mirror of https://github.com/hpcaitech/ColossalAI
[ddp] add save/load state dict for ColoDDP (#1127)
* add save/load state dict for ColoDDP * add unit test * refactor unit test folder * polish unit test * rename unit testpull/1138/head
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GitHub
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commit
d26902645e
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@ -1,4 +1,5 @@
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import torch
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import itertools
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import torch.distributed as dist
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from colossalai.core import global_context as gpc
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from colossalai.context import ParallelMode
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@ -7,8 +8,14 @@ from colossalai.zero.utils.zero_hook_v2 import ZeROHookV2
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from colossalai.tensor.chunk import TensorState, Chunk
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from colossalai.tensor.param_op_hook import ParamOpHookManager
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from colossalai.gemini.gemini_mgr import GeminiManager
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from typing import Dict, Iterable
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from typing import Dict, Iterable, List
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from colossalai.logging import get_dist_logger
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from collections import OrderedDict
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from colossalai.tensor.colo_parameter import ColoParameter
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try:
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from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX, _IncompatibleKeys
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except ImportError:
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_EXTRA_STATE_KEY_SUFFIX = '_extra_state'
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def free_storage(data: torch.Tensor) -> None:
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@ -122,6 +129,12 @@ class ColoDDP(torch.nn.Module):
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for p in params_to_ignore:
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p._ddp_to_ignore = True
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def state_dict(self, destination=None, prefix='', keep_vars=False):
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return self.module.state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)
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def load_state_dict(self, state_dict: 'OrderedDict[str, torch.Tensor]', strict: bool = True):
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return self.module.load_state_dict(state_dict, strict)
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class ColoDDPV2(ColoDDP):
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@ -130,7 +143,7 @@ class ColoDDPV2(ColoDDP):
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self.gemini_manager = gemini_manager
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self.chunk_manager = gemini_manager.chunk_manager
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self.param_op_hook = ZeROHookV2(gemini_manager)
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self.fp32_params = []
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self.fp32_params: List[ColoParameter] = []
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self.overflow_counter = 0
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self.grads_device: Dict[torch.Tensor, torch.device] = {}
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self.chunk_manager.create_group('fp16_param', force_data_on_cuda=True)
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@ -205,3 +218,208 @@ class ColoDDPV2(ColoDDP):
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def _set_chunk_grad_device(self, chunk: Chunk, device: torch.device) -> None:
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for tensor in chunk.get_tensors():
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self.grads_device[tensor] = device
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def state_dict(self, destination=None, prefix='', keep_vars=False):
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r"""Returns a dictionary containing a whole state of the module.
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Both parameters and persistent buffers (e.g. running averages) are
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included. Keys are corresponding parameter and buffer names.
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Parameters and buffers set to ``None`` are not included.
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Returns:
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dict:
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a dictionary containing a whole state of the module
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Example::
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>>> module.state_dict().keys()
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['bias', 'weight']
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"""
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if destination is None:
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destination = OrderedDict()
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destination._metadata = OrderedDict()
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destination._metadata[prefix[:-1]] = local_metadata = dict(version=self._version)
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self._save_to_state_dict(destination, prefix, keep_vars)
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for hook in self._state_dict_hooks.values():
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hook_result = hook(self, destination, prefix, local_metadata)
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if hook_result is not None:
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destination = hook_result
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return destination
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def _save_to_state_dict(self, destination, prefix, keep_vars):
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r"""Saves module state to `destination` dictionary, containing a state
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of the module, but not its descendants. This is called on every
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submodule in :meth:`~torch.nn.Module.state_dict`.
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In rare cases, subclasses can achieve class-specific behavior by
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overriding this method with custom logic.
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Args:
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destination (dict): a dict where state will be stored
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prefix (str): the prefix for parameters and buffers used in this
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module
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"""
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chunks = self.chunk_manager.get_chunks(self.fp32_params)
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for chunk in chunks:
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self.chunk_manager.access_chunk(chunk)
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for (name, p), fp32_p in zip(self.named_parameters(), self.fp32_params):
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if p is not None:
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destination[prefix + name] = fp32_p.clone() if keep_vars else fp32_p.clone().detach()
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for chunk in chunks:
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self.chunk_manager.release_chunk(chunk)
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for name, buf in self.named_buffers():
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if buf is not None and name not in self._non_persistent_buffers_set:
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destination[prefix + name] = buf if keep_vars else buf.detach()
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extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
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if getattr(self.__class__, "get_extra_state",
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torch.nn.Module.get_extra_state) is not torch.nn.Module.get_extra_state:
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destination[extra_state_key] = self.get_extra_state()
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def load_state_dict(self, state_dict: 'OrderedDict[str, torch.Tensor]', strict: bool = True):
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r"""Copies parameters and buffers from :attr:`state_dict` into
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this module and its descendants. If :attr:`strict` is ``True``, then
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the keys of :attr:`state_dict` must exactly match the keys returned
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by this module's :meth:`~torch.nn.Module.state_dict` function.
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Args:
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state_dict (dict): a dict containing parameters and
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persistent buffers.
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strict (bool, optional): whether to strictly enforce that the keys
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in :attr:`state_dict` match the keys returned by this module's
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:meth:`~torch.nn.Module.state_dict` function. Default: ``True``
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Returns:
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``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
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* **missing_keys** is a list of str containing the missing keys
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* **unexpected_keys** is a list of str containing the unexpected keys
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Note:
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If a parameter or buffer is registered as ``None`` and its corresponding key
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exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a
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``RuntimeError``.
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"""
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missing_keys: List[str] = []
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unexpected_keys: List[str] = []
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error_msgs: List[str] = []
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# copy state_dict so _load_from_state_dict can modify it
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metadata = getattr(state_dict, '_metadata', None)
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state_dict = state_dict.copy()
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if metadata is not None:
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# mypy isn't aware that "_metadata" exists in state_dict
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state_dict._metadata = metadata # type: ignore[attr-defined]
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prefix = ''
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local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
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self._load_from_state_dict(state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
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if strict:
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if len(unexpected_keys) > 0:
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error_msgs.insert(
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0, 'Unexpected key(s) in state_dict: {}. '.format(', '.join(
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'"{}"'.format(k) for k in unexpected_keys)))
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if len(missing_keys) > 0:
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error_msgs.insert(
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0, 'Missing key(s) in state_dict: {}. '.format(', '.join('"{}"'.format(k) for k in missing_keys)))
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if len(error_msgs) > 0:
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raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
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self.__class__.__name__, "\n\t".join(error_msgs)))
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return _IncompatibleKeys(missing_keys, unexpected_keys)
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def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
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error_msgs):
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r"""Copies parameters and buffers from :attr:`state_dict` into only
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this module, but not its descendants. This is called on every submodule
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in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this
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module in input :attr:`state_dict` is provided as :attr:`local_metadata`.
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For state dicts without metadata, :attr:`local_metadata` is empty.
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Subclasses can achieve class-specific backward compatible loading using
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the version number at `local_metadata.get("version", None)`.
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.. note::
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:attr:`state_dict` is not the same object as the input
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:attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. So
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it can be modified.
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Args:
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state_dict (dict): a dict containing parameters and
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persistent buffers.
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prefix (str): the prefix for parameters and buffers used in this
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module
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local_metadata (dict): a dict containing the metadata for this module.
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See
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strict (bool): whether to strictly enforce that the keys in
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:attr:`state_dict` with :attr:`prefix` match the names of
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parameters and buffers in this module
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missing_keys (list of str): if ``strict=True``, add missing keys to
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this list
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unexpected_keys (list of str): if ``strict=True``, add unexpected
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keys to this list
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error_msgs (list of str): error messages should be added to this
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list, and will be reported together in
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:meth:`~torch.nn.Module.load_state_dict`
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"""
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for hook in self._load_state_dict_pre_hooks.values():
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hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
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persistent_buffers = {k: v for k, v in self.named_buffers() if k not in self._non_persistent_buffers_set}
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local_name_params = itertools.chain(self.named_parameters(), persistent_buffers.items())
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local_state = {k: v for k, v in local_name_params if v is not None}
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def load(name, dest_tensor, copy_func):
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key = prefix + name
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if key in state_dict:
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input_param = state_dict[key]
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# Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+
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if len(dest_tensor.shape) == 0 and len(input_param.shape) == 1:
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input_param = input_param[0]
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if input_param.shape != dest_tensor.shape:
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# local shape should match the one in checkpoint
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error_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, '
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'the shape in current model is {}.'.format(key, input_param.shape,
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dest_tensor.shape))
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return
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try:
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with torch.no_grad():
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# self.chunk_manager.copy_tensor_to_chunk_slice(fp32_p, input_param)
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copy_func(input_param)
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except Exception as ex:
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error_msgs.append('While copying the parameter named "{}", '
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'whose dimensions in the model are {} and '
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'whose dimensions in the checkpoint are {}, '
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'an exception occurred : {}.'.format(key, dest_tensor.size(), input_param.size(),
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ex.args))
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elif strict:
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missing_keys.append(key)
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def load_fp32_p(fp32_p, data):
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if fp32_p.storage().size() > 0:
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self.chunk_manager.copy_tensor_to_chunk_slice(fp32_p, data)
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for (name, p), fp32_p in zip(self.named_parameters(), self.fp32_params):
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if p is not None:
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load(name, fp32_p, partial(load_fp32_p, fp32_p))
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self.chunk_manager.copy_chunk_group('fp16_param', 'fp32_param')
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for name, buf in persistent_buffers.items():
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if buf is not None:
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load(name, buf, buf.copy_)
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extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
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if getattr(self.__class__, "set_extra_state",
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torch.nn.Module.set_extra_state) is not torch.nn.Module.set_extra_state:
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if extra_state_key in state_dict:
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self.set_extra_state(state_dict[extra_state_key])
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elif strict:
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missing_keys.append(extra_state_key)
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elif strict and (extra_state_key in state_dict):
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unexpected_keys.append(extra_state_key)
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if strict:
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for key in state_dict.keys():
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if key.startswith(prefix) and key != extra_state_key:
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input_name = key[len(prefix):]
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if input_name not in local_state:
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unexpected_keys.append(key)
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@ -0,0 +1,66 @@
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import pytest
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import colossalai
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import torch
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import torch.multiprocessing as mp
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils import free_port
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from colossalai.tensor import ChunkManager
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from functools import partial
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from tests.components_to_test.registry import non_distributed_component_funcs
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from colossalai.nn.parallel import ColoDDPV2, ColoDDP
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from colossalai.gemini.gemini_mgr import GeminiManager
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from typing import Callable
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from collections import OrderedDict
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def check_state_dict_equal(state_dict: OrderedDict, other_state_dict: OrderedDict):
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for (k1, t1), (k2, t2) in zip(state_dict.items(), other_state_dict.items()):
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assert k1 == k2
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assert torch.allclose(t1, t2, atol=1e-3, rtol=1e-3)
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def init_ddp(module: torch.nn.Module) -> ColoDDP:
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return ColoDDP(module)
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def init_ddpv2(module: torch.nn.Module, use_chunk: bool = False, use_zero: bool = False) -> ColoDDPV2:
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chunk_size = ChunkManager.search_chunk_size(module, 64, 4) if use_chunk else None
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chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero)
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gemini_manager = GeminiManager('cuda', chunk_manager)
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return ColoDDPV2(module, gemini_manager)
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def run_state_dict(ddp_init_func: Callable[[torch.nn.Module], ColoDDP]):
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get_components_func = non_distributed_component_funcs.get_callable('nested_model')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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torch_model = model_builder().cuda()
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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model = ddp_init_func(model)
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torch_state_dict = torch_model.state_dict()
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model.load_state_dict(torch_state_dict)
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state_dict = model.state_dict()
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check_state_dict_equal(torch_state_dict, state_dict)
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def run_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_state_dict(init_ddp)
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run_state_dict(partial(init_ddpv2, use_chunk=False, use_zero=False))
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run_state_dict(partial(init_ddpv2, use_chunk=False, use_zero=True))
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run_state_dict(partial(init_ddpv2, use_chunk=True, use_zero=False))
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run_state_dict(partial(init_ddpv2, use_chunk=True, use_zero=True))
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 2])
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@rerun_if_address_is_in_use()
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def test_state_dict(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_state_dict(2)
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