mirror of https://github.com/hpcaitech/ColossalAI
794 lines
37 KiB
Python
794 lines
37 KiB
Python
import itertools
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from collections import OrderedDict
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from contextlib import nullcontext
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from functools import partial
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from typing import Dict, Iterator, List, Optional, Set, Tuple, Union
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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.checkpoint_io.utils import calculate_tensor_size
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from colossalai.lazy import LazyTensor
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from colossalai.logging import get_dist_logger
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from colossalai.nn.parallel.data_parallel import ColoDDP, _cast_float, free_storage
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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 .chunk import Chunk, ChunkManager, TensorState, init_chunk_manager
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from .gemini_hook import GeminiZeROHook
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from .gemini_mgr import GeminiManager
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from .memory_tracer import MemStats, OrderedParamGenerator
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from .utils import get_temp_total_chunk_on_cuda
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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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__all__ = [
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'ZeroDDP',
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'GeminiDDP',
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]
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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 memory 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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scatter_after_inference (bool): If set to True, the model will be scattered after inference. This will save memory but slow down the consecutive inference.
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mixed_precision (torch.dtype): If set to torch.float16, the model will be trained in fp16. Otherwise, the model will be trained in bf16. Defaults to torch.float16.
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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,
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scatter_after_inference: bool = True,
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mixed_precision: torch.dtype = torch.float16) -> None:
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assert mixed_precision in (torch.float16, torch.bfloat16)
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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.scatter_after_inference = scatter_after_inference
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self.mixed_precision = mixed_precision
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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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super().__init__(module, process_group=ColoProcessGroup())
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self._non_persistent_buffers_set = self._get_non_persistent_buffers_set(module)
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self._cast_buffers()
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def _get_non_persistent_buffers_set(self,
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module,
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memo: Optional[Set[nn.Module]] = None,
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prefix: str = '',
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remove_duplicate: bool = True):
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r"""
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Args:
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memo: a memo to store the set of modules already added to the result
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prefix: a prefix that will be added to the name of the module
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remove_duplicate: whether to remove the duplicated module instances in the result
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or not
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"""
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if memo is None:
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memo = set()
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self_non_persistent_set = set()
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if module not in memo:
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if remove_duplicate:
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memo.add(module)
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self_non_persistent_set = set(
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map(lambda key: prefix + ('.' if prefix else '') + key, module._non_persistent_buffers_set))
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for name, sub_module in module._modules.items():
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if sub_module is None:
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continue
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submodule_prefix = prefix + ('.' if prefix else '') + name
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child_non_persistent_set = self._get_non_persistent_buffers_set(sub_module, memo, submodule_prefix,
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remove_duplicate)
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self_non_persistent_set = set.union(self_non_persistent_set, child_non_persistent_set)
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return self_non_persistent_set
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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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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, self.mixed_precision), _cast_float(kwargs, self.mixed_precision)
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self.module.zero_grad(set_to_none=True)
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if not grad_flag:
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outputs = self._inference_forward(*args, **kwargs)
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else:
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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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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 _inference_forward(self, *args, **kwargs):
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"""This function is only triggered for inference.
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"""
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fwd_ctx = ColoParamOpHookManager.use_hooks(self.param_op_hook)
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if not self.scatter_after_inference:
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# gather all chunks
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for chunk in self.chunk_manager.get_chunks(self.fp16_params):
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self.chunk_manager.access_chunk(chunk)
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fwd_ctx = nullcontext()
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with fwd_ctx:
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outputs = self.module(*args, **kwargs)
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if self.scatter_after_inference:
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# scatter chunks
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self._post_forward()
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# reset all recorded attributes
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self.gemini_manager.reset_attributes()
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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,
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destination=None,
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prefix='',
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keep_vars=False,
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only_rank_0: bool = True,
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dtype: torch.dtype = torch.float16):
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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, dtype)
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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_chunk_to_save_data(self, chunk: Chunk, only_rank_0: bool, dtype: torch.dtype = torch.float16) -> Dict:
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"""
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get gathered chunk content.
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Args:
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chunk (Chunk): a chunk
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only_rank_0 (bool): whether to only save data on rank 0
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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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chunk_to_save_data = dict()
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temp_chunk = get_temp_total_chunk_on_cuda(chunk)
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if torch.is_floating_point(temp_chunk):
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temp_chunk = temp_chunk.to(dtype)
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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 chunk_to_save_data
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chunk_to_save_data[tensor] = record_tensor
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del temp_chunk
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return chunk_to_save_data
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def _get_param_to_save_data(self, param_list: List[torch.nn.Parameter], only_rank_0: bool,
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dtype: torch.dtype) -> 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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param_to_save_data.update(self._get_chunk_to_save_data(chunk, only_rank_0, dtype))
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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, dtype=torch.float16):
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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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# as memory of fp16_params may be reused by grad, it's not reliable, we should use fp32_params and convert to fp16
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param_to_save_data = self._get_param_to_save_data(self.fp32_params, only_rank_0, dtype)
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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]
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del p_mapping
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del param_to_save_data
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# save all buffers
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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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# save extra states
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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(
|
|
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():
|
|
self._preprocess_param(p)
|
|
assert type(p) is 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=self.mixed_precision)
|
|
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.to(self.mixed_precision)
|
|
|
|
# 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():
|
|
if isinstance(buffer, LazyTensor):
|
|
buffer.materialize()
|
|
buffer.data = buffer.cuda()
|
|
if torch.is_floating_point(buffer):
|
|
buffer.data = buffer.to(self.mixed_precision)
|
|
|
|
def _preprocess_param(self, p: Union[nn.Parameter, ColoParameter, 'LazyTensor']) -> None:
|
|
"""Convert parameter to ColoParameter in-place.
|
|
Args:
|
|
p (Union[nn.Parameter, ColoParameter, LazyTensor]): parameter to be converted
|
|
"""
|
|
if type(p) is ColoParameter:
|
|
# model is initialized with ColoInitContext
|
|
return
|
|
requires_grad = p.requires_grad
|
|
if isinstance(p, LazyTensor):
|
|
# model is initialized with LazyInitContext
|
|
p.materialize()
|
|
p.__class__ = ColoParameter
|
|
p.__init__(p, requires_grad=requires_grad)
|
|
|
|
def state_dict_shard(self,
|
|
prefix: str = '',
|
|
keep_vars: bool = False,
|
|
max_shard_size: int = 1024,
|
|
only_rank_0: bool = True,
|
|
dtype: torch.dtype = torch.float16) -> Iterator[Tuple[OrderedDict, int]]:
|
|
"""Returns dictionaries containing a whole state of the module one by one. The max size of dictionary shard is specified by ``max_shard_size``.
|
|
|
|
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.
|
|
|
|
Args:
|
|
prefix (str, optional): the prefix for parameters and buffers used in this
|
|
module. Defaults to ''.
|
|
keep_vars (bool, optional): whether to keep variables. Defaults to False.
|
|
max_shard_size (int, optional): max size of state dict shard (in MB). Defaults to 1024.
|
|
only_rank_0 (bool, optional): only get data on rank0. Defaults to True.
|
|
|
|
|
|
Yields:
|
|
Iterator[OrderedDict]: A generator of state dict shard
|
|
"""
|
|
sharder = _StateDictSharder(max_shard_size)
|
|
|
|
# get the mapping between copies and fp16 parameters
|
|
fp16_to_fp32 = dict()
|
|
for p, fp32_p in zip(self.fp16_params, self.fp32_params):
|
|
fp16_to_fp32[p] = fp32_p
|
|
|
|
# key is fp32 param, and value is gathered param on CPU
|
|
gathered_param_buffer = dict()
|
|
for name, param in self.name2param.items():
|
|
if param is not None:
|
|
if is_ddp_ignored(param):
|
|
# deal with ddp ignored parameters
|
|
gathered_param = param if keep_vars else param.detach()
|
|
else:
|
|
# as memory of fp16 param may be reused, we should use fp32 param and then convert to fp16
|
|
fp32_param = fp16_to_fp32[param]
|
|
if fp32_param not in gathered_param_buffer:
|
|
chunk = self.chunk_manager.get_chunk(fp32_param)
|
|
gathered_param_buffer.update(self._get_chunk_to_save_data(chunk, only_rank_0, dtype))
|
|
gathered_param = gathered_param_buffer.pop(fp32_param)
|
|
|
|
block, block_size = sharder.append(prefix + name, gathered_param)
|
|
if block is not None:
|
|
yield block, block_size
|
|
|
|
del fp16_to_fp32
|
|
del gathered_param_buffer
|
|
|
|
# save all buffers
|
|
for name, buf in self.named_buffers():
|
|
if buf is not None and name not in self._non_persistent_buffers_set:
|
|
buffer = buf if keep_vars else buf.detach()
|
|
block, block_size = sharder.append(prefix + name, buffer)
|
|
if block is not None:
|
|
yield block, block_size
|
|
# 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:
|
|
extra_state = self.get_extra_state()
|
|
block, block_size = sharder.append(extra_state_key, extra_state)
|
|
if block is not None:
|
|
yield block, block_size
|
|
|
|
yield sharder.current_block, sharder.current_block_size
|
|
|
|
|
|
class _StateDictSharder:
|
|
|
|
def __init__(self, max_shard_size: int) -> None:
|
|
self.max_shard_size = max_shard_size
|
|
self.current_block = OrderedDict()
|
|
self.current_block_size = 0
|
|
|
|
def append(self, name: str, tensor: torch.Tensor) -> Tuple[Optional[OrderedDict], int]:
|
|
tensor_size = calculate_tensor_size(tensor)
|
|
ret_block = None
|
|
ret_block_size = 0
|
|
|
|
# before we return the current block and create a new block,
|
|
# we need to ensure that the current block is not empty
|
|
if self.current_block_size + tensor_size > self.max_shard_size and self.current_block_size > 0:
|
|
ret_block = self.current_block
|
|
ret_block_size = self.current_block_size
|
|
self.current_block = OrderedDict()
|
|
self.current_block_size = 0
|
|
self.current_block[name] = tensor
|
|
self.current_block_size += tensor_size
|
|
return ret_block, ret_block_size
|
|
|
|
|
|
class GeminiDDP(ZeroDDP):
|
|
|
|
def __init__(self,
|
|
module: torch.nn.Module,
|
|
device: torch.device,
|
|
placement_policy: str = "cpu",
|
|
pin_memory: bool = False,
|
|
force_outputs_fp32: bool = False,
|
|
strict_ddp_mode: bool = False,
|
|
scatter_after_inference: bool = True,
|
|
search_range_m: int = 32,
|
|
hidden_dim: Optional[int] = None,
|
|
min_chunk_size_m: float = 32,
|
|
memstats: Optional[MemStats] = None,
|
|
mixed_precision: torch.dtype = torch.float16,
|
|
verbose: bool = False) -> None:
|
|
"""
|
|
A torch.Module wrapper using ZeRO-DP and Gemini.
|
|
ZeRO is for parallel. Gemini is for memory management.
|
|
WARNING: The class will modify the module inline!
|
|
|
|
Example:
|
|
model is initialized under the context of ColoInitContext
|
|
>>> model = GeminiDDP(model, torch.cuda.current_device(), "cuda")
|
|
>>> logits = model(x)
|
|
>>> loss = criterion(logits, labels)
|
|
>>> model.backward(loss)
|
|
|
|
Args:
|
|
module (torch.nn.Module): the model to be wrapped.
|
|
device (torch.device): device to place the model.
|
|
placement_policy (str, optional): "cpu", "cuda", "auto". Defaults to "cpu".
|
|
pin_memory (bool, optional): use pin memory on CPU. Defaults to False.
|
|
force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False.
|
|
search_range_m (int, optional): chunk size searching range divided by 2^20. Defaults to 32.
|
|
hidden_dim (int, optional): the hidden dimension of DNN.
|
|
Users can provide this argument to speed up searching.
|
|
If users do not know this argument before training, it is ok. We will use a default value 1024.
|
|
min_chunk_size_m (float, optional): the minimum chunk size divided by 2^20.
|
|
If the aggregate size of parameters is still smaller than the minimum chunk size,
|
|
all parameters will be compacted into one small chunk.
|
|
memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer.
|
|
"""
|
|
# some ugly hotfix for the compatibility with Lightning
|
|
if search_range_m is None:
|
|
search_range_m = 32
|
|
|
|
chunk_manager = init_chunk_manager(model=module,
|
|
init_device=device,
|
|
hidden_dim=hidden_dim,
|
|
search_range_m=search_range_m,
|
|
min_chunk_size_m=min_chunk_size_m,
|
|
strict_ddp_flag=strict_ddp_mode,
|
|
verbose=verbose)
|
|
gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats)
|
|
super().__init__(module,
|
|
gemini_manager,
|
|
pin_memory,
|
|
force_outputs_fp32,
|
|
strict_ddp_mode,
|
|
scatter_after_inference,
|
|
mixed_precision=mixed_precision)
|