A DataProto is a data structure that aims to provide a standard protocol for data exchange between functions. It contains a batch (TensorDict) and a meta_info (Dict). The batch is a TensorDict https://pytorch.org/tensordict/. TensorDict allows you to manipulate a dictionary of Tensors l
| 97 | |
| 98 | @dataclass |
| 99 | class DataProto: |
| 100 | """ |
| 101 | A DataProto is a data structure that aims to provide a standard protocol for data exchange between functions. |
| 102 | It contains a batch (TensorDict) and a meta_info (Dict). The batch is a TensorDict https://pytorch.org/tensordict/. |
| 103 | TensorDict allows you to manipulate a dictionary of Tensors like a single Tensor. Ideally, the tensors with the |
| 104 | same batch size should be put inside batch. |
| 105 | """ |
| 106 | batch: TensorDict = None |
| 107 | non_tensor_batch: Dict = field(default_factory=dict) |
| 108 | meta_info: Dict = field(default_factory=dict) |
| 109 | |
| 110 | def __post_init__(self): |
| 111 | # perform necessary checking |
| 112 | self.check_consistency() |
| 113 | |
| 114 | def __len__(self): |
| 115 | return self.batch.batch_size[0] |
| 116 | |
| 117 | def __getitem__(self, item): |
| 118 | tensor_data = self.batch[item] |
| 119 | non_tensor_data = {key: val[item] for key, val in self.non_tensor_batch.items()} |
| 120 | return DataProtoItem(batch=tensor_data, non_tensor_batch=non_tensor_data, meta_info=self.meta_info) |
| 121 | |
| 122 | def slice(self, index): |
| 123 | tensor_data = self.batch[index] |
| 124 | non_tensor_data = {key: val[index] for key, val in self.non_tensor_batch.items()} |
| 125 | return DataProto(batch=tensor_data, non_tensor_batch=non_tensor_data, meta_info=self.meta_info) |
| 126 | |
| 127 | def slice_batch(self, start, length, dim=0): |
| 128 | """ |
| 129 | Note that this operation is in-place |
| 130 | """ |
| 131 | for key, val in self.batch.items(): |
| 132 | self.batch[key] = val.narrow(start=start, length=length, dim=dim) |
| 133 | |
| 134 | |
| 135 | def __getstate__(self): |
| 136 | import io |
| 137 | buffer = io.BytesIO() |
| 138 | if tensordict.__version__ >= '0.5.0' and self.batch is not None: |
| 139 | self.batch = self.batch.contiguous() |
| 140 | self.batch = self.batch.consolidate() |
| 141 | torch.save(self.batch, buffer) |
| 142 | return buffer, self.non_tensor_batch, self.meta_info |
| 143 | |
| 144 | def __setstate__(self, data): |
| 145 | batch_deserialized, non_tensor_batch, meta_info = data |
| 146 | batch_deserialized.seek(0) |
| 147 | batch = torch.load(batch_deserialized, |
| 148 | weights_only=False, |
| 149 | map_location='cpu' if not torch.cuda.is_available() else None) |
| 150 | self.batch = batch |
| 151 | self.non_tensor_batch = non_tensor_batch |
| 152 | self.meta_info = meta_info |
| 153 | |
| 154 | def check_consistency(self): |
| 155 | """Check the consistency of the DataProto. Mainly for batch and non_tensor_batch |
| 156 | We expose this function as a public one so that user can call themselves directly |
no outgoing calls
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