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from typing import (
Any,
Dict,
List,
Optional,
)
from .operator import StreamInstanceOperator
from .type_utils import isoftype
class Format(StreamInstanceOperator):
pass
class SystemFormat(Format):
r"""Generates the whole input to the model, from constant strings that are given as args, and from values found in specified fields of the instance.
SystemFormat expects the input instance to contain:
1. A field named "source" whose value is a string verbalizing the original values in the instance (as read
from the source dataset), in the context of the underlying task.
2. A field named "instruction" that contains a (non-None) string.
3. A field named with the value in arg 'demos_field', containing a list of dicts, each dict with fields "source"
and "target", representing a single demo.
SystemFormat formats the above fields into a single string to be inputted to the model. This string overwrites
field "source" of the instance. Formatting is driven by two args: 'demo_format' and 'model_input_format'.
SystemFormat also pops field "instruction" and the field containing the demos out from the input instance.
Args:
demos_field (str): the name of the field that contains the demos, being a list of dicts, each with "source" and "target" keys
demo_format (str): formatting string for a single demo, combining fields "source" and "target"
model_input_format (str) overall product format, combining instruction and source (as read from fields "instruction"
and "source" of the input instance), together with demos (as formatted into one string)
Example:
when input instance:
{
"source": "1+1",
"target": "2",
"instruction": "Solve the math exercises.",
"demos": [{"source": "1+2", "target": "3"}, {"source": "4-2", "target": "2"}]
}
is process-ed by
system_format = SystemFormat(
demos_field="demos",
demo_format="Input: {source}\nOutput: {target}\n\n",
model_input_format="Instruction: {instruction}\n\n{demos}Input: {source}\nOutput: ",
)
the resulting instance is:
{
"target": "2",
"source": "Instruction: Solve the math exercises.\n\nInput: 1+2\nOutput: 3\n\nInput: 4-2\nOutput: 2\n\nInput: 1+1\nOutput: ",
}
"""
demos_field: str = "demos"
demo_format: str = (
"{source}\n{target}\n\n" # example: "User: {source}\nAgent: {target}\n\n"
)
model_input_format: str = "{instruction}{demos}{source}\n"
@staticmethod
def _retrieve_field_and_assert_not_none(instance, field_name) -> str:
if field_name is not None and field_name in instance:
field_value = instance[field_name]
assert (
field_value is not None
), f"Value in field '{field_name}' should not be none. Received instance: {instance}"
return field_value
return ""
def process(
self, instance: Dict[str, Any], stream_name: Optional[str] = None
) -> Dict[str, Any]:
assert (
"source" in instance
), f"field 'source' is expected to be in the input instance. Received instance: {instance}"
source = self._retrieve_field_and_assert_not_none(
instance=instance, field_name="source"
)
instruction = self._retrieve_field_and_assert_not_none(
instance=instance, field_name="instruction"
)
# pop "instruction" from instance
if "instruction" in instance:
instance.pop("instruction")
demo_instances = []
if self.demos_field is not None and self.demos_field in instance:
demos = instance[self.demos_field]
assert (
demos is not None and isoftype(demos, List[Dict[str, Any]])
), f"A list of dict-s is expected in field '{self.demos_field}'. Received instance: {instance}"
demo_instances = demos
# pop demos from instance
instance.pop(self.demos_field)
demos_string = ""
for demo_instance in demo_instances:
demo_str = self.demo_format.format(**demo_instance)
demos_string += demo_str
output = self.model_input_format.format(
instruction=instruction,
demos=demos_string,
source=source,
)
instance["source"] = output
return instance
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