feat: inference passthrough provider (#1166)

##  What does this PR do?
In this PR, we implement a passthrough inference provider that works for
any endpoints that respect llama stack inference API definition.

## Test Plan
config some endpoint that respect llama stack inference API definition
and got the inference results successfully

<img width="1268" alt="Screenshot 2025-02-19 at 8 52 51 PM"
src="https://github.com/user-attachments/assets/447816e4-ea7a-4365-b90c-386dc7dcf4a1"
/>

llama_stack/providers/registry/inference.py

@@ -215,4 +215,14 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="passthrough",
+                pip_packages=[],
+                module="llama_stack.providers.remote.inference.passthrough",
+                config_class="llama_stack.providers.remote.inference.passthrough.PassthroughImplConfig",
+                provider_data_validator="llama_stack.providers.remote.inference.passthrough.PassthroughProviderDataValidator",
+            ),
+        ),
     ]

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

@@ -0,0 +1,23 @@
+# 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 PassthroughImplConfig
+
+
+class PassthroughProviderDataValidator(BaseModel):
+    url: str
+    api_key: str
+
+
+async def get_adapter_impl(config: PassthroughImplConfig, _deps):
+    from .passthrough import PassthroughInferenceAdapter
+
+    assert isinstance(config, PassthroughImplConfig), f"Unexpected config type: {type(config)}"
+    impl = PassthroughInferenceAdapter(config)
+    await impl.initialize()
+    return impl

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

@@ -0,0 +1,31 @@
+# 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 pydantic import BaseModel, Field, SecretStr
+
+from llama_stack.schema_utils import json_schema_type
+
+
+@json_schema_type
+class PassthroughImplConfig(BaseModel):
+    url: str = Field(
+        default=None,
+        description="The URL for the passthrough endpoint",
+    )
+
+    api_key: Optional[SecretStr] = Field(
+        default=None,
+        description="API Key for the passthrouth endpoint",
+    )
+
+    @classmethod
+    def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
+        return {
+            "url": "${env.PASSTHROUGH_URL}",
+            "api_key": "${env.PASSTHROUGH_API_KEY}",
+        }

llama_stack/providers/remote/inference/passthrough/passthrough.py

@@ -0,0 +1,148 @@
+# 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
+
+from llama_stack_client import LlamaStackClient
+
+from llama_stack.apis.common.content_types import InterleavedContent
+from llama_stack.apis.inference import (
+    EmbeddingsResponse,
+    Inference,
+    LogProbConfig,
+    Message,
+    ResponseFormat,
+    SamplingParams,
+    ToolChoice,
+    ToolConfig,
+    ToolDefinition,
+    ToolPromptFormat,
+)
+from llama_stack.apis.models import Model
+from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
+
+from .config import PassthroughImplConfig
+
+
+class PassthroughInferenceAdapter(Inference):
+    def __init__(self, config: PassthroughImplConfig) -> None:
+        ModelRegistryHelper.__init__(self, [])
+        self.config = config
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def unregister_model(self, model_id: str) -> None:
+        pass
+
+    async def register_model(self, model: Model) -> Model:
+        return model
+
+    def _get_client(self) -> LlamaStackClient:
+        passthrough_url = None
+        passthrough_api_key = None
+        provider_data = None
+
+        if self.config.url is not None:
+            passthrough_url = self.config.url
+        else:
+            provider_data = self.get_request_provider_data()
+            if provider_data is None or not provider_data.passthrough_url:
+                raise ValueError(
+                    'Pass url of the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_url": <your passthrough url>}'
+                )
+            passthrough_url = provider_data.passthrough_url
+
+        if self.config.api_key is not None:
+            passthrough_api_key = self.config.api_key.get_secret_value()
+        else:
+            provider_data = self.get_request_provider_data()
+            if provider_data is None or not provider_data.passthrough_api_key:
+                raise ValueError(
+                    'Pass API Key for the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_api_key": <your api key>}'
+                )
+            passthrough_api_key = provider_data.passthrough_api_key
+
+        return LlamaStackClient(
+            base_url=passthrough_url,
+            api_key=passthrough_api_key,
+            provider_data=provider_data,
+        )
+
+    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:
+        client = self._get_client()
+        model = await self.model_store.get_model(model_id)
+
+        params = {
+            "model_id": model.provider_resource_id,
+            "content": content,
+            "sampling_params": sampling_params,
+            "response_format": response_format,
+            "stream": stream,
+            "logprobs": logprobs,
+        }
+
+        params = {key: value for key, value in params.items() if value is not None}
+
+        # only pass through the not None params
+        return client.inference.completion(**params)
+
+    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,
+        tool_config: Optional[ToolConfig] = None,
+    ) -> AsyncGenerator:
+        client = self._get_client()
+        model = await self.model_store.get_model(model_id)
+
+        params = {
+            "model_id": model.provider_resource_id,
+            "messages": messages,
+            "sampling_params": sampling_params,
+            "tools": tools,
+            "tool_choice": tool_choice,
+            "tool_prompt_format": tool_prompt_format,
+            "response_format": response_format,
+            "stream": stream,
+            "logprobs": logprobs,
+        }
+
+        params = {key: value for key, value in params.items() if value is not None}
+
+        # only pass through the not None params
+        return client.inference.chat_completion(**params)
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedContent],
+    ) -> EmbeddingsResponse:
+        client = self._get_client()
+        model = await self.model_store.get_model(model_id)
+
+        return client.inference.embeddings(
+            model_id=model.provider_resource_id,
+            contents=contents,
+        )

llama_stack/templates/passthrough/build.yaml

@@ -0,0 +1,32 @@
+version: '2'
+distribution_spec:
+  description: Use for running LLM inference with the endpoint that compatible with Llama Stack API
+  providers:
+    inference:
+    - remote::passthrough
+    vector_io:
+    - inline::faiss
+    - remote::chromadb
+    - remote::pgvector
+    safety:
+    - inline::llama-guard
+    agents:
+    - inline::meta-reference
+    telemetry:
+    - inline::meta-reference
+    eval:
+    - inline::meta-reference
+    datasetio:
+    - remote::huggingface
+    - inline::localfs
+    scoring:
+    - inline::basic
+    - inline::llm-as-judge
+    - inline::braintrust
+    tool_runtime:
+    - remote::brave-search
+    - remote::tavily-search
+    - inline::code-interpreter
+    - inline::rag-runtime
+    - remote::model-context-protocol
+image_type: conda

llama_stack/templates/passthrough/run.yaml

@@ -0,0 +1,120 @@
+version: '2'
+image_name: passthrough
+apis:
+- agents
+- datasetio
+- eval
+- inference
+- safety
+- scoring
+- telemetry
+- tool_runtime
+- vector_io
+providers:
+  inference:
+  - provider_id: passthrough
+    provider_type: remote::passthrough
+    config:
+      url: ${env.PASSTHROUGH_URL}
+      api_key: ${env.PASSTHROUGH_API_KEY}
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+    config: {}
+  vector_io:
+  - provider_id: faiss
+    provider_type: inline::faiss
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/faiss_store.db
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config: {}
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/agents_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
+      sinks: ${env.TELEMETRY_SINKS:console,sqlite}
+      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/passthrough/trace_store.db}
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config: {}
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config: {}
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config: {}
+  scoring:
+  - provider_id: basic
+    provider_type: inline::basic
+    config: {}
+  - provider_id: llm-as-judge
+    provider_type: inline::llm-as-judge
+    config: {}
+  - provider_id: braintrust
+    provider_type: inline::braintrust
+    config:
+      openai_api_key: ${env.OPENAI_API_KEY:}
+  tool_runtime:
+  - provider_id: brave-search
+    provider_type: remote::brave-search
+    config:
+      api_key: ${env.BRAVE_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: tavily-search
+    provider_type: remote::tavily-search
+    config:
+      api_key: ${env.TAVILY_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: code-interpreter
+    provider_type: inline::code-interpreter
+    config: {}
+  - provider_id: rag-runtime
+    provider_type: inline::rag-runtime
+    config: {}
+  - provider_id: model-context-protocol
+    provider_type: remote::model-context-protocol
+    config: {}
+metadata_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-llama}/registry.db
+models:
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-8B-Instruct
+  provider_id: passthrough
+  provider_model_id: llama3.1-8b-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
+  provider_id: passthrough
+  provider_model_id: llama3.2-11b-vision-instruct
+  model_type: llm
+shields:
+- shield_id: meta-llama/Llama-Guard-3-8B
+vector_dbs: []
+datasets: []
+scoring_fns: []
+eval_tasks: []
+tool_groups:
+- toolgroup_id: builtin::websearch
+  provider_id: tavily-search
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
+- toolgroup_id: builtin::code_interpreter
+  provider_id: code-interpreter
+server:
+  port: 8321