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Code Pull requests Releases Packages 2 Activity Actions

fix integeration

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This commit is contained in:
Xi Yan 2025-03-15 17:36:39 -07:00
parent a6fa3aa5a2
commit 5cf7779b8f
2 changed files with 87 additions and 29 deletions
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76
llama_stack/providers/inline/eval/meta_reference/eval.py
View file

@ -19,11 +19,7 @@ from llama_stack.providers.datatypes import BenchmarksProtocolPrivate
from llama_stack.providers.inline.agents.meta_reference.agent_instance import (
MEMORY_QUERY_TOOL,
)
from llama_stack.providers.utils.common.data_schema_validator import (
ColumnName,
get_valid_schemas,
validate_dataset_schema,
)
from llama_stack.providers.utils.common.data_schema_validator import ColumnName
from llama_stack.providers.utils.kvstore import kvstore_impl
from .....apis.common.job_types import Job
@ -89,10 +85,14 @@ class MetaReferenceEvalImpl(
dataset_id = task_def.dataset_id
scoring_functions = task_def.scoring_functions
dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.eval.value))
# TODO: validate dataset schema
all_rows = await self.datasetio_api.iterrows(
dataset_id=dataset_id,
limit=(-1 if benchmark_config.num_examples is None else benchmark_config.num_examples),
limit=(
-1
if benchmark_config.num_examples is None
else benchmark_config.num_examples
),
)
res = await self.evaluate_rows(
benchmark_id=benchmark_id,
@ -118,10 +118,14 @@ class MetaReferenceEvalImpl(
for i, x in tqdm(enumerate(input_rows)):
assert ColumnName.chat_completion_input.value in x, "Invalid input row"
input_messages = json.loads(x[ColumnName.chat_completion_input.value])
input_messages = [UserMessage(**x) for x in input_messages if x["role"] == "user"]
input_messages = [
UserMessage(**x) for x in input_messages if x["role"] == "user"
]
# 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_create_response = await self.agents_api.create_agent_session(
agent_id, f"session-{i}"
)
session_id = session_create_response.session_id
turn_request = dict(
@ -130,7 +134,12 @@ class MetaReferenceEvalImpl(
messages=input_messages,
stream=True,
)
turn_response = [chunk async for chunk in await self.agents_api.create_agent_turn(**turn_request)]
turn_response = [
chunk
async for chunk in await self.agents_api.create_agent_turn(
**turn_request
)
]
final_event = turn_response[-1].event.payload
# check if there's a memory retrieval step and extract the context
@ -139,10 +148,14 @@ class MetaReferenceEvalImpl(
if step.step_type == StepType.tool_execution.value:
for tool_response in step.tool_responses:
if tool_response.tool_name == MEMORY_QUERY_TOOL:
memory_rag_context = " ".join(x.text for x in tool_response.content)
memory_rag_context = " ".join(
x.text for x in tool_response.content
)
agent_generation = {}
agent_generation[ColumnName.generated_answer.value] = final_event.turn.output_message.content
agent_generation[ColumnName.generated_answer.value] = (
final_event.turn.output_message.content
)
if memory_rag_context:
agent_generation[ColumnName.context.value] = memory_rag_context
@ -154,7 +167,9 @@ class MetaReferenceEvalImpl(
self, input_rows: List[Dict[str, Any]], benchmark_config: BenchmarkConfig
) -> List[Dict[str, Any]]:
candidate = benchmark_config.eval_candidate
assert candidate.sampling_params.max_tokens is not None, "SamplingParams.max_tokens must be provided"
assert (
candidate.sampling_params.max_tokens is not None
), "SamplingParams.max_tokens must be provided"
generations = []
for x in tqdm(input_rows):
@ -165,21 +180,39 @@ class MetaReferenceEvalImpl(
content=input_content,
sampling_params=candidate.sampling_params,
)
generations.append({ColumnName.generated_answer.value: response.completion_message.content})
generations.append(
{
ColumnName.generated_answer.value: response.completion_message.content
}
)
elif ColumnName.chat_completion_input.value in x:
chat_completion_input_json = json.loads(x[ColumnName.chat_completion_input.value])
input_messages = [UserMessage(**x) for x in chat_completion_input_json if x["role"] == "user"]
chat_completion_input_json = json.loads(
x[ColumnName.chat_completion_input.value]
)
input_messages = [
UserMessage(**x)
for x in chat_completion_input_json
if x["role"] == "user"
]
messages = []
if candidate.system_message:
messages.append(candidate.system_message)
messages += [SystemMessage(**x) for x in chat_completion_input_json if x["role"] == "system"]
messages += [
SystemMessage(**x)
for x in chat_completion_input_json
if x["role"] == "system"
]
messages += input_messages
response = await self.inference_api.chat_completion(
model_id=candidate.model,
messages=messages,
sampling_params=candidate.sampling_params,
)
generations.append({ColumnName.generated_answer.value: response.completion_message.content})
generations.append(
{
ColumnName.generated_answer.value: response.completion_message.content
}
)
else:
raise ValueError("Invalid input row")
@ -202,7 +235,8 @@ class MetaReferenceEvalImpl(
# scoring with generated_answer
score_input_rows = [
input_r | generated_r for input_r, generated_r in zip(input_rows, generations, strict=False)
input_r | generated_r
for input_r, generated_r in zip(input_rows, generations, strict=False)
]
if benchmark_config.scoring_params is not None:
@ -211,7 +245,9 @@ class MetaReferenceEvalImpl(
for scoring_fn_id in scoring_functions
}
else:
scoring_functions_dict = {scoring_fn_id: None for scoring_fn_id in scoring_functions}
scoring_functions_dict = {
scoring_fn_id: None for scoring_fn_id in scoring_functions
}
score_response = await self.scoring_api.score(
input_rows=score_input_rows, scoring_functions=scoring_functions_dict

40
tests/integration/eval/test_eval.py
View file

@ -3,11 +3,13 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import os
import uuid
from pathlib import Path
import pytest
from ..datasetio.test_datasetio import register_dataset
from ..datasets.test_datasets import data_url_from_file
# How to run this test:
#
@ -16,12 +18,20 @@ from ..datasetio.test_datasetio import register_dataset
@pytest.mark.parametrize("scoring_fn_id", ["basic::equality"])
def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
register_dataset(llama_stack_client, for_generation=True, dataset_id="test_dataset_for_eval")
dataset = llama_stack_client.datasets.register(
purpose="eval/messages-answer",
source={
"type": "uri",
"uri": data_url_from_file(
Path(__file__).parent.parent / "datasets" / "test_dataset.csv"
),
},
)
response = llama_stack_client.datasets.list()
assert any(x.identifier == "test_dataset_for_eval" for x in response)
assert any(x.identifier == dataset.identifier for x in response)
rows = llama_stack_client.datasets.iterrows(
dataset_id="test_dataset_for_eval",
dataset_id=dataset.identifier,
limit=3,
)
assert len(rows.data) == 3
@ -32,7 +42,7 @@ def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
benchmark_id = str(uuid.uuid4())
llama_stack_client.benchmarks.register(
benchmark_id=benchmark_id,
dataset_id="test_dataset_for_eval",
dataset_id=dataset.identifier,
scoring_functions=scoring_functions,
)
list_benchmarks = llama_stack_client.benchmarks.list()
@ -59,11 +69,19 @@ def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
@pytest.mark.parametrize("scoring_fn_id", ["basic::subset_of"])
def test_evaluate_benchmark(llama_stack_client, text_model_id, scoring_fn_id):
register_dataset(llama_stack_client, for_generation=True, dataset_id="test_dataset_for_eval_2")
dataset = llama_stack_client.datasets.register(
purpose="eval/messages-answer",
source={
"type": "uri",
"uri": data_url_from_file(
Path(__file__).parent.parent / "datasets" / "test_dataset.csv"
),
},
)
benchmark_id = str(uuid.uuid4())
llama_stack_client.benchmarks.register(
benchmark_id=benchmark_id,
dataset_id="test_dataset_for_eval_2",
dataset_id=dataset.identifier,
scoring_functions=[scoring_fn_id],
)
@ -80,10 +98,14 @@ def test_evaluate_benchmark(llama_stack_client, text_model_id, scoring_fn_id):
},
)
assert response.job_id == "0"
job_status = llama_stack_client.eval.jobs.status(job_id=response.job_id, benchmark_id=benchmark_id)
job_status = llama_stack_client.eval.jobs.status(
job_id=response.job_id, benchmark_id=benchmark_id
)
assert job_status and job_status == "completed"
eval_response = llama_stack_client.eval.jobs.retrieve(job_id=response.job_id, benchmark_id=benchmark_id)
eval_response = llama_stack_client.eval.jobs.retrieve(
job_id=response.job_id, benchmark_id=benchmark_id
)
assert eval_response is not None
assert len(eval_response.generations) == 5
assert scoring_fn_id in eval_response.scores