import warnings from typing import Optional, Union import torch import torch.distributed as dist import torch.nn as nn import torch.optim as optim from coati.models.base import LM, Actor, RewardModel from coati.models.lora import LoraLinear from torch.optim import Optimizer from transformers.modeling_utils import PreTrainedModel from transformers.tokenization_utils_base import PreTrainedTokenizerBase import colossalai from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import CPUAdam, HybridAdam from colossalai.tensor import ProcessGroup, ShardSpec from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP, zero_model_wrapper, zero_optim_wrapper from colossalai.zero.gemini.utils import get_static_torch_model from .base import Strategy from .ddp import DDPStrategy logger = get_dist_logger(__name__) class ColossalAIStrategy(DDPStrategy): """ The strategy for training with ColossalAI. Args: stage(int): The stage to use in ZeRO. Choose in (1, 2, 3) precision(str): The precision to use. Choose in ('fp32', 'fp16'). Stage 3 only supports fp16. seed(int): The seed for the random number generator. shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3. This is not compativle with `from_pretrained()`. We temporarily disable this and will support it in the future. placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda') If it is “cpu”, parameters, gradients and optimizer states will be offloaded to CPU, If it is “cuda”, they will not be offloaded, which means max CUDA memory will be used. It is the fastest. pin_memory(bool): Whether to pin the memory for the data loader. Only for ZeRO-3. force_outputs_fp32(bool): Whether to force the outputs to be fp32. Only for ZeRO-3. search_range_mb(int): The search range in MB for the chunk size. Only for ZeRO-3. hidden_dim(optional, int): The hidden dimension for the gemini. Only for ZeRO-3. min_chunk_size_mb(float): The minimum chunk size in MB. Only for ZeRO-3. gpu_margin_mem_ratio(float): The margin memory ratio for the GPU. Only for ZeRO-3. reduce_bugket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2. overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2. initial_scale(float): The initial scale for the optimizer. growth_factor(float): The growth factor for the optimizer. backoff_factor(float): The backoff factor for the optimizer. growth_interval(int): The growth interval for the optimizer. hysteresis(int): The hysteresis for the optimizer. min_scale(float): The minimum scale for the optimizer. max_scale(float): The maximum scale for the optimizer. max_norm(float): The maximum norm for the optimizer. norm_type(float): The norm type for the optimizer. """ def __init__( self, stage: int = 3, precision: str = 'fp16', seed: int = 42, shard_init: bool = False, # only for stage 3 placement_policy: str = 'cuda', pin_memory: bool = True, # only for stage 3 force_outputs_fp32: bool = False, # only for stage 3 search_range_mb: int = 32, # only for stage 3 hidden_dim: Optional[int] = None, # only for stage 3 min_chunk_size_mb: float = 32, # only for stage 3 gpu_margin_mem_ratio: float = 0.0, # only for stage 3 reduce_bucket_size: int = 12 * 1024**2, # only for stage 1&2 overlap_communication: bool = True, # only for stage 1&2 initial_scale: float = 2**16, growth_factor: float = 2, backoff_factor: float = 0.5, growth_interval: int = 1000, hysteresis: int = 2, min_scale: float = 1, max_scale: float = 2**32, max_norm: float = 0.0, norm_type: float = 2.0) -> None: super().__init__(seed) assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy "{placement_policy}"' assert precision in ('fp32', 'fp16'), f'Unsupported precision "{precision}"' self.stage = stage # TODO(ver217): support shard_init when using from_pretrained() if shard_init: warnings.warn( f'Shard init is not supported model.from_pretrained() yet. Please load weights after strategy.prepare()' ) if stage == 3 and precision == 'fp32': warnings.warn(f'Stage 3 only supports fp16. Precision is set to fp16.') precision = 'fp16' self.precision = precision self.shard_init = shard_init self.gemini_config = dict(device=get_current_device(), placement_policy=placement_policy, pin_memory=pin_memory, force_outputs_fp32=force_outputs_fp32, strict_ddp_mode=shard_init, search_range_mb=search_range_mb, hidden_dim=hidden_dim, min_chunk_size_mb=min_chunk_size_mb) if stage == 3: self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio) else: self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size, overlap_communication=overlap_communication, cpu_offload=(placement_policy == 'cpu')) self.optim_kwargs = dict(initial_scale=initial_scale, growth_factor=growth_factor, backoff_factor=backoff_factor, growth_interval=growth_interval, hysteresis=hysteresis, min_scale=min_scale, max_scale=max_scale, max_norm=max_norm, norm_type=norm_type) def setup_distributed(self) -> None: colossalai.launch_from_torch({}, seed=self.seed) def model_init_context(self): if self.stage == 3: world_size = dist.get_world_size() shard_pg = ProcessGroup(tp_degree=world_size) if self.shard_init else None default_dist_spec = ShardSpec([-1], [world_size]) if self.shard_init else None return ColoInitContext(device=get_current_device(), dtype=torch.half, default_pg=shard_pg, default_dist_spec=default_dist_spec) return super().model_init_context() def setup_model(self, model: nn.Module) -> nn.Module: model = zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config) if self.stage != 3 and self.precision == 'fp16': model = model.half() return model def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer: assert isinstance(optimizer, (CPUAdam, HybridAdam)), f'Unsupported optimizer {type(optimizer)}' return zero_optim_wrapper(model, optimizer, optim_config=self.zero_optim_config, **self.optim_kwargs) def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None: optimizer.backward(loss) def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: optimizer.step() @staticmethod def _unwrap_actor(actor: Actor) -> nn.Module: model: Union[nn.Module, ZeroDDP] = Strategy._unwrap_actor(actor) if isinstance(model, ZeroDDP): return model.module return model def _unwrap_model(self, model: Union[nn.Module, ZeroDDP]) -> nn.Module: if isinstance(model, ZeroDDP) and self.stage == 3: logger.info(f"model type: {type(model)}, get static torch model") model = get_static_torch_model(model) logger.info(f"unwrapped_model type: {type(model)}") return super()._unwrap_model(model) def save_model(self, model: nn.Module, path: str, only_rank0: bool = True, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: if only_rank0 and dist.get_rank() != 0: return None unwrapped_model = self._unwrap_model(model) # TODO : better way to get torch model from gemini model # to get torch model from gemini model for module in unwrapped_model.modules(): if isinstance(module, LoraLinear): module.merge_weights = True module.eval() if isinstance(unwrapped_model, RewardModel): state_dict = unwrapped_model.state_dict() if only_rank0 and dist.get_rank() != 0: return torch.save(state_dict, path) else: try: if isinstance(unwrapped_model, LM): unwrapped_model = unwrapped_model.model logger.info(f'Saving model to {path}', ranks=[0]) unwrapped_model.save_pretrained(path) logger.info(f'Model saved to {path} Successfully', ranks=[0]) if tokenizer is not None: logger.info(f'Saving tokenizer to {path}', ranks=[0]) tokenizer.save_pretrained(path) logger.info(f'Tokenizer saved to {path} Successfully', ranks=[0]) except AttributeError: state_dict = unwrapped_model.state_dict() if only_rank0 and dist.get_rank() != 0: return torch.save(state_dict, path) def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: if only_rank0: raise RuntimeError( f'Optimizer states are sharded when using ColossalAIStrategy. Only rank0 is not supported.') torch.save(optimizer.state_dict(), path)