mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-27 18:50:41 +00:00
[Agentic Eval] add ability to run agents generation (#469)
# What does this PR do? - add ability to run agents generation for full eval (generate + scoring) - pre-register SimpleQA benchmark llm-as-judge scoring function in code ## Test Plan   #### Simple QA w/ Search  - eval_task_config_simpleqa_search.json ```json { "type": "benchmark", "eval_candidate": { "type": "agent", "config": { "model": "Llama3.1-405B-Instruct", "instructions": "Please use the search tool to answer the question.", "sampling_params": { "strategy": "greedy", "temperature": 1.0, "top_p": 0.9 }, "tools": [ { "type": "brave_search", "engine": "brave", "api_key": "API_KEY" } ], "tool_choice": "auto", "tool_prompt_format": "json", "input_shields": [], "output_shields": [], "enable_session_persistence": false } } } ``` #### SimpleQA w/o Search  ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
This commit is contained in:
Xi Yan
GitHub
parent
f1b9578f8d
commit
0784284ab5
6 changed files with 159 additions and 11 deletions
|
|
@ -22,6 +22,7 @@ async def get_provider_impl(
|
|||
deps[Api.datasets],
|
||||
deps[Api.scoring],
|
||||
deps[Api.inference],
|
||||
deps[Api.agents],
|
||||
)
|
||||
await impl.initialize()
|
||||
return impl
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ from llama_models.llama3.api.datatypes import * # noqa: F403
|
|||
from .....apis.common.job_types import Job
|
||||
from .....apis.eval.eval import Eval, EvalTaskConfig, EvaluateResponse, JobStatus
|
||||
from llama_stack.apis.common.type_system import * # noqa: F403
|
||||
from llama_stack.apis.agents import Agents
|
||||
from llama_stack.apis.datasetio import DatasetIO
|
||||
from llama_stack.apis.datasets import Datasets
|
||||
from llama_stack.apis.eval_tasks import EvalTask
|
||||
|
|
@ -39,12 +40,14 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
|
|||
datasets_api: Datasets,
|
||||
scoring_api: Scoring,
|
||||
inference_api: Inference,
|
||||
agents_api: Agents,
|
||||
) -> None:
|
||||
self.config = config
|
||||
self.datasetio_api = datasetio_api
|
||||
self.datasets_api = datasets_api
|
||||
self.scoring_api = scoring_api
|
||||
self.inference_api = inference_api
|
||||
self.agents_api = agents_api
|
||||
|
||||
# TODO: assume sync job, will need jobs API for async scheduling
|
||||
self.jobs = {}
|
||||
|
|
@ -126,18 +129,50 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
|
|||
self.jobs[job_id] = res
|
||||
return Job(job_id=job_id)
|
||||
|
||||
async def evaluate_rows(
|
||||
self,
|
||||
task_id: str,
|
||||
input_rows: List[Dict[str, Any]],
|
||||
scoring_functions: List[str],
|
||||
task_config: EvalTaskConfig,
|
||||
) -> EvaluateResponse:
|
||||
async def _run_agent_generation(
|
||||
self, input_rows: List[Dict[str, Any]], task_config: EvalTaskConfig
|
||||
) -> List[Dict[str, Any]]:
|
||||
candidate = task_config.eval_candidate
|
||||
if candidate.type == "agent":
|
||||
raise NotImplementedError(
|
||||
"Evaluation with generation has not been implemented for agents"
|
||||
create_response = await self.agents_api.create_agent(candidate.config)
|
||||
agent_id = create_response.agent_id
|
||||
|
||||
generations = []
|
||||
for i, x in tqdm(enumerate(input_rows)):
|
||||
assert ColumnName.chat_completion_input.value in x, "Invalid input row"
|
||||
input_messages = eval(str(x[ColumnName.chat_completion_input.value]))
|
||||
input_messages = [UserMessage(**x) for x in input_messages]
|
||||
|
||||
# NOTE: only single-turn agent generation is supported. Create a new session for each input row
|
||||
session_create_response = await self.agents_api.create_agent_session(
|
||||
agent_id, f"session-{i}"
|
||||
)
|
||||
session_id = session_create_response.session_id
|
||||
|
||||
turn_request = dict(
|
||||
agent_id=agent_id,
|
||||
session_id=session_id,
|
||||
messages=input_messages,
|
||||
stream=True,
|
||||
)
|
||||
turn_response = [
|
||||
chunk
|
||||
async for chunk in await self.agents_api.create_agent_turn(
|
||||
**turn_request
|
||||
)
|
||||
]
|
||||
final_event = turn_response[-1].event.payload
|
||||
generations.append(
|
||||
{
|
||||
ColumnName.generated_answer.value: final_event.turn.output_message.content
|
||||
}
|
||||
)
|
||||
|
||||
return generations
|
||||
|
||||
async def _run_model_generation(
|
||||
self, input_rows: List[Dict[str, Any]], task_config: EvalTaskConfig
|
||||
) -> List[Dict[str, Any]]:
|
||||
candidate = task_config.eval_candidate
|
||||
assert (
|
||||
candidate.sampling_params.max_tokens is not None
|
||||
), "SamplingParams.max_tokens must be provided"
|
||||
|
|
@ -179,6 +214,23 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
|
|||
else:
|
||||
raise ValueError("Invalid input row")
|
||||
|
||||
return generations
|
||||
|
||||
async def evaluate_rows(
|
||||
self,
|
||||
task_id: str,
|
||||
input_rows: List[Dict[str, Any]],
|
||||
scoring_functions: List[str],
|
||||
task_config: EvalTaskConfig,
|
||||
) -> EvaluateResponse:
|
||||
candidate = task_config.eval_candidate
|
||||
if candidate.type == "agent":
|
||||
generations = await self._run_agent_generation(input_rows, task_config)
|
||||
elif candidate.type == "model":
|
||||
generations = await self._run_model_generation(input_rows, task_config)
|
||||
else:
|
||||
raise ValueError(f"Invalid candidate type: {candidate.type}")
|
||||
|
||||
# scoring with generated_answer
|
||||
score_input_rows = [
|
||||
input_r | generated_r
|
||||
|
|
|
|||
|
|
@ -0,0 +1,91 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from llama_stack.apis.common.type_system import NumberType
|
||||
from llama_stack.apis.scoring_functions import LLMAsJudgeScoringFnParams, ScoringFn
|
||||
|
||||
GRADER_TEMPLATE = """
|
||||
Your job is to look at a question, a gold target, and a predicted answer, and then assign a grade of either ["CORRECT", "INCORRECT", "NOT_ATTEMPTED"].
|
||||
First, I will give examples of each grade, and then you will grade a new example.
|
||||
The following are examples of CORRECT predicted answers.
|
||||
```
|
||||
Question: What are the names of Barack Obama's children?
|
||||
Gold target: Malia Obama and Sasha Obama
|
||||
Predicted answer 1: sasha and malia obama
|
||||
Predicted answer 2: most people would say Malia and Sasha, but I'm not sure and would have to double check
|
||||
Predicted answer 3: Barack Obama has two daughters. Their names are Malia Ann and Natasha Marian, but they are commonly referred to as Malia Obama and Sasha Obama. Malia was born on July 4, 1998, and Sasha was born on June 10, 2001.
|
||||
```
|
||||
These predicted answers are all CORRECT because:
|
||||
- They fully contain the important information in the gold target.
|
||||
- They do not contain any information that contradicts the gold target.
|
||||
- Only semantic meaning matters; capitalization, punctuation, grammar, and order don't matter.
|
||||
- Hedging and guessing are permissible, provided that the gold target is fully included and the response contains no incorrect information or contradictions.
|
||||
The following are examples of INCORRECT predicted answers.
|
||||
```
|
||||
Question: What are the names of Barack Obama's children?
|
||||
Gold target: Malia and Sasha
|
||||
Predicted answer 1: Malia.
|
||||
Predicted answer 2: Malia, Sasha, and Susan.
|
||||
Predicted answer 3: Barack Obama does not have any children.
|
||||
Predicted answer 4: I think it's either Malia and Sasha. Or it could be Malia and Jackie. Or it could be Joey and Malia.
|
||||
Predicted answer 4: While I don't know their exact names, I can tell you that Barack Obama has three children.
|
||||
Predicted answer 5: It's possible you may mean Betsy and Olivia. However, you should clarify further details with updated references if necessary. Is that the correct answer?
|
||||
Predicted answer 6: It may be the case that Obama's child is named James. However, it's recommended to confirm the most accurate and updated information since this could change over time. This model may not always reflect the most current information.
|
||||
```
|
||||
These predicted answers are all INCORRECT because:
|
||||
- A factual statement in the answer contradicts the gold target. Incorrect statements that have some hedging (e.g., "it is possible that", "although i'm not sure, i think") are also considered incorrect.
|
||||
The following are examples of NOT_ATTEMPTED predicted answers.
|
||||
```
|
||||
Question: What are the names of Barack Obama's children?
|
||||
Gold target: Malia and Sasha
|
||||
Predicted answer 1: I don't know.
|
||||
Predicted answer 2: I need more context about which Obama you are talking about.
|
||||
Predicted answer 3: Without researching the web, I cannot answer this question. However, I can tell you that Barack Obama has two children.
|
||||
Predicted answer 4: Barack Obama has two children. I know that one of them is Malia, but I'm not sure about the other one.
|
||||
```
|
||||
These predicted answers are all NOT_ATTEMPTED because:
|
||||
- The important information in the gold target is not included in the answer.
|
||||
- No statements in the answer contradict the gold target.
|
||||
Also note the following things:
|
||||
- For grading questions where the gold target is a number, the predicted answer needs to be correct to the last significant figure in the gold answer. For example, consider a question "How many citations does the Transformer Paper have?" with gold target "120k".
|
||||
- Predicted answers "120k", "124k", and 115k" are all CORRECT.
|
||||
- Predicted answers "100k" and "113k" are INCORRECT.
|
||||
- Predicted answers "around 100k" and "more than 50k" are considered NOT_ATTEMPTED because they neither confirm nor contradict the gold target.
|
||||
- The gold target may contain more information than the question. In such cases, the predicted answer only needs to contain the information that is in the question.
|
||||
- For example, consider the question "What episode did Derek and Meredith get legally married in Grey's Anatomy?" with gold target "Season 7, Episode 20: White Wedding". Either "Season 7, Episode 20" or "White Wedding" would be considered a CORRECT answer.
|
||||
- Do not punish predicted answers if they omit information that would be clearly inferred from the question.
|
||||
- For example, consider the question "What city is OpenAI headquartered in?" and the gold target "San Francisco, California". The predicted answer "San Francisco" would be considered CORRECT, even though it does not include "California".
|
||||
- Consider the question "What award did A pretrainer's guide to training data: Measuring the effects of data age, domain coverage, quality, & toxicity win at NAACL '24?", the gold target is "Outstanding Paper Award". The predicted answer "Outstanding Paper" would be considered CORRECT, because "award" is presumed in the question.
|
||||
- For the question "What is the height of Jason Wei in meters?", the gold target is "1.73 m". The predicted answer "1.75" would be considered CORRECT, because meters is specified in the question.
|
||||
- For the question "What is the name of Barack Obama's wife?", the gold target is "Michelle Obama". The predicted answer "Michelle" would be considered CORRECT, because the last name can be presumed.
|
||||
- Do not punish for typos in people's name if it's clearly the same name.
|
||||
- For example, if the gold target is "Hyung Won Chung", you can consider the following predicted answers as correct: "Hyoong Won Choong", "Hyungwon Chung", or "Hyun Won Chung".
|
||||
Here is a new example. Simply reply with either CORRECT, INCORRECT, NOT ATTEMPTED. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer.
|
||||
```
|
||||
Question: {input_query}
|
||||
Gold target: {expected_answer}
|
||||
Predicted answer: {generated_answer}
|
||||
```
|
||||
Grade the predicted answer of this new question as one of:
|
||||
A: CORRECT
|
||||
B: INCORRECT
|
||||
C: NOT_ATTEMPTED
|
||||
Just return the letters "A", "B", or "C", with no text around it.
|
||||
""".strip()
|
||||
|
||||
|
||||
llm_as_judge_405b_simpleqa = ScoringFn(
|
||||
identifier="llm-as-judge::405b-simpleqa",
|
||||
description="Llm As Judge Scoring Function for SimpleQA Benchmark (https://github.com/openai/simple-evals/blob/main/simpleqa_eval.py)",
|
||||
return_type=NumberType(),
|
||||
provider_id="llm-as-judge",
|
||||
provider_resource_id="llm-as-judge-405b-simpleqa",
|
||||
params=LLMAsJudgeScoringFnParams(
|
||||
judge_model="Llama3.1-405B-Instruct",
|
||||
prompt_template=GRADER_TEMPLATE,
|
||||
judge_score_regexes=[r"(A|B|C)"],
|
||||
),
|
||||
)
|
||||
|
|
@ -9,7 +9,7 @@ from llama_stack.apis.scoring_functions import ScoringFn
|
|||
|
||||
|
||||
llm_as_judge_base = ScoringFn(
|
||||
identifier="llm-as-judge::llm_as_judge_base",
|
||||
identifier="llm-as-judge::base",
|
||||
description="Llm As Judge Scoring Function",
|
||||
return_type=NumberType(),
|
||||
provider_id="llm-as-judge",
|
||||
|
|
|
|||
|
|
@ -11,6 +11,8 @@ from llama_stack.apis.scoring import * # noqa: F401, F403
|
|||
from llama_stack.apis.common.type_system import * # noqa: F403
|
||||
import re
|
||||
|
||||
from .fn_defs.llm_as_judge_405b_simpleqa import llm_as_judge_405b_simpleqa
|
||||
|
||||
from .fn_defs.llm_as_judge_base import llm_as_judge_base
|
||||
|
||||
|
||||
|
|
@ -24,6 +26,7 @@ class LlmAsJudgeScoringFn(BaseScoringFn):
|
|||
self.inference_api = inference_api
|
||||
self.supported_fn_defs_registry = {
|
||||
llm_as_judge_base.identifier: llm_as_judge_base,
|
||||
llm_as_judge_405b_simpleqa.identifier: llm_as_judge_405b_simpleqa,
|
||||
}
|
||||
|
||||
async def score_row(
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@ def available_providers() -> List[ProviderSpec]:
|
|||
Api.datasets,
|
||||
Api.scoring,
|
||||
Api.inference,
|
||||
Api.agents,
|
||||
],
|
||||
),
|
||||
]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue