forked from phoenix-oss/llama-stack-mirror
a626b7bce3
# What does this PR do? create a new dataset BFCL_v3 from https://gorilla.cs.berkeley.edu/blogs/13_bfcl_v3_multi_turn.html overall each question asks the model to perform a task described in natural language, and additionally a set of available functions and their schema are given for the model to choose from. the model is required to write the function call form including function name and parameters , to achieve the stated purpose. the results are validated against provided ground truth, to make sure that the generated function call and the ground truth function call are syntactically and semantically equivalent, by checking their AST . ## Test Plan start server by ``` llama stack run ./llama_stack/templates/ollama/run.yaml ``` then send traffic ``` llama-stack-client eval run-benchmark "bfcl" --model-id meta-llama/Llama-3.2-3B-Instruct --output-dir /tmp/gpqa --num-examples 2 ``` [//]: # (## Documentation)
103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from enum import Enum
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from typing import Any, Dict, List
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from llama_stack.apis.common.type_system import (
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ChatCompletionInputType,
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CompletionInputType,
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StringType,
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)
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from llama_stack.distribution.datatypes import Api
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class ColumnName(Enum):
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input_query = "input_query"
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expected_answer = "expected_answer"
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chat_completion_input = "chat_completion_input"
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completion_input = "completion_input"
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generated_answer = "generated_answer"
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context = "context"
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dialog = "dialog"
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function = "function"
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language = "language"
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id = "id"
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ground_truth = "ground_truth"
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VALID_SCHEMAS_FOR_SCORING = [
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.generated_answer.value: StringType(),
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},
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.generated_answer.value: StringType(),
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ColumnName.context.value: StringType(),
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},
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.generated_answer.value: StringType(),
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ColumnName.function.value: StringType(),
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ColumnName.language.value: StringType(),
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ColumnName.id.value: StringType(),
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ColumnName.ground_truth.value: StringType(),
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},
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]
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VALID_SCHEMAS_FOR_EVAL = [
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.chat_completion_input.value: ChatCompletionInputType(),
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},
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.completion_input.value: CompletionInputType(),
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},
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.generated_answer.value: StringType(),
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ColumnName.function.value: StringType(),
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ColumnName.language.value: StringType(),
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ColumnName.id.value: StringType(),
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ColumnName.ground_truth.value: StringType(),
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},
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]
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def get_valid_schemas(api_str: str):
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if api_str == Api.scoring.value:
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return VALID_SCHEMAS_FOR_SCORING
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elif api_str == Api.eval.value:
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return VALID_SCHEMAS_FOR_EVAL
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else:
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raise ValueError(f"Invalid API string: {api_str}")
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def validate_dataset_schema(
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dataset_schema: Dict[str, Any],
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expected_schemas: List[Dict[str, Any]],
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):
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if dataset_schema not in expected_schemas:
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raise ValueError(f"Dataset {dataset_schema} does not have a correct input schema in {expected_schemas}")
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def validate_row_schema(
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input_row: Dict[str, Any],
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expected_schemas: List[Dict[str, Any]],
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):
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for schema in expected_schemas:
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if all(key in input_row for key in schema):
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return
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raise ValueError(f"Input row {input_row} does not match any of the expected schemas in {expected_schemas}")
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