""" Copyright 2023-2024 SGLang Team Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ """Conversion between OpenAI APIs and native SRT APIs""" import asyncio import json import logging import os import time import uuid from http import HTTPStatus from typing import Dict, List, Optional from fastapi import HTTPException, Request, UploadFile from fastapi.responses import JSONResponse, StreamingResponse from pydantic import ValidationError from sglang.srt.conversation import ( Conversation, SeparatorStyle, chat_template_exists, generate_chat_conv, register_conv_template, ) from sglang.srt.managers.io_struct import EmbeddingReqInput, GenerateReqInput from sglang.srt.openai_api.protocol import ( BatchRequest, BatchResponse, ChatCompletionRequest, ChatCompletionResponse, ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice, ChatCompletionStreamResponse, ChatCompletionTokenLogprob, ChatMessage, ChoiceLogprobs, CompletionRequest, CompletionResponse, CompletionResponseChoice, CompletionResponseStreamChoice, CompletionStreamResponse, DeltaMessage, EmbeddingObject, EmbeddingRequest, EmbeddingResponse, ErrorResponse, FileDeleteResponse, FileRequest, FileResponse, LogProbs, TopLogprob, UsageInfo, ) logger = logging.getLogger(__name__) chat_template_name = None class FileMetadata: def __init__(self, filename: str, purpose: str): self.filename = filename self.purpose = purpose # In-memory storage for batch jobs and files batch_storage: Dict[str, BatchResponse] = {} file_id_request: Dict[str, FileMetadata] = {} file_id_response: Dict[str, FileResponse] = {} # map file id to file path in SGLang backend file_id_storage: Dict[str, str] = {} # backend storage directory storage_dir = None def format_finish_reason(finish_reason) -> Optional[str]: if finish_reason.startswith("None"): return None elif finish_reason.startswith("FINISH_MATCHED"): return "stop" elif finish_reason.startswith("FINISH_LENGTH"): return "length" elif finish_reason.startswith("FINISH_ABORT"): return "abort" else: return "unknown" def create_error_response( message: str, err_type: str = "BadRequestError", status_code: HTTPStatus = HTTPStatus.BAD_REQUEST, ): error = ErrorResponse(message=message, type=err_type, code=status_code.value) return JSONResponse(content=error.model_dump(), status_code=error.code) def create_streaming_error_response( message: str, err_type: str = "BadRequestError", status_code: HTTPStatus = HTTPStatus.BAD_REQUEST, ) -> str: error = ErrorResponse(message=message, type=err_type, code=status_code.value) json_str = json.dumps({"error": error.model_dump()}) return json_str def load_chat_template_for_openai_api(tokenizer_manager, chat_template_arg): global chat_template_name logger.info(f"Use chat template: {chat_template_arg}") if not chat_template_exists(chat_template_arg): if not os.path.exists(chat_template_arg): raise RuntimeError( f"Chat template {chat_template_arg} is not a built-in template name " "or a valid chat template file path." ) if chat_template_arg.endswith(".jinja"): with open(chat_template_arg, "r") as f: chat_template = "".join(f.readlines()).strip("\n") tokenizer_manager.tokenizer.chat_template = chat_template.replace( "\\n", "\n" ) chat_template_name = None else: assert chat_template_arg.endswith( ".json" ), "unrecognized format of chat template file" with open(chat_template_arg, "r") as filep: template = json.load(filep) try: sep_style = SeparatorStyle[template["sep_style"]] except KeyError: raise ValueError( f"Unknown separator style: {template['sep_style']}" ) from None register_conv_template( Conversation( name=template["name"], system_template=template["system"] + "\n{system_message}", system_message=template.get("system_message", ""), roles=(template["user"], template["assistant"]), sep_style=sep_style, sep=template.get("sep", "\n"), stop_str=template["stop_str"], ), override=True, ) chat_template_name = template["name"] else: chat_template_name = chat_template_arg async def v1_files_create(file: UploadFile, purpose: str, file_storage_pth: str = None): try: global storage_dir if file_storage_pth: storage_dir = file_storage_pth # Read the file content file_content = await file.read() # Create an instance of RequestBody request_body = FileRequest(file=file_content, purpose=purpose) # Save the file to the sglang_oai_storage directory os.makedirs(storage_dir, exist_ok=True) file_id = f"backend_input_file-{uuid.uuid4()}" filename = f"{file_id}.jsonl" file_path = os.path.join(storage_dir, filename) with open(file_path, "wb") as f: f.write(request_body.file) # add info to global file map file_id_request[file_id] = FileMetadata(filename=file.filename, purpose=purpose) file_id_storage[file_id] = file_path # Return the response in the required format response = FileResponse( id=file_id, bytes=len(request_body.file), created_at=int(time.time()), filename=file.filename, purpose=request_body.purpose, ) file_id_response[file_id] = response return response except ValidationError as e: return {"error": "Invalid input", "details": e.errors()} async def v1_delete_file(file_id: str): # Retrieve the file job from the in-memory storage file_response = file_id_response.get(file_id) if file_response is None: raise HTTPException(status_code=404, detail="File not found") file_path = file_id_storage.get(file_id) if file_path is None: raise HTTPException(status_code=404, detail="File not found") os.remove(file_path) del file_id_response[file_id] del file_id_storage[file_id] return FileDeleteResponse(id=file_id, deleted=True) async def v1_batches(tokenizer_manager, raw_request: Request): try: body = await raw_request.json() batch_request = BatchRequest(**body) batch_id = f"batch_{uuid.uuid4()}" # Create an instance of BatchResponse batch_response = BatchResponse( id=batch_id, endpoint=batch_request.endpoint, input_file_id=batch_request.input_file_id, completion_window=batch_request.completion_window, created_at=int(time.time()), metadata=batch_request.metadata, ) batch_storage[batch_id] = batch_response # Start processing the batch asynchronously asyncio.create_task(process_batch(tokenizer_manager, batch_id, batch_request)) # Return the initial batch_response return batch_response except ValidationError as e: return {"error": "Invalid input", "details": e.errors()} except Exception as e: return {"error": str(e)} async def process_batch(tokenizer_manager, batch_id: str, batch_request: BatchRequest): try: # Update the batch status to "in_progress" batch_storage[batch_id].status = "in_progress" batch_storage[batch_id].in_progress_at = int(time.time()) # Retrieve the input file content input_file_request = file_id_request.get(batch_request.input_file_id) if not input_file_request: raise ValueError("Input file not found") # Parse the JSONL file and process each request input_file_path = file_id_storage.get(batch_request.input_file_id) with open(input_file_path, "r", encoding="utf-8") as f: lines = f.readlines() total_requests = len(lines) completed_requests = 0 failed_requests = 0 all_ret = [] end_point = batch_storage[batch_id].endpoint file_request_list = [] all_requests = [] request_ids = [] for line in lines: request_data = json.loads(line) file_request_list.append(request_data) body = request_data["body"] request_ids.append(request_data["custom_id"]) # Although streaming is supported for standalone completions, it is not supported in # batch mode (multiple completions in single request). if body.get("stream", False): raise ValueError("Streaming requests are not supported in batch mode") if end_point == "/v1/chat/completions": all_requests.append(ChatCompletionRequest(**body)) elif end_point == "/v1/completions": all_requests.append(CompletionRequest(**body)) if end_point == "/v1/chat/completions": adapted_request, request = v1_chat_generate_request( all_requests, tokenizer_manager, request_ids=request_ids ) elif end_point == "/v1/completions": adapted_request, request = v1_generate_request( all_requests, request_ids=request_ids ) try: ret = await tokenizer_manager.generate_request(adapted_request).__anext__() if not isinstance(ret, list): ret = [ret] if end_point == "/v1/chat/completions": responses = v1_chat_generate_response(request, ret, to_file=True) else: responses = v1_generate_response( request, ret, tokenizer_manager, to_file=True ) except Exception as e: error_json = { "id": f"batch_req_{uuid.uuid4()}", "custom_id": request_data.get("custom_id"), "response": None, "error": {"message": str(e)}, } all_ret.append(error_json) failed_requests += len(file_request_list) for idx, response in enumerate(responses): # the batch_req here can be changed to be named within a batch granularity response_json = { "id": f"batch_req_{uuid.uuid4()}", "custom_id": file_request_list[idx].get("custom_id"), "response": response, "error": None, } all_ret.append(response_json) completed_requests += 1 # Write results to a new file output_file_id = f"backend_result_file-{uuid.uuid4()}" global storage_dir output_file_path = os.path.join(storage_dir, f"{output_file_id}.jsonl") with open(output_file_path, "w", encoding="utf-8") as f: for ret in all_ret: f.write(json.dumps(ret) + "\n") # Update batch response with output file information retrieve_batch = batch_storage[batch_id] retrieve_batch.output_file_id = output_file_id file_id_storage[output_file_id] = output_file_path file_id_response[output_file_id] = FileResponse( id=output_file_id, bytes=os.path.getsize(output_file_path), created_at=int(time.time()), filename=f"{output_file_id}.jsonl", purpose="batch_result", ) # Update batch status to "completed" retrieve_batch.status = "completed" retrieve_batch.completed_at = int(time.time()) retrieve_batch.request_counts = { "total": total_requests, "completed": completed_requests, "failed": failed_requests, } except Exception as e: logger.error("error in SGLang:", e) # Update batch status to "failed" retrieve_batch = batch_storage[batch_id] retrieve_batch.status = "failed" retrieve_batch.failed_at = int(time.time()) retrieve_batch.errors = {"message": str(e)} async def v1_retrieve_batch(batch_id: str): # Retrieve the batch job from the in-memory storage batch_response = batch_storage.get(batch_id) if batch_response is None: raise HTTPException(status_code=404, detail="Batch not found") return batch_response async def v1_cancel_batch(tokenizer_manager, batch_id: str): # Retrieve the batch job from the in-memory storage batch_response = batch_storage.get(batch_id) if batch_response is None: raise HTTPException(status_code=404, detail="Batch not found") # Only do cancal when status is "validating" or "in_progress" if batch_response.status in ["validating", "in_progress"]: # Start cancelling the batch asynchronously asyncio.create_task( cancel_batch( tokenizer_manager=tokenizer_manager, batch_id=batch_id, input_file_id=batch_response.input_file_id, ) ) # Update batch status to "cancelling" batch_response.status = "cancelling" return batch_response else: raise HTTPException( status_code=500, detail=f"Current status is {batch_response.status}, no need to cancel", ) async def cancel_batch(tokenizer_manager, batch_id: str, input_file_id: str): try: # Update the batch status to "cancelling" batch_storage[batch_id].status = "cancelling" # Retrieve the input file content input_file_request = file_id_request.get(input_file_id) if not input_file_request: raise ValueError("Input file not found") # Parse the JSONL file and process each request input_file_path = file_id_storage.get(input_file_id) with open(input_file_path, "r", encoding="utf-8") as f: lines = f.readlines() file_request_list = [] request_ids = [] for line in lines: request_data = json.loads(line) file_request_list.append(request_data) request_ids.append(request_data["custom_id"]) # Cancel requests by request_ids for rid in request_ids: tokenizer_manager.abort_request(rid=rid) retrieve_batch = batch_storage[batch_id] retrieve_batch.status = "cancelled" except Exception as e: logger.error("error in SGLang:", e) # Update batch status to "failed" retrieve_batch = batch_storage[batch_id] retrieve_batch.status = "failed" retrieve_batch.failed_at = int(time.time()) retrieve_batch.errors = {"message": str(e)} async def v1_retrieve_file(file_id: str): # Retrieve the batch job from the in-memory storage file_response = file_id_response.get(file_id) if file_response is None: raise HTTPException(status_code=404, detail="File not found") return file_response async def v1_retrieve_file_content(file_id: str): file_pth = file_id_storage.get(file_id) if not file_pth or not os.path.exists(file_pth): raise HTTPException(status_code=404, detail="File not found") def iter_file(): with open(file_pth, mode="rb") as file_like: yield from file_like return StreamingResponse(iter_file(), media_type="application/octet-stream") def v1_generate_request( all_requests: List[CompletionRequest], request_ids: List[str] = None ): prompts = [] sampling_params_list = [] return_logprobs = [] logprob_start_lens = [] top_logprobs_nums = [] # NOTE: with openai API, the prompt's logprobs are always not computed first_prompt_type = type(all_requests[0].prompt) for request in all_requests: assert ( type(request.prompt) == first_prompt_type ), "All prompts must be of the same type in file input settings" if len(all_requests) > 1 and request.n > 1: raise ValueError( "Parallel sampling is not supported for completions from files" ) if request.echo and request.logprobs: logger.warning( "Echo is not compatible with logprobs. " "To compute logprobs of input prompt, please use SGLang /request API." ) for request in all_requests: prompts.append(request.prompt) return_logprobs.append(request.logprobs is not None and request.logprobs > 0) logprob_start_lens.append(-1) top_logprobs_nums.append( request.logprobs if request.logprobs is not None else 0 ) sampling_params_list.append( { "temperature": request.temperature, "max_new_tokens": request.max_tokens, "min_new_tokens": request.min_tokens, "stop": request.stop, "stop_token_ids": request.stop_token_ids, "top_p": request.top_p, "presence_penalty": request.presence_penalty, "frequency_penalty": request.frequency_penalty, "repetition_penalty": request.repetition_penalty, "regex": request.regex, "json_schema": request.json_schema, "n": request.n, "ignore_eos": request.ignore_eos, } ) if len(all_requests) == 1: prompt = prompts[0] sampling_params_list = sampling_params_list[0] logprob_start_lens = logprob_start_lens[0] return_logprobs = return_logprobs[0] top_logprobs_nums = top_logprobs_nums[0] if isinstance(prompt, str) or isinstance(prompt[0], str): prompt_kwargs = {"text": prompt} else: prompt_kwargs = {"input_ids": prompt} else: if isinstance(prompts[0], str): prompt_kwargs = {"text": prompts} else: prompt_kwargs = {"input_ids": prompts} adapted_request = GenerateReqInput( **prompt_kwargs, sampling_params=sampling_params_list, return_logprob=return_logprobs, top_logprobs_num=top_logprobs_nums, logprob_start_len=logprob_start_lens, return_text_in_logprobs=True, stream=all_requests[0].stream, rid=request_ids, ) if len(all_requests) == 1: return adapted_request, all_requests[0] return adapted_request, all_requests def v1_generate_response(request, ret, tokenizer_manager, to_file=False): choices = [] echo = False if (not isinstance(request, list)) and request.echo: # TODO: handle the case propmt is token ids if isinstance(request.prompt, list) and isinstance(request.prompt[0], str): # for the case of multiple str prompts prompts = request.prompt elif isinstance(request.prompt, list) and isinstance(request.prompt[0], list): # for the case of multiple token ids prompts prompts = [ tokenizer_manager.tokenizer.decode(prompt, skip_special_tokens=True) for prompt in request.prompt ] elif isinstance(request.prompt, list) and isinstance(request.prompt[0], int): # for the case of single token ids prompt prompts = [ tokenizer_manager.tokenizer.decode( request.prompt, skip_special_tokens=True ) ] else: # for the case of single str prompt prompts = [request.prompt] echo = True for idx, ret_item in enumerate(ret): text = ret_item["text"] if isinstance(request, list) and request[idx].echo: echo = True text = request[idx].prompt + text if (not isinstance(request, list)) and echo: prompt_index = idx // request.n text = prompts[prompt_index] + text logprobs = False if isinstance(request, list) and request[idx].logprobs: logprobs = True elif (not isinstance(request, list)) and request.logprobs: logprobs = True if logprobs: if echo: input_token_logprobs = ret_item["meta_info"]["input_token_logprobs"] input_top_logprobs = ret_item["meta_info"]["input_top_logprobs"] else: input_token_logprobs = None input_top_logprobs = None logprobs = to_openai_style_logprobs( input_token_logprobs=input_token_logprobs, input_top_logprobs=input_top_logprobs, output_token_logprobs=ret_item["meta_info"]["output_token_logprobs"], output_top_logprobs=ret_item["meta_info"]["output_top_logprobs"], ) else: logprobs = None if to_file: # to make the choise data json serializable choice_data = { "index": 0, "text": text, "logprobs": logprobs, "finish_reason": format_finish_reason( ret_item["meta_info"]["finish_reason"] ), } else: choice_data = CompletionResponseChoice( index=idx, text=text, logprobs=logprobs, finish_reason=format_finish_reason( ret_item["meta_info"]["finish_reason"] ), ) choices.append(choice_data) if to_file: responses = [] for i, choice in enumerate(choices): response = { "status_code": 200, "request_id": ret[i]["meta_info"]["id"], "body": { # remain the same but if needed we can change that "id": ret[i]["meta_info"]["id"], "object": "text_completion", "created": int(time.time()), "model": request[i].model, "choices": choice, "usage": { "prompt_tokens": ret[i]["meta_info"]["prompt_tokens"], "completion_tokens": ret[i]["meta_info"]["completion_tokens"], "total_tokens": ret[i]["meta_info"]["prompt_tokens"] + ret[i]["meta_info"]["completion_tokens"], }, "system_fingerprint": None, }, } responses.append(response) return responses else: prompt_tokens = sum( ret[i]["meta_info"]["prompt_tokens"] for i in range(0, len(ret), request.n) ) completion_tokens = sum(item["meta_info"]["completion_tokens"] for item in ret) response = CompletionResponse( id=ret[0]["meta_info"]["id"], model=request.model, choices=choices, usage=UsageInfo( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens, ), ) return response async def v1_completions(tokenizer_manager, raw_request: Request): request_json = await raw_request.json() all_requests = [CompletionRequest(**request_json)] adapted_request, request = v1_generate_request(all_requests) if adapted_request.stream: async def generate_stream_resp(): stream_buffers = {} n_prev_tokens = {} prompt_tokens = {} completion_tokens = {} try: async for content in tokenizer_manager.generate_request( adapted_request, raw_request ): index = content["index"] stream_buffer = stream_buffers.get(index, "") n_prev_token = n_prev_tokens.get(index, 0) text = content["text"] prompt_tokens[index] = content["meta_info"]["prompt_tokens"] completion_tokens[index] = content["meta_info"]["completion_tokens"] if not stream_buffer: # The first chunk if request.echo: if isinstance(request.prompt, str): # for the case of single str prompts prompts = request.prompt elif isinstance(request.prompt, list): if isinstance(request.prompt[0], str): # for the case of multiple str prompts prompts = request.prompt[index // request.n] elif isinstance(request.prompt[0], int): # for the case of single token ids prompt prompts = tokenizer_manager.tokenizer.decode( request.prompt, skip_special_tokens=True ) elif isinstance(request.prompt[0], list) and isinstance( request.prompt[0][0], int ): # for the case of multiple token ids prompts prompts = tokenizer_manager.tokenizer.decode( request.prompt[index // request.n], skip_special_tokens=True, ) # Prepend prompt in response text. text = prompts + text if request.logprobs: # The first chunk and echo is enabled. if not stream_buffer and request.echo: input_token_logprobs = content["meta_info"][ "input_token_logprobs" ] input_top_logprobs = content["meta_info"][ "input_top_logprobs" ] else: input_token_logprobs = None input_top_logprobs = None logprobs = to_openai_style_logprobs( input_token_logprobs=input_token_logprobs, input_top_logprobs=input_top_logprobs, output_token_logprobs=content["meta_info"][ "output_token_logprobs" ][n_prev_token:], output_top_logprobs=content["meta_info"][ "output_top_logprobs" ][n_prev_token:], ) n_prev_token = len( content["meta_info"]["output_token_logprobs"] ) else: logprobs = None delta = text[len(stream_buffer) :] stream_buffer = stream_buffer + delta choice_data = CompletionResponseStreamChoice( index=index, text=delta, logprobs=logprobs, finish_reason=format_finish_reason( content["meta_info"]["finish_reason"] ), ) chunk = CompletionStreamResponse( id=content["meta_info"]["id"], object="text_completion", choices=[choice_data], model=request.model, ) stream_buffers[index] = stream_buffer n_prev_tokens[index] = n_prev_token yield f"data: {chunk.model_dump_json()}\n\n" if request.stream_options and request.stream_options.include_usage: total_prompt_tokens = sum( tokens for i, tokens in prompt_tokens.items() if i % request.n == 0 ) total_completion_tokens = sum( tokens for tokens in completion_tokens.values() ) usage = UsageInfo( prompt_tokens=total_prompt_tokens, completion_tokens=total_completion_tokens, total_tokens=total_prompt_tokens + total_completion_tokens, ) final_usage_chunk = CompletionStreamResponse( id=str(uuid.uuid4().hex), choices=[], model=request.model, usage=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: error = create_streaming_error_response(str(e)) yield f"data: {error}\n\n" yield "data: [DONE]\n\n" return StreamingResponse( generate_stream_resp(), media_type="text/event-stream", background=tokenizer_manager.create_abort_task(adapted_request), ) # Non-streaming response. try: ret = await tokenizer_manager.generate_request( adapted_request, raw_request ).__anext__() except ValueError as e: return create_error_response(str(e)) if not isinstance(ret, list): ret = [ret] response = v1_generate_response(request, ret, tokenizer_manager) return response def v1_chat_generate_request( all_requests: List[ChatCompletionRequest], tokenizer_manager, request_ids: List[str] = None, ): input_ids = [] sampling_params_list = [] image_data_list = [] return_logprobs = [] logprob_start_lens = [] top_logprobs_nums = [] # NOTE: with openai API, the prompt's logprobs are always not computed for request in all_requests: # Prep the data needed for the underlying GenerateReqInput: # - prompt: The full prompt string. # - stop: Custom stop tokens. # - image_data: None or a list of image strings (URLs or base64 strings). # None skips any image processing in GenerateReqInput. if not isinstance(request.messages, str): # Apply chat template and its stop strings. if chat_template_name is None: openai_compatible_messages = [] for message in request.messages: if isinstance(message.content, str): openai_compatible_messages.append( {"role": message.role, "content": message.content} ) else: content_list = message.dict()["content"] for content in content_list: if content["type"] == "text": openai_compatible_messages.append( {"role": message.role, "content": content["text"]} ) prompt_ids = tokenizer_manager.tokenizer.apply_chat_template( openai_compatible_messages, tokenize=True, add_generation_prompt=True, ) stop = request.stop image_data = None else: conv = generate_chat_conv(request, chat_template_name) prompt = conv.get_prompt() image_data = conv.image_data stop = conv.stop_str or [] if request.stop: if isinstance(request.stop, str): stop.append(request.stop) else: stop.extend(request.stop) prompt_ids = tokenizer_manager.tokenizer.encode(prompt) else: # Use the raw prompt and stop strings if the messages is already a string. prompt_ids = request.messages stop = request.stop image_data = None input_ids.append(prompt_ids) return_logprobs.append(request.logprobs) logprob_start_lens.append(-1) top_logprobs_nums.append(request.top_logprobs) sampling_params_list.append( { "temperature": request.temperature, "max_new_tokens": request.max_tokens, "min_new_tokens": request.min_tokens, "stop": stop, "stop_token_ids": request.stop_token_ids, "top_p": request.top_p, "presence_penalty": request.presence_penalty, "frequency_penalty": request.frequency_penalty, "repetition_penalty": request.repetition_penalty, "regex": request.regex, "json_schema": request.json_schema, "n": request.n, } ) image_data_list.append(image_data) if len(all_requests) == 1: input_ids = input_ids[0] if isinstance(input_ids, str): prompt_kwargs = {"text": input_ids} else: prompt_kwargs = {"input_ids": input_ids} sampling_params_list = sampling_params_list[0] image_data = image_data_list[0] return_logprobs = return_logprobs[0] logprob_start_lens = logprob_start_lens[0] top_logprobs_nums = top_logprobs_nums[0] else: if isinstance(input_ids[0], str): prompt_kwargs = {"text": input_ids} else: prompt_kwargs = {"input_ids": input_ids} adapted_request = GenerateReqInput( **prompt_kwargs, image_data=image_data, sampling_params=sampling_params_list, return_logprob=return_logprobs, logprob_start_len=logprob_start_lens, top_logprobs_num=top_logprobs_nums, stream=all_requests[0].stream, return_text_in_logprobs=True, rid=request_ids, ) if len(all_requests) == 1: return adapted_request, all_requests[0] return adapted_request, all_requests def v1_chat_generate_response(request, ret, to_file=False): choices = [] for idx, ret_item in enumerate(ret): logprobs = False if isinstance(request, list) and request[idx].logprobs: logprobs = True elif (not isinstance(request, list)) and request.logprobs: logprobs = True if logprobs: logprobs = to_openai_style_logprobs( output_token_logprobs=ret_item["meta_info"]["output_token_logprobs"], output_top_logprobs=ret_item["meta_info"]["output_top_logprobs"], ) token_logprobs = [] for token, logprob in zip(logprobs.tokens, logprobs.token_logprobs): token_bytes = list(token.encode("utf-8")) top_logprobs = [] if logprobs.top_logprobs: for top_token, top_logprob in logprobs.top_logprobs[0].items(): top_token_bytes = list(top_token.encode("utf-8")) top_logprobs.append( TopLogprob( token=top_token, bytes=top_token_bytes, logprob=top_logprob, ) ) token_logprobs.append( ChatCompletionTokenLogprob( token=token, bytes=token_bytes, logprob=logprob, top_logprobs=top_logprobs, ) ) choice_logprobs = ChoiceLogprobs(content=token_logprobs) else: choice_logprobs = None if to_file: # to make the choice data json serializable choice_data = { "index": 0, "message": {"role": "assistant", "content": ret_item["text"]}, "logprobs": choice_logprobs, "finish_reason": format_finish_reason( ret_item["meta_info"]["finish_reason"] ), } else: choice_data = ChatCompletionResponseChoice( index=idx, message=ChatMessage(role="assistant", content=ret_item["text"]), logprobs=choice_logprobs, finish_reason=format_finish_reason( ret_item["meta_info"]["finish_reason"] ), ) choices.append(choice_data) if to_file: responses = [] for i, choice in enumerate(choices): response = { "status_code": 200, "request_id": ret[i]["meta_info"]["id"], "body": { # remain the same but if needed we can change that "id": ret[i]["meta_info"]["id"], "object": "chat.completion", "created": int(time.time()), "model": request[i].model, "choices": choice, "usage": { "prompt_tokens": ret[i]["meta_info"]["prompt_tokens"], "completion_tokens": ret[i]["meta_info"]["completion_tokens"], "total_tokens": ret[i]["meta_info"]["prompt_tokens"] + ret[i]["meta_info"]["completion_tokens"], }, "system_fingerprint": None, }, } responses.append(response) return responses else: prompt_tokens = sum( ret[i]["meta_info"]["prompt_tokens"] for i in range(0, len(ret), request.n) ) completion_tokens = sum(item["meta_info"]["completion_tokens"] for item in ret) response = ChatCompletionResponse( id=ret[0]["meta_info"]["id"], model=request.model, choices=choices, usage=UsageInfo( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens, ), ) return response async def v1_chat_completions(tokenizer_manager, raw_request: Request): request_json = await raw_request.json() all_requests = [ChatCompletionRequest(**request_json)] adapted_request, request = v1_chat_generate_request(all_requests, tokenizer_manager) if adapted_request.stream: async def generate_stream_resp(): is_firsts = {} stream_buffers = {} n_prev_tokens = {} prompt_tokens = {} completion_tokens = {} try: async for content in tokenizer_manager.generate_request( adapted_request, raw_request ): index = content["index"] is_first = is_firsts.get(index, True) stream_buffer = stream_buffers.get(index, "") n_prev_token = n_prev_tokens.get(index, 0) prompt_tokens[index] = content["meta_info"]["prompt_tokens"] completion_tokens[index] = content["meta_info"]["completion_tokens"] if request.logprobs: logprobs = to_openai_style_logprobs( output_token_logprobs=content["meta_info"][ "output_token_logprobs" ][n_prev_token:], output_top_logprobs=content["meta_info"][ "output_top_logprobs" ][n_prev_token:], ) n_prev_token = len( content["meta_info"]["output_token_logprobs"] ) token_logprobs = [] for token, logprob in zip( logprobs.tokens, logprobs.token_logprobs ): token_bytes = list(token.encode("utf-8")) top_logprobs = [] if logprobs.top_logprobs: for top_token, top_logprob in logprobs.top_logprobs[ 0 ].items(): top_token_bytes = list(top_token.encode("utf-8")) top_logprobs.append( TopLogprob( token=top_token, bytes=top_token_bytes, logprob=top_logprob, ) ) token_logprobs.append( ChatCompletionTokenLogprob( token=token, bytes=token_bytes, logprob=logprob, top_logprobs=top_logprobs, ) ) choice_logprobs = ChoiceLogprobs(content=token_logprobs) else: choice_logprobs = None if is_first: # First chunk with role is_first = False choice_data = ChatCompletionResponseStreamChoice( index=index, delta=DeltaMessage(role="assistant"), finish_reason=format_finish_reason( content["meta_info"]["finish_reason"] ), logprobs=choice_logprobs, ) chunk = ChatCompletionStreamResponse( id=content["meta_info"]["id"], choices=[choice_data], model=request.model, ) yield f"data: {chunk.model_dump_json()}\n\n" text = content["text"] delta = text[len(stream_buffer) :] stream_buffer = stream_buffer + delta choice_data = ChatCompletionResponseStreamChoice( index=index, delta=DeltaMessage(content=delta), finish_reason=format_finish_reason( content["meta_info"]["finish_reason"] ), logprobs=choice_logprobs, ) chunk = ChatCompletionStreamResponse( id=content["meta_info"]["id"], choices=[choice_data], model=request.model, ) is_firsts[index] = is_first stream_buffers[index] = stream_buffer n_prev_tokens[index] = n_prev_token yield f"data: {chunk.model_dump_json()}\n\n" if request.stream_options and request.stream_options.include_usage: total_prompt_tokens = sum( tokens for i, tokens in prompt_tokens.items() if i % request.n == 0 ) total_completion_tokens = sum( tokens for tokens in completion_tokens.values() ) usage = UsageInfo( prompt_tokens=total_prompt_tokens, completion_tokens=total_completion_tokens, total_tokens=total_prompt_tokens + total_completion_tokens, ) final_usage_chunk = ChatCompletionStreamResponse( id=str(uuid.uuid4().hex), choices=[], model=request.model, usage=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: error = create_streaming_error_response(str(e)) yield f"data: {error}\n\n" yield "data: [DONE]\n\n" return StreamingResponse( generate_stream_resp(), media_type="text/event-stream", background=tokenizer_manager.create_abort_task(adapted_request), ) # Non-streaming response. try: ret = await tokenizer_manager.generate_request( adapted_request, raw_request ).__anext__() except ValueError as e: return create_error_response(str(e)) if not isinstance(ret, list): ret = [ret] response = v1_chat_generate_response(request, ret) return response def v1_embedding_request(all_requests, tokenizer_manager): prompts = [] sampling_params_list = [] first_prompt_type = type(all_requests[0].input) for request in all_requests: prompt = request.input assert ( type(prompt) == first_prompt_type ), "All prompts must be of the same type in file input settings" prompts.append(prompt) if len(all_requests) == 1: prompt = prompts[0] if isinstance(prompt, str) or isinstance(prompt[0], str): prompt_kwargs = {"text": prompt} else: prompt_kwargs = {"input_ids": prompt} else: if isinstance(prompts[0], str) or isinstance(propmt[0][0], str): prompt_kwargs = {"text": prompts} else: prompt_kwargs = {"input_ids": prompts} adapted_request = EmbeddingReqInput( **prompt_kwargs, ) if len(all_requests) == 1: return adapted_request, all_requests[0] return adapted_request, all_requests def v1_embedding_response(ret, model_path, to_file=False): embedding_objects = [] prompt_tokens = 0 for idx, ret_item in enumerate(ret): embedding_objects.append( EmbeddingObject( embedding=ret[idx]["embedding"], index=idx, ) ) prompt_tokens += ret[idx]["meta_info"]["prompt_tokens"] return EmbeddingResponse( data=embedding_objects, model=model_path, usage=UsageInfo( prompt_tokens=prompt_tokens, total_tokens=prompt_tokens, ), ) async def v1_embeddings(tokenizer_manager, raw_request: Request): request_json = await raw_request.json() all_requests = [EmbeddingRequest(**request_json)] adapted_request, request = v1_embedding_request(all_requests, tokenizer_manager) try: ret = await tokenizer_manager.generate_request( adapted_request, raw_request ).__anext__() except ValueError as e: return create_error_response(str(e)) if not isinstance(ret, list): ret = [ret] response = v1_embedding_response(ret, tokenizer_manager.model_path) return response def to_openai_style_logprobs( input_token_logprobs=None, output_token_logprobs=None, input_top_logprobs=None, output_top_logprobs=None, ): ret_logprobs = LogProbs() def append_token_logprobs(token_logprobs): for logprob, _, token_text in token_logprobs: ret_logprobs.tokens.append(token_text) ret_logprobs.token_logprobs.append(logprob) # Not supported yet ret_logprobs.text_offset.append(-1) def append_top_logprobs(top_logprobs): for tokens in top_logprobs: if tokens is not None: ret_logprobs.top_logprobs.append( {token[2]: token[0] for token in tokens} ) else: ret_logprobs.top_logprobs.append(None) if input_token_logprobs is not None: append_token_logprobs(input_token_logprobs) if output_token_logprobs is not None: append_token_logprobs(output_token_logprobs) if input_top_logprobs is not None: append_top_logprobs(input_top_logprobs) if output_top_logprobs is not None: append_top_logprobs(output_top_logprobs) return ret_logprobs