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Merge pull request #6877 from LDOUBLEV/dyg_pts

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[tipc]fix fp16 benchmark train
This commit is contained in:
Double_V
2022-07-14 11:44:48 +08:00
committed by GitHub
parent 2d722451ab 1d7499876d
commit 4bb366cccd
2 changed files with 14 additions and 8 deletions
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test_tipc/benchmark_train.sh
+10 -4
View File
@@ -139,8 +139,8 @@ else
device_num=${params_list[4]}
IFS=";"
if [ ${precision} = "null" ];then
precision="fp32"
if [ ${precision} = "fp16" ];then
precision="amp"
fi
fp_items_list=($precision)
@@ -150,10 +150,16 @@ fi
IFS="|"
for batch_size in ${batch_size_list[*]}; do
for precision in ${fp_items_list[*]}; do
for train_precision in ${fp_items_list[*]}; do
for device_num in ${device_num_list[*]}; do
# sed batchsize and precision
func_sed_params "$FILENAME" "${line_precision}" "$precision"
if [ ${train_precision} = "amp" ];then
precision="fp16"
else
precision="fp32"
fi
func_sed_params "$FILENAME" "${line_precision}" "$train_precision"
func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size"
func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$epoch"
gpu_id=$(set_gpu_id $device_num)
test_tipc/test_train_inference_python.sh
+4 -4
View File
@@ -265,7 +265,7 @@ else
if [ ${run_train} = "null" ]; then
continue
fi
set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
@@ -287,11 +287,11 @@ else
set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
elif [ ${#ips} -le 15 ];then # train with multi-gpu
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
else # train with multi-machine
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
fi
# run train
eval $cmd