add util for ram efficient loading of model when using fsdp

src/transformers/modeling_utils.py

@@ -73,6 +73,7 @@
  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 @@
 _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"
@@ -2225,6 +2238,9 @@ def from_pretrained(
  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
@@ -3180,7 +3196,8 @@ def _fix_key(key):
  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)
@@ -3385,23 +3402,35 @@ def _find_mismatched_keys(
  )
 
  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 @@ def __init__(
  ):
  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 @@ def _wrap_model(self, model, training=True, dataloader=None):
  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 @@ def train(
  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 @@ def _inner_training_loop(
 
  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 @@ def create_accelerator_and_postprocess(self):
  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).
 
@@ -454,14 +454,22 @@ 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.
  - limit_all_gathers (`bool`, *optional*, defaults to `False`)
  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 @@ def __post_init__(self):
  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 @@ def __post_init__(self):
  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(