Spaces:
Paused
Paused
File size: 22,883 Bytes
8f99309 5491dc5 8f99309 5491dc5 8f99309 690332d 5491dc5 8f99309 690332d 5491dc5 8f99309 5491dc5 690332d 8f99309 5491dc5 8f99309 690332d 8f99309 690332d 5491dc5 8f99309 5491dc5 8f99309 690332d 8f99309 5491dc5 8f99309 690332d 8f99309 690332d 8f99309 690332d 8f99309 690332d 5491dc5 8f99309 5491dc5 8f99309 5491dc5 8f99309 690332d 8f99309 5491dc5 8f99309 690332d 8f99309 690332d 5491dc5 690332d 8f99309 5491dc5 8f99309 5491dc5 690332d 5491dc5 690332d 5491dc5 690332d 8f99309 5491dc5 8f99309 690332d 8f99309 690332d 5491dc5 8f99309 690332d 8f99309 690332d 5491dc5 8f99309 5491dc5 8f99309 5491dc5 8f99309 5491dc5 8f99309 5491dc5 8f99309 690332d 5491dc5 8f99309 690332d 8f99309 690332d 8f99309 690332d 8f99309 690332d 8f99309 5491dc5 8f99309 690332d 8f99309 690332d 8f99309 690332d 8f99309 690332d 8f99309 5491dc5 8f99309 5491dc5 8f99309 5491dc5 8f99309 690332d 8f99309 5491dc5 8f99309 5491dc5 8f99309 5491dc5 8f99309 5491dc5 8f99309 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 |
import time
from typing import (AsyncGenerator, AsyncIterator, Awaitable, Dict, 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.async_llm_engine import AsyncLLMEngine
from vllm.entrypoints.chat_utils import (ConversationMessage,
load_chat_template,
parse_chat_message_content)
from vllm.entrypoints.logger import RequestLogger
from vllm.entrypoints.openai.protocol import (
ChatCompletionLogProb, ChatCompletionLogProbs,
ChatCompletionLogProbsContent, ChatCompletionNamedToolChoiceParam,
ChatCompletionRequest, ChatCompletionResponse,
ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse,
FunctionCall, ToolCall, UsageInfo)
from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
OpenAIServing,
PromptAdapterPath)
from vllm.inputs import PromptInputs
from vllm.logger import init_logger
from vllm.model_executor.guided_decoding import (
get_guided_decoding_logits_processor)
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.utils import random_uuid
logger = init_logger(__name__)
class OpenAIServingChat(OpenAIServing):
def __init__(
self,
engine: AsyncLLMEngine,
model_config: ModelConfig,
served_model_names: List[str],
response_role: str,
*,
lora_modules: Optional[List[LoRAModulePath]],
prompt_adapters: Optional[List[PromptAdapterPath]],
request_logger: Optional[RequestLogger],
chat_template: Optional[str],
):
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)
self.response_role = response_role
# If this is None we use the tokenizer's default chat template
self.chat_template = load_chat_template(chat_template)
async def create_chat_completion(
self,
request: ChatCompletionRequest,
raw_request: Optional[Request] = None
) -> Union[ErrorResponse, AsyncGenerator[str, None],
ChatCompletionResponse]:
"""Completion API similar to OpenAI's API.
See https://platform.openai.com/docs/api-reference/chat/create
for the API specification. This API mimics the OpenAI
ChatCompletion API.
NOTE: Currently we do not support the following feature:
- function_call (Users should implement this by themselves)
"""
error_check_ret = await self._check_model(request)
if error_check_ret is not None:
return error_check_ret
try:
(
lora_request,
prompt_adapter_request,
) = self._maybe_get_adapters(request)
model_config = self.model_config
tokenizer = await self.engine.get_tokenizer(lora_request)
conversation: List[ConversationMessage] = []
mm_futures: List[Awaitable[MultiModalDataDict]] = []
for msg in request.messages:
chat_parsed_result = parse_chat_message_content(
msg, model_config, tokenizer)
conversation.extend(chat_parsed_result.messages)
mm_futures.extend(chat_parsed_result.mm_futures)
tool_dicts = None if request.tools is None else [
tool.model_dump() for tool in request.tools
]
prompt = tokenizer.apply_chat_template(
conversation=conversation,
tokenize=False,
add_generation_prompt=request.add_generation_prompt,
tools=tool_dicts,
documents=request.documents,
chat_template=request.chat_template or self.chat_template,
**(request.chat_template_kwargs or {}),
)
except Exception as e:
logger.error("Error in applying chat template from request: %s", e)
return self.create_error_response(str(e))
mm_data: Optional[MultiModalDataDict] = None
try:
if len(mm_futures):
# since we support only single mm data currently
assert len(
mm_futures
) == 1, "Multiple 'image_url' input is currently not supported."
mm_data = await mm_futures[0]
except Exception as e:
logger.error("Error in loading multi-modal data: %s", e)
return self.create_error_response(str(e))
request_id = f"chat-{random_uuid()}"
try:
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_logits_processor = (
await
get_guided_decoding_logits_processor(guided_decoding_backend,
request, tokenizer))
if guided_decode_logits_processor:
if sampling_params.logits_processors is None:
sampling_params.logits_processors = []
sampling_params.logits_processors.append(
guided_decode_logits_processor)
prompt_inputs = self._tokenize_prompt_input(
request,
tokenizer,
prompt,
truncate_prompt_tokens=sampling_params.truncate_prompt_tokens,
add_special_tokens=request.add_special_tokens,
)
self._log_inputs(request_id,
prompt_inputs,
params=sampling_params,
lora_request=lora_request,
prompt_adapter_request=prompt_adapter_request)
engine_inputs: PromptInputs = {
"prompt_token_ids": prompt_inputs["prompt_token_ids"],
}
if mm_data is not None:
engine_inputs["multi_modal_data"] = mm_data
is_tracing_enabled = await self.engine.is_tracing_enabled()
trace_headers = None
if is_tracing_enabled and raw_request:
trace_headers = extract_trace_headers(raw_request.headers)
if (not is_tracing_enabled and raw_request
and contains_trace_headers(raw_request.headers)):
log_tracing_disabled_warning()
result_generator = self.engine.generate(
engine_inputs,
sampling_params,
request_id,
lora_request=lora_request,
trace_headers=trace_headers,
prompt_adapter_request=prompt_adapter_request,
)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
# Streaming response
if request.stream:
return self.chat_completion_stream_generator(
request, result_generator, request_id, conversation, tokenizer)
else:
try:
return await self.chat_completion_full_generator(
request, raw_request, result_generator, request_id,
conversation, tokenizer)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
if request.add_generation_prompt:
return self.response_role
else:
return request.messages[-1]["role"]
async def chat_completion_stream_generator(
self,
request: ChatCompletionRequest,
result_generator: AsyncIterator[RequestOutput],
request_id: str,
conversation: List[ConversationMessage],
tokenizer: PreTrainedTokenizer,
) -> AsyncGenerator[str, None]:
model_name = self.served_model_names[0]
created_time = int(time.time())
chunk_object_type = "chat.completion.chunk"
first_iteration = True
# Send response for each token for each request.n (index)
num_choices = 1 if request.n is None else request.n
previous_texts = [""] * num_choices
previous_num_tokens = [0] * num_choices
finish_reason_sent = [False] * num_choices
try:
async for res in result_generator:
# We need to do it here, because if there are exceptions in
# the result_generator, it needs to be sent as the FIRST
# response (by the try...catch).
if first_iteration:
# Send first response for each request.n (index) with
# the role
role = self.get_chat_request_role(request)
for i in range(num_choices):
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=DeltaMessage(role=role),
logprobs=None,
finish_reason=None)
chunk = ChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name)
if (request.stream_options
and request.stream_options.include_usage):
chunk.usage = None
data = chunk.model_dump_json(exclude_unset=True)
yield f"data: {data}\n\n"
# Send response to echo the input portion of the
# last message
if request.echo:
last_msg_content = ""
if conversation and conversation[-1].get(
"content") and conversation[-1].get(
"role") == role:
last_msg_content = conversation[-1]["content"]
if last_msg_content:
for i in range(num_choices):
choice_data = (
ChatCompletionResponseStreamChoice(
index=i,
delta=DeltaMessage(
content=last_msg_content),
logprobs=None,
finish_reason=None))
chunk = ChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name)
if (request.stream_options and
request.stream_options.include_usage):
chunk.usage = None
data = chunk.model_dump_json(
exclude_unset=True)
yield f"data: {data}\n\n"
first_iteration = False
for output in res.outputs:
i = output.index
if finish_reason_sent[i]:
continue
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 and request.top_logprobs is not None:
assert out_logprobs is not None, (
"Did not output logprobs")
logprobs = self._create_chat_logprobs(
token_ids=delta_token_ids,
top_logprobs=out_logprobs,
tokenizer=tokenizer,
num_output_top_logprobs=request.top_logprobs,
)
else:
logprobs = None
delta_text = output.text[len(previous_texts[i]):]
previous_texts[i] = output.text
previous_num_tokens[i] = len(output.token_ids)
if request.tool_choice and type(
request.tool_choice
) is ChatCompletionNamedToolChoiceParam:
delta_message = DeltaMessage(tool_calls=[
ToolCall(function=FunctionCall(
name=request.tool_choice.function.name,
arguments=delta_text))
])
else:
delta_message = DeltaMessage(content=delta_text)
if output.finish_reason is None:
# Send token-by-token response for each request.n
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=delta_message,
logprobs=logprobs,
finish_reason=None)
chunk = ChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name)
if (request.stream_options
and request.stream_options.include_usage):
chunk.usage = None
data = chunk.model_dump_json(exclude_unset=True)
yield f"data: {data}\n\n"
else:
# Send the finish response for each request.n only once
prompt_tokens = len(res.prompt_token_ids)
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=delta_message,
logprobs=logprobs,
finish_reason=output.finish_reason,
stop_reason=output.stop_reason)
chunk = ChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name)
if (request.stream_options
and request.stream_options.include_usage):
chunk.usage = None
data = chunk.model_dump_json(exclude_unset=True)
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],
)
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
data = self.create_streaming_error_response(str(e))
yield f"data: {data}\n\n"
# Send the final done message after all response.n are finished
yield "data: [DONE]\n\n"
async def chat_completion_full_generator(
self,
request: ChatCompletionRequest,
raw_request: Optional[Request],
result_generator: AsyncIterator[RequestOutput],
request_id: str,
conversation: List[ConversationMessage],
tokenizer: PreTrainedTokenizer,
) -> Union[ErrorResponse, ChatCompletionResponse]:
model_name = self.served_model_names[0]
created_time = int(time.time())
final_res: Optional[RequestOutput] = None
async for res in result_generator:
if raw_request is not None and await raw_request.is_disconnected():
# Abort the request if the client disconnects.
await self.engine.abort(request_id)
return self.create_error_response("Client disconnected")
final_res = res
assert final_res is not None
choices: List[ChatCompletionResponseChoice] = []
role = self.get_chat_request_role(request)
for output in final_res.outputs:
token_ids = output.token_ids
out_logprobs = output.logprobs
if request.logprobs and request.top_logprobs is not None:
assert out_logprobs is not None, "Did not output logprobs"
logprobs = self._create_chat_logprobs(
token_ids=token_ids,
top_logprobs=out_logprobs,
num_output_top_logprobs=request.top_logprobs,
tokenizer=tokenizer,
)
else:
logprobs = None
if request.tool_choice and type(
request.tool_choice) is ChatCompletionNamedToolChoiceParam:
message = ChatMessage(
role=role,
content="",
tool_calls=[
ToolCall(function=FunctionCall(
name=request.tool_choice.function.name,
arguments=output.text))
])
elif not request.tool_choice or request.tool_choice == "none":
message = ChatMessage(role=role, content=output.text)
choice_data = ChatCompletionResponseChoice(
index=output.index,
message=message,
logprobs=logprobs,
finish_reason=output.finish_reason,
stop_reason=output.stop_reason)
choices.append(choice_data)
if request.echo:
last_msg_content = ""
if conversation and conversation[-1].get(
"content") and conversation[-1].get("role") == role:
last_msg_content = conversation[-1]["content"]
for choice in choices:
full_message = last_msg_content + choice.message.content
choice.message.content = full_message
num_prompt_tokens = len(final_res.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,
)
response = ChatCompletionResponse(
id=request_id,
created=created_time,
model=model_name,
choices=choices,
usage=usage,
)
return response
def _get_top_logprobs(
self, logprobs: Dict[int, Logprob], top_logprobs: Optional[int],
tokenizer: PreTrainedTokenizer) -> List[ChatCompletionLogProb]:
return [
ChatCompletionLogProb(
token=(token := self._get_decoded_token(p[1], p[0],
tokenizer)),
logprob=max(p[1].logprob, -9999.0),
bytes=list(token.encode("utf-8", errors="replace")))
for i, p in enumerate(logprobs.items())
if top_logprobs and i < top_logprobs
]
def _create_chat_logprobs(
self,
token_ids: GenericSequence[int],
top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
tokenizer: PreTrainedTokenizer,
num_output_top_logprobs: Optional[int] = None,
) -> ChatCompletionLogProbs:
"""Create OpenAI-style logprobs."""
logprobs_content = []
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)
logprobs_content.append(
ChatCompletionLogProbsContent(
token=token,
bytes=list(token.encode("utf-8", errors="replace"))))
else:
logprobs_content.append(
ChatCompletionLogProbsContent(
token=step_top_logprobs[token_id].decoded_token,
logprob=max(step_top_logprobs[token_id].logprob,
-9999.0),
bytes=list(
step_top_logprobs[token_id].decoded_token.encode(
"utf-8", errors="replace")),
top_logprobs=self._get_top_logprobs(
step_top_logprobs, num_output_top_logprobs,
tokenizer)))
return ChatCompletionLogProbs(content=logprobs_content) |