OpenAI Compatible Frontend

cacheflow/core/block_manager.py

@@ -148,7 +148,7 @@ def _get_physical_blocks(self, seq_group: SequenceGroup) -> List[PhysicalTokenBl
         # the sequences in the same group.
         blocks: Set[PhysicalTokenBlock] = set()
         for seq in seq_group.get_seqs():
-            if seq.status == SequenceStatus.FINISHED:
+            if SequenceStatus.is_finished(seq.status):
                 continue
             block_table = self.block_tables[seq.seq_id]
             for block in block_table:
@@ -169,7 +169,7 @@ def swap_in(self, seq_group: SequenceGroup) -> Dict[int, int]:
         # CPU block -> GPU block.
         mapping: Dict[PhysicalTokenBlock, PhysicalTokenBlock] = {}
         for seq in seq_group.get_seqs():
-            if seq.status == SequenceStatus.FINISHED:
+            if SequenceStatus.is_finished(seq.status):
                 continue
             new_block_table: BlockTable = []
             block_table = self.block_tables[seq.seq_id]
@@ -200,7 +200,7 @@ def swap_out(self, seq_group: SequenceGroup) -> Dict[int, int]:
         # GPU block -> CPU block.
         mapping: Dict[PhysicalTokenBlock, PhysicalTokenBlock] = {}
         for seq in seq_group.get_seqs():
-            if seq.status == SequenceStatus.FINISHED:
+            if SequenceStatus.is_finished(seq.status):
                 continue
             new_block_table: BlockTable = []
             block_table = self.block_tables[seq.seq_id]

cacheflow/core/scheduler.py

@@ -292,10 +292,12 @@ def update(
                 # Append a new token to the sequence.
                 output = seq_outputs[seq.seq_id]
                 seq.append_token_id(output.output_token, output.logprobs)
+        # Return a shallow copy of the running queue to prevent the queue
+        # from being modified by the caller.
         return self.running.copy()
 
-    def free_seq(self, seq: Sequence) -> None:
-        seq.status = SequenceStatus.FINISHED
+    def free_seq(self, seq: Sequence, finish_status: SequenceStatus) -> None:
+        seq.status = finish_status
         self.block_manager.free(seq)
 
     def free_finished_seq_groups(self) -> None:

cacheflow/entrypoints/openai/openai_frontend.py

@@ -0,0 +1,300 @@
+# Adapted from https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/serve/openai_api_server.py
+
+import argparse
+from http import HTTPStatus
+import json
+import time
+from typing import AsyncGenerator, Dict, List, Optional
+
+import fastapi
+from fastapi.exceptions import RequestValidationError
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse, JSONResponse
+import uvicorn
+
+from cacheflow.outputs import RequestOutput
+from cacheflow.server.arg_utils import ServerArgs
+from cacheflow.server.async_llm_server import AsyncLLMServer
+from cacheflow.server.tokenizer_utils import get_tokenizer
+from cacheflow.logger import init_logger
+from cacheflow.sampling_params import SamplingParams
+from cacheflow.utils import random_uuid
+from cacheflow.entrypoints.openai.protocol import (
+    CompletionRequest,
+    CompletionResponse,
+    CompletionResponseChoice,
+    CompletionResponseStreamChoice,
+    CompletionStreamResponse,
+    ErrorResponse,
+    LogProbs,
+    ModelCard,
+    ModelList,
+    ModelPermission,
+    UsageInfo,
+)
+
+
+logger = init_logger(__name__)
+served_model = None
+app = fastapi.FastAPI()
+
+
+def create_error_response(status_code: HTTPStatus,
+                          message: str) -> JSONResponse:
+    return JSONResponse(
+        ErrorResponse(message=message, type="invalid_request_error").dict(),
+        status_code=status_code.value
+    )
+
+
+@app.exception_handler(RequestValidationError)
+async def validation_exception_handler(request, exc):
+    return create_error_response(HTTPStatus.BAD_REQUEST, str(exc))
+
+
+async def check_model(request) -> Optional[JSONResponse]:
+    if request.model == served_model:
+        return
+    ret = create_error_response(
+        HTTPStatus.NOT_FOUND,
+        f"The model `{request.model}` does not exist.",
+    )
+    return ret
+
+
+@app.get("/v1/models")
+async def show_available_models():
+    """Show available models. Right now we only have one model."""
+    model_cards = [ModelCard(id=served_model, root=served_model,
+                             permission=[ModelPermission()])]
+    return ModelList(data=model_cards)
+
+
+def create_logprobs(token_ids: List[int],
+                    id_logprobs: List[Dict[int, float]],
+                    initial_text_offset: int = 0) -> LogProbs:
+    """Create OpenAI-style logprobs."""
+    logprobs = LogProbs()
+    last_token_len = 0
+    for token_id, id_logprob in zip(token_ids, id_logprobs):
+        token = tokenizer.convert_ids_to_tokens(token_id)
+        logprobs.tokens.append(token)
+        logprobs.token_logprobs.append(id_logprob[token_id])
+        if len(logprobs.text_offset) == 0:
+            logprobs.text_offset.append(initial_text_offset)
+        else:
+            logprobs.text_offset.append(logprobs.text_offset[-1] + last_token_len)
+        last_token_len = len(token)
+
+        logprobs.top_logprobs.append(
+            {tokenizer.convert_ids_to_tokens(i): p
+             for i, p in id_logprob.items()})
+    return logprobs
+
+
+@app.post("/v1/completions")
+async def create_completion(request: CompletionRequest):
+    logger.info(f"Received completion request: {request}")
+
+    error_check_ret = await check_model(request)
+    if error_check_ret is not None:
+        return error_check_ret
+
+    if request.echo:
+        # We do not support echo since the cacheflow server does not
+        # currently support getting the logprobs of prompt tokens.
+        return create_error_response(HTTPStatus.BAD_REQUEST,
+                                     "echo is not currently supported")
+
+    if request.suffix is not None:
+        # The language models we currently support do not support suffix.
+        return create_error_response(HTTPStatus.BAD_REQUEST,
+                                    "suffix is not currently supported")
+
+    if request.logit_bias is not None:
+        # TODO: support logit_bias in cacheflow server.
+        return create_error_response(HTTPStatus.BAD_REQUEST,
+                                     "logit_bias is not currently supported")
+
+    model_name = request.model
+    request_id = f"cmpl-{random_uuid()}"
+    prompt = request.prompt
+    created_time = int(time.time())
+    try:
+        sampling_params = SamplingParams(
+            n=request.n,
+            best_of=request.best_of,
+            presence_penalty=request.presence_penalty,
+            frequency_penalty=request.frequency_penalty,
+            temperature=request.temperature,
+            top_p=request.top_p,
+            top_k=request.top_k,
+            stop=request.stop,
+            ignore_eos=request.ignore_eos,
+            max_tokens=request.max_tokens,
+            logprobs=request.logprobs,
+            use_beam_search=request.use_beam_search,
+        )
+    except ValueError as e:
+        return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
+
+    result_generator = server.generate(prompt, sampling_params,
+                                       request_id=request_id)
+
+    # 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)
+
+    def create_stream_response_json(index: int,
+                                    text: str,
+                                    logprobs: Optional[LogProbs] = None,
+                                    finish_reason: Optional[str] = None) -> str:
+        choice_data = CompletionResponseStreamChoice(
+            index=index,
+            text=text,
+            logprobs=logprobs,
+            finish_reason=finish_reason,
+        )
+        response = CompletionStreamResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            choices=[choice_data],
+        )
+        response_json = response.json(ensure_ascii=False)
+
+        return response_json
+
+    async def completion_stream_generator() -> AsyncGenerator[str, None]:
+        previous_texts = [""] * request.n
+        previous_num_tokens = [0] * request.n
+        async for res in result_generator:
+            res: RequestOutput
+            for output in res.outputs:
+                i = output.index
+                delta_text = output.text[len(previous_texts[i]):]
+                if request.logprobs is not None:
+                    logprobs = create_logprobs(
+                        output.token_ids[previous_num_tokens[i]:],
+                        output.logprobs[previous_num_tokens[i]:],
+                        len(previous_texts[i]))
+                else:
+                    logprobs = None
+                previous_texts[i] = output.text
+                previous_num_tokens[i] = len(output.token_ids)
+                response_json = create_stream_response_json(
+                    index=i,
+                    text=delta_text,
+                    logprobs=logprobs,
+                )
+                yield f"data: {response_json}\n\n"
+                if output.finish_reason is not None:
+                    logprobs = LogProbs() if request.logprobs is not None else None
+                    response_json = create_stream_response_json(
+                        index=i,
+                        text="",
+                        logprobs=logprobs,
+                        finish_reason=output.finish_reason,
+                    )
+                    yield f"data: {response_json}\n\n"
+            yield "data: [DONE]\n\n"
+
+    # Streaming response
+    if stream:
+        return StreamingResponse(completion_stream_generator(),
+                                 media_type="text/event-stream")
+
+    # Non-streaming response
+    final_res: RequestOutput = None
+    async for res in result_generator:
+        final_res = res
+    assert final_res is not None
+    choices = []
+    for output in final_res.outputs:
+        if request.logprobs is not None:
+            logprobs = create_logprobs(output.token_ids, output.logprobs)
+        else:
+            logprobs = None
+        choice_data = CompletionResponseChoice(
+            index=output.index,
+            text=output.text,
+            logprobs=logprobs,
+            finish_reason=output.finish_reason,
+        )
+        choices.append(choice_data)
+
+    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 = CompletionResponse(
+        id=request_id,
+        created=created_time,
+        model=model_name,
+        choices=choices,
+        usage=usage,
+    )
+
+    if request.stream:
+        # When user requests streaming but we don't stream, we still need to
+        # return a streaming response with a single event.
+        response_json = response.json(ensure_ascii=False)
+        async def fake_stream_generator() -> AsyncGenerator[str, None]:
+            yield f"data: {response_json}\n\n"
+            yield "data: [DONE]\n\n"
+        return StreamingResponse(fake_stream_generator(),
+                                 media_type="text/event-stream")
+
+    return response
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="CacheFlow OpenAI-Compatible RESTful API server."
+    )
+    parser.add_argument("--host", type=str, default="localhost", help="host name")
+    parser.add_argument("--port", type=int, default=8000, help="port number")
+    parser.add_argument(
+        "--allow-credentials", action="store_true", help="allow credentials"
+    )
+    parser.add_argument(
+        "--allowed-origins", type=json.loads, default=["*"], help="allowed origins"
+    )
+    parser.add_argument(
+        "--allowed-methods", type=json.loads, default=["*"], help="allowed methods"
+    )
+    parser.add_argument(
+        "--allowed-headers", type=json.loads, default=["*"], help="allowed headers"
+    )
+    parser.add_argument("--served-model-name", type=str, default=None,
+                        help="The model name used in the API. If not specified, "
+                             "the model name will be the same as the "
+                             "huggingface name.")
+    parser = ServerArgs.add_cli_args(parser)
+    args = parser.parse_args()
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=args.allowed_origins,
+        allow_credentials=args.allow_credentials,
+        allow_methods=args.allowed_methods,
+        allow_headers=args.allowed_headers,
+    )
+
+    logger.info(f"args: {args}")
+
+    served_model = args.served_model_name or args.model
+
+    server_args = ServerArgs.from_cli_args(args)
+    server = AsyncLLMServer.from_server_args(server_args)
+
+    # A separate tokenizer to map token IDs to strings.
+    tokenizer = get_tokenizer(args.model)
+
+    uvicorn.run(app, host=args.host, port=args.port, log_level="info")