86 lines
2.4 KiB
Python
86 lines
2.4 KiB
Python
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"""Extension of torchrec.dataset.utils.Batch to cover any dataset.
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"""
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# flake8: noqa
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from __future__ import annotations
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from typing import Dict
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import abc
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from dataclasses import dataclass
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import dataclasses
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import torch
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from torchrec.streamable import Pipelineable
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class BatchBase(Pipelineable, abc.ABC):
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@abc.abstractmethod
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def as_dict(self) -> Dict:
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raise NotImplementedError
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def to(self, device: torch.device, non_blocking: bool = False):
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args = {}
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for feature_name, feature_value in self.as_dict().items():
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args[feature_name] = feature_value.to(device=device, non_blocking=non_blocking)
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return self.__class__(**args)
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def record_stream(self, stream: torch.cuda.streams.Stream) -> None:
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for feature_value in self.as_dict().values():
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feature_value.record_stream(stream)
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def pin_memory(self):
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args = {}
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for feature_name, feature_value in self.as_dict().items():
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args[feature_name] = feature_value.pin_memory()
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return self.__class__(**args)
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def __repr__(self) -> str:
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def obj2str(v):
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return f"{v.size()}" if hasattr(v, "size") else f"{v.length_per_key()}"
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return "\n".join([f"{k}: {obj2str(v)}," for k, v in self.as_dict().items()])
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@property
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def batch_size(self) -> int:
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for tensor in self.as_dict().values():
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if tensor is None:
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continue
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if not isinstance(tensor, torch.Tensor):
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continue
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return tensor.shape[0]
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raise Exception("Could not determine batch size from tensors.")
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@dataclass
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class DataclassBatch(BatchBase):
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@classmethod
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def feature_names(cls):
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return list(cls.__dataclass_fields__.keys())
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def as_dict(self):
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return {
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feature_name: getattr(self, feature_name)
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for feature_name in self.feature_names()
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if hasattr(self, feature_name)
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}
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@staticmethod
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def from_schema(name: str, schema):
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"""Instantiates a custom batch subclass if all columns can be represented as a torch.Tensor."""
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return dataclasses.make_dataclass(
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cls_name=name,
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fields=[(name, torch.Tensor, dataclasses.field(default=None)) for name in schema.names],
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bases=(DataclassBatch,),
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)
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@staticmethod
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def from_fields(name: str, fields: dict):
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return dataclasses.make_dataclass(
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cls_name=name,
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fields=[(_name, _type, dataclasses.field(default=None)) for _name, _type in fields.items()],
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bases=(DataclassBatch,),
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)
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class DictionaryBatch(BatchBase, dict):
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def as_dict(self) -> Dict:
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return self
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