load ${checkpoint_path} \
+    --save ${checkpoint_path} \
+    --no-async-tensor-model-parallel-allreduce \
+    --tensorboard-queue-size 1 \
+    --log-timers-to-tensorboard \
+    --log-batch-size-to-tensorboard \
+    --log-validation-ppl-to-tensorboard \
+    --tensorboard-dir ${tensorboard_path}"
+
+# test megatron activation checkpointing
+# we fixed bug in the code of this activation checkpointing, i.e., --recompute-granularity full --recompute-method uniform
+# the two arguments can be found in megatron/arguments.py
+if [ "${activation_checkpoint}" = "true" ]; then
+megatron_options="${megatron_options} \
+    --recompute-granularity full \
+    --recompute-method uniform \
+    --recompute-num-layers 1"
+fi
+
+if [ "${log_optimizer_state}" = "true" ]; then
+megatron_options="${megatron_options} \
+    --log-optimizer-states-to-tensorboard"
+fi
+
+config_json="ds_config_gbs${global_batch_size}_mbs${batch_size}_log${log_interval}_zero${zero_stage}.json"
+template_json="ds_config_gpt_TEMPLATE.json"
+sed "s/GBSIZE/${global_batch_size}/" ${template_json} \
+    | sed "s/MBSIZE/${batch_size}/" \
+    | sed "s/LOG_INTERVAL/${log_interval}/" \
+    | sed "s/ZERO_STAGE/${zero_stage}/" \
+    | sed "s/PRESCALE_GRAD/${prescale_grad}/" \
+      > ${config_json}
+
+deepspeed_options=" \
+    --pipeline-model-parallel-size ${pp_size}"
+
+if [[ "${no_pp}" = "true" ]]; then
+deepspeed_options="${deepspeed_options} \
+    --no-pipeline-parallel"
+fi
+
+# disable the deepspeed activation checkpointing
+
+# if [ "${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
+
+deepspeed ${dir}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &>> ${log_path}/${jobname}_${host}_${current_time}.log

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/rebase/ds_pretrain_gpt_125M.sh

@@ -0,0 +1,331 @@
+#!/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
+
+## init_std is 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)
+
+## We changed min_lr to a lower number (1.0e-6), which we found is able to
+## provide better zero-shot eval results.
+
+## 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=1.0e-6
+init_std=0.02
+
+## 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=1.0e-6
+# init_std=0.018
+
+## 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=1.0e-6
+# init_std=0.015
+
+## 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=1.0e-6
+# init_std=0.013
+
+## 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=1.0e-6
+# init_std=0.011
+
+## 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=1.0e-6
+# init_std=0.009
+
+## 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-6
+# init_std=0.008
+
+## 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=1.0e-6
+# init_std=0.005
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens.
+train_tokens_in_billion=300
+train_tokens=$((${train_tokens_in_billion} * 1000000000))
+
+## 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 data efficiency techniques may change num tokens 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=$(( 300 * 1000000000 * 2 / ${seq_len} ))
+
+## Another wall-clock time termination condition in minutes. Set it large
+## enough to avoid undesired early termination.
+exit_duration=30000000
+###############################################################################
+### lr configs
+## lr warmup and decay duration.
+## Original GPT-3 paper uses 375M warmup tokens and 260B cosine decay tokens.
+## Here we increase the warmup tokens to 3B since when batch size warmup is not
+## used, there are more tokens per step. Thus we need to increase warmup tokens
+## to make sure there are enough warmup steps, which is important for training
+## stability.
+lr_warmup_tokens_in_million=3000
+lr_warmup_tokens=$((${lr_warmup_tokens_in_million} * 1000000))
+## Here we changed the LR decay tokens to align with total train tokens, since
+## related works (e.g., https://arxiv.org/abs/2203.15556) find that setting the
+## learning rate schedule to match the number of training tokens results in the
+## best final model quality 
+lr_decay_tokens_in_billion=${train_tokens_in_billion}
+lr_decay_tokens=$((${lr_decay_tokens_in_billion} * 1000000000))
+lr_decay_style="cosine"
+###############################################################################
+### Parallelism configs
+## Model parallelism, 1 is no MP
+mp_size=2
+
+## Pipeline parallelism. To disable PP, set pp_size to 1 and no_pp to true.
+## Note that currently both curriculum learning and random-LTD are NOT
+## compatible with pipeline parallelism.
+pp_size=2
+no_pp="false"
+
+## ZeRO-based data parallelism, stage=0 will disable ZeRO
+zero_stage=1
+
+## Total number of GPUs. ds_ssh is from DeepSpeed library.
+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} / ${num_gpus_pernode} ))
+
+## Data parallel size.
+dp_size=$(( ${num_gpus} / ${pp_size} / ${mp_size} ))
+
+## Micro batch size per GPU
+## Make sure that batch_size <= global_batch_size*pp_size*mp_size/num_gpus
+## Reduce it manually if GPU OOM
+# batch_size=$(( ${global_batch_size} / ${dp_size} ))
+batch_size=2
+###############################################################################
+### Misc configs
+log_interval=10
+eval_iters=10
+eval_interval=100
+# num_save controls how frequent to save checkpoint. num_save=20 means that a
+# checkpoint will be saved every 5% of training. For longer training you would
+# want larger num_save to save more frequently, and vice versa.
+num_save=100
+estimated_train_iter=$((${train_tokens} / ${seq_len} / ${global_batch_size}))
+# save_interval=$((${estimated_train_iter} / ${num_save}))
+save_interval=100
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+activation_checkpoint="true"
+# activation_checkpoint="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+log_optimizer_state="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d_%H.%M.%S")
+host="${HOSTNAME}"
+seed=1234
+num_workers=0
+
+data_path="BookCorpusDataset_text_document"
+if [ ! -f "BookCorpusDataset_text_document.bin" ]; then
+    wget https://the-eye.eu/public/AI/pile_neox/data/BookCorpusDataset_text_document.bin
+fi
+if [ ! -f "BookCorpusDataset_text_document.idx" ]; then
+    wget https://the-eye.eu/public/AI/pile_neox/data/BookCorpusDataset_text_document.idx
+fi
+
+vocab_path="gpt2-vocab.json"
+if [ ! -f "$vocab_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json
+fi
+merge_path="gpt2-merges.txt"
+if [ ! -f "$merge_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt
+fi
+
+prescale_grad="true"
+jobname="gpt_${model_size}B_tok${train_tokens_in_billion}B"
+jobname="${jobname}_lr${lr}_min${min_lr}_w${lr_warmup_tokens_in_million}M_d${lr_decay_tokens_in_billion}B_${lr_decay_style}"
+jobname="${jobname}_gbs${global_batch_size}_mbs${batch_size}_g${num_gpus}"
+if [[ $zero_stage -gt 0 ]]; then
+    jobname="${jobname}_z${zero_stage}"
+    prescale_grad="false"
+fi
+if [[ $mp_size -gt 1 ]]; then
+    jobname="${jobname}_mp${mp_size}"
+fi
+if [ "${no_pp}" = "false" ]; then
+    jobname="${jobname}_pp${pp_size}"
+fi
+jobname="${jobname}_seed${seed}_rebase"
+
+username=$(whoami)
+output_home="output"
+log_path="${output_home}/log/"
+checkpoint_path="${output_home}/checkpoint/${jobname}"
+tensorboard_dir="${output_home}/tensorboard/"
+tensorboard_path="${tensorboard_dir}${jobname}_${host}_${current_time}"
+mkdir -p ${log_path}
+mkdir -p ${checkpoint_path}
+mkdir -p ${tensorboard_path}
+###############################################################################
+data_options=" \
+    --vocab-file ${vocab_path} \
+    --merge-file ${merge_path} \
+    --data-path ${data_path} \
+    --data-impl mmap"
+
+## If CL is used, make sure to set "--split" the same as what you used during
+## offline data analysis&indexing.
+megatron_options=" \
+    --override-opt_param-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 ${lr_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 ${lr_decay_style} \
+    --split 949,50,1 \
+    --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 ${num_workers} \
+    --fp16 \
+    --seed ${seed} \
+    --load ${checkpoint_path} \
+    --save ${checkpoint_path} \
+    --no-async-tensor-model-parallel-allreduce \
+    --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="${megatron_options} \
+    --log-optimizer-states-to-tensorboard"
+fi
+
+config_json="ds_config_gbs${global_batch_size}_mbs${batch_size}_log${log_interval}_zero${zero_stage}.json"
+template_json="ds_config_gpt_TEMPLATE.json"
+sed "s/GBSIZE/${global_batch_size}/" ${template_json} \
+    | sed "s/MBSIZE/${batch_size}/" \
+    | sed "s/LOG_INTERVAL/${log_interval}/" \
+    | sed "s/ZERO_STAGE/${zero_stage}/" \
+    | sed "s/PRESCALE_GRAD/${prescale_grad}/" \
+      > ${config_json}
+
+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
+
+deepspeed ${dir}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &>> ${log_path}/${jobname}_${host}_${current_time}.log

docker/intel_code/llama13b/Megatron-DeepSpeed/examples_deepspeed/rebase/ds_pretrain_gpt_13B.sh

@@ -0,0 +1,332 @@
+#!/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
+
+## init_std is 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)
+
+## We changed min_lr to a lower number (1.0e-6), which we found is able to
+## provide better zero-shot eval results.
+
+## 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=1.0e-6
+# init_std=0.02
+
+## 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=1.0e-6
+# init_std=0.018
+
+## 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=1.0e-6
+# init_std=0.015
+
+## 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=1.0e-6
+# init_std=0.013
+
+## 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=1.0e-6
+# init_std=0.011
+
+## 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=1.0e-6
+# init_std=0.009
+
+## 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-6
+init_std=0.008
+
+## 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=1.0e-6
+# init_std=0.005
+###############################################################################
+### Training duration configs
+## The main termination condition, original GPT-3 paper trains for 300B tokens.
+train_tokens_in_billion=300
+train_tokens=$((${train_tokens_in_billion} * 1000000000))
+
+## 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 data efficiency techniques may change num tokens 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=$(( 300 * 1000000000 * 2 / ${seq_len} ))
+
+## Another wall-clock time termination condition in minutes. Set it large
+## enough to avoid undesired early termination.
+exit_duration=30000000
+###############################################################################
+### lr configs
+## lr warmup and decay duration.
+## Original GPT-3 paper uses 375M warmup tokens and 260B cosine decay tokens.
+## Here we increase the warmup tokens to 3B since when batch size warmup is not
+## used, there are more tokens per step. Thus we need to increase warmup tokens
+## to make sure there are enough warmup steps, which is important for training
+## stability.
+lr_warmup_tokens_in_million=3000
+lr_warmup_tokens=$((${lr_warmup_tokens_in_million} * 1000000))
+## Here we changed the LR decay tokens to align with total train tokens, since
+## related works (e.g., https://arxiv.org/abs/2203.15556) find that setting the
+## learning rate schedule to match the number of training tokens results in the
+## best final model quality 
+lr_decay_tokens_in_billion=${train_tokens_in_billion}
+lr_decay_tokens=$((${lr_decay_tokens_in_billion} * 1000000000))
+lr_decay_style="cosine"
+###############################################################################
+### Parallelism configs
+## Model parallelism, 1 is no MP
+mp_size=4
+
+## Pipeline parallelism. To disable PP, set pp_size to 1 and no_pp to true.
+## Note that currently both curriculum learning and random-LTD are NOT
+## compatible with pipeline parallelism.
+pp_size=8
+no_pp="false"
+
+## ZeRO-based data parallelism, stage=0 will disable ZeRO
+zero_stage=1
+
+## Total number of GPUs. ds_ssh is from DeepSpeed library.
+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} / ${num_gpus_pernode} ))
+
+## Data parallel size.
+dp_size=$(( ${num_gpus} / ${pp_size} / ${mp_size} ))
+
+## Micro batch size per GPU
+## Make sure that batch_size <= global_batch_size*pp_size*mp_size/num_gpus
+## Reduce it manually if GPU OOM
+# batch_size=$(( ${global_batch_size} / ${dp_size} ))
+batch_size=2
+###############################################################################
+### Misc configs
+log_interval=10
+eval_iters=10
+eval_interval=100
+# num_save controls how frequent to save checkpoint. num_save=20 means that a
+# checkpoint will be saved every 5% of training. For longer training you would
+# want larger num_save to save more frequently, and vice versa.
+num_save=100
+estimated_train_iter=$((${train_tokens} / ${seq_len} / ${global_batch_size}))
+# save_interval=$((${estimated_train_iter} / ${num_save}))
+save_interval=100
+
+## Activation checkpointing saves GPU memory, but reduces training speed
+activation_checkpoint="true"
+# activation_checkpoint="false"
+
+## Whether or not log optimizer states (norms, max abs values) to tensorboard.
+## This is not required for training and might save GPU memory when turned off.
+log_optimizer_state="true"
+###############################################################################
+### Output and data configs
+current_time=$(date "+%Y.%m.%d_%H.%M.%S")
+host="${HOSTNAME}"
+seed=1234
+num_workers=0
+
+## Public the Pile dataset, can be downloaded at
+## https://mystic.the-eye.eu/public/AI/pile_neox/ or 
+## https://the-eye.eu/public/AI/pile_neox/ Change data_home to where you
+## store the pile_text_document.bin and pile_text_document.idx.
+data_home="/vc_data_blob/users/conglli/the_pile_public_merged_nopreprocessing"
+data_path="${data_home}/pile_text_document"
+
+vocab_path="gpt2-vocab.json"
+if [ ! -f "$vocab_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json
+fi
+merge_path="gpt2-merges.txt"
+if [ ! -f "$merge_path" ]; then
+    wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt
+fi
+
+prescale_grad="true"
+jobname="gpt_${model_size}B_tok${train_tokens_in_billion}B"
+jobname="${jobname}_lr${lr}_min${min_lr}_w${lr_warmup_tokens_in_million}M_d${lr_decay_tokens_in_billion}B_${lr_decay_style}"
+jobname="${jobname}_gbs${global_batch_size}_mbs${batch_size}_g${num_gpus}"
+if [[ $zero_stage -gt 0 ]]; then
+    jobname="${jobname}_z${zero_stage}"
+    prescale_grad="false"
+fi
+if [[ $mp_size -gt 1 ]]; then
+    jobname="${jobname}_mp${mp_size}"
+fi
+if [ "${no_pp}" = "false" ]; then
+    jobname="${jobname}_pp${pp_size}"
+fi
+jobname="${jobname}_seed${seed}_rebase"
+
+username=$(whoami)
+output_home="/blob/users/${username}/project/data_efficient_gpt"
+log_path="${output_home}/log/"
+checkpoint_path="${output_home}/checkpoint/${jobname}"
+## Microsoft internal constraint: because tensorboard is logged by last rank,
+## it's better to put the path in NFS instead of Blob.
+tensorboard_dir="/vc_data/users/${username}/project/data_efficient_gpt/tensorboard/"
+tensorboard_path="${tensorboard_dir}${jobname}_${host}_${current_time}"
+mkdir -p ${log_path}
+mkdir -p ${checkpoint_path}
+mkdir -p ${tensorboard_path}
+###############################################################################
+data_options=" \
+    --vocab-file ${vocab_path} \
+    --merge-file ${merge_path} \
+    --data-path ${data_path} \
+    --data-impl mmap"
+
+## If CL is used, make sure to set "--split" the same as what you used during
+## offline data analysis&indexing.
+megatron_options=" \
+    --override-opt_param-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 ${lr_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 ${lr_decay_style} \
+    --split 949,50,1 \
+    --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 ${num_workers} \
+    --fp16 \
+    --seed ${seed} \
+    --load ${checkpoint_path} \
+    --save ${checkpoint_path} \
+    --no-async-tensor-model-parallel-allreduce \
+    --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="${megatron_options} \
+    --log-optimizer-states-to-tensorboard"
+fi
+
+config_json="ds_config_gbs${global_batch_size}_mbs${batch_size}_log${log_interval}_zero${zero_stage}.json"
+template_json="ds_config_gpt_TEMPLATE.json"
+sed "s/GBSIZE/${global_batch_size}/" ${template_json} \
+    | sed "s/MBSIZE/${batch_size}/" \
+    | sed "s/LOG_INTERVAL/${log_interval}/" \
+    | sed "s/ZERO_STAGE/${zero_stage}/" \
+    | sed "s/PRESCALE_GRAD/${prescale_grad}/" \
+      > ${config_json}
+
+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
+
+deepspeed ${dir}/../../pretrain_gpt.py ${megatron_options} ${data_options} ${deepspeed_options} &>> ${log_path}/${jobname}_${host}_${current_time}.log