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Unverified Commit baaedfdb authored by Cyrus Leung's avatar Cyrus Leung Committed by GitHub
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[mypy] Enable following imports for entrypoints (#7248) 


Co-authored-by: default avatarWoosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: default avatarFei <dfdfcai4@gmail.com>
parent 45066412
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Showing with 186 additions and 94 deletions
vllm/entrypoints/openai/rpc/server.py
View file @ baaedfdb
......@@ -23,8 +23,8 @@ class AsyncEngineRPCServer:
def __init__(self, async_engine_args: AsyncEngineArgs,
usage_context: UsageContext, rpc_path: str):
# Initialize engine first.
self.engine = AsyncLLMEngine.from_engine_args(async_engine_args,
usage_context)
self.engine = AsyncLLMEngine.from_engine_args(
async_engine_args, usage_context=usage_context)
# Initialize context.
self.context = zmq.asyncio.Context()
......@@ -39,7 +39,7 @@ class AsyncEngineRPCServer:
self.context.destroy()
self.engine.shutdown_background_loop()
# Clear the engine reference so that it can be GC'ed.
self.engine = None
del self.engine
async def get_model_config(self, identity):
"""Send the ModelConfig"""
......
vllm/entrypoints/openai/serving_chat.py
View file @ baaedfdb
import asyncio
import time
from typing import AsyncGenerator, AsyncIterator, Dict, List, Optional
from typing import AsyncGenerator, AsyncIterator, Dict, Final, List, Optional
from typing import Sequence as GenericSequence
from typing import Union
from fastapi import Request
from transformers import PreTrainedTokenizer
from vllm.config import ModelConfig
from vllm.engine.protocol import AsyncEngineClient
......@@ -24,13 +23,14 @@ from vllm.entrypoints.openai.protocol import (
from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
OpenAIServing,
PromptAdapterPath)
from vllm.inputs import PromptInputs
from vllm.inputs import TokensPrompt
from vllm.logger import init_logger
from vllm.multimodal import MultiModalDataDict
from vllm.outputs import RequestOutput
from vllm.sequence import Logprob
from vllm.tracing import (contains_trace_headers, extract_trace_headers,
log_tracing_disabled_warning)
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import iterate_with_cancellation, random_uuid
logger = init_logger(__name__)
......@@ -67,9 +67,9 @@ class OpenAIServingChat(OpenAIServing):
async def create_chat_completion(
self,
request: ChatCompletionRequest,
raw_request: Optional[Request] = None
) -> Union[ErrorResponse, AsyncGenerator[str, None],
ChatCompletionResponse]:
raw_request: Optional[Request] = None,
) -> Union[AsyncGenerator[str, None], ChatCompletionResponse,
ErrorResponse]:
"""Completion API similar to OpenAI's API.
See https://platform.openai.com/docs/api-reference/chat/create
......@@ -83,16 +83,6 @@ class OpenAIServingChat(OpenAIServing):
if error_check_ret is not None:
return error_check_ret
if request.prompt_logprobs is not None:
if request.stream and request.prompt_logprobs > 0:
return self.create_error_response(
"Prompt_logprobs are not available when stream is enabled")
if request.prompt_logprobs < 0:
return self.create_error_response(
f"Prompt_logprobs set to invalid "
f"negative value: {request.prompt_logprobs}")
try:
(
lora_request,
......@@ -160,9 +150,8 @@ class OpenAIServingChat(OpenAIServing):
lora_request=lora_request,
prompt_adapter_request=prompt_adapter_request)
engine_inputs: PromptInputs = {
"prompt_token_ids": prompt_inputs["prompt_token_ids"],
}
engine_inputs = TokensPrompt(
prompt_token_ids=prompt_inputs["prompt_token_ids"])
if mm_data is not None:
engine_inputs["multi_modal_data"] = mm_data
......@@ -214,11 +203,11 @@ class OpenAIServingChat(OpenAIServing):
result_generator: AsyncIterator[RequestOutput],
request_id: str,
conversation: List[ConversationMessage],
tokenizer: PreTrainedTokenizer,
tokenizer: AnyTokenizer,
) -> AsyncGenerator[str, None]:
model_name = self.served_model_names[0]
created_time = int(time.time())
chunk_object_type = "chat.completion.chunk"
chunk_object_type: Final = "chat.completion.chunk"
first_iteration = True
# Send response for each token for each request.n (index)
......@@ -438,7 +427,7 @@ class OpenAIServingChat(OpenAIServing):
result_generator: AsyncIterator[RequestOutput],
request_id: str,
conversation: List[ConversationMessage],
tokenizer: PreTrainedTokenizer,
tokenizer: AnyTokenizer,
) -> Union[ErrorResponse, ChatCompletionResponse]:
model_name = self.served_model_names[0]
......@@ -523,7 +512,7 @@ class OpenAIServingChat(OpenAIServing):
def _get_top_logprobs(
self, logprobs: Dict[int, Logprob], top_logprobs: Optional[int],
tokenizer: PreTrainedTokenizer) -> List[ChatCompletionLogProb]:
tokenizer: AnyTokenizer) -> List[ChatCompletionLogProb]:
return [
ChatCompletionLogProb(token=(token := self._get_decoded_token(
p[1],
......@@ -541,12 +530,11 @@ class OpenAIServingChat(OpenAIServing):
self,
token_ids: GenericSequence[int],
top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
tokenizer: PreTrainedTokenizer,
tokenizer: AnyTokenizer,
num_output_top_logprobs: Optional[int] = None,
) -> ChatCompletionLogProbs:
"""Create OpenAI-style logprobs."""
logprobs_content = []
logprobs_content: List[ChatCompletionLogProbsContent] = []
for i, token_id in enumerate(token_ids):
step_top_logprobs = top_logprobs[i]
......@@ -554,23 +542,32 @@ class OpenAIServingChat(OpenAIServing):
token = tokenizer.decode(token_id)
if self.return_tokens_as_token_ids:
token = f"token_id:{token_id}"
logprobs_content.append(
ChatCompletionLogProbsContent(
token=token,
bytes=list(token.encode("utf-8", errors="replace"))))
bytes=list(token.encode("utf-8", errors="replace")),
))
else:
step_token = step_top_logprobs[token_id]
step_decoded = step_token.decoded_token
logprobs_content.append(
ChatCompletionLogProbsContent(
token=self._get_decoded_token(
step_top_logprobs[token_id], token_id, tokenizer,
self.return_tokens_as_token_ids),
logprob=max(step_top_logprobs[token_id].logprob,
-9999.0),
bytes=list(
step_top_logprobs[token_id].decoded_token.encode(
"utf-8", errors="replace")),
step_token,
token_id,
tokenizer,
self.return_tokens_as_token_ids,
),
logprob=max(step_token.logprob, -9999.0),
bytes=None if step_decoded is None else list(
step_decoded.encode("utf-8", errors="replace")),
top_logprobs=self._get_top_logprobs(
step_top_logprobs, num_output_top_logprobs,
tokenizer)))
step_top_logprobs,
num_output_top_logprobs,
tokenizer,
),
))
return ChatCompletionLogProbs(content=logprobs_content)
vllm/entrypoints/openai/serving_completion.py
View file @ baaedfdb
......@@ -3,10 +3,9 @@ import time
from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List,
Optional)
from typing import Sequence as GenericSequence
from typing import Tuple, cast
from typing import Tuple, Union, cast
from fastapi import Request
from transformers import PreTrainedTokenizer
from vllm.config import ModelConfig
from vllm.engine.protocol import AsyncEngineClient
......@@ -19,7 +18,7 @@ from vllm.entrypoints.openai.protocol import (CompletionLogProbs,
CompletionResponseChoice,
CompletionResponseStreamChoice,
CompletionStreamResponse,
UsageInfo)
ErrorResponse, UsageInfo)
# yapf: enable
from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
OpenAIServing,
......@@ -29,6 +28,7 @@ from vllm.outputs import RequestOutput
from vllm.sequence import Logprob
from vllm.tracing import (contains_trace_headers, extract_trace_headers,
log_tracing_disabled_warning)
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import merge_async_iterators, random_uuid
logger = init_logger(__name__)
......@@ -60,8 +60,11 @@ class OpenAIServingCompletion(OpenAIServing):
request_logger=request_logger,
return_tokens_as_token_ids=return_tokens_as_token_ids)
async def create_completion(self, request: CompletionRequest,
raw_request: Request):
async def create_completion(
self,
request: CompletionRequest,
raw_request: Request,
) -> Union[AsyncGenerator[str, None], CompletionResponse, ErrorResponse]:
"""Completion API similar to OpenAI's API.
See https://platform.openai.com/docs/api-reference/completions/create
......@@ -84,15 +87,6 @@ class OpenAIServingCompletion(OpenAIServing):
request_id = f"cmpl-{random_uuid()}"
created_time = int(time.time())
if request.prompt_logprobs is not None:
if request.stream and request.prompt_logprobs > 0:
return self.create_error_response(
"Prompt_logprobs are not available when stream is enabled")
elif request.prompt_logprobs < 0:
return self.create_error_response(
f"Prompt_logprobs set to invalid negative "
f"value: {request.prompt_logprobs}")
# Schedule the request and get the result generator.
generators: List[AsyncGenerator[RequestOutput, None]] = []
try:
......@@ -153,8 +147,7 @@ class OpenAIServingCompletion(OpenAIServing):
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
result_generator: AsyncIterator[Tuple[
int, RequestOutput]] = merge_async_iterators(
result_generator = merge_async_iterators(
*generators, is_cancelled=raw_request.is_disconnected)
# Similar to the OpenAI API, when n != best_of, we do not stream the
......@@ -227,7 +220,7 @@ class OpenAIServingCompletion(OpenAIServing):
created_time: int,
model_name: str,
num_prompts: int,
tokenizer: PreTrainedTokenizer,
tokenizer: AnyTokenizer,
) -> AsyncGenerator[str, None]:
num_choices = 1 if request.n is None else request.n
previous_texts = [""] * num_choices * num_prompts
......@@ -236,6 +229,13 @@ class OpenAIServingCompletion(OpenAIServing):
try:
async for prompt_idx, res in result_generator:
prompt_token_ids = res.prompt_token_ids
prompt_logprobs = res.prompt_logprobs
prompt_text = res.prompt
delta_token_ids: GenericSequence[int]
out_logprobs: Optional[GenericSequence[Optional[Dict[
int, Logprob]]]]
for output in res.outputs:
i = output.index + prompt_idx * num_choices
......@@ -244,19 +244,25 @@ class OpenAIServingCompletion(OpenAIServing):
assert request.max_tokens is not None
if request.echo and request.max_tokens == 0:
assert prompt_text is not None
# only return the prompt
delta_text = res.prompt
delta_token_ids = res.prompt_token_ids
out_logprobs = res.prompt_logprobs
delta_text = prompt_text
delta_token_ids = prompt_token_ids
out_logprobs = prompt_logprobs
has_echoed[i] = True
elif (request.echo and request.max_tokens > 0
and not has_echoed[i]):
assert prompt_text is not None
assert prompt_logprobs is not None
# echo the prompt and first token
delta_text = res.prompt + output.text
delta_token_ids = (res.prompt_token_ids +
output.token_ids)
out_logprobs = res.prompt_logprobs + (output.logprobs
or [])
delta_text = prompt_text + output.text
delta_token_ids = [
*prompt_token_ids, *output.token_ids
]
out_logprobs = [
*prompt_logprobs,
*(output.logprobs or []),
]
has_echoed[i] = True
else:
# return just the delta
......@@ -301,7 +307,7 @@ class OpenAIServingCompletion(OpenAIServing):
and request.stream_options.include_usage):
if (request.stream_options.continuous_usage_stats
or output.finish_reason is not None):
prompt_tokens = len(res.prompt_token_ids)
prompt_tokens = len(prompt_token_ids)
completion_tokens = len(output.token_ids)
usage = UsageInfo(
prompt_tokens=prompt_tokens,
......@@ -342,7 +348,7 @@ class OpenAIServingCompletion(OpenAIServing):
request_id: str,
created_time: int,
model_name: str,
tokenizer: PreTrainedTokenizer,
tokenizer: AnyTokenizer,
) -> CompletionResponse:
choices: List[CompletionResponseChoice] = []
num_prompt_tokens = 0
......@@ -353,16 +359,31 @@ class OpenAIServingCompletion(OpenAIServing):
prompt_logprobs = final_res.prompt_logprobs
prompt_text = final_res.prompt
token_ids: GenericSequence[int]
out_logprobs: Optional[GenericSequence[Optional[Dict[int,
Logprob]]]]
for output in final_res.outputs:
assert request.max_tokens is not None
if request.echo and request.max_tokens == 0:
assert prompt_text is not None
token_ids = prompt_token_ids
out_logprobs = prompt_logprobs
output_text = prompt_text
elif request.echo and request.max_tokens > 0:
token_ids = prompt_token_ids + list(output.token_ids)
out_logprobs = (prompt_logprobs + output.logprobs
if request.logprobs is not None else None)
assert prompt_text is not None
token_ids = [*prompt_token_ids, *output.token_ids]
if request.logprobs is None:
out_logprobs = None
else:
assert prompt_logprobs is not None
assert output.logprobs is not None
out_logprobs = [
*prompt_logprobs,
*output.logprobs,
]
output_text = prompt_text + output.text
else:
token_ids = output.token_ids
......@@ -413,7 +434,7 @@ class OpenAIServingCompletion(OpenAIServing):
token_ids: GenericSequence[int],
top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
num_output_top_logprobs: int,
tokenizer: PreTrainedTokenizer,
tokenizer: AnyTokenizer,
initial_text_offset: int = 0,
) -> CompletionLogProbs:
"""Create logprobs for OpenAI Completion API."""
......@@ -430,17 +451,21 @@ class OpenAIServingCompletion(OpenAIServing):
token = tokenizer.decode(token_id)
if self.return_tokens_as_token_ids:
token = f"token_id:{token_id}"
out_tokens.append(token)
out_token_logprobs.append(None)
out_top_logprobs.append(None)
else:
step_token = step_top_logprobs[token_id]
token = self._get_decoded_token(
step_top_logprobs[token_id],
step_token,
token_id,
tokenizer,
return_as_token_id=self.return_tokens_as_token_ids)
token_logprob = max(step_top_logprobs[token_id].logprob,
-9999.0)
return_as_token_id=self.return_tokens_as_token_ids,
)
token_logprob = max(step_token.logprob, -9999.0)
out_tokens.append(token)
out_token_logprobs.append(token_logprob)
......
vllm/entrypoints/openai/serving_embedding.py
View file @ baaedfdb
import asyncio
import base64
import time
from typing import (AsyncGenerator, AsyncIterator, List, Optional, Tuple,
Union, cast)
from typing import AsyncGenerator, List, Literal, Optional, Union, cast
import numpy as np
from fastapi import Request
from typing_extensions import assert_never
from vllm.config import ModelConfig
from vllm.engine.protocol import AsyncEngineClient
......@@ -16,7 +16,7 @@ from vllm.entrypoints.openai.protocol import (EmbeddingRequest,
ErrorResponse, UsageInfo)
from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.logger import init_logger
from vllm.outputs import EmbeddingRequestOutput
from vllm.outputs import EmbeddingOutput, EmbeddingRequestOutput
from vllm.utils import merge_async_iterators, random_uuid
logger = init_logger(__name__)
......@@ -24,18 +24,28 @@ logger = init_logger(__name__)
TypeTokenIDs = List[int]
def _get_embedding(
output: EmbeddingOutput,
encoding_format: Literal["float", "base64"],
) -> Union[List[float], str]:
if encoding_format == "float":
return output.embedding
elif encoding_format == "base64":
embedding_bytes = np.array(output.embedding).tobytes()
return base64.b64encode(embedding_bytes).decode("utf-8")
assert_never(encoding_format)
def request_output_to_embedding_response(
final_res_batch: List[EmbeddingRequestOutput], request_id: str,
created_time: int, model_name: str,
encoding_format: str) -> EmbeddingResponse:
encoding_format: Literal["float", "base64"]) -> EmbeddingResponse:
data: List[EmbeddingResponseData] = []
num_prompt_tokens = 0
for idx, final_res in enumerate(final_res_batch):
prompt_token_ids = final_res.prompt_token_ids
embedding = final_res.outputs.embedding
if encoding_format == "base64":
embedding_bytes = np.array(embedding).tobytes()
embedding = base64.b64encode(embedding_bytes).decode("utf-8")
embedding = _get_embedding(final_res.outputs, encoding_format)
embedding_data = EmbeddingResponseData(index=idx, embedding=embedding)
data.append(embedding_data)
......@@ -76,8 +86,8 @@ class OpenAIServingEmbedding(OpenAIServing):
async def create_embedding(
self,
request: EmbeddingRequest,
raw_request: Optional[Request] = None
) -> Union[ErrorResponse, EmbeddingResponse]:
raw_request: Optional[Request] = None,
) -> Union[EmbeddingResponse, ErrorResponse]:
"""Completion API similar to OpenAI's API.
See https://platform.openai.com/docs/api-reference/embeddings/create
......@@ -89,8 +99,7 @@ class OpenAIServingEmbedding(OpenAIServing):
if error_check_ret is not None:
return error_check_ret
encoding_format = (request.encoding_format
if request.encoding_format else "float")
encoding_format = request.encoding_format
if request.dimensions is not None:
return self.create_error_response(
"dimensions is currently not supported")
......@@ -145,11 +154,10 @@ class OpenAIServingEmbedding(OpenAIServing):
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
result_generator: AsyncIterator[Tuple[
int, EmbeddingRequestOutput]] = merge_async_iterators(
result_generator = merge_async_iterators(
*generators,
is_cancelled=raw_request.is_disconnected
if raw_request else None)
is_cancelled=raw_request.is_disconnected if raw_request else None,
)
# Non-streaming response
final_res_batch: List[Optional[EmbeddingRequestOutput]]
......@@ -175,7 +183,7 @@ class OpenAIServingEmbedding(OpenAIServing):
return response
def _check_embedding_mode(self, embedding_mode: bool):
def _check_embedding_mode(self, embedding_mode: bool) -> bool:
if not embedding_mode:
logger.warning(
"embedding_mode is False. Embedding API will not work.")
......
vllm/entrypoints/openai/serving_engine.py
View file @ baaedfdb
......@@ -31,7 +31,7 @@ from vllm.pooling_params import PoolingParams
from vllm.prompt_adapter.request import PromptAdapterRequest
from vllm.sampling_params import LogitsProcessor, SamplingParams
from vllm.sequence import Logprob
from vllm.transformers_utils.tokenizer_group import AnyTokenizer
from vllm.transformers_utils.tokenizer import AnyTokenizer
logger = init_logger(__name__)
......
vllm/sampling_params.py
View file @ baaedfdb
......@@ -153,6 +153,68 @@ class SamplingParams(
output_text_buffer_length: int = 0
_all_stop_token_ids: Set[int] = msgspec.field(default_factory=set)
@staticmethod
def from_optional(
n: Optional[int] = 1,
best_of: Optional[int] = None,
presence_penalty: Optional[float] = 0.0,
frequency_penalty: Optional[float] = 0.0,
repetition_penalty: Optional[float] = 1.0,
temperature: Optional[float] = 1.0,
top_p: Optional[float] = 1.0,
top_k: int = -1,
min_p: float = 0.0,
seed: Optional[int] = None,
use_beam_search: bool = False,
length_penalty: float = 1.0,
early_stopping: Union[bool, str] = False,
stop: Optional[Union[str, List[str]]] = None,
stop_token_ids: Optional[List[int]] = None,
include_stop_str_in_output: bool = False,
ignore_eos: bool = False,
max_tokens: Optional[int] = 16,
min_tokens: int = 0,
logprobs: Optional[int] = None,
prompt_logprobs: Optional[int] = None,
detokenize: bool = True,
skip_special_tokens: bool = True,
spaces_between_special_tokens: bool = True,
logits_processors: Optional[List[LogitsProcessor]] = None,
truncate_prompt_tokens: Optional[Annotated[int,
msgspec.Meta(ge=1)]] = None,
) -> "SamplingParams":
return SamplingParams(
n=1 if n is None else n,
best_of=best_of,
presence_penalty=0.0
if presence_penalty is None else presence_penalty,
frequency_penalty=0.0
if frequency_penalty is None else frequency_penalty,
repetition_penalty=1.0
if repetition_penalty is None else repetition_penalty,
temperature=1.0 if temperature is None else temperature,
top_p=1.0 if top_p is None else top_p,
top_k=top_k,
min_p=min_p,
seed=seed,
use_beam_search=use_beam_search,
length_penalty=length_penalty,
early_stopping=early_stopping,
stop=stop,
stop_token_ids=stop_token_ids,
include_stop_str_in_output=include_stop_str_in_output,
ignore_eos=ignore_eos,
max_tokens=max_tokens,
min_tokens=min_tokens,
logprobs=logprobs,
prompt_logprobs=prompt_logprobs,
detokenize=detokenize,
skip_special_tokens=skip_special_tokens,
spaces_between_special_tokens=spaces_between_special_tokens,
logits_processors=logits_processors,
truncate_prompt_tokens=truncate_prompt_tokens,
)
def __post_init__(self) -> None:
self.best_of = self.best_of or self.n
if 0 < self.temperature < _MAX_TEMP:
......
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