Add centml as remote inference provider

llama_stack/providers/remote/inference/centml/__init__.py

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+# 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 pydantic import BaseModel
+
+from .config import CentMLImplConfig
+
+class CentMLProviderDataValidator(BaseModel):
+    centml_api_key: str
+
+async def get_adapter_impl(config: CentMLImplConfig, _deps):
+    """
+    Factory function to construct and initialize the CentML adapter.
+
+    :param config: Instance of CentMLImplConfig, containing `url`, `api_key`, etc.
+    :param _deps: Additional dependencies provided by llama-stack (unused here).
+    """
+    from .centml import CentMLInferenceAdapter
+
+    # Ensure the provided config is indeed a CentMLImplConfig
+    assert isinstance(
+        config, CentMLImplConfig
+    ), f"Unexpected config type: {type(config)}"
+
+    # Instantiate and initialize the adapter
+    adapter = CentMLInferenceAdapter(config)
+    await adapter.initialize()
+    return adapter

llama_stack/providers/remote/inference/centml/centml.py

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+# 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, List, Optional, Union
+
+from openai import OpenAI
+
+from llama_models.datatypes import CoreModelId
+from llama_models.llama3.api.chat_format import ChatFormat
+from llama_models.llama3.api.tokenizer import Tokenizer
+
+from llama_stack.apis.common.content_types import InterleavedContent
+from llama_stack.apis.inference import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    CompletionRequest,
+    EmbeddingsResponse,
+    Inference,
+    LogProbConfig,
+    Message,
+    ResponseFormat,
+    ResponseFormatType,
+    SamplingParams,
+    ToolChoice,
+    ToolDefinition,
+    ToolPromptFormat,
+)
+from llama_stack.distribution.request_headers import NeedsRequestProviderData
+from llama_stack.providers.utils.inference.model_registry import (
+    build_model_alias,
+    ModelRegistryHelper,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    convert_message_to_openai_dict,
+    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,
+    content_has_media,
+    interleaved_content_as_str,
+    request_has_media,
+)
+
+from .config import CentMLImplConfig
+
+# Example model aliases that map from CentML’s
+# published model identifiers to llama-stack's `CoreModelId`.
+MODEL_ALIASES = [
+    build_model_alias(
+        "meta-llama/Llama-3.3-70B-Instruct",
+        CoreModelId.llama3_3_70b_instruct.value,
+    ),
+    build_model_alias(
+        "meta-llama/Llama-3.1-405B-Instruct-FP8",
+        CoreModelId.llama3_1_405b_instruct.value,
+    ),
+]
+
+
+class CentMLInferenceAdapter(ModelRegistryHelper, Inference,
+                             NeedsRequestProviderData):
+    """
+    Adapter to use CentML's serverless inference endpoints,
+    which adhere to the OpenAI chat/completions API spec,
+    inside llama-stack.
+    """
+
+    def __init__(self, config: CentMLImplConfig) -> None:
+        super().__init__(MODEL_ALIASES)
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    def _get_api_key(self) -> str:
+        """
+        Obtain the CentML API key either from the adapter config
+        or from the dynamic provider data in request headers.
+        """
+        if self.config.api_key is not None:
+            return self.config.api_key.get_secret_value()
+        else:
+            provider_data = self.get_request_provider_data()
+            if provider_data is None or not provider_data.centml_api_key:
+                raise ValueError(
+                    'Pass CentML API Key in the header X-LlamaStack-ProviderData as { "centml_api_key": "<your-api-key>" }'
+                )
+            return provider_data.centml_api_key
+
+    def _get_client(self) -> OpenAI:
+        """
+        Creates an OpenAI-compatible client pointing to CentML's base URL,
+        using the user's CentML API key.
+        """
+        api_key = self._get_api_key()
+        return OpenAI(api_key=api_key, base_url=self.config.url)
+
+    #
+    # COMPLETION (non-chat)
+    #
+
+    async def completion(
+        self,
+        model_id: str,
+        content: InterleavedContent,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        """
+        For "completion" style requests (non-chat).
+        """
+        model = await self.model_store.get_model(model_id)
+        request = CompletionRequest(
+            model=model.provider_resource_id,
+            content=content,
+            sampling_params=sampling_params,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        if stream:
+            return self._stream_completion(request)
+        else:
+            return await self._nonstream_completion(request)
+
+    async def _nonstream_completion(
+            self, request: CompletionRequest) -> ChatCompletionResponse:
+        params = await self._get_params(request)
+        # Using the older "completions" route for non-chat
+        response = self._get_client().completions.create(**params)
+        return process_completion_response(response, self.formatter)
+
+    async def _stream_completion(self,
+                                 request: CompletionRequest) -> AsyncGenerator:
+        params = await self._get_params(request)
+
+        async def _to_async_generator():
+            stream = self._get_client().completions.create(**params)
+            for chunk in stream:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_completion_stream_response(
+                stream, self.formatter):
+            yield chunk
+
+    #
+    # CHAT COMPLETION
+    #
+
+    async def chat_completion(
+        self,
+        model_id: 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] = None,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        """
+        For "chat completion" style requests.
+        """
+        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,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        if stream:
+            return self._stream_chat_completion(request)
+        else:
+            return await self._nonstream_chat_completion(request)
+
+    async def _nonstream_chat_completion(
+            self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+        params = await self._get_params(request)
+
+        # For chat requests, if "messages" is in params -> .chat.completions
+        if "messages" in params:
+            response = self._get_client().chat.completions.create(**params)
+        else:
+            # fallback if we ended up only with "prompt"
+            response = self._get_client().completions.create(**params)
+
+        return process_chat_completion_response(response, self.formatter)
+
+    async def _stream_chat_completion(
+            self, request: ChatCompletionRequest) -> AsyncGenerator:
+        params = await self._get_params(request)
+
+        async def _to_async_generator():
+            if "messages" in params:
+                stream = self._get_client().chat.completions.create(**params)
+            else:
+                stream = self._get_client().completions.create(**params)
+            for chunk in stream:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_chat_completion_stream_response(
+                stream, self.formatter):
+            yield chunk
+
+    #
+    # HELPER METHODS
+    #
+
+    async def _get_params(
+            self, request: Union[ChatCompletionRequest,
+                                 CompletionRequest]) -> dict:
+        """
+        Build the 'params' dict that the OpenAI (CentML) client expects.
+        For chat requests, we always prefer "messages" so that it calls
+        the chat endpoint properly.
+        """
+        input_dict = {}
+        media_present = request_has_media(request)
+
+        if isinstance(request, ChatCompletionRequest):
+            # For chat requests, always build "messages" from the user messages
+            input_dict["messages"] = [
+                await convert_message_to_openai_dict(m)
+                for m in request.messages
+            ]
+
+        else:
+            # Non-chat (CompletionRequest)
+            assert not media_present, "CentML does not support media for completions"
+            input_dict["prompt"] = await completion_request_to_prompt(
+                request, self.formatter)
+
+        return {
+            "model":
+            request.model,
+            **input_dict,
+            "stream":
+            request.stream,
+            **self._build_options(request.sampling_params, request.response_format),
+        }
+
+    def _build_options(
+        self,
+        sampling_params: Optional[SamplingParams],
+        fmt: Optional[ResponseFormat],
+    ) -> dict:
+        """
+        Build temperature, max_tokens, top_p, etc., plus any response format data.
+        """
+        options = get_sampling_options(sampling_params)
+        options.setdefault("max_tokens", 512)
+
+        if fmt:
+            if fmt.type == ResponseFormatType.json_schema.value:
+                options["response_format"] = {
+                    "type": "json_object",
+                    "schema": fmt.json_schema,
+                }
+            elif fmt.type == ResponseFormatType.grammar.value:
+                raise NotImplementedError(
+                    "Grammar response format not supported yet")
+            else:
+                raise ValueError(f"Unknown response format {fmt.type}")
+
+        return options
+
+    #
+    # EMBEDDINGS
+    #
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedContent],
+    ) -> EmbeddingsResponse:
+        model = await self.model_store.get_model(model_id)
+        # CentML does not support media
+        assert all(not content_has_media(c) for c in contents), \
+            "CentML does not support media for embeddings"
+
+        resp = self._get_client().embeddings.create(
+            model=model.provider_resource_id,
+            input=[interleaved_content_as_str(c) for c in contents],
+        )
+        embeddings = [item.embedding for item in resp.data]
+        return EmbeddingsResponse(embeddings=embeddings)

llama_stack/providers/remote/inference/centml/config.py

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+# 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 Any, Dict, Optional
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field, SecretStr
+
+
+@json_schema_type
+class CentMLImplConfig(BaseModel):
+    url: str = Field(
+        default="https://api.centml.com/openai/v1",
+        description="The CentML API server URL",
+    )
+    api_key: Optional[SecretStr] = Field(
+        default=None,
+        description="The CentML API Key",
+    )
+
+    @classmethod
+    def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
+        return {
+            "url": "https://api.centml.com/openai/v1",
+            "api_key": "${env.CENTML_API_KEY}",
+        }