Add SambaNova Providier

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -0,0 +1,219 @@
+# 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.
+
+import json
+from typing import AsyncGenerator
+
+from llama_models.datatypes import CoreModelId, SamplingStrategy
+
+from llama_models.llama3.api.chat_format import ChatFormat
+
+from llama_models.llama3.api.datatypes import Message, ImageMedia
+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.model_registry import (
+    build_model_alias,
+    ModelRegistryHelper,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    process_chat_completion_response,
+    process_chat_completion_stream_response,
+)
+
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    convert_message_to_dict,
+)
+
+from .config import SambaNovaImplConfig
+
+MODEL_ALIASES = [
+    build_model_alias(
+        "Meta-Llama-3.1-8B-Instruct",
+        CoreModelId.llama3_1_8b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.1-70B-Instruct",
+        CoreModelId.llama3_1_70b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.1-405B-Instruct",
+        CoreModelId.llama3_1_405b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.2-1B-Instruct",
+        CoreModelId.llama3_2_1b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.2-3B-Instruct",
+        CoreModelId.llama3_2_3b_instruct.value,
+    ),
+    build_model_alias(
+        "Llama-3.2-11B-Vision-Instruct",
+        CoreModelId.llama3_2_11b_vision_instruct.value,
+    ),
+    build_model_alias(
+        "Llama-3.2-90B-Vision-Instruct",
+        CoreModelId.llama3_2_90b_vision_instruct.value,
+    ),
+]
+
+
+class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
+    def __init__(self, config: SambaNovaImplConfig) -> None:
+        ModelRegistryHelper.__init__(
+            self,
+            model_aliases=MODEL_ALIASES,
+        )
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+    async def initialize(self) -> None:
+        return
+
+    async def shutdown(self) -> None:
+        pass
+
+    def _get_client(self) -> OpenAI:
+        return OpenAI(base_url=self.config.url, api_key=self.config.api_key)
+
+    async def completion(
+        self,
+        model_id: str,
+        content: InterleavedTextMedia,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        raise NotImplementedError()
+    
+    async def chat_completion(
+        self,
+        model_id: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        model = await self.model_store.get_model(model_id)
+
+        request = ChatCompletionRequest(
+            model=model.provider_resource_id,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        request_sambanova = await self.convert_chat_completion_request(request)
+
+        client = OpenAI(base_url=self.config.url, api_key=self.config.api_key)
+        if stream:
+            return self._stream_chat_completion(request_sambanova, client)
+        else:
+            return await self._nonstream_chat_completion(request_sambanova, client)
+        
+    async def _nonstream_chat_completion(
+        self, request: ChatCompletionRequest, client: OpenAI
+    ) -> ChatCompletionResponse:
+        r = client.chat.completions.create(**request)
+        return process_chat_completion_response(r, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: ChatCompletionRequest, client: OpenAI
+    ) -> AsyncGenerator:
+        async def _to_async_generator():
+            s = client.chat.completions.create(**request)
+            for chunk in s:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_chat_completion_stream_response(
+            stream, self.formatter
+        ):
+            yield chunk
+        
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()
+    
+    async def convert_chat_completion_request(self, request: ChatCompletionRequest) -> dict:
+        compatible_request = self.convert_sampling_params(request.sampling_params)
+        compatible_request["model"] = request.model
+        compatible_request["messages"] = await self.convert_to_sambanova_message(request.messages)
+        compatible_request["stream"] = request.stream
+        compatible_request["logprobs"] = False
+        compatible_request["extra_headers"] = {
+            b"User-Agent": b"llama-stack: sambanova-inference-adapter",
+        }
+        return compatible_request
+
+    def convert_sampling_params(self, sampling_params: SamplingParams, legacy: bool = False) -> dict:
+        params = {}
+
+        if sampling_params:
+            params["frequency_penalty"] = sampling_params.repetition_penalty
+
+            if sampling_params.max_tokens:
+                if legacy:
+                    params["max_tokens"] = sampling_params.max_tokens
+                else:
+                    params["max_completion_tokens"] = sampling_params.max_tokens
+
+            if sampling_params.strategy == SamplingStrategy.top_p:
+                params["top_p"] = sampling_params.top_p
+            elif sampling_params.strategy == "top_k":
+                params["extra_body"]["top_k"] = sampling_params.top_k
+            elif sampling_params.strategy == "greedy":
+                params["temperature"] = sampling_params.temperature
+        
+        return params
+
+    async def convert_to_sambanova_message(self, messages: List[Message]) -> List[dict]:
+        conversation = []
+        for message in messages:
+            content = await convert_message_to_dict(message)
+            
+            # Need to override role
+            if isinstance(message, UserMessage):
+                content["role"] = "user"
+            elif isinstance(message, CompletionMessage):
+                content["role"] = "assistant"
+                tools = []
+                for tool_call in message.tool_calls:
+                    tools.append({
+                        "id": tool_call.call_id,
+                        "function": {
+                            "name": tool_call.name,
+                            "arguments": json.dumps(tool_call.arguments),
+                        },
+                        "type": "function",
+                    })
+                content["tool_calls"] = tools
+            elif isinstance(message, ToolResponseMessage):
+                content["role"] = "tool"
+                content["tool_call_id"] = message.call_id
+            elif isinstance(message, SystemMessage):
+                content["role"] = "system"
+
+            conversation.append(content)
+
+        return conversation
+        
+