mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-27 18:50:41 +00:00
2b7d70ba86
* wip * scoring fn api * eval api * eval task * evaluate api update * pre commit * unwrap context -> config * config field doc * typo * naming fix * separate benchmark / app eval * api name * rename * wip tests * wip * datasetio test * delete unused * fixture * scoring resolve * fix scoring register * scoring test pass * score batch * scoring fix * fix eval * test eval works * huggingface provider * datasetdef files * mmlu scoring fn * test wip * remove type ignore * api refactor * add default task_eval_id for routing * add eval_id for jobs * remove type ignore * huggingface provider * wip huggingface register * only keep 1 run_eval * fix optional * register task required * register task required * delete old tests * fix * mmlu loose * refactor * msg * fix tests * move benchmark task def to file * msg * gen openapi * openapi gen * move dataset to hf llamastack repo * remove todo * refactor * add register model to unit test * rename * register to client * delete preregistered dataset/eval task * comments * huggingface -> remote adapter * openapi gen
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
# 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 typing import Literal, Optional, Protocol, Union
|
|
|
|
from typing_extensions import Annotated
|
|
|
|
from llama_models.llama3.api.datatypes import * # noqa: F403
|
|
from llama_models.schema_utils import json_schema_type, webmethod
|
|
from llama_stack.apis.scoring_functions import * # noqa: F403
|
|
from llama_stack.apis.agents import AgentConfig
|
|
from llama_stack.apis.common.job_types import Job, JobStatus
|
|
from llama_stack.apis.scoring import * # noqa: F403
|
|
from llama_stack.apis.eval_tasks import * # noqa: F403
|
|
|
|
|
|
@json_schema_type
|
|
class ModelCandidate(BaseModel):
|
|
type: Literal["model"] = "model"
|
|
model: str
|
|
sampling_params: SamplingParams
|
|
system_message: Optional[SystemMessage] = None
|
|
|
|
|
|
@json_schema_type
|
|
class AgentCandidate(BaseModel):
|
|
type: Literal["agent"] = "agent"
|
|
config: AgentConfig
|
|
|
|
|
|
EvalCandidate = Annotated[
|
|
Union[ModelCandidate, AgentCandidate], Field(discriminator="type")
|
|
]
|
|
|
|
|
|
@json_schema_type
|
|
class BenchmarkEvalTaskConfig(BaseModel):
|
|
type: Literal["benchmark"] = "benchmark"
|
|
eval_candidate: EvalCandidate
|
|
num_examples: Optional[int] = Field(
|
|
description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
|
|
default=None,
|
|
)
|
|
|
|
|
|
@json_schema_type
|
|
class AppEvalTaskConfig(BaseModel):
|
|
type: Literal["app"] = "app"
|
|
eval_candidate: EvalCandidate
|
|
scoring_params: Dict[str, ScoringFnParams] = Field(
|
|
description="Map between scoring function id and parameters for each scoring function you want to run",
|
|
default_factory=dict,
|
|
)
|
|
num_examples: Optional[int] = Field(
|
|
description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
|
|
default=None,
|
|
)
|
|
# we could optinally add any specific dataset config here
|
|
|
|
|
|
EvalTaskConfig = Annotated[
|
|
Union[BenchmarkEvalTaskConfig, AppEvalTaskConfig], Field(discriminator="type")
|
|
]
|
|
|
|
|
|
@json_schema_type
|
|
class EvaluateResponse(BaseModel):
|
|
generations: List[Dict[str, Any]]
|
|
# each key in the dict is a scoring function name
|
|
scores: Dict[str, ScoringResult]
|
|
|
|
|
|
class Eval(Protocol):
|
|
@webmethod(route="/eval/run_eval", method="POST")
|
|
async def run_eval(
|
|
self,
|
|
task_id: str,
|
|
task_config: EvalTaskConfig,
|
|
) -> Job: ...
|
|
|
|
@webmethod(route="/eval/evaluate_rows", method="POST")
|
|
async def evaluate_rows(
|
|
self,
|
|
task_id: str,
|
|
input_rows: List[Dict[str, Any]],
|
|
scoring_functions: List[str],
|
|
task_config: EvalTaskConfig,
|
|
) -> EvaluateResponse: ...
|
|
|
|
@webmethod(route="/eval/job/status", method="GET")
|
|
async def job_status(self, task_id: str, job_id: str) -> Optional[JobStatus]: ...
|
|
|
|
@webmethod(route="/eval/job/cancel", method="POST")
|
|
async def job_cancel(self, task_id: str, job_id: str) -> None: ...
|
|
|
|
@webmethod(route="/eval/job/result", method="GET")
|
|
async def job_result(self, task_id: str, job_id: str) -> EvaluateResponse: ...
|