[hotfix] fix lora load (#6231)

* [hotfix] fix lora load

* [hotfix] fix hp load

* accelerate deepseek loading

applications/ColossalChat/examples/training_scripts/lora_finetune.py

@@ -257,7 +257,7 @@ def train(args) -> None:
     )
 
     torch.set_default_dtype(torch.float)
-    booster.load_model(model, args.pretrained)
+    booster.load_model(model, args.pretrained, low_cpu_mem_mode=False, num_threads=8)
 
     coordinator.print_on_master(
         f"Booster init max device memory: {accelerator.max_memory_allocated() / 1024 ** 2:.2f} MB"

colossalai/booster/plugin/gemini_plugin.py

@@ -85,11 +85,11 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
             if use_async:
                 from colossalai.utils.safetensors import save
 
-                if id(model) not in self.pinned_state_dicts:
-                    self.pinned_state_dicts[id(model)] = create_pinned_state_dict(state_dict)
+                if hash(model) not in self.pinned_state_dicts:
+                    self.pinned_state_dicts[hash(model)] = create_pinned_state_dict(state_dict)
                 for k, v in state_dict.items():
-                    self.pinned_state_dicts[id(model)][k].copy_(v)
-                    state_dict[k] = self.pinned_state_dicts[id(model)][k]
+                    self.pinned_state_dicts[hash(model)][k].copy_(v)
+                    state_dict[k] = self.pinned_state_dicts[hash(model)][k]
                 writer = save(checkpoint, state_dict)
                 self.async_writers.append(writer)
             else:
@@ -172,9 +172,9 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
         Path(checkpoint_path).mkdir(parents=True, exist_ok=True)
 
         if use_async and self.coordinator.is_master():
-            if id(model) not in self.pinned_state_dicts:
-                self.pinned_state_dicts[id(model)] = {}
-            pinned_state_dicts = self.pinned_state_dicts[id(model)]
+            if hash(model) not in self.pinned_state_dicts:
+                self.pinned_state_dicts[hash(model)] = {}
+            pinned_state_dicts = self.pinned_state_dicts[hash(model)]
         else:
             pinned_state_dicts = None
         state_dict_shard = model.state_dict_shard(

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -26,6 +26,7 @@ from colossalai.amp.naive_amp.mixed_precision_optimizer import MixedPrecisionOpt
 from colossalai.checkpoint_io import CheckpointIO, HybridParallelCheckpointIO
 from colossalai.cluster import ProcessGroupMesh
 from colossalai.interface import AMPModelMixin, ModelWrapper, OptimizerWrapper
+from colossalai.interface.model import PeftUnwrapMixin
 from colossalai.interface.optimizer import DistributedOptim
 from colossalai.logging import get_dist_logger
 from colossalai.nn.optimizer import DistGaloreAwamW, cast_to_distributed
@@ -225,7 +226,7 @@ class HybridParallelModule(ModelWrapper, AMPModelMixin):
         if isinstance(model, DDP):
             model = model.module
         if unwrap_peft and isinstance(model, PeftModel):
-            model = model.get_base_model()
+            model = PeftUnwrapMixin(model)
         return model
 
     def _force_wait_all_gather(self):

colossalai/booster/plugin/torch_ddp_plugin.py

@@ -12,6 +12,7 @@ from torch.utils.data import DataLoader
 from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO
 from colossalai.cluster import DistCoordinator
 from colossalai.interface import ModelWrapper, OptimizerWrapper
+from colossalai.interface.model import PeftUnwrapMixin
 from colossalai.logging import get_dist_logger
 from colossalai.quantization import BnbQuantizationConfig, quantize_model
 from colossalai.utils import get_current_device
@@ -201,7 +202,7 @@ class TorchDDPModel(ModelWrapper):
     def unwrap(self, unwrap_peft: bool = True) -> nn.Module:
         model = self.module.module
         if unwrap_peft and isinstance(model, PeftModel):
-            model = model.get_base_model()
+            model = PeftUnwrapMixin(model)
         return model
 
 

colossalai/booster/plugin/torch_fsdp_plugin.py

@@ -103,11 +103,11 @@ class TorchFSDPCheckpointIO(GeneralCheckpointIO):
             if use_async:
                 from colossalai.utils.safetensors import save
 
-                if id(model) not in self.pinned_state_dicts:
-                    self.pinned_state_dicts[id(model)] = create_pinned_state_dict(full_model_state)
+                if hash(model) not in self.pinned_state_dicts:
+                    self.pinned_state_dicts[hash(model)] = create_pinned_state_dict(full_model_state)
                 for k, v in full_model_state.items():
-                    self.pinned_state_dicts[id(model)][k].copy_(v)
-                    full_model_state[k] = self.pinned_state_dicts[id(model)][k]
+                    self.pinned_state_dicts[hash(model)][k].copy_(v)
+                    full_model_state[k] = self.pinned_state_dicts[hash(model)][k]
                 writer = save(checkpoint, full_model_state)
                 self.async_writers.append(writer)
             else:
@@ -186,9 +186,9 @@ class TorchFSDPCheckpointIO(GeneralCheckpointIO):
             state_dict = model.unwrap().state_dict()
 
         if use_async and self.coordinator.is_master():
-            if id(model) not in self.pinned_state_dicts:
-                self.pinned_state_dicts[id(model)] = {}
-            pinned_state_dicts = self.pinned_state_dicts[id(model)]
+            if hash(model) not in self.pinned_state_dicts:
+                self.pinned_state_dicts[hash(model)] = {}
+            pinned_state_dicts = self.pinned_state_dicts[hash(model)]
         else:
             pinned_state_dicts = None
         state_dict_shard = utils.shard_model_checkpoint(

colossalai/checkpoint_io/general_checkpoint_io.py

@@ -60,9 +60,9 @@ class GeneralCheckpointIO(CheckpointIO):
         if use_async:
             from colossalai.utils.safetensors import move_and_save
 
-            if id(model) not in self.pinned_state_dicts:
-                self.pinned_state_dicts[id(model)] = create_pinned_state_dict(state_dict)
-            writer = move_and_save(checkpoint, state_dict, self.pinned_state_dicts[id(model)])
+            if hash(model) not in self.pinned_state_dicts:
+                self.pinned_state_dicts[hash(model)] = create_pinned_state_dict(state_dict)
+            writer = move_and_save(checkpoint, state_dict, self.pinned_state_dicts[hash(model)])
             self.async_writers.append(writer)
         else:
             # save the checkpoint
@@ -234,7 +234,7 @@ class GeneralCheckpointIO(CheckpointIO):
         index_file = CheckpointIndexFile(checkpoint_path)
 
         if use_async:
-            pinned_state_dict = self.pinned_state_dicts.get(id(model), None)
+            pinned_state_dict = self.pinned_state_dicts.get(hash(model), None)
             total_size, new_pinned_state_dict, writers = async_move_save_state_dict_shards(
                 sharded_state_dict=state_dict_shard,
                 checkpoint=checkpoint_path,
@@ -243,7 +243,7 @@ class GeneralCheckpointIO(CheckpointIO):
                 is_master=True,
                 pinned_state_dict=pinned_state_dict,
             )
-            self.pinned_state_dicts[id(model)] = new_pinned_state_dict
+            self.pinned_state_dicts[hash(model)] = new_pinned_state_dict
             self.async_writers.extend(writers)
         else:
             # Save shards of optimizer states.

colossalai/checkpoint_io/hybrid_parallel_checkpoint_io.py

@@ -249,9 +249,9 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
         # Only devices with tp_rank == 0 are responsible for model saving.
         control_saving = self.tp_rank == 0 and self.sp_rank == 0
         if control_saving and use_async:
-            if id(model) not in self.pinned_state_dicts:
-                self.pinned_state_dicts[id(model)] = {}
-            pinned_state_dicts = self.pinned_state_dicts[id(model)]
+            if hash(model) not in self.pinned_state_dicts:
+                self.pinned_state_dicts[hash(model)] = {}
+            pinned_state_dicts = self.pinned_state_dicts[hash(model)]
         else:
             pinned_state_dicts = None
         state_dict_shard = HybridParallelCheckpointIO._model_sharder(
@@ -789,11 +789,11 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
                 if use_async:
                     from colossalai.utils.safetensors import save
 
-                    if id(model) not in self.pinned_state_dicts:
-                        self.pinned_state_dicts[id(model)] = create_pinned_state_dict(state_dict)
+                    if hash(model) not in self.pinned_state_dicts:
+                        self.pinned_state_dicts[hash(model)] = create_pinned_state_dict(state_dict)
                     for name, param in state_dict.items():
-                        self.pinned_state_dicts[id(model)][name].copy_(param)
-                        state_dict[name] = self.pinned_state_dicts[id(model)][name]
+                        self.pinned_state_dicts[hash(model)][name].copy_(param)
+                        state_dict[name] = self.pinned_state_dicts[hash(model)][name]
                     writer = save(path=checkpoint, state_dict=state_dict)
                     self.async_writers.append(writer)
                 else:
@@ -811,11 +811,11 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
                 if use_async:
                     from colossalai.utils.safetensors import save
 
-                    if id(model) not in self.pinned_state_dicts:
-                        self.pinned_state_dicts[id(model)] = create_pinned_state_dict(complete_state_dict)
+                    if hash(model) not in self.pinned_state_dicts:
+                        self.pinned_state_dicts[hash(model)] = create_pinned_state_dict(complete_state_dict)
                     for name, param in complete_state_dict.items():
-                        self.pinned_state_dicts[id(model)][name].copy_(param)
-                        complete_state_dict[name] = self.pinned_state_dicts[id(model)][name]
+                        self.pinned_state_dicts[hash(model)][name].copy_(param)
+                        complete_state_dict[name] = self.pinned_state_dicts[hash(model)][name]
                     writer = save(path=checkpoint, state_dict=complete_state_dict)
                     self.async_writers.append(writer)
                 else:

colossalai/checkpoint_io/moe_checkpoint.py

@@ -701,15 +701,18 @@ class MoECheckpointIO(HybridParallelCheckpointIO):
                         all_param = None
                     # gather param from every ep rank
                     # dist.all_gather(all_param, param, group=ep_group)
-                    dist.gather(param, all_param, group=ep_group)
+                    dist.gather(param, all_param, dst=dist.get_global_rank(ep_group, 0), group=ep_group)
                     if ep_rank == 0:
                         all_param = torch.cat(all_param, dim=0)
                         state_dict[name] = all_param.cpu()
 
         if self.pp_size > 1:
             if self.dp_rank == 0:
-                out = [None for _ in range(self.pp_size)]
-                dist.gather_object(state_dict, out, group=self.pp_group)
+                if self.pp_rank == 0:
+                    out = [None for _ in range(self.pp_size)]
+                else:
+                    out = None
+                dist.gather_object(state_dict, out, dst=dist.get_global_rank(self.pp_group, 0), group=self.pp_group)
                 if self.pp_rank == 0:
                     new_state_dict = {}
                     for o in out:

colossalai/checkpoint_io/utils.py

@@ -20,6 +20,7 @@ from torch.optim import Optimizer
 from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten
 
 from colossalai.accelerator import get_accelerator
+from colossalai.interface.model import PeftUnwrapMixin
 from colossalai.tensor.d_tensor import (
     is_customized_distributed_tensor,
     is_distributed_tensor,
@@ -554,6 +555,8 @@ def save_config_file(model: nn.Module, checkpoint_path: str, is_master: bool = T
         from transformers.modeling_utils import unwrap_model as unwrap_huggingface_model
     except ImportError:
         return
+    if isinstance(model, PeftUnwrapMixin):
+        model = model.base_model
     if not isinstance(model, PreTrainedModel):
         return
 
@@ -692,6 +695,9 @@ def load_state_dict_into_model(
         state_dict (dict): a dict containing parameters and
             persistent buffers.
     """
+    if isinstance(model, PeftUnwrapMixin):
+        state_dict = model.patch_state_dict(state_dict)
+        model = model.base_model
     if not isinstance(state_dict, Mapping):
         raise TypeError("Expected state_dict to be dict-like, got {}.".format(type(state_dict)))
 

colossalai/interface/model.py

@@ -1,5 +1,102 @@
+import re
+from typing import Dict, Set
+
+import torch
 import torch.nn as nn
-from peft import PeftModel
+from peft import PeftModel, PeftType
+
+
+def extract_lora_layers(model: PeftModel, names: Set[str], adapter_name: str = "default"):
+    config = model.peft_config[adapter_name]
+    if config.peft_type != PeftType.LORA:
+        raise ValueError(f"Adapter {adapter_name} is not a LORA adapter.")
+    # to_return = lora_state_dict(model, bias=model.peft_config.bias)
+    # adapted from `https://github.com/microsoft/LoRA/blob/main/loralib/utils.py`
+    # to be used directly with the state dict which is necessary when using DeepSpeed or FSDP
+    bias = config.bias
+    if bias == "none":
+        to_return = {k for k in names if "lora_" in k}
+    elif bias == "all":
+        to_return = {k for k in names if "lora_" in k or "bias" in k}
+    elif bias == "lora_only":
+        to_return = set()
+        for k in names:
+            if "lora_" in k:
+                to_return.add(k)
+                bias_name = k.split("lora_")[0] + "bias"
+                if bias_name in names:
+                    to_return.add(bias_name)
+    else:
+        raise NotImplementedError
+    to_return = {k for k in to_return if (("lora_" in k and adapter_name in k) or ("bias" in k))}
+    if config.use_dora:
+        # Here we take care of a refactor of DoRA which changed lora_magnitude_vector from a ParameterDict to a
+        # ModuleDict with a DoraLayer instance. The old parameter is now the "weight" attribute of that layer. Since
+        # we want the state_dict format not to change, we remove the "weight" part.
+        new_dora_suffix = f"lora_magnitude_vector.{adapter_name}.weight"
+
+        def renamed_dora_weights(k):
+            if k.endswith(new_dora_suffix):
+                k = k[:-7]  # remove ".weight"
+            return k
+
+        to_return = {renamed_dora_weights(k) for k in to_return}
+
+    to_return = {re.sub(f"lora_\S\.{adapter_name}\.(weight|bias)", "base_layer", k) for k in to_return}
+    return to_return
+
+
+class PeftUnwrapMixin:
+    def __init__(self, peft_model: PeftModel):
+        self.base_model = peft_model.get_base_model()
+        # peft does not affect buffers
+        self.lora_layers = extract_lora_layers(peft_model, set(n for n, p in self.base_model.named_parameters()))
+        potential_lora_weights = set()
+        for n in self.lora_layers:
+            potential_lora_weights.add(f"{n}.weight")
+            potential_lora_weights.add(f"{n}.bias")
+        self.lora_param_to_origin_param = {n: n.replace("base_layer.", "") for n in potential_lora_weights}
+        self.origin_param_to_lora_param = {v: k for k, v in self.lora_param_to_origin_param.items()}
+
+    def named_parameters(self):
+        for n, p in self.base_model.named_parameters():
+            if n in self.lora_param_to_origin_param:
+                n = self.lora_param_to_origin_param[n]
+            yield n, p
+
+    def named_buffers(self):
+        return self.base_model.named_buffers()
+
+    @property
+    def _modules(self):
+        return self.base_model._modules
+
+    @property
+    def _non_persistent_buffers_set(self):
+        return self.base_model._non_persistent_buffers_set
+
+    def patch_state_dict(self, state_dict: Dict[str, torch.Tensor]):
+        new_state_dict = {}
+        for k, v in state_dict.items():
+            if k in self.origin_param_to_lora_param:
+                k = self.origin_param_to_lora_param[k]
+            new_state_dict[k] = v
+        return new_state_dict
+
+    def state_dict(self):
+        state_dict = {}
+        for k, v in self.base_model.state_dict().items():
+            if k in self.lora_param_to_origin_param:
+                k = self.lora_param_to_origin_param[k]
+            state_dict[k] = v
+        return state_dict
+
+    def load_state_dict(self, state_dict, strict: bool = True, assign: bool = False):
+        state_dict = self.patch_state_dict(state_dict)
+        self.base_model.load_state_dict(state_dict, strict=strict, assign=assign)
+
+    def __hash__(self):
+        return hash(self.base_model)
 
 
 class ModelWrapper(nn.Module):
@@ -23,7 +120,7 @@ class ModelWrapper(nn.Module):
         else:
             model = self.module
         if unwrap_peft and isinstance(model, PeftModel):
-            model = model.get_base_model()
+            model = PeftUnwrapMixin(model)
         return model
 
     def forward(self, *args, **kwargs):