Added moe parallel example (#140)

examples/widenet_tiny_cifar10_moe/README.md

@@ -0,0 +1,30 @@
+# Overview
+
+MoE is a new technique to enlarge neural networks while keeping the same throughput in our training. 
+It is designed to improve the performance of our models without any additional time penalty. But now using 
+our temporary moe parallelism will cause a moderate computation overhead and additoinal communication time.
+The communication time depends on the topology of network in running environment. At present, moe parallelism
+may not meet what you want. Optimized version of moe parallelism will come soon.
+
+This is a simple example about how to run widenet-tiny on cifar10. More information about widenet can be 
+found [here](https://arxiv.org/abs/2107.11817).
+
+# How to run
+
+On a single server, you can directly use torchrun to start pre-training on multiple GPUs in parallel. 
+If you use the script here to train, just use follow instruction in your terminal. `n_proc` is the 
+number of processes which commonly equals to the number GPUs.
+
+```shell
+torchrun --nnodes=1 --nproc_per_node=4 train.py \
+    --config ./config.py
+```
+
+If you want to use multi servers, please check our document about environment initialization.
+
+Make sure to initialize moe running environment by `moe_set_seed` before building the model.
+
+# Result
+
+The result of training widenet-tiny on cifar10 from scratch is 89.93%. Since moe makes the model larger
+than other vit-tiny models, mixup and rand augmentation is needed.

examples/widenet_tiny_cifar10_moe/config.py

@@ -0,0 +1,15 @@
+BATCH_SIZE = 512
+LEARNING_RATE = 2e-3
+WEIGHT_DECAY = 3e-2
+
+NUM_EPOCHS = 200
+WARMUP_EPOCHS = 40
+
+WORLD_SIZE = 4
+MOE_MODEL_PARALLEL_SIZE = 4
+
+parallel = dict(
+    moe=dict(size=MOE_MODEL_PARALLEL_SIZE)
+)
+
+LOG_PATH = f"./cifar10_moe"

examples/widenet_tiny_cifar10_moe/train.py

@@ -0,0 +1,119 @@
+import os
+
+import colossalai
+import torch
+import torchvision
+from torchvision import transforms
+from colossalai.core import global_context as gpc
+from colossalai.logging import get_dist_logger
+from colossalai.nn import Accuracy
+from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
+from colossalai.trainer import Trainer
+from colossalai.trainer.hooks import (AccuracyHook, LogMemoryByEpochHook,
+                                      LogMetricByEpochHook,
+                                      LogMetricByStepHook,
+                                      LogTimingByEpochHook, LossHook,
+                                      LRSchedulerHook, ThroughputHook)
+from colossalai.utils import MultiTimer, get_dataloader
+from colossalai.nn.loss import MoeCrossEntropyLoss
+from model_zoo.moe.models import Widenet
+from colossalai.context.random import moe_set_seed
+
+DATASET_PATH = str(os.environ['DATA'])  # The directory of your dataset
+
+
+def build_cifar(batch_size):
+    transform_train = transforms.Compose([
+        transforms.RandomCrop(32, padding=4),
+        transforms.AutoAugment(policy=transforms.AutoAugmentPolicy.CIFAR10),
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+    ])
+    transform_test = transforms.Compose([
+        transforms.Resize(32),
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+    ])
+
+    train_dataset = torchvision.datasets.CIFAR10(root=DATASET_PATH,
+                                                 train=True,
+                                                 download=True,
+                                                 transform=transform_train)
+    test_dataset = torchvision.datasets.CIFAR10(root=DATASET_PATH, train=False, transform=transform_test)
+    train_dataloader = get_dataloader(dataset=train_dataset,
+                                      shuffle=True,
+                                      batch_size=batch_size,
+                                      num_workers=4,
+                                      pin_memory=True)
+    test_dataloader = get_dataloader(dataset=test_dataset, batch_size=batch_size, num_workers=4, pin_memory=True)
+    return train_dataloader, test_dataloader
+
+
+def train_cifar():
+    args = colossalai.get_default_parser().parse_args()
+    colossalai.launch_from_torch(config=args.config)
+
+    logger = get_dist_logger()
+    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)
+
+    moe_set_seed(42)
+    model = Widenet(
+        num_experts=4,
+        capacity_factor=1.2,
+        img_size=32,
+        patch_size=4,
+        num_classes=10,
+        depth=6,
+        d_model=512,
+        num_heads=2,
+        d_kv=128,
+        d_ff=2048
+    )
+
+    train_dataloader, test_dataloader = build_cifar(gpc.config.BATCH_SIZE // gpc.data_parallel_size)
+    criterion = MoeCrossEntropyLoss(aux_weight=0.01, label_smoothing=0.1)
+    optimizer = torch.optim.AdamW(model.parameters(), lr=gpc.config.LEARNING_RATE,
+                                  weight_decay=gpc.config.WEIGHT_DECAY)
+
+    lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer,
+                                           total_steps=gpc.config.NUM_EPOCHS,
+                                           warmup_steps=gpc.config.WARMUP_EPOCHS)
+
+    engine, train_dataloader, test_dataloader, lr_scheduler = colossalai.initialize(model=model,
+                                                                                    optimizer=optimizer,
+                                                                                    criterion=criterion,
+                                                                                    train_dataloader=train_dataloader,
+                                                                                    test_dataloader=test_dataloader,
+                                                                                    lr_scheduler=lr_scheduler)
+
+    logger.info("Engine is built", ranks=[0])
+
+    timer = MultiTimer()
+    trainer = Trainer(engine=engine, logger=logger, timer=timer)
+    logger.info("Trainer is built", ranks=[0])
+
+    hooks = [
+        LogMetricByEpochHook(logger=logger),
+        LogMetricByStepHook(),
+        AccuracyHook(accuracy_func=Accuracy()),
+        LossHook(),
+        ThroughputHook(),
+        LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=True)
+    ]
+
+    logger.info("Train start", ranks=[0])
+    trainer.fit(train_dataloader=train_dataloader,
+                test_dataloader=test_dataloader,
+                epochs=gpc.config.NUM_EPOCHS,
+                hooks=hooks,
+                display_progress=True,
+                test_interval=1)
+
+
+if __name__ == '__main__':
+    train_cifar()

model_zoo/moe/models.py

@@ -14,6 +14,7 @@ from colossalai.utils import get_current_device
 class VanillaSelfAttention(nn.Module):
     """Standard ViT self attention.
     """
+
     def __init__(self,
                  d_model: int,
                  n_heads: int,
@@ -57,6 +58,7 @@ class VanillaSelfAttention(nn.Module):
 class VanillaFFN(nn.Module):
     """FFN composed with two linear layers, also called MLP.
     """
+
     def __init__(self,
                  d_model: int,
                  d_ff: int,
@@ -72,8 +74,7 @@ class VanillaFFN(nn.Module):
         drop1 = nn.Dropout(drop_rate) if dropout1 is None else dropout1
         drop2 = nn.Dropout(drop_rate) if dropout2 is None else dropout2
 
-        self.ffn = nn.Sequential(
-            dense1, act, drop1,dense2, drop2)
+        self.ffn = nn.Sequential(dense1, act, drop1, dense2, drop2)
 
     def forward(self, x):
         return self.ffn(x)
@@ -91,7 +92,7 @@ class Widenet(nn.Module):
                  d_model: int = 768,
                  num_heads: int = 12,
                  d_kv: int = 64,
-                 d_ff: int = 3072,
+                 d_ff: int = 4096,
                  attention_drop: float = 0.,
                  drop_rate: float = 0.1,
                  drop_path: float = 0.):