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import time
from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List,
Optional)
from typing import Sequence as GenericSequence
from typing import Tuple, cast
from fastapi import Request
from transformers import PreTrainedTokenizer
from vllm.config import ModelConfig
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.entrypoints.logger import RequestLogger
# yapf conflicts with isort for this block
# yapf: disable
from vllm.entrypoints.openai.protocol import (CompletionLogProbs,
CompletionRequest,
CompletionResponse,
CompletionResponseChoice,
CompletionResponseStreamChoice,
CompletionStreamResponse,
UsageInfo)
# yapf: enable
from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
OpenAIServing,
PromptAdapterPath)
from vllm.logger import init_logger
from vllm.model_executor.guided_decoding import (
get_guided_decoding_logits_processor)
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.utils import merge_async_iterators, random_uuid
logger = init_logger(__name__)
TypeTokenIDs = List[int]
TypeTopLogProbs = List[Optional[Dict[int, float]]]
TypeCreateLogProbsFn = Callable[
[TypeTokenIDs, TypeTopLogProbs, Optional[int], int], CompletionLogProbs]
class OpenAIServingCompletion(OpenAIServing):
def __init__(
self,
engine: AsyncLLMEngine,
model_config: ModelConfig,
served_model_names: List[str],
*,
lora_modules: Optional[List[LoRAModulePath]],
prompt_adapters: Optional[List[PromptAdapterPath]],
request_logger: Optional[RequestLogger],
):
super().__init__(engine=engine,
model_config=model_config,
served_model_names=served_model_names,
lora_modules=lora_modules,
prompt_adapters=prompt_adapters,
request_logger=request_logger)
async def create_completion(self, request: CompletionRequest,
raw_request: Request):
"""Completion API similar to OpenAI's API.
See https://platform.openai.com/docs/api-reference/completions/create
for the API specification. This API mimics the OpenAI Completion API.
NOTE: Currently we do not support the following feature:
- suffix (the language models we currently support do not support
suffix)
"""
error_check_ret = await self._check_model(request)
if error_check_ret is not None:
return error_check_ret
# Return error for unsupported features.
if request.suffix is not None:
return self.create_error_response(
"suffix is not currently supported")
model_name = self.served_model_names[0]
request_id = f"cmpl-{random_uuid()}"
created_time = int(time.time())
# Schedule the request and get the result generator.
generators: List[AsyncIterator[RequestOutput]] = []
try:
(
lora_request,
prompt_adapter_request,
) = self._maybe_get_adapters(request)
tokenizer = await self.engine.get_tokenizer(lora_request)
sampling_params = request.to_sampling_params()
decoding_config = await self.engine.get_decoding_config()
guided_decoding_backend = request.guided_decoding_backend \
or decoding_config.guided_decoding_backend
guided_decode_logit_processor = (
await
get_guided_decoding_logits_processor(guided_decoding_backend,
request, tokenizer))
if guided_decode_logit_processor is not None:
if sampling_params.logits_processors is None:
sampling_params.logits_processors = []
sampling_params.logits_processors.append(
guided_decode_logit_processor)
prompts = list(
self._tokenize_prompt_input_or_inputs(
request,
tokenizer,
request.prompt,
truncate_prompt_tokens=sampling_params.
truncate_prompt_tokens,
add_special_tokens=request.add_special_tokens,
))
for i, prompt_inputs in enumerate(prompts):
request_id_item = f"{request_id}-{i}"
self._log_inputs(request_id_item,
prompt_inputs,
params=sampling_params,
lora_request=lora_request,
prompt_adapter_request=prompt_adapter_request)
is_tracing_enabled = await self.engine.is_tracing_enabled()
trace_headers = None
if is_tracing_enabled:
trace_headers = extract_trace_headers(raw_request.headers)
if not is_tracing_enabled and contains_trace_headers(
raw_request.headers):
log_tracing_disabled_warning()
generator = self.engine.generate(
{"prompt_token_ids": prompt_inputs["prompt_token_ids"]},
sampling_params,
request_id_item,
lora_request=lora_request,
prompt_adapter_request=prompt_adapter_request,
trace_headers=trace_headers,
)
generators.append(generator)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
result_generator: AsyncIterator[Tuple[
int, RequestOutput]] = merge_async_iterators(*generators)
# Similar to the OpenAI API, when n != best_of, we do not stream the
# results. In addition, we do not stream the results when use
# beam search.
stream = (request.stream
and (request.best_of is None or request.n == request.best_of)
and not request.use_beam_search)
# Streaming response
if stream:
return self.completion_stream_generator(request,
raw_request,
result_generator,
request_id,
created_time,
model_name,
num_prompts=len(prompts),
tokenizer=tokenizer)
# Non-streaming response
final_res_batch: List[Optional[RequestOutput]] = [None] * len(prompts)
try:
async for i, res in result_generator:
if await raw_request.is_disconnected():
# Abort the request if the client disconnects.
await self.engine.abort(f"{request_id}-{i}")
return self.create_error_response("Client disconnected")
final_res_batch[i] = res
for i, final_res in enumerate(final_res_batch):
assert final_res is not None
# The output should contain the input text
# We did not pass it into vLLM engine to avoid being redundant
# with the inputs token IDs
if final_res.prompt is None:
final_res.prompt = prompts[i]["prompt"]
final_res_batch_checked = cast(List[RequestOutput],
final_res_batch)
response = self.request_output_to_completion_response(
final_res_batch_checked,
request,
request_id,
created_time,
model_name,
tokenizer,
)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
# When user requests streaming but we don't stream, we still need to
# return a streaming response with a single event.
if request.stream:
response_json = response.model_dump_json()
async def fake_stream_generator() -> AsyncGenerator[str, None]:
yield f"data: {response_json}\n\n"
yield "data: [DONE]\n\n"
return fake_stream_generator()
return response
async def completion_stream_generator(
self,
request: CompletionRequest,
raw_request: Request,
result_generator: AsyncIterator[Tuple[int, RequestOutput]],
request_id: str,
created_time: int,
model_name: str,
num_prompts: int,
tokenizer: PreTrainedTokenizer,
) -> AsyncGenerator[str, None]:
num_choices = 1 if request.n is None else request.n
previous_texts = [""] * num_choices * num_prompts
previous_num_tokens = [0] * num_choices * num_prompts
has_echoed = [False] * num_choices * num_prompts
try:
async for prompt_idx, res in result_generator:
# Abort the request if the client disconnects.
if await raw_request.is_disconnected():
await self.engine.abort(f"{request_id}-{prompt_idx}")
raise StopAsyncIteration()
for output in res.outputs:
i = output.index + prompt_idx * num_choices
# TODO(simon): optimize the performance by avoiding full
# text O(n^2) sending.
assert request.max_tokens is not None
if request.echo and request.max_tokens == 0:
# only return the prompt
delta_text = res.prompt
delta_token_ids = res.prompt_token_ids
out_logprobs = res.prompt_logprobs
has_echoed[i] = True
elif (request.echo and request.max_tokens > 0
and not has_echoed[i]):
# 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 [])
has_echoed[i] = True
else:
# return just the delta
delta_text = output.text[len(previous_texts[i]):]
delta_token_ids = output.token_ids[
previous_num_tokens[i]:]
out_logprobs = output.logprobs[previous_num_tokens[
i]:] if output.logprobs else None
if request.logprobs is not None:
assert out_logprobs is not None, (
"Did not output logprobs")
logprobs = self._create_completion_logprobs(
token_ids=delta_token_ids,
top_logprobs=out_logprobs,
num_output_top_logprobs=request.logprobs,
tokenizer=tokenizer,
initial_text_offset=len(previous_texts[i]),
)
else:
logprobs = None
previous_texts[i] = output.text
previous_num_tokens[i] = len(output.token_ids)
finish_reason = output.finish_reason
stop_reason = output.stop_reason
chunk = CompletionStreamResponse(
id=request_id,
created=created_time,
model=model_name,
choices=[
CompletionResponseStreamChoice(
index=i,
text=delta_text,
logprobs=logprobs,
finish_reason=finish_reason,
stop_reason=stop_reason,
)
])
if (request.stream_options
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)
completion_tokens = len(output.token_ids)
usage = UsageInfo(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
if request.stream_options.continuous_usage_stats:
chunk.usage = usage
else:
chunk.usage = None
response_json = chunk.model_dump_json(exclude_unset=False)
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=usage,
)
final_usage_data = (final_usage_chunk.model_dump_json(
exclude_unset=False, 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))
yield f"data: {data}\n\n"
yield "data: [DONE]\n\n"
def request_output_to_completion_response(
self,
final_res_batch: List[RequestOutput],
request: CompletionRequest,
request_id: str,
created_time: int,
model_name: str,
tokenizer: PreTrainedTokenizer,
) -> CompletionResponse:
choices: List[CompletionResponseChoice] = []
num_prompt_tokens = 0
num_generated_tokens = 0
for final_res in final_res_batch:
prompt_token_ids = final_res.prompt_token_ids
prompt_logprobs = final_res.prompt_logprobs
prompt_text = final_res.prompt
for output in final_res.outputs:
assert request.max_tokens is not None
if request.echo and request.max_tokens == 0:
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)
output_text = prompt_text + output.text
else:
token_ids = output.token_ids
out_logprobs = output.logprobs
output_text = output.text
if request.logprobs is not None:
assert out_logprobs is not None, "Did not output logprobs"
logprobs = self._create_completion_logprobs(
token_ids=token_ids,
top_logprobs=out_logprobs,
tokenizer=tokenizer,
num_output_top_logprobs=request.logprobs,
)
else:
logprobs = None
choice_data = CompletionResponseChoice(
index=len(choices),
text=output_text,
logprobs=logprobs,
finish_reason=output.finish_reason,
stop_reason=output.stop_reason,
)
choices.append(choice_data)
num_prompt_tokens += len(prompt_token_ids)
num_generated_tokens += sum(
len(output.token_ids) for output in final_res.outputs)
usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=num_generated_tokens,
total_tokens=num_prompt_tokens + num_generated_tokens,
)
return CompletionResponse(
id=request_id,
created=created_time,
model=model_name,
choices=choices,
usage=usage,
)
def _create_completion_logprobs(
self,
token_ids: GenericSequence[int],
top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
num_output_top_logprobs: int,
tokenizer: PreTrainedTokenizer,
initial_text_offset: int = 0,
) -> CompletionLogProbs:
"""Create logprobs for OpenAI Completion API."""
out_text_offset: List[int] = []
out_token_logprobs: List[Optional[float]] = []
out_tokens: List[str] = []
out_top_logprobs: List[Optional[Dict[str, float]]] = []
last_token_len = 0
for i, token_id in enumerate(token_ids):
step_top_logprobs = top_logprobs[i]
if step_top_logprobs is None:
token = tokenizer.decode(token_id)
out_tokens.append(token)
out_token_logprobs.append(None)
out_top_logprobs.append(None)
else:
token = self._get_decoded_token(step_top_logprobs[token_id],
token_id, tokenizer)
token_logprob = max(step_top_logprobs[token_id].logprob,
-9999.0)
out_tokens.append(token)
out_token_logprobs.append(token_logprob)
# makes sure to add the top num_output_top_logprobs + 1
# logprobs, as defined in the openai API
# (cf. https://github.com/openai/openai-openapi/blob/
# 893ba52242dbd5387a97b96444ee1c742cfce9bd/openapi.yaml#L7153)
out_top_logprobs.append({
# Convert float("-inf") to the
# JSON-serializable float that OpenAI uses
self._get_decoded_token(top_lp[1], top_lp[0], tokenizer):
max(top_lp[1].logprob, -9999.0)
for i, top_lp in enumerate(step_top_logprobs.items())
if num_output_top_logprobs >= i
})
if len(out_text_offset) == 0:
out_text_offset.append(initial_text_offset)
else:
out_text_offset.append(out_text_offset[-1] + last_token_len)
last_token_len = len(token)
return CompletionLogProbs(
text_offset=out_text_offset,
token_logprobs=out_token_logprobs,
tokens=out_tokens,
top_logprobs=out_top_logprobs,
) |