add util for ram efficient loading of model when using fsdp (#25107)

* add util for ram efficient loading of model when using fsdp

* make fix-copies

* fixes 😅

* docs

* making it further easier to use

* rename the function

* refactor to handle fsdp ram efficiency in `from_pretrained`

* fixes

* fixes

* fixes

* update

* fixes

* revert `load_pretrained_model_only_on_rank0`

* resolve `load_from_checkpoint`

src/transformers/modeling_utils.py

@@ -73,6 +73,7 @@ from .utils import (
     is_torch_tpu_available,
     logging,
     replace_return_docstrings,
+    strtobool,
 )
 from .utils.hub import convert_file_size_to_int, get_checkpoint_shard_files
 from .utils.import_utils import ENV_VARS_TRUE_VALUES, is_sagemaker_mp_enabled, is_torch_fx_proxy
@@ -106,6 +107,14 @@ logger = logging.get_logger(__name__)
 _init_weights = True
 
 
+def is_fsdp_enabled():
+    return strtobool(os.environ.get("ACCELERATE_USE_FSDP", "False")) == 1
+
+
+def is_fsdp_enabled_and_dist_rank_0():
+    return is_fsdp_enabled() and torch.distributed.is_initialized() and torch.distributed.get_rank() == 0
+
+
 if is_sagemaker_mp_enabled():
     import smdistributed.modelparallel.torch as smp
     from smdistributed.modelparallel import __version__ as SMP_VERSION
@@ -458,7 +467,11 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
             )
         return safe_load_file(checkpoint_file)
     try:
-        if is_deepspeed_zero3_enabled() and torch.distributed.is_initialized() and torch.distributed.get_rank() > 0:
+        if (
+            (is_deepspeed_zero3_enabled() or is_fsdp_enabled)
+            and torch.distributed.is_initialized()
+            and torch.distributed.get_rank() > 0
+        ):
             map_location = "meta"
         else:
             map_location = "cpu"
@@ -2283,6 +2296,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         commit_hash = kwargs.pop("_commit_hash", None)
         variant = kwargs.pop("variant", None)
 
+        if is_fsdp_enabled():
+            low_cpu_mem_usage = True
+
         if use_auth_token is not None:
             warnings.warn(
                 "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.", FutureWarning
@@ -3238,7 +3254,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             model_buffers = {".".join([prefix, key]) for key in model_buffers}
         unexpected_keys = list(unexpected_keys - model_buffers)
 
-        if device_map is None:
+        model.tie_weights()
+        if device_map is None and not is_fsdp_enabled():
             ptrs = collections.defaultdict(list)
             for name, tensor in model.state_dict().items():
                 id_tensor = id_tensor_storage(tensor)
@@ -3443,23 +3460,35 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 )
 
                 if low_cpu_mem_usage:
-                    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
-                        model_to_load,
-                        state_dict,
-                        loaded_keys,
-                        start_prefix,
-                        expected_keys,
-                        device_map=device_map,
-                        offload_folder=offload_folder,
-                        offload_index=offload_index,
-                        state_dict_folder=state_dict_folder,
-                        state_dict_index=state_dict_index,
-                        dtype=dtype,
-                        is_quantized=is_quantized,
-                        is_safetensors=is_safetensors,
-                        keep_in_fp32_modules=keep_in_fp32_modules,
-                    )
-                    error_msgs += new_error_msgs
+                    if not is_fsdp_enabled() or is_fsdp_enabled_and_dist_rank_0():
+                        new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
+                            model_to_load,
+                            state_dict,
+                            loaded_keys,
+                            start_prefix,
+                            expected_keys,
+                            device_map=device_map,
+                            offload_folder=offload_folder,
+                            offload_index=offload_index,
+                            state_dict_folder=state_dict_folder,
+                            state_dict_index=state_dict_index,
+                            dtype=dtype,
+                            is_quantized=is_quantized,
+                            is_safetensors=is_safetensors,
+                            keep_in_fp32_modules=keep_in_fp32_modules,
+                        )
+                        error_msgs += new_error_msgs
+                    else:
+                        for key, param in model_to_load.state_dict().items():
+                            if param.device == torch.device("meta"):
+                                if not (is_quantized):
+                                    set_module_tensor_to_device(
+                                        model, key, "cpu", torch.empty(*param.size(), dtype=dtype)
+                                    )
+                                else:
+                                    set_module_quantized_tensor_to_device(
+                                        model, key, "cpu", torch.empty(*param.size(), dtype=dtype)
+                                    )
                 else:
                     error_msgs += _load_state_dict_into_model(model_to_load, state_dict, start_prefix)
 

src/transformers/trainer.py

@@ -465,10 +465,6 @@ class Trainer:
             ):
                 self.backward_prefetch = BackwardPrefetch.BACKWARD_POST
 
-            self.forward_prefetch = False
-            if self.args.fsdp_config.get("forward_prefetch", False):
-                self.forward_prefetch = True
-
             self.limit_all_gathers = False
             if self.args.fsdp_config.get("limit_all_gathers", False):
                 self.limit_all_gathers = True
@@ -1379,12 +1375,12 @@ class Trainer:
             auto_wrapper_callable = None
             default_transformer_cls_names_to_wrap = getattr(model, "_no_split_modules", None)
             fsdp_transformer_layer_cls_to_wrap = self.args.fsdp_config.get(
-                "fsdp_transformer_layer_cls_to_wrap", default_transformer_cls_names_to_wrap
+                "transformer_layer_cls_to_wrap", default_transformer_cls_names_to_wrap
             )
 
-            if self.args.fsdp_config["fsdp_min_num_params"] > 0:
+            if self.args.fsdp_config["min_num_params"] > 0:
                 auto_wrap_policy = functools.partial(
-                    size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config["fsdp_min_num_params"]
+                    size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config["min_num_params"]
                 )
             elif fsdp_transformer_layer_cls_to_wrap is not None:
                 transformer_cls_to_wrap = set()
@@ -1517,7 +1513,12 @@ class Trainer:
             if resume_from_checkpoint is None:
                 raise ValueError(f"No valid checkpoint found in output directory ({args.output_dir})")
 
-        if resume_from_checkpoint is not None and not is_sagemaker_mp_enabled() and not self.is_deepspeed_enabled:
+        if (
+            resume_from_checkpoint is not None
+            and not is_sagemaker_mp_enabled()
+            and not self.is_deepspeed_enabled
+            and not self.is_fsdp_enabled
+        ):
             self._load_from_checkpoint(resume_from_checkpoint)
 
         # If model was re-initialized, put it on the right device and update self.model_wrapped
@@ -1651,7 +1652,7 @@ class Trainer:
 
         model = self._wrap_model(self.model_wrapped)
 
-        if is_sagemaker_mp_enabled() and resume_from_checkpoint is not None:
+        if (is_sagemaker_mp_enabled() or self.is_fsdp_enabled) and resume_from_checkpoint is not None:
             self._load_from_checkpoint(resume_from_checkpoint, model)
 
         # as the model is wrapped, don't use `accelerator.prepare`
@@ -3886,7 +3887,6 @@ class Trainer:
             fsdp_plugin.limit_all_gathers = self.args.fsdp_config.get(
                 "limit_all_gathers", fsdp_plugin.limit_all_gathers
             )
-            fsdp_plugin.use_orig_params = self.args.fsdp_config.get("use_orig_params", fsdp_plugin.use_orig_params)
 
         if self.is_deepspeed_enabled:
             if getattr(self.args, "hf_deepspeed_config", None) is None:

src/transformers/training_args.py

@@ -436,13 +436,13 @@ class TrainingArguments:
             deepspeed json config file (e.g., `ds_config.json`) or an already loaded json file as `dict`.
 
             A List of config and its options:
-                - fsdp_min_num_params (`int`, *optional*, defaults to `0`):
+                - min_num_params (`int`, *optional*, defaults to `0`):
                     FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is
                     passed).
-                - fsdp_transformer_layer_cls_to_wrap (`List[str]`, *optional*):
+                - transformer_layer_cls_to_wrap (`List[str]`, *optional*):
                     List of transformer layer class names (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`,
                     `T5Block` .... (useful only when `fsdp` flag is passed).
-                - fsdp_backward_prefetch (`str`, *optional*)
+                - backward_prefetch (`str`, *optional*)
                     FSDP's backward prefetch mode. Controls when to prefetch next set of parameters (useful only when
                     `fsdp` field is passed).
 
@@ -454,7 +454,7 @@ class TrainingArguments:
                     - `"backward_post"` : This prefetches the next set of parameters after the current set of
                       parameter’s
                         gradient computation.
-                - fsdp_forward_prefetch (`bool`, *optional*, defaults to `False`)
+                - forward_prefetch (`bool`, *optional*, defaults to `False`)
                     FSDP's forward prefetch mode (useful only when `fsdp` field is passed).
                      If `"True"`, then FSDP explicitly prefetches the next upcoming all-gather while executing in the
                      forward pass.
@@ -462,6 +462,14 @@ class TrainingArguments:
                     FSDP's limit_all_gathers (useful only when `fsdp` field is passed).
                      If `"True"`, FSDP explicitly synchronizes the CPU thread to prevent too many in-flight
                      all-gathers.
+                - use_orig_params (`bool`, *optional*, defaults to `False`)
+                    If `"True"`, allows non-uniform `requires_grad` during init, which means support for interspersed
+                    frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please
+                    refer this
+                    [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019
+                - sync_module_states (`bool`, *optional*, defaults to `True`)
+                    If `"True"`, each individually wrapped FSDP unit will broadcast module parameters from rank 0 to
+                    ensure they are the same across all ranks after initialization
                 - xla (`bool`, *optional*, defaults to `False`):
                     Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature
                     and its API may evolve in the future.
@@ -1520,44 +1528,44 @@ class TrainingArguments:
             self.fsdp_config = {}
 
         if isinstance(self.fsdp_config, str):
+            if len(self.fsdp) == 0:
+                warnings.warn("`--fsdp_config` is useful only when `--fsdp` is specified.")
             with io.open(self.fsdp_config, "r", encoding="utf-8") as f:
                 self.fsdp_config = json.load(f)
+                for k, v in self.fsdp_config.items():
+                    if k.startswith("fsdp_"):
+                        self.fsdp_config[k.replace("fsdp_", "")] = v
+                        del self.fsdp_config[k]
 
         if self.fsdp_min_num_params > 0:
             warnings.warn("using `--fsdp_min_num_params` is deprecated. Use fsdp_config instead ", FutureWarning)
 
-        self.fsdp_config["fsdp_min_num_params"] = max(
-            self.fsdp_config.get("fsdp_min_num_params", 0), self.fsdp_min_num_params
-        )
+        self.fsdp_config["min_num_params"] = max(self.fsdp_config.get("min_num_params", 0), self.fsdp_min_num_params)
 
-        # if fsdp_config["fsdp_transformer_layer_cls_to_wrap"] is specified as a string, convert it to a list with a single object
-        if isinstance(self.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None), str):
-            self.fsdp_config["fsdp_transformer_layer_cls_to_wrap"] = [
-                self.fsdp_config["fsdp_transformer_layer_cls_to_wrap"]
-            ]
+        # if fsdp_config["transformer_layer_cls_to_wrap"] is specified as a string, convert it to a list with a single object
+        if isinstance(self.fsdp_config.get("transformer_layer_cls_to_wrap", None), str):
+            self.fsdp_config["transformer_layer_cls_to_wrap"] = [self.fsdp_config["transformer_layer_cls_to_wrap"]]
 
         if self.fsdp_transformer_layer_cls_to_wrap is not None:
             warnings.warn(
                 "using `--fsdp_transformer_layer_cls_to_wrap` is deprecated. Use fsdp_config instead ", FutureWarning
             )
-            self.fsdp_config["fsdp_transformer_layer_cls_to_wrap"] = self.fsdp_config.get(
-                "fsdp_transformer_layer_cls_to_wrap", []
+            self.fsdp_config["transformer_layer_cls_to_wrap"] = self.fsdp_config.get(
+                "transformer_layer_cls_to_wrap", []
             ) + [self.fsdp_transformer_layer_cls_to_wrap]
 
-        if len(self.fsdp) == 0 and self.fsdp_config["fsdp_min_num_params"] > 0:
-            warnings.warn("`--fsdp_min_num_params` is useful only when `--fsdp` is specified.")
+        if len(self.fsdp) == 0 and self.fsdp_config["min_num_params"] > 0:
+            warnings.warn("`min_num_params` is useful only when `--fsdp` is specified.")
 
-        if len(self.fsdp) == 0 and self.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None:
-            warnings.warn("`--fsdp_transformer_layer_cls_to_wrap` is useful only when `--fsdp` is specified.")
+        if len(self.fsdp) == 0 and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None:
+            warnings.warn("`transformer_layer_cls_to_wrap` is useful only when `--fsdp` is specified.")
 
         if (
             len(self.fsdp) > 0
-            and self.fsdp_config["fsdp_min_num_params"] > 0
-            and self.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None
+            and self.fsdp_config["min_num_params"] > 0
+            and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None
         ):
-            raise ValueError(
-                "`--fsdp_min_num_params` and `--fsdp_transformer_layer_cls_to_wrap` are mutually exclusive."
-            )
+            raise ValueError("`min_num_params` and `transformer_layer_cls_to_wrap` are mutually exclusive.")
         self.fsdp_config["xla"] = self.fsdp_config.get("xla", False)
         self.fsdp_config["xla_fsdp_grad_ckpt"] = self.fsdp_config.get("xla_fsdp_grad_ckpt", False)
         if self.fsdp_config["xla"]:
@@ -1583,23 +1591,29 @@ class TrainingArguments:
                 FSDP_SHARDING_STRATEGY,
             )
 
+            prefix = "FSDP_"
             for fsdp_option in self.fsdp:
                 if fsdp_option.upper() in FSDP_SHARDING_STRATEGY:
                     # set environment variable for FSDP sharding strategy
-                    os.environ["FSDP_SHARDING_STRATEGY"] = str(FSDP_SHARDING_STRATEGY.index(fsdp_option.upper()) + 1)
+                    os.environ[f"{prefix}SHARDING_STRATEGY"] = str(
+                        FSDP_SHARDING_STRATEGY.index(fsdp_option.upper()) + 1
+                    )
                 elif fsdp_option == FSDPOption.OFFLOAD:
-                    os.environ["FSDP_OFFLOAD_PARAMS"] = "true"
+                    os.environ[f"{prefix}OFFLOAD_PARAMS"] = "true"
                 elif fsdp_option == FSDPOption.AUTO_WRAP:
-                    os.environ["FSDP_AUTO_WRAP_POLICY"] = FSDP_AUTO_WRAP_POLICY[0]
-                    if self.fsdp_config["fsdp_min_num_params"] > 0:
-                        os.environ["FSDP_MIN_NUM_PARAMS"] = str(self.fsdp_config["fsdp_min_num_params"])
-                        os.environ["FSDP_AUTO_WRAP_POLICY"] = FSDP_AUTO_WRAP_POLICY[1]
-                    elif self.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None:
-                        os.environ["FSDP_TRANSFORMER_CLS_TO_WRAP"] = ",".join(
-                            self.fsdp_config["fsdp_transformer_layer_cls_to_wrap"]
+                    os.environ[f"{prefix}AUTO_WRAP_POLICY"] = FSDP_AUTO_WRAP_POLICY[0]
+                    if self.fsdp_config["min_num_params"] > 0:
+                        os.environ[f"{prefix}MIN_NUM_PARAMS"] = str(self.fsdp_config["min_num_params"])
+                        os.environ[f"{prefix}AUTO_WRAP_POLICY"] = FSDP_AUTO_WRAP_POLICY[1]
+                    elif self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None:
+                        os.environ[f"{prefix}TRANSFORMER_CLS_TO_WRAP"] = ",".join(
+                            self.fsdp_config["transformer_layer_cls_to_wrap"]
                         )
             prefetch_policy = self.fsdp_config.get("fsdp_backward_prefetch", "NO_PREFETCH")
-            os.environ["FSDP_BACKWARD_PREFETCH"] = prefetch_policy.upper()
+            os.environ[f"{prefix}BACKWARD_PREFETCH"] = prefetch_policy.upper()
+            os.environ[f"{prefix}FORWARD_PREFETCH"] = self.fsdp_config.get("forward_prefect", "false")
+            os.environ[f"{prefix}SYNC_MODULE_STATES"] = self.fsdp_config.get("sync_module_states", "true")
+            os.environ[f"{prefix}USE_ORIG_PARAMS"] = self.fsdp_config.get("use_orig_params", "false")
 
         if self.tpu_metrics_debug:
             warnings.warn(