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Commit ef59b687 authored by Jared Casper's avatar Jared Casper
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Merge branch 'maanug/distributed-pretrain-examples' into 'main' 

Fix distributed pretraining examples

See merge request ADLR/megatron-lm!523
parents 0dce440a 94dbfd1c
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Showing with 333 additions and 212 deletions
examples/pretrain_bert_distributed.sh
View file @ ef59b687
#!/bin/bash #!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8 GPUS_PER_NODE=8
# Change for multinode config # Change for multinode config
MASTER_ADDR=localhost MASTER_ADDR=localhost
...@@ -8,37 +10,55 @@ NNODES=1 ...@@ -8,37 +10,55 @@ NNODES=1
NODE_RANK=0 NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
DATA_PATH=<Specify path and file prefix>_text_sentence
CHECKPOINT_PATH=<Specify path> CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/bert-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
BERT_ARGS="
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--micro-batch-size 4 \
--global-batch-size 32 \
--lr 0.0001 \
--train-iters 1000000 \
--lr-decay-iters 990000 \
--lr-decay-style linear \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
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_bert.py \
$BERT_ARGS \
python -m torch.distributed.launch $DISTRIBUTED_ARGS \ $DATA_ARGS \
pretrain_bert.py \ $OUTPUT_ARGS \
--num-layers 24 \ --distributed-backend nccl \
--hidden-size 1024 \ --save $CHECKPOINT_PATH \
--num-attention-heads 16 \ --load $CHECKPOINT_PATH
--micro-batch-size 4 \
--global-batch-size 32 \
--seq-length 512 \
--max-position-embeddings 512 \
--train-iters 1000000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file bert-vocab.txt \
--data-impl mmap \
--split 949,50,1 \
--distributed-backend nccl \
--lr 0.0001 \
--lr-decay-style linear \
--min-lr 1.0e-5 \
--lr-decay-iters 990000 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--lr-warmup-fraction .01 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--fp16
examples/pretrain_bert_distributed_with_mp.sh
View file @ ef59b687
#!/bin/bash #!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8 GPUS_PER_NODE=8
# Change for multinode config # Change for multinode config
MASTER_ADDR=localhost MASTER_ADDR=localhost
...@@ -8,40 +10,57 @@ NNODES=1 ...@@ -8,40 +10,57 @@ NNODES=1
NODE_RANK=0 NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
DATA_PATH=<Specify path and file prefix>_text_sentence
VOCAB_FILE=<Specify path to vocab.txt>
CHECKPOINT_PATH=<Specify path> CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/bert-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
BERT_ARGS="
--tensor-model-parallel-size 2 \
--pipeline-model-parallel-size 2 \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--micro-batch-size 2 \
--global-batch-size 16 \
--lr 0.0001 \
--train-iters 1000000 \
--lr-decay-iters 990000 \
--lr-decay-style linear \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
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_bert.py \
$BERT_ARGS \
python -m torch.distributed.launch $DISTRIBUTED_ARGS \ $DATA_ARGS \
pretrain_bert.py \ $OUTPUT_ARGS \
--tensor-model-parallel-size 2 \ --distributed-backend nccl \
--pipeline-model-parallel-size 2 \ --save $CHECKPOINT_PATH \
--num-layers 24 \ --load $CHECKPOINT_PATH
--hidden-size 1024 \
--num-attention-heads 16 \
--micro-batch-size 2 \
--global-batch-size 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--train-iters 1000000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1 \
--distributed-backend nccl \
--lr 0.0001 \
--lr-decay-style linear \
--min-lr 1.0e-5 \
--lr-decay-iters 990000 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--lr-warmup-fraction .01 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--fp16
examples/pretrain_gpt_distributed.sh
View file @ ef59b687
#! /bin/bash #!/bin/bash
# Runs the "345M" parameter model # Runs the "345M" parameter model
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8 GPUS_PER_NODE=8
# Change for multinode config # Change for multinode config
MASTER_ADDR=localhost MASTER_ADDR=localhost
...@@ -10,39 +12,57 @@ NNODES=1 ...@@ -10,39 +12,57 @@ NNODES=1
NODE_RANK=0 NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
DATA_PATH=<Specify path and file prefix>_text_document
CHECKPOINT_PATH=<Specify path> CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/gpt2-vocab.json
MERGE_FILE=<Specify path to file>/gpt2-merges.txt
DATA_PATH=<Specify path and file prefix>_text_document
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
GPT_ARGS="
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size 8 \
--global-batch-size 64 \
--lr 0.00015 \
--train-iters 500000 \
--lr-decay-iters 320000 \
--lr-decay-style cosine \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
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 \
$GPT_ARGS \
python -m torch.distributed.launch $DISTRIBUTED_ARGS \ $DATA_ARGS \
pretrain_gpt.py \ $OUTPUT_ARGS \
--num-layers 24 \ --distributed-backend nccl \
--hidden-size 1024 \ --save $CHECKPOINT_PATH \
--num-attention-heads 16 \ --load $CHECKPOINT_PATH
--micro-batch-size 8 \
--global-batch-size 64 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--train-iters 500000 \
--lr-decay-iters 320000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file gpt2-vocab.json \
--merge-file gpt2-merges.txt \
--data-impl mmap \
--split 949,50,1 \
--distributed-backend nccl \
--lr 0.00015 \
--lr-decay-style cosine \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--lr-warmup-fraction .01 \
--activations-checkpoint-method uniform \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--fp16
examples/pretrain_gpt_distributed_with_mp.sh
View file @ ef59b687
#! /bin/bash #!/bin/bash
# Runs the "345M" parameter model # Runs the "345M" parameter model
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8 GPUS_PER_NODE=8
# Change for multinode config # Change for multinode config
MASTER_ADDR=localhost MASTER_ADDR=localhost
...@@ -10,42 +12,61 @@ NNODES=1 ...@@ -10,42 +12,61 @@ NNODES=1
NODE_RANK=0 NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
DATA_PATH=<Specify path and file prefix>_text_document
CHECKPOINT_PATH=<Specify path> CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/gpt2-vocab.json
MERGE_FILE=<Specify path to file>/gpt2-merges.txt
DATA_PATH=<Specify path and file prefix>_text_document
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
GPT_ARGS="
--tensor-model-parallel-size 2 \
--pipeline-model-parallel-size 2 \
--sequence-parallel \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size 4 \
--global-batch-size 16 \
--lr 0.00015 \
--train-iters 500000 \
--lr-decay-iters 320000 \
--lr-decay-style cosine \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
torchrun $DISTRIBUTED_ARGS pretrain_gpt.py \
$GPT_ARGS \
$DATA_ARGS \
$OUTPUT_ARGS \
--distributed-backend nccl \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
python -m torch.distributed.launch $DISTRIBUTED_ARGS \
pretrain_gpt.py \
--tensor-model-parallel-size 2 \
--pipeline-model-parallel-size 2 \
--sequence-parallel \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--micro-batch-size 4 \
--global-batch-size 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--train-iters 500000 \
--lr-decay-iters 320000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file gpt2-vocab.json \
--merge-file gpt2-merges.txt \
--data-impl mmap \
--split 949,50,1 \
--distributed-backend nccl \
--lr 0.00015 \
--lr-decay-style cosine \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--lr-warmup-fraction .01 \
--activations-checkpoint-method uniform \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--fp16
examples/pretrain_t5_distributed.sh
View file @ ef59b687
#!/bin/bash #!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8 GPUS_PER_NODE=8
# Change for multinode config # Change for multinode config
MASTER_ADDR=localhost MASTER_ADDR=localhost
...@@ -8,41 +10,59 @@ NNODES=1 ...@@ -8,41 +10,59 @@ NNODES=1
NODE_RANK=0 NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
DATA_PATH=<Specify path and file prefix>
VOCAB_FILE=<Specify path to vocab.txt>
CHECKPOINT_PATH=<Specify path> CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/t5-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
T5_ARGS="
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--kv-channels 64 \
--ffn-hidden-size 3072 \
--encoder-seq-length 512 \
--decoder-seq-length 128 \
--max-position-embeddings 512 \
--micro-batch-size 16 \
--global-batch-size 128 \
--lr 0.0001 \
--train-iters 1000000 \
--lr-decay-iters 1000000 \
--lr-decay-style linear \
--min-lr 0.00001 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16 \
--vocab-extra-ids 100
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
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_t5.py \
$T5_ARGS \
python -m torch.distributed.launch $DISTRIBUTED_ARGS \ $DATA_ARGS \
pretrain_t5.py \ $OUTPUT_ARGS \
--num-layers 12 \ --distributed-backend nccl \
--hidden-size 768 \ --save $CHECKPOINT_PATH \
--num-attention-heads 12 \ --load $CHECKPOINT_PATH
--kv-channels 64 \
--ffn-hidden-size 3072 \
--encoder-seq-length 512 \
--decoder-seq-length 128 \
--micro-batch-size 16 \
--global-batch-size 128 \
--max-position-embeddings 512 \
--train-iters 1000000 \
--lr-decay-iters 1000000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1 \
--lr 0.0001 \
--min-lr 0.00001 \
--lr-decay-style linear \
--lr-warmup-fraction .01 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--fp16 \
--vocab-extra-ids 100
examples/pretrain_t5_distributed_with_mp.sh
View file @ ef59b687
#!/bin/bash #!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8 GPUS_PER_NODE=8
# Change for multinode config # Change for multinode config
MASTER_ADDR=localhost MASTER_ADDR=localhost
...@@ -8,41 +10,60 @@ NNODES=1 ...@@ -8,41 +10,60 @@ NNODES=1
NODE_RANK=0 NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
DATA_PATH=<Specify path and file prefix>
CHECKPOINT_PATH=<Specify path> CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/t5-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
T5_ARGS="
--tensor-model-parallel-size 2 \
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--kv-channels 64 \
--ffn-hidden-size 3072 \
--encoder-seq-length 512 \
--decoder-seq-length 128 \
--max-position-embeddings 512 \
--micro-batch-size 16 \
--global-batch-size 128 \
--lr 0.0001 \
--train-iters 1000000 \
--lr-decay-iters 1000000 \
--lr-decay-style linear \
--min-lr 0.00001 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16 \
--vocab-extra-ids 100
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
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_t5.py \
$T5_ARGS \
python -m torch.distributed.launch $DISTRIBUTED_ARGS \ $DATA_ARGS \
pretrain_t5.py \ $OUTPUT_ARGS \
--tensor-model-parallel-size 2 \ --distributed-backend nccl \
--num-layers 12 \ --save $CHECKPOINT_PATH \
--hidden-size 768 \ --load $CHECKPOINT_PATH
--num-attention-heads 12 \
--kv-channels 64 \
--ffn-hidden-size 3072 \
--encoder-seq-length 512 \
--decoder-seq-length 128 \
--micro-batch-size 16 \
--global-batch-size 128 \
--max-position-embeddings 512 \
--train-iters 1000000 \
--lr-decay-iters 1000000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file t5-vocab.txt \
--data-impl mmap \
--split 949,50,1 \
--lr 0.0001 \
--min-lr 0.00001 \
--lr-decay-style linear \
--lr-warmup-fraction .01 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--fp16 \
--vocab-extra-ids 100
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