[examples] run all examples with torchrun

examples/aishell/NST/run_nst.sh

@@ -23,14 +23,16 @@
 # Use this to control how many gpu you use, It's 1-gpu training if you specify
 # just 1gpu, otherwise it's is multiple gpu training based on DDP in pytorch
 export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
-# The NCCL_SOCKET_IFNAME variable specifies which IP interface to use for nccl
-# communication. More details can be found in
-# https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html
-# export NCCL_SOCKET_IFNAME=ens4f1
-export NCCL_DEBUG=INFO
+
 stage=1 # start from 0 if you need to start from data preparation
 stop_stage=8
 
+# You should change the following two parameters for multiple machine training,
+# see https://pytorch.org/docs/stable/elastic/run.html
+HOST_NODE_ADDR="localhost:0"
+num_nodes=1
+
+
 # here are extra parameters used in NST
 cer_out_dir=""
 dir=""
@@ -61,15 +63,6 @@ cer_hypo_dir="wenet_cer_hypo"
 cer_label_dir="wenet_cer_label"
 pseudo_data_ratio=0.75
 
-# The num of machines(nodes) for multi-machine training, 1 is for one machine.
-# NFS is required if num_nodes > 1.
-
-num_nodes=1
-
-# The rank of each node or machine, which ranges from 0 to `num_nodes - 1`.
-# You should set the node_ranHk=0 on the first machine, set the node_rank=1
-# on the second machine, and so on.
-node_rank=0
 dict=data/dict/lang_char.txt
 
 # data_type can be `raw` or `shard`. Typically, raw is used for small dataset,
@@ -119,9 +112,6 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
   num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
   # Use "nccl" if it works, otherwise use "gloo"
   dist_backend="gloo"
-  world_size=`expr $num_gpus \* $num_nodes`
-  echo "total gpus is: $world_size"
-
   # the global_cmvn file need to be calculated by combining both supervised/unsupervised datasets,
   # and it should be positioned at data/${train_set}/global_cmvn .
   cmvn_opts=
@@ -132,15 +122,8 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
   # and output dimension, and $dir/train.yaml will be used for inference
   # and export.
   echo "checkpoint is "  ${checkpoint}
-  for ((i = 0; i < $num_gpus; ++i)); do
-  {
-    gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
-    echo "gpu number  $i "
-    # Rank of each gpu/process used for knowing whether it is
-    # the master of a worker.
-
-    rank=`expr $node_rank \* $num_gpus + $i`
-    python wenet/bin/train.py --gpu $gpu_id \
+  torchrun --nnodes=$num_nodes --nproc_per_node=$num_gpus --rdzv_endpoint=$HOST_NODE_ADDR \
+    python wenet/bin/train.py \
       --config $train_config \
       --data_type $data_type \
       --symbol_table $dict \
@@ -149,15 +132,10 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
       ${checkpoint:+--checkpoint $checkpoint} \
       --model_dir $dir \
       --ddp.init_method $init_method \
-      --ddp.world_size $world_size \
-      --ddp.rank $rank \
       --ddp.dist_backend $dist_backend \
       --num_workers 1 \
       $cmvn_opts \
       --pin_memory
-  } &
-  done
-  wait
 fi
 
 # In stage 2, we get the averaged final checkpoint and calculate the test and dev accuracy

examples/aishell/paraformer/run.sh

@@ -6,22 +6,14 @@
 # Use this to control how many gpu you use, It's 1-gpu training if you specify
 # just 1gpu, otherwise it's is multiple gpu training based on DDP in pytorch
 export CUDA_VISIBLE_DEVICES="0,1,2,3"
-# The NCCL_SOCKET_IFNAME variable specifies which IP interface to use for nccl
-# communication. More details can be found in
-# https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html
-# export NCCL_SOCKET_IFNAME=ens4f1
-export NCCL_DEBUG=INFO
 stage=0 # start from 0 if you need to start from data preparation
 stop_stage=5
 
-# The num of machines(nodes) for multi-machine training, 1 is for one machine.
-# NFS is required if num_nodes > 1.
+# You should change the following two parameters for multiple machine training,
+# see https://pytorch.org/docs/stable/elastic/run.html
+HOST_NODE_ADDR="localhost:0"
 num_nodes=1
 
-# The rank of each node or machine, which ranges from 0 to `num_nodes - 1`.
-# You should set the node_rank=0 on the first machine, set the node_rank=1
-# on the second machine, and so on.
-node_rank=0
 # The aishell dataset location, please change this to your own path
 # make sure of using absolute path. DO-NOT-USE relative path!
 data=/export/data/asr-data/OpenSLR/33/
@@ -120,39 +112,26 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
   num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
   # Use "nccl" if it works, otherwise use "gloo"
   dist_backend="gloo"
-  world_size=`expr $num_gpus \* $num_nodes`
-  echo "total gpus is: $world_size"
   cmvn_opts=
   $cmvn && cp data/${train_set}/global_cmvn $dir
   $cmvn && cmvn_opts="--cmvn ${dir}/global_cmvn"
 
   # train.py rewrite $train_config to $dir/train.yaml with model input
   # and output dimension, and $dir/train.yaml will be used for inference
   # and export.
-  for ((i = 0; i < $num_gpus; ++i)); do
-  {
-    gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
-    # Rank of each gpu/process used for knowing whether it is
-    # the master of a worker.
-    rank=`expr $node_rank \* $num_gpus + $i`
-    python3 wenet/bin/train.py --gpu $gpu_id \
+  torchrun --nnodes=$num_nodes --nproc_per_node=$num_gpus --rdzv_endpoint=$HOST_NODE_ADDR \
+    python3 wenet/bin/train.py \
       --config $train_config \
       --data_type $data_type \
       --symbol_table $dict \
       --train_data data/$train_set/data.list \
       --cv_data data/test/data.list \
       ${checkpoint:+--checkpoint $checkpoint} \
       --model_dir $dir \
-      --ddp.init_method $init_method \
-      --ddp.world_size $world_size \
-      --ddp.rank $rank \
       --ddp.dist_backend $dist_backend \
       --num_workers 8 \
       $cmvn_opts \
       --pin_memory
-  } &
-  done
-  wait
 fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then

examples/aishell/rnnt/run.sh

@@ -12,14 +12,11 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 stage=0 # start from 0 if you need to start from data preparation
 stop_stage=5
 
-# The num of machines(nodes) for multi-machine training, 1 is for one machine.
-# NFS is required if num_nodes > 1.
+# You should change the following two parameters for multiple machine training,
+# see https://pytorch.org/docs/stable/elastic/run.html
+HOST_NODE_ADDR="localhost:0"
 num_nodes=1
 
-# The rank of each node or machine, which ranges from 0 to `num_nodes - 1`.
-# You should set the node_rank=0 on the first machine, set the node_rank=1
-# on the second machine, and so on.
-node_rank=0
 # The aishell dataset location, please change this to your own path
 # make sure of using absolute path. DO-NOT-USE relatvie path!
 data=/export/data/asr-data/OpenSLR/33/
@@ -112,22 +109,15 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
   num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
   # Use "nccl" if it works, otherwise use "gloo"
   dist_backend="gloo"
-  world_size=`expr $num_gpus \* $num_nodes`
-  echo "total gpus is: $world_size"
   cmvn_opts=
   $cmvn && cp data/${train_set}/global_cmvn $dir
   $cmvn && cmvn_opts="--cmvn ${dir}/global_cmvn"
 
   # train.py rewrite $train_config to $dir/train.yaml with model input
   # and output dimension, and $dir/train.yaml will be used for inference
   # and export.
-  for ((i = 0; i < $num_gpus; ++i)); do
-  {
-    gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
-    # Rank of each gpu/process used for knowing whether it is
-    # the master of a worker.
-    rank=`expr $node_rank \* $num_gpus + $i`
-    python wenet/bin/train.py --gpu $gpu_id \
+  torchrun --nnodes=$num_nodes --nproc_per_node=$num_gpus --rdzv_endpoint=$HOST_NODE_ADDR \
+    python wenet/bin/train.py \
       --config $train_config \
       --data_type $data_type \
       --symbol_table $dict \
@@ -136,15 +126,10 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
       ${checkpoint:+--checkpoint $checkpoint} \
       --model_dir $dir \
       --ddp.init_method $init_method \
-      --ddp.world_size $world_size \
-      --ddp.rank $rank \
       --ddp.dist_backend $dist_backend \
       --num_workers 1 \
       $cmvn_opts \
       --pin_memory
-  } &
-  done
-  wait
 fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then

examples/aishell2/rnnt/run.sh

@@ -11,14 +11,10 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 stage=0 # start from 0 if you need to start from data preparation
 stop_stage=5
-# The num of nodes or machines used for multi-machine training
-# Default 1 for single machine/node
-# NFS will be needed if you want run multi-machine training
+# You should change the following two parameters for multiple machine training,
+# see https://pytorch.org/docs/stable/elastic/run.html
+HOST_NODE_ADDR="localhost:0"
 num_nodes=1
-# The rank of each node or machine, range from 0 to num_nodes -1
-# The first node/machine sets node_rank 0, the second one sets node_rank 1
-# the third one set node_rank 2, and so on. Default 0
-node_rank=0
 
 # modify this to your AISHELL-2 data path
 # Note: the evaluation data (dev & test) is available at AISHELL.
@@ -110,43 +106,26 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
     # Use "nccl" if it works, otherwise use "gloo"
     dist_backend="gloo"
-    #dist_backend="nccl"
-    # The total number of processes/gpus, so that the master knows
-    # how many workers to wait for.
-    # More details about ddp can be found in
-    # https://pytorch.org/tutorials/intermediate/dist_tuto.html
-    world_size=`expr $num_gpus \* $num_nodes`
-    echo "total gpus is: $world_size"
     cmvn_opts=
     $cmvn && cp data/${train_set}/global_cmvn $dir
     $cmvn && cmvn_opts="--cmvn ${dir}/global_cmvn"
     # train.py will write $train_config to $dir/train.yaml with model input
     # and output dimension, train.yaml will be used for inference or model
     # export later
-    for ((i = 0; i < $num_gpus; ++i)); do
-    {
-        gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
-    # Rank of each gpu/process used for knowing whether it is
-    # the master of a worker.
-    rank=`expr $node_rank \* $num_gpus + $i`
-    python wenet/bin/train.py --gpu $gpu_id \
-            --config $train_config \
-            --data_type raw \
-            --symbol_table $dict \
-            --train_data data/$train_set/data.list \
-            --cv_data data/dev/data.list \
-            ${checkpoint:+--checkpoint $checkpoint} \
-            --model_dir $dir \
-            --ddp.init_method $init_method \
-            --ddp.world_size $world_size \
-            --ddp.rank $rank \
-            --ddp.dist_backend $dist_backend \
-            --num_workers 4 \
-            $cmvn_opts \
-            2>&1 | tee -a $dir/train.log || exit 1;
-    } &
-    done
-    wait
+    torchrun --nnodes=$num_nodes --nproc_per_node=$num_gpus --rdzv_endpoint=$HOST_NODE_ADDR \
+      python wenet/bin/train.py \
+              --config $train_config \
+              --data_type raw \
+              --symbol_table $dict \
+              --train_data data/$train_set/data.list \
+              --cv_data data/dev/data.list \
+              ${checkpoint:+--checkpoint $checkpoint} \
+              --model_dir $dir \
+              --ddp.init_method $init_method \
+              --ddp.dist_backend $dist_backend \
+              --num_workers 4 \
+              $cmvn_opts \
+              2>&1 | tee -a $dir/train.log || exit 1;
 fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then

examples/aishell2/s0/run.sh

@@ -6,21 +6,13 @@
 # Use this to control how many gpu you use, It's 1-gpu training if you specify
 # just 1gpu, otherwise it's is multiple gpu training based on DDP in pytorch
 export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
-# The NCCL_SOCKET_IFNAME variable specifies which IP interface to use for nccl
-# communication. More details can be found in
-# https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html
-# export NCCL_SOCKET_IFNAME=ens4f1
-export NCCL_DEBUG=INFO
 stage=0 # start from 0 if you need to start from data preparation
 stop_stage=6
-# The num of nodes or machines used for multi-machine training
-# Default 1 for single machine/node
-# NFS will be needed if you want run multi-machine training
+
+# You should change the following two parameters for multiple machine training,
+# see https://pytorch.org/docs/stable/elastic/run.html
+HOST_NODE_ADDR="localhost:0"
 num_nodes=1
-# The rank of each node or machine, range from 0 to num_nodes -1
-# The first node/machine sets node_rank 0, the second one sets node_rank 1
-# the third one set node_rank 2, and so on. Default 0
-node_rank=0
 
 # modify this to your AISHELL-2 data path
 # Note: the evaluation data (dev & test) is available at AISHELL.
@@ -106,41 +98,25 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
     # Use "nccl" if it works, otherwise use "gloo"
     dist_backend="gloo"
-    # The total number of processes/gpus, so that the master knows
-    # how many workers to wait for.
-    # More details about ddp can be found in
-    # https://pytorch.org/tutorials/intermediate/dist_tuto.html
-    world_size=`expr $num_gpus \* $num_nodes`
-    echo "total gpus is: $world_size"
     cmvn_opts=
     $cmvn && cp data/${train_set}/global_cmvn $dir
     $cmvn && cmvn_opts="--cmvn ${dir}/global_cmvn"
     # train.py will write $train_config to $dir/train.yaml with model input
     # and output dimension, train.yaml will be used for inference or model
     # export later
-    for ((i = 0; i < $num_gpus; ++i)); do
-    {
-        gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
-    # Rank of each gpu/process used for knowing whether it is
-    # the master of a worker.
-    rank=`expr $node_rank \* $num_gpus + $i`
-    python wenet/bin/train.py --gpu $gpu_id \
-            --config $train_config \
-            --data_type raw \
-            --symbol_table $dict \
-            --train_data data/$train_set/data.list \
-            --cv_data data/dev/data.list \
-            ${checkpoint:+--checkpoint $checkpoint} \
-            --model_dir $dir \
-            --ddp.init_method $init_method \
-            --ddp.world_size $world_size \
-            --ddp.rank $rank \
-            --ddp.dist_backend $dist_backend \
-            --num_workers 2 \
-            $cmvn_opts
-    } &
-    done
-    wait
+    torchrun --nnodes=$num_nodes --nproc_per_node=$num_gpus --rdzv_endpoint=$HOST_NODE_ADDR \
+      python wenet/bin/train.py \
+              --config $train_config \
+              --data_type raw \
+              --symbol_table $dict \
+              --train_data data/$train_set/data.list \
+              --cv_data data/dev/data.list \
+              ${checkpoint:+--checkpoint $checkpoint} \
+              --model_dir $dir \
+              --ddp.init_method $init_method \
+              --ddp.dist_backend $dist_backend \
+              --num_workers 2 \
+              $cmvn_opts
 fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then