[chat] fix gemini strategy

applications/Chat/benchmarks/benchmark_opt_lora_dummy.py

@@ -76,9 +76,9 @@ def main(args):
     if args.strategy == "ddp":
         strategy = DDPStrategy()
     elif args.strategy == "colossalai_gemini":
-        strategy = GeminiStrategy(placement_policy="cuda", initial_scale=2**5)
+        strategy = GeminiStrategy(placement_policy="static",initial_scale=2**5)
     elif args.strategy == "colossalai_gemini_cpu":
-        strategy = GeminiStrategy(placement_policy="cpu", initial_scale=2**5)
+        strategy = GeminiStrategy(placement_policy="static", offload_optim_frac=1.0, offload_param_frac=1.0, initial_scale=2**5)
     elif args.strategy == "colossalai_zero2":
         strategy = LowLevelZeroStrategy(stage=2, placement_policy="cuda")
     elif args.strategy == "colossalai_zero2_cpu":

applications/Chat/coati/models/base/actor.py

@@ -30,3 +30,4 @@ def forward(
         """Returns model output."""
         output = self.model(input_ids, attention_mask=attention_mask, **model_kwargs)
         return output
+

applications/Chat/coati/ray/utils.py

@@ -71,11 +71,11 @@ def get_strategy_from_args(strategy: str):
     if strategy == "ddp":
         strategy_ = DDPStrategy()
     elif strategy == "colossalai_gemini":
-        strategy_ = GeminiStrategy(placement_policy="cuda", initial_scale=2**5)
+        strategy_ = GeminiStrategy(placement_policy="static", initial_scale=2**5)
     elif strategy == "colossalai_zero2":
         strategy_ = LowLevelZeroStrategy(stage=2, placement_policy="cuda")
     elif strategy == "colossalai_gemini_cpu":
-        strategy_ = GeminiStrategy(placement_policy="cpu", initial_scale=2**5)
+        strategy_ = GeminiStrategy(placement_policy="static", offload_optim_frac=1.0, offload_param_frac=1.0, initial_scale=2**5)
     elif strategy == "colossalai_zero2_cpu":
         strategy_ = LowLevelZeroStrategy(stage=2, placement_policy="cpu")
     else:

applications/Chat/coati/trainer/strategies/base.py

@@ -110,8 +110,8 @@ def unwrap_model(model: nn.Module) -> nn.Module:
         """
         return model
 
-    def save_model(self, model: nn.Module, path: str, only_rank0: bool = True, **kwargs) -> None:
-        self.booster.save_model(model, path, shard=not only_rank0, **kwargs)
+    def save_model(self, model: nn.Module, path: str, shard: bool = False, **kwargs) -> None:
+        self.booster.save_model(model, path, shard=shard, **kwargs)
 
     def load_model(self, model: nn.Module, path: str, strict: bool = True) -> None:
         self.booster.load_model(model, path, strict)

applications/Chat/coati/trainer/strategies/colossalai.py

@@ -6,7 +6,6 @@
 import colossalai
 from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin
 from colossalai.booster.plugin.low_level_zero_plugin import LowLevelZeroModel
-from colossalai.lazy.lazy_init import LazyInitContext
 from colossalai.utils import get_current_device
 from colossalai.zero.gemini.gemini_ddp import GeminiDDP
 
@@ -130,6 +129,9 @@ def __init__(
         seed: int = 42,
         shard_init: bool = False,  # only for stage 3
         placement_policy: str = "auto",
+        shard_param_frac: float = 1.0,  # only for static placement
+        offload_optim_frac: float = 0.0,  # only for static placement
+        offload_param_frac: float = 0.0,  # only for static placement
         pin_memory: bool = True,  # only for stage 3
         force_outputs_fp32: bool = False,  # only for stage 3
         search_range_m: int = 32,  # only for stage 3
@@ -160,6 +162,9 @@ def __init__(
         plugin_initializer = lambda: GeminiPlugin(
             chunk_init_device=get_current_device(),
             placement_policy=placement_policy,
+            shard_param_frac=shard_param_frac,
+            offload_optim_frac=offload_optim_frac,
+            offload_param_frac=offload_param_frac,
             precision="fp16",
             pin_memory=pin_memory,
             force_outputs_fp32=force_outputs_fp32,
@@ -188,7 +193,7 @@ def setup_distributed(self) -> None:
         colossalai.launch_from_torch({}, seed=self.seed)
 
     def model_init_context(self):
-        return LazyInitContext(default_device=get_current_device())
+        return super().model_init_context()
 
     def unwrap_model(self, model: nn.Module) -> nn.Module:
         assert isinstance(model, GeminiDDP)

applications/Chat/coati/trainer/strategies/ddp.py

@@ -87,9 +87,9 @@ def unwrap_model(self, model: nn.Module) -> nn.Module:
         return model.unwrap()
 
     def save_pretrained(
-        self, model: nn.Module, path: str, only_rank0: bool = True, tokenizer: Optional[PreTrainedTokenizerBase] = None
+        self, model: nn.Module, path: str, shard: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None
     ) -> None:
-        if not only_rank0 or dist.get_rank() == 0:
+        if dist.get_rank() == 0:
             unwrapped_model = self.unwrap_model(model)
             assert isinstance(unwrapped_model, (Actor, Critic, RewardModel))
             pretrained_model = unwrapped_model.model
@@ -98,19 +98,19 @@ def save_pretrained(
             pretrained_model.save_pretrained(path, save_function=lambda *args, **kwargs: None)
             if tokenizer is not None:
                 tokenizer.save_pretrained(path)
-        model_path = os.path.join(path, "pytorch_model.bin")
-        self.save_model(model, model_path, only_rank0=only_rank0)
 
+        model_path = os.path.join(path, "pytorch_model.bin")
+        self.save_model(model, model_path, shard=shard)
         def _replace_keys(model_path: str, replace_fn: Callable):
             state_dict = torch.load(model_path, map_location="cpu")
             state_dict = {replace_fn(k): v for k, v in state_dict.items()}
             torch.save(state_dict, model_path)
-
         # FIXME: save_model would add "model." prefix to keys of pytorch_model.bin
         # HACK: rename keys of pytorch_model.bin
         if dist.get_rank() == 0:
             _replace_keys(model_path, lambda k: k.replace("model.", "", 1))
 
+
     def get_model_state_dict_shard(self, model: nn.Module, **config):
         # TODO: implement sharding on naive strategy
         model = self.unwrap_model(model)

applications/Chat/examples/community/peft/train_peft_prompts.py

@@ -24,7 +24,7 @@ def main(args):
     if args.strategy == "ddp":
         strategy = DDPStrategy()
     elif args.strategy == "colossalai_gemini":
-        strategy = GeminiStrategy(placement_policy="cpu", initial_scale=2**5)
+        strategy = GeminiStrategy(placement_policy="static", offload_optim_frac=1.0, offload_param_frac=1.0, initial_scale=2**5)
     elif args.strategy == "colossalai_zero2":
         strategy = LowLevelZeroStrategy(stage=2, placement_policy="cpu")
     else:

applications/Chat/examples/community/peft/train_peft_sft.py

@@ -24,7 +24,7 @@ def train(args):
     if args.strategy == "ddp":
         strategy = DDPStrategy()
     elif args.strategy == "colossalai_gemini":
-        strategy = GeminiStrategy(placement_policy="cuda")
+        strategy = GeminiStrategy(placement_policy="static")
     elif args.strategy == "colossalai_zero2":
         strategy = LowLevelZeroStrategy(stage=2, placement_policy="cuda")
     else:

applications/Chat/examples/requirements.txt

@@ -1,3 +1,3 @@
 pandas>=1.4.1
 sentencepiece
-colossalai>=0.3.1
+colossalai==0.3.3

applications/Chat/examples/train_prompts.py

@@ -23,7 +23,7 @@ def main(args):
     if args.strategy == "ddp":
         strategy = DDPStrategy()
     elif args.strategy == "colossalai_gemini":
-        strategy = GeminiStrategy(placement_policy="auto", initial_scale=2**5)
+        strategy = GeminiStrategy(placement_policy="static", initial_scale=2**5)
     elif args.strategy == "colossalai_zero2":
         strategy = LowLevelZeroStrategy(stage=2, placement_policy="cuda")
     else:
@@ -33,6 +33,10 @@ def main(args):
         warnings.warn("LoRA weights should be merged with the model weights")
         state_dict = torch.load(args.rm_path, map_location="cpu")
 
+    if args.lora_rank > 0:
+        warnings.warn("Lora is not supported yet.")
+        args.lora_rank = 0
+
     with strategy.model_init_context():
         # configure model
         if args.model == "gpt2":
@@ -193,7 +197,7 @@ def main(args):
     )
 
     # save model checkpoint after fitting
-    strategy.save_model(actor, args.save_path, only_rank0=True)
+    strategy.save_pretrained(actor, path=args.save_path)
     # save optimizer checkpoint on all ranks
     if args.need_optim_ckpt:
         strategy.save_optimizer(

applications/Chat/examples/train_reward_model.py

@@ -1,4 +1,5 @@
 import argparse
+import warnings
 
 import torch
 import torch.distributed as dist
@@ -33,6 +34,10 @@ def train(args):
         raise ValueError(f'Unsupported strategy "{args.strategy}"')
 
     # configure model
+    if args.lora_rank > 0:
+        warnings.warn("Lora is not supported yet.")
+        args.lora_rank = 0
+
     with strategy.model_init_context():
         if args.model == "bloom":
             model = BLOOMRM(pretrained=args.pretrain, lora_rank=args.lora_rank)
@@ -158,7 +163,8 @@ def train(args):
         use_wandb=args.use_wandb,
     )
     # save model checkpoint after fitting on only rank0
-    strategy.save_model(model, args.save_path, only_rank0=True)
+    state_dict = model.state_dict()
+    torch.save(state_dict, args.save_path)
     # save optimizer checkpoint on all ranks
     if args.need_optim_ckpt:
         strategy.save_optimizer(

applications/Chat/examples/train_sft.py

@@ -40,8 +40,9 @@ def train(args):
 
     # configure model
     if args.lora_rank > 0:
-        warnings.warn("Gradient checkpoint is disabled when using LoRA")
-        args.grad_checkpoint = False
+        warnings.warn("Lora is not supported yet.")
+        args.lora_rank = 0
+
     with strategy.model_init_context():
         if args.model == "bloom":
             model = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank, checkpoint=args.grad_checkpoint)
@@ -178,7 +179,7 @@ def train(args):
     )
 
     # save model checkpoint after fitting on only rank0
-    strategy.save_pretrained(model, path=args.save_path, only_rank0=True, tokenizer=tokenizer)
+    strategy.save_pretrained(model, path=args.save_path, tokenizer=tokenizer)
     # save optimizer checkpoint on all ranks
     if args.need_optim_ckpt:
         strategy.save_optimizer(

applications/Chat/requirements-test.txt

@@ -1,2 +1,2 @@
 pytest
-colossalai>=0.3.1
+colossalai==0.3.3

applications/Chat/requirements.txt

@@ -2,7 +2,7 @@ transformers>=4.20.1
 tqdm
 datasets
 loralib
-colossalai>=0.3.1
+colossalai==0.3.3
 torch<2.0.0, >=1.12.1
 langchain
 tokenizers

applications/Chat/tests/test_checkpoint.py

@@ -57,9 +57,9 @@ def run_test_checkpoint(strategy_name: str, shard: bool):
         rank0_dirname = rank0_dirname[0]
 
         model_path = os.path.join(rank0_dirname, "model" if shard else f"model.pt")
-        strategy.save_model(actor, model_path, only_rank0=not shard)
+        strategy.save_model(actor, model_path)
         optim_path = os.path.join(rank0_dirname, "optim" if shard else "optim.pt")
-        strategy.save_optimizer(actor_optim, optim_path, only_rank0=not shard)
+        strategy.save_optimizer(actor_optim, optim_path)
         dist.barrier()
 
         strategy.load_model(actor, model_path, strict=False)

applications/Chat/tests/test_train.sh

@@ -41,6 +41,7 @@ MODELS_DIR=$BASE_DIR/examples/models_config
 MODELS=('gpt2' 'bloom' 'opt' 'llama')
 STRATEGIES=('ddp' 'colossalai_gemini' 'colossalai_zero2')
 
+
 export OMP_NUM_THREADS=8
 
 # install requirements
@@ -80,13 +81,10 @@ SKIPPED_TESTS=(
     "llama-ddp"
     "llama-colossalai_gemini"
     "llama-colossalai_zero2"
-    "gpt2-colossalai_gemini"
-    "opt-colossalai_gemini"
-    "bloom-colossalai_gemini"
 )
 
 GRAD_CKPTS=('' '--grad_checkpoint')
-for lora_rank in '0' '4'; do
+for lora_rank in '0'; do
     for model in ${MODELS[@]}; do
         strategies=($(shuf -e "${STRATEGIES[@]}"))
         for strategy in ${strategies[@]}; do
@@ -135,14 +133,11 @@ SKIPPED_TESTS=(
     "llama-ddp"
     "llama-colossalai_gemini"
     "llama-colossalai_zero2"
-    "gpt2-colossalai_gemini"
-    "opt-colossalai_gemini"
-    "bloom-colossalai_gemini"
 )
 
 LOSS_FNS=('log_sig' 'log_exp')
 DATASETS=('Anthropic/hh-rlhf' 'Dahoas/rm-static')
-for lora_rank in '0' '4'; do
+for lora_rank in '0'; do
     for model in ${MODELS[@]}; do
         strategies=($(shuf -e "${STRATEGIES[@]}"))
         for strategy in ${strategies[@]}; do
@@ -193,13 +188,10 @@ SKIPPED_TESTS=(
     "llama-ddp"
     "llama-colossalai_gemini"
     "llama-colossalai_zero2"
-    "gpt2-colossalai_gemini"
-    "opt-colossalai_gemini"
-    "bloom-colossalai_gemini"
 )
 
 for model in ${MODELS[@]}; do
-    for lora_rank in '0' '4'; do
+    for lora_rank in '0'; do
         strategies=($(shuf -e "${STRATEGIES[@]}"))
         for strategy in ${strategies[@]}; do
             if [[ " ${SKIPPED_TESTS[*]} " =~ " $model-$strategy-$lora_rank " ]]; then
@@ -223,7 +215,7 @@ for model in ${MODELS[@]}; do
                     --experience_batch_size 2 --train_batch_size 1 --lora_rank $lora_rank \
                     --pretrain $EXAMPLES_DIR/rlhf_models/sft_ckpt_${model}_${lora_rank} \
                     $rm_pretrain_model --rm_path $EXAMPLES_DIR/rlhf_models/rm_ckpt_${model}_${lora_rank}.pt \
-                    --save_path $EXAMPLES_DIR/rlhf_models/actor_checkpoint_prompts.pt
+                    --save_path $EXAMPLES_DIR/rlhf_models/actor_checkpoint_prompts
                 passed=$?
                 if [ $passed -eq 0 ]; then
                     break
@@ -238,4 +230,4 @@ for model in ${MODELS[@]}; do
         rm $EXAMPLES_DIR/rlhf_models/rm_ckpt_${model}_${lora_rank}.pt
     done
 done
-rm $EXAMPLES_DIR/rlhf_models/actor_checkpoint_prompts.pt
+rm -rf $EXAMPLES_DIR/rlhf_models/actor_checkpoint_prompts