Callbacks
PredictionWriter
Bases: BasePredictionWriter
, Callback
A callback that writes predictions to disk at specified intervals during training.
Logits, Embeddings, Hiddens, Input IDs, and Labels may all be saved to the disk depending on trainer configuration. Batch Idxs are provided for each prediction in the same dictionary. These must be used to maintain order between multi device predictions and single device predictions.
Source code in bionemo/llm/utils/callbacks.py
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data_parallel_rank
property
Returns the data parallel rank.
data_parallel_world_size
property
Returns the data parallel world size.
should_write_predictions
property
Ensures that predictions are only written on TP/CP rank 0 and that it is the last stage of the pipeline.
__init__(output_dir, write_interval, batch_dim_key_defaults=None, seq_dim_key_defaults=None, save_all_model_parallel_ranks=False, files_per_subdir=None)
Initializes the callback.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
output_dir
|
str | PathLike
|
The directory where predictions will be written. |
required |
write_interval
|
IntervalT
|
The interval at which predictions will be written (batch, epoch). Epoch may not be used with multi-device trainers. |
required |
batch_dim_key_defaults
|
dict[str, int] | None
|
The default batch dimension for each key, if different from the standard 0. |
None
|
seq_dim_key_defaults
|
dict[str, int] | None
|
The default sequence dimension for each key, if different from the standard 1. |
None
|
save_all_model_parallel_ranks
|
bool
|
Whether to save predictions for all model parallel ranks. Generally these will be redundant. |
False
|
files_per_subdir
|
int | None
|
Number of files to write to each subdirectory. If provided, subdirectories with N files each will be created. Ignored unless write_interval is 'batch'. |
None
|
Source code in bionemo/llm/utils/callbacks.py
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setup(trainer, pl_module, *args, **kwargs)
Invoked with Trainer.fit, validate, test, and predict are called. Will immediately fail when 'write_interval' is 'epoch' and 'trainer.num_devices' > 1.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
trainer
|
Trainer
|
The Trainer instance. |
required |
pl_module
|
LightningModule
|
The LightningModule instance. |
required |
Source code in bionemo/llm/utils/callbacks.py
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write_on_batch_end(trainer, pl_module, prediction, batch_indices, batch, batch_idx, dataloader_idx)
Writes predictions to disk at the end of each batch.
Predictions files follow the naming pattern, where rank is the active GPU in which the predictions were made. predictions__rank_{rank}__batch_{batch_idx}.pt
Parameters:
Name | Type | Description | Default |
---|---|---|---|
trainer
|
Trainer
|
The Trainer instance. |
required |
pl_module
|
LightningModule
|
The LightningModule instance. |
required |
prediction
|
Any
|
The prediction made by the model. |
required |
batch_indices
|
Sequence[int] | None
|
The indices of the batch. |
required |
batch
|
Any
|
The batch data. |
required |
batch_idx
|
int
|
The index of the batch. |
required |
dataloader_idx
|
int
|
The index of the dataloader. |
required |
Source code in bionemo/llm/utils/callbacks.py
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write_on_epoch_end(trainer, pl_module, predictions, batch_indices)
Writes predictions to disk at the end of each epoch.
Writing all predictions on epoch end is memory intensive. It is recommended to use the batch writer instead for large predictions.
Multi-device predictions will likely yield predictions in an order that is inconsistent with single device predictions and the input data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
trainer
|
Trainer
|
The Trainer instance. |
required |
pl_module
|
LightningModule
|
The LightningModule instance. |
required |
predictions
|
Any
|
The predictions made by the model. |
required |
batch_indices
|
Sequence[int]
|
The indices of the batch. |
required |
Source code in bionemo/llm/utils/callbacks.py
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