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feat: [New Eval Benchamark] IfEval (#1708)

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# What does this PR do?
In this PR, we added a new eval open benchmark IfEval based on paper
https://arxiv.org/abs/2311.07911 to measure the model capability of
instruction following.


## Test Plan
spin up a llama stack server with open-benchmark template

run `llama-stack-client --endpoint xxx eval run-benchmark
"meta-reference-ifeval" --model-id "meta-llama/Llama-3.3-70B-Instruct"
--output-dir "/home/markchen1015/" --num-examples 20` on client side and
get the eval aggregate results
This commit is contained in:
Botao Chen 2025-03-19 16:39:59 -07:00 • committed by GitHub
parent a7008dc15d
commit f369871083
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1
.github/workflows/integration-tests.yml vendored
View file

@ -52,6 +52,7 @@ jobs:
# always test against the latest version of the client
uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main
uv pip install -e .
llama stack build --template ollama --image-type venv
- name: Wait for Ollama to start
run: |

57
distributions/dependencies.json
View file

@ -7,10 +7,12 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -23,6 +25,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -41,10 +44,12 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"matplotlib",
"nltk",
"numpy",
@ -56,6 +61,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -75,10 +81,12 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"fastapi",
"fire",
"fireworks-ai",
"httpx",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -91,6 +99,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -112,11 +121,13 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"huggingface_hub",
"langdetect",
"matplotlib",
"nltk",
"numpy",
@ -128,6 +139,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -147,10 +159,12 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"fastapi",
"fire",
"fireworks-ai",
"httpx",
"langdetect",
"litellm",
"matplotlib",
"mcp",
@ -164,6 +178,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -184,11 +199,13 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"fireworks-ai",
"httpx",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -201,6 +218,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -219,10 +237,12 @@
"blobfile",
"chardet",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"litellm",
"matplotlib",
"nltk",
@ -235,6 +255,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -253,11 +274,13 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"huggingface_hub",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -270,6 +293,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -288,11 +312,13 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"huggingface_hub",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -305,6 +331,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -325,11 +352,13 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"fairscale",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"lm-format-enforcer",
"matplotlib",
"mcp",
@ -343,6 +372,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -365,12 +395,14 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"fairscale",
"faiss-cpu",
"fastapi",
"fbgemm-gpu",
"fire",
"httpx",
"langdetect",
"lm-format-enforcer",
"matplotlib",
"mcp",
@ -384,6 +416,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -403,10 +436,12 @@
"aiosqlite",
"blobfile",
"chardet",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"matplotlib",
"nltk",
"numpy",
@ -418,6 +453,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -436,10 +472,12 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -453,6 +491,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -470,9 +509,11 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"fastapi",
"fire",
"httpx",
"langdetect",
"litellm",
"matplotlib",
"mcp",
@ -486,6 +527,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -505,10 +547,12 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -521,6 +565,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -540,10 +585,12 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -556,6 +603,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -605,11 +653,13 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"huggingface_hub",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -622,6 +672,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -641,10 +692,12 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -657,6 +710,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",
@ -677,10 +731,12 @@
"chardet",
"chromadb-client",
"datasets",
"emoji",
"faiss-cpu",
"fastapi",
"fire",
"httpx",
"langdetect",
"matplotlib",
"mcp",
"nltk",
@ -693,6 +749,7 @@
"psycopg2-binary",
"pymongo",
"pypdf",
"pythainlp",
"redis",
"requests",
"scikit-learn",

1
docs/_static/llama-stack-spec.html vendored
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@ -6268,6 +6268,7 @@
"type": "string",
"enum": [
"average",
"weighted_average",
"median",
"categorical_count",
"accuracy"

1
docs/_static/llama-stack-spec.yaml vendored
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@ -4389,6 +4389,7 @@ components:
type: string
enum:
- average
- weighted_average
- median
- categorical_count
- accuracy

1
llama_stack/apis/scoring_functions/scoring_functions.py
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@ -36,6 +36,7 @@ class ScoringFnParamsType(Enum):
@json_schema_type
class AggregationFunctionType(Enum):
average = "average"
weighted_average = "weighted_average"
median = "median"
categorical_count = "categorical_count"
accuracy = "accuracy"

2
llama_stack/providers/inline/scoring/basic/scoring.py
View file

@ -25,6 +25,7 @@ from .config import BasicScoringConfig
from .scoring_fn.bfcl_scoring_fn import BFCLScoringFn
from .scoring_fn.docvqa_scoring_fn import DocVQAScoringFn
from .scoring_fn.equality_scoring_fn import EqualityScoringFn
from .scoring_fn.ifeval_scoring_fn import IfEvalScoringFn
from .scoring_fn.regex_parser_math_response_scoring_fn import (
RegexParserMathResponseScoringFn,
)
@ -37,6 +38,7 @@ FIXED_FNS = [
RegexParserScoringFn,
RegexParserMathResponseScoringFn,
BFCLScoringFn,
IfEvalScoringFn,
DocVQAScoringFn,
]

23
llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/ifeval.py Normal file
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@ -0,0 +1,23 @@
# 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 (
AggregationFunctionType,
BasicScoringFnParams,
ScoringFn,
)
ifeval = ScoringFn(
identifier="basic::ifeval",
description="Eval intruction follow capacity by checkping how many instructions can be followed in each example",
return_type=NumberType(),
provider_id="basic",
provider_resource_id="ifeval",
params=BasicScoringFnParams(
aggregation_functions=[AggregationFunctionType.weighted_average],
),
)

79
llama_stack/providers/inline/scoring/basic/scoring_fn/ifeval_scoring_fn.py Normal file
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@ -0,0 +1,79 @@
# 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 Any, Dict, Optional
from llama_stack.apis.scoring import ScoringResultRow
from llama_stack.apis.scoring_functions import ScoringFnParams
from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn
from ..utils.ifeval_utils import INSTRUCTION_DICT, INSTRUCTION_LIST
from .fn_defs.ifeval import (
ifeval,
)
class IfEvalScoringFn(RegisteredBaseScoringFn):
"""
A scoring_fn Instruction-Following Eval (IFEval) benchmark
"""
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.supported_fn_defs_registry = {
ifeval.identifier: ifeval,
}
async def score_row(
self,
input_row: Dict[str, Any],
scoring_fn_identifier: Optional[str] = None,
scoring_params: Optional[ScoringFnParams] = None,
) -> ScoringResultRow:
assert scoring_fn_identifier is not None, "Scoring function identifier not found."
fn_def = self.supported_fn_defs_registry[scoring_fn_identifier]
if scoring_params is not None:
fn_def.params = scoring_params
instruction_list = input_row["instruction_id_list"]
generated_answer = input_row["generated_answer"].strip()
is_following_list = []
results = dict(
{k + "_correct": 0.0 for k in INSTRUCTION_LIST},
**{k + "_total": 0.0 for k in INSTRUCTION_LIST},
)
for index, instruction_id in enumerate(instruction_list):
instruction_cls = INSTRUCTION_DICT[instruction_id]
instruction = instruction_cls(instruction_id)
results[instruction_id + "_total"] += 1.0
results[instruction_id.split(":")[0] + "_total"] += 1.0
clean_input_row = {k: v for k, v in input_row["kwargs"][index].items() if v is not None}
print(clean_input_row)
instruction.build_description(**clean_input_row)
args = instruction.get_instruction_args()
if args and "prompt" in args:
instruction.build_description(prompt=input_row["prompt"])
if generated_answer and instruction.check_following(generated_answer):
is_following_list.append(True)
results[instruction_id + "_correct"] += 1.0
results[instruction_id.split(":")[0] + "_correct"] += 1.0
else:
is_following_list.append(False)
if len(is_following_list) == 0:
return {
"score": 0.0,
"weight": 0.0,
}
return {
"score": float(sum(is_following_list)) / float(len(is_following_list)),
"weight": float(len(is_following_list)),
}

3319
llama_stack/providers/inline/scoring/basic/utils/ifeval_utils.py Normal file
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2
llama_stack/providers/registry/eval.py
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@ -14,7 +14,7 @@ def available_providers() -> List[ProviderSpec]:
InlineProviderSpec(
api=Api.eval,
provider_type="inline::meta-reference",
pip_packages=["tree_sitter"],
pip_packages=["tree_sitter", "pythainlp", "langdetect", "emoji", "nltk"],
module="llama_stack.providers.inline.eval.meta_reference",
config_class="llama_stack.providers.inline.eval.meta_reference.MetaReferenceEvalConfig",
api_dependencies=[

12
llama_stack/providers/utils/scoring/aggregation_utils.py
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@ -28,6 +28,17 @@ def aggregate_average(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]
}
def aggregate_weighted_average(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
return {
"weighted_average": sum(
result["score"] * result["weight"]
for result in scoring_results
if result["score"] is not None and result["weight"] is not None
)
/ sum(result["weight"] for result in scoring_results if result["weight"] is not None),
}
def aggregate_categorical_count(
scoring_results: List[ScoringResultRow],
) -> Dict[str, Any]:
@ -46,6 +57,7 @@ def aggregate_median(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
AGGREGATION_FUNCTIONS = {
AggregationFunctionType.accuracy: aggregate_accuracy,
AggregationFunctionType.average: aggregate_average,
AggregationFunctionType.weighted_average: aggregate_weighted_average,
AggregationFunctionType.categorical_count: aggregate_categorical_count,
AggregationFunctionType.median: aggregate_median,
}

12
llama_stack/templates/open-benchmark/open_benchmark.py
View file

@ -203,6 +203,13 @@ def get_distribution_template() -> DistributionTemplate:
uri="huggingface://datasets/llamastack/bfcl_v3?split=train",
),
),
DatasetInput(
dataset_id="ifeval",
purpose=DatasetPurpose.eval_messages_answer,
source=URIDataSource(
uri="huggingface://datasets/llamastack/IfEval?split=train",
),
),
DatasetInput(
dataset_id="docvqa",
purpose=DatasetPurpose.eval_messages_answer,
@ -238,6 +245,11 @@ def get_distribution_template() -> DistributionTemplate:
dataset_id="bfcl",
scoring_functions=["basic::bfcl"],
),
BenchmarkInput(
benchmark_id="meta-reference-ifeval",
dataset_id="ifeval",
scoring_functions=["basic::ifeval"],
),
BenchmarkInput(
benchmark_id="meta-reference-docvqa",
dataset_id="docvqa",

11
llama_stack/templates/open-benchmark/run.yaml
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@ -188,6 +188,12 @@ datasets:
uri: huggingface://datasets/llamastack/bfcl_v3?split=train
metadata: {}
dataset_id: bfcl
- purpose: eval/messages-answer
source:
type: uri
uri: huggingface://datasets/llamastack/IfEval?split=train
metadata: {}
dataset_id: ifeval
- purpose: eval/messages-answer
source:
type: uri
@ -221,6 +227,11 @@ benchmarks:
- basic::bfcl
metadata: {}
benchmark_id: meta-reference-bfcl
- dataset_id: ifeval
scoring_functions:
- basic::ifeval
metadata: {}
benchmark_id: meta-reference-ifeval
- dataset_id: docvqa
scoring_functions:
- basic::docvqa