ColossalAI/colossalai/shardformer/layer/parallel_module.py

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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import itertools
from abc import ABC, abstractmethod
from typing import List, Union
import torch
import torch.nn as nn
from torch.distributed import ProcessGroup
from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX, Module
from colossalai.tensor.d_tensor import (
distribute_tensor,
distribute_tensor_with_customization,
get_device_mesh,
get_sharding_spec,
is_customized_distributed_tensor,
is_distributed_tensor,
sharded_tensor_to_param,
to_global,
to_global_for_customized_distributed_tensor,
)
__all__ = ['ParallelModule']
class ParallelModule(nn.Module, ABC):
@abstractmethod
def from_native_module(module: nn.Module,
process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "ParallelModule":
"""
Convert a native PyTorch module to a parallelized module.
Args:
module (nn.Module): the module to be converted.
process_group (ProcessGroup or list[ProcessGroup]): the process group(s) to be used for communication.
If this is a list, the process group at the ith index of the list will correspond to the process group
in the ith axis of the device mesh. Defaults to None, which means the global process group.
"""
pass
def _save_to_state_dict(self, destination, prefix, keep_vars):
r"""Saves module state to `destination` dictionary, containing a state
of the module, but not its descendants. This is called on every
submodule in :meth:`~torch.nn.Module.state_dict`.
In rare cases, subclasses can achieve class-specific behavior by
overriding this method with custom logic.
Args:
destination (dict): a dict where state will be stored
prefix (str): the prefix for parameters and buffers used in this
module
"""
for name, param in self._parameters.items():
if param is not None:
param_ = param if keep_vars else param.detach()
if is_distributed_tensor(param_):
destination[prefix + name] = to_global(param_)
elif is_customized_distributed_tensor(param_):
destination[prefix + name] = to_global_for_customized_distributed_tensor(param_)
else:
destination[prefix + name] = param_
for name, buf in self._buffers.items():
if buf is not None and name not in self._non_persistent_buffers_set:
destination[prefix + name] = buf if keep_vars else buf.detach()
extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
if getattr(self.__class__, "get_extra_state", Module.get_extra_state) is not Module.get_extra_state:
destination[extra_state_key] = self.get_extra_state()
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
error_msgs):
r"""Copies parameters and buffers from :attr:`state_dict` into only
this module, but not its descendants. This is called on every submodule
in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this
module in input :attr:`state_dict` is provided as :attr:`local_metadata`.
For state dicts without metadata, :attr:`local_metadata` is empty.
Subclasses can achieve class-specific backward compatible loading using
the version number at `local_metadata.get("version", None)`.
.. note::
:attr:`state_dict` is not the same object as the input
:attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. So
it can be modified.
Args:
state_dict (dict): a dict containing parameters and
persistent buffers.
prefix (str): the prefix for parameters and buffers used in this
module
local_metadata (dict): a dict containing the metadata for this module.
See
strict (bool): whether to strictly enforce that the keys in
:attr:`state_dict` with :attr:`prefix` match the names of
parameters and buffers in this module
missing_keys (list of str): if ``strict=True``, add missing keys to
this list
unexpected_keys (list of str): if ``strict=True``, add unexpected
keys to this list
error_msgs (list of str): error messages should be added to this
list, and will be reported together in
:meth:`~torch.nn.Module.load_state_dict`
"""
for hook in self._load_state_dict_pre_hooks.values():
hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set}
local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items())
local_state = {k: v for k, v in local_name_params if v is not None}
for name, param in local_state.items():
key = prefix + name
if key in state_dict:
input_param = state_dict[key]
if not torch.overrides.is_tensor_like(input_param):
error_msgs.append('While copying the parameter named "{}", '
'expected torch.Tensor or Tensor-like object from checkpoint but '
'received {}'.format(key, type(input_param)))
continue
if is_distributed_tensor(param):
# shard the input param
device_mesh = get_device_mesh(param)
sharding_spec = get_sharding_spec(param)
sharded_tensor = distribute_tensor(input_param, device_mesh, sharding_spec)
input_param = sharded_tensor_to_param(sharded_tensor)
elif is_customized_distributed_tensor(param):
input_param = distribute_tensor_with_customization(input_param, param.shard_fn, param.gather_fn)
# This is used to avoid copying uninitialized parameters into
# non-lazy modules, since they dont have the hook to do the checks
# in such case, it will error when accessing the .shape attribute.
is_param_lazy = torch.nn.parameter.is_lazy(param)
# Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+
if not is_param_lazy and len(param.shape) == 0 and len(input_param.shape) == 1:
input_param = input_param[0]
if not is_param_lazy and input_param.shape != param.shape:
# local shape should match the one in checkpoint
error_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, '
'the shape in current model is {}.'.format(key, input_param.shape, param.shape))
continue
try:
with torch.no_grad():
param.copy_(input_param)
except Exception as ex:
error_msgs.append('While copying the parameter named "{}", '
'whose dimensions in the model are {} and '
'whose dimensions in the checkpoint are {}, '
'an exception occurred : {}.'.format(key, param.size(), input_param.size(),
ex.args))
elif strict:
missing_keys.append(key)
extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
if getattr(self.__class__, "set_extra_state", Module.set_extra_state) is not Module.set_extra_state:
if extra_state_key in state_dict:
self.set_extra_state(state_dict[extra_state_key])
elif strict:
missing_keys.append(extra_state_key)
elif strict and (extra_state_key in state_dict):
unexpected_keys.append(extra_state_key)
if strict:
for key in state_dict.keys():
if key.startswith(prefix) and key != extra_state_key:
input_name = key[len(prefix):]
input_name = input_name.split('.', 1)[0] # get the name of param/buffer/child
if input_name not in self._modules and input_name not in local_state:
unexpected_keys.append(key)