ColossalAI/colossalai/zero/gemini/utils.py

from collections import OrderedDict
from copy import copy
from typing import Optional, Set

import torch
import torch.distributed as dist
import torch.nn as nn

from colossalai.utils import get_current_device

from .chunk import Chunk


def get_temp_total_chunk_on_cuda(chunk: Chunk):
    if chunk.is_gathered:
        return chunk.cuda_global_chunk

    if chunk.cuda_shard is not None:
        shard_temp = chunk.cuda_shard
    else:
        shard_temp = chunk.cpu_shard.to(get_current_device())

    total_temp = torch.zeros(chunk.chunk_size, dtype=chunk.dtype, device=get_current_device())
    gather_list = list(torch.chunk(input=total_temp, chunks=chunk.pg_size, dim=0))
    dist.all_gather(tensor_list=gather_list, tensor=shard_temp, group=chunk.torch_pg)

    return total_temp


def _get_dfs_module_list(module: nn.Module, memo: Optional[Set[nn.Module]] = None, prefix: str = ""):
    """Get a dfs module list of the given module. Its order is same as the order of creations of modules."""
    if memo is None:
        memo = set()
    if module not in memo:
        for name, submodule in module._modules.items():
            if submodule is None:
                continue
            submodule_prefix = prefix + ("." if prefix else "") + name
            for m in _get_dfs_module_list(submodule, memo, submodule_prefix):
                yield m

        memo.add(module)
        yield prefix, module


def _get_shallow_copy_model(model: nn.Module):
    """Get a shallow copy of the given model. Each submodule is different from the original submodule.
    But the new submodule and the old submodule share all attributes.
    """
    old_to_new = dict()
    for name, module in _get_dfs_module_list(model):
        new_module = copy(module)
        new_module._modules = OrderedDict()
        for subname, submodule in module._modules.items():
            if submodule is None:
                continue
            setattr(new_module, subname, old_to_new[submodule])
        old_to_new[module] = new_module
    return old_to_new[model]


def get_static_torch_model(
    zero_ddp_model, device=torch.device("cpu"), dtype=torch.float32, only_rank_0=True
) -> torch.nn.Module:
    """Get a static torch.nn.Module model from the given GeminiDDP module.
    You should notice that the original GeminiDDP model is not modified.
    Thus, you can use the original model in further training.
    But you should not use the returned torch model to train, this can cause unexpected errors.

    Args:
        zero_ddp_model (GeminiDDP): a zero ddp model
        device (torch.device): the device of the final torch model
        dtype (torch.dtype): the dtype of the final torch model
        only_rank_0 (bool): if True, only rank0 has the converted torch model

    Returns:
        torch.nn.Module: a static torch model used for saving checkpoints or numeric checks
    """
    from colossalai.zero.gemini.gemini_ddp import GeminiDDP

    assert isinstance(zero_ddp_model, GeminiDDP)

    state_dict = zero_ddp_model.state_dict(only_rank_0=only_rank_0)
    colo_model = zero_ddp_model.module
    torch_model = _get_shallow_copy_model(colo_model)

    if not only_rank_0 or dist.get_rank() == 0:
        for (name, colo_module), (_, torch_module) in zip(
            _get_dfs_module_list(colo_model), _get_dfs_module_list(torch_model)
        ):
            # clean the parameter list of the new torch module
            torch_module._parameters = OrderedDict()
            for sufix_param_name, param in colo_module.named_parameters(recurse=False):
                # get the full name of the parameter
                full_param_name = name + ("." if name else "") + sufix_param_name
                assert (
                    full_param_name in state_dict
                ), f"Can not find parameter `{full_param_name}` in the GeminiDDP module"
                state_param = state_dict[full_param_name]
                torch_param = torch.nn.Parameter(state_param.data.to(device=device, dtype=dtype))

                setattr(torch_module, sufix_param_name, torch_param)
    dist.barrier()

    return torch_model