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[Feature][Frontend]: Continued stream_options implementation also in CompletionRequest (#5319)

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This commit is contained in:
Itay Etelis
2024-06-10 17:22:09 +03:00
committed by GitHub
parent 6b29d6fe70
commit 774d1035e4
4 changed files with 180 additions and 126 deletions
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236
tests/entrypoints/test_openai_server.py
View File

@ -478,8 +478,6 @@ async def test_completion_streaming(server, client: openai.AsyncOpenAI,
temperature=0.0,
)
single_output = single_completion.choices[0].text
single_usage = single_completion.usage
stream = await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
@ -495,7 +493,6 @@ async def test_completion_streaming(server, client: openai.AsyncOpenAI,
assert finish_reason_count == 1
assert chunk.choices[0].finish_reason == "length"
assert chunk.choices[0].text
assert chunk.usage == single_usage
assert "".join(chunks) == single_output
@ -550,6 +547,138 @@ async def test_chat_streaming(server, client: openai.AsyncOpenAI,
assert "".join(chunks) == output
@pytest.mark.asyncio
@pytest.mark.parametrize(
"model_name",
["HuggingFaceH4/zephyr-7b-beta", "zephyr-lora"],
)
async def test_chat_completion_stream_options(server,
client: openai.AsyncOpenAI,
model_name: str):
messages = [{
"role": "system",
"content": "You are a helpful assistant."
}, {
"role": "user",
"content": "What is the capital of France?"
}]
# Test stream=True, stream_options={"include_usage": False}
stream = await client.chat.completions.create(
model=model_name,
messages=messages,
max_tokens=10,
temperature=0.0,
stream=True,
stream_options={"include_usage": False})
async for chunk in stream:
assert chunk.usage is None
# Test stream=True, stream_options={"include_usage": True}
stream = await client.chat.completions.create(
model=model_name,
messages=messages,
max_tokens=10,
temperature=0.0,
stream=True,
stream_options={"include_usage": True})
async for chunk in stream:
if chunk.choices[0].finish_reason is None:
assert chunk.usage is None
else:
assert chunk.usage is None
final_chunk = await stream.__anext__()
assert final_chunk.usage is not None
assert final_chunk.usage.prompt_tokens > 0
assert final_chunk.usage.completion_tokens > 0
assert final_chunk.usage.total_tokens == (
final_chunk.usage.prompt_tokens +
final_chunk.usage.completion_tokens)
assert final_chunk.choices == []
# Test stream=False, stream_options={"include_usage": None}
with pytest.raises(BadRequestError):
await client.chat.completions.create(
model=model_name,
messages=messages,
max_tokens=10,
temperature=0.0,
stream=False,
stream_options={"include_usage": None})
# Test stream=False, stream_options={"include_usage": True}
with pytest.raises(BadRequestError):
await client.chat.completions.create(
model=model_name,
messages=messages,
max_tokens=10,
temperature=0.0,
stream=False,
stream_options={"include_usage": True})
@pytest.mark.asyncio
@pytest.mark.parametrize(
"model_name",
["HuggingFaceH4/zephyr-7b-beta", "zephyr-lora"],
)
async def test_completion_stream_options(server, client: openai.AsyncOpenAI,
model_name: str):
prompt = "What is the capital of France?"
# Test stream=True, stream_options={"include_usage": False}
stream = await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
stream_options={"include_usage": False})
async for chunk in stream:
assert chunk.usage is None
# Test stream=True, stream_options={"include_usage": True}
stream = await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
stream_options={"include_usage": True})
async for chunk in stream:
if chunk.choices[0].finish_reason is None:
assert chunk.usage is None
else:
assert chunk.usage is None
final_chunk = await stream.__anext__()
assert final_chunk.usage is not None
assert final_chunk.usage.prompt_tokens > 0
assert final_chunk.usage.completion_tokens > 0
assert final_chunk.usage.total_tokens == (
final_chunk.usage.prompt_tokens +
final_chunk.usage.completion_tokens)
assert final_chunk.choices == []
# Test stream=False, stream_options={"include_usage": None}
with pytest.raises(BadRequestError):
await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=False,
stream_options={"include_usage": None})
# Test stream=False, stream_options={"include_usage": True}
with pytest.raises(BadRequestError):
await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=False,
stream_options={"include_usage": True})
@pytest.mark.asyncio
@pytest.mark.parametrize(
# just test 1 lora hereafter
@ -1343,106 +1472,5 @@ async def test_batch_embedding(embedding_server, client: openai.AsyncOpenAI,
assert embeddings.usage.total_tokens == 17
@pytest.mark.parametrize(
"model_name",
[MODEL_NAME],
)
async def test_stream_options(server, client: openai.AsyncOpenAI,
model_name: str):
prompt = "What is the capital of France?"
# Test stream=True, stream_options=None
stream = await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
stream_options=None,
)
chunks = []
async for chunk in stream:
chunks.append(chunk.choices[0].text)
assert len(chunks) > 0
assert "usage" not in chunk
# Test stream=True, stream_options={"include_usage": False}
stream = await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
stream_options={"include_usage": False},
)
chunks = []
async for chunk in stream:
chunks.append(chunk.choices[0].text)
assert len(chunks) > 0
assert "usage" not in chunk
# Test stream=True, stream_options={"include_usage": True}
stream = await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True,
stream_options={"include_usage": True},
)
chunks = []
finish_reason_count = 0
async for chunk in stream:
if chunk.choices[0].finish_reason is None:
assert chunk.usage is None
chunks.append(chunk.choices[0].text)
else:
assert chunk.usage is None
finish_reason_count += 1
# The last message should have usage and no choices
last_message = await stream.__anext__()
assert last_message.usage is not None
assert last_message.usage.prompt_tokens > 0
assert last_message.usage.completion_tokens > 0
assert last_message.usage.total_tokens == (
last_message.usage.prompt_tokens +
last_message.usage.completion_tokens)
assert last_message.choices == []
# Test stream=False, stream_options={"include_usage": None}
with pytest.raises(BadRequestError):
await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=False,
stream_options={"include_usage": None},
)
# Test stream=False, stream_options={"include_usage": False}
with pytest.raises(BadRequestError):
await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=False,
stream_options={"include_usage": False},
)
# Test stream=False, stream_options={"include_usage": True}
with pytest.raises(BadRequestError):
await client.completions.create(
model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=False,
stream_options={"include_usage": True},
)
if __name__ == "__main__":
pytest.main([__file__])

9
vllm/entrypoints/openai/protocol.py
View File

@ -346,6 +346,7 @@ class CompletionRequest(OpenAIBaseModel):
le=torch.iinfo(torch.long).max)
stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
stream: Optional[bool] = False
stream_options: Optional[StreamOptions] = None
suffix: Optional[str] = None
temperature: Optional[float] = 1.0
top_p: Optional[float] = 1.0
@ -482,6 +483,14 @@ class CompletionRequest(OpenAIBaseModel):
" in the interval [0, 5]."))
return data
@model_validator(mode="before")
@classmethod
def validate_stream_options(cls, data):
if data.get("stream_options") and not data.get("stream"):
raise ValueError(
"Stream options can only be defined when stream is True.")
return data
class EmbeddingRequest(BaseModel):
# Ordered by official OpenAI API documentation

35
vllm/entrypoints/openai/serving_chat.py
View File

@ -441,25 +441,24 @@ class OpenAIServingChat(OpenAIServing):
yield f"data: {data}\n\n"
finish_reason_sent[i] = True
if (request.stream_options
and request.stream_options.include_usage):
final_usage = UsageInfo(
prompt_tokens=prompt_tokens,
completion_tokens=previous_num_tokens[i],
total_tokens=prompt_tokens +
previous_num_tokens[i],
)
if (request.stream_options
and request.stream_options.include_usage):
final_usage = UsageInfo(
prompt_tokens=prompt_tokens,
completion_tokens=previous_num_tokens[i],
total_tokens=prompt_tokens + previous_num_tokens[i],
)
final_usage_chunk = ChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[],
model=model_name,
usage=final_usage)
final_usage_data = (final_usage_chunk.model_dump_json(
exclude_unset=True, exclude_none=True))
yield f"data: {final_usage_data}\n\n"
final_usage_chunk = ChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[],
model=model_name,
usage=final_usage)
final_usage_data = (final_usage_chunk.model_dump_json(
exclude_unset=True, exclude_none=True))
yield f"data: {final_usage_data}\n\n"
except ValueError as e:
# TODO: Use a vllm-specific Validation Error

26
vllm/entrypoints/openai/serving_completion.py
View File

@ -264,7 +264,8 @@ class OpenAIServingCompletion(OpenAIServing):
)
else:
final_usage = None
response_json = CompletionStreamResponse(
chunk = CompletionStreamResponse(
id=request_id,
created=created_time,
model=model_name,
@ -276,10 +277,27 @@ class OpenAIServingCompletion(OpenAIServing):
finish_reason=finish_reason,
stop_reason=stop_reason,
)
],
usage=final_usage,
).model_dump_json(exclude_unset=True)
])
if (request.stream_options
and request.stream_options.include_usage):
chunk.usage = None
response_json = chunk.model_dump_json(exclude_unset=True)
yield f"data: {response_json}\n\n"
if (request.stream_options
and request.stream_options.include_usage):
final_usage_chunk = CompletionStreamResponse(
id=request_id,
created=created_time,
model=model_name,
choices=[],
usage=final_usage,
)
final_usage_data = (final_usage_chunk.model_dump_json(
exclude_unset=True, exclude_none=True))
yield f"data: {final_usage_data}\n\n"
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
data = self.create_streaming_error_response(str(e))