Merge a133571dc7d073290b927877b54ca4cad06b0ad9 into f2387fe523c6f89987f3723bfa967095e8de5127

egs/librispeech/ASR/conformer_ctc/decode.py

@@ -383,7 +383,7 @@ def decode_one_batch(
             ans[lm_scale_str] = hyps
     else:
         for lm_scale in lm_scale_list:
-            ans[lm_scale_str] = [[] * lattice.shape[0]]
+            ans[f"{lm_scale}"] = [[] * lattice.shape[0]]
     return ans
 
 

egs/librispeech/ASR/conformer_ctc/run-multi-node-multi-gpu.sh

@@ -0,0 +1,125 @@
+#!/usr/bin/env bash
+#
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# This script is the entry point to start model training
+# with multi-node multi-GPU.
+#
+# Read the usage instructions below for how to run this script.
+
+set -e
+
+cur_dir=$(cd $(dirname $BASH_SOURCE) && pwd)
+
+# DDP related parameters
+master_addr=
+node_rank=
+num_nodes=
+master_port=1234
+
+# Training script parameters
+# You can add more if you like
+#
+# Use ./conformer_ctc/train.py --help to see more
+#
+# If you add more parameters here, remember to append them to the
+# end of this file.
+#
+max_duration=200
+bucketing_sampler=1
+full_libri=1
+start_epoch=0
+num_epochs=2
+exp_dir=conformer_ctc/exp3
+lang_dir=data/lang_bpe_500
+
+. $cur_dir/../shared/parse_options.sh
+
+function usage() {
+  echo "Usage: "
+  echo ""
+  echo "    $0 \\"
+  echo "      --master-addr <IP of master> \\"
+  echo "      --master-port <Port of master> \\"
+  echo "      --node-rank <rank of this node> \\"
+  echo "      --num-nodes <Number of node>"
+  echo ""
+  echo " --master-addr   The ip address of the master node."
+  echo " --master-port   The port of the master node."
+  echo " --node-rank     Rank of this node."
+  echo " --num-nodes     Number of nodes in DDP training."
+  echo ""
+  echo "Usage example:"
+  echo "Suppose you want to use DDP with two machines:"
+  echo "  (1) Machine 1 has 4 GPUs. You want to use"
+  echo "      GPU 0, 1, and 3 for training"
+  echo "      IP of machine 1 is: 10.177.41.71"
+  echo "  (2) Machine 2 has 4 GPUs. You want to use"
+  echo "      GPU 0, 2, and 3 for training"
+  echo "      IP of machine 2 is: 10.177.41.72"
+  echo "You want to select machine 1 as the master node and"
+  echo "assume that the port 1234 is free on machine 1."
+  echo ""
+  echo "On machine 1, you run:"
+  echo ""
+  echo "  export CUDA_VISIBLE_DEVICES=\"0,1,3\""
+  echo "  ./conformer_ctc/run-multi-node-multi-gpu.sh --master-addr 10.177.41.71 --master-port 1234 --node-rank 0 --num-nodes 2"
+  echo ""
+  echo "On machine 2, you run:"
+  echo ""
+  echo "  export CUDA_VISIBLE_DEVICES=\"0,2,3\""
+  echo "  ./conformer_ctc/run-multi-node-multi-gpu.sh --master-addr 10.177.41.71 --master-port 1234 --node-rank 1 --num-nodes 2"
+  echo ""
+  echo "Note 1:"
+  echo "  You use CUDA_VISIBLE_DEVICES to decide which GPUs are used for training."
+  echo ""
+  echo "Note 2:"
+  echo "  If you use torch < 1.9.0, then every node has to use the same number of GPUs for training."
+  echo "  If you use torch >= 1.9.0, different nodes can have a different number of GPUs for training."
+  exit 1
+}
+
+default='\033[0m'
+bold='\033[1m'
+red='\033[31m'
+
+function error() {
+  printf "${bold}${red}[ERROR]${default} $1\n"
+}
+
+[ ! -z $CUDA_VISIBLE_DEVICES ] || ( echo; error "Please set CUDA_VISIBLE_DEVICES"; echo; usage )
+[ ! -z $master_addr ] || ( echo; error "Please set --master-addr"; echo; usage )
+[ ! -z $master_port ] || ( echo; error "Please set --master-port"; echo; usage )
+[ ! -z $node_rank ] || ( echo; error "Please set --node-rank"; echo; usage )
+[ ! -z $num_nodes ] || ( echo; error "Please set --num-nodes"; echo; usage )
+
+# Number of GPUs this node has
+num_gpus=$(python3 -c "s=\"$CUDA_VISIBLE_DEVICES\"; print(len(s.split(',')))")
+
+echo "CUDA_VISIBLE_DEVICES: $CUDA_VISIBLE_DEVICES"
+echo "num_gpus: $num_gpus"
+echo "master_addr: $master_addr"
+
+export MASTER_ADDR=$master_addr
+export MASTER_PORT=$master_port
+
+set -x
+
+python -m torch.distributed.launch \
+  --use_env \
+  --nproc_per_node $num_gpus \
+  --nnodes $num_nodes \
+  --node_rank $node_rank \
+  --master_addr $master_addr \
+  --master_port $master_port \
+  \
+  $cur_dir/train.py \
+    --use-multi-node true \
+    --master-port $master_port \
+    --max-duration $max_duration \
+    --bucketing-sampler $bucketing_sampler \
+    --full-libri $full_libri \
+    --start-epoch $start_epoch \
+    --num-epochs $num_epochs \
+    --exp-dir $exp_dir \
+    --lang-dir $lang_dir

egs/librispeech/ASR/conformer_ctc/train.py

@@ -42,7 +42,13 @@ from transformer import Noam
 from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
 from icefall.checkpoint import load_checkpoint
 from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
-from icefall.dist import cleanup_dist, setup_dist
+from icefall.dist import (
+    cleanup_dist,
+    get_local_rank,
+    get_rank,
+    get_world_size,
+    setup_dist,
+)
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
@@ -58,6 +64,17 @@ def get_parser():
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
 
+    parser.add_argument(
+        "--use-multi-node",
+        type=str2bool,
+        default=False,
+        help="""True if using multi-node multi-GPU.
+        You are not supposed to set it directly.
+        See ./conformer_ctc/run-multi-node-multi-gpu.sh
+        for details.
+        """,
+    )
+
     parser.add_argument(
         "--world-size",
         type=int,
@@ -96,6 +113,23 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="conformer_ctc/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved.
+        """,
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_bpe",
+        help="""It contains language related input files such as lexicon.txt
+        """,
+    )
+
     return parser
 
 
@@ -110,12 +144,6 @@ def get_params() -> AttributeDict:
 
     Explanation of options saved in `params`:
 
-        - exp_dir: It specifies the directory where all training related
-                   files, e.g., checkpoints, log, etc, are saved
-
-        - lang_dir: It contains language related input files such as
-                    "lexicon.txt"
-
         - best_train_loss: Best training loss so far. It is used to select
                            the model that has the lowest training loss. It is
                            updated during the training.
@@ -533,9 +561,17 @@ def run(rank, world_size, args):
     params = get_params()
     params.update(vars(args))
 
+    if args.use_multi_node:
+        local_rank = get_local_rank()
+    else:
+        local_rank = rank
+    logging.info(
+        f"rank: {rank}, world_size: {world_size}, local_rank: {local_rank}"
+    )
+
     fix_random_seed(42)
     if world_size > 1:
-        setup_dist(rank, world_size, params.master_port)
+        setup_dist(rank, world_size, params.master_port, args.use_multi_node)
 
     setup_logger(f"{params.exp_dir}/log/log-train")
     logging.info("Training started")
@@ -552,7 +588,8 @@ def run(rank, world_size, args):
 
     device = torch.device("cpu")
     if torch.cuda.is_available():
-        device = torch.device("cuda", rank)
+        device = torch.device("cuda", local_rank)
+    logging.info(f"Device: {device}, rank: {rank}, local_rank: {local_rank}")
 
     graph_compiler = BpeCtcTrainingGraphCompiler(
         params.lang_dir,
@@ -577,7 +614,7 @@ def run(rank, world_size, args):
 
     model.to(device)
     if world_size > 1:
-        model = DDP(model, device_ids=[rank])
+        model = DDP(model, device_ids=[local_rank])
 
     optimizer = Noam(
         model.parameters(),
@@ -638,9 +675,21 @@ def main():
     parser = get_parser()
     LibriSpeechAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+    args.lang_dir = Path(args.lang_dir)
+
+    if args.use_multi_node:
+        # for multi-node multi-GPU training
+        rank = get_rank()
+        world_size = get_world_size()
+        args.world_size = world_size
+        print(f"rank: {rank}, world_size: {world_size}")
+        run(rank=rank, world_size=world_size, args=args)
+        return
 
     world_size = args.world_size
     assert world_size >= 1
+
     if world_size > 1:
         mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
     else:

egs/librispeech/ASR/prepare.sh

@@ -191,5 +191,3 @@ if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
     ./local/compile_hlg.py --lang-dir $lang_dir
   done
 fi
-
-cd data && ln -sfv lang_bpe_5000 lang_bpe

icefall/dist.py

@@ -21,14 +21,46 @@ import torch
 from torch import distributed as dist
 
 
-def setup_dist(rank, world_size, master_port=None):
+def setup_dist(rank, world_size, master_port=None, is_multi_node=False):
-    os.environ["MASTER_ADDR"] = "localhost"
+    """
-    os.environ["MASTER_PORT"] = (
+    rank and world_size are used only if is_multi_node is False.
-        "12354" if master_port is None else str(master_port)
+    """
-    )
+    if "MASTER_ADDR" not in os.environ:
-    dist.init_process_group("nccl", rank=rank, world_size=world_size)
+        os.environ["MASTER_ADDR"] = "localhost"
-    torch.cuda.set_device(rank)
+
+    if "MASTER_PORT" not in os.environ:
+        os.environ["MASTER_PORT"] = (
+            "12354" if master_port is None else str(master_port)
+        )
+
+    if is_multi_node is False:
+        dist.init_process_group("nccl", rank=rank, world_size=world_size)
+        torch.cuda.set_device(rank)
+    else:
+        dist.init_process_group("nccl")
 
 
 def cleanup_dist():
     dist.destroy_process_group()
+
+
+def get_world_size():
+    if "WORLD_SIZE" in os.environ:
+        return int(os.environ["WORLD_SIZE"])
+    if dist.is_available() and dist.is_initialized():
+        return dist.get_world_size()
+    else:
+        return 1
+
+
+def get_rank():
+    if "RANK" in os.environ:
+        return int(os.environ["RANK"])
+    elif dist.is_available() and dist.is_initialized():
+        return dist.rank()
+    else:
+        return 1
+
+
+def get_local_rank():
+    return int(os.environ.get("LOCAL_RANK", 0))