[ckpt] Add async ckpt api (#6136)

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

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* fix

* fix

.github/workflows/build_on_pr.yml

@@ -117,7 +117,7 @@ jobs:
           cd TensorNVMe
           conda install cmake
           pip install -r requirements.txt
-          DISABLE_URING=1 pip install -v .
+          DISABLE_URING=1 pip install -v --no-cache-dir .
 
       - name: Store TensorNVMe Cache
         run: |

colossalai/booster/booster.py

@@ -325,6 +325,7 @@ class Booster:
                 names to compose the keys in state_dict. Defaults to None.
             size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024.
             use_safetensors (bool, optional): whether to use safe tensors. Default: False. If set to True, the checkpoint will be saved.
+            use_async (bool, optional): whether to save the state_dict of model asynchronously. Default: False.
         """
         self.checkpoint_io.save_model(
             model,

colossalai/booster/plugin/gemini_plugin.py

@@ -65,7 +65,14 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
         self.coordinator = DistCoordinator()
         self.logger = get_dist_logger()
 
-    def save_unsharded_model(self, model: GeminiDDP, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
+    def save_unsharded_model(
+        self,
+        model: GeminiDDP,
+        checkpoint: str,
+        gather_dtensor: bool,
+        use_safetensors: bool,
+        use_async: bool = False,
+    ):
         """
         Save sharded model to checkpoint but only on master process.
         The model should be unwrapped in self.load_model via ModelWrapper.unwrap.
@@ -74,7 +81,10 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
         assert isinstance(model, GeminiDDP), "Please boost the model before saving!"
         state_dict = model.state_dict(only_rank_0=True)
         if self.coordinator.is_master():
-            save_state_dict(state_dict, checkpoint, use_safetensors)
+            if use_async:
+                super().save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors, use_async)
+            else:
+                save_state_dict(state_dict, checkpoint, use_safetensors)
 
     def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool = True):
         """
@@ -112,6 +122,7 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
         prefix: Optional[str] = None,
         max_shard_size: int = 1024,
         use_safetensors: bool = False,
+        use_async: bool = False,
     ):
         """
         Save sharded model.
@@ -130,27 +141,33 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
 
         # Save shards of optimizer states.
         is_master = self.coordinator.is_master()
-        total_size = save_state_dict_shards(
-            sharded_state_dict=state_dict_shard,
-            checkpoint=checkpoint_path,
-            index_file=index_file,
-            base_filename=weights_name,
-            is_master=is_master,
-            use_safetensors=use_safetensors,
-        )
+        if use_async:
+            super().save_sharded_model(
+                model, checkpoint_path, gather_dtensor, prefix, max_shard_size, use_safetensors, use_async
+            )
 
-        # only save the index file on the master rank
-        if self.coordinator.is_master():
-            index_file.append_meta_data("total_size", total_size)
-            index_file.write_index_file(save_index_file)
-            save_config_file(model.unwrap(), checkpoint_path)
-            self.logger.info(
-                f"The model is split into checkpoint shards. "
-                f"You can find where each parameters has been saved in the "
-                f"index located at {save_index_file}.",
-                ranks=[0],
+        else:
+            total_size = save_state_dict_shards(
+                sharded_state_dict=state_dict_shard,
+                checkpoint=checkpoint_path,
+                index_file=index_file,
+                base_filename=weights_name,
+                is_master=is_master,
+                use_safetensors=use_safetensors,
             )
 
+            # only save the index file on the master rank
+            if self.coordinator.is_master():
+                index_file.append_meta_data("total_size", total_size)
+                index_file.write_index_file(save_index_file)
+                save_config_file(model.unwrap(), checkpoint_path)
+                self.logger.info(
+                    f"The model is split into checkpoint shards. "
+                    f"You can find where each parameters has been saved in the "
+                    f"index located at {save_index_file}.",
+                    ranks=[0],
+                )
+
     def load_sharded_model(
         self, model: GeminiDDP, checkpoint_index_file: Path, strict: bool = False, use_safetensors: bool = False
     ):

colossalai/booster/plugin/torch_fsdp_plugin.py

@@ -54,7 +54,9 @@ class TorchFSDPCheckpointIO(GeneralCheckpointIO):
         sharded_osd = FSDP.scatter_full_optim_state_dict(checkpoint, fsdp_model)
         optimizer.load_state_dict(sharded_osd)
 
-    def save_unsharded_model(self, model: ModelWrapper, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
+    def save_unsharded_model(
+        self, model: ModelWrapper, checkpoint: str, gather_dtensor: bool, use_safetensors: bool, use_async: bool = False
+    ):
         """
         Save model to checkpoint but only on master process.
         """
@@ -82,6 +84,7 @@ class TorchFSDPCheckpointIO(GeneralCheckpointIO):
         prefix: Optional[str] = None,
         size_per_shard: int = 1024,
         use_safetensors: bool = False,
+        use_async: bool = False,
     ):
         """
         Save model to checkpoint but only on master process.

colossalai/checkpoint_io/checkpoint_io_base.py

@@ -176,10 +176,10 @@ class CheckpointIO(ABC):
 
         if shard:
             self.save_sharded_model(
-                model, checkpoint, gather_dtensor, prefix, size_per_shard, use_safetensors, use_async=use_async
+                model, checkpoint, gather_dtensor, prefix, size_per_shard, use_safetensors, use_async
             )
         else:
-            self.save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors, use_async=use_async)
+            self.save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors, use_async)
 
     def load_optimizer(self, optimizer: Optimizer, checkpoint: str, prefix: str = None, size_per_shard: int = 1024):
         """

colossalai/checkpoint_io/general_checkpoint_io.py

@@ -61,8 +61,8 @@ class GeneralCheckpointIO(CheckpointIO):
                 self.pinned_state_dicts[id(model)] = create_pinned_state_dict(state_dict)
             self.async_writers.append(writer)
             move_and_save(writer, state_dict, self.pinned_state_dicts[id(model)])
-        else:
 
+        else:
             # save the checkpoint
             save_state_dict(state_dict, checkpoint, use_safetensors)
 

colossalai/checkpoint_io/hybrid_parallel_checkpoint_io.py

@@ -27,6 +27,8 @@ from .general_checkpoint_io import GeneralCheckpointIO
 from .index_file import CheckpointIndexFile
 from .utils import (
     StateDictSharder,
+    async_save_state_dict_shards,
+    create_pinned_state_dict,
     gather_distributed_param,
     get_model_base_filenames,
     get_optimizer_base_filenames,
@@ -177,6 +179,7 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
         prefix: Optional[str] = None,
         size_per_shard: int = 1024,
         use_safetensors: bool = False,
+        use_async: bool = False,
     ) -> None:
         """
         Save sharded model checkpoint under the given checkpointing path.
@@ -194,6 +197,7 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
             prefix (str, optional): Perfix of file to save. Defaults to None.
             size_per_shard (int, optional): Size per shard in MB. Defaults to 1024.
             use_safetensors (bool, optional): Whether to use safe tensors. Defaults to False.
+            use_async (bool, optional): Whether to save the state_dicts of model asynchronously. Defaults to False.
         """
 
         assert isinstance(model, ModelWrapper), "Please boost the model before saving!"
@@ -219,24 +223,27 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
 
         if self.pp_size == 1:
             # When pipeline is not used, save the model shards as in general checkpointIO
-            total_size = save_state_dict_shards(
-                sharded_state_dict=state_dict_shard,
-                checkpoint=checkpoint,
-                index_file=index_file,
-                base_filename=weights_name,
-                is_master=control_saving,
-                use_safetensors=use_safetensors,
-            )
-            if control_saving:
-                index_file.append_meta_data("total_size", total_size)
-                index_file.write_index_file(save_index_file)
-                save_config_file(model, checkpoint)
-                if self.verbose and self.coordinator.is_master():
-                    logging.info(
-                        f"The model is split into checkpoint shards. "
-                        f"You can find where each parameters has been saved in the "
-                        f"index located at {save_index_file}."
-                    )
+            if use_async:
+                super().save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors, use_async=use_async)
+            else:
+                total_size = save_state_dict_shards(
+                    sharded_state_dict=state_dict_shard,
+                    checkpoint=checkpoint,
+                    index_file=index_file,
+                    base_filename=weights_name,
+                    is_master=control_saving,
+                    use_safetensors=use_safetensors,
+                )
+                if control_saving:
+                    index_file.append_meta_data("total_size", total_size)
+                    index_file.write_index_file(save_index_file)
+                    save_config_file(model, checkpoint)
+                    if self.verbose and self.coordinator.is_master():
+                        logging.info(
+                            f"The model is split into checkpoint shards. "
+                            f"You can find where each parameters has been saved in the "
+                            f"index located at {save_index_file}."
+                        )
 
         else:
             # When pipeline is used, each stage produces its own shard files and index files.
@@ -251,7 +258,16 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
             weights_name = weights_name.replace(".safetensors", f"-stage-{self.pp_rank+1:05d}-shard.safetensors")
             save_index_file = save_index_file.replace(".json", f"-stage-{self.pp_rank+1:05d}.json")
             save_index_file = os.path.join("tmp_index_files", save_index_file)
-
+            if use_async:
+                total_size, returned_state_dict, writers = async_save_state_dict_shards(
+                    sharded_state_dict=state_dict_shard,
+                    checkpoint=checkpoint,
+                    index_file=index_file,
+                    base_filename=weights_name,
+                    is_master=control_saving,
+                    use_pp_format=True,
+                    n_write_entries=191,
+                )
             total_size = save_state_dict_shards(
                 sharded_state_dict=state_dict_shard,
                 checkpoint=checkpoint,
@@ -626,7 +642,9 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
         if self.verbose and self.coordinator.is_master():
             logging.info(f"The optimizer has been successfully loaded from sharded checkpoint: {ckpt_root_path}.")
 
-    def save_unsharded_model(self, model: ModelWrapper, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
+    def save_unsharded_model(
+        self, model: ModelWrapper, checkpoint: str, gather_dtensor: bool, use_safetensors: bool, use_async: bool = False
+    ):
         """
         Save model state dict to a single file with given checkpointing path.
 
@@ -635,6 +653,7 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
             checkpoint (str): Checkpointing path which should be a file path. Can be absolute or relative path.
             gather_dtensor (bool, optional): Whether to gather dtensor, currently not used. Defaults to True.
             use_safetensors (bool, optional): Whether to use safe tensors. Defaults to False.
+            use_async (bool, optional): Whether to save the state_dicts of model asynchronously. Defaults to False.
         """
         if self.coordinator.is_master():
             logging.warning("Please avoid using unsharded checkpointing methods when dealing with large models!")
@@ -651,7 +670,10 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
         if self.pp_size == 1:
             # When pipeline is not used, let master rank directly save the collected state_dict.
             if self.tp_rank == 0:
-                save_state_dict(state_dict, checkpoint, use_safetensors)
+                if use_async:
+                    super().save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors, use_async)
+                else:
+                    save_state_dict(state_dict, checkpoint, use_safetensors)
         else:
             # When pipeline is used, first collect state_dict from every pipeline stage, then save the complete state_dict.
             state_dict_list = [None for _ in range(self.pp_size)]
@@ -662,7 +684,18 @@ class HybridParallelCheckpointIO(GeneralCheckpointIO):
                 complete_state_dict = dict()
                 for _state_dict in state_dict_list:
                     complete_state_dict.update(_state_dict)
-                save_state_dict(complete_state_dict, checkpoint, use_safetensors)
+                if use_async:
+                    from tensornvme.async_file_io import AsyncFileWriter
+
+                    from colossalai.utils.safetensors import move_and_save
+
+                    writer = AsyncFileWriter(open(checkpoint, "wb"), self.N_WRITE_ENTRIES, backend="pthread")
+                    if id(model) not in self.pinned_state_dicts:
+                        self.pinned_state_dicts[id(model)] = create_pinned_state_dict(state_dict)
+                    self.async_writers.append(writer)
+                    move_and_save(writer, state_dict, self.pinned_state_dicts[id(model)])
+                else:
+                    save_state_dict(complete_state_dict, checkpoint, use_safetensors)
 
     def load_unsharded_model(self, model: ModelWrapper, checkpoint: str, strict: bool = False):
         """

colossalai/checkpoint_io/moe_checkpoint.py

@@ -117,6 +117,7 @@ class MoECheckpointIO(HybridParallelCheckpointIO):
         prefix: Optional[str] = None,
         size_per_shard: int = 1024,
         use_safetensors: bool = False,
+        use_async: bool = False,
     ) -> None:
         """
         Save sharded model checkpoint under the given checkpointing path.
@@ -161,24 +162,27 @@ class MoECheckpointIO(HybridParallelCheckpointIO):
 
         if self.pp_size == 1 and self.ep_size == 1:
             # When pipeline is not used, save the model shards as in general checkpointIO
-            total_size = save_state_dict_shards(
-                sharded_state_dict=state_dict_shard,
-                checkpoint=checkpoint,
-                index_file=index_file,
-                base_filename=weights_name,
-                is_master=control_saving,
-                use_safetensors=use_safetensors,
-            )
-            if control_saving:
-                index_file.append_meta_data("total_size", total_size)
-                index_file.write_index_file(save_index_file)
-                save_config_file(model, checkpoint)
-                if self.verbose and self.coordinator.is_master():
-                    logging.info(
-                        f"The model is split into checkpoint shards. "
-                        f"You can find where each parameters has been saved in the "
-                        f"index located at {save_index_file}."
-                    )
+            if use_async:
+                super().save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors, use_async=use_async)
+            else:
+                total_size = save_state_dict_shards(
+                    sharded_state_dict=state_dict_shard,
+                    checkpoint=checkpoint,
+                    index_file=index_file,
+                    base_filename=weights_name,
+                    is_master=control_saving,
+                    use_safetensors=use_safetensors,
+                )
+                if control_saving:
+                    index_file.append_meta_data("total_size", total_size)
+                    index_file.write_index_file(save_index_file)
+                    save_config_file(model, checkpoint)
+                    if self.verbose and self.coordinator.is_master():
+                        logging.info(
+                            f"The model is split into checkpoint shards. "
+                            f"You can find where each parameters has been saved in the "
+                            f"index located at {save_index_file}."
+                        )
 
             dist.barrier()
         else:
@@ -708,10 +712,20 @@ class MoECheckpointIO(HybridParallelCheckpointIO):
         checkpoint: str,
         gather_dtensor: bool,
         use_safetensors: bool,
+        use_async: bool = False,
     ):
         state_dict = self.pre_save_model(model)
         if dist.get_rank() == 0:
-            torch.save(state_dict, checkpoint)
+            if use_async:
+                super().save_unsharded_model(
+                    model=model,
+                    checkpoint=checkpoint,
+                    gather_dtensor=gather_dtensor,
+                    use_safetensors=use_safetensors,
+                    use_async=use_async,
+                )
+            else:
+                torch.save(state_dict, checkpoint)
         dist.barrier()
 
     # Copied from colossalai.moe

colossalai/checkpoint_io/utils.py

@@ -371,7 +371,11 @@ def shard_optimizer_checkpoint(state_dict: dict, max_shard_size: int = 1024) ->
 # ======================================
 
 
-def save_state_dict(state_dict: dict, checkpoint_file_path: str, use_safetensors: bool) -> None:
+def save_state_dict(
+    state_dict: dict,
+    checkpoint_file_path: str,
+    use_safetensors: bool,
+) -> None:
     """
     Save state dict to checkpoint.
 
@@ -581,14 +585,7 @@ def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool = False):
         raise Exception("load the model using `safetensors`, but no file endwith .safetensors")
     if use_safetensors:
         from safetensors.torch import load_file as safe_load_file
-        from safetensors.torch import safe_open
 
-        with safe_open(checkpoint_file, framework="pt") as f:
-            metadata = f.metadata()
-        if metadata["format"] != "pt":
-            raise NotImplementedError(
-                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, map_location=torch.device("cpu"))

colossalai/utils/safetensors.py

@@ -28,14 +28,12 @@ class PreparedData:
 
 
 def prepare(data: Dict[str, torch.Tensor]) -> Tuple[PreparedData, List[torch.Tensor], List[str]]:
-    sorted_data = sorted(data.items(), key=lambda x: (x[1].dtype, x[0]))
 
     tensors = []
     tensor_keys = []
     metadata = {}
     offset = 0
-
-    for name, tensor in sorted_data:
+    for name, tensor in data.items():
         n = tensor.numel() * tensor.element_size()
         tensor_info = TensorInfo(
             dtype=_TYPES_INV[tensor.dtype], shape=list(tensor.shape), data_offsets=(offset, offset + n)

tests/test_checkpoint_io/test_gemini_checkpoint_io.py

@@ -63,10 +63,15 @@ def exam_state_dict_with_origin(placement_config, model_name, use_safetensors: b
         model_size = sum(p.numel() * p.element_size() for p in bert_model.parameters()) / 1024**2
 
         booster.save_model(
-            bert_model, pretrained_path, True, True, "", (model_size / 3), use_safetensors=use_safetensors
+            bert_model,
+            pretrained_path,
+            True,
+            True,
+            "",
+            (model_size / 3),
+            use_safetensors=use_safetensors,
         )
         dist.barrier()
-
         new_bert_model = BertForSequenceClassification.from_pretrained(pretrained_path)
         check_state_dict_equal(bert_model.state_dict(only_rank_0=False), new_bert_model.state_dict())
 
@@ -119,7 +124,12 @@ def exam_state_dict(placement_config, shard: bool, model_name: str, size_per_sha
     with shared_tempdir() as tempdir:
         model_ckpt_path = f"{tempdir}/model"
         optimizer_ckpt_path = f"{tempdir}/optimizer"
-        booster.save_model(model, model_ckpt_path, shard=shard, size_per_shard=size_per_shard)
+        booster.save_model(
+            model,
+            model_ckpt_path,
+            shard=shard,
+            size_per_shard=size_per_shard,
+        )
 
         booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard, size_per_shard=size_per_shard)
         dist.barrier()

tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py

@@ -26,9 +26,10 @@ from tests.kit.model_zoo import model_zoo
 # only test 2 is fine
 @clear_cache_before_run()
 @parameterize("stage", [2])
-@parameterize("shard", [True, False])
+@parameterize("shard", [False, True])
 @parameterize("offload", [False, True])
-def check_low_level_zero_checkpointIO(stage: int, shard: bool, offload: bool):
+@parameterize("use_async", [False, True])
+def check_low_level_zero_checkpointIO(stage: int, shard: bool, offload: bool, use_async: bool):
     plugin = LowLevelZeroPlugin(stage=stage, max_norm=1.0, initial_scale=32, cpu_offload=offload)
     booster = Booster(plugin=plugin)
     model = resnet18()
@@ -41,13 +42,26 @@ def check_low_level_zero_checkpointIO(stage: int, shard: bool, offload: bool):
     loss = criterion(output)
     booster.backward(loss, optimizer)
     optimizer.step()
+
     with shared_tempdir() as tempdir:
+
         model_ckpt_path = f"{tempdir}/model"
         optimizer_ckpt_path = f"{tempdir}/optimizer"
+        if not shard and not use_async:
+            model_ckpt_path = f"{model_ckpt_path}.pt"
+        if not shard and use_async:
+            model_ckpt_path = f"{model_ckpt_path}.safetensors"
+        booster.save_model(
+            model,
+            model_ckpt_path,
+            shard=shard,
+            use_async=use_async,
+        )
+
         # lr scheduler is tested in test_torch_ddp_checkpoint_io.py and low level zero does not change it, we can skip it here
-        booster.save_model(model, model_ckpt_path, shard=shard)
         booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard)
-
+        booster.checkpoint_io._sync_d2h()
+        booster.checkpoint_io._sync_io()
         dist.barrier()
 
         new_model = resnet18()
@@ -71,6 +85,7 @@ def check_low_level_zero_checkpointIO(stage: int, shard: bool, offload: bool):
 
         booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
         check_state_dict_equal(optimizer.optim.state_dict(), new_optimizer.optim.state_dict())
+
     torch.cuda.empty_cache()