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Commit c788823b authored by wxj's avatar wxj
Browse files

删除用于dcu的te的batchgemm, 添加nv上的llama和qwen脚本

parent 475293db
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Showing with 1099 additions and 14 deletions
dcu_megatron/adaptor/megatron_adaptor.py
View file @ c788823b
......@@ -140,13 +140,13 @@ class CoreAdaptation(MegatronAdaptationABC):
from ..core.extensions.transformer_engine import TEDotProductAttentionPatch
from megatron.core.extensions.transformer_engine import TEGroupedLinear
if not is_te_min_version("1.10.0"):
# kv channels, te_min_version 1.10.0 -> 1.9.0
MegatronAdaptation.register('megatron.core.extensions.transformer_engine.TEDotProductAttention.__init__',
TEDotProductAttentionPatch.__init__)
# if not is_te_min_version("1.10.0"):
# # kv channels, te_min_version 1.10.0 -> 1.9.0
# MegatronAdaptation.register('megatron.core.extensions.transformer_engine.TEDotProductAttention.__init__',
# TEDotProductAttentionPatch.__init__)
if int(os.getenv("GROUPED_GEMM_BatchLinear", '0')):
TEGroupedLinear.__bases__ = (te.pytorch.BatchedLinear if is_te_min_version("2.3.0.dev0") else te.pytorch.BatchLinear,)
# if int(os.getenv("GROUPED_GEMM_BatchLinear", '0')):
# TEGroupedLinear.__bases__ = (te.pytorch.BatchedLinear if is_te_min_version("2.3.0.dev0") else te.pytorch.BatchLinear,)
def patch_tensor_parallel(self):
from ..core.tensor_parallel.cross_entropy import VocabParallelCrossEntropy
......
examples/llama/hostfile 0 → 100644
View file @ c788823b
a121 slots=8
a124 slots=8
\ No newline at end of file
examples/llama/run_llama2_13B.sh 0 → 100755
View file @ c788823b
for para in $*
do
if [[ $para == --profiling* ]];then
profiling=${para#*=}
fi
done
# Those variables need to modify
GPUS="16" # how many gpus to use
HOST="a121" # hostname
PORT="11452" # port id
DATA_PATH="/data/datasets/oscar-1GB_head-llama2_text_document" # path to oscar-1GB_head-llama2_text_document
TOKENIZER_MODEL_PATH="/data/models/llama2/tokenizer.model" # path to tokenizer.model
CHECKPOINT_PATH="./ckpt" # path to ckpt
# Runs Llama2 7B model
mpirun -np ${GPUS} --hostfile hostfile \
--allow-run-as-root \
--bind-to none \
--mca plm_rsh_no_tree_spawn 1 \
--mca plm_rsh_args "-p 11451" \
bash -c "
./train_llama2_13b_2nodes.sh \
${HOST} \
${PORT} \
--data_path=$DATA_PATH \
--tokenizer_path=$TOKENIZER_MODEL_PATH \
--checkpoint_path=$CHECKPOINT_PATH \
--profiling=$profiling" > ./logs/log-$((${GPUS} / 8))nodes-`date +%F-%H%M`.log 2>&1
wait
\ No newline at end of file
examples/llama/run_llama2_7B.sh
View file @ c788823b
......@@ -6,9 +6,9 @@ do
done
# Those variables need to modify
GPUS="4" # how many gpus to use
HOST="localhost" # hostname
PORT="11451" # port id
GPUS="16" # how many gpus to use
HOST="a121" # hostname
PORT="11452" # port id
DATA_PATH="/data/datasets/oscar-1GB_head-llama2_text_document" # path to oscar-1GB_head-llama2_text_document
TOKENIZER_MODEL_PATH="/data/models/llama2/tokenizer.model" # path to tokenizer.model
CHECKPOINT_PATH="./ckpt" # path to ckpt
......@@ -18,13 +18,14 @@ mpirun -np ${GPUS} --hostfile hostfile \
--allow-run-as-root \
--bind-to none \
--mca plm_rsh_no_tree_spawn 1 \
--mca plm_rsh_args "-p 11451" \
bash -c "
./train_llama2_7b_1nodes.sh \
./train_llama2_7b_2nodes.sh \
${HOST} \
${PORT} \
--data_path=$DATA_PATH \
--tokenizer_path=$TOKENIZER_MODEL_PATH \
--checkpoint_path=$CHECKPOINT_PATH \
--profiling=$profiling" > log-$((${GPUS} / 8))nodes-`date +%F-%H%M`.log 2>&1
--profiling=$profiling" > ./logs/log-$((${GPUS} / 8))nodes-`date +%F-%H%M`.log 2>&1
wait
\ No newline at end of file
examples/llama/train_llama2_13b_1nodes.sh 0 → 100755
View file @ c788823b
#!/bin/bash
INITIALIZATION_ARGS=( --num-workers 2)
for para in $*
do
if [[ $para == --data_path* ]];then
data_path=${para#*=}
elif [[ $para == --tokenizer_path* ]];then
tokenizer_path=${para#*=}
elif [[ $para == --checkpoint_path* ]];then
checkpoint_path=${para#*=}
elif [[ $para == --profiling* ]];then
profiling=${para#*=}
elif [[ $para == --reproduce* ]];then
INITIALIZATION_ARGS=( --reproduce --num-workers 0)
export MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC=1 # miopen 确定算法打开
export ROCBLAS_ATOMICS_MOD=0 # rocblas 关闭原子操作
# 关闭miopen中的atomic操作算法, 只保留gemm算法
export MIOPEN_DEBUG_CONV_FFT=0
export MIOPEN_DEBUG_CONV_DIRECT=0
export MIOPEN_DEBUG_CONV_GEMM=1
export MIOPEN_DEBUG_CONV_WINOGRAD=0
export MIOPEN_DEBUG_CONV_IMPLICIT_GEMM=0
fi
done
# data path
DATA_PATH=${data_path}
TOKENIZER_MODEL_PATH=${tokenizer_path}
CHECKPOINT_PATH=${checkpoint_path}
# default env
DIST_URL=${1}
DIST_PORT=${2}
RANK=$OMPI_COMM_WORLD_RANK
LOCAL_RANK=$OMPI_COMM_WORLD_LOCAL_RANK
WORLD_SIZE=$OMPI_COMM_WORLD_SIZE
CURRENT_DIR="$( cd "$( dirname "$0" )" && pwd )"
MEGATRON_PATH=$( dirname $( dirname ${CURRENT_DIR}))
export GLOG_minloglevel=3
export CUDA_DEVICE_MAX_CONNECTIONS=1
export HSA_FORCE_FINE_GRAIN_PCIE=1
export OMP_NUM_THREADS=1
export GPU_MAX_HW_QUEUES=10
export PYTHONPATH=${MEGATRON_PATH}/Megatron-LM:$PYTHONPATH
# torch控制多流转单流
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export SENDRECV_STREAM_WITH_COMPUTE=1
#增加编译缓存
export cache_size_limit=64
DISTRIBUTED_ARGS=(
--rank ${RANK}
--world-size ${WORLD_SIZE}
--local-rank ${LOCAL_RANK}
--dist-url tcp://${DIST_URL}:${DIST_PORT}
)
GPT_MODEL_ARGS=(
--seq-length 4096
--num-layers 40
--hidden-size 5120
--ffn-hidden-size 13824
--num-attention-heads 40
--max-position-embeddings 4096
--normalization RMSNorm
--position-embedding-type rope
--untie-embeddings-and-output-weights
)
TRAINING_ARGS=(
--transformer-impl local
--use-legacy-models
--micro-batch-size 1
--global-batch-size 256
--train-iters 50
--weight-decay 0.1
--adam-beta1 0.9
--adam-beta2 0.95
--init-method-std 0.006
--clip-grad 1.0
--bf16
--disable-bias-linear
--attention-dropout 0
--hidden-dropout 0
--swiglu
--lr 3.0e-5
--lr-decay-style cosine
--min-lr 3.0e-6
--lr-warmup-iters 1
--ckpt-format torch
--ddp-average-in-collective
--overlap-grad-reduce
--use-flash-attn
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size 2
--pipeline-model-parallel-size 2
--context-parallel-size 1
--use-distributed-optimizer
--sequence-parallel
)
DATA_ARGS=(
--tokenizer-type Llama2Tokenizer
--tokenizer-model ${TOKENIZER_MODEL_PATH}
--data-path ${DATA_PATH}
--split 949,50,1
)
EVAL_AND_LOGGING_ARGS=(
--log-throughput
--eval-iters 5
--log-interval 1
--save-interval 1000
--eval-interval 1000
--save $CHECKPOINT_PATH
--load $CHECKPOINT_PATH
--tensorboard-dir "${CHECKPOINT_PATH}/tensorboard"
)
TORCH_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 3
--profile-step-end 4
--profile-dir torch_prof_llama_1nodes_tp1-pp2-cp1
--use-pytorch-profiler
)
HIP_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 4
--profile-step-end 5
--use-hip-profiler
)
APP="python -u ${MEGATRON_PATH}/pretrain_gpt.py \
${GPT_MODEL_ARGS[@]} \
${TRAINING_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS[@]} \
${EVAL_AND_LOGGING_ARGS[@]} \
${DISTRIBUTED_ARGS[@]} \
${INITIALIZATION_ARGS[@]} \
"
if [[ $profiling == "torch" ]]; then
APP+=" ${TORCH_PROFIE_ARGS[@]}"
elif [[ $profiling == "hip" ]]; then
mkdir -p hip_prof_data
APP+=" ${HIP_PROFIE_ARGS[@]}"
APP="hipprof -d hip_prof_data --hip-trace --trace-off ${APP}"
fi
#for hygon cpu
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
${APP}
\ No newline at end of file
examples/llama/train_llama2_13b_2nodes.sh 0 → 100755
View file @ c788823b
#!/bin/bash
INITIALIZATION_ARGS=( --num-workers 2)
for para in $*
do
if [[ $para == --data_path* ]];then
data_path=${para#*=}
elif [[ $para == --tokenizer_path* ]];then
tokenizer_path=${para#*=}
elif [[ $para == --checkpoint_path* ]];then
checkpoint_path=${para#*=}
elif [[ $para == --profiling* ]];then
profiling=${para#*=}
elif [[ $para == --reproduce* ]];then
INITIALIZATION_ARGS=( --reproduce --num-workers 0)
export MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC=1 # miopen 确定算法打开
export ROCBLAS_ATOMICS_MOD=0 # rocblas 关闭原子操作
# 关闭miopen中的atomic操作算法, 只保留gemm算法
export MIOPEN_DEBUG_CONV_FFT=0
export MIOPEN_DEBUG_CONV_DIRECT=0
export MIOPEN_DEBUG_CONV_GEMM=1
export MIOPEN_DEBUG_CONV_WINOGRAD=0
export MIOPEN_DEBUG_CONV_IMPLICIT_GEMM=0
fi
done
# data path
DATA_PATH=${data_path}
TOKENIZER_MODEL_PATH=${tokenizer_path}
CHECKPOINT_PATH=${checkpoint_path}
# default env
DIST_URL=${1}
DIST_PORT=${2}
RANK=$OMPI_COMM_WORLD_RANK
LOCAL_RANK=$OMPI_COMM_WORLD_LOCAL_RANK
WORLD_SIZE=$OMPI_COMM_WORLD_SIZE
CURRENT_DIR="$( cd "$( dirname "$0" )" && pwd )"
MEGATRON_PATH=$( dirname $( dirname ${CURRENT_DIR}))
export GLOG_minloglevel=3
export CUDA_DEVICE_MAX_CONNECTIONS=1
export HSA_FORCE_FINE_GRAIN_PCIE=1
export OMP_NUM_THREADS=1
export GPU_MAX_HW_QUEUES=10
export PYTHONPATH=${MEGATRON_PATH}/Megatron-LM:$PYTHONPATH
# torch控制多流转单流
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export SENDRECV_STREAM_WITH_COMPUTE=1
#增加编译缓存
export cache_size_limit=64
DISTRIBUTED_ARGS=(
--rank ${RANK}
--world-size ${WORLD_SIZE}
--local-rank ${LOCAL_RANK}
--dist-url tcp://${DIST_URL}:${DIST_PORT}
)
GPT_MODEL_ARGS=(
--seq-length 4096
--num-layers 40
--hidden-size 5120
--ffn-hidden-size 13824
--num-attention-heads 40
--max-position-embeddings 4096
--normalization RMSNorm
--position-embedding-type rope
--untie-embeddings-and-output-weights
)
TRAINING_ARGS=(
--transformer-impl local
--use-legacy-models
--micro-batch-size 1
--global-batch-size 256
--train-iters 50
--weight-decay 0.1
--adam-beta1 0.9
--adam-beta2 0.95
--init-method-std 0.006
--clip-grad 1.0
--bf16
--disable-bias-linear
--attention-dropout 0
--hidden-dropout 0
--swiglu
--lr 3.0e-5
--lr-decay-style cosine
--min-lr 3.0e-6
--lr-warmup-iters 1
--ckpt-format torch
--ddp-average-in-collective
--overlap-grad-reduce
--use-flash-attn
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size 2
--pipeline-model-parallel-size 2
--context-parallel-size 1
--use-distributed-optimizer
--sequence-parallel
)
DATA_ARGS=(
--tokenizer-type Llama2Tokenizer
--tokenizer-model ${TOKENIZER_MODEL_PATH}
--data-path ${DATA_PATH}
--split 949,50,1
)
EVAL_AND_LOGGING_ARGS=(
--log-throughput
--eval-iters 5
--log-interval 1
--save-interval 1000
--eval-interval 1000
--save $CHECKPOINT_PATH
--load $CHECKPOINT_PATH
--tensorboard-dir "${CHECKPOINT_PATH}/tensorboard"
)
TORCH_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 3
--profile-step-end 4
--profile-dir torch_prof_llama_1nodes_tp1-pp2-cp1
--use-pytorch-profiler
)
HIP_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 4
--profile-step-end 5
--use-hip-profiler
)
APP="python -u ${MEGATRON_PATH}/pretrain_gpt.py \
${GPT_MODEL_ARGS[@]} \
${TRAINING_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS[@]} \
${EVAL_AND_LOGGING_ARGS[@]} \
${DISTRIBUTED_ARGS[@]} \
${INITIALIZATION_ARGS[@]} \
"
if [[ $profiling == "torch" ]]; then
APP+=" ${TORCH_PROFIE_ARGS[@]}"
elif [[ $profiling == "hip" ]]; then
mkdir -p hip_prof_data
APP+=" ${HIP_PROFIE_ARGS[@]}"
APP="hipprof -d hip_prof_data --hip-trace --trace-off ${APP}"
fi
#for hygon cpu
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
${APP}
\ No newline at end of file
examples/llama/train_llama2_7b_1nodes.sh
View file @ c788823b
......@@ -72,8 +72,8 @@ GPT_MODEL_ARGS=(
)
TRAINING_ARGS=(
--transformer-impl local
--use-legacy-models
--transformer-impl transformer_engine
--use-mcore-models
--micro-batch-size 1
--global-batch-size 64
--train-iters 50
......@@ -159,5 +159,5 @@ elif [[ $profiling == "hip" ]]; then
fi
#for hygon cpu
export CUDA_VISIBLE_DEVICES=4,5,6,7
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
${APP}
\ No newline at end of file
examples/llama/train_llama2_7b_2nodes.sh 0 → 100755
View file @ c788823b
#!/bin/bash
INITIALIZATION_ARGS=( --num-workers 2)
for para in $*
do
if [[ $para == --data_path* ]];then
data_path=${para#*=}
elif [[ $para == --tokenizer_path* ]];then
tokenizer_path=${para#*=}
elif [[ $para == --checkpoint_path* ]];then
checkpoint_path=${para#*=}
elif [[ $para == --profiling* ]];then
profiling=${para#*=}
elif [[ $para == --reproduce* ]];then
INITIALIZATION_ARGS=( --reproduce --num-workers 0)
export MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC=1 # miopen 确定算法打开
export ROCBLAS_ATOMICS_MOD=0 # rocblas 关闭原子操作
# 关闭miopen中的atomic操作算法, 只保留gemm算法
export MIOPEN_DEBUG_CONV_FFT=0
export MIOPEN_DEBUG_CONV_DIRECT=0
export MIOPEN_DEBUG_CONV_GEMM=1
export MIOPEN_DEBUG_CONV_WINOGRAD=0
export MIOPEN_DEBUG_CONV_IMPLICIT_GEMM=0
fi
done
# data path
DATA_PATH=${data_path}
TOKENIZER_MODEL_PATH=${tokenizer_path}
CHECKPOINT_PATH=${checkpoint_path}
# default env
DIST_URL=${1}
DIST_PORT=${2}
RANK=$OMPI_COMM_WORLD_RANK
LOCAL_RANK=$OMPI_COMM_WORLD_LOCAL_RANK
WORLD_SIZE=$OMPI_COMM_WORLD_SIZE
CURRENT_DIR="$( cd "$( dirname "$0" )" && pwd )"
MEGATRON_PATH=$( dirname $( dirname ${CURRENT_DIR}))
export GLOG_minloglevel=3
export CUDA_DEVICE_MAX_CONNECTIONS=1
export HSA_FORCE_FINE_GRAIN_PCIE=1
export OMP_NUM_THREADS=1
export GPU_MAX_HW_QUEUES=10
export PYTHONPATH=${MEGATRON_PATH}/Megatron-LM:$PYTHONPATH
# torch控制多流转单流
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export SENDRECV_STREAM_WITH_COMPUTE=1
#增加编译缓存
export cache_size_limit=64
DISTRIBUTED_ARGS=(
--rank ${RANK}
--world-size ${WORLD_SIZE}
--local-rank ${LOCAL_RANK}
--dist-url tcp://${DIST_URL}:${DIST_PORT}
)
GPT_MODEL_ARGS=(
--seq-length 4096
--num-layers 32
--hidden-size 4096
--ffn-hidden-size 11008
--num-attention-heads 32
--max-position-embeddings 4096
--normalization RMSNorm
--position-embedding-type rope
--untie-embeddings-and-output-weights
)
TRAINING_ARGS=(
--transformer-impl local
--use-legacy-models
--micro-batch-size 1
--global-batch-size 256
--train-iters 50
--weight-decay 0.1
--adam-beta1 0.9
--adam-beta2 0.95
--init-method-std 0.006
--clip-grad 1.0
--bf16
--disable-bias-linear
--attention-dropout 0
--hidden-dropout 0
--swiglu
--lr 3.0e-5
--lr-decay-style cosine
--min-lr 3.0e-6
--lr-warmup-iters 1
--ckpt-format torch
--ddp-average-in-collective
--overlap-grad-reduce
--use-flash-attn
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size 1
--pipeline-model-parallel-size 2
--context-parallel-size 1
--use-distributed-optimizer
--sequence-parallel
)
DATA_ARGS=(
--tokenizer-type Llama2Tokenizer
--tokenizer-model ${TOKENIZER_MODEL_PATH}
--data-path ${DATA_PATH}
--split 949,50,1
)
EVAL_AND_LOGGING_ARGS=(
--log-throughput
--eval-iters 5
--log-interval 1
--save-interval 1000
--eval-interval 1000
--save $CHECKPOINT_PATH
--load $CHECKPOINT_PATH
--tensorboard-dir "${CHECKPOINT_PATH}/tensorboard"
)
TORCH_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 3
--profile-step-end 4
--profile-dir torch_prof_llama_1nodes_tp1-pp2-cp1
--use-pytorch-profiler
)
HIP_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 4
--profile-step-end 5
--use-hip-profiler
)
APP="python -u ${MEGATRON_PATH}/pretrain_gpt.py \
${GPT_MODEL_ARGS[@]} \
${TRAINING_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS[@]} \
${EVAL_AND_LOGGING_ARGS[@]} \
${DISTRIBUTED_ARGS[@]} \
${INITIALIZATION_ARGS[@]} \
"
if [[ $profiling == "torch" ]]; then
APP+=" ${TORCH_PROFIE_ARGS[@]}"
elif [[ $profiling == "hip" ]]; then
mkdir -p hip_prof_data
APP+=" ${HIP_PROFIE_ARGS[@]}"
APP="hipprof -d hip_prof_data --hip-trace --trace-off ${APP}"
fi
#for hygon cpu
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
${APP}
\ No newline at end of file
examples/qwen/hostfile 0 → 100644
View file @ c788823b
a121 slots=8
a124 slots=8
\ No newline at end of file
examples/qwen/run_qwen1.5_14B.sh 0 → 100755
View file @ c788823b
for para in $*
do
if [[ $para == --profiling* ]];then
profiling=${para#*=}
fi
done
# Those variables need to modify
GPUS="16" # how many gpus to use
HOST="a121" # hostname
PORT="11452" # port id
DATA_PATH="/data/datasets/oscar-1GB_head-qwen_text_document" # path to oscar-1GB_head-llama2_text_document
TOKENIZER_MODEL_PATH="/data/models/qwen1.5" # path to tokenizer.model
CHECKPOINT_PATH="./ckpt" # path to ckpt
# Runs Llama2 7B model
mpirun -np ${GPUS} --hostfile hostfile \
--allow-run-as-root \
--bind-to none \
--mca plm_rsh_no_tree_spawn 1 \
--mca plm_rsh_args "-p 11451" \
bash -c "
./train_qwen1.5_14b_2nodes.sh \
${HOST} \
${PORT} \
--data_path=$DATA_PATH \
--tokenizer_path=$TOKENIZER_MODEL_PATH \
--checkpoint_path=$CHECKPOINT_PATH \
--profiling=$profiling" > ./logs/log-$((${GPUS} / 8))nodes-`date +%F-%H%M`.log 2>&1
wait
\ No newline at end of file
examples/qwen/run_qwen1.5_32B.sh 0 → 100755
View file @ c788823b
for para in $*
do
if [[ $para == --profiling* ]];then
profiling=${para#*=}
fi
done
# Those variables need to modify
GPUS="16" # how many gpus to use
HOST="a121" # hostname
PORT="11452" # port id
DATA_PATH="/data/datasets/oscar-1GB_head-qwen_text_document" # path to oscar-1GB_head-llama2_text_document
TOKENIZER_MODEL_PATH="/data/models/qwen1.5" # path to tokenizer.model
CHECKPOINT_PATH="./ckpt" # path to ckpt
# Runs Llama2 7B model
mpirun -np ${GPUS} --hostfile hostfile \
--allow-run-as-root \
--bind-to none \
--mca plm_rsh_no_tree_spawn 1 \
--mca plm_rsh_args "-p 11451" \
bash -c "
./train_qwen1.5_14b_1nodes.sh \
${HOST} \
${PORT} \
--data_path=$DATA_PATH \
--tokenizer_path=$TOKENIZER_MODEL_PATH \
--checkpoint_path=$CHECKPOINT_PATH \
--profiling=$profiling" > ./logs/log-$((${GPUS} / 8))nodes-`date +%F-%H%M`.log 2>&1
wait
\ No newline at end of file
examples/qwen/train_qwen1.5_14b_1nodes.sh 0 → 100755
View file @ c788823b
#!/bin/bash
INITIALIZATION_ARGS=( --num-workers 2)
for para in $*
do
if [[ $para == --data_path* ]];then
data_path=${para#*=}
elif [[ $para == --tokenizer_path* ]];then
tokenizer_path=${para#*=}
elif [[ $para == --checkpoint_path* ]];then
checkpoint_path=${para#*=}
elif [[ $para == --profiling* ]];then
profiling=${para#*=}
elif [[ $para == --reproduce* ]];then
INITIALIZATION_ARGS=( --reproduce --num-workers 0)
export MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC=1 # miopen 确定算法打开
export ROCBLAS_ATOMICS_MOD=0 # rocblas 关闭原子操作
# 关闭miopen中的atomic操作算法, 只保留gemm算法
export MIOPEN_DEBUG_CONV_FFT=0
export MIOPEN_DEBUG_CONV_DIRECT=0
export MIOPEN_DEBUG_CONV_GEMM=1
export MIOPEN_DEBUG_CONV_WINOGRAD=0
export MIOPEN_DEBUG_CONV_IMPLICIT_GEMM=0
fi
done
# data path
DATA_PATH=${data_path}
TOKENIZER_MODEL_PATH=${tokenizer_path}
CHECKPOINT_PATH=${checkpoint_path}
# default env
DIST_URL=${1}
DIST_PORT=${2}
RANK=$OMPI_COMM_WORLD_RANK
LOCAL_RANK=$OMPI_COMM_WORLD_LOCAL_RANK
WORLD_SIZE=$OMPI_COMM_WORLD_SIZE
CURRENT_DIR="$( cd "$( dirname "$0" )" && pwd )"
MEGATRON_PATH=$( dirname $( dirname ${CURRENT_DIR}))
export GLOG_minloglevel=3
export CUDA_DEVICE_MAX_CONNECTIONS=1
export HSA_FORCE_FINE_GRAIN_PCIE=1
export OMP_NUM_THREADS=1
export GPU_MAX_HW_QUEUES=10
export PYTHONPATH=${MEGATRON_PATH}/Megatron-LM:$PYTHONPATH
# torch控制多流转单流
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export SENDRECV_STREAM_WITH_COMPUTE=1
#增加编译缓存
export cache_size_limit=64
DISTRIBUTED_ARGS=(
--rank ${RANK}
--world-size ${WORLD_SIZE}
--local-rank ${LOCAL_RANK}
--dist-url tcp://${DIST_URL}:${DIST_PORT}
)
GPT_MODEL_ARGS=(
--seq-length 4096
--num-layers 40
--hidden-size 5120
--ffn-hidden-size 13696
--num-attention-heads 40
--max-position-embeddings 32768
--normalization RMSNorm
--position-embedding-type rope
--untie-embeddings-and-output-weights
)
TRAINING_ARGS=(
--transformer-impl transformer_engine
--use-mcore-models
--micro-batch-size 1
--global-batch-size 256
--train-iters 50
--weight-decay 0.1
--adam-beta1 0.9
--adam-beta2 0.95
--init-method-std 0.006
--clip-grad 1.0
--bf16
--disable-bias-linear
--attention-dropout 0
--hidden-dropout 0
--swiglu
--add-qkv-bias
--lr 3.0e-5
--lr-decay-style cosine
--min-lr 3.0e-6
--lr-warmup-iters 1
--ckpt-format torch
--ddp-average-in-collective
--overlap-grad-reduce
--use-flash-attn
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size 2
--pipeline-model-parallel-size 4
--context-parallel-size 1
--use-distributed-optimizer
--sequence-parallel
)
DATA_ARGS=(
--tokenizer-type QwenTokenizer
--merge-file ${TOKENIZER_MODEL_PATH}/merges.txt
--vocab-file ${TOKENIZER_MODEL_PATH}/vocab.json
--data-path ${DATA_PATH}
--split 949,50,1
)
EVAL_AND_LOGGING_ARGS=(
--log-throughput
--eval-iters 5
--log-interval 1
--save-interval 1000
--eval-interval 1000
--save $CHECKPOINT_PATH
--load $CHECKPOINT_PATH
--tensorboard-dir "${CHECKPOINT_PATH}/tensorboard"
)
TORCH_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 3
--profile-step-end 4
--profile-dir torch_prof_llama_1nodes_tp1-pp2-cp1
--use-pytorch-profiler
)
HIP_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 4
--profile-step-end 5
--use-hip-profiler
)
APP="python -u ${MEGATRON_PATH}/pretrain_gpt.py \
${GPT_MODEL_ARGS[@]} \
${TRAINING_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS[@]} \
${EVAL_AND_LOGGING_ARGS[@]} \
${DISTRIBUTED_ARGS[@]} \
${INITIALIZATION_ARGS[@]} \
"
if [[ $profiling == "torch" ]]; then
APP+=" ${TORCH_PROFIE_ARGS[@]}"
elif [[ $profiling == "hip" ]]; then
mkdir -p hip_prof_data
APP+=" ${HIP_PROFIE_ARGS[@]}"
APP="hipprof -d hip_prof_data --hip-trace --trace-off ${APP}"
fi
#for hygon cpu
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
${APP}
\ No newline at end of file
examples/qwen/train_qwen1.5_14b_2nodes.sh 0 → 100755
View file @ c788823b
#!/bin/bash
INITIALIZATION_ARGS=( --num-workers 2)
for para in $*
do
if [[ $para == --data_path* ]];then
data_path=${para#*=}
elif [[ $para == --tokenizer_path* ]];then
tokenizer_path=${para#*=}
elif [[ $para == --checkpoint_path* ]];then
checkpoint_path=${para#*=}
elif [[ $para == --profiling* ]];then
profiling=${para#*=}
elif [[ $para == --reproduce* ]];then
INITIALIZATION_ARGS=( --reproduce --num-workers 0)
export MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC=1 # miopen 确定算法打开
export ROCBLAS_ATOMICS_MOD=0 # rocblas 关闭原子操作
# 关闭miopen中的atomic操作算法, 只保留gemm算法
export MIOPEN_DEBUG_CONV_FFT=0
export MIOPEN_DEBUG_CONV_DIRECT=0
export MIOPEN_DEBUG_CONV_GEMM=1
export MIOPEN_DEBUG_CONV_WINOGRAD=0
export MIOPEN_DEBUG_CONV_IMPLICIT_GEMM=0
fi
done
# data path
DATA_PATH=${data_path}
TOKENIZER_MODEL_PATH=${tokenizer_path}
CHECKPOINT_PATH=${checkpoint_path}
# default env
DIST_URL=${1}
DIST_PORT=${2}
RANK=$OMPI_COMM_WORLD_RANK
LOCAL_RANK=$OMPI_COMM_WORLD_LOCAL_RANK
WORLD_SIZE=$OMPI_COMM_WORLD_SIZE
CURRENT_DIR="$( cd "$( dirname "$0" )" && pwd )"
MEGATRON_PATH=$( dirname $( dirname ${CURRENT_DIR}))
export GLOG_minloglevel=3
export CUDA_DEVICE_MAX_CONNECTIONS=1
export HSA_FORCE_FINE_GRAIN_PCIE=1
export OMP_NUM_THREADS=1
export GPU_MAX_HW_QUEUES=10
export PYTHONPATH=${MEGATRON_PATH}/Megatron-LM:$PYTHONPATH
# torch控制多流转单流
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export SENDRECV_STREAM_WITH_COMPUTE=1
#增加编译缓存
export cache_size_limit=64
DISTRIBUTED_ARGS=(
--rank ${RANK}
--world-size ${WORLD_SIZE}
--local-rank ${LOCAL_RANK}
--dist-url tcp://${DIST_URL}:${DIST_PORT}
)
GPT_MODEL_ARGS=(
--seq-length 4096
--num-layers 40
--hidden-size 5120
--ffn-hidden-size 13696
--num-attention-heads 40
--max-position-embeddings 32768
--normalization RMSNorm
--position-embedding-type rope
--untie-embeddings-and-output-weights
)
TRAINING_ARGS=(
--transformer-impl transformer_engine
--use-mcore-models
--micro-batch-size 1
--global-batch-size 256
--train-iters 50
--weight-decay 0.1
--adam-beta1 0.9
--adam-beta2 0.95
--init-method-std 0.006
--clip-grad 1.0
--bf16
--disable-bias-linear
--attention-dropout 0
--hidden-dropout 0
--swiglu
--add-qkv-bias
--lr 3.0e-5
--lr-decay-style cosine
--min-lr 3.0e-6
--lr-warmup-iters 1
--ckpt-format torch
--ddp-average-in-collective
--overlap-grad-reduce
--use-flash-attn
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size 2
--pipeline-model-parallel-size 2
--context-parallel-size 1
--use-distributed-optimizer
--sequence-parallel
)
DATA_ARGS=(
--tokenizer-type QwenTokenizer
--merge-file ${TOKENIZER_MODEL_PATH}/merges.txt
--vocab-file ${TOKENIZER_MODEL_PATH}/vocab.json
--data-path ${DATA_PATH}
--split 949,50,1
)
EVAL_AND_LOGGING_ARGS=(
--log-throughput
--eval-iters 5
--log-interval 1
--save-interval 1000
--eval-interval 1000
--save $CHECKPOINT_PATH
--load $CHECKPOINT_PATH
--tensorboard-dir "${CHECKPOINT_PATH}/tensorboard"
)
TORCH_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 3
--profile-step-end 4
--profile-dir torch_prof_llama_1nodes_tp1-pp2-cp1
--use-pytorch-profiler
)
HIP_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 4
--profile-step-end 5
--use-hip-profiler
)
APP="python -u ${MEGATRON_PATH}/pretrain_gpt.py \
${GPT_MODEL_ARGS[@]} \
${TRAINING_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS[@]} \
${EVAL_AND_LOGGING_ARGS[@]} \
${DISTRIBUTED_ARGS[@]} \
${INITIALIZATION_ARGS[@]} \
"
if [[ $profiling == "torch" ]]; then
APP+=" ${TORCH_PROFIE_ARGS[@]}"
elif [[ $profiling == "hip" ]]; then
mkdir -p hip_prof_data
APP+=" ${HIP_PROFIE_ARGS[@]}"
APP="hipprof -d hip_prof_data --hip-trace --trace-off ${APP}"
fi
#for hygon cpu
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
${APP}
\ No newline at end of file
examples/qwen/train_qwen1.5_32b_2nodes.sh 0 → 100755
View file @ c788823b
#!/bin/bash
INITIALIZATION_ARGS=( --num-workers 2)
for para in $*
do
if [[ $para == --data_path* ]];then
data_path=${para#*=}
elif [[ $para == --tokenizer_path* ]];then
tokenizer_path=${para#*=}
elif [[ $para == --checkpoint_path* ]];then
checkpoint_path=${para#*=}
elif [[ $para == --profiling* ]];then
profiling=${para#*=}
elif [[ $para == --reproduce* ]];then
INITIALIZATION_ARGS=( --reproduce --num-workers 0)
export MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC=1 # miopen 确定算法打开
export ROCBLAS_ATOMICS_MOD=0 # rocblas 关闭原子操作
# 关闭miopen中的atomic操作算法, 只保留gemm算法
export MIOPEN_DEBUG_CONV_FFT=0
export MIOPEN_DEBUG_CONV_DIRECT=0
export MIOPEN_DEBUG_CONV_GEMM=1
export MIOPEN_DEBUG_CONV_WINOGRAD=0
export MIOPEN_DEBUG_CONV_IMPLICIT_GEMM=0
fi
done
# data path
DATA_PATH=${data_path}
TOKENIZER_MODEL_PATH=${tokenizer_path}
CHECKPOINT_PATH=${checkpoint_path}
# default env
DIST_URL=${1}
DIST_PORT=${2}
RANK=$OMPI_COMM_WORLD_RANK
LOCAL_RANK=$OMPI_COMM_WORLD_LOCAL_RANK
WORLD_SIZE=$OMPI_COMM_WORLD_SIZE
CURRENT_DIR="$( cd "$( dirname "$0" )" && pwd )"
MEGATRON_PATH=$( dirname $( dirname ${CURRENT_DIR}))
export GLOG_minloglevel=3
export CUDA_DEVICE_MAX_CONNECTIONS=1
export HSA_FORCE_FINE_GRAIN_PCIE=1
export OMP_NUM_THREADS=1
export GPU_MAX_HW_QUEUES=10
export PYTHONPATH=${MEGATRON_PATH}/Megatron-LM:$PYTHONPATH
# torch控制多流转单流
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export SENDRECV_STREAM_WITH_COMPUTE=1
#增加编译缓存
export cache_size_limit=64
DISTRIBUTED_ARGS=(
--rank ${RANK}
--world-size ${WORLD_SIZE}
--local-rank ${LOCAL_RANK}
--dist-url tcp://${DIST_URL}:${DIST_PORT}
)
GPT_MODEL_ARGS=(
--seq-length 4096
--num-layers 64
--hidden-size 5120
--ffn-hidden-size 27392
--num-attention-heads 40
--max-position-embeddings 32768
--num-query-groups 8
--group-query-attention
--normalization RMSNorm
--position-embedding-type rope
--untie-embeddings-and-output-weights
)
TRAINING_ARGS=(
--transformer-impl transformer_engine
--use-mcore-models
--micro-batch-size 1
--global-batch-size 1024
--train-iters 50
--weight-decay 0.1
--adam-beta1 0.9
--adam-beta2 0.95
--init-method-std 0.006
--clip-grad 1.0
--bf16
--disable-bias-linear
--attention-dropout 0
--hidden-dropout 0
--swiglu
--add-qkv-bias
--lr 3.0e-5
--lr-decay-style cosine
--min-lr 3.0e-6
--lr-warmup-iters 1
--ckpt-format torch
--ddp-average-in-collective
--overlap-grad-reduce
--use-flash-attn
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size 4
--pipeline-model-parallel-size 4
--context-parallel-size 1
--use-distributed-optimizer
--sequence-parallel
)
DATA_ARGS=(
--tokenizer-type QwenTokenizer
--merge-file ${TOKENIZER_MODEL_PATH}/merges.txt
--vocab-file ${TOKENIZER_MODEL_PATH}/vocab.json
--data-path ${DATA_PATH}
--split 949,50,1
)
EVAL_AND_LOGGING_ARGS=(
--log-throughput
--eval-iters 5
--log-interval 1
--save-interval 1000
--eval-interval 1000
--save $CHECKPOINT_PATH
--load $CHECKPOINT_PATH
--tensorboard-dir "${CHECKPOINT_PATH}/tensorboard"
)
TORCH_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 3
--profile-step-end 4
--profile-dir torch_prof_llama_1nodes_tp1-pp2-cp1
--use-pytorch-profiler
)
HIP_PROFIE_ARGS=(
--profile
--profile-ranks 0 1 2 3 4 5 6 7
--profile-step-start 4
--profile-step-end 5
--use-hip-profiler
)
APP="python -u ${MEGATRON_PATH}/pretrain_gpt.py \
${GPT_MODEL_ARGS[@]} \
${TRAINING_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS[@]} \
${EVAL_AND_LOGGING_ARGS[@]} \
${DISTRIBUTED_ARGS[@]} \
${INITIALIZATION_ARGS[@]} \
"
if [[ $profiling == "torch" ]]; then
APP+=" ${TORCH_PROFIE_ARGS[@]}"
elif [[ $profiling == "hip" ]]; then
mkdir -p hip_prof_data
APP+=" ${HIP_PROFIE_ARGS[@]}"
APP="hipprof -d hip_prof_data --hip-trace --trace-off ${APP}"
fi
#for hygon cpu
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
${APP}
\ No newline at end of file
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