Add Runpod Provider + Distribution (#362)

Add Runpod as a inference provider for openAI compatible managed
endpoints.

Testing 
- Configured llama stack from scratch, set `remote::runpod` as a
inference provider.
- Added Runpod Endpoint URL and API key. 
- Started llama-stack server - llama stack run my-local-stack --port
3000
```
curl http://localhost:5000/inference/chat_completion \
-H "Content-Type: application/json" \
-d '{
	"model": "Llama3.1-8B-Instruct",
	"messages": [
		{"role": "system", "content": "You are a helpful assistant."},
		{"role": "user", "content": "Write me a 2 sentence poem about the moon"}
	],
	"sampling_params": {"temperature": 0.7, "seed": 42, "max_tokens": 512}
}' ```

---------

Signed-off-by: pandyamarut <pandyamarut@gmail.com>

distributions/runpod/build.yaml

@@ -0,0 +1,9 @@
+name: runpod
+distribution_spec:
+  description: Use Runpod for running LLM inference
+  providers:
+    inference: remote::runpod
+    memory: meta-reference
+    safety: meta-reference
+    agents: meta-reference
+    telemetry: meta-reference

llama_stack/providers/adapters/inference/runpod/__init__.py

@@ -0,0 +1,17 @@
+# 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 .config import RunpodImplConfig
+from .runpod import RunpodInferenceAdapter
+
+
+async def get_adapter_impl(config: RunpodImplConfig, _deps):
+    assert isinstance(
+        config, RunpodImplConfig 
+    ), f"Unexpected config type: {type(config)}"
+    impl = RunpodInferenceAdapter(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/adapters/inference/runpod/config.py

@@ -0,0 +1,22 @@
+# 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 Optional
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field
+
+
+@json_schema_type
+class RunpodImplConfig(BaseModel):
+    url: Optional[str] = Field(
+        default=None,
+        description="The URL for the Runpod model serving endpoint",
+    )
+    api_token: Optional[str] = Field(
+        default=None,
+        description="The API token",
+    )

llama_stack/providers/adapters/inference/runpod/runpod.py

@@ -0,0 +1,133 @@
+# 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 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 openai import OpenAI
+
+from llama_stack.apis.inference import *  # noqa: F403
+# from llama_stack.providers.datatypes import ModelsProtocolPrivate
+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 llama_stack.providers.utils.inference.prompt_adapter import (
+    chat_completion_request_to_prompt,
+)
+
+from .config import RunpodImplConfig
+
+RUNPOD_SUPPORTED_MODELS = {
+    "Llama3.1-8B": "meta-llama/Llama-3.1-8B",
+    "Llama3.1-70B": "meta-llama/Llama-3.1-70B",
+    "Llama3.1-405B:bf16-mp8": "meta-llama/Llama-3.1-405B",
+    "Llama3.1-405B": "meta-llama/Llama-3.1-405B-FP8",
+    "Llama3.1-405B:bf16-mp16": "meta-llama/Llama-3.1-405B",
+    "Llama3.1-8B-Instruct": "meta-llama/Llama-3.1-8B-Instruct",
+    "Llama3.1-70B-Instruct": "meta-llama/Llama-3.1-70B-Instruct",
+    "Llama3.1-405B-Instruct:bf16-mp8": "meta-llama/Llama-3.1-405B-Instruct",
+    "Llama3.1-405B-Instruct": "meta-llama/Llama-3.1-405B-Instruct-FP8",
+    "Llama3.1-405B-Instruct:bf16-mp16": "meta-llama/Llama-3.1-405B-Instruct",
+    "Llama3.2-1B": "meta-llama/Llama-3.2-1B",
+    "Llama3.2-3B": "meta-llama/Llama-3.2-3B",
+}
+class RunpodInferenceAdapter(ModelRegistryHelper, Inference):
+    def __init__(self, config: RunpodImplConfig) -> None:
+        ModelRegistryHelper.__init__(
+            self, stack_to_provider_models_map=RUNPOD_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:
+        raise NotImplementedError()
+
+    async def chat_completion(
+        self,
+        model: 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:
+        request = ChatCompletionRequest(
+            model=model,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+
+        client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
+        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: OpenAI
+    ) -> ChatCompletionResponse:
+        params = self._get_params(request)
+        r = client.completions.create(**params)
+        return process_chat_completion_response(r, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: ChatCompletionRequest, client: OpenAI
+    ) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        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(
+            stream, self.formatter
+        ):
+            yield chunk
+
+    def _get_params(self, request: ChatCompletionRequest) -> dict:
+        return {
+            "model": self.map_to_provider_model(request.model),
+            "prompt": chat_completion_request_to_prompt(request, self.formatter),
+            "stream": request.stream,
+            **get_sampling_options(request.sampling_params),
+        }
+
+    async def embeddings(
+        self,
+        model: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()

llama_stack/providers/registry/inference.py

@@ -195,4 +195,13 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="runpod",
+                pip_packages=["openai"],
+                module="llama_stack.providers.adapters.inference.runpod",
+                config_class="llama_stack.providers.adapters.inference.runpod.RunpodImplConfig",
+            ),
+        ),
     ]