adopt multi machine traiing

examples/wenetspeech/asr1/conf/conformer.yaml

@@ -67,7 +67,7 @@ maxlen_out: 150  # if output length(number of tokens) > maxlen-out, data is auto
 resample_rate: 16000
 shuffle_size: 1500
 sort_size: 1000
-num_workers: 0
+num_workers: 8
 prefetch_factor: 10
 dist_sampler: True
 num_encs: 1

examples/wenetspeech/asr1/local/train.sh

@@ -45,8 +45,7 @@ python3 -u ${BIN_DIR}/train.py \
 --benchmark-batch-size ${benchmark_batch_size} \
 --benchmark-max-step ${benchmark_max_step}
 else
-#NCCL_SOCKET_IFNAME=eth0
-python3 -m paddle.distributed.launch --gpus=${CUDA_VISIBLE_DEVICES} ${ips_config} ${BIN_DIR}/train.py \
+NCCL_SOCKET_IFNAME=eth0 python3 -m paddle.distributed.launch --gpus=${CUDA_VISIBLE_DEVICES} ${ips_config} ${BIN_DIR}/train.py \
 --ngpu ${ngpu} \
 --seed ${seed} \
 --config ${config_path} \

paddlespeech/audio/streamdata/shardlists.py

@@ -65,6 +65,7 @@ class SimpleShardList(IterableDataset):
 
 def split_by_node(src, group=None):
     rank, world_size, worker, num_workers = utils.paddle_worker_info(group=group)
+    logger.info(f"world_size:{world_size}, rank:{rank}")
     if world_size > 1:
         for s in islice(src, rank, None, world_size):
             yield s
@@ -83,6 +84,7 @@ def single_node_only(src, group=None):
 
 def split_by_worker(src):
     rank, world_size, worker, num_workers = utils.paddle_worker_info()
+    logger.info(f"num_workers:{num_workers}, worker:{worker}")
     if num_workers > 1:
         for s in islice(src, worker, None, num_workers):
             yield s

paddlespeech/audio/streamdata/utils.py

@@ -16,6 +16,9 @@ import re
 import sys
 from typing import Any, Callable, Iterator, Optional, Union
 
+from ..utils.log import Logger
+
+logger = Logger(__name__)
 
 def make_seed(*args):
     seed = 0
@@ -112,13 +115,14 @@ def paddle_worker_info(group=None):
         num_workers = int(os.environ["NUM_WORKERS"])
     else:
         try:
-            import paddle.io.get_worker_info 
+            from paddle.io import get_worker_info
             worker_info = paddle.io.get_worker_info()
             if worker_info is not None:
                 worker = worker_info.id
                 num_workers = worker_info.num_workers
-        except ModuleNotFoundError:
-            pass
+        except ModuleNotFoundError as E:
+            logger.info(f"not found {E}")
+            exit(-1)
 
     return rank, world_size, worker, num_workers