[example] updated large-batch optimizer tutorial

examples/tutorial/large_batch_optimizer/README.md

@@ -1,31 +1,35 @@
 # Comparison of Large Batch Training Optimization
 
-## 🚀Quick Start
-Run with synthetic data
-```bash
-colossalai run --nproc_per_node 4 train.py --config config.py -s
-```
+## Table of contents
 
+- [Overview](#-overview)
+- [Quick Start](#-quick-start)
 
-## Prepare Dataset
+## 📚 Overview
 
-We use CIFAR10 dataset in this example. You should invoke the `donwload_cifar10.py` in the tutorial root directory or directly run the `auto_parallel_with_resnet.py`.
-The dataset will be downloaded to `colossalai/examples/tutorials/data` by default.
-If you wish to use customized directory for the dataset. You can set the environment variable `DATA` via the following command.
+This example lets you to quickly try out the large batch training optimization provided by Colossal-AI. We use synthetic dataset to go through the process, thus, you don't need to prepare any dataset. You can try out the `Lamb` and `Lars` optimizers from Colossal-AI with the following code.
 
-```bash
-export DATA=/path/to/data
+```python
+from colossalai.nn.optimizer import Lamb, Lars
 ```
 
-You can also use synthetic data for this tutorial if you don't wish to download the `CIFAR10` dataset by adding the `-s` or `--synthetic` flag to the command.
+## 🚀 Quick Start
+
+1. Install PyTorch
 
+2. Install the dependencies.
+
+```bash
+pip install -r requirements.txt
+```
 
-## Run on 2*2 device mesh
+3. Run the training scripts with synthetic data.
 
 ```bash
-# run with cifar10
-colossalai run --nproc_per_node 4 train.py --config config.py
+# run on 4 GPUs
+# run with lars
+colossalai run --nproc_per_node 4 train.py --config config.py --optimizer lars
 
-# run with synthetic dataset
-colossalai run --nproc_per_node 4 train.py --config config.py -s
+# run with lamb
+colossalai run --nproc_per_node 4 train.py --config config.py --optimizer lamb
 ```

examples/tutorial/large_batch_optimizer/config.py

@@ -6,31 +6,11 @@
 BATCH_SIZE = 512
 LEARNING_RATE = 3e-3
 WEIGHT_DECAY = 0.3
-NUM_EPOCHS = 10
-WARMUP_EPOCHS = 3
+NUM_EPOCHS = 2
+WARMUP_EPOCHS = 1
 
 # model config
-IMG_SIZE = 224
-PATCH_SIZE = 16
-HIDDEN_SIZE = 512
-DEPTH = 4
-NUM_HEADS = 4
-MLP_RATIO = 2
-NUM_CLASSES = 1000
-CHECKPOINT = False
-SEQ_LENGTH = (IMG_SIZE // PATCH_SIZE)**2 + 1    # add 1 for cls token
-
-# parallel setting
-TENSOR_PARALLEL_SIZE = 2
-TENSOR_PARALLEL_MODE = '1d'
-
-parallel = dict(
-    pipeline=2,
-    tensor=dict(mode=TENSOR_PARALLEL_MODE, size=TENSOR_PARALLEL_SIZE),
-)
+NUM_CLASSES = 10
 
 fp16 = dict(mode=AMP_TYPE.NAIVE)
 clip_grad_norm = 1.0
-
-# pipeline config
-NUM_MICRO_BATCHES = parallel['pipeline']

examples/tutorial/large_batch_optimizer/requirements.txt

@@ -1,2 +1,3 @@
-colossalai >= 0.1.12
-torch >= 1.8.1
+colossalai
+torch
+titans

examples/tutorial/large_batch_optimizer/test_ci.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+set -euxo pipefail
+
+pip install -r requirements.txt
+
+# run test
+colossalai run --nproc_per_node 4 --master_port 29500 train.py --config config.py --optimizer lars
+colossalai run --nproc_per_node 4 --master_port 29501 train.py --config config.py --optimizer lamb

examples/tutorial/large_batch_optimizer/train.py

@@ -1,19 +1,13 @@
-import os
-
 import torch
-from titans.dataloader.cifar10 import build_cifar
-from titans.model.vit.vit import _create_vit_model
+import torch.nn as nn
+from torchvision.models import resnet18
 from tqdm import tqdm
 
 import colossalai
-from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.logging import get_dist_logger
-from colossalai.nn import CrossEntropyLoss
 from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
 from colossalai.nn.optimizer import Lamb, Lars
-from colossalai.pipeline.pipelinable import PipelinableContext
-from colossalai.utils import get_dataloader, is_using_pp
 
 
 class DummyDataloader():
@@ -45,7 +39,10 @@ def __len__(self):
 def main():
     # initialize distributed setting
     parser = colossalai.get_default_parser()
-    parser.add_argument('-s', '--synthetic', action="store_true", help="whether use synthetic data")
+    parser.add_argument('--optimizer',
+                        choices=['lars', 'lamb'],
+                        help="Choose your large-batch optimizer",
+                        required=True)
     args = parser.parse_args()
 
     # launch from torch
@@ -55,59 +52,22 @@ def main():
     logger = get_dist_logger()
     logger.info("initialized distributed environment", ranks=[0])
 
-    if hasattr(gpc.config, 'LOG_PATH'):
-        if gpc.get_global_rank() == 0:
-            log_path = gpc.config.LOG_PATH
-            if not os.path.exists(log_path):
-                os.mkdir(log_path)
-            logger.log_to_file(log_path)
-
-    use_pipeline = is_using_pp()
-
-    # create model
-    model_kwargs = dict(img_size=gpc.config.IMG_SIZE,
-                        patch_size=gpc.config.PATCH_SIZE,
-                        hidden_size=gpc.config.HIDDEN_SIZE,
-                        depth=gpc.config.DEPTH,
-                        num_heads=gpc.config.NUM_HEADS,
-                        mlp_ratio=gpc.config.MLP_RATIO,
-                        num_classes=10,
-                        init_method='jax',
-                        checkpoint=gpc.config.CHECKPOINT)
-
-    if use_pipeline:
-        pipelinable = PipelinableContext()
-        with pipelinable:
-            model = _create_vit_model(**model_kwargs)
-        pipelinable.to_layer_list()
-        pipelinable.policy = "uniform"
-        model = pipelinable.partition(1, gpc.pipeline_parallel_size, gpc.get_local_rank(ParallelMode.PIPELINE))
-    else:
-        model = _create_vit_model(**model_kwargs)
-
-    # count number of parameters
-    total_numel = 0
-    for p in model.parameters():
-        total_numel += p.numel()
-    if not gpc.is_initialized(ParallelMode.PIPELINE):
-        pipeline_stage = 0
-    else:
-        pipeline_stage = gpc.get_local_rank(ParallelMode.PIPELINE)
-    logger.info(f"number of parameters: {total_numel} on pipeline stage {pipeline_stage}")
-
-    # create dataloaders
-    root = os.environ.get('DATA', '../data/')
-    if args.synthetic:
-        train_dataloader = DummyDataloader(length=30, batch_size=gpc.config.BATCH_SIZE)
-        test_dataloader = DummyDataloader(length=10, batch_size=gpc.config.BATCH_SIZE)
-    else:
-        train_dataloader, test_dataloader = build_cifar(gpc.config.BATCH_SIZE, root, pad_if_needed=True)
+    # create synthetic dataloaders
+    train_dataloader = DummyDataloader(length=10, batch_size=gpc.config.BATCH_SIZE)
+    test_dataloader = DummyDataloader(length=5, batch_size=gpc.config.BATCH_SIZE)
+
+    # build model
+    model = resnet18(num_classes=gpc.config.NUM_CLASSES)
 
     # create loss function
-    criterion = CrossEntropyLoss(label_smoothing=0.1)
+    criterion = nn.CrossEntropyLoss()
 
     # create optimizer
-    optimizer = Lars(model.parameters(), lr=gpc.config.LEARNING_RATE, weight_decay=gpc.config.WEIGHT_DECAY)
+    if args.optimizer == "lars":
+        optim_cls = Lars
+    elif args.optimizer == "lamb":
+        optim_cls = Lamb
+    optimizer = optim_cls(model.parameters(), lr=gpc.config.LEARNING_RATE, weight_decay=gpc.config.WEIGHT_DECAY)
 
     # create lr scheduler
     lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer,