mirror of
https://github.com/pytorch/pytorch.git
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b7b2178204
Applies PLW0108 which removes useless lambda calls in Python, the rule is in preview so it is not ready to be enabled by default just yet. These are the autofixes from the rule. Pull Request resolved: https://github.com/pytorch/pytorch/pull/113602 Approved by: https://github.com/albanD
511 lines
15 KiB
Python
511 lines
15 KiB
Python
from abc import ABC, abstractmethod
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import queue
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import threading
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import collections
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from dataclasses import dataclass
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import os
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import dataclasses
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import io
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import pickle
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from typing import List, Union, Dict, cast
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import torch
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from torch import Tensor
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from torch.futures import Future
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from pathlib import Path
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from .metadata import (
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Metadata,
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MetadataIndex,
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)
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from .storage import (
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StorageReader,
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StorageWriter,
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WriteResult,
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)
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from .planner import (
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LoadItemType,
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LoadPlanner,
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LoadPlan,
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SavePlan,
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SavePlanner,
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ReadItem,
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WriteItem,
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WriteItemType,
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)
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from .utils import _create_file_view
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from torch.distributed._shard._utils import narrow_tensor_by_index
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from torch._utils import _get_device_module
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__all__ = [
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"FileSystemWriter",
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"FileSystemReader",
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]
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@dataclass
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class _StorageInfo:
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"""This is the per entry storage info."""
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relative_path: str
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offset: int
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length: int
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@dataclass
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class _StoragePrefix:
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prefix: str
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DEFAULT_SUFFIX = ".distcp"
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def _trim(tensor: torch.Tensor) -> torch.Tensor:
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tensor = tensor.detach().cpu()
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if tensor._typed_storage()._size() != tensor.numel():
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tensor = tensor.clone()
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return tensor
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def _result_from_write_item(
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item: WriteItem, size_in_bytes, storage_data
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) -> WriteResult:
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return WriteResult(
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index=item.index, size_in_bytes=size_in_bytes, storage_data=storage_data
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)
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class _TensorLoader(ABC):
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@abstractmethod
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def add(self, size, obj):
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pass
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@abstractmethod
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def start_loading(self):
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pass
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@abstractmethod
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def values(self):
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pass
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class _SerialCpuLoader(_TensorLoader):
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def __init__(self, resolve_fun):
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self.resolve_fun = resolve_fun
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self.items = []
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def add(self, size, obj):
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self.items.append((size, obj))
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def start_loading(self):
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pass
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def values(self):
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for _, obj in self.items:
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tensor = self.resolve_fun(obj).detach()
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tensor = tensor.cpu()
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if tensor.storage().size() != tensor.numel():
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tensor = tensor.clone()
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yield (
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tensor,
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obj,
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)
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class _OverlappingCpuLoader(_TensorLoader):
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def __init__(self, resolve_fun, stream=None, inflight_threshhold=1_000_000):
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self.resolve_fun = resolve_fun
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self.items = []
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self.inflight_threshhold = inflight_threshhold
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self.in_flight_data = 0
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self.current_items: collections.deque = collections.deque()
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self.idx = 0
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self.started = False
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self.device_type = stream.device_type if stream else torch.device("cuda").type
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self.device_module = _get_device_module(self.device_type)
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self.stream = stream or self.device_module.current_stream()
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if self.stream != self.device_module.current_stream():
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self.stream.wait_stream(self.device_module.current_stream())
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@property
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def _done(self):
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return self.idx >= len(self.items)
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def _drain(self):
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drained = []
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if self.in_flight_data >= self.inflight_threshhold:
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self.stream.synchronize()
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while self.in_flight_data >= self.inflight_threshhold:
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val = self.current_items.popleft()
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self.in_flight_data -= val[0].numel() * val[0].element_size()
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drained.append(val)
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return drained
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def _refill(self):
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with self.device_module.stream(self.stream):
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while (
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not self._done
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and self.in_flight_data < self.inflight_threshhold
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):
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_, obj = self.items[self.idx]
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self.idx += 1
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tensor = self.resolve_fun(obj).detach()
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if tensor.device.type == self.device_type:
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tensor = tensor.to(device="cpu", non_blocking=True)
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elif tensor.device == torch.device("cpu"):
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if tensor.storage().size() != tensor.numel():
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# this forces the tensor to be both contiguous and with minimal storage
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tensor = tensor.clone()
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self.current_items.append(
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(
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tensor,
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obj,
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)
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)
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self.in_flight_data += tensor.numel() * tensor.element_size()
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def _finish(self):
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assert self._done
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if len(self.current_items) > 0:
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self.stream.synchronize()
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return self.current_items
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def add(self, size, obj):
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if self.started:
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raise RuntimeError("cannot add items after loading started")
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self.items.append((size, obj))
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def start_loading(self):
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if self.started:
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return
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self.started = True
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self.items.sort(key=lambda x: x[0])
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self._refill()
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def values(self):
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self.start_loading()
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while not self._done:
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drained = self._drain()
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self._refill()
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yield from drained
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yield from self._finish()
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def _item_size(item: WriteItem) -> int:
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size = 1
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assert item.tensor_data is not None
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# can't use math.prod as PT needs to support older python
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for s in item.tensor_data.size:
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size *= s
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dtype = item.tensor_data.properties.dtype
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return size * torch._utils._element_size(dtype)
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def _split_by_size_and_type(
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bins, items: List[WriteItem]
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) -> List[List[WriteItem]]:
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if bins == 1:
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return [items]
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bytes_w = [wi for wi in items if wi.type == WriteItemType.BYTE_IO]
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tensor_w = [wi for wi in items if wi.type != WriteItemType.BYTE_IO]
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buckets: List[List[WriteItem]] = [[] for _ in range(bins)]
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bucket_sizes = [0 for _ in range(bins)]
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tensor_w.sort(key=_item_size, reverse=True)
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for i, wi in enumerate(bytes_w):
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buckets[i % bins].append(wi)
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for wi in tensor_w:
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# TODO replace with headq
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idx = min(enumerate(bucket_sizes), key=lambda x: x[1])[0]
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buckets[idx].append(wi)
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bucket_sizes[idx] += _item_size(wi)
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return buckets
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def _write_item(stream, data, write_item, storage_key):
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offset = stream.tell()
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if write_item.type == WriteItemType.BYTE_IO:
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assert isinstance(data, io.BytesIO)
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stream.write(data.getbuffer())
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else:
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assert isinstance(data, torch.Tensor)
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assert data.device == torch.device("cpu")
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torch.save(data, stream)
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length = stream.tell() - offset
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return _result_from_write_item(
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write_item, length, _StorageInfo(storage_key, offset, length)
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)
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def _write_files_from_queue(
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file_queue: queue.Queue,
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result_queue: queue.Queue,
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planner: SavePlanner,
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inflight_threshhold: int,
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use_fsync: bool,
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):
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try:
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while True:
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file_name, storage_key, write_items = file_queue.get_nowait()
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loader: _TensorLoader
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if torch.cuda.is_available() and inflight_threshhold > 0:
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loader = _OverlappingCpuLoader(
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planner.resolve_data,
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inflight_threshhold=inflight_threshhold,
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)
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else:
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loader = _SerialCpuLoader(
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planner.resolve_data,
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)
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tensor_w = [
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wi for wi in write_items if wi.type != WriteItemType.BYTE_IO
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]
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for write_item in tensor_w:
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loader.add(_item_size(write_item), write_item)
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loader.start_loading()
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bytes_w = [
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wi for wi in write_items if wi.type == WriteItemType.BYTE_IO
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]
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write_results = []
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with file_name.open("wb") as stream:
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for write_item in bytes_w:
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data = planner.resolve_data(write_item)
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write_results.append(
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_write_item(stream, data, write_item, storage_key)
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)
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for tensor, write_item in loader.values():
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assert tensor.is_cpu
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write_results.append(
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_write_item(stream, tensor, write_item, storage_key)
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)
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if use_fsync:
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os.fsync(stream.fileno())
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result_queue.put(write_results)
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except queue.Empty:
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pass
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class FileSystemWriter(StorageWriter):
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"""
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Basic implementation of StorageWriter using file IO.
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This implementation makes the following assumptions and simplifications:
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* The checkpoint path is an empty or non-existing directory.
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* File creation is atomic
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The checkpoint consist of one file per write request plus
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a `.metadata` file with the serialized metadata.
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"""
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def __init__(
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self,
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path: Union[str, os.PathLike],
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single_file_per_rank: bool = True,
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sync_files: bool = True,
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thread_count: int = 1,
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per_thread_copy_ahead: int = 10_000_000,
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) -> None:
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"""
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Initialize the writer pointing to `path`.
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Args:
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path: directory where the checkpoint will be written to.
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single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True.
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sync_files : force files to be synced to permanent storage. Default to True.
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thread_count: Number of IO threads to use to write. Default to 1.
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per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb.
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N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure.
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"""
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super().__init__()
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if not isinstance(path, Path):
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path = Path(path)
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self.path = path
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self.single_file_per_rank = single_file_per_rank
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self.sync_files = sync_files
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self.thread_count = thread_count
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self.per_thread_copy_ahead = per_thread_copy_ahead
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def set_up_storage_writer(self, is_coordinator: bool) -> None:
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pass
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def prepare_local_plan(self, plan: SavePlan) -> SavePlan:
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self.path.mkdir(parents=True, exist_ok=True)
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return plan
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def prepare_global_plan(
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self, global_plan: List[SavePlan]
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) -> List[SavePlan]:
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new_plans = [
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dataclasses.replace(plan, storage_data=_StoragePrefix(f"__{i}_"))
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for i, plan in enumerate(global_plan)
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]
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return new_plans
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def write_data(
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self,
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plan: SavePlan,
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planner: SavePlanner,
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) -> Future[List[WriteResult]]:
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storage_plan: _StoragePrefix = plan.storage_data
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file_count = 0
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def gen_file():
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nonlocal file_count
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file_name = f"{storage_plan.prefix}{file_count}{DEFAULT_SUFFIX}"
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file_count += 1
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return file_name
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file_queue: queue.Queue = queue.Queue()
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if self.single_file_per_rank:
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for bucket in _split_by_size_and_type(
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self.thread_count, plan.items
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):
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file_name = gen_file()
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file_queue.put((self.path / file_name, file_name, bucket))
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else:
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for item in plan.items:
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file_name = gen_file()
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file_queue.put((self.path / file_name, file_name, [item]))
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result_queue: queue.Queue = queue.Queue()
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threads = []
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for _ in range(1, self.thread_count):
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t = threading.Thread(
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target=_write_files_from_queue,
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args=(
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file_queue,
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result_queue,
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planner,
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self.per_thread_copy_ahead,
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self.sync_files,
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),
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)
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t.start()
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threads.append(t)
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_write_files_from_queue(
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file_queue=file_queue,
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result_queue=result_queue,
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planner=planner,
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inflight_threshhold=self.per_thread_copy_ahead,
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use_fsync=self.sync_files,
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)
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for t in threads:
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t.join()
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res = []
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try:
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while True:
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res += result_queue.get_nowait()
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except queue.Empty:
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pass
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fut: Future[List[WriteResult]] = Future()
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fut.set_result(res)
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return fut
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def finish(
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self, metadata: Metadata, results: List[List[WriteResult]]
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) -> None:
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storage_md = dict()
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for wr_list in results:
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storage_md.update({wr.index: wr.storage_data for wr in wr_list})
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metadata.storage_data = storage_md
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with (self.path / ".metadata.tmp").open("wb") as metadata_file:
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pickle.dump(metadata, metadata_file)
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if self.sync_files:
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os.fsync(metadata_file.fileno())
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(self.path / ".metadata.tmp").rename(self.path / ".metadata")
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class FileSystemReader(StorageReader):
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def __init__(self, path: Union[str, os.PathLike]) -> None:
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super().__init__()
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if not isinstance(path, Path):
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path = Path(path)
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self.path = path
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self.storage_data: Dict[MetadataIndex, _StorageInfo] = dict()
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def _slice_file(self, file, sinfo: _StorageInfo):
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return _create_file_view(file, sinfo.offset, sinfo.length)
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def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
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# group requests by file
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per_file: Dict[str, List[ReadItem]] = dict()
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for read_item in plan.items:
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item_md = self.storage_data[read_item.storage_index]
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path = item_md.relative_path
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per_file.setdefault(path, []).append(read_item)
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for relative_path, reqs in per_file.items():
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with (self.path / relative_path).open("rb") as file:
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# TODO sort by offset and cache the reading
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for req in reqs:
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item_md = self.storage_data[req.storage_index]
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file_slice = self._slice_file(file, item_md)
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if req.type == LoadItemType.BYTE_IO:
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bytes = io.BytesIO(file_slice.read(item_md.length))
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bytes.seek(0)
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planner.load_bytes(req, bytes)
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else:
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tensor = cast(
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Tensor, torch.load(file_slice, map_location="cpu")
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)
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tensor = narrow_tensor_by_index(
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tensor, req.storage_offsets, req.lengths
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)
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target_tensor = planner.resolve_tensor(req).detach()
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assert (
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target_tensor.size() == tensor.size()
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), f"req {req.storage_index} mismatch sizes {target_tensor.size()} vs {tensor.size()}"
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target_tensor.copy_(tensor)
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planner.commit_tensor(req, target_tensor)
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fut: Future = Future()
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fut.set_result(None)
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return fut
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# Implementing the abstract function in StorageReader
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def read_metadata(self) -> Metadata:
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with (self.path / ".metadata").open("rb") as metadata_file:
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return pickle.load(metadata_file)
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def set_up_storage_reader(self, metadata: Metadata, is_coordinator: bool) -> None:
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self.storage_data = metadata.storage_data
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assert self.storage_data is not None
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def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan:
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return plan
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def prepare_global_plan(
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self, global_plan: List[LoadPlan]
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) -> List[LoadPlan]:
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return global_plan
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