[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

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* fix

* fix

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* fix

* fix

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* 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

fix

Author: flybird11111

colossalai/booster/plugin/low_level_zero_plugin.py

@@ -1,12 +1,15 @@
+import enum
 import logging
 import os
+import warnings
 from functools import partial
 from pathlib import Path
 from types import MethodType
 from typing import Callable, Dict, Iterator, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
+from torch.nn import Parameter
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
 from torch.utils._pytree import tree_map
@@ -42,6 +45,12 @@ def _convert_floating_point(x, dtype: torch.dtype = torch.float16):
 SUPPORTED_PRECISION = ["fp16", "bf16", "fp32"]
 
 
+class OptimizerParamCheckState(enum.Enum):
+    ORIGIN_PARAM_FINDED = 0
+    ORIGIN_PARAM_NOT_FIND = -1
+    LORA_PARM_EXISTED = -2
+
+
 class LowLevelZeroModel(ModelWrapper, AMPModelMixin):
     def __init__(self, module: nn.Module, precision: str) -> None:
         super().__init__(module)
@@ -209,6 +218,19 @@ def load_sharded_model(
         super().load_sharded_model(model, checkpoint_index_file, strict, use_safetensors, load_sub_module)
         model.update_master_params()
 
+    def save_lora_as_pretrained(self, model, checkpoint, use_safetensors):
+        if os.path.isfile(checkpoint):
+            logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
+            return
+        from peft import PeftModel
+
+        assert isinstance(model, ModelWrapper), "Please boost the model before saving!"
+        peft_model = model.unwrap()
+        assert isinstance(
+            peft_model, PeftModel
+        ), "The model doesn't have lora adapters, please enable lora before saving."
+        return peft_model.save_pretrained(checkpoint, safe_serialization=use_safetensors)
+
 
 class LowLevelZeroPlugin(DPPluginBase):
     """
@@ -288,6 +310,7 @@ def __init__(
             cpu_offload=cpu_offload,
             master_weights=master_weights,
         )
+        self.lora_enabled = False
         self.verbose = verbose
 
         # set class name with stage, for better error message
@@ -311,6 +334,72 @@ def control_device(self) -> bool:
     def supported_devices(self) -> List[str]:
         return ["cuda", "npu"]
 
+    def support_lora(self) -> bool:
+        return True
+
+    def enable_lora(
+        self, model: nn.Module, pretrained_dir: Optional[str] = None, lora_config: Optional[Dict] = None
+    ) -> nn.Module:
+        from peft import PeftModel, get_peft_model
+
+        assert not isinstance(model, LowLevelZeroModel), "Lora should be enabled before boosting the model."
+        self.lora_enabled = True
+        warnings.warn("You have enabled LoRa training. Please check the hyperparameters such as lr")
+
+        if pretrained_dir is None:
+            peft_model = get_peft_model(model, lora_config)
+        else:
+            peft_model = PeftModel.from_pretrained(model, pretrained_dir, is_trainable=True)
+        return peft_model
+
+    def get_param_group_id(self, optimizer: Optimizer, origin_param: Parameter):
+        origin_param_id = id(origin_param)
+        for group_id, param_group in enumerate(optimizer.param_groups):
+            for p in param_group["params"]:
+                if id(p) == origin_param_id:
+                    return group_id
+        return -1
+
+    def get_param_group_id(self, optimizer: Optimizer, origin_param: Parameter, lora_param: Parameter):
+        origin_param_id = id(origin_param)
+        lora_param_id = id(lora_param)
+        target_group_id = None
+        for group_id, param_group in enumerate(optimizer.param_groups):
+            for p in param_group["params"]:
+                if id(p) == lora_param_id:
+                    # check if the lora parameter exists.
+                    return target_group_id, OptimizerParamCheckState.LORA_PARM_EXISTED
+                if id(p) == origin_param_id:
+                    target_group_id = group_id
+        if target_group_id is not None:
+            return target_group_id, OptimizerParamCheckState.ORIGIN_PARAM_FINDED
+        else:
+            return target_group_id, OptimizerParamCheckState.ORIGIN_PARAM_NOT_FIND
+
+    def add_lora_params_to_optimizer(self, model, optimizer):
+        """add lora parameters to optimizer"""
+        name2param = {}
+        for name, param in model.named_parameters():
+            name2param[name] = param
+
+        for name, param in name2param.items():
+            if "lora_A" in name or "lora_B" in name:
+                origin_key = name.replace("lora_A.", "")
+                origin_key = origin_key.replace("lora_B.", "")
+                origin_key = origin_key.replace(f"{model.active_adapter}", "base_layer")
+                origin_param = name2param[origin_key]
+                group_id, check_state = self.get_param_group_id(optimizer, origin_param, param)
+                if check_state == OptimizerParamCheckState.ORIGIN_PARAM_NOT_FIND:
+                    warnings.warn(
+                        "Origin parameter {origin_key} related to {name} doesn't exist in optimizer param_groups."
+                    )
+                elif (
+                    check_state == OptimizerParamCheckState.ORIGIN_PARAM_FINDED
+                    and group_id is not None
+                    and group_id >= 0
+                ):
+                    optimizer.param_groups[group_id]["params"].append(param)
+
     def configure(
         self,
         model: nn.Module,
@@ -319,6 +408,15 @@ def configure(
         dataloader: Optional[DataLoader] = None,
         lr_scheduler: Optional[LRScheduler] = None,
     ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]:
+        if self.lora_enabled:
+            from peft import PeftModel
+
+            assert isinstance(
+                model, PeftModel
+            ), "The model should have been wrapped as a PeftModel when self.lora_enabled is True"
+            if optimizer is not None:
+                self.add_lora_params_to_optimizer(model, optimizer)
+
         if not isinstance(model, ModelWrapper):
             model = LowLevelZeroModel(model, self.precision)
 
@@ -340,8 +438,3 @@ def get_checkpoint_io(self) -> CheckpointIO:
     def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[None]:
         assert isinstance(optimizer, LowLevelZeroOptimizer)
         return optimizer.no_sync()
-
-    def enable_lora(
-        self, model: nn.Module, pretrained_dir: Optional[str] = None, lora_config: Optional[Dict] = None
-    ) -> nn.Module:
-        raise NotImplementedError

colossalai/pipeline/p2p.py

@@ -45,6 +45,18 @@ def _cuda_safe_tensor_to_object(tensor: torch.Tensor, tensor_size: torch.Size) -
     return unpickle
 
 
+def check_for_nccl_backend(group):
+    pg = group or c10d._get_default_group()
+    # Gate PG wrapper check on Gloo availability.
+    if c10d._GLOO_AVAILABLE:
+        # It is not expected for PG to be wrapped many times, but support it just
+        # in case
+        while isinstance(pg, c10d._ProcessGroupWrapper):
+            pg = pg.wrapped_pg
+
+    return c10d.is_nccl_available() and pg.name() == c10d.Backend.NCCL
+
+
 # NOTE: FIXME: NPU DOES NOT support isend nor irecv, so broadcast is kept for future use
 def _broadcast_object_list(
     object_list: List[Any], src: int, group: ProcessGroup, device: Optional[Union[torch.device, str, int]] = None

colossalai/zero/low_level/bookkeeping/gradient_store.py

@@ -82,6 +82,9 @@ def get_working_grads_by_group_id(self, group_id: int) -> List:
         """
 
         grad_list = []
+        # When using LoRa and the user sets multiple param_groups, it is possible that some param_groups have no parameters with gradients.
+        if group_id not in self._grads_of_params.keys():
+            return grad_list
         for param_grads in self._grads_of_params[group_id].values():
             grad_list.append(param_grads[self._working_index])
 

requirements/requirements-test.txt

@@ -18,5 +18,5 @@ flash_attn
 datasets
 pydantic
 ray
-peft
+peft>=0.7.1
 #auto-gptq now not support torch1.12

requirements/requirements.txt

@@ -17,3 +17,4 @@ sentencepiece
 google
 protobuf
 transformers==4.36.2
+peft>=0.7.1

tests/test_booster/test_plugin/test_dp_plugin_base.py

@@ -1,4 +1,4 @@
-from typing import Callable, Iterator, List, Tuple, Union
+from typing import Callable, Dict, Iterator, List, Tuple, Union
 
 import torch
 import torch.distributed as dist
@@ -51,6 +51,12 @@ def supported_precisions(self) -> List[str]:
     def no_sync(self, model: nn.Module) -> Iterator[None]:
         pass
 
+    def enable_lora(self, model: nn.Module, pretrained_dir: str, lora_config: Dict) -> nn.Module:
+        pass
+
+    def support_lora(self) -> bool:
+        pass
+
 
 def check_dataloader_sharding():
     plugin = DPPluginWrapper()

tests/test_booster/test_plugin/test_low_level_zero_plugin.py

@@ -2,6 +2,7 @@
 
 import torch
 import torch.distributed as dist
+from peft import LoraConfig
 from torch.optim import Adam
 
 import colossalai
@@ -22,13 +23,17 @@
 
 
 @clear_cache_before_run()
-def run_fn(stage, model_fn, data_gen_fn, output_transform_fn) -> Optional[str]:
+def run_fn(stage, model_fn, data_gen_fn, output_transform_fn, lora_config=None) -> Optional[str]:
     device = get_accelerator().get_current_device()
     try:
         plugin = LowLevelZeroPlugin(stage=stage, max_norm=1.0, initial_scale=2**5)
         booster = Booster(plugin=plugin)
         model = model_fn()
         optimizer = Adam(model.parameters(), lr=1e-3)
+
+        if lora_config is not None:
+            model = booster.enable_lora(model, lora_config=lora_config)
+
         criterion = lambda x: x.mean()
         data = data_gen_fn()
 
@@ -48,6 +53,7 @@ def run_fn(stage, model_fn, data_gen_fn, output_transform_fn) -> Optional[str]:
 
     except Exception as e:
         return repr(e)
+        # raise e
 
 
 @parameterize("stage", [2])
@@ -91,10 +97,42 @@ def check_low_level_zero_plugin(stage: int, early_stop: bool = True):
     assert len(failed_info) == 0, "\n".join([f"{k}: {v}" for k, v in failed_info.items()])
 
 
+@parameterize("stage", [2])
+@parameterize("model_name", ["transformers_llama"])
+def check_low_level_zero_lora(stage, model_name, early_stop: bool = True):
+    passed_models = []
+    failed_info = {}  # (model_name, error) pair
+
+    sub_model_zoo = model_zoo.get_sub_registry(model_name)
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        task_type = None
+        if name == "transformers_llama_for_casual_lm":
+            task_type = "CAUSAL_LM"
+        if name == "transformers_llama_for_sequence_classification":
+            task_type = "SEQ_CLS"
+        lora_config = LoraConfig(task_type=task_type, r=8, lora_alpha=32, lora_dropout=0.1)
+        err = run_fn(stage, 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, early_stop: bool = True):
     # init dist env
     colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host="localhost")
     check_low_level_zero_plugin(early_stop=early_stop)
+    check_low_level_zero_lora(early_stop=early_stop)
 
 
 @rerun_if_address_is_in_use()