[example] update gemini examples

examples/language/gpt/gemini/test_ci.sh

@@ -3,7 +3,7 @@ $(cd `dirname $0`;pwd)
 export TRAIN_STEP=4
 
 for MODEL_TYPE in "gpt2_medium"; do
-  for DISTPLAN in "colossalai"; do
+  for DISTPLAN in "CAI_Gemini"; do
     for BATCH_SIZE in 2; do
       for GPUNUM in 1 4; do
         for TPDEGREE in 1 2; do

examples/language/gpt/gemini/train_gpt_demo.py

@@ -11,11 +11,13 @@
 from torch.nn.parallel import DistributedDataParallel as DDP
 
 import colossalai
+from colossalai.booster import Booster
+from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
 from colossalai.logging import disable_existing_loggers, get_dist_logger
 from colossalai.nn.optimizer import HybridAdam
 from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec
 from colossalai.utils import get_current_device
-from colossalai.zero import ColoInitContext, zero_model_wrapper, zero_optim_wrapper
+from colossalai.zero import ColoInitContext
 
 CAI_VERSION = colossalai.__version__
 
@@ -236,23 +238,6 @@ def main():
             tensor_parallelize(model, tp_pg)
 
         # asign running configurations
-        gemini_config = None
-        if args.distplan.startswith("CAI_ZeRO"):
-            optim_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True, verbose=True)
-        elif args.distplan == "CAI_Gemini":
-            gemini_config = dict(strict_ddp_mode=args.tp_degree == 1,
-                                 device=get_current_device(),
-                                 placement_policy=args.placement,
-                                 pin_memory=True,
-                                 hidden_dim=model.config.n_embd,
-                                 search_range_mb=128)
-            optim_config = dict(gpu_margin_mem_ratio=0.)
-        else:
-            raise RuntimeError
-
-        # build a highly optimized gpu/cpu optimizer
-        optimizer = HybridAdam(model.parameters(), lr=1e-3)
-
         if args.distplan == "CAI_ZeRO1":
             zero_stage = 1
         elif args.distplan == "CAI_ZeRO2":
@@ -262,22 +247,42 @@ def main():
         else:
             raise RuntimeError
 
-        # wrap your model and optimizer
-        model = zero_model_wrapper(model, zero_stage, gemini_config)
-        optimizer = zero_optim_wrapper(model, optimizer, optim_config=optim_config)
+        plugin = None
+        if args.distplan.startswith("CAI_ZeRO"):
+            plugin = LowLevelZeroPlugin(stage=zero_stage,
+                                        reduce_bucket_size_in_m=12 * 1024 * 1024,
+                                        overlap_communication=True,
+                                        verbose=True)
+        elif args.distplan == "CAI_Gemini":
+            plugin = GeminiPlugin(device=get_current_device(),
+                                  placement_policy=args.placement,
+                                  pin_memory=True,
+                                  strict_ddp_mode=args.tp_degree == 1,
+                                  search_range_mb=128,
+                                  hidden_dim=model.config.n_embd,
+                                  gpu_margin_mem_ratio=0.)
+        else:
+            raise RuntimeError
+
+        # build a highly optimized gpu/cpu optimizer
+        optimizer = HybridAdam(model.parameters(), lr=1e-3)
 
         logger.info(get_mem_info(prefix='After init optim, '), ranks=[0])
     elif args.distplan.startswith("Pytorch"):
         assert args.tp_degree == 1, "The degree of TP should be 1 for DDP examples."
         model = model_builder(args.model_type)(checkpoint=True).cuda()
-        model = DDP(model)
+        plugin = TorchDDPPlugin()
         if args.distplan.endswith("DDP"):
             optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
         elif args.distplan.endswith("ZeRO"):
             from torch.distributed.optim import ZeroRedundancyOptimizer
             optimizer = ZeroRedundancyOptimizer(model.parameters(), optimizer_class=torch.optim.Adam, lr=1e-3)
+
     else:
         raise RuntimeError
+    # wrap your model and optimizer
+    booster = Booster(plugin=plugin)
+    model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
 
     # model is shared after TP
     numel = get_model_size(model)
@@ -305,13 +310,7 @@ def train_step():
         fwd_end = time()
         fwd_time = fwd_end - start
         logger.info(get_mem_info(prefix=f'[{n + 1}/{NUM_STEPS}] Forward '), ranks=[0])
-
-        if args.distplan.startswith("CAI"):
-            optimizer.backward(loss)
-        elif args.distplan.startswith("Pytorch"):
-            loss.backward()
-        else:
-            raise RuntimeError
+        booster.backward(loss, optimizer)
 
         torch.cuda.synchronize()
         bwd_end = time()