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