|
| 1 | +import logging |
| 2 | +import pickle |
| 3 | +import time |
| 4 | +from contextlib import contextmanager |
| 5 | +from dataclasses import dataclass |
| 6 | +from datetime import timedelta |
| 7 | +from typing import Generator, List, Tuple, TypeVar, Union, cast |
| 8 | + |
| 9 | +import torch |
| 10 | +from torch.distributed import Work |
| 11 | +from torch.distributed.tensor import DTensor, _DTensorSpec |
| 12 | +from torch.utils._pytree import TreeSpec, tree_flatten, tree_unflatten |
| 13 | + |
| 14 | +from torchft.checkpointing.transport import CheckpointTransport |
| 15 | +from torchft.process_group import ProcessGroup |
| 16 | + |
| 17 | +logger: logging.Logger = logging.getLogger(__name__) |
| 18 | + |
| 19 | +T = TypeVar("T") |
| 20 | + |
| 21 | + |
| 22 | +@dataclass |
| 23 | +class _TensorMeta: |
| 24 | + """ |
| 25 | + This is the metadata for a tensor that is used to transfer checkpoints. |
| 26 | + It contains the shape, the dtype, the storage offset and the stride of the |
| 27 | + tensor. |
| 28 | +
|
| 29 | + This must be pickleable so that it can be sent over the wire. |
| 30 | + """ |
| 31 | + |
| 32 | + shape: torch.Size |
| 33 | + dtype: torch.dtype |
| 34 | + storage_offset: int |
| 35 | + stride: Tuple[int, ...] |
| 36 | + nbytes: int |
| 37 | + |
| 38 | + |
| 39 | +@dataclass |
| 40 | +class _DTensorMeta: |
| 41 | + """ |
| 42 | + This is the metadata for a DTensor that is used to transfer checkpoints. |
| 43 | + It contains the metadata for the local tensor and the spec of the DTensor. |
| 44 | +
|
| 45 | + This must be pickleable so that it can be sent over the wire. |
| 46 | + """ |
| 47 | + |
| 48 | + local: _TensorMeta |
| 49 | + spec: _DTensorSpec |
| 50 | + |
| 51 | + |
| 52 | +@dataclass |
| 53 | +class _StateDictMeta: |
| 54 | + """ |
| 55 | + This is the metadata for a state dict that is used to transfer checkpoints. |
| 56 | + It contains the step, the pytree spec of the state dict and the metadata for |
| 57 | + each tensor in the state dict. |
| 58 | +
|
| 59 | + This must be pickleable so that it can be sent over the wire. |
| 60 | +
|
| 61 | + Args: |
| 62 | + step: the step of the checkpoint to verify consistency |
| 63 | + treespec: the pytree spec of the state dict |
| 64 | + non_tensor_leaves: the metadata for each tensor in the state dict and any |
| 65 | + non-tensor leaves in the state dict |
| 66 | + """ |
| 67 | + |
| 68 | + step: int |
| 69 | + treespec: TreeSpec |
| 70 | + non_tensor_leaves: List[Union[object, _TensorMeta, _DTensorMeta]] |
| 71 | + |
| 72 | + |
| 73 | +@contextmanager |
| 74 | +def _timeit(name: str) -> Generator[None, None, None]: |
| 75 | + start = time.perf_counter() |
| 76 | + yield |
| 77 | + dur = time.perf_counter() - start |
| 78 | + logger.info(f"{name} took {dur}s") |
| 79 | + |
| 80 | + |
| 81 | +def _prepare_tensor(tensor: torch.Tensor) -> Tuple[torch.Tensor, _TensorMeta]: |
| 82 | + return ( |
| 83 | + _cast_tensor(tensor, torch.uint8), |
| 84 | + _TensorMeta( |
| 85 | + shape=tensor.shape, |
| 86 | + dtype=tensor.dtype, |
| 87 | + storage_offset=cast(int, tensor.storage_offset()), |
| 88 | + stride=tensor.stride(), |
| 89 | + nbytes=tensor.untyped_storage().nbytes(), |
| 90 | + ), |
| 91 | + ) |
| 92 | + |
| 93 | + |
| 94 | +def _prepare_state_dict( |
| 95 | + state_dict: object, |
| 96 | + step: int, |
| 97 | + device: torch.device, |
| 98 | +) -> Tuple[_StateDictMeta, List[torch.Tensor]]: |
| 99 | + leaves, treespec = tree_flatten(state_dict) |
| 100 | + |
| 101 | + non_tensor_leaves = [] |
| 102 | + tensors = [] |
| 103 | + for v in leaves: |
| 104 | + if isinstance(v, DTensor): |
| 105 | + tensor, tensor_meta = _prepare_tensor(v._local_tensor) |
| 106 | + |
| 107 | + tensors.append(tensor) |
| 108 | + |
| 109 | + non_tensor_leaves.append( |
| 110 | + _DTensorMeta( |
| 111 | + local=tensor_meta, |
| 112 | + spec=v._spec, |
| 113 | + ) |
| 114 | + ) |
| 115 | + elif isinstance(v, torch.Tensor): |
| 116 | + tensor, tensor_meta = _prepare_tensor(v) |
| 117 | + tensors.append(tensor) |
| 118 | + non_tensor_leaves.append(tensor_meta) |
| 119 | + else: |
| 120 | + non_tensor_leaves.append(v) |
| 121 | + |
| 122 | + return ( |
| 123 | + _StateDictMeta( |
| 124 | + step=step, |
| 125 | + treespec=treespec, |
| 126 | + non_tensor_leaves=non_tensor_leaves, |
| 127 | + ), |
| 128 | + tensors, |
| 129 | + ) |
| 130 | + |
| 131 | + |
| 132 | +def _cast_tensor(tensor: torch.Tensor, dtype: torch.dtype) -> torch.Tensor: |
| 133 | + """ |
| 134 | + Casts the underlying storage to a tensor of the given dtype. |
| 135 | +
|
| 136 | + The returned tensor will be of size ``storage.nbytes``. |
| 137 | +
|
| 138 | + This works for all datatypes and supports strided/offset tensors with the |
| 139 | + caveat that the cast tensor may be larger than the original tensor due to |
| 140 | + the differences in striding. |
| 141 | + """ |
| 142 | + storage = tensor.untyped_storage() |
| 143 | + ret = torch.tensor(storage, dtype=dtype, device=tensor.device) |
| 144 | + assert ret.untyped_storage() is storage, "storage should be the same" |
| 145 | + return ret |
| 146 | + |
| 147 | + |
| 148 | +class PGTransport(CheckpointTransport[T]): |
| 149 | + """ |
| 150 | + This is a checkpoint transport that uses the process group to transfer checkpoints. |
| 151 | + This allows for fast recovery of workers by fetching the current weights |
| 152 | + from an existing worker. |
| 153 | + Args: |
| 154 | + state_dict: a callable that returns the state dict to be transferred |
| 155 | + """ |
| 156 | + |
| 157 | + def __init__( |
| 158 | + self, pg: ProcessGroup, timeout: timedelta, device: torch.device |
| 159 | + ) -> None: |
| 160 | + self._work: List[Work] = [] |
| 161 | + self._pg = pg |
| 162 | + self._timeout = timeout |
| 163 | + self._device = device |
| 164 | + |
| 165 | + def metadata(self) -> str: |
| 166 | + return "<n/a>" |
| 167 | + |
| 168 | + def disallow_checkpoint(self) -> None: |
| 169 | + pass |
| 170 | + |
| 171 | + def send_checkpoint( |
| 172 | + self, dst_ranks: List[int], step: int, state_dict: T, timeout: timedelta |
| 173 | + ) -> None: |
| 174 | + with _timeit("preparing state_dict"): |
| 175 | + meta, tensors = _prepare_state_dict(state_dict, step, device=self._device) |
| 176 | + |
| 177 | + work = [] |
| 178 | + |
| 179 | + with _timeit("send pickle"): |
| 180 | + buf = pickle.dumps(meta) |
| 181 | + len_t = torch.tensor([len(buf)], dtype=torch.int64, device=self._device) |
| 182 | + buf_t = torch.frombuffer(buf, dtype=torch.uint8).to(self._device) |
| 183 | + for dst_rank in dst_ranks: |
| 184 | + work.append(self._pg.send([len_t], dst_rank, tag=1)) |
| 185 | + work.append(self._pg.send([buf_t], dst_rank, tag=2)) |
| 186 | + |
| 187 | + with _timeit("send tensors"): |
| 188 | + for i, t in enumerate(tensors): |
| 189 | + t = t.to(self._device) |
| 190 | + for dst_rank in dst_ranks: |
| 191 | + work.append(self._pg.send([t], dst_rank, tag=3 + i)) |
| 192 | + |
| 193 | + # allow 3 concurrent transfers at a time to avoid OOMs |
| 194 | + while len(work) > (3 * len(dst_ranks)): |
| 195 | + work.pop(0).wait(timeout) |
| 196 | + |
| 197 | + for w in work: |
| 198 | + w.wait(timeout) |
| 199 | + |
| 200 | + def recv_checkpoint( |
| 201 | + self, src_rank: int, metadata: str, step: int, timeout: timedelta |
| 202 | + ) -> T: |
| 203 | + len_t = torch.zeros(1, dtype=torch.int64, device=self._device) |
| 204 | + self._pg.recv([len_t], src_rank, tag=1).wait(timeout) |
| 205 | + length = cast(int, len_t.item()) |
| 206 | + |
| 207 | + assert length > 0, f"invalid metadata length {length=}" |
| 208 | + |
| 209 | + buf = torch.empty(length, dtype=torch.uint8, device=self._device) |
| 210 | + self._pg.recv([buf], src_rank, tag=2).wait(timeout) |
| 211 | + |
| 212 | + meta: _StateDictMeta = pickle.loads(buf.cpu().numpy().tobytes()) |
| 213 | + assert meta.step == step |
| 214 | + |
| 215 | + i: int = 0 |
| 216 | + |
| 217 | + def recv(v: _TensorMeta) -> torch.Tensor: |
| 218 | + nonlocal i |
| 219 | + |
| 220 | + t = torch.empty(v.nbytes, dtype=torch.uint8, device=self._device) |
| 221 | + # TODO: parallelize receives |
| 222 | + self._pg.recv([t], src_rank, tag=3 + i).wait(timeout) |
| 223 | + i += 1 |
| 224 | + |
| 225 | + # TODO: allow in place receives to avoid having to copy to cpu to |
| 226 | + # avoid OOMs |
| 227 | + t = t.cpu() |
| 228 | + |
| 229 | + return torch.as_strided( |
| 230 | + t.view(v.dtype), |
| 231 | + size=v.shape, |
| 232 | + stride=v.stride, |
| 233 | + storage_offset=v.storage_offset, |
| 234 | + ) |
| 235 | + |
| 236 | + values = [] |
| 237 | + for v in meta.non_tensor_leaves: |
| 238 | + if isinstance(v, _TensorMeta): |
| 239 | + values.append(recv(v)) |
| 240 | + elif isinstance(v, _DTensorMeta): |
| 241 | + tensor = recv(v.local) |
| 242 | + # pyre-fixme[29]: DTensor is not a function |
| 243 | + values.append(DTensor(tensor, v.spec, requires_grad=False)) |
| 244 | + else: |
| 245 | + values.append(v) |
| 246 | + |
| 247 | + return tree_unflatten(values, meta.treespec) |
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