mirror of https://github.com/hpcaitech/ColossalAI
696 lines
31 KiB
Python
696 lines
31 KiB
Python
import itertools
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from collections import OrderedDict
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from functools import partial
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from typing import Dict, Iterable, List, Optional, Set
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from colossalai.gemini.chunk import Chunk, ChunkManager, TensorState
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from colossalai.gemini.gemini_mgr import GeminiManager
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from colossalai.gemini.memory_tracer import OrderedParamGenerator
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from colossalai.logging import get_dist_logger
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from colossalai.nn.parallel.utils import get_temp_total_chunk_on_cuda
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from colossalai.tensor import ProcessGroup as ColoProcessGroup
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from colossalai.tensor import ReplicaSpec
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from colossalai.tensor.colo_parameter import ColoParameter, ColoTensor, ColoTensorSpec
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from colossalai.tensor.param_op_hook import ColoParamOpHookManager
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from colossalai.utils import get_current_device, is_ddp_ignored
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from colossalai.zero.utils.gemini_hook import GeminiZeROHook
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from .reducer import Reducer
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from .utils import get_static_torch_model
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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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"""Free underlying storage of a Tensor."""
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if data.storage().size() > 0:
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# Since we're modifying the Tensor's Storage directly, make sure the Tensor
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# is the sole occupant of the Storage.
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assert data.storage_offset() == 0
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data.storage().resize_(0)
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def _cast_float(args, dtype: torch.dtype):
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if isinstance(args, torch.Tensor) and torch.is_floating_point(args):
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args = args.to(dtype)
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elif isinstance(args, (list, tuple)):
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args = type(args)(_cast_float(t, dtype) for t in args)
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elif isinstance(args, dict):
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args = {k: _cast_float(v, dtype) for k, v in args.items()}
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return args
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class ColoDDP(torch.nn.Module):
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"""Distributed data parallel for ColoTensor. Nested ColoDDP is not supported now.
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Example:
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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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>>> model = torch.nn.Linear(20, 1)
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>>> pg = ProcessGroup(tp_degree = world_size//2)
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>>> model = ColoDDP(model, pg)
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>>> logits = model(x)
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>>> loss = criterion(logits, labels)
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>>> model.backward(loss)
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Args:
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module (torch.nn.Module): Module to apply DDP.
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process_group (Optional[dist.ProcessGroup], optional): The process group which DDP uses.
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If it's None, the default data parallel group will be used. Defaults to None.
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"""
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def __init__(self,
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module: torch.nn.Module,
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process_group: ColoProcessGroup,
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bucket_cap_mb: int = 25,
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rebuild_bucket: bool = True) -> None:
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assert not isinstance(module, ColoDDP)
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super().__init__()
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self.module = module
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self.comm_stream: torch.cuda.Stream = torch.cuda.Stream()
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assert process_group
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self.process_group = process_group
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self.dp_world_size = self.process_group.dp_world_size()
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self.reducer = Reducer(bucket_cap_mb)
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self.rebuild_bucket = rebuild_bucket
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for p in module.parameters():
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if is_ddp_ignored(p):
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continue
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if p.requires_grad:
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p.register_hook(partial(self.grad_handle, p))
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def parameters(self, recurse: bool = True):
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return self.module.parameters(recurse)
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def named_parameters(self, prefix: str = '', recurse: bool = True):
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return self.module.named_parameters(prefix, recurse)
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def named_buffers(self, prefix: str = '', recurse: bool = True):
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return self.module.named_buffers(prefix, recurse)
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def named_children(self):
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return self.module.named_children()
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def named_modules(self,
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memo: Optional[Set[torch.nn.Module]] = None,
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prefix: str = '',
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remove_duplicate: bool = True):
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return self.module.named_modules(memo, prefix, remove_duplicate)
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def forward(self, *args, **kwargs):
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self.module.zero_grad(set_to_none=True)
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return self.module(*args, **kwargs)
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def backward(self, loss: torch.Tensor):
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loss.backward()
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with torch.cuda.stream(self.comm_stream):
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self.reducer.flush()
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torch.cuda.current_stream().wait_stream(self.comm_stream)
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if self.rebuild_bucket:
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self.reducer.free()
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for p in self.module.parameters():
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if is_ddp_ignored(p):
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continue
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if p.grad.device.type != "cpu":
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p.grad = p._saved_grad
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def grad_handle(self, p, grad):
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if grad.device.type != "cpu":
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empty_grad = torch.empty_like(grad)
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free_storage(empty_grad)
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if self.dp_world_size > 1:
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grad = grad / self.dp_world_size
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self.comm_stream.wait_stream(torch.cuda.current_stream())
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with torch.cuda.stream(self.comm_stream):
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self.reducer.all_reduce_async(grad,
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group=self.process_group.dp_process_group(),
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callback_fn=partial(self._save_grad, p))
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grad.record_stream(self.comm_stream)
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else:
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ColoDDP._save_grad(p, grad)
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return empty_grad
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else:
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# TODO(jiaruifang) fixme
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self.process_group.set_cpu_groups()
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dist.all_reduce(grad, group=self.process_group.cpu_dp_process_group())
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return grad
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@staticmethod
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def _save_grad(p, grad):
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if hasattr(p, '_saved_grad'):
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p._saved_grad.add_(grad)
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else:
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p._saved_grad = grad
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def zero_grad(self, set_to_none: bool = False) -> None:
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self.module.zero_grad(set_to_none=True)
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for p in self.module.parameters():
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if getattr(p, '_saved_grad', None) is not None:
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if set_to_none:
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p._saved_grad = None
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else:
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if p._saved_grad.grad_fn is not None:
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p._saved_grad.detach_()
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else:
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p._saved_grad.requires_grad_(False)
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p._saved_grad.zero_()
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@staticmethod
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def set_params_to_ignore(params_to_ignore: Iterable[torch.Tensor]) -> None:
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"""Sets parameters to be ignored by DDP.
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This method must be called before initializing ColoDDP.
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Example:
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>>> params_to_ignore = []
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>>> for p in module.parameters():
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>>> if should_ignore(p):
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>>> params_to_ignore.append(p)
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>>> ColoDDP.set_params_to_ignore(params_to_ignore)
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>>> module = ColoDDP(module)
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Args:
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params_to_ignore (Iterable[torch.Tensor]): A list of parameters to be ignored.
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"""
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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 ZeroDDP(ColoDDP):
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"""ZeRO DDP for ColoTensor.
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Warning: Nested ZeroDDP is not supported now.
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It is designed to be used with ChunkManager and GeminiManager.
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For more details, see the API reference of ``ChunkManager`` and ``GeminiManager``.
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Args:
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module (torch.nn.Module): Module to apply ZeRO-DP.
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gemini_manager (GeminiManager): Manages the chunk manager and heterogeneous momery space.
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For more details, see the API reference of ``GeminiManager``.
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pin_memory (bool): Chunks on CPU Memory use pin-memory.
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force_outputs_fp32 (bool): If set to True, outputs will be fp32. Otherwise, outputs will be fp16.
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Defaults to False.
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strict_ddp_mode (bool): If set to True, there is no tensor sharding, each tensor is replicated.
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Defaults to False. Users can set it to True, when they clearly know that they only need DDP.
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"""
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def __init__(self,
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module: torch.nn.Module,
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gemini_manager: GeminiManager,
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pin_memory: bool = False,
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force_outputs_fp32: bool = False,
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strict_ddp_mode: bool = False) -> None:
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super().__init__(module, process_group=ColoProcessGroup())
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self.gemini_manager = gemini_manager
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self.chunk_manager: ChunkManager = gemini_manager.chunk_manager
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self.force_outputs_fp32 = force_outputs_fp32
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self.param_op_hook = GeminiZeROHook(gemini_manager)
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self.fp32_params: List[ColoTensor] = list()
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self.fp16_params: List[ColoParameter] = list()
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self.overflow_counter = 0
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self.grads_device: Dict[torch.Tensor, torch.device] = dict()
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self.param2name: Dict[nn.Parameter, str] = dict()
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self.name2param: Dict[str, nn.Parameter] = dict()
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self._cast_buffers()
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self._logger = get_dist_logger()
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if self.gemini_manager._premade_memstats_:
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# build chunk in param runtime visited order.
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param_order = self.gemini_manager.memstats()._param_runtime_order
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else:
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# build chunk in param initialized order.
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# Note: in this way, it can not get filter unused params during runtime.
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param_order = OrderedParamGenerator()
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for p in module.parameters():
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param_order.append(p)
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self._init_chunks(param_order=param_order,
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strict_ddp_mode=strict_ddp_mode,
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cpu_offload=self.gemini_manager.policy_name != 'cuda',
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pin_memory=pin_memory)
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for name, param in module.named_parameters():
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self.param2name[param] = name
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for m_name, m_var in module.named_modules():
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for p_name, p_var in m_var.named_parameters(recurse=False):
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param_name = m_name + '.' + p_name if m_name else p_name
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self.name2param[param_name] = p_var
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def _post_forward(self):
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"""This function is only triggered for inference.
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"""
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access_list = list(self.chunk_manager.accessed_chunks)
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# we need to scatter all accessed chunks and move them to their original places
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for chunk in access_list:
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if chunk.keep_gathered:
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self.chunk_manager.fake_release_chunk(chunk)
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else:
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assert chunk.can_release
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self.chunk_manager.release_chunk(chunk)
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first_param = next(iter(chunk.tensors_info))
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self.chunk_manager.move_chunk(chunk, self.grads_device[first_param])
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assert self.chunk_manager.accessed_mem == 0
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# reset all recorded attributes
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self.gemini_manager.reset_attributes()
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def forward(self, *args, **kwargs):
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# check whether we are in a inference mode
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grad_flag = torch.is_grad_enabled()
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if not grad_flag:
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assert not self.gemini_manager.need_warmup or not self.gemini_manager.is_warmup(
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), "You should run a completed iteration as your warmup iter"
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args, kwargs = _cast_float(args, torch.half), _cast_float(kwargs, torch.half)
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self.module.zero_grad(set_to_none=True)
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self.gemini_manager.pre_iter(*args)
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with ColoParamOpHookManager.use_hooks(self.param_op_hook):
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outputs = self.module(*args, **kwargs)
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# scatter chunks in the inference mode
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if not grad_flag:
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self._post_forward()
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if self.force_outputs_fp32:
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return _cast_float(outputs, torch.float)
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return outputs
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def _setup_grads_ptr(self):
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for p in self.module.parameters():
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if is_ddp_ignored(p):
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continue
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p.grad = None
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def _pre_backward(self):
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# set a visit label for all parameters
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# the label is used to check whether the parameter is correctly reduced
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for param in self.param2name:
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if not is_ddp_ignored(param):
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setattr(param, "_gemini_reduced", False)
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def _post_backward(self):
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if self.chunk_manager.accessed_mem != 0:
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error_params = ["Reduction failed at followed parameters:"]
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for param in self.param2name:
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if not is_ddp_ignored(param) and not getattr(param, "_gemini_reduced"):
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error_params.append(self.param2name[param])
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error_str = "\n\t".join(error_params)
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raise RuntimeError("ZERO DDP error: the synchronization of gradients doesn't exit properly.",
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"The most possible reason is that the model is not compatible with ZeroDDP.\n",
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f"{error_str}")
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self._setup_grads_ptr()
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self._logger.debug(
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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}'
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)
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self.gemini_manager.post_iter()
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def backward(self, loss: torch.Tensor):
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self._pre_backward()
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with self.param_op_hook.switch_to_backward(), ColoParamOpHookManager.use_hooks(self.param_op_hook):
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loss.backward()
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self._post_backward()
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def backward_by_grad(self, tensor, grad):
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with self.param_op_hook.switch_to_backward(), ColoParamOpHookManager.use_hooks(self.param_op_hook):
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torch.autograd.backward(tensor, grad)
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self._post_backward()
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def grad_handle(self, p, grad):
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empty_grad = torch.empty_like(grad)
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free_storage(empty_grad)
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with torch._C.DisableTorchFunction():
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chunk = self.chunk_manager.get_chunk(p)
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if chunk.tensors_info[p].state != TensorState.HOLD_AFTER_BWD:
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raise RuntimeError(f"Parameter `{self.param2name[p]}` failed at the gradient reduction. "
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"Some unsupported torch function is operated upon this parameter.")
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self.chunk_manager.trans_tensor_state(p, TensorState.READY_FOR_REDUCE)
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chunk.copy_tensor_to_chunk_slice(p, grad)
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reduced = self.chunk_manager.reduce_chunk(chunk)
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if reduced:
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if chunk.is_gathered:
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chunk.cuda_global_chunk.div_(chunk.pg_size)
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else:
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chunk.cuda_shard.div_(chunk.pg_size)
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# check overflow elements
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self.overflow_counter += chunk.has_inf_or_nan
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# record l2 norm for gradient clipping
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if chunk.l2_norm_flag:
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chunk.set_l2_norm()
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self.chunk_manager.move_chunk(chunk, self.grads_device[p], force_copy=True)
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return empty_grad
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def zero_grad(self, set_to_none: bool = False) -> None:
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self.module.zero_grad(set_to_none=True)
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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, only_rank_0: bool = True):
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"""Returns a dictionary containing a whole state of the module.
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Both parameters and persistent buffers (e.g. running averages) are included.
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Keys are corresponding parameter and buffer names.
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Parameters and buffers set to ``None`` are not included.
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Warning: The non strict state dict would ignore the parameters if the tensors of the parameters
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are shared with other parameters which have been included in the dictionary.
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When you need to load the state dict, you should set the argument `strict` to False.
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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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"""
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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, only_rank_0)
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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 _get_param_to_save_data(self, param_list: List[torch.nn.Parameter], only_rank_0: bool) -> Dict:
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"""
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get param content from chunks.
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Args:
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param_list (_type_): a list of torch.nn.Parameters
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only_rank_0 (_type_): _description_
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Returns:
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Dict: a dict whose key is param name and value is param with correct payload
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"""
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# save parameters
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param_to_save_data = dict()
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chunk_list = self.chunk_manager.get_chunks(param_list)
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for chunk in chunk_list:
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temp_chunk = get_temp_total_chunk_on_cuda(chunk)
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for tensor, tensor_info in chunk.tensors_info.items():
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record_tensor = torch.empty([0])
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record_flag = (not only_rank_0) | (dist.get_rank(chunk.torch_pg) == 0)
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if record_flag:
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record_tensor = temp_chunk[tensor_info.offset:tensor_info.end].view(tensor.shape).cpu()
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assert tensor not in param_to_save_data
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param_to_save_data[tensor] = record_tensor
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del temp_chunk
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return param_to_save_data
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def _save_to_state_dict(self, destination, prefix, keep_vars, only_rank_0=True):
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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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assert keep_vars is False, "`state_dict` with parameter, `keep_vars=True`, is not supported now."
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# get copies of fp32 parameters in CPU
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param_to_save_data = self._get_param_to_save_data(self.fp32_params, only_rank_0)
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# get the mapping between copies and fp16 parameters
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p_mapping = dict()
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for p, fp32_p in zip(self.fp16_params, self.fp32_params):
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name = self.param2name[p]
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assert fp32_p in param_to_save_data, "Parameter '{}' is neglected in the chunk list".format(name)
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record_parameter = param_to_save_data[fp32_p]
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p_mapping[p] = record_parameter
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for name, param in self.name2param.items():
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if param is not None:
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if is_ddp_ignored(param):
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# deal with ddp ignored parameters
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destination[prefix + name] = param if keep_vars else param.detach()
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else:
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destination[prefix + name] = p_mapping[param]
|
|
del p_mapping
|
|
del param_to_save_data
|
|
|
|
# save all buffers
|
|
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()
|
|
# save extra states
|
|
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(param_name, dest_tensor, copy_func):
|
|
state_key = prefix + param_name
|
|
if state_key in state_dict:
|
|
input_param = state_dict[state_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(state_key, input_param.shape,
|
|
dest_tensor.shape))
|
|
return
|
|
try:
|
|
with torch.no_grad():
|
|
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(state_key, dest_tensor.size(),
|
|
input_param.size(), ex.args))
|
|
elif strict:
|
|
missing_keys.append(state_key)
|
|
|
|
def load_fp32_parameter(chunk_slice, data):
|
|
chunk_slice.copy_(data.flatten())
|
|
|
|
for name, param in self.named_parameters():
|
|
if is_ddp_ignored(param):
|
|
# deal with ddp ignored parameters
|
|
load(name, param, param.copy_)
|
|
|
|
fp32_to_name = dict()
|
|
for p, fp32_p in zip(self.fp16_params, self.fp32_params):
|
|
if p is not None:
|
|
name = self.param2name[p]
|
|
fp32_to_name[fp32_p] = name
|
|
|
|
chunk_list = self.chunk_manager.get_chunks(self.fp32_params)
|
|
for chunk in chunk_list:
|
|
temp_chunk = get_temp_total_chunk_on_cuda(chunk)
|
|
|
|
for tensor, tensor_info in chunk.tensors_info.items():
|
|
parameter_name = fp32_to_name[tensor]
|
|
parameter_slice = temp_chunk[tensor_info.offset:tensor_info.end]
|
|
load(parameter_name, tensor, partial(load_fp32_parameter, parameter_slice))
|
|
|
|
if chunk.is_gathered:
|
|
chunk.cuda_global_chunk.copy_(temp_chunk)
|
|
elif chunk.cuda_shard is not None:
|
|
chunk.cuda_shard.copy_(temp_chunk[chunk.shard_begin:chunk.shard_end])
|
|
else:
|
|
chunk.cpu_shard.copy_(temp_chunk[chunk.shard_begin:chunk.shard_end])
|
|
|
|
del temp_chunk
|
|
|
|
for chunk_32 in chunk_list:
|
|
chunk_16 = chunk_32.paired_chunk
|
|
assert chunk_16 is not None
|
|
chunk_16.optim_update()
|
|
|
|
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 _init_chunks(self, param_order, strict_ddp_mode: bool, cpu_offload: bool, pin_memory: bool):
|
|
ddp_pg = ColoProcessGroup()
|
|
for p in param_order.generate():
|
|
assert isinstance(p, ColoParameter)
|
|
|
|
# gather sharded parameters in the strict ddp mode
|
|
if strict_ddp_mode:
|
|
if not p.is_replicate():
|
|
p.set_dist_spec(ReplicaSpec())
|
|
p.set_process_group(pg=ddp_pg)
|
|
|
|
# ignore the parameters with no gradient
|
|
if not p.requires_grad:
|
|
self.set_params_to_ignore([p])
|
|
|
|
# move ignored parameters to CUDA
|
|
if is_ddp_ignored(p):
|
|
p.data = p.data.to(device=get_current_device(), dtype=torch.float16)
|
|
continue
|
|
|
|
# create a fp32 parameter
|
|
fp32_data = p.data.float()
|
|
fp32_p = ColoTensor(fp32_data, spec=ColoTensorSpec(p.process_group))
|
|
# create a fp16 parameter
|
|
p.data = p.data.half()
|
|
|
|
# register the fp16 parameter and fp32 parameter in the chunk manager
|
|
dp_world_size = p.process_group.dp_world_size()
|
|
self.chunk_manager.register_tensor(tensor=p,
|
|
group_type='fp16_param',
|
|
config_key=dp_world_size,
|
|
cpu_offload=cpu_offload,
|
|
pin_memory=pin_memory)
|
|
self.chunk_manager.register_tensor(tensor=fp32_p,
|
|
group_type='fp32_param',
|
|
config_key=dp_world_size,
|
|
cpu_offload=cpu_offload,
|
|
pin_memory=pin_memory)
|
|
|
|
self.fp16_params.append(p)
|
|
self.fp32_params.append(fp32_p)
|
|
self.grads_device[p] = self.gemini_manager.default_device
|
|
|
|
self.chunk_manager.close_all_groups()
|
|
|
|
for p, fp32_p in zip(self.fp16_params, self.fp32_params):
|
|
chunk_16 = self.chunk_manager.get_chunk(p)
|
|
chunk_32 = self.chunk_manager.get_chunk(fp32_p)
|
|
chunk_32.init_pair(chunk_16)
|
|
|
|
# keep gathered chunks are in CUDA
|
|
if chunk_16.keep_gathered:
|
|
self.grads_device[p] = get_current_device()
|
|
|
|
def _cast_buffers(self):
|
|
for buffer in self.module.buffers():
|
|
buffer.data = buffer.cuda()
|
|
if torch.is_floating_point(buffer):
|
|
buffer.data = buffer.half()
|