impls -> inline, adapters -> remote (#381)

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -0,0 +1,212 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import AsyncGenerator
+
+from fireworks.client import Fireworks
+
+from llama_models.llama3.api.chat_format import ChatFormat
+
+from llama_models.llama3.api.datatypes import Message
+from llama_models.llama3.api.tokenizer import Tokenizer
+
+from llama_stack.apis.inference import *  # noqa: F403
+
+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,
+    process_completion_response,
+    process_completion_stream_response,
+)
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    chat_completion_request_to_prompt,
+    completion_request_to_prompt,
+    convert_message_to_dict,
+    request_has_media,
+)
+
+from .config import FireworksImplConfig
+
+
+FIREWORKS_SUPPORTED_MODELS = {
+    "Llama3.1-8B-Instruct": "fireworks/llama-v3p1-8b-instruct",
+    "Llama3.1-70B-Instruct": "fireworks/llama-v3p1-70b-instruct",
+    "Llama3.1-405B-Instruct": "fireworks/llama-v3p1-405b-instruct",
+    "Llama3.2-1B-Instruct": "fireworks/llama-v3p2-1b-instruct",
+    "Llama3.2-3B-Instruct": "fireworks/llama-v3p2-3b-instruct",
+    "Llama3.2-11B-Vision-Instruct": "fireworks/llama-v3p2-11b-vision-instruct",
+    "Llama3.2-90B-Vision-Instruct": "fireworks/llama-v3p2-90b-vision-instruct",
+}
+
+
+class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
+    def __init__(self, config: FireworksImplConfig) -> None:
+        ModelRegistryHelper.__init__(
+            self, stack_to_provider_models_map=FIREWORKS_SUPPORTED_MODELS
+        )
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+    async def initialize(self) -> None:
+        return
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def completion(
+        self,
+        model: str,
+        content: InterleavedTextMedia,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        request = CompletionRequest(
+            model=model,
+            content=content,
+            sampling_params=sampling_params,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        client = Fireworks(api_key=self.config.api_key)
+        if stream:
+            return self._stream_completion(request, client)
+        else:
+            return await self._nonstream_completion(request, client)
+
+    async def _nonstream_completion(
+        self, request: CompletionRequest, client: Fireworks
+    ) -> CompletionResponse:
+        params = await self._get_params(request)
+        r = await client.completion.acreate(**params)
+        return process_completion_response(r, self.formatter)
+
+    async def _stream_completion(
+        self, request: CompletionRequest, client: Fireworks
+    ) -> AsyncGenerator:
+        params = await self._get_params(request)
+
+        stream = client.completion.acreate(**params)
+        async for chunk in process_completion_stream_response(stream, self.formatter):
+            yield chunk
+
+    async def chat_completion(
+        self,
+        model: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        request = ChatCompletionRequest(
+            model=model,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+
+        client = Fireworks(api_key=self.config.api_key)
+        if stream:
+            return self._stream_chat_completion(request, client)
+        else:
+            return await self._nonstream_chat_completion(request, client)
+
+    async def _nonstream_chat_completion(
+        self, request: ChatCompletionRequest, client: Fireworks
+    ) -> ChatCompletionResponse:
+        params = await self._get_params(request)
+        if "messages" in params:
+            r = await client.chat.completions.acreate(**params)
+        else:
+            r = await client.completion.acreate(**params)
+        return process_chat_completion_response(r, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: ChatCompletionRequest, client: Fireworks
+    ) -> AsyncGenerator:
+        params = await self._get_params(request)
+
+        if "messages" in params:
+            stream = client.chat.completions.acreate(**params)
+        else:
+            stream = client.completion.acreate(**params)
+
+        async for chunk in process_chat_completion_stream_response(
+            stream, self.formatter
+        ):
+            yield chunk
+
+    async def _get_params(
+        self, request: Union[ChatCompletionRequest, CompletionRequest]
+    ) -> dict:
+        input_dict = {}
+        media_present = request_has_media(request)
+
+        if isinstance(request, ChatCompletionRequest):
+            if media_present:
+                input_dict["messages"] = [
+                    await convert_message_to_dict(m) for m in request.messages
+                ]
+            else:
+                input_dict["prompt"] = chat_completion_request_to_prompt(
+                    request, self.formatter
+                )
+        elif isinstance(request, CompletionRequest):
+            assert (
+                not media_present
+            ), "Fireworks does not support media for Completion requests"
+            input_dict["prompt"] = completion_request_to_prompt(request, self.formatter)
+        else:
+            raise ValueError(f"Unknown request type {type(request)}")
+
+        # Fireworks always prepends with BOS
+        if "prompt" in input_dict:
+            if input_dict["prompt"].startswith("<|begin_of_text|>"):
+                input_dict["prompt"] = input_dict["prompt"][len("<|begin_of_text|>") :]
+
+        options = get_sampling_options(request.sampling_params)
+        options.setdefault("max_tokens", 512)
+
+        if fmt := request.response_format:
+            if fmt.type == ResponseFormatType.json_schema.value:
+                options["response_format"] = {
+                    "type": "json_object",
+                    "schema": fmt.json_schema,
+                }
+            elif fmt.type == ResponseFormatType.grammar.value:
+                options["response_format"] = {
+                    "type": "grammar",
+                    "grammar": fmt.bnf,
+                }
+            else:
+                raise ValueError(f"Unknown response format {fmt.type}")
+
+        return {
+            "model": self.map_to_provider_model(request.model),
+            **input_dict,
+            "stream": request.stream,
+            **options,
+        }
+
+    async def embeddings(
+        self,
+        model: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()