mirror of https://github.com/hpcaitech/ColossalAI
[LowLevelZero] low level zero support lora (#5153)
* low level zero support lora low level zero support lora * add checkpoint test * add checkpoint test * fix * fix * fix * fix fix fix fix * fix * fix fix fix fix fix fix fix * fix * fix fix fix fix fix fix fix * fix * test ci * git # This is a combination of 3 commits. Update low_level_zero_plugin.py Update low_level_zero_plugin.py fix fix fix * fix naming fix naming fix naming fixpull/5001/merge
flybird11111
GitHub
parent
c5fd4aa6e8
commit
cabc1286ca
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@ -1,12 +1,15 @@
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import logging
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import warnings
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import enum
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import os
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from functools import partial
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from pathlib import Path
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from types import MethodType
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from typing import Callable, Dict, Iterator, List, Optional, Tuple
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from typing import Callable, Dict, Iterator, List, Optional, Tuple, Dict
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import torch
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import torch.nn as nn
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from torch.nn import Parameter
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
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from torch.utils._pytree import tree_map
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@ -41,6 +44,11 @@ def _convert_floating_point(x, dtype: torch.dtype = torch.float16):
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SUPPORTED_PRECISION = ["fp16", "bf16", "fp32"]
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class OptimizerParamCheckState(enum.Enum):
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ORIGIN_PARAM_FINDED = 0
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ORIGIN_PARAM_NOT_FIND = -1
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LORA_PARM_EXISTED = -2
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class LowLevelZeroModel(ModelWrapper, AMPModelMixin):
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def __init__(self, module: nn.Module, precision: str) -> None:
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@ -208,6 +216,18 @@ class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO):
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super().load_sharded_model(model, checkpoint_index_file, strict, use_safetensors, load_sub_module)
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model.update_master_params()
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def save_lora_as_pretrained(self, model, checkpoint, use_safetensors):
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if os.path.isfile(checkpoint):
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logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
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return
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from peft import PeftModel
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assert isinstance(model, ModelWrapper), "Please boost the model before saving!"
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peft_model = model.unwrap()
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assert isinstance(
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peft_model, PeftModel
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), "The model doesn't have lora adapters, please enable lora before saving."
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return peft_model.save_pretrained(checkpoint, safe_serialization=use_safetensors)
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class LowLevelZeroPlugin(DPPluginBase):
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"""
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@ -287,6 +307,7 @@ class LowLevelZeroPlugin(DPPluginBase):
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cpu_offload=cpu_offload,
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master_weights=master_weights,
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)
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self.lora_enabled = False
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self.verbose = verbose
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# set class name with stage, for better error message
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@ -310,6 +331,66 @@ class LowLevelZeroPlugin(DPPluginBase):
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def supported_devices(self) -> List[str]:
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return ["cuda"]
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def support_lora(self) -> bool:
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return True
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def enable_lora(
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self, model: nn.Module, pretrained_dir: Optional[str] = None, lora_config: Optional[Dict] = None
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) -> nn.Module:
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from peft import PeftModel, get_peft_model
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assert not isinstance(model, LowLevelZeroModel), "Lora should be enabled before boosting the model."
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self.lora_enabled = True
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warnings.warn("You have enabled LoRa training. Please check the hyperparameters such as lr")
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if pretrained_dir is None:
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peft_model = get_peft_model(model, lora_config)
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else:
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peft_model = PeftModel.from_pretrained(model, pretrained_dir, is_trainable=True)
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return peft_model
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def get_param_group_id(self, optimizer: Optimizer, origin_param: Parameter):
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origin_param_id = id(origin_param)
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for group_id, param_group in enumerate(optimizer.param_groups):
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for p in param_group['params']:
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if id(p) == origin_param_id:
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return group_id
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return -1
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def get_param_group_id(self, optimizer: Optimizer, origin_param: Parameter, lora_param: Parameter):
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origin_param_id = id(origin_param)
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lora_param_id = id(lora_param)
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target_group_id = None
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for group_id, param_group in enumerate(optimizer.param_groups):
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for p in param_group['params']:
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if id(p) == lora_param_id:
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# check if the lora parameter exists.
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return target_group_id, OptimizerParamCheckState.LORA_PARM_EXISTED
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if id(p) == origin_param_id:
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target_group_id = group_id
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if target_group_id is not None:
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return target_group_id, OptimizerParamCheckState.ORIGIN_PARAM_FINDED
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else:
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return target_group_id, OptimizerParamCheckState.ORIGIN_PARAM_NOT_FIND
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def add_lora_params_to_optimizer(self, model, optimizer):
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""" add lora parameters to optimizer """
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name2param= {}
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for name, param in model.named_parameters():
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name2param[name] = param
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for name, param in name2param.items():
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if 'lora_A' in name or 'lora_B' in name:
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origin_key = name.replace("lora_A.", "")
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origin_key = origin_key.replace("lora_B.", "")
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origin_key = origin_key.replace(f"{model.active_adapter}", "base_layer")
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origin_param = name2param[origin_key]
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group_id, check_state = self.get_param_group_id(optimizer, origin_param, param)
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if check_state == OptimizerParamCheckState.ORIGIN_PARAM_NOT_FIND:
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warnings.warn("Origin parameter {origin_key} related to {name} doesn't exist in optimizer param_groups.")
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elif check_state == OptimizerParamCheckState.ORIGIN_PARAM_FINDED and group_id is not None and group_id >= 0:
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optimizer.param_groups[group_id]['params'].append(param)
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def configure(
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self,
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model: nn.Module,
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@ -318,6 +399,13 @@ class LowLevelZeroPlugin(DPPluginBase):
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dataloader: Optional[DataLoader] = None,
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lr_scheduler: Optional[LRScheduler] = None,
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) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]:
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if self.lora_enabled:
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from peft import PeftModel
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assert isinstance(model, PeftModel), "The model should have been wrapped as a PeftModel when self.lora_enabled is True"
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if optimizer is not None:
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self.add_lora_params_to_optimizer(model, optimizer)
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if not isinstance(model, ModelWrapper):
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model = LowLevelZeroModel(model, self.precision)
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@ -339,8 +427,3 @@ class LowLevelZeroPlugin(DPPluginBase):
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def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[None]:
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assert isinstance(optimizer, LowLevelZeroOptimizer)
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return optimizer.no_sync()
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def enable_lora(
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self, model: nn.Module, pretrained_dir: Optional[str] = None, lora_config: Optional[Dict] = None
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) -> nn.Module:
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raise NotImplementedError
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@ -44,6 +44,20 @@ def _cuda_safe_tensor_to_object(tensor: torch.Tensor, tensor_size: torch.Size) -
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return unpickle
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def check_for_nccl_backend(group):
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pg = group or c10d._get_default_group()
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# Gate PG wrapper check on Gloo availability.
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if c10d._GLOO_AVAILABLE:
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# It is not expected for PG to be wrapped many times, but support it just
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# in case
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while isinstance(pg, c10d._ProcessGroupWrapper):
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pg = pg.wrapped_pg
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return (
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c10d.is_nccl_available() and
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pg.name() == c10d.Backend.NCCL
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)
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def _broadcast_object_list(
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object_list: List[Any], src: int, group: ProcessGroup, device: Optional[Union[torch.device, str, int]] = None
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@ -65,7 +79,7 @@ def _broadcast_object_list(
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c10d._warn_not_in_group("broadcast_object_list")
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return
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is_nccl_backend = c10d._check_for_nccl_backend(group)
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is_nccl_backend = check_for_nccl_backend(group)
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current_device = None
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if device is not None:
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@ -82,6 +82,9 @@ class GradientStore(BaseStore):
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"""
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grad_list = []
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# When using LoRa and the user sets multiple param_groups, it is possible that some param_groups have no parameters with gradients.
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if group_id not in self._grads_of_params.keys():
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return grad_list
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for param_grads in self._grads_of_params[group_id].values():
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grad_list.append(param_grads[self._working_index])
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@ -18,5 +18,5 @@ SentencePiece
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ninja
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flash_attn==2.0.5
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datasets
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peft
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peft>=0.7.1
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#auto-gptq now not support torch1.12
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@ -14,3 +14,4 @@ einops
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sentencepiece
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google
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protobuf
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peft>=0.7.1
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@ -1,4 +1,4 @@
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from typing import Callable, Iterator, List, Tuple, Union
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from typing import Callable, Iterator, List, Tuple, Union, Dict
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import torch
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import torch.distributed as dist
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@ -51,6 +51,12 @@ class DPPluginWrapper(DPPluginBase):
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def no_sync(self, model: nn.Module) -> Iterator[None]:
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pass
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def enable_lora(self, model: nn.Module, pretrained_dir: str, lora_config: Dict) -> nn.Module:
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pass
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def support_lora(self) -> bool:
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pass
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def check_dataloader_sharding():
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plugin = DPPluginWrapper()
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@ -2,6 +2,7 @@ from typing import Optional
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import torch
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import torch.distributed as dist
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from peft import LoraConfig
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import colossalai
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from colossalai.booster import Booster
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@ -18,12 +19,16 @@ _LOW_LEVEL_ZERO_ERR_MODELS = ["dlrm_interactionarch"]
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_STUCK_MODELS = ["transformers_albert_for_multiple_choice"]
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def run_fn(stage, model_fn, data_gen_fn, output_transform_fn) -> Optional[str]:
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def run_fn(stage, model_fn, data_gen_fn, output_transform_fn, lora_config=None) -> Optional[str]:
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try:
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plugin = LowLevelZeroPlugin(stage=stage, max_norm=1.0, initial_scale=2**5)
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booster = Booster(plugin=plugin)
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model = model_fn()
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optimizer = HybridAdam(model.parameters(), lr=1e-3)
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if lora_config is not None:
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model = booster.enable_lora(model, lora_config=lora_config)
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criterion = lambda x: x.mean()
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data = data_gen_fn()
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@ -43,6 +48,8 @@ def run_fn(stage, model_fn, data_gen_fn, output_transform_fn) -> Optional[str]:
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except Exception as e:
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return repr(e)
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# raise e
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@parameterize("stage", [2])
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@ -81,10 +88,41 @@ def check_low_level_zero_plugin(stage: int, early_stop: bool = True):
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assert len(failed_info) == 0, "\n".join([f"{k}: {v}" for k, v in failed_info.items()])
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@parameterize("stage", [2])
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@parameterize("model_name", ["transformers_llama"])
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def check_low_level_zero_lora(stage, model_name, early_stop: bool = True):
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passed_models = []
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failed_info = {} # (model_name, error) pair
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sub_model_zoo = model_zoo.get_sub_registry(model_name)
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for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
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task_type = None
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if name == "transformers_llama_for_casual_lm":
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task_type = "CAUSAL_LM"
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if name == "transformers_llama_for_sequence_classification":
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task_type = "SEQ_CLS"
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lora_config = LoraConfig(task_type=task_type, r=8, lora_alpha=32, lora_dropout=0.1)
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err = run_fn(stage, model_fn, data_gen_fn, output_transform_fn, lora_config)
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torch.cuda.empty_cache()
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if err is None:
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passed_models.append(name)
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else:
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failed_info[name] = err
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if early_stop:
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break
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if dist.get_rank() == 0:
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print(f"Passed models({len(passed_models)}): {passed_models}\n\n")
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print(f"Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n")
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assert len(failed_info) == 0, "\n".join([f"{k}: {v}" for k, v in failed_info.items()])
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def run_dist(rank, world_size, port, early_stop: bool = True):
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# init dist env
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colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host="localhost")
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check_low_level_zero_plugin(early_stop=early_stop)
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check_low_level_zero_lora(early_stop=early_stop)
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@rerun_if_address_is_in_use()
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@ -2,6 +2,9 @@ import torch
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import torch.distributed as dist
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from torchvision.models import resnet18
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from utils import shared_tempdir
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from typing import Optional
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from peft import LoraConfig
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from copy import deepcopy
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import colossalai
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from colossalai.booster import Booster
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@ -15,6 +18,7 @@ from colossalai.testing import (
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spawn,
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)
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from colossalai.zero import LowLevelZeroOptimizer
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from tests.kit.model_zoo import model_zoo
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# stage 1 and 2 process the optimizer/mode the same way
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@ -69,9 +73,103 @@ def check_low_level_zero_checkpointIO(stage: int, shard: bool, offload: bool):
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torch.cuda.empty_cache()
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def run_fn(stage, shard, offload, model_fn, data_gen_fn, output_transform_fn, lora_config=None) -> Optional[str]:
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try:
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plugin = LowLevelZeroPlugin(stage=stage, max_norm=1.0, initial_scale=2**5, cpu_offload=offload)
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new_plugin = LowLevelZeroPlugin(stage=stage, max_norm=1.0, initial_scale=2**5, cpu_offload=offload)
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booster = Booster(plugin=plugin)
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new_booster = Booster(plugin=new_plugin)
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model = model_fn()
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optimizer = HybridAdam(model.parameters(), lr=1e-3)
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new_model = deepcopy(model)
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new_optimizer = HybridAdam(new_model.parameters(), lr=1e-3)
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model = booster.enable_lora(model, lora_config=lora_config)
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criterion = lambda x: x.mean()
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data = data_gen_fn()
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data = {
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k: v.to("cuda") if torch.is_tensor(v) or "Tensor" in v.__class__.__name__ else v for k, v in data.items()
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}
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model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
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output = model(**data)
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output = output_transform_fn(output)
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output_key = list(output.keys())[0]
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loss = criterion(output[output_key])
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booster.backward(loss, optimizer)
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optimizer.step()
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with shared_tempdir() as tempdir:
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model_ckpt_path = f"{tempdir}/model"
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optimizer_ckpt_path = f"{tempdir}/optimizer"
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booster.save_lora_as_pretrained(model, model_ckpt_path)
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booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=False)
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new_model = new_booster.enable_lora(new_model, pretrained_dir=model_ckpt_path, lora_config=lora_config)
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new_model, new_optimizer, criterion, _, _ = new_booster.boost(new_model, new_optimizer, criterion)
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check_state_dict_equal(model.state_dict(), new_model.state_dict(), False)
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# check master weight
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assert isinstance(new_optimizer, LowLevelZeroOptimizer)
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working_param_id_set = set(id(p) for p in new_model.parameters())
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for p_id, master_param in new_optimizer._param_store.working_to_master_param.items():
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assert p_id in working_param_id_set
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working_param = new_optimizer._param_store.master_to_working_param[id(master_param)]
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padding = new_optimizer._param_store.get_param_padding_size(working_param)
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padded_param = torch.nn.functional.pad(working_param.data.view(-1), (0, padding))
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working_shard = padded_param.chunk(dist.get_world_size())[dist.get_rank()]
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assert torch.equal(
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working_shard, master_param.data.view(-1).to(dtype=padded_param.dtype, device=padded_param.device)
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)
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new_booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
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check_state_dict_equal(optimizer.optim.state_dict(), new_optimizer.optim.state_dict(), False)
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except Exception as e:
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# return repr(e)
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raise e
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@clear_cache_before_run()
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@parameterize("stage", [2])
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@parameterize("shard", [True, False])
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@parameterize("offload", [False, True])
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@parameterize("model_name", ["transformers_llama"])
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def check_low_level_zero_lora_checkpointIO(stage: int, shard: bool, offload: bool, model_name: str, early_stop: bool = True):
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passed_models = []
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failed_info = {} # (model_name, error) pair
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sub_model_zoo = model_zoo.get_sub_registry(model_name)
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for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
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if name != "transformers_llama":
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continue
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task_type = None
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if name == "transformers_llama_for_casual_lm":
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task_type = "CAUSAL_LM"
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if name == "transformers_llama_for_sequence_classification":
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task_type = "SEQ_CLS"
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lora_config = LoraConfig(task_type=task_type, r=8, lora_alpha=32, lora_dropout=0.1)
|
||||
err = run_fn(stage, shard, offload, model_fn, data_gen_fn, output_transform_fn, lora_config)
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
if err is None:
|
||||
passed_models.append(name)
|
||||
else:
|
||||
failed_info[name] = err
|
||||
if early_stop:
|
||||
break
|
||||
|
||||
if dist.get_rank() == 0:
|
||||
print(f"Passed models({len(passed_models)}): {passed_models}\n\n")
|
||||
print(f"Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n")
|
||||
assert len(failed_info) == 0, "\n".join([f"{k}: {v}" for k, v in failed_info.items()])
|
||||
|
||||
def run_dist(rank, world_size, port):
|
||||
colossalai.launch(config=(dict()), rank=rank, world_size=world_size, port=port, host="localhost")
|
||||
check_low_level_zero_checkpointIO()
|
||||
check_low_level_zero_lora_checkpointIO()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue