Merge branch 'main' into feature/shardformer

applications/Chat/examples/train_sft.py

@@ -9,13 +9,15 @@ from coati.models.bloom import BLOOMActor
 from coati.models.gpt import GPTActor
 from coati.models.llama import LlamaActor
 from coati.models.opt import OPTActor
+from coati.models.chatglm import ChatGLMActor
 from coati.trainer import SFTTrainer
 from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy
 from datasets import load_dataset
 from torch.optim import Adam
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
-from transformers import AutoTokenizer, BloomTokenizerFast, LlamaTokenizer
+from transformers import AutoTokenizer, BloomTokenizerFast, LlamaTokenizer, AutoModel
+from coati.models.chatglm.chatglm_tokenizer import ChatGLMTokenizer
 from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
 from transformers.trainer import get_scheduler
 
@@ -58,6 +60,8 @@ def train(args):
             model = LlamaActor(pretrained=args.pretrain,
                                lora_rank=args.lora_rank,
                                checkpoint=args.grad_checkpoint)
+        elif args.model == 'chatglm':
+            model = ChatGLMActor(pretrained=args.pretrain)
         else:
             raise ValueError(f'Unsupported model "{args.model}"')
 
@@ -81,6 +85,9 @@ def train(args):
             "hf-internal-testing/llama-tokenizer" if args.tokenizer is None else args.tokenizer)
         tokenizer.eos_token = '<\s>'
         tokenizer.pad_token = tokenizer.unk_token
+    elif args.model == 'chatglm':
+        tokenizer = ChatGLMTokenizer.from_pretrained(
+            "THUDM/chatglm-6b" if args.tokenizer is None else args.tokenizer, trust_remote_code=True)
     else:
         raise ValueError(f'Unsupported model "{args.model}"')
 
@@ -99,7 +106,6 @@ def train(args):
         optim = HybridAdam(model.parameters(), lr=args.lr, clipping_norm=1.0)
     else:
         optim = Adam(model.parameters(), lr=args.lr)
-
     logger = get_dist_logger()
 
     # configure dataset
@@ -185,7 +191,7 @@ if __name__ == '__main__':
     parser.add_argument('--strategy',
                         choices=['ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_zero2_cpu'],
                         default='colossalai_zero2')
-    parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom')
+    parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama', 'chatglm'], default='bloom')
     parser.add_argument('--tokenizer', type=str, default=None)
     parser.add_argument('--pretrain', type=str, default=None)
     parser.add_argument('--dataset', type=str, default=None)

applications/Chat/requirements-test.txt

 pytest
+colossalai==0.3.1
\ No newline at end of file

applications/Chat/requirements.txt

@@ -2,7 +2,7 @@ transformers>=4.20.1
 tqdm
 datasets
 loralib
-colossalai>=0.2.4
+colossalai==0.3.1
 torch<2.0.0, >=1.12.1
 langchain
 tokenizers

applications/Chat/tests/test_dataset.py

@@ -11,7 +11,7 @@ from coati.dataset.sft_dataset import IGNORE_INDEX, SFTDataset, SupervisedDatase
 from datasets import load_dataset
 from transformers import AutoTokenizer, BloomTokenizerFast, LlamaTokenizer, PreTrainedTokenizer
 from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
-
+from coati.models.chatglm.chatglm_tokenizer import ChatGLMTokenizer
 SFT_DATASET = [
     {
         "instruction":
@@ -80,6 +80,8 @@ def make_tokenizer(model: str):
     elif model == "llama":
         tokenizer = LlamaTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
         tokenizer.pad_token = tokenizer.unk_token
+    elif model == "chatglm":
+        tokenizer = ChatGLMTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
     else:
         raise ValueError(f"Unsupported model '{model}'")
     return tokenizer
@@ -93,12 +95,18 @@ def check_content(input_ids_stripped: torch.Tensor, tokenizer: PreTrainedTokeniz
     elif model == "llama":
         assert input_ids_stripped[0] == tokenizer.bos_token_id
         input_ids_stripped = input_ids_stripped[1:]
-
+    elif model == "chatglm":
+        assert input_ids_stripped[0] == tokenizer.bos_token_id
+        assert input_ids_stripped[-1] == tokenizer.eos_token_id
+        input_ids_stripped = input_ids_stripped[1:-1]
     assert torch.all(input_ids_stripped != tokenizer.pad_token_id)
     assert torch.all(input_ids_stripped != tokenizer.bos_token_id)
     assert torch.all(input_ids_stripped != tokenizer.eos_token_id)
     assert input_ids_stripped != tokenizer.sep_token_id
     assert input_ids_stripped != tokenizer.cls_token_id
+    if model == "chatglm":
+        assert torch.all(input_ids_stripped != tokenizer.mask_token_id)
+    else:
         assert input_ids_stripped != tokenizer.mask_token_id
 
 
@@ -190,7 +198,8 @@ def test_reward_dataset(model: str, dataset_path: str, subset: Optional[str], ma
             assert torch.all(r_mask)
 
 
-@pytest.mark.parametrize("model", ["gpt2", "bloom", "opt", "llama"])
+
+@pytest.mark.parametrize("model", ["gpt2", "bloom", "opt", "llama", "chatglm"])
 @pytest.mark.parametrize("dataset_path", ["yizhongw/self_instruct", None])
 @pytest.mark.parametrize("max_dataset_size", [2])
 @pytest.mark.parametrize("max_length", [32, 1024])
@@ -211,6 +220,19 @@ def test_sft_dataset(model: str, dataset_path: Optional[str], max_dataset_size:
                                             max_length=max_length)
         assert len(sft_dataset) == min(max_dataset_size, len(SFT_DATASET))
 
+    if isinstance(tokenizer, ChatGLMTokenizer):
+        for i in range(max_dataset_size):
+            assert isinstance(sft_dataset[i], dict)
+            assert list(sft_dataset[i].keys()) == ["input_ids", "labels"]
+            input_ids = sft_dataset[i]["input_ids"]
+            labels = sft_dataset[i]["labels"]
+            assert input_ids.shape == labels.shape == torch.Size([max_length])
+            
+            ignore_mask = labels == IGNORE_INDEX
+            assert input_ids.masked_select(torch.logical_not(ignore_mask))[0] == tokenizer.bos_token_id
+            check_content(input_ids.masked_select(torch.logical_not(ignore_mask)), tokenizer, model)
+            return
+        
     for i in range(max_dataset_size):
         assert isinstance(sft_dataset[i], dict)
         assert list(sft_dataset[i].keys()) == ["input_ids", "labels", "attention_mask"]
@@ -238,4 +260,7 @@ if __name__ == "__main__":
                         max_datasets_size=8,
                         max_length=256)
 
-    test_prompt_dataset(model="opt", max_datasets_size=2, max_length=128)
+    test_prompt_dataset(model="opt",
+                        max_datasets_size=2,
+                        max_length=128)
+

applications/Chat/tests/test_models.py

@@ -9,11 +9,12 @@ from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic
 from coati.models.generation import generate
 from coati.models.gpt import GPTRM, GPTActor, GPTCritic
 from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM
+from coati.models.chatglm import ChatGLMActor
 from coati.models.lora import LoraLinear, convert_to_lora_module
 from coati.models.loss import GPTLMLoss, LogExpLoss, LogSigLoss, PolicyLoss, ValueLoss
 from coati.models.opt import OPTRM, OPTActor, OPTCritic
 from coati.models.utils import calc_action_log_probs, compute_reward, masked_mean
-
+from coati.models.chatglm.chatglm_tokenizer import ChatGLMTokenizer
 
 @pytest.mark.parametrize("batch_size", [4])
 @pytest.mark.parametrize("seq_len", [32])
@@ -24,8 +25,10 @@ from coati.models.utils import calc_action_log_probs, compute_reward, masked_mea
         lambda: GPTActor(),
     # HACK: skip llama due to long execution time
     # lambda: LlamaActor(),
-        lambda: OPTActor()
-    ])
+    lambda: OPTActor(),
+    # lambda: ChatGLMActor(),
+])
+
 @pytest.mark.parametrize("generate_kwargs", [{
     "max_length": 64,
     "use_cache": True,
@@ -116,10 +119,12 @@ def test_lora(lora_rank: int, num_dim: int, num_layers: int):
     # HACK: skip llama due to long execution time
     # lambda: (LlamaActor(), LlamaCritic(), LlamaRM()),
     lambda: (OPTActor(), OPTCritic(), OPTRM()),
-    ])
+    lambda: (ChatGLMActor(), None, None),
+])
 @torch.no_grad()
-def test_models(models_maker: Callable[[], Tuple[Actor, Critic, RewardModel]], batch_size: int, seq_len: int):
-
+def test_models(models_maker: Callable[[], Tuple[Actor, Critic, RewardModel]],
+                batch_size: int,
+                seq_len: int):
     actor_input = {
         "input_ids": torch.randint(0, 100, (batch_size, seq_len)),
         "attention_mask": torch.randint(0, 2, (batch_size, seq_len))
@@ -135,20 +140,30 @@ def test_models(models_maker: Callable[[], Tuple[Actor, Critic, RewardModel]], b
     }
 
     actor, critic, rm = models_maker()
+    if isinstance(actor, ChatGLMActor):
+        actor = actor.float()
+        tokenizer = ChatGLMTokenizer.from_pretrained( "THUDM/chatglm-6b", trust_remote_code=True)
+        chatglm_special_token = torch.tensor([tokenizer.gmask_token_id, tokenizer.bos_token_id]).repeat(batch_size, 1)
+        actor_input ={
+        "input_ids": torch.cat((torch.randint(0, 100, (batch_size, seq_len//2)), chatglm_special_token, torch.randint(0, 100, (batch_size, seq_len//2 - 2))), dim=1),
+        "attention_mask": torch.randint(0, 2, (batch_size, 1, seq_len, seq_len))
+    }
     assert isinstance(actor, Actor)
     base_actor_model = get_base_model(actor)
+    actor_output = actor(**actor_input)
+    assert actor_output.logits.shape[:2] == (batch_size, seq_len)
+
+    if critic:
         assert isinstance(critic, Critic)
         base_critic_model = get_base_model(critic)
+        critic_output = critic(**critic_input)
+        assert critic_output.shape == (batch_size, )
+    
+    if rm:
         assert isinstance(rm, RewardModel)
         base_rm_model = get_base_model(rm)
-
-    actor_output = actor(**actor_input)
-    critic_output = critic(**critic_input)
         rm_output = rm(**rm_input)
-
-    assert actor_output.logits.shape[:2] == (batch_size, seq_len)
-    assert critic_output.shape == (batch_size,)
-    assert rm_output.shape == (batch_size,)
+        assert rm_output.shape == (batch_size, )
 
 
 @pytest.mark.parametrize("batch_size", [16])

colossalai/auto_parallel/passes/runtime_preparation_pass.py

@@ -144,7 +144,7 @@ def size_value_converting_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMesh
 
     # DeviceMesh information instructs the scaling of the size value
     device_mesh_info = {}
-    for dim, dim_size in enumerate(device_mesh.mesh_shape):
+    for dim, dim_size in enumerate(device_mesh.shape):
         device_mesh_info[dim] = dim_size
 
     def _extract_target_dim(node):

colossalai/booster/plugin/gemini_plugin.py

 import gc
 import logging
 import os
-import warnings
 from pathlib import Path
-from typing import Callable, Iterator, List, Optional, Tuple, Union
+from typing import Callable, Iterator, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
-from torch import Tensor
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
 from torch.utils.data import DataLoader
@@ -16,7 +14,6 @@ from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO, GeneralC
 from colossalai.checkpoint_io.utils import (
     get_model_base_filenames,
     get_optimizer_base_filenames,
-    get_shard_filename,
     load_shard_state_dict,
     save_config_file,
     save_state_dict,
@@ -25,8 +22,7 @@ from colossalai.checkpoint_io.utils import (
 from colossalai.cluster import DistCoordinator
 from colossalai.interface import ModelWrapper, OptimizerWrapper
 from colossalai.utils import get_current_device
-from colossalai.zero import GeminiDDP, zero_model_wrapper, zero_optim_wrapper
-from colossalai.zero.gemini import ZeroOptimizer
+from colossalai.zero import GeminiDDP, GeminiOptimizer
 from colossalai.zero.gemini.memory_tracer import MemStats
 
 from .dp_plugin_base import DPPluginBase
@@ -134,11 +130,7 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
         As there is communication when getting state dict, this must be called on all processes.
         """
 
-        # If optimizer is wrapped, unwrap it.
-        if isinstance(optimizer, OptimizerWrapper):
-            optimizer = optimizer.unwrap()
-
-        assert isinstance(optimizer, ZeroOptimizer)
+        assert isinstance(optimizer, GeminiOptimizer)
 
         if os.path.isfile(checkpoint):
             logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
@@ -185,11 +177,7 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
         if not os.path.isfile(checkpoint_index_file):
             logging.error(f"Provided path ({checkpoint_index_file}) should be a file")
 
-        # If optimizer is wrapped, unwrap it.
-        if isinstance(optimizer, OptimizerWrapper):
-            optimizer = optimizer.unwrap()
-
-        assert isinstance(optimizer, ZeroOptimizer)
+        assert isinstance(optimizer, GeminiOptimizer)
 
         # Read checkpoint index file.
         ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file)
@@ -222,47 +210,6 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
             super().save_lr_scheduler(lr_scheduler, checkpoint)
 
 
-class GeminiModel(ModelWrapper):
-
-    def __init__(self, module: nn.Module, gemini_config: dict, verbose: bool = False) -> None:
-        super().__init__(module)
-        self.module = zero_model_wrapper(module, zero_stage=3, gemini_config=gemini_config, verbose=verbose)
-
-    def unwrap(self):
-        # as save/load state dict is coupled with the GeminiDDP, we only return GeminiDDP model
-        return self.module
-
-
-class GeminiOptimizer(OptimizerWrapper):
-
-    def __init__(self,
-                 module: GeminiDDP,
-                 optimizer: Optimizer,
-                 zero_optim_config: dict,
-                 optim_kwargs: dict,
-                 verbose: bool = False) -> None:
-        optimizer = zero_optim_wrapper(module,
-                                       optimizer,
-                                       optim_config=zero_optim_config,
-                                       **optim_kwargs,
-                                       verbose=verbose)
-        super().__init__(optimizer)
-
-    def backward(self, loss: Tensor, *args, **kwargs):
-        self.optim.backward(loss)
-
-    def clip_grad_by_norm(self,
-                          max_norm: Union[float, int],
-                          norm_type: Union[float, int] = 2,
-                          error_if_nonfinite: bool = False,
-                          *args,
-                          **kwargs) -> Tensor:
-        warnings.warn(f'Gemini controls grad clipping by itself, so you should not use clip_grad_by_norm')
-
-    def clip_grad_by_value(self, clip_value: float, *args, **kwargs) -> None:
-        raise NotImplementedError('Gemini does not support clip_grad_by_value')
-
-
 class GeminiPlugin(DPPluginBase):
     """
     Plugin for Gemini.
@@ -279,8 +226,20 @@ class GeminiPlugin(DPPluginBase):
         >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
 
     Args:
-        device (torch.device): device to place the model.
-        placement_policy (str, optional): "cpu", "cuda", "auto". Defaults to "cpu".
+        chunk_config_dict (dict, optional): chunk configuration dictionary.
+        chunk_init_device (torch.device, optional): device to initialize the chunk.
+        placement_policy (str, optional): "static" and "auto". Defaults to "static".
+        shard_param_frac (float, optional): fraction of parameters to be sharded. Only for "static" placement.
+            If `shard_param_frac` is 1.0, it's equal to zero-3. If `shard_param_frac` is 0.0, it's equal to zero-2. Defaults to 1.0.
+        offload_optim_frac (float, optional): fraction of optimizer states to be offloaded. Only for "static" placement.
+            If `shard_param_frac` is 1.0 and `offload_optim_frac` is 0.0, it's equal to old "cuda" placement. Defaults to 0.0.
+        offload_param_frac (float, optional): fraction of parameters to be offloaded. Only for "static" placement.
+            For efficiency, this argument is useful only when `shard_param_frac` is 1.0 and `offload_optim_frac` is 1.0.
+            If `shard_param_frac` is 1.0, `offload_optim_frac` is 1.0 and `offload_param_frac` is 1.0, it's equal to old "cpu" placement.
+            When using static placement, we recommend users to tune `shard_param_frac` first and then `offload_optim_frac`.
+            Defaults to 0.0.
+        warmup_non_model_data_ratio (float, optional): ratio of expected non-model data memory during warmup. Only for "auto" placement. Defaults to 0.8.
+        steady_cuda_cap_ratio (float, optional): ratio of allowed cuda capacity for model data during steady state. Only for "auto" placement. Defaults to 0.9.
         precision (str, optional): precision. Support 'fp16' and 'bf16'. Defaults to 'fp16'.
         pin_memory (bool, optional): use pin memory on CPU. Defaults to False.
         force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False.
@@ -312,8 +271,14 @@ class GeminiPlugin(DPPluginBase):
 
     def __init__(
         self,
-        device: Optional[torch.device] = None,
-        placement_policy: str = "cpu",
+        chunk_config_dict: Optional[dict] = None,
+        chunk_init_device: Optional[torch.device] = None,
+        placement_policy: str = "static",
+        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
+        warmup_non_model_data_ratio: float = 0.8,    # only for auto placement
+        steady_cuda_cap_ratio: float = 0.9,    # only for auto placement
         precision: str = "fp16",
         pin_memory: bool = False,
         force_outputs_fp32: bool = False,
@@ -337,8 +302,14 @@ class GeminiPlugin(DPPluginBase):
         super().__init__()
         assert precision in SUPPORTED_PRECISION, f'precision {precision} is not supported'
         self.gemini_config = dict(
-            device=(device or get_current_device()),
+            chunk_config_dict=chunk_config_dict,
+            chunk_init_device=(chunk_init_device or 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,
+            warmup_non_model_data_ratio=warmup_non_model_data_ratio,
+            steady_cuda_cap_ratio=steady_cuda_cap_ratio,
             pin_memory=pin_memory,
             force_outputs_fp32=force_outputs_fp32,
             strict_ddp_mode=strict_ddp_mode,
@@ -395,12 +366,15 @@ class GeminiPlugin(DPPluginBase):
             # model = nn.SyncBatchNorm.convert_sync_batchnorm(model, None)
 
             # wrap the model with Gemini
-            model = GeminiModel(model, self.gemini_config, self.verbose)
+            model = GeminiDDP(model, **self.gemini_config, verbose=self.verbose)
 
         if optimizer is not None and \
                 not isinstance(optimizer, OptimizerWrapper):
-            optimizer = GeminiOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs,
-                                        self.verbose)
+            optimizer = GeminiOptimizer(optimizer,
+                                        model.unwrap(),
+                                        **self.zero_optim_config,
+                                        **self.optim_kwargs,
+                                        verbose=self.verbose)
 
         return model, optimizer, criterion, dataloader, lr_scheduler
 

colossalai/booster/plugin/low_level_zero_plugin.py

@@ -17,8 +17,13 @@ from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO
 from colossalai.checkpoint_io.utils import (
     get_optimizer_base_filenames,
     get_shard_filename,
+    load_param_groups_into_optimizer,
+    load_shard_state_dict,
+    load_states_into_optimizer,
     save_param_groups,
     save_state_dict,
+    sharded_optimizer_loading_epilogue,
+    unwrap_optimizer,
 )
 from colossalai.interface import ModelWrapper, OptimizerWrapper
 from colossalai.utils import get_current_device
@@ -126,9 +131,26 @@ class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO):
             index_file_path (str): Path to the index file
             prefix (str): Not used.
         """
-        super().load_sharded_optimizer(optimizer, index_file_path, prefix)
-        current_rank_state_dict = optimizer.optim.state_dict()['state']
-        for param_idx, state in current_rank_state_dict.items():
+        # If optimizer is wrapped, unwrap it.
+        if isinstance(optimizer, OptimizerWrapper):
+            optimizer = unwrap_optimizer(optimizer)
+
+        # Read checkpoint index file.
+        ckpt_index_file = CheckpointIndexFile.from_file(index_file_path)
+
+        # Load param_groups
+        param_group_path = ckpt_index_file.get_param_group_filename()
+        if param_group_path is None:
+            raise RuntimeError(f'Invalid index file path {index_file_path} for an optimizer. \
+                               Lacking param group file under current directory.')
+        id_map = load_param_groups_into_optimizer(optimizer, param_group_path)
+
+        checkpoint_files, _ = ckpt_index_file.get_checkpoint_filenames()
+
+        for shard_file in checkpoint_files:
+            state_dict = load_shard_state_dict(Path(shard_file), use_safetensors=False)
+            # shard state dict
+            for param_idx, state in state_dict.items():
                 for k, v in state.items():
                     if isinstance(v, torch.Tensor) and k != 'step':
                         padding_size = (self.coordinator.world_size -
@@ -138,7 +160,10 @@ class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO):
                             if padding_size > 0:
                                 v = torch.nn.functional.pad(v, [0, padding_size])
                             v_list = v.split(v.numel() // self.coordinator.world_size)
-                        current_rank_state_dict[param_idx][k] = v_list[self.coordinator.rank].detach()
+                            state_dict[param_idx][k] = v_list[self.coordinator.rank].detach().clone()
+            load_states_into_optimizer(optimizer, state_dict, id_map)
+
+        sharded_optimizer_loading_epilogue(optimizer)
 
 
 class LowLevelZeroModel(ModelWrapper):

colossalai/checkpoint_io/general_checkpoint_io.py

@@ -79,8 +79,6 @@ class GeneralCheckpointIO(CheckpointIO):
         for shard_file in checkpoint_files:
             state_dict = load_shard_state_dict(Path(shard_file), use_safetensors=False)
             load_states_into_optimizer(optimizer, state_dict, id_map)
-            del state_dict
-            gc.collect()
 
         sharded_optimizer_loading_epilogue(optimizer)
 

colossalai/checkpoint_io/utils.py

@@ -514,7 +514,7 @@ def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool = False):
                 f"Conversion from a {metadata['format']} safetensors archive to PyTorch is not implemented yet.")
         return safe_load_file(checkpoint_file)
     else:
-        return torch.load(checkpoint_file)
+        return torch.load(checkpoint_file, map_location=torch.device('cpu'))
 
 
 def load_state_dict_into_model(model: nn.Module,
@@ -574,7 +574,7 @@ def load_param_groups_into_optimizer(optimizer: Optimizer, param_group_path: str
 
     # Load list of param_groups from given file path.
     # The params in saved_groups are in the form of integer indices.
-    saved_groups = torch.load(param_group_path)
+    saved_groups = torch.load(param_group_path, map_location=torch.device('cpu'))
     if not isinstance(saved_groups, List):
         raise ValueError(f'The param_groups saved at {param_group_path} is not of List type')
 
@@ -730,7 +730,7 @@ def load_state_dict(checkpoint_file_path: Path):
 
     else:
         # load with torch
-        return torch.load(checkpoint_file_path)
+        return torch.load(checkpoint_file_path, map_location=torch.device('cpu'))
 
 
 def add_prefix(weights_name: str, prefix: Optional[str] = None) -> str:

colossalai/cli/launcher/run.py

@@ -265,6 +265,10 @@ def launch_multi_processes(args: Config) -> None:
     # establish remote connection
     runner.connect(host_info_list=active_device_pool, workdir=curr_path, env=env)
 
+    # overwrite master addr when num_nodes > 1 and not specified
+    if len(active_device_pool) > 1 and args.master_addr == "127.0.0.1":
+        args.master_addr = active_device_pool.hostinfo_list[0].hostname
+
     # execute distributed launching command
     for node_id, hostinfo in enumerate(active_device_pool):
         cmd = get_launch_command(master_addr=args.master_addr,

colossalai/kernel/cuda_native/mha/mem_eff_attn.py

@@ -2,7 +2,13 @@ import warnings
 
 HAS_MEM_EFF_ATTN = False
 try:
-    from xformers.ops.fmha import memory_efficient_attention
+    from xformers.ops.fmha import MemoryEfficientAttentionCutlassOp, memory_efficient_attention
+    from xformers.ops.fmha.attn_bias import (
+        BlockDiagonalCausalMask,
+        BlockDiagonalMask,
+        LowerTriangularMask,
+        LowerTriangularMaskWithTensorBias,
+    )
     HAS_MEM_EFF_ATTN = True
 except ImportError:
     warnings.warn('please install xformers from https://github.com/facebookresearch/xformers')
@@ -16,13 +22,6 @@ if HAS_MEM_EFF_ATTN:
     from typing import Optional
 
     import torch
-    from xformers.ops.fmha import MemoryEfficientAttentionCutlassOp
-    from xformers.ops.fmha.attn_bias import (
-        BlockDiagonalCausalMask,
-        BlockDiagonalMask,
-        LowerTriangularMask,
-        LowerTriangularMaskWithTensorBias,
-    )
 
     from .utils import SeqLenInfo
 

colossalai/tensor/colo_parameter.py

@@ -3,9 +3,15 @@ from typing import Optional
 import torch
 
 from colossalai.tensor.colo_tensor import ColoTensor
-from colossalai.tensor.const import TensorType
 from colossalai.tensor.param_op_hook import ColoParamOpHookManager
-from colossalai.tensor.tensor_spec import ColoTensorSpec
+
+from .colo_tensor import _convert_output
+
+WHITE_LIST_FUNCS = {torch.Tensor.__getitem__}
+
+
+def is_no_hook_op(func) -> bool:
+    return func.__name__.startswith('__') and func not in WHITE_LIST_FUNCS
 
 
 def filter_colo_parameters(*args, **kwargs):
@@ -41,44 +47,16 @@ class ColoParameter(ColoTensor, torch.nn.Parameter):
 
     """
 
-    def __new__(cls,
-                data: Optional[torch.Tensor] = None,
-                requires_grad: bool = True,
-                spec: ColoTensorSpec = None) -> 'ColoParameter':
+    def __new__(cls, data: Optional[torch.Tensor] = None, requires_grad: bool = True) -> 'ColoParameter':
         if data is None:
             data = torch.empty(0)
         return torch.Tensor._make_subclass(cls, data, requires_grad)
 
-    def __init__(self,
-                 data: Optional[torch.Tensor] = None,
-                 requires_grad: bool = True,
-                 spec: ColoTensorSpec = None) -> None:
-        ColoTensor.__init__(self, data, spec)
-        self._type = TensorType.MODEL
-        # a list contains modules sharing this ColoParameter with others.
-        self._shared_param_modules = []
-
-    @property
-    def shared_param_modules(self):
-        return self._shared_param_modules
-
-    @staticmethod
-    def from_torch_tensor(tensor: torch.Tensor,
-                          requires_grad: bool = True,
-                          spec: ColoTensorSpec = None) -> 'ColoParameter':
-        tensor = tensor.as_subclass(ColoParameter)
-        tensor.__init__(tensor, requires_grad=requires_grad, spec=spec)
-        return tensor
-
-    def __repr__(self):
-        return super(ColoParameter, self).__repr__()
-
     @classmethod
     def __torch_function__(cls, func, types, args=..., kwargs=None):
-        if ColoParamOpHookManager.has_hook():
-            if not func.__name__.startswith('__'):
         if kwargs is None:
             kwargs = {}
+        if ColoParamOpHookManager.has_hook() and not is_no_hook_op(func):
             params = filter_colo_parameters(*args, **kwargs)
             if len(params) > 0:
                 with torch._C.DisableTorchFunction():
@@ -87,7 +65,7 @@ class ColoParameter(ColoTensor, torch.nn.Parameter):
                 ret = super().__torch_function__(func, types, args, kwargs)
                 with torch._C.DisableTorchFunction():
                     ret = ColoParamOpHookManager.post_op(params, ret)
-                    return ret
+                return _convert_output(ret, func)
         return super().__torch_function__(func, types, args, kwargs)
 
     def __deepcopy__(self, memo):
@@ -96,9 +74,7 @@ class ColoParameter(ColoTensor, torch.nn.Parameter):
         else:
             with torch._C.DisableTorchFunction():
                 data = self.data.clone()
-            tensor = ColoParameter(data,
-                                   self.requires_grad,
-                                   spec=ColoTensorSpec(self.get_process_group(), self.dist_spec, self.compute_spec))
+            tensor = ColoParameter(data, self.requires_grad)
             memo[id(self)] = tensor
             return tensor
 

colossalai/tensor/colo_tensor.py

-import operator
-from copy import copy
-from functools import lru_cache, reduce
-from typing import Callable, Optional, Set
+from functools import lru_cache
+from typing import Callable, Set
 
 import torch
 
-from colossalai.tensor.dist_spec_mgr import DistSpecManager
-from colossalai.tensor.distspec import DistPlacementPattern, ReplicaSpec, _DistSpec
-from colossalai.tensor.process_group import ProcessGroup
-from colossalai.tensor.tensor_spec import ColoTensorSpec
-
-from .const import TensorType
-from .op_wrapper import _COLOSSAL_OPS
+INPALCE_MAPPING = {
+    torch.Tensor.add_: torch.Tensor.add,
+    torch.Tensor.sub_: torch.Tensor.sub,
+    torch.Tensor.mul_: torch.Tensor.mul,
+    torch.Tensor.div_: torch.Tensor.div
+}
 
 
 @lru_cache(None)
@@ -25,61 +22,37 @@ def _get_my_nowrap_functions() -> Set[Callable]:
     }
 
 
-def _convert_output(output, colo_spec: ColoTensorSpec):
-    if type(output) == torch.Tensor:
-        return ColoTensor.from_torch_tensor(output, colo_spec)
+def _convert(output):
+    if isinstance(output, torch.Tensor) and not isinstance(output, ColoTensor):
+        output.__class__ = ColoTensor
     elif isinstance(output, (list, tuple)):
-        return type(output)(_convert_output(o, colo_spec) for o in output)
-    else:
+        output = type(output)(_convert(o) for o in output)
     return output
 
 
-def _get_spec_from_args(args, kwargs) -> ColoTensorSpec:
-    for elem in args:
-        if isinstance(elem, ColoTensor):
-            pg = elem.get_process_group()
-            dp = elem.dist_spec
-            return ColoTensorSpec(pg, dp)
-        elif isinstance(elem, (list, tuple)):
-            spec = _get_spec_from_args(elem, {})
-            if spec is not None:
-                return spec
-    for k, v in kwargs.items():
-        if isinstance(v, ColoTensor):
-            pg = v.get_process_group()
-            dp = v.dist_spec
-            return ColoTensorSpec(pg, dp)
-    return None
+def _convert_output(output, func):
+    if func in _get_my_nowrap_functions():
+        return output
+    return _convert(output)
 
 
 class ColoTensor(torch.Tensor):
     """ Data Structure for Tensor in Colossal-AI. It is a subclass of torch.Tensor.
 
-    The Colotensor can be initialized with a PyTorch tensor in the following ways.
-
-        >>> pg = ProcessGroup()
-        >>> colo_t1 = ColoTensor(torch.randn(2,3), spec = ColoTensorSpec(pg, ReplicaSpec()))
-        >>> # The tensor passed in is a tensor after sharding but not a global tensor.
-        >>> shard_spec = ShardSpec(process_group=ProcessGroup(tp=world_size),
-        >>>                 dims=[0],
-        >>>                 num_partitions=[world_size])
-        >>> tensor_spec = ColoTensorSpec(pg, shard_spec)
-        >>> colo_t2 = ColoTensor.from_torch_tensor(t_ref.clone(), tensor_spec)
+    It is only used to trigger the torch function hook.
 
     Args:
         data (torch.Tensor): a torch tensor used as the payload the colotensor.
-        spec (ColoTensorSpec, optional): the tensor spec of initialization. Defaults to ColoTensorSpec(ReplicaSpec()).
     """
     torch_major = int(torch.__version__.split('.')[0])
     torch_minor = int(torch.__version__.split('.')[1])
 
-    def __new__(cls, data: torch.Tensor, spec: ColoTensorSpec) -> 'ColoTensor':
+    def __new__(cls, data: torch.Tensor) -> 'ColoTensor':
         """
         The signature of the __new__ has to be consistent with the torch.Tensor.
 
         Args:
             data (torch.Tensor): a torch tensor used as the payload the colotensor.
-            spec (TensorSpec, optional): the tensor spec of initialization.
 
         Returns:
             ColoTensor: a ColoTensor wrappers the data.
@@ -88,86 +61,6 @@ class ColoTensor(torch.Tensor):
             data = torch.empty(0)
         return torch.Tensor._make_subclass(cls, data, data.requires_grad)
 
-    def __init__(self, data: torch.Tensor, spec: Optional[ColoTensorSpec] = None) -> None:
-        # If not set spec, use a DP process group and replicate dist spec
-        if spec is None:
-            self.has_initialized = False
-            self.dist_spec = ReplicaSpec()
-            self.compute_spec = None
-            self.process_group = ProcessGroup()
-        else:
-            self.has_initialized = True
-            self.dist_spec = spec.dist_attr
-            self.compute_spec = spec.compute_attr
-            if spec.pg is None:
-                self.process_group = ProcessGroup()
-            else:
-                self.process_group = spec.pg
-
-        self._type = TensorType.NONMODEL
-
-    def has_compute_spec(self) -> bool:
-        return self.compute_spec is not None
-
-    def is_model_data(self) -> bool:
-        return self._type == TensorType.MODEL
-
-    def get_process_group(self) -> 'ProcessGroup':
-        return self.process_group
-
-    def set_process_group(self, pg: ProcessGroup):
-        """set_process_group
-        change the pg of the ColoTensor. Note that the valid use cases is limited.
-        It works for the target pg is DP and TP only and current dist spec of the Tensor is Replica.
-
-        Args:
-            pg (ProcessGroup): target pg
-
-        """
-        assert isinstance(pg, ProcessGroup), f"pg as type {type(pg)} is invalid"
-        # if the new pg is the same as the old pg, just returns
-        if self.process_group == pg:
-            return
-        assert self.process_group.tp_world_size() == 1 or self.process_group.dp_world_size() == 1, \
-            "Can not set_process_group on a ColoTensor whose process_group is both tp > 1 and world group > 1"
-        assert self.dist_spec.placement.value == 'r', \
-            "Can not set_process_group on a ColoTensor whose dist spec is not Replica"
-
-        self.process_group = pg
-
-    def get_tp_world_size(self) -> int:
-        return self.process_group.tp_world_size()
-
-    def get_dp_world_size(self) -> int:
-        """get_dp_world_size
-        get the dp world size of the tensor.
-
-        Returns:
-            int: dp world size
-        """
-        return self.process_group.dp_world_size()
-
-    def set_dist_spec(self, dist_spec: _DistSpec):
-        """set_dist_spec
-        set dist spec and change the payloads.
-
-        Args:
-            dist_spec (_DistSpec): target dist spec.
-        """
-        assert isinstance(dist_spec, _DistSpec)
-        assert self.process_group is not None
-        self._redistribute(dist_spec)
-
-    def set_tensor_spec(self, dist_spec, compute_spec):
-        if dist_spec is not None:
-            assert isinstance(dist_spec, _DistSpec), f"{type(dist_spec)}"
-            self.set_dist_spec(dist_spec)
-        if compute_spec is not None:
-            self.compute_spec = compute_spec
-
-    def has_compute_pattern(self, compute_pattern):
-        return self.compute_spec.compute_pattern == compute_pattern
-
     @classmethod
     def __torch_function__(cls, func, types, args=(), kwargs=None):
         if kwargs is None:
@@ -175,9 +68,6 @@ class ColoTensor(torch.Tensor):
 
         if not all(issubclass(cls, t) for t in types):
             return NotImplemented
-        global _COLOSSAL_OPS
-        if func in _COLOSSAL_OPS:
-            func = _COLOSSAL_OPS[func]
 
         if cls.torch_major > 1 or (cls.torch_major == 1 and cls.torch_minor >= 12):
             # in order to trigger pre-op hook in the forward of checkpoint module
@@ -189,94 +79,16 @@ class ColoTensor(torch.Tensor):
                 tensor_kwargs = {k: torch.Tensor(v) if torch.is_tensor(v) else v for k, v in kwargs.items()}
                 return backward_tensor.backward(**tensor_kwargs)
 
+        # replace the in-place function
+        if func in INPALCE_MAPPING:
+            func = INPALCE_MAPPING[func]
+        # set the 'inplace' kwargs to False
+        if 'inplace' in kwargs:
+            kwargs['inplace'] = False
+
         with torch._C.DisableTorchFunction():
             ret = func(*args, **kwargs)
-            if func in _get_my_nowrap_functions():
-                return ret
-            else:
-                colo_spec = _get_spec_from_args(args, kwargs)
-                return _convert_output(ret, colo_spec)
-
-    def __repr__(self):
-        output_list = [super(ColoTensor, self).__repr__()]
-        output_list.append(str(self.process_group))
-        output_list.append(str(self.dist_spec))
-        if self.compute_spec is not None:
-            output_list.append(str(self.compute_spec))
-        return "\n".join(output_list)
-
-    def _redistribute(self, dist_spec: _DistSpec) -> None:
-        """_redistribute
-        Note the function will not handle the logic of backward propagation!
-        It is used during model tensor initializations as an internal function.
-
-        Args:
-            dist_spec (_DistSpec): the target dist. spec.
-        """
-        assert self.grad_fn is None, "Current tensor has grad_fn and it can't get converted"
-        with DistSpecManager.no_grad():
-            self.data = DistSpecManager.handle_trans_spec(self.data, self.dist_spec, dist_spec, self.process_group)
-        self.dist_spec = dist_spec
-
-    def redistribute(self, dist_spec: _DistSpec, pg: Optional[ProcessGroup] = None) -> 'ColoTensor':
-        """redistribute
-        Redistribute the tensor among processes. The rule is like this:
-
-        1. If the pg is None, then redistribute the tensor payload among the TP process group. Keep the
-        DP process group not changed.
-
-        2. If the pg is not not None and not equal to the current process group.
-        First, convert the tensor as replicated among the TP process group.
-        Second, reset the process group to the new pg.
-        Third, convert the tensor (new replicated both among the tp process group) to the new dist_spec.
-
-        Args:
-            dist_spec (_DistSpec): the new dist spec.
-            pg (Optional[ProcessGroup], optional): the new process group . Defaults to None.
-
-        Returns:
-            ColoTensor: a redistributed colotensor
-        """
-        if pg is not None and pg != self.get_process_group():
-            # if the pg is not equal, convert the current tensor to replicated
-            handled = self.redistribute(ReplicaSpec())
-        else:
-            handled = self
-            pg = self.process_group
-
-        ret = DistSpecManager.handle_trans_spec(handled, handled.dist_spec, dist_spec, pg)
-        return ColoTensor.from_torch_tensor(ret, ColoTensorSpec(pg=pg, dist_attr=dist_spec))
-
-    def to_replicate_(self):
-        """to_replicate_
-
-        an inline member function, converting dist spec of the tensor to REPLICATE
-        """
-        self._redistribute(dist_spec=ReplicaSpec())
-
-    def to_replicate(self) -> 'ColoTensor':
-        """to_replicate
-
-        converting dist spec of the tensor to ReplicaSpec()
-        """
-        return self.redistribute(ReplicaSpec())
-
-    @staticmethod
-    def from_torch_tensor(tensor: torch.Tensor, spec: Optional[ColoTensorSpec] = None) -> 'ColoTensor':
-        """from_torch_tensor
-
-        A static method builds a `ColoTensor` from a PyTorch Tensor.
-
-        Args:
-            tensor (torch.Tensor): the pytorch tensor, which is a local tensor for this rank not a global tensor.
-            spec (Optional[ColoTensorSpec], optional): tensor spec. Defaults to None.
-
-        Returns:
-            ColoTensor: a ColoTensor
-        """
-        tensor = tensor.as_subclass(ColoTensor)
-        tensor.__init__(tensor, spec=spec)
-        return tensor
+            return _convert_output(ret, func)
 
     def __deepcopy__(self, memo):
         if id(self) in memo:
@@ -284,60 +96,6 @@ class ColoTensor(torch.Tensor):
         else:
             with torch._C.DisableTorchFunction():
                 data = self.data.clone()
-            tensor = ColoTensor(data, spec=copy(ColoTensorSpec(self.process_group, self.dist_spec, self.compute_spec)))
+            tensor = ColoTensor(data)
             memo[id(self)] = tensor
             return tensor
-
-    # override builtin functions which must use tensor in replicate placement #
-
-    def size_local(self, *args) -> torch.Size:
-        with torch._C.DisableTorchFunction():
-            return super().size(*args)
-
-    def size_global(self, *args) -> torch.Size:
-        """size_global
-
-        override the torch building size()
-        the shape passed in must be in a replicate placement.
-
-        Returns:
-            torch.Size: the global tensor shape
-        """
-        if self.is_replicate():
-            return self.size_local(*args)
-        spec = self.dist_spec
-        dims = spec.dims
-        num_partitions = spec.num_partitions
-        # import inspect
-        # print(*['{:40}| {}:{}\n'.format(x.function, x.filename, x.lineno) for x in inspect.stack()])
-        size_list = list(self.size_local())
-        for dim, num_partition in zip(dims, num_partitions):
-            size_list[dim] *= num_partition
-        if args == ():
-            return torch.Size(size_list)
-        else:
-            return size_list[args[0]]
-
-    def numel_global(self):
-        """Returns the number of elements in the tensor when it's replicated.
-        """
-        return reduce(operator.mul, self.size_global(), 1)
-
-    # Some API for dist spec check
-
-    def is_replicate(self):
-        return self.dist_spec.placement == DistPlacementPattern.REPLICATE \
-               or (len(self.dist_spec.num_partitions) == 1
-                   and self.dist_spec.num_partitions[0] == 1) \
-               or (self.process_group.tp_world_size() == 1)
-
-    def is_shard_1dcol(self):
-        return self.dist_spec.placement == DistPlacementPattern.SHARD \
-               and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == -1
-
-    def is_shard_1drow(self):
-        return self.dist_spec.placement == DistPlacementPattern.SHARD \
-               and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == 0
-
-    def is_sharded(self):
-        return self.dist_spec.placement == DistPlacementPattern.SHARD

colossalai/tensor/param_op_hook.py

@@ -3,9 +3,7 @@ from contextlib import contextmanager
 from typing import Any, List, Tuple
 
 import torch
-
-from colossalai.tensor.colo_tensor import ColoTensor
-from colossalai.tensor.tensor_spec import ColoTensorSpec
+from torch.utils._pytree import TreeSpec, tree_flatten, tree_unflatten
 
 
 class ColoParamOpHook(ABC):
@@ -82,26 +80,18 @@ class ColoParamOpHookManager:
     @staticmethod
     def pre_op(params: List[torch.Tensor], *args: Any) -> list:
         ColoParamOpHookManager._trigger_pre_forward(params)
-        grad_args, rear_args = _get_grad_args(*args)
-        colo_info = _get_colo_tensors_info(*grad_args)
-        rets = PreFwdPostBwd.apply(params, *grad_args)
-        update_args = _update_colo_tensors(colo_info, *rets)
-        if rear_args is None:
-            return update_args
-        else:
-            arg_zero = (tuple(update_args),)
-            return arg_zero + rear_args
+        # auto grad function can only recognize torch.Tensor, thus we have to flatten the input
+        # if one of the input requires grad, all the output will be treated as requires grad
+        # and will have grad fn even the corresponding input does not require grad
+        # we have to extract tensors requiring grad into flat list and then merge them back
+        grad_args, other_args, grad_flags, spec = _flatten_grad_args(args)
+        new_grad_args = PreFwdPostBwd.apply(params, *grad_args)
+        return _merge_args(new_grad_args, other_args, grad_flags, spec)
 
     @staticmethod
     def post_op(params: List[torch.Tensor], arg: Any) -> Any:
         ColoParamOpHookManager._trigger_post_forward(params)
-        colo_info = _get_colo_tensors_info(arg)
-        ret = PostFwdPreBwd.apply(params, arg)
-        res = _update_colo_tensors(colo_info, ret)
-        if len(res) == 1:
-            return res[0]
-        else:
-            return res
+        return PostFwdPreBwd.apply(params, arg)
 
     @staticmethod
     def has_hook() -> bool:
@@ -141,57 +131,24 @@ def _is_grad_tensor(obj) -> bool:
     return False
 
 
-def _has_grad_tensor(obj) -> bool:
-    if isinstance(obj, tuple) or isinstance(obj, list):
-        for x in obj:
-            if _has_grad_tensor(x):
-                return True
-        return False
-    elif isinstance(obj, dict):
-        for x in obj.values():
-            if _has_grad_tensor(x):
-                return True
-        return False
-    else:
-        return _is_grad_tensor(obj)
-
-
-def _get_grad_args(*args):
-    # if there is no grad tensors, do nothing
-    if not _has_grad_tensor(args):
-        return args, None
-    # returns the identical args if there is a grad tensor
-    for obj in args:
-        if _is_grad_tensor(obj):
-            return args, None
-    # otherwise, the first argument should be a tuple of grad tensors
-    # if there is no grad tensor, the backward of PreFwdPostBwd can't be triggered
-    arg_zero = args[0]
-    if not isinstance(arg_zero, tuple):
-        raise NotImplementedError("Some torch function is incompatible because of its complicated inputs.")
-    check_grad_flag = False
-    for obj in arg_zero:
-        check_grad_flag |= _is_grad_tensor(obj)
-    if not check_grad_flag:
-        raise NotImplementedError("Some torch function is incompatible because of its complicated inputs.")
-    return arg_zero, args[1:]
-
-
-def _get_colo_tensors_info(*args) -> list:
-    info = []
-    for arg in args:
-        if isinstance(arg, ColoTensor):
-            info.append((arg.__class__, ColoTensorSpec(arg.get_process_group(), arg.dist_spec, arg.compute_spec)))
+def _flatten_grad_args(args) -> Tuple[list, list, List[bool], TreeSpec]:
+    flat_args, spec = tree_flatten(args)
+    grad_args = []
+    other_args = []
+    grad_flags = []
+    for arg in flat_args:
+        flag = _is_grad_tensor(arg)
+        grad_flags.append(flag)
+        if flag:
+            grad_args.append(arg)
         else:
-            info.append(None)
-    return info
-
-
-def _update_colo_tensors(info, *args) -> list:
-    ret = []
-    for t_info, arg in zip(info, args):
-        if t_info is not None:
-            t_cls, spec = t_info
-            arg = t_cls.from_torch_tensor(arg, spec=spec)
-        ret.append(arg)
-    return ret
+            other_args.append(arg)
+    assert len(grad_args) > 0
+    return grad_args, other_args, grad_flags, spec
+
+
+def _merge_args(grad_args, other_args, grad_flags, spec):
+    grad_iter = iter(grad_args)
+    other_iter = iter(other_args)
+    flat_args = [next(grad_iter) if flag else next(other_iter) for flag in grad_flags]
+    return tree_unflatten(flat_args, spec)

colossalai/zero/__init__.py

@@ -2,8 +2,7 @@ from .gemini import (
     ColoInitContext,
     GeminiAdamOptimizer,
     GeminiDDP,
-    ZeroDDP,
-    ZeroOptimizer,
+    GeminiOptimizer,
     get_static_torch_model,
     post_process_colo_init_ctx,
 )
@@ -11,6 +10,6 @@ from .low_level import LowLevelZeroOptimizer
 from .wrapper import zero_model_wrapper, zero_optim_wrapper
 
 __all__ = [
-    'ZeroDDP', 'GeminiDDP', 'ZeroOptimizer', 'GeminiAdamOptimizer', 'zero_model_wrapper', 'zero_optim_wrapper',
+    'GeminiDDP', 'GeminiOptimizer', 'GeminiAdamOptimizer', 'zero_model_wrapper', 'zero_optim_wrapper',
     'LowLevelZeroOptimizer', 'ColoInitContext', 'post_process_colo_init_ctx', 'get_static_torch_model'
 ]

colossalai/zero/gemini/__init__.py

 from .chunk import ChunkManager, TensorInfo, TensorState, search_chunk_configuration
 from .colo_init_context import ColoInitContext, post_process_colo_init_ctx
-from .gemini_ddp import GeminiDDP, ZeroDDP
+from .gemini_ddp import GeminiDDP
 from .gemini_mgr import GeminiManager
-from .gemini_optimizer import GeminiAdamOptimizer, ZeroOptimizer
+from .gemini_optimizer import GeminiAdamOptimizer, GeminiOptimizer
 from .utils import get_static_torch_model
 
 __all__ = [
-    'GeminiManager', 'TensorInfo', 'TensorState', 'ChunkManager', 'search_chunk_configuration', 'ZeroDDP', 'GeminiDDP',
-    'get_static_torch_model', 'GeminiAdamOptimizer', 'ZeroOptimizer', 'ColoInitContext', 'post_process_colo_init_ctx'
+    'GeminiManager', 'TensorInfo', 'TensorState', 'ChunkManager', 'search_chunk_configuration', 'GeminiDDP',
+    'get_static_torch_model', 'GeminiAdamOptimizer', 'GeminiOptimizer', 'ColoInitContext', 'post_process_colo_init_ctx'
 ]

colossalai/zero/gemini/chunk/chunk.py

@@ -4,8 +4,8 @@ from typing import Dict, List, Optional
 
 import torch
 import torch.distributed as dist
+from torch.distributed import ProcessGroup
 
-from colossalai.tensor import ProcessGroup as ColoProcessGroup
 from colossalai.utils import get_current_device
 
 
@@ -55,7 +55,7 @@ class Chunk:
 
     def __init__(self,
                  chunk_size: int,
-                 process_group: ColoProcessGroup,
+                 process_group: ProcessGroup,
                  dtype: torch.dtype,
                  init_device: Optional[torch.device] = None,
                  cpu_shard_init: bool = False,
@@ -69,7 +69,7 @@ class Chunk:
 
         Args:
             chunk_size (int): the number of elements in the chunk
-            process_group (ColoProcessGroup): the process group of this chunk
+            process_group (ProcessGroup): the process group of this chunk
             dtype (torch.dtype): the data type of the chunk
             init_device (torch.device): optional, During the chunk construction process, where the tensor is stored.
                 The default value is None, which is the current GPU
@@ -83,7 +83,7 @@ class Chunk:
         self.chunk_size = chunk_size
         self.utilized_size = 0
 
-        self.torch_pg = process_group.dp_process_group()
+        self.torch_pg = process_group
         self.pg_size = dist.get_world_size(self.torch_pg)
         self.pg_rank = dist.get_rank(self.torch_pg)
 

colossalai/zero/gemini/chunk/manager.py

@@ -2,8 +2,9 @@ from collections import deque
 from typing import Deque, Dict, Iterable, List, Optional, Set, Tuple
 
 import torch
+import torch.distributed as dist
+from torch.distributed import ProcessGroup
 
-from colossalai.tensor import ColoTensor
 from colossalai.utils import get_current_device
 
 from .chunk import Chunk, ChunkFullError, TensorState
@@ -27,16 +28,17 @@ class ChunkManager:
             self.dp_degree_chunk_size_dict[k] = v.pop('chunk_size')
             v['init_device'] = self.device
 
-        self.chunk_groups: Dict[str, Deque] = dict()
+        self.chunk_groups: Dict[str, Deque[Chunk]] = dict()
         self.tensor_chunk_map: Dict[torch.Tensor, Chunk] = dict()
         self.accessed_chunks: Set[Chunk] = set()
         self.accessed_mem: int = 0
         self.total_mem: Dict[str, int] = {'cpu': 0, 'cuda': 0}
 
     def register_tensor(self,
-                        tensor: ColoTensor,
+                        tensor: torch.Tensor,
                         group_type: str,
                         config_key: int,
+                        process_group: ProcessGroup,
                         cpu_offload: bool = False,
                         pin_memory: bool = False) -> None:
         """
@@ -51,7 +53,7 @@ class ChunkManager:
             pin_memory: whether the chunk is pinned in the cpu memory
         """
         assert tensor not in self.tensor_chunk_map
-        assert isinstance(tensor, ColoTensor), "Please feed ColoTensor to this ChunkManager"
+        assert isinstance(tensor, torch.Tensor), "Please feed Tensor to this ChunkManager"
         assert config_key in self.dp_degree_chunk_size_dict
 
         chunk_size = self.dp_degree_chunk_size_dict[config_key]
@@ -73,12 +75,12 @@ class ChunkManager:
 
             if tensor.numel() > chunk_size:
                 chunk_size = tensor.numel()
-                dp_size = tensor.get_dp_world_size()
+                dp_size = dist.get_world_size(process_group)
                 chunk_size = chunk_size + (-chunk_size % dp_size)
 
             chunk = Chunk(
                 chunk_size=chunk_size,
-                process_group=tensor.process_group,
+                process_group=process_group,
                 dtype=tensor.dtype,
                 cpu_shard_init=cpu_offload,
                 pin_memory=pin_memory,
@@ -220,7 +222,7 @@ class ChunkManager:
                 msg.append(f'[{i}] {chunk}\n')
         return ''.join(msg)
 
-    def __get_chunk_group(self, group_name: str) -> Deque:
+    def __get_chunk_group(self, group_name: str) -> Deque[Chunk]:
         """Register a chunk group.
         """
         if group_name not in self.chunk_groups:

colossalai/zero/gemini/chunk/search_utils.py

@@ -4,6 +4,7 @@ from typing import Dict, List, Optional, Tuple
 import numpy as np
 import torch.distributed as dist
 import torch.nn as nn
+from torch.distributed import ProcessGroup
 
 from colossalai.tensor import ColoParameter
 from colossalai.utils import is_ddp_ignored
@@ -59,7 +60,7 @@ def _get_unused_byte(size_list: List[int], chunk_size: int) -> int:
     return left + acc
 
 
-def _tensor_numel(local_param: ColoParameter, strict_ddp_flag: bool) -> int:
+def _tensor_numel(local_param: ColoParameter) -> int:
     """_tensor_numel
 
     Get the number of elements of a tensor.
@@ -71,15 +72,12 @@ def _tensor_numel(local_param: ColoParameter, strict_ddp_flag: bool) -> int:
     Returns:
         int: the number of elements.
     """
-    if strict_ddp_flag and type(local_param) is ColoParameter:
-        return local_param.numel_global()
-    else:
-        # if local_param is not ColoParameter, we assume it's replicated
+    # TODO(ver217): support dtensor here
     return local_param.numel()
 
 
 def classify_params_by_dp_degree(param_order: OrderedParamGenerator,
-                                 strict_ddp_flag: bool = False) -> Dict[int, List[ColoParameter]]:
+                                 process_group: ProcessGroup) -> Dict[int, List[ColoParameter]]:
     """classify_params_by_dp_degree
 
     Classify the parameters by their dp degree
@@ -97,13 +95,7 @@ def classify_params_by_dp_degree(param_order: OrderedParamGenerator,
         # assert isinstance(param, ColoParameter), "please init model in the ColoInitContext"
         if is_ddp_ignored(param):
             continue
-
-        if strict_ddp_flag or type(param) is not ColoParameter:
-            # if model is not initialized with ColoInitContext, we assume it's replicated
-            # TODO(ver217): integrate DTensor
-            param_key = dist.get_world_size()
-        else:
-            param_key = param.process_group.dp_world_size()
+        param_key = dist.get_world_size(process_group)
 
         if param_key not in params_dict:
             params_dict[param_key] = []
@@ -119,6 +111,7 @@ def search_chunk_configuration(
         min_chunk_size_m: float = 32,
         filter_exlarge_params: bool = True,
         strict_ddp_flag: bool = False,
+        process_group: Optional[ProcessGroup] = None,
         memstas: Optional[MemStats] = None) -> Tuple[Dict, int, int]:
     """search_chunk_configuration
 
@@ -149,7 +142,7 @@ def search_chunk_configuration(
     min_chunk_size = round(min_chunk_size_m * 1024**2)
     assert search_range >= 0
 
-    params_dict = classify_params_by_dp_degree(param_order, strict_ddp_flag)
+    params_dict = classify_params_by_dp_degree(param_order, process_group)
     size_lcm = np.lcm.reduce(list(params_dict.keys()))
     config_dict: Dict[int, Dict] = dict()
     total_param_size = 0
@@ -157,7 +150,7 @@ def search_chunk_configuration(
     size_dict: Dict[int, List[int]] = dict()
     for dp_degree in params_dict:
         params_list = params_dict[dp_degree]
-        size_list = [_tensor_numel(p, strict_ddp_flag) for p in params_list]
+        size_list = [_tensor_numel(p) for p in params_list]
         group_acc_size = sum(size_list)
         total_param_size += group_acc_size