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refactor: tests/unittests -> tests/unit; tests/api -> tests/integration

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Ashwin Bharambe 2025-03-04 09:55:05 -08:00
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tests/api/inference/test_text_inference.py
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# 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.
import pytest
from pydantic import BaseModel
from llama_stack.models.llama.sku_list import resolve_model
from llama_stack.providers.tests.test_cases.test_case import TestCase
PROVIDER_LOGPROBS_TOP_K = {"remote::together", "remote::fireworks", "remote::vllm"}
def skip_if_model_doesnt_support_completion(client_with_models, model_id):
models = {m.identifier: m for m in client_with_models.models.list()}
provider_id = models[model_id].provider_id
providers = {p.provider_id: p for p in client_with_models.providers.list()}
provider = providers[provider_id]
if provider.provider_type in ("remote::openai", "remote::anthropic", "remote::gemini", "remote::groq"):
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support completion")
def get_llama_model(client_with_models, model_id):
models = {}
for m in client_with_models.models.list():
models[m.identifier] = m
models[m.provider_resource_id] = m
assert model_id in models, f"Model {model_id} not found"
model = models[model_id]
ids = (model.identifier, model.provider_resource_id)
for mid in ids:
if resolve_model(mid):
return mid
return model.metadata.get("llama_model", None)
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:sanity",
],
)
def test_text_completion_non_streaming(client_with_models, text_model_id, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
tc = TestCase(test_case)
response = client_with_models.inference.completion(
content=tc["content"],
stream=False,
model_id=text_model_id,
sampling_params={
"max_tokens": 50,
},
)
assert len(response.content) > 10
# assert "blue" in response.content.lower().strip()
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:sanity",
],
)
def test_text_completion_streaming(client_with_models, text_model_id, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
tc = TestCase(test_case)
response = client_with_models.inference.completion(
content=tc["content"],
stream=True,
model_id=text_model_id,
sampling_params={
"max_tokens": 50,
},
)
streamed_content = [chunk.delta for chunk in response]
content_str = "".join(streamed_content).lower().strip()
# assert "blue" in content_str
assert len(content_str) > 10
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:log_probs",
],
)
def test_text_completion_log_probs_non_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
tc = TestCase(test_case)
response = client_with_models.inference.completion(
content=tc["content"],
stream=False,
model_id=text_model_id,
sampling_params={
"max_tokens": 5,
},
logprobs={
"top_k": 1,
},
)
assert response.logprobs, "Logprobs should not be empty"
assert 1 <= len(response.logprobs) <= 5 # each token has 1 logprob and here max_tokens=5
assert all(len(logprob.logprobs_by_token) == 1 for logprob in response.logprobs)
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:log_probs",
],
)
def test_text_completion_log_probs_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
tc = TestCase(test_case)
response = client_with_models.inference.completion(
content=tc["content"],
stream=True,
model_id=text_model_id,
sampling_params={
"max_tokens": 5,
},
logprobs={
"top_k": 1,
},
)
streamed_content = list(response)
for chunk in streamed_content:
if chunk.delta: # if there's a token, we expect logprobs
assert chunk.logprobs, "Logprobs should not be empty"
assert all(len(logprob.logprobs_by_token) == 1 for logprob in chunk.logprobs)
else: # no token, no logprobs
assert not chunk.logprobs, "Logprobs should be empty"
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:structured_output",
],
)
def test_text_completion_structured_output(client_with_models, text_model_id, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
class AnswerFormat(BaseModel):
name: str
year_born: str
year_retired: str
tc = TestCase(test_case)
user_input = tc["user_input"]
response = client_with_models.inference.completion(
model_id=text_model_id,
content=user_input,
stream=False,
sampling_params={
"max_tokens": 50,
},
response_format={
"type": "json_schema",
"json_schema": AnswerFormat.model_json_schema(),
},
)
answer = AnswerFormat.model_validate_json(response.content)
expected = tc["expected"]
assert answer.name == expected["name"]
assert answer.year_born == expected["year_born"]
assert answer.year_retired == expected["year_retired"]
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:non_streaming_01",
"inference:chat_completion:non_streaming_02",
],
)
def test_text_chat_completion_non_streaming(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
question = tc["question"]
expected = tc["expected"]
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=[
{
"role": "user",
"content": question,
}
],
stream=False,
)
message_content = response.completion_message.content.lower().strip()
assert len(message_content) > 0
assert expected.lower() in message_content
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:streaming_01",
"inference:chat_completion:streaming_02",
],
)
def test_text_chat_completion_streaming(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
question = tc["question"]
expected = tc["expected"]
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=[{"role": "user", "content": question}],
stream=True,
)
streamed_content = [str(chunk.event.delta.text.lower().strip()) for chunk in response]
assert len(streamed_content) > 0
assert expected.lower() in "".join(streamed_content)
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_text_chat_completion_with_tool_calling_and_non_streaming(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_choice="auto",
stream=False,
)
# some models can return content for the response in addition to the tool call
assert response.completion_message.role == "assistant"
assert len(response.completion_message.tool_calls) == 1
assert response.completion_message.tool_calls[0].tool_name == tc["tools"][0]["tool_name"]
assert response.completion_message.tool_calls[0].arguments == tc["expected"]
# Will extract streamed text and separate it from tool invocation content
# The returned tool inovcation content will be a string so it's easy to comapare with expected value
# e.g. "[get_weather, {'location': 'San Francisco, CA'}]"
def extract_tool_invocation_content(response):
tool_invocation_content: str = ""
for chunk in response:
delta = chunk.event.delta
if delta.type == "tool_call" and delta.parse_status == "succeeded":
call = delta.tool_call
tool_invocation_content += f"[{call.tool_name}, {call.arguments}]"
return tool_invocation_content
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_text_chat_completion_with_tool_calling_and_streaming(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_choice="auto",
stream=True,
)
tool_invocation_content = extract_tool_invocation_content(response)
expected_tool_name = tc["tools"][0]["tool_name"]
expected_argument = tc["expected"]
assert tool_invocation_content == f"[{expected_tool_name}, {expected_argument}]"
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_text_chat_completion_with_tool_choice_required(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_config={
"tool_choice": "required",
},
stream=True,
)
tool_invocation_content = extract_tool_invocation_content(response)
expected_tool_name = tc["tools"][0]["tool_name"]
expected_argument = tc["expected"]
assert tool_invocation_content == f"[{expected_tool_name}, {expected_argument}]"
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_text_chat_completion_with_tool_choice_none(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_config={"tool_choice": "none"},
stream=True,
)
tool_invocation_content = extract_tool_invocation_content(response)
assert tool_invocation_content == ""
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:structured_output",
],
)
def test_text_chat_completion_structured_output(client_with_models, text_model_id, test_case):
class NBAStats(BaseModel):
year_for_draft: int
num_seasons_in_nba: int
class AnswerFormat(BaseModel):
first_name: str
last_name: str
year_of_birth: int
nba_stats: NBAStats
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
response_format={
"type": "json_schema",
"json_schema": AnswerFormat.model_json_schema(),
},
stream=False,
)
answer = AnswerFormat.model_validate_json(response.completion_message.content)
expected = tc["expected"]
assert answer.first_name == expected["first_name"]
assert answer.last_name == expected["last_name"]
assert answer.year_of_birth == expected["year_of_birth"]
assert answer.nba_stats.num_seasons_in_nba == expected["num_seasons_in_nba"]
assert answer.nba_stats.year_for_draft == expected["year_for_draft"]
@pytest.mark.parametrize("streaming", [True, False])
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling_tools_absent",
],
)
def test_text_chat_completion_tool_calling_tools_not_in_request(
client_with_models, text_model_id, test_case, streaming
):
tc = TestCase(test_case)
# TODO: more dynamic lookup on tool_prompt_format for model family
tool_prompt_format = "json" if "3.1" in text_model_id else "python_list"
request = {
"model_id": text_model_id,
"messages": tc["messages"],
"tools": tc["tools"],
"tool_choice": "auto",
"tool_prompt_format": tool_prompt_format,
"stream": streaming,
}
response = client_with_models.inference.chat_completion(**request)
if streaming:
for chunk in response:
delta = chunk.event.delta
if delta.type == "tool_call" and delta.parse_status == "succeeded":
assert delta.tool_call.tool_name == "get_object_namespace_list"
if delta.type == "tool_call" and delta.parse_status == "failed":
# expect raw message that failed to parse in tool_call
assert isinstance(delta.tool_call, str)
assert len(delta.tool_call) > 0
else:
for tc in response.completion_message.tool_calls:
assert tc.tool_name == "get_object_namespace_list"