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188 lines
8.2 KiB
188 lines
8.2 KiB
from colossalai.tensor.distspec import _DistSpec
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# from colossalai.nn.layer.utils import divide
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from numpy import prod
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from contextlib import contextmanager
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import torch
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import torch.distributed as dist
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from packaging import version
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from colossalai.logging import get_dist_logger
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from colossalai.tensor import ProcessGroup
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# TODO(jiaruifang) circle import, move the divide to colossalai.commons.
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# colossalai.tensor shall not import any submodule from colossal.nn
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def divide(numerator, denominator):
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"""Only allow exact division.
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Args:
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numerator (int): Numerator of the division.
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denominator (int): Denominator of the division.
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Returns:
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int: the result of exact division.
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"""
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assert denominator != 0, 'denominator can not be zero'
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assert numerator % denominator == 0, \
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'{} is not divisible by {}'.format(numerator, denominator)
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return numerator // denominator
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class TransformDistSpec(torch.autograd.Function):
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@staticmethod
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def forward(ctx, tensor, old_dist_spec, dist_spec, pg, forward_trans_func, backward_trans_func):
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ctx.old_dist_spec = old_dist_spec
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ctx.dist_spec = dist_spec
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ctx.backward_trans_func = backward_trans_func
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ctx.pg = pg
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return forward_trans_func(tensor, old_dist_spec, dist_spec, pg)
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@staticmethod
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def backward(ctx, grad_outputs):
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return ctx.backward_trans_func(grad_outputs, ctx.dist_spec, ctx.old_dist_spec,
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ctx.pg), None, None, None, None, None
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class DistSpecManager:
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_use_autograd_function: bool = True
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@staticmethod
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def _sanity_check(old_dist_spec: _DistSpec, dist_spec: _DistSpec) -> None:
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pass
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@staticmethod
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def _shard_as(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec,
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pg: ProcessGroup) -> torch.Tensor:
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"""_shard_as: shard the tensor w.r.t a distributed specification.
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Assuming the tensor passed in is a global (replicated) tensor.
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Args:
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tensor (torch.Tensor): a global (replicated) tensor before shard
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dist_spec (_DistSpec): the distributed spec. to be sharded as.
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pg (ProcessGrouo): the process group of the corresponding colotensor
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Returns:
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torch.Tensor: a torch tensor after sharded.
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"""
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assert old_dist_spec.placement.value == 'r', f"The old_dist_spec of DistSpecManager._shard_as must be REPLICATE!"
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DistSpecManager._sanity_check(old_dist_spec, dist_spec)
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chunk = tensor
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idx = pg.tp_local_rank()
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num_parts = prod(dist_spec.num_partitions)
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for i, dim in enumerate(dist_spec.dims):
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num_parts //= dist_spec.num_partitions[i]
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chunk_size = divide(tensor.size(dim), dist_spec.num_partitions[i])
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chunk = chunk.narrow(dim, idx // num_parts * chunk_size, chunk_size)
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idx %= num_parts
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return chunk.clone().detach().contiguous()
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@staticmethod
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def _gather(tensor: torch.Tensor, old_dist_spec: _DistSpec, pg: ProcessGroup) -> torch.Tensor:
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"""_gather gather sharded tensors to a replicated one.
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Args:
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tensor (torch.Tensor): a shared torch tensor
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old_dist_spec (_DistSpec): the distributed spec. of the tensor.
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Returns:
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torch.Tensor: a replicated tensor.
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"""
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assert old_dist_spec.placement.value == 's', f"The old_dist_spec of DistSpecManager._gather must be SHARD!"
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is_cpu_tensor = False
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if tensor.device.type == 'cpu':
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# pytorch lower than 1.11 dose not support gather a cpu tensor.
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# Therefore, we transfer tensor to GPU before gather.
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saved_dev = tensor.device
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tensor.data = tensor.data.cuda()
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is_cpu_tensor = True
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buffer = [torch.empty_like(tensor) for _ in range(pg.tp_world_size())]
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assert tensor.device.type == 'cuda'
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dist.all_gather(buffer, tensor, group=pg.tp_process_group())
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for i in range(len(old_dist_spec.dims) - 1, -1, -1):
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new_buffer = []
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dim = old_dist_spec.dims[i]
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num_parts = old_dist_spec.num_partitions[i]
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for start in range(0, len(buffer), num_parts):
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new_buffer.append(torch.cat(buffer[start:start + num_parts], dim))
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buffer = new_buffer
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assert len(buffer) == 1
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if is_cpu_tensor:
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buffer[0].data = buffer[0].data.to(saved_dev)
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return buffer[0]
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@staticmethod
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def _all_to_all(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec,
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pg: ProcessGroup) -> torch.Tensor:
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world_size = pg.tp_world_size()
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if world_size == 1:
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return tensor
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assert tensor.device.type == "cuda", \
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"Currently, only CUDA Tensor with NCCL backend is supported for the requested AlltoAll " \
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f"collective function, however, we got {tensor.device.type} device"
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gather_dim = old_dist_spec.dims[0]
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scatter_dim = dist_spec.dims[0]
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shapes = list(tensor.shape)
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scattered_dim_size = shapes[scatter_dim] // world_size
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gathered_dim_size = shapes[gather_dim] * world_size
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shapes[scatter_dim] = scattered_dim_size
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scatter_list = [t.contiguous() for t in torch.tensor_split(tensor, world_size, scatter_dim)]
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gather_list = [torch.empty(*shapes, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)]
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dist.all_to_all(gather_list, scatter_list, group=pg.tp_process_group())
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output_ = torch.cat(gather_list, dim=gather_dim).contiguous()
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assert output_.shape[scatter_dim] == scattered_dim_size and output_.shape[gather_dim] == gathered_dim_size
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return output_
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@staticmethod
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def _r2r(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec, pg: ProcessGroup) -> torch.Tensor:
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DistSpecManager._sanity_check(old_dist_spec, dist_spec)
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return tensor
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@staticmethod
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def _r2s(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec, pg: ProcessGroup) -> torch.Tensor:
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DistSpecManager._sanity_check(old_dist_spec, dist_spec)
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return DistSpecManager._shard_as(tensor, old_dist_spec, dist_spec, pg)
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@staticmethod
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def _s2r(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec, pg: ProcessGroup) -> torch.Tensor:
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DistSpecManager._sanity_check(old_dist_spec, dist_spec)
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return DistSpecManager._gather(tensor, old_dist_spec, pg)
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@staticmethod
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def _s2s(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec, pg: ProcessGroup) -> torch.Tensor:
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DistSpecManager._sanity_check(old_dist_spec, dist_spec)
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if old_dist_spec == dist_spec:
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return tensor
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if len(old_dist_spec.dims) == 1 and len(dist_spec.dims) == 1:
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# use all-to-all to save memory
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return DistSpecManager._all_to_all(tensor, old_dist_spec, dist_spec, pg)
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tensor = DistSpecManager._gather(tensor, old_dist_spec, pg)
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return DistSpecManager._shard_as(tensor, old_dist_spec, dist_spec, pg)
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@staticmethod
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def handle_trans_spec(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSpec,
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pg: ProcessGroup) -> torch.Tensor:
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assert isinstance(old_dist_spec, _DistSpec), f"{type(old_dist_spec)} should be _DistSpec"
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assert isinstance(dist_spec, _DistSpec), f"{type(dist_spec)} should be _DistSpec"
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forward_trans_handle = getattr(DistSpecManager, f'_{old_dist_spec.placement.value}2{dist_spec.placement.value}')
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if not DistSpecManager._use_autograd_function:
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return forward_trans_handle(tensor, old_dist_spec, dist_spec, pg)
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backward_trans_handle = getattr(DistSpecManager,
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f'_{dist_spec.placement.value}2{old_dist_spec.placement.value}')
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return TransformDistSpec.apply(tensor, old_dist_spec, dist_spec, pg, forward_trans_handle,
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backward_trans_handle)
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@staticmethod
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@contextmanager
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def no_grad():
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try:
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DistSpecManager._use_autograd_function = False
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yield
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finally:
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DistSpecManager._use_autograd_function = True
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