Implement OpenAI to OpenAI adapter for gateway

gateway/src/dstack/gateway/common.py

@@ -1,6 +1,7 @@
 import asyncio
 import functools
-from typing import Callable, ParamSpec, TypeVar
+import json
+from typing import Any, AsyncIterator, Callable, Dict, ParamSpec, TypeVar
 
 import httpx
 
@@ -19,3 +20,9 @@ def __del__(self):
             asyncio.get_running_loop().create_task(self.aclose())
         except Exception:
             pass
+
+    async def stream_sse(self, url: str, **kwargs) -> AsyncIterator[Dict[str, Any]]:
+        async with self.stream("POST", url, **kwargs) as resp:
+            async for line in resp.aiter_lines():
+                if line.startswith("data:"):
+                    yield json.loads(line[len("data:") :].strip("\n"))

gateway/src/dstack/gateway/openai/clients/__init__.py

@@ -7,6 +7,8 @@
     ChatCompletionsResponse,
 )
 
+DEFAULT_TIMEOUT = 60
+
 
 class ChatCompletionsClient(ABC):
     @abstractmethod

gateway/src/dstack/gateway/openai/clients/openai.py

@@ -0,0 +1,29 @@
+from typing import AsyncIterator, Optional
+
+from dstack.gateway.common import AsyncClientWrapper
+from dstack.gateway.errors import GatewayError
+from dstack.gateway.openai.clients import DEFAULT_TIMEOUT, ChatCompletionsClient
+from dstack.gateway.openai.schemas import (
+    ChatCompletionsChunk,
+    ChatCompletionsRequest,
+    ChatCompletionsResponse,
+)
+
+
+class OpenAIChatCompletions(ChatCompletionsClient):
+    def __init__(self, base_url: str, host: Optional[str] = None):
+        self.client = AsyncClientWrapper(
+            base_url=base_url.rstrip("/"),
+            headers={} if host is None else {"Host": host},
+            timeout=DEFAULT_TIMEOUT,
+        )
+
+    async def generate(self, request: ChatCompletionsRequest) -> ChatCompletionsResponse:
+        resp = await self.client.post("/chat/completions", json=request.model_dump())
+        if resp.status_code != 200:
+            raise GatewayError(resp.text)
+        return ChatCompletionsResponse.model_validate(resp.json())
+
+    async def stream(self, request: ChatCompletionsRequest) -> AsyncIterator[ChatCompletionsChunk]:
+        async for data in self.client.stream_sse("/chat/completions", json=request.model_dump()):
+            yield ChatCompletionsChunk.model_validate(data)

gateway/src/dstack/gateway/openai/models.py

@@ -3,22 +3,32 @@
 from pydantic import BaseModel, Field
 
 
-class ChatModel(BaseModel):
-    type: Literal["chat"] = "chat"
+class BaseChatModel(BaseModel):
+    type: Literal["chat"]
     name: str
+    format: str
+
+
+class TGIChatModel(BaseChatModel):
     format: Literal["tgi"]
     chat_template: str
     eos_token: str
 
 
-ANY_MODEL = Union[ChatModel]
+class OpenAIChatModel(BaseChatModel):
+    format: Literal["openai"]
+    prefix: str
+
+
+ChatModel = Annotated[Union[TGIChatModel, OpenAIChatModel], Field(discriminator="format")]
+AnyModel = Union[ChatModel]  # embeddings and etc.
 
 
 class OpenAIOptions(BaseModel):
-    model: Annotated[ANY_MODEL, Field(discriminator="type")]
+    model: AnyModel  # TODO(egor-s): add discriminator by type
 
 
 class ServiceModel(BaseModel):
-    model: ANY_MODEL
+    model: AnyModel
     domain: str
     created: int

gateway/src/dstack/gateway/openai/routes.py

@@ -46,6 +46,7 @@ async def post_chat_completions(
             return StreamingResponse(
                 stream_chunks(client.stream(body)),
                 media_type="text/event-stream",
+                headers={"X-Accel-Buffering": "no"},
             )
     except GatewayError as e:
         raise e.http()

gateway/src/dstack/gateway/openai/schemas.py

@@ -37,7 +37,7 @@ class ChatCompletionsChoice(BaseModel):
 
 class ChatCompletionsChunkChoice(BaseModel):
     delta: object
-    logprobs: Optional[object]
+    logprobs: object = {}
     finish_reason: Optional[FinishReason]
     index: int
 
@@ -53,7 +53,7 @@ class ChatCompletionsResponse(BaseModel):
     choices: List[ChatCompletionsChoice]
     created: int
     model: str
-    system_fingerprint: str
+    system_fingerprint: str = ""
     object: Literal["chat.completion"] = "chat.completion"
     usage: ChatCompletionsUsage
 
@@ -63,7 +63,7 @@ class ChatCompletionsChunk(BaseModel):
     choices: List[ChatCompletionsChunkChoice]
     created: int
     model: str
-    system_fingerprint: str
+    system_fingerprint: str = ""
     object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
 
 

gateway/src/dstack/gateway/openai/store.py

@@ -7,6 +7,7 @@
 
 from dstack.gateway.errors import GatewayError, NotFoundError
 from dstack.gateway.openai.clients import ChatCompletionsClient
+from dstack.gateway.openai.clients.openai import OpenAIChatCompletions
 from dstack.gateway.openai.clients.tgi import TGIChatCompletions
 from dstack.gateway.openai.models import OpenAIOptions, ServiceModel
 from dstack.gateway.openai.schemas import Model
@@ -81,6 +82,11 @@ async def get_chat_client(self, project: str, model_name: str) -> ChatCompletion
                     chat_template=service.model.chat_template,
                     eos_token=service.model.eos_token,
                 )
+            elif service.model.format == "openai":
+                return OpenAIChatCompletions(
+                    base_url=f"http://localhost/{service.model.prefix.lstrip('/')}",
+                    host=service.domain,
+                )
             else:
                 raise GatewayError(f"Unsupported model format: {service.model.format}")
 

src/dstack/_internal/core/models/configurations.py

@@ -7,6 +7,7 @@
 
 from dstack._internal.core.errors import ConfigurationError
 from dstack._internal.core.models.common import ForbidExtra
+from dstack._internal.core.models.gateways import AnyModel
 from dstack._internal.core.models.repos.base import Repo
 from dstack._internal.core.models.repos.virtual import VirtualRepo
 from dstack._internal.core.models.resources import ResourcesSpec
@@ -78,26 +79,6 @@ class Artifact(ForbidExtra):
     ] = False
 
 
-class ModelInfo(ForbidExtra):
-    """
-    Mapping of the model for the OpenAI-compatible endpoint.
-
-    Attributes:
-        type (str): The type of the model, e.g. "chat"
-        name (str): The name of the model. This name will be used both to load model configuration from the HuggingFace Hub and in the OpenAI-compatible endpoint.
-        format (str): The format of the model, e.g. "tgi" if the model is served with HuggingFace's Text Generation Inference.
-        chat_template (Optional[str]): The custom prompt template for the model. If not specified, the default prompt template the HuggingFace Hub configuration will be used.
-        eos_token (Optional[str]): The custom end of sentence token. If not specified, the default custom end of sentence token from the HuggingFace Hub configuration will be used.
-    """
-
-    type: Annotated[Literal["chat"], Field(description="The type of the model")]
-    name: Annotated[str, Field(description="The name of the model")]
-    format: Annotated[Literal["tgi"], Field(description="The serving format")]
-
-    chat_template: Optional[str] = None  # TODO(egor-s): use discriminator and root model
-    eos_token: Optional[str] = None
-
-
 class BaseConfiguration(ForbidExtra):
     type: Literal["none"]
     image: Annotated[Optional[str], Field(description="The name of the Docker image to run")]
@@ -206,7 +187,7 @@ class ServiceConfiguration(BaseConfiguration):
         Field(description="The port, that application listens to or the mapping"),
     ]
     model: Annotated[
-        Optional[ModelInfo],
+        Optional[AnyModel],
         Field(description="Mapping of the model for the OpenAI-compatible endpoint"),
     ] = None
     auth: Annotated[bool, Field(description="Enable the authorization")] = True

src/dstack/_internal/core/models/gateways.py

@@ -1,7 +1,8 @@
 import datetime
-from typing import Optional
+from typing import Optional, Union
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
+from typing_extensions import Annotated, Literal
 
 from dstack._internal.core.models.backends.base import BackendType
 
@@ -15,3 +16,45 @@ class Gateway(BaseModel):
     default: bool
     created_at: datetime.datetime
     backend: BackendType
+
+
+class BaseChatModel(BaseModel):
+    type: Annotated[Literal["chat"], Field(description="The type of the model")]
+    name: Annotated[str, Field(description="The name of the model")]
+    format: Annotated[str, Field(description="The serving format")]
+
+
+class TGIChatModel(BaseChatModel):
+    """
+    Mapping of the model for the OpenAI-compatible endpoint.
+
+    Attributes:
+        type (str): The type of the model, e.g. "chat"
+        name (str): The name of the model. This name will be used both to load model configuration from the HuggingFace Hub and in the OpenAI-compatible endpoint.
+        format (str): The format of the model, e.g. "tgi" if the model is served with HuggingFace's Text Generation Inference.
+        chat_template (Optional[str]): The custom prompt template for the model. If not specified, the default prompt template the HuggingFace Hub configuration will be used.
+        eos_token (Optional[str]): The custom end of sentence token. If not specified, the default custom end of sentence token from the HuggingFace Hub configuration will be used.
+    """
+
+    format: Literal["tgi"]
+    chat_template: Optional[str] = None  # will be set before registering the service
+    eos_token: Optional[str] = None
+
+
+class OpenAIChatModel(BaseChatModel):
+    """
+    Mapping of the model for the OpenAI-compatible endpoint.
+
+    Attributes:
+        type (str): The type of the model, e.g. "chat"
+        name (str): The name of the model. This name will be used both to load model configuration from the HuggingFace Hub and in the OpenAI-compatible endpoint.
+        format (str): The format of the model, i.e. "openai".
+        prefix (str): The `base_url` prefix: `http://hostname/{prefix}/chat/completions`. Defaults to `/v1`.
+    """
+
+    format: Literal["openai"]
+    prefix: Annotated[str, Field(description="The `base_url` prefix (after hostname)")] = "/v1"
+
+
+ChatModel = Annotated[Union[TGIChatModel, OpenAIChatModel], Field(discriminator="format")]
+AnyModel = Annotated[Union[ChatModel], Field(discriminator="type")]  # embeddings and etc.

src/dstack/_internal/server/services/jobs/configurators/extensions/openai.py

@@ -1,19 +1,23 @@
 import requests
 
 from dstack._internal.core.errors import ConfigurationError
-from dstack._internal.core.models.configurations import ModelInfo
+from dstack._internal.core.models.gateways import AnyModel
 
 
-def complete_model(model_info: ModelInfo) -> dict:
+def complete_model(model_info: AnyModel) -> dict:
     model_info = model_info.copy(deep=True)
-    # TODO(egor-s): support more types and formats
 
-    if model_info.chat_template is None or model_info.eos_token is None:
-        tokenizer_config = get_tokenizer_config(model_info.name)
-        if model_info.chat_template is None:
-            model_info.chat_template = tokenizer_config["chat_template"]  # TODO(egor-s): default
-        if model_info.eos_token is None:
-            model_info.eos_token = tokenizer_config["eos_token"]  # TODO(egor-s): default
+    if model_info.type == "chat" and model_info.format == "tgi":
+        if model_info.chat_template is None or model_info.eos_token is None:
+            tokenizer_config = get_tokenizer_config(model_info.name)
+            if model_info.chat_template is None:
+                model_info.chat_template = tokenizer_config[
+                    "chat_template"
+                ]  # TODO(egor-s): default
+            if model_info.eos_token is None:
+                model_info.eos_token = tokenizer_config["eos_token"]  # TODO(egor-s): default
+    elif model_info.type == "chat" and model_info.format == "openai":
+        pass  # nothing to do
 
     return {"model": model_info.dict()}
 

src/dstack/api/__init__.py

@@ -1,12 +1,12 @@
 # ruff: noqa: F401
 from dstack._internal.core.errors import ClientError
 from dstack._internal.core.models.backends.base import BackendType
-from dstack._internal.core.models.configurations import ModelInfo as _ModelInfo
 from dstack._internal.core.models.configurations import RegistryAuth
 from dstack._internal.core.models.configurations import (
     ServiceConfiguration as _ServiceConfiguration,
 )
 from dstack._internal.core.models.configurations import TaskConfiguration as _TaskConfiguration
+from dstack._internal.core.models.gateways import OpenAIChatModel, TGIChatModel
 from dstack._internal.core.models.repos.local import LocalRepo
 from dstack._internal.core.models.repos.remote import RemoteRepo
 from dstack._internal.core.models.repos.virtual import VirtualRepo
@@ -18,6 +18,5 @@
 from dstack.api._public.resources import GPU, Disk, Resources
 from dstack.api._public.runs import Run, RunStatus
 
-ModelMapping = _ModelInfo
 Service = _ServiceConfiguration
 Task = _TaskConfiguration