data_juicer.ops.op_fusion module#

data_juicer.ops.op_fusion.fuse_operators(ops, probe_res=None)[source]#

Fuse the input ops list and return the fused ops list.

Parameters:
  • ops – the corresponding list of op objects.

  • probe_res – the probed speed for each OP from Monitor.

Returns:

a list of fused op objects.

data_juicer.ops.op_fusion.fuse_filter_group(original_filter_group)[source]#

Fuse single filter group and return the fused filter group.

Parameters:

original_filter_group – the original filter group, including op definitions and objects.

Returns:

the fused definitions and objects of the input filter group.

class data_juicer.ops.op_fusion.FusedFilter(name: str, fused_filters: List)[source]#

Bases: Filter

A fused operator for filters.

__init__(name: str, fused_filters: List)[source]#

Initialization method.

Parameters:

fused_filters – a list of filters to be fused.

compute_stats_batched(samples, rank=None)[source]#
process_batched(samples)[source]#
class data_juicer.ops.op_fusion.GeneralFusedOP(batch_size: int = 1, fused_op_list: List = None, *args, **kwargs)[source]#

Bases: Mapper

An explicitly fused operator designed to execute multiple sequential operations (OPs) on the same batch, enabling fine-grained control over data processing.

__init__(batch_size: int = 1, fused_op_list: List = None, *args, **kwargs)[source]#

Base class that conducts data editing.

Parameters:
  • text_key – the key name of field that stores sample texts to be processed.

  • image_key – the key name of field that stores sample image list to be processed

  • audio_key – the key name of field that stores sample audio list to be processed

  • video_key – the key name of field that stores sample video list to be processed

  • image_bytes_key – the key name of field that stores sample image bytes list to be processed

  • query_key – the key name of field that stores sample queries

  • response_key – the key name of field that stores responses

  • history_key – the key name of field that stores history of queries and responses

process_batched(samples, rank=None)[source]#
run(dataset, *, exporter=None, tracer=None)[source]#