mirror of
https://github.com/RYDE-WORK/MiniCPM.git
synced 2026-01-19 12:53:36 +08:00
692 lines
28 KiB
Python
692 lines
28 KiB
Python
import time
|
|
from typing import AsyncGenerator, AsyncIterator, 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.protocol import AsyncEngineClient
|
|
from vllm.entrypoints.chat_utils import (
|
|
ConversationMessage,
|
|
load_chat_template,
|
|
parse_chat_messages,
|
|
)
|
|
from vllm.entrypoints.logger import RequestLogger
|
|
from 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.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
|
|
from utils import decode_function_call
|
|
import json
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
class OpenAIServingChat(OpenAIServing):
|
|
def __init__(
|
|
self,
|
|
async_engine_client: AsyncEngineClient,
|
|
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],
|
|
return_tokens_as_token_ids: bool = False,
|
|
):
|
|
super().__init__(
|
|
async_engine_client=async_engine_client,
|
|
model_config=model_config,
|
|
served_model_names=served_model_names,
|
|
lora_modules=lora_modules,
|
|
prompt_adapters=prompt_adapters,
|
|
request_logger=request_logger,
|
|
return_tokens_as_token_ids=return_tokens_as_token_ids,
|
|
)
|
|
|
|
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.async_engine_client.get_tokenizer(lora_request)
|
|
|
|
conversation, mm_futures = parse_chat_messages(
|
|
request.messages, model_config, tokenizer
|
|
)
|
|
print('conversation:', conversation)
|
|
# parse_chat_messages ignores tool_calls and tool_call_id
|
|
# we have to fix this
|
|
conversation = request.messages
|
|
for msg in conversation:
|
|
if 'tool_calls' in msg and msg['tool_calls'] is not None:
|
|
msg['tool_calls'] = [tc for tc in msg['tool_calls']]
|
|
print('fixed conversation:', conversation)
|
|
|
|
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 {}),
|
|
)
|
|
assert isinstance(prompt, str)
|
|
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:
|
|
guided_decode_logits_processor = await self._guided_decode_logits_processor(
|
|
request, tokenizer
|
|
)
|
|
|
|
prompt_inputs = self._tokenize_prompt_input(
|
|
request,
|
|
tokenizer,
|
|
prompt,
|
|
truncate_prompt_tokens=request.truncate_prompt_tokens,
|
|
add_special_tokens=request.add_special_tokens,
|
|
)
|
|
|
|
sampling_params = request.to_sampling_params(
|
|
tokenizer,
|
|
guided_decode_logits_processor,
|
|
default_max_tokens=self.max_model_len
|
|
- len(prompt_inputs["prompt_token_ids"]),
|
|
)
|
|
|
|
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.async_engine_client.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.async_engine_client.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
|
|
):
|
|
if request.stream_options.continuous_usage_stats:
|
|
prompt_tokens = len(res.prompt_token_ids)
|
|
usage = UsageInfo(
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=0,
|
|
total_tokens=prompt_tokens,
|
|
)
|
|
chunk.usage = usage
|
|
else:
|
|
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
|
|
):
|
|
if request.stream_options.continuous_usage_stats:
|
|
prompt_tokens = len(res.prompt_token_ids)
|
|
usage = UsageInfo(
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=0,
|
|
total_tokens=prompt_tokens,
|
|
)
|
|
chunk.usage = usage
|
|
else:
|
|
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
|
|
|
|
# if have tool
|
|
if request.tools is not None and len(request.tools) > 0:
|
|
if output.finish_reason is not None:
|
|
msg = decode_function_call(output.text)
|
|
if "tool_calls" in msg and msg["tool_calls"] is not None and len(msg["tool_calls"]) > 0:
|
|
delta_message = DeltaMessage(
|
|
content=msg.get("thought", ""),
|
|
tool_calls=[
|
|
ToolCall(
|
|
id=f"chatcmpl-tool-{random_uuid()}",
|
|
function=FunctionCall(
|
|
name=fc["function"]["name"],
|
|
arguments=json.dumps(fc["function"]["arguments"], ensure_ascii=False),
|
|
)
|
|
)
|
|
for fc in msg["tool_calls"]
|
|
]
|
|
)
|
|
else:
|
|
delta_message = DeltaMessage(content=msg.get("content", ""))
|
|
else:
|
|
# only return the last one
|
|
continue
|
|
else:
|
|
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
|
|
):
|
|
if request.stream_options.continuous_usage_stats:
|
|
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,
|
|
)
|
|
chunk.usage = usage
|
|
else:
|
|
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
|
|
):
|
|
if request.stream_options.continuous_usage_stats:
|
|
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,
|
|
)
|
|
chunk.usage = usage
|
|
else:
|
|
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.async_engine_client.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)
|
|
msg = decode_function_call(output.text)
|
|
if "tool_calls" in msg and msg["tool_calls"] is not None and len(msg["tool_calls"]) > 0:
|
|
message = ChatMessage(
|
|
role=role,
|
|
content=msg.get("thought", ""),
|
|
tool_calls=[
|
|
ToolCall(
|
|
function=FunctionCall(
|
|
name=fc["function"]["name"],
|
|
arguments=json.dumps(fc["function"]["arguments"], ensure_ascii=False),
|
|
)
|
|
)
|
|
for fc in msg["tool_calls"]
|
|
],
|
|
)
|
|
else:
|
|
message = ChatMessage(role=role, content=msg.get("content", ""))
|
|
|
|
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,
|
|
return_as_token_id=self.return_tokens_as_token_ids,
|
|
)
|
|
),
|
|
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)
|
|
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"))
|
|
)
|
|
)
|
|
else:
|
|
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"
|
|
)
|
|
),
|
|
top_logprobs=self._get_top_logprobs(
|
|
step_top_logprobs, num_output_top_logprobs, tokenizer
|
|
),
|
|
)
|
|
)
|
|
|
|
return ChatCompletionLogProbs(content=logprobs_content)
|