[example] Modify palm example with the new booster API (#3913)

* Modify torch version requirement to adapt torch 2.0

* modify palm example using new booster API

* roll back

* fix port

* polish

* polish

examples/language/palm/README.md

@@ -43,6 +43,9 @@ palm = PaLM(
 )
 ```
 
+## New API
+We have modified our previous implementation of PaLM with our new Booster API, which offers a more flexible and efficient way to train your model. The new API is more user-friendly and easy to use. You can find the new API in train.py. We have also offer a shell script test_ci.sh for you to go through all our plugins for the booster. For more information about the booster API you can refer to https://colossalai.org/docs/basics/booster_api/.
+
 ## Test on Enwik8
 
 ```bash

examples/language/palm/run.sh

@@ -3,9 +3,11 @@ export DISTPAN="colossalai"
 
 # The following options only valid when DISTPAN="colossalai"
 export TPDEGREE=1
-export GPUNUM=1
+export GPUNUM=4
 export PLACEMENT='cpu'
 export USE_SHARD_INIT=False
-export BATCH_SIZE=4
+export BATCH_SIZE=1
 
-env OMP_NUM_THREADS=12 torchrun  --standalone --nproc_per_node=${GPUNUM}  --master_port 29501  train.py  --tp_degree=${TPDEGREE} --batch_size=${BATCH_SIZE} --placement ${PLACEMENT} --shardinit ${USE_SHARD_INIT} --distplan ${DISTPAN} 2>&1 | tee run.log
+env OMP_NUM_THREADS=12 torchrun  --standalone --nproc_per_node=${GPUNUM}  --master_port 29501  train.py  \
+--dummy_data=True --tp_degree=${TPDEGREE} --batch_size=${BATCH_SIZE} --plugin='gemini' \
+--placement ${PLACEMENT} --shardinit ${USE_SHARD_INIT} --distplan ${DISTPAN} 2>&1 | tee run.log

examples/language/palm/test_ci.sh

@@ -4,6 +4,6 @@ for BATCH_SIZE in 2
 do
 for GPUNUM in 1 4
 do
-env OMP_NUM_THREADS=12 torchrun  --standalone --nproc_per_node=${GPUNUM}  --master_port 29501  train.py --dummy_data=True --batch_size=${BATCH_SIZE}  2>&1 | tee run.log
+env OMP_NUM_THREADS=12 torchrun  --standalone --nproc_per_node=${GPUNUM}  --standalone  train.py --dummy_data=True --batch_size=${BATCH_SIZE}  --plugin='gemini' 2>&1 | tee run.log
 done
 done

examples/language/palm/train.py

@@ -9,6 +9,8 @@ import torch.nn as nn
 import torch.optim as optim
 import tqdm
 from packaging import version
+
+from colossalai.nn import HybridAdam
 from palm_pytorch import PaLM
 from palm_pytorch.autoregressive_wrapper import AutoregressiveWrapper
 from torch.utils.data import DataLoader, Dataset
@@ -18,6 +20,8 @@ from colossalai.logging import disable_existing_loggers, get_dist_logger
 from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec
 from colossalai.utils import MultiTimer, get_current_device
 from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, ZeroDDP
+from colossalai.booster import Booster
+from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
 
 # constants
 
@@ -58,6 +62,12 @@ def parse_args():
         help=
         "Shard the tensors when init the model to shrink peak memory size on the assigned device. Valid when using colossalai as dist plan.",
     )
+    parser.add_argument('-p',
+                        '--plugin',
+                        type=str,
+                        default='torch_ddp',
+                        choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'],
+                        help="plugin to use")
     parser.add_argument(
         "--batch_size",
         type=int,
@@ -101,28 +111,6 @@ def get_model_size(model: nn.Module):
     return total_numel
 
 
-# Gemini + ZeRO DDP
-def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"):
-    cai_version = colossalai.__version__
-    if version.parse(cai_version) > version.parse("0.1.10"):
-        from colossalai.nn.parallel import GeminiDDP
-        model = GeminiDDP(model,
-                          device=get_current_device(),
-                          placement_policy=placement_policy,
-                          pin_memory=True,
-                          search_range_mb=32)
-    elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
-        from colossalai.gemini import ChunkManager, GeminiManager
-        chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
-        gemini_manager = GeminiManager(placement_policy, chunk_manager)
-        chunk_manager = ChunkManager(chunk_size,
-                                     pg,
-                                     enable_distributed_storage=True,
-                                     init_device=GeminiManager.get_default_device(placement_policy))
-        model = ZeroDDP(model, gemini_manager)
-    else:
-        raise NotImplemented(f"CAI version {cai_version} is not supported")
-    return model
 
 
 # Parameter Sharding Strategies for Tensor Parallelism
@@ -218,6 +206,18 @@ val_loader = cycle(DataLoader(val_dataset, batch_size=args.batch_size))
 if args.distplan == "colossalai":
     # instantiate GPT-like decoder model
 
+    booster_kwargs = {}
+    if args.plugin == 'torch_ddp_fp16':
+        booster_kwargs['mixed_precision'] = 'fp16'
+    if args.plugin.startswith('torch_ddp'):
+        plugin = TorchDDPPlugin()
+    elif args.plugin == 'gemini':
+        plugin = GeminiPlugin(placement_policy=args.placement, strict_ddp_mode=True, initial_scale=2 ** 5)
+    elif args.plugin == 'low_level_zero':
+        plugin = LowLevelZeroPlugin(initial_scale=2 ** 5)
+    logger.info(f"plugin: {plugin}")
+    booster = Booster(plugin=plugin, **booster_kwargs)
+
     default_pg = ProcessGroup(tp_degree=args.tp_degree)
     default_dist_spec = ShardSpec([-1], [args.tp_degree]) if args.shardinit else None
     ctx = ColoInitContext(device='cpu', default_dist_spec=default_dist_spec, default_pg=default_pg)
@@ -228,12 +228,12 @@ if args.distplan == "colossalai":
 
     pg = default_pg
     tensor_parallelize(model, pg)
-    model = gemini_zero_dpp(model, pg, args.placement)
 
     # optimizer
 
-    #optimizer = GeminiAdamOptimizer(model, lr=1e-7, initial_scale=2**5)
-    optimizer = GeminiAdamOptimizer(model, lr=LEARNING_RATE, initial_scale=2**5)
+    optimizer = HybridAdam(model.parameters(), lr=LEARNING_RATE, initial_scale=2**5)
+    model, optimizer, _, _, _ = booster.boost(model, optimizer)
+
 else:
     model = PaLM(num_tokens=256, dim=512, depth=8)
     model = AutoregressiveWrapper(model, max_seq_len=2048)