ColossalAI/colossalai/nn/parallel/data_parallel.py

492 lines
22 KiB
Python

import torch
import itertools
import torch.distributed as dist
from functools import partial
from colossalai.zero.utils.zero_hook_v2 import ZeROHookV2
from colossalai.gemini.chunk import TensorState, Chunk
from colossalai.tensor.param_op_hook import ParamOpHookManager
from colossalai.gemini.gemini_mgr import GeminiManager
from typing import Dict, Iterable, List, Optional
from colossalai.logging import get_dist_logger
from collections import OrderedDict
from colossalai.tensor.colo_parameter import ColoParameter
from colossalai.tensor import ProcessGroup as ColoProcessGroup
from .reducer import Reducer
try:
from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX, _IncompatibleKeys
except ImportError:
_EXTRA_STATE_KEY_SUFFIX = '_extra_state'
def free_storage(data: torch.Tensor) -> None:
"""Free underlying storage of a Tensor."""
if data.storage().size() > 0:
# Since we're modifying the Tensor's Storage directly, make sure the Tensor
# is the sole occupant of the Storage.
assert data.storage_offset() == 0
data.storage().resize_(0)
def _cast_float(args, dtype: torch.dtype):
if isinstance(args, torch.Tensor) and torch.is_floating_point(args):
args = args.to(dtype)
elif isinstance(args, (list, tuple)):
args = type(args)(_cast_float(t, dtype) for t in args)
elif isinstance(args, dict):
args = {k: _cast_float(v, dtype) for k, v in args.items()}
return args
class ColoDDP(torch.nn.Module):
"""Distributed data parallel for ColoTensor. Nested ColoDDP is not supported now.
Example:
>>> from colossalai.core import global_context as gpc
>>> from colossalai.context import ParallelMode
>>> model = torch.nn.Linear(20, 1)
>>> pg = ProcessGroup(tp_degree = world_size//2)
>>> model = ColoDDP(model, pg)
>>> logits = model(x)
>>> loss = criterion(logits, labels)
>>> model.backward(loss)
Args:
module (torch.nn.Module): Module to apply DDP.
process_group (Optional[dist.ProcessGroup], optional): The process group which DDP uses.
If it's None, the default data parallel group will be used. Defaults to None.
"""
def __init__(self,
module: torch.nn.Module,
process_group: ColoProcessGroup,
bucket_cap_mb: int = 25,
rebuild_bucket: bool = True) -> None:
assert not isinstance(module, ColoDDP)
super().__init__()
self.module = module
self.comm_stream: torch.cuda.Stream = torch.cuda.Stream()
assert process_group
self.process_group = process_group
self.dp_world_size = self.process_group.dp_world_size()
self.reducer = Reducer(bucket_cap_mb)
self.rebuild_bucket = rebuild_bucket
for p in module.parameters():
if getattr(p, '_ddp_to_ignore', False):
continue
if p.requires_grad:
p.register_hook(partial(self.grad_handle, p))
def parameters(self, recurse: bool = True):
return self.module.parameters(recurse)
def named_parameters(self, prefix: str = '', recurse: bool = True):
return self.module.named_parameters(prefix, recurse)
def forward(self, *args, **kwargs):
self.module.zero_grad(set_to_none=True)
return self.module(*args, **kwargs)
def backward(self, loss: torch.Tensor):
loss.backward()
with torch.cuda.stream(self.comm_stream):
self.reducer.flush()
torch.cuda.current_stream().wait_stream(self.comm_stream)
if self.rebuild_bucket:
self.reducer.free()
for p in self.module.parameters():
if getattr(p, '_ddp_to_ignore', False):
continue
if p.grad.device.type != "cpu":
p.grad = p._saved_grad
def grad_handle(self, p, grad):
if grad.device.type != "cpu":
empty_grad = torch.empty_like(grad)
free_storage(empty_grad)
if self.dp_world_size > 1:
grad = grad / self.dp_world_size
self.comm_stream.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(self.comm_stream):
self.reducer.all_reduce_async(grad,
group=self.process_group.dp_process_group(),
callback_fn=partial(self._save_grad, p))
grad.record_stream(self.comm_stream)
else:
ColoDDP._save_grad(p, grad)
return empty_grad
else:
# TODO(jiaruifang) fixme
self.process_group.set_cpu_groups()
dist.all_reduce(grad, group=self.process_group.cpu_dp_process_group())
return grad
@staticmethod
def _save_grad(p, grad):
if hasattr(p, '_saved_grad'):
p._saved_grad.add_(grad)
else:
p._saved_grad = grad
def zero_grad(self, set_to_none: bool = False) -> None:
self.module.zero_grad(set_to_none=True)
for p in self.module.parameters():
if getattr(p, '_saved_grad', None) is not None:
if set_to_none:
p._saved_grad = None
else:
if p._saved_grad.grad_fn is not None:
p._saved_grad.detach_()
else:
p._saved_grad.requires_grad_(False)
p._saved_grad.zero_()
@staticmethod
def set_params_to_ignore(params_to_ignore: Iterable[torch.Tensor]) -> None:
"""Sets parameters to be ignored by DDP.
This method must be called before initializing ColoDDP.
Example:
>>> params_to_ignore = []
>>> for p in module.parameters():
>>> if should_ignore(p):
>>> params_to_ignore.append(p)
>>> ColoDDP.set_params_to_ignore(params_to_ignore)
>>> module = ColoDDP(module)
Args:
params_to_ignore (Iterable[torch.Tensor]): A list of parameters to be ignored.
"""
for p in params_to_ignore:
p._ddp_to_ignore = True
def state_dict(self, destination=None, prefix='', keep_vars=False):
return self.module.state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)
def load_state_dict(self, state_dict: 'OrderedDict[str, torch.Tensor]', strict: bool = True):
return self.module.load_state_dict(state_dict, strict)
class ZeroDDP(ColoDDP):
"""ZeRO-DP for ColoTensor. Nested ZeroDDP is not supported now.
We can configure chunk and gemini via ChunkManager and GeminiManager respectively.
For more details, see the API reference of ``ChunkManager`` and ``GeminiManager``.
Example:
>>> model = torch.nn.Linear(20, 1)
>>> placement_policy = 'cuda'
>>> chunk_size = ChunkManager.search_chunk_size(model, search_range, n_grids) if use_chunk else None
>>> chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero, init_device=GeminiManager.get_default_device(placement_policy))
>>> gemini_manager = GeminiManager(placement_policy, chunk_manager)
>>> model = ZeroDDP(model, gemini_manager)
>>> logits = model(x)
>>> loss = criterion(logits, labels)
>>> model.backward(loss)
Args:
module (torch.nn.Module): Module to apply ZeRO-DP.
gemini_manager (GeminiManager): Manages the chunk manager and heterogeneous momery space.
For more details, see the API reference of ``GeminiManager``.
"""
def __init__(self, module: torch.nn.Module, gemini_manager: GeminiManager) -> None:
super().__init__(module, process_group=gemini_manager.chunk_manager.process_group)
self.gemini_manager = gemini_manager
self.chunk_manager = gemini_manager.chunk_manager
self.param_op_hook = ZeROHookV2(gemini_manager)
self.fp32_params: List[ColoParameter] = []
self.overflow_counter = 0
self.grads_device: Dict[torch.Tensor, torch.device] = {}
self.chunk_manager.create_group('fp16_param', force_data_on_cuda=True)
self.chunk_manager.create_group('fp32_param')
# TODO: get param order and filter unused params
for p in module.parameters():
if getattr(p, '_ddp_to_ignore', False):
p.data = p.half()
continue
fp32_p = p.float().detach()
p.data = p.half()
self.chunk_manager.append_tensor(p, 'fp16_param')
self.chunk_manager.append_tensor(fp32_p, 'fp32_param')
self.fp32_params.append(fp32_p)
self.grads_device[p] = self.gemini_manager.default_device
self._cast_buffers()
self._logger = get_dist_logger()
def forward(self, *args, **kwargs):
args, kwargs = _cast_float(args, torch.half), _cast_float(kwargs, torch.half)
self.module.zero_grad(set_to_none=True)
self.gemini_manager.pre_iter()
with ParamOpHookManager.use_hooks(self.param_op_hook):
outputs = self.module(*args, **kwargs)
self.chunk_manager.exec_lazy_release()
return _cast_float(outputs, torch.float)
def _setup_grads_ptr(self):
for p in self.module.parameters():
if getattr(p, '_ddp_to_ignore', False):
continue
if self.chunk_manager.get_chunk(p).is_empty or not p.requires_grad:
p.grad = None
else:
p.grad = p.data
def _post_backward(self):
self.chunk_manager.exec_lazy_release()
self._setup_grads_ptr()
self._logger.debug(
f'comp cuda demand time: {self.gemini_manager._comp_cuda_demand_time}, layout time: {self.gemini_manager._layout_time}, evict time: {self.gemini_manager._evict_time}, CPU->CUDA vol: {self.gemini_manager._h2d_volume}B, CUDA->CPU vol: {self.gemini_manager._d2h_volume}'
)
self.gemini_manager.post_iter()
def backward(self, loss: torch.Tensor):
with self.param_op_hook.switch_to_backward(), ParamOpHookManager.use_hooks(self.param_op_hook):
loss.backward()
self._post_backward()
def backward_by_grad(self, tensor, grad):
with self.param_op_hook.switch_to_backward(), ParamOpHookManager.use_hooks(self.param_op_hook):
torch.autograd.backward(tensor, grad)
self._post_backward()
def grad_handle(self, p, grad):
empty_grad = torch.empty_like(grad)
free_storage(empty_grad)
with torch._C.DisableTorchFunction():
self.chunk_manager.trans_tensor_state(p, TensorState.READY_FOR_REDUCE)
if self.dp_world_size > 1:
grad = grad / self.dp_world_size
self.chunk_manager.copy_tensor_to_chunk_slice(p, grad)
chunk = self.chunk_manager.get_chunk(p)
reduced = self.chunk_manager.reduce_chunk(chunk)
self.chunk_manager.release_chunk(chunk)
if reduced and not chunk.is_empty:
self.overflow_counter += chunk.has_inf_or_nan
self.chunk_manager.move_chunk(chunk, self.grads_device[p])
return empty_grad
def zero_grad(self, set_to_none: bool = False) -> None:
self.module.zero_grad(set_to_none=True)
def _set_chunk_grad_device(self, chunk: Chunk, device: torch.device) -> None:
for tensor in chunk.get_tensors():
self.grads_device[tensor] = device
def state_dict(self, destination=None, prefix='', keep_vars=False):
r"""Returns a dictionary containing a whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to ``None`` are not included.
Returns:
dict:
a dictionary containing a whole state of the module
Example:
>>> module.state_dict().keys()
['bias', 'weight']
"""
if destination is None:
destination = OrderedDict()
destination._metadata = OrderedDict()
destination._metadata[prefix[:-1]] = local_metadata = dict(version=self._version)
self._save_to_state_dict(destination, prefix, keep_vars)
for hook in self._state_dict_hooks.values():
hook_result = hook(self, destination, prefix, local_metadata)
if hook_result is not None:
destination = hook_result
return destination
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
"""
chunks = self.chunk_manager.get_chunks(self.fp32_params)
chunks_orig_device_type = []
for chunk in chunks:
chunks_orig_device_type.append(chunk.device_type)
self.chunk_manager.access_chunk(chunk)
for (name, p), fp32_p in zip(self.named_parameters(), self.fp32_params):
if p is not None:
rec_p = fp32_p.clone() if fp32_p.device.type == 'cpu' else fp32_p.cpu()
destination[prefix + name] = rec_p if keep_vars else rec_p.detach()
for orig_dvice_type, chunk in zip(chunks_orig_device_type, chunks):
self.chunk_manager.release_chunk(chunk)
if not chunk.is_empty and orig_dvice_type == 'cpu':
self.chunk_manager.move_chunk(chunk, torch.device('cpu'))
for name, buf in self.named_buffers():
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",
torch.nn.Module.get_extra_state) is not torch.nn.Module.get_extra_state:
destination[extra_state_key] = self.get_extra_state()
def load_state_dict(self, state_dict: 'OrderedDict[str, torch.Tensor]', strict: bool = True):
r"""Copies parameters and buffers from :attr:`state_dict` into
this module and its descendants. If :attr:`strict` is ``True``, then
the keys of :attr:`state_dict` must exactly match the keys returned
by this module's :meth:`~torch.nn.Module.state_dict` function.
Args:
state_dict (dict): a dict containing parameters and
persistent buffers.
strict (bool, optional): whether to strictly enforce that the keys
in :attr:`state_dict` match the keys returned by this module's
:meth:`~torch.nn.Module.state_dict` function. Default: ``True``
Returns:
``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
* **missing_keys** is a list of str containing the missing keys
* **unexpected_keys** is a list of str containing the unexpected keys
Note:
If a parameter or buffer is registered as ``None`` and its corresponding key
exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a
``RuntimeError``.
"""
missing_keys: List[str] = []
unexpected_keys: List[str] = []
error_msgs: List[str] = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
# mypy isn't aware that "_metadata" exists in state_dict
state_dict._metadata = metadata # type: ignore[attr-defined]
prefix = ''
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
self._load_from_state_dict(state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
if strict:
if len(unexpected_keys) > 0:
error_msgs.insert(
0, 'Unexpected key(s) in state_dict: {}. '.format(', '.join(
'"{}"'.format(k) for k in unexpected_keys)))
if len(missing_keys) > 0:
error_msgs.insert(
0, 'Missing key(s) in state_dict: {}. '.format(', '.join('"{}"'.format(k) for k in missing_keys)))
if len(error_msgs) > 0:
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
self.__class__.__name__, "\n\t".join(error_msgs)))
return _IncompatibleKeys(missing_keys, unexpected_keys)
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.named_buffers() if k not in self._non_persistent_buffers_set}
local_name_params = itertools.chain(self.named_parameters(), persistent_buffers.items())
local_state = {k: v for k, v in local_name_params if v is not None}
def load(name, dest_tensor, copy_func):
key = prefix + name
if key in state_dict:
input_param = state_dict[key]
# Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+
if len(dest_tensor.shape) == 0 and len(input_param.shape) == 1:
input_param = input_param[0]
if input_param.shape != dest_tensor.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,
dest_tensor.shape))
return
try:
with torch.no_grad():
# self.chunk_manager.copy_tensor_to_chunk_slice(fp32_p, input_param)
copy_func(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, dest_tensor.size(), input_param.size(),
ex.args))
elif strict:
missing_keys.append(key)
def load_fp32_p(fp32_p, data):
if fp32_p.storage().size() > 0:
self.chunk_manager.copy_tensor_to_chunk_slice(fp32_p, data)
for (name, p), fp32_p in zip(self.named_parameters(), self.fp32_params):
if p is not None:
load(name, fp32_p, partial(load_fp32_p, fp32_p))
self.chunk_manager.copy_chunk_group('fp16_param', 'fp32_param')
for name, buf in persistent_buffers.items():
if buf is not None:
load(name, buf, buf.copy_)
extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
if getattr(self.__class__, "set_extra_state",
torch.nn.Module.set_extra_state) is not torch.nn.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):]
if input_name not in local_state:
unexpected_keys.append(key)
def _cast_buffers(self):
for buffer in self.module.buffers():
buffer.data = buffer.cuda()
if torch.is_floating_point(buffer):
buffer.data = buffer.half()