[tutorial] added synthetic data for sequence parallel (#1927)

* [tutorial] added synthetic data for sequence parallel

* polish code

examples/tutorial/sequence_parallel/README.md

@@ -133,7 +133,7 @@ machine setting.
   start your script. A sample command is like below:
 
   ```bash
-    python -m torch.distributed.launch --nproc_per_node <num_gpus_on_this_machine> --master_addr localhost --master_port 29500 train.py
+    colossalai run --nproc_per_node <num_gpus_on_this_machine> --master_addr localhost --master_port 29500 train.py
   ```
 
 - If you are using multiple machines with multiple GPUs, we suggest that you refer to `colossalai

examples/tutorial/sequence_parallel/config.py

@@ -31,10 +31,8 @@ SEED = 1234
 NUM_MICRO_BATCHES = 4
 
 # colossalai config
-parallel = dict(pipeline=1, tensor=dict(size=4, mode='sequence'))
+parallel = dict(pipeline=1, tensor=dict(size=2, mode='sequence'))
 
 fp16 = dict(mode=AMP_TYPE.NAIVE, verbose=True)
 
-clip_grad_norm = 1.0
-
 gradient_handler = [dict(type='SequenceParallelGradientHandler')]

examples/tutorial/sequence_parallel/data/datasets/bert_dataset.py

@@ -14,19 +14,30 @@
 # limitations under the License.
 """BERT Style dataset."""
 
-from colossalai.logging import get_dist_logger
+import os
+import time
+
 import numpy as np
 import torch
 from torch.utils.data import Dataset
 
-from ..tokenizer import get_tokenizer
-from .dataset_utils import (get_a_and_b_segments, truncate_segments, create_tokens_and_tokentypes,
-                            create_masked_lm_predictions, pad_and_convert_to_numpy)
-from colossalai.core import global_context as gpc
 from colossalai.context import ParallelMode
-import time
-import os
-from . import helpers
+from colossalai.core import global_context as gpc
+from colossalai.logging import get_dist_logger
+
+from ..tokenizer import get_tokenizer
+from .dataset_utils import (
+    create_masked_lm_predictions,
+    create_tokens_and_tokentypes,
+    get_a_and_b_segments,
+    pad_and_convert_to_numpy,
+    truncate_segments,
+)
+
+try:
+    from . import helpers
+except:
+    print("helper is not built, ignore this message if you are using synthetic data.")
 
 
 class BertDataset(Dataset):

examples/tutorial/sequence_parallel/train.py

@@ -1,21 +1,22 @@
+import argparse
+
+import torch
+from data import build_train_valid_test_data_iterators
+from data.bert_helper import SequenceParallelDataIterator, get_batch_for_sequence_parallel
+from data.tokenizer import get_padded_vocab_size, initialize_tokenizer
+from loss_func.bert_loss import BertLoss
+from lr_scheduler import AnnealingLR
+from model.bert import BertForPretrain, build_pipeline_bert
+
 import colossalai
+from colossalai.amp import AMP_TYPE
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from data import build_train_valid_test_data_iterators
-from data.tokenizer import initialize_tokenizer, get_padded_vocab_size
-from data.bert_helper import get_batch_for_sequence_parallel, SequenceParallelDataIterator
-from colossalai.amp import AMP_TYPE
-from colossalai.logging import get_dist_logger
-from colossalai.utils import MultiTimer, is_using_pp
-from model.bert import BertForPretrain
-from lr_scheduler import AnnealingLR
-from loss_func.bert_loss import BertLoss
-import torch
 from colossalai.engine.schedule import PipelineSchedule
-from colossalai.amp import AMP_TYPE
-from colossalai.nn.optimizer import FusedAdam
 from colossalai.kernel import LayerNorm
-from model.bert import build_pipeline_bert
+from colossalai.logging import get_dist_logger
+from colossalai.nn.optimizer import FusedAdam
+from colossalai.utils import MultiTimer, is_using_pp
 
 
 def process_batch_data(batch_data):
@@ -28,13 +29,21 @@ def process_batch_data(batch_data):
     return data, label
 
 
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-s', '--synthetic', action="store_true", help="whether use synthetic data")
+    return parser.parse_args()
+
+
 def main():
     # initialize
+    args = parse_args()
     colossalai.launch_from_torch(config='./config.py', seed=1234, backend='nccl')
 
     logger = get_dist_logger()
 
     # build dataloader
+    if not args.synthetic:
         initialize_tokenizer(gpc.config.VOCAB_FILE_PATH, tokenizer_type='BertWordPieceLowerCase')
         VOCAB_SIZE = get_padded_vocab_size()
         trainloader, validloader, testloader = build_train_valid_test_data_iterators(
@@ -52,6 +61,17 @@ def main():
             skip_warmup=True,
             binary_head=False,
         )
+    else:
+        from data.dummy_dataloader import DummyDataloader
+
+        BATCH_SIZE_PER_GPUS = gpc.config.GLOBAL_BATCH_SIZE // gpc.get_world_size(ParallelMode.DATA)
+        VOCAB_SIZE = 30528
+        trainloader = DummyDataloader(batch_size=BATCH_SIZE_PER_GPUS,
+                                      vocab_size=VOCAB_SIZE,
+                                      seq_length=gpc.config.SEQ_LENGTH)
+        validloader = DummyDataloader(batch_size=BATCH_SIZE_PER_GPUS,
+                                      vocab_size=VOCAB_SIZE,
+                                      seq_length=gpc.config.SEQ_LENGTH)
 
     logger.info("Dataloaders are built", ranks=[0])
 
@@ -121,11 +141,7 @@ def main():
     logger.info(f"LR Scheduler is built with {warmup_steps} warmup steps and {gpc.config.DECAY_ITERS} decay steps")
 
     # # init
-    engine, *dummy = colossalai.initialize(
-        model,
-        optimizer,
-        criterion,
-    )
+    engine, *dummy = colossalai.initialize(model, optimizer, criterion, verbose=True)
 
     # build timer
     timer = MultiTimer()
@@ -140,6 +156,8 @@ def main():
         train_data_iter = SequenceParallelDataIterator(trainloader)
         valid_data_iter = SequenceParallelDataIterator(validloader)
 
+    logger.info("start training")
+
     for step in range(1, gpc.config.TRAIN_ITERS + 1):
         timer.start('train-iterations')
         engine.train()