Convert TGI to work with openai_compat

llama_stack/providers/adapters/inference/databricks/databricks.py

@@ -8,16 +8,22 @@ from typing import AsyncGenerator
 
 from llama_models.llama3.api.chat_format import ChatFormat
 
-from llama_models.llama3.api.datatypes import Message, StopReason
+from llama_models.llama3.api.datatypes import Message
 from llama_models.llama3.api.tokenizer import Tokenizer
 
 from openai import OpenAI
 
 from llama_stack.apis.inference import *  # noqa: F403
+
 from llama_stack.providers.utils.inference.augment_messages import (
-    augment_messages_for_tools,
+    chat_completion_request_to_prompt,
 )
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
+from llama_stack.providers.utils.inference.openai_compat import (
+    get_sampling_options,
+    process_chat_completion_response,
+    process_chat_completion_stream_response,
+)
 
 from .config import DatabricksImplConfig
 
@@ -34,12 +40,7 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
             self, stack_to_provider_models_map=DATABRICKS_SUPPORTED_MODELS
         )
         self.config = config
-        tokenizer = Tokenizer.get_instance()
+        self.formatter = ChatFormat(Tokenizer.get_instance())
-        self.formatter = ChatFormat(tokenizer)
-
-    @property
-    def client(self) -> OpenAI:
-        return OpenAI(base_url=self.config.url, api_key=self.config.api_token)
 
     async def initialize(self) -> None:
         return
@@ -47,35 +48,10 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
     async def shutdown(self) -> None:
         pass
 
-    async def validate_routing_keys(self, routing_keys: list[str]) -> None:
+    def completion(self, request: CompletionRequest) -> AsyncGenerator:
-        # these are the model names the Llama Stack will use to route requests to this provider
-        # perform validation here if necessary
-        pass
-
-    async def completion(self, request: CompletionRequest) -> AsyncGenerator:
         raise NotImplementedError()
 
-    def _messages_to_databricks_messages(self, messages: list[Message]) -> list:
+    def chat_completion(
-        databricks_messages = []
-        for message in messages:
-            if message.role == "ipython":
-                role = "tool"
-            else:
-                role = message.role
-            databricks_messages.append({"role": role, "content": message.content})
-
-        return databricks_messages
-
-    def get_databricks_chat_options(self, request: ChatCompletionRequest) -> dict:
-        options = {}
-        if request.sampling_params is not None:
-            for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
-                if getattr(request.sampling_params, attr):
-                    options[attr] = getattr(request.sampling_params, attr)
-
-        return options
-
-    async def chat_completion(
         self,
         model: str,
         messages: List[Message],
@@ -97,146 +73,39 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
             logprobs=logprobs,
         )
 
-        messages = augment_messages_for_tools(request)
+        client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
-        options = self.get_databricks_chat_options(request)
+        if stream:
-        databricks_model = self.map_to_provider_model(request.model)
+            return self._stream_chat_completion(request, client)
-
-        if not request.stream:
-            r = self.client.chat.completions.create(
-                model=databricks_model,
-                messages=self._messages_to_databricks_messages(messages),
-                stream=False,
-                **options,
-            )
-
-            stop_reason = None
-            if r.choices[0].finish_reason:
-                if r.choices[0].finish_reason == "stop":
-                    stop_reason = StopReason.end_of_turn
-                elif r.choices[0].finish_reason == "length":
-                    stop_reason = StopReason.out_of_tokens
-
-            completion_message = self.formatter.decode_assistant_message_from_content(
-                r.choices[0].message.content, stop_reason
-            )
-            yield ChatCompletionResponse(
-                completion_message=completion_message,
-                logprobs=None,
-            )
         else:
-            yield ChatCompletionResponseStreamChunk(
+            return self._nonstream_chat_completion(request, client)
-                event=ChatCompletionResponseEvent(
-                    event_type=ChatCompletionResponseEventType.start,
-                    delta="",
-                )
-            )
 
-            buffer = ""
+    async def _nonstream_chat_completion(
-            ipython = False
+        self, request: ChatCompletionRequest, client: OpenAI
-            stop_reason = None
+    ) -> ChatCompletionResponse:
+        params = self._get_params(request)
+        r = client.completions.create(**params)
+        return process_chat_completion_response(request, r, self.formatter)
 
-            for chunk in self.client.chat.completions.create(
+    async def _stream_chat_completion(
-                model=databricks_model,
+        self, request: ChatCompletionRequest, client: OpenAI
-                messages=self._messages_to_databricks_messages(messages),
+    ) -> AsyncGenerator:
-                stream=True,
+        params = self._get_params(request)
-                **options,
+
+        async def _to_async_generator():
+            s = client.completions.create(**params)
+            for chunk in s:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_chat_completion_stream_response(
+            request, stream, self.formatter
         ):
-                if chunk.choices[0].finish_reason:
+            yield chunk
-                    if stop_reason is None and chunk.choices[0].finish_reason == "stop":
-                        stop_reason = StopReason.end_of_turn
-                    elif (
-                        stop_reason is None
-                        and chunk.choices[0].finish_reason == "length"
-                    ):
-                        stop_reason = StopReason.out_of_tokens
-                    break
 
-                text = chunk.choices[0].delta.content
+    def _get_params(self, request: ChatCompletionRequest) -> dict:
-
+        return {
-                if text is None:
+            "model": self.map_to_provider_model(request.model),
-                    continue
+            "prompt": chat_completion_request_to_prompt(request, self.formatter),
-
+            "stream": request.stream,
-                # check if its a tool call ( aka starts with <|python_tag|> )
+            **get_sampling_options(request),
-                if not ipython and text.startswith("<|python_tag|>"):
+        }
-                    ipython = True
-                    yield ChatCompletionResponseStreamChunk(
-                        event=ChatCompletionResponseEvent(
-                            event_type=ChatCompletionResponseEventType.progress,
-                            delta=ToolCallDelta(
-                                content="",
-                                parse_status=ToolCallParseStatus.started,
-                            ),
-                        )
-                    )
-                    buffer += text
-                    continue
-
-                if ipython:
-                    if text == "<|eot_id|>":
-                        stop_reason = StopReason.end_of_turn
-                        text = ""
-                        continue
-                    elif text == "<|eom_id|>":
-                        stop_reason = StopReason.end_of_message
-                        text = ""
-                        continue
-
-                    buffer += text
-                    delta = ToolCallDelta(
-                        content=text,
-                        parse_status=ToolCallParseStatus.in_progress,
-                    )
-
-                    yield ChatCompletionResponseStreamChunk(
-                        event=ChatCompletionResponseEvent(
-                            event_type=ChatCompletionResponseEventType.progress,
-                            delta=delta,
-                            stop_reason=stop_reason,
-                        )
-                    )
-                else:
-                    buffer += text
-                    yield ChatCompletionResponseStreamChunk(
-                        event=ChatCompletionResponseEvent(
-                            event_type=ChatCompletionResponseEventType.progress,
-                            delta=text,
-                            stop_reason=stop_reason,
-                        )
-                    )
-
-            # parse tool calls and report errors
-            message = self.formatter.decode_assistant_message_from_content(
-                buffer, stop_reason
-            )
-            parsed_tool_calls = len(message.tool_calls) > 0
-            if ipython and not parsed_tool_calls:
-                yield ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=ChatCompletionResponseEventType.progress,
-                        delta=ToolCallDelta(
-                            content="",
-                            parse_status=ToolCallParseStatus.failure,
-                        ),
-                        stop_reason=stop_reason,
-                    )
-                )
-
-            for tool_call in message.tool_calls:
-                yield ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=ChatCompletionResponseEventType.progress,
-                        delta=ToolCallDelta(
-                            content=tool_call,
-                            parse_status=ToolCallParseStatus.success,
-                        ),
-                        stop_reason=stop_reason,
-                    )
-                )
-
-            yield ChatCompletionResponseStreamChunk(
-                event=ChatCompletionResponseEvent(
-                    event_type=ChatCompletionResponseEventType.complete,
-                    delta="",
-                    stop_reason=stop_reason,
-                )
-            )

llama_stack/providers/adapters/inference/tgi/tgi.py

@@ -10,13 +10,19 @@ from typing import AsyncGenerator
 
 from huggingface_hub import AsyncInferenceClient, HfApi
 from llama_models.llama3.api.chat_format import ChatFormat
-from llama_models.llama3.api.datatypes import StopReason
 from llama_models.llama3.api.tokenizer import Tokenizer
 from llama_models.sku_list import resolve_model
 
 from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.providers.utils.inference.augment_messages import (
-    augment_messages_for_tools,
+    chat_completion_request_to_model_input_info,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    get_sampling_options,
+    OpenAICompatCompletionChoice,
+    OpenAICompatCompletionResponse,
+    process_chat_completion_response,
+    process_chat_completion_stream_response,
 )
 
 from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
@@ -30,8 +36,7 @@ class _HfAdapter(Inference):
     model_id: str
 
     def __init__(self) -> None:
-        self.tokenizer = Tokenizer.get_instance()
+        self.formatter = ChatFormat(Tokenizer.get_instance())
-        self.formatter = ChatFormat(self.tokenizer)
 
     async def register_model(self, model: ModelDef) -> None:
         resolved_model = resolve_model(model.identifier)
@@ -49,7 +54,7 @@ class _HfAdapter(Inference):
     async def shutdown(self) -> None:
         pass
 
-    async def completion(
+    def completion(
         self,
         model: str,
         content: InterleavedTextMedia,
@@ -59,16 +64,7 @@ class _HfAdapter(Inference):
     ) -> AsyncGenerator:
         raise NotImplementedError()
 
-    def get_chat_options(self, request: ChatCompletionRequest) -> dict:
+    def chat_completion(
-        options = {}
-        if request.sampling_params is not None:
-            for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
-                if getattr(request.sampling_params, attr):
-                    options[attr] = getattr(request.sampling_params, attr)
-
-        return options
-
-    async def chat_completion(
         self,
         model: str,
         messages: List[Message],
@@ -90,145 +86,64 @@ class _HfAdapter(Inference):
             logprobs=logprobs,
         )
 
-        messages = augment_messages_for_tools(request)
+        if stream:
-        model_input = self.formatter.encode_dialog_prompt(messages)
+            return self._stream_chat_completion(request)
-        prompt = self.tokenizer.decode(model_input.tokens)
+        else:
+            return self._nonstream_chat_completion(request)
 
-        input_tokens = len(model_input.tokens)
+    async def _nonstream_chat_completion(
+        self, request: ChatCompletionRequest
+    ) -> ChatCompletionResponse:
+        params = self._get_params(request)
+        r = await self.client.text_generation(**params)
+
+        choice = OpenAICompatCompletionChoice(
+            finish_reason=r.details.finish_reason,
+            text="".join(t.text for t in r.details.tokens),
+        )
+        response = OpenAICompatCompletionResponse(
+            choices=[choice],
+        )
+        return process_chat_completion_response(request, response, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: ChatCompletionRequest
+    ) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        async def _generate_and_convert_to_openai_compat():
+            s = await self.client.text_generation(**params)
+            async for chunk in s:
+                token_result = chunk.token
+
+                choice = OpenAICompatCompletionChoice(text=token_result.text)
+                yield OpenAICompatCompletionResponse(
+                    choices=[choice],
+                )
+
+        stream = _generate_and_convert_to_openai_compat()
+        async for chunk in process_chat_completion_stream_response(
+            request, stream, self.formatter
+        ):
+            yield chunk
+
+    def _get_params(self, request: ChatCompletionRequest) -> dict:
+        prompt, input_tokens = chat_completion_request_to_model_input_info(
+            request, self.formatter
+        )
         max_new_tokens = min(
             request.sampling_params.max_tokens or (self.max_tokens - input_tokens),
             self.max_tokens - input_tokens - 1,
         )
-
+        options = get_sampling_options(request)
-        options = self.get_chat_options(request)
+        return dict(
-        if not request.stream:
-            response = await self.client.text_generation(
             prompt=prompt,
-                stream=False,
+            stream=request.stream,
             details=True,
             max_new_tokens=max_new_tokens,
             stop_sequences=["<|eom_id|>", "<|eot_id|>"],
             **options,
         )
-            stop_reason = None
-            if response.details.finish_reason:
-                if response.details.finish_reason in ["stop", "eos_token"]:
-                    stop_reason = StopReason.end_of_turn
-                elif response.details.finish_reason == "length":
-                    stop_reason = StopReason.out_of_tokens
-
-            generated_text = "".join(t.text for t in response.details.tokens)
-            completion_message = self.formatter.decode_assistant_message_from_content(
-                generated_text,
-                stop_reason,
-            )
-            yield ChatCompletionResponse(
-                completion_message=completion_message,
-                logprobs=None,
-            )
-
-        else:
-            yield ChatCompletionResponseStreamChunk(
-                event=ChatCompletionResponseEvent(
-                    event_type=ChatCompletionResponseEventType.start,
-                    delta="",
-                )
-            )
-            buffer = ""
-            ipython = False
-            stop_reason = None
-            tokens = []
-
-            async for response in await self.client.text_generation(
-                prompt=prompt,
-                stream=True,
-                details=True,
-                max_new_tokens=max_new_tokens,
-                stop_sequences=["<|eom_id|>", "<|eot_id|>"],
-                **options,
-            ):
-                token_result = response.token
-
-                buffer += token_result.text
-                tokens.append(token_result.id)
-
-                if not ipython and buffer.startswith("<|python_tag|>"):
-                    ipython = True
-                    yield ChatCompletionResponseStreamChunk(
-                        event=ChatCompletionResponseEvent(
-                            event_type=ChatCompletionResponseEventType.progress,
-                            delta=ToolCallDelta(
-                                content="",
-                                parse_status=ToolCallParseStatus.started,
-                            ),
-                        )
-                    )
-                    buffer = buffer[len("<|python_tag|>") :]
-                    continue
-
-                if token_result.text == "<|eot_id|>":
-                    stop_reason = StopReason.end_of_turn
-                    text = ""
-                elif token_result.text == "<|eom_id|>":
-                    stop_reason = StopReason.end_of_message
-                    text = ""
-                else:
-                    text = token_result.text
-
-                if ipython:
-                    delta = ToolCallDelta(
-                        content=text,
-                        parse_status=ToolCallParseStatus.in_progress,
-                    )
-                else:
-                    delta = text
-
-                if stop_reason is None:
-                    yield ChatCompletionResponseStreamChunk(
-                        event=ChatCompletionResponseEvent(
-                            event_type=ChatCompletionResponseEventType.progress,
-                            delta=delta,
-                            stop_reason=stop_reason,
-                        )
-                    )
-
-            if stop_reason is None:
-                stop_reason = StopReason.out_of_tokens
-
-            # parse tool calls and report errors
-            message = self.formatter.decode_assistant_message(tokens, stop_reason)
-            parsed_tool_calls = len(message.tool_calls) > 0
-            if ipython and not parsed_tool_calls:
-                yield ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=ChatCompletionResponseEventType.progress,
-                        delta=ToolCallDelta(
-                            content="",
-                            parse_status=ToolCallParseStatus.failure,
-                        ),
-                        stop_reason=stop_reason,
-                    )
-                )
-
-            for tool_call in message.tool_calls:
-                yield ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=ChatCompletionResponseEventType.progress,
-                        delta=ToolCallDelta(
-                            content=tool_call,
-                            parse_status=ToolCallParseStatus.success,
-                        ),
-                        stop_reason=stop_reason,
-                    )
-                )
-
-            yield ChatCompletionResponseStreamChunk(
-                event=ChatCompletionResponseEvent(
-                    event_type=ChatCompletionResponseEventType.complete,
-                    delta="",
-                    stop_reason=stop_reason,
-                )
-            )
 
 
 class TGIAdapter(_HfAdapter):

llama_stack/providers/adapters/inference/together/together.py

@@ -48,10 +48,6 @@ class TogetherInferenceAdapter(
         self.config = config
         self.formatter = ChatFormat(Tokenizer.get_instance())
 
-    @property
-    def client(self) -> Together:
-        return Together(api_key=self.config.api_key)
-
     async def initialize(self) -> None:
         return
 
@@ -91,7 +87,6 @@ class TogetherInferenceAdapter(
             together_api_key = provider_data.together_api_key
 
         client = Together(api_key=together_api_key)
-        # wrapper request to make it easier to pass around (internal only, not exposed to API)
         request = ChatCompletionRequest(
             model=model,
             messages=messages,

llama_stack/providers/tests/inference/test_inference.py

@@ -55,8 +55,8 @@ def get_expected_stop_reason(model: str):
 @pytest_asyncio.fixture(
     scope="session",
     params=[
-        # {"model": Llama_8B},
+        {"model": Llama_8B},
-        {"model": Llama_3B},
+        # {"model": Llama_3B},
     ],
     ids=lambda d: d["model"],
 )

llama_stack/providers/utils/inference/augment_messages.py

@@ -3,8 +3,11 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+from typing import Tuple
+
 from llama_models.llama3.api.chat_format import ChatFormat
 from termcolor import cprint
+
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 from llama_stack.apis.inference import *  # noqa: F403
 from llama_models.datatypes import ModelFamily
@@ -28,6 +31,17 @@ def chat_completion_request_to_prompt(
     return formatter.tokenizer.decode(model_input.tokens)
 
 
+def chat_completion_request_to_model_input_info(
+    request: ChatCompletionRequest, formatter: ChatFormat
+) -> Tuple[str, int]:
+    messages = augment_messages_for_tools(request)
+    model_input = formatter.encode_dialog_prompt(messages)
+    return (
+        formatter.tokenizer.decode(model_input.tokens),
+        len(model_input.tokens),
+    )
+
+
 def augment_messages_for_tools(request: ChatCompletionRequest) -> List[Message]:
     """Reads chat completion request and augments the messages to handle tools.
     For eg. for llama_3_1, add system message with the appropriate tools or

llama_stack/providers/utils/inference/openai_compat.py

@@ -60,6 +60,8 @@ def process_chat_completion_response(
     if reason := choice.finish_reason:
         if reason in ["stop", "eos"]:
             stop_reason = StopReason.end_of_turn
+        elif reason == "eom":
+            stop_reason = StopReason.end_of_message
         elif reason == "length":
             stop_reason = StopReason.out_of_tokens
 
@@ -96,7 +98,7 @@ async def process_chat_completion_stream_response(
         finish_reason = choice.finish_reason
 
         if finish_reason:
-            if stop_reason is None and finish_reason in ["stop", "eos"]:
+            if stop_reason is None and finish_reason in ["stop", "eos", "eos_token"]:
                 stop_reason = StopReason.end_of_turn
             elif stop_reason is None and finish_reason == "length":
                 stop_reason = StopReason.out_of_tokens
@@ -118,7 +120,6 @@ async def process_chat_completion_stream_response(
             buffer += text
             continue
 
-        if ipython:
         if text == "<|eot_id|>":
             stop_reason = StopReason.end_of_turn
             text = ""
@@ -128,6 +129,7 @@ async def process_chat_completion_stream_response(
             text = ""
             continue
 
+        if ipython:
             buffer += text
             delta = ToolCallDelta(
                 content=text,