Merge 999c28e809 into 188a56af5c

llama_stack/core/routers/inference.py

@@ -611,7 +611,7 @@ class InferenceRouter(Inference):
                         completion_text += "".join(choice_data["content_parts"])
 
                     # Add metrics to the chunk
-                    if self.telemetry and chunk.usage:
+                    if self.telemetry and hasattr(chunk, "usage") and chunk.usage:
                         metrics = self._construct_metrics(
                             prompt_tokens=chunk.usage.prompt_tokens,
                             completion_tokens=chunk.usage.completion_tokens,

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

@@ -4,19 +4,12 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.apis.inference import Inference
-
 from .config import WatsonXConfig
 
 
-async def get_adapter_impl(config: WatsonXConfig, _deps) -> Inference:
+async def get_adapter_impl(config: WatsonXConfig, _deps):
-    # import dynamically so `llama stack build` does not fail due to missing dependencies
+    # import dynamically so the import is used only when it is needed
     from .watsonx import WatsonXInferenceAdapter
 
-    if not isinstance(config, WatsonXConfig):
-        raise RuntimeError(f"Unexpected config type: {type(config)}")
     adapter = WatsonXInferenceAdapter(config)
     return adapter
-
-
-__all__ = ["get_adapter_impl", "WatsonXConfig"]

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

@@ -27,11 +27,11 @@ class WatsonXConfig(RemoteInferenceProviderConfig):
     )
     api_key: SecretStr | None = Field(
         default_factory=lambda: os.getenv("WATSONX_API_KEY"),
-        description="The watsonx API key",
+        description="The watsonx.ai API key",
     )
     project_id: str | None = Field(
         default_factory=lambda: os.getenv("WATSONX_PROJECT_ID"),
-        description="The Project ID key",
+        description="The watsonx.ai project ID",
     )
     timeout: int = Field(
         default=60,

llama_stack/providers/remote/inference/watsonx/models.py

@@ -1,47 +0,0 @@
-# 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 llama_stack.models.llama.sku_types import CoreModelId
-from llama_stack.providers.utils.inference.model_registry import build_hf_repo_model_entry
-
-MODEL_ENTRIES = [
-    build_hf_repo_model_entry(
-        "meta-llama/llama-3-3-70b-instruct",
-        CoreModelId.llama3_3_70b_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "meta-llama/llama-2-13b-chat",
-        CoreModelId.llama2_13b.value,
-    ),
-    build_hf_repo_model_entry(
-        "meta-llama/llama-3-1-70b-instruct",
-        CoreModelId.llama3_1_70b_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "meta-llama/llama-3-1-8b-instruct",
-        CoreModelId.llama3_1_8b_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "meta-llama/llama-3-2-11b-vision-instruct",
-        CoreModelId.llama3_2_11b_vision_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "meta-llama/llama-3-2-1b-instruct",
-        CoreModelId.llama3_2_1b_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "meta-llama/llama-3-2-3b-instruct",
-        CoreModelId.llama3_2_3b_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "meta-llama/llama-3-2-90b-vision-instruct",
-        CoreModelId.llama3_2_90b_vision_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "meta-llama/llama-guard-3-11b-vision",
-        CoreModelId.llama_guard_3_11b_vision.value,
-    ),
-]

llama_stack/providers/remote/inference/watsonx/watsonx.py

@@ -4,246 +4,105 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from collections.abc import AsyncGenerator, AsyncIterator
+import asyncio
 from typing import Any
 
-from ibm_watsonx_ai.foundation_models import Model
+import requests
-from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
-from openai import AsyncOpenAI
 
-from llama_stack.apis.inference import (
+from llama_stack.apis.inference import ChatCompletionRequest
-    ChatCompletionRequest,
+from llama_stack.apis.models import Model
-    CompletionRequest,
+from llama_stack.apis.models.models import ModelType
-    GreedySamplingStrategy,
+from llama_stack.providers.remote.inference.watsonx.config import WatsonXConfig
-    Inference,
+from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
-    OpenAIChatCompletion,
-    OpenAIChatCompletionChunk,
-    OpenAICompletion,
-    OpenAIEmbeddingsResponse,
-    OpenAIMessageParam,
-    OpenAIResponseFormatParam,
-    TopKSamplingStrategy,
-    TopPSamplingStrategy,
-)
-from llama_stack.log import get_logger
-from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
-from llama_stack.providers.utils.inference.openai_compat import (
-    prepare_openai_completion_params,
-)
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
-    completion_request_to_prompt,
-    request_has_media,
-)
-
-from . import WatsonXConfig
-from .models import MODEL_ENTRIES
-
-logger = get_logger(name=__name__, category="inference::watsonx")
 
 
-# Note on structured output
+class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
-# WatsonX returns responses with a json embedded into a string.
+    _config: WatsonXConfig
-# Examples:
+    __provider_id__: str = "watsonx"
 
-# ChatCompletionResponse(completion_message=CompletionMessage(content='```json\n{\n
+    def __init__(self, config: WatsonXConfig):
-# "first_name": "Michael",\n  "last_name": "Jordan",\n'...)
+        LiteLLMOpenAIMixin.__init__(
-# Not even a valid JSON, but we can still extract the JSON from the content
+            self,
+            litellm_provider_name="watsonx",
+            api_key_from_config=config.api_key.get_secret_value(),
+            provider_data_api_key_field="watsonx_api_key",
+        )
+        self.available_models = None
+        self.config = config
 
-# CompletionResponse(content=' \nThe best answer is $\\boxed{\\{"name": "Michael Jordan",
+    # get_api_key = LiteLLMOpenAIMixin.get_api_key
-# "year_born": "1963", "year_retired": "2003"\\}}$')
-# Find the start of the boxed content
 
+    def get_base_url(self) -> str:
+        return self.config.url
 
-class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
+    async def initialize(self):
-    def __init__(self, config: WatsonXConfig) -> None:
+        await super().initialize()
-        ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
 
-        logger.info(f"Initializing watsonx InferenceAdapter({config.url})...")
+    async def shutdown(self):
-        self._config = config
+        await super().shutdown()
-        self._openai_client: AsyncOpenAI | None = None
 
-        self._project_id = self._config.project_id
+    async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]:
+        # Get base parameters from parent
+        params = await super()._get_params(request)
 
-    async def initialize(self) -> None:
+        # Add watsonx.ai specific parameters
-        pass
+        params["project_id"] = self.config.project_id
-
+        params["time_limit"] = self.config.timeout
-    async def shutdown(self) -> None:
-        pass
-
-    def _get_client(self, model_id) -> Model:
-        config_api_key = self._config.api_key.get_secret_value() if self._config.api_key else None
-        config_url = self._config.url
-        project_id = self._config.project_id
-        credentials = {"url": config_url, "apikey": config_api_key}
-
-        return Model(model_id=model_id, credentials=credentials, project_id=project_id)
-
-    def _get_openai_client(self) -> AsyncOpenAI:
-        if not self._openai_client:
-            self._openai_client = AsyncOpenAI(
-                base_url=f"{self._config.url}/openai/v1",
-                api_key=self._config.api_key,
-            )
-        return self._openai_client
-
-    async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict:
-        input_dict = {"params": {}}
-        media_present = request_has_media(request)
-        llama_model = self.get_llama_model(request.model)
-        if isinstance(request, ChatCompletionRequest):
-            input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
-        else:
-            assert not media_present, "Together does not support media for Completion requests"
-            input_dict["prompt"] = await completion_request_to_prompt(request)
-        if request.sampling_params:
-            if request.sampling_params.strategy:
-                input_dict["params"][GenParams.DECODING_METHOD] = request.sampling_params.strategy.type
-            if request.sampling_params.max_tokens:
-                input_dict["params"][GenParams.MAX_NEW_TOKENS] = request.sampling_params.max_tokens
-            if request.sampling_params.repetition_penalty:
-                input_dict["params"][GenParams.REPETITION_PENALTY] = request.sampling_params.repetition_penalty
-
-            if isinstance(request.sampling_params.strategy, TopPSamplingStrategy):
-                input_dict["params"][GenParams.TOP_P] = request.sampling_params.strategy.top_p
-                input_dict["params"][GenParams.TEMPERATURE] = request.sampling_params.strategy.temperature
-            if isinstance(request.sampling_params.strategy, TopKSamplingStrategy):
-                input_dict["params"][GenParams.TOP_K] = request.sampling_params.strategy.top_k
-            if isinstance(request.sampling_params.strategy, GreedySamplingStrategy):
-                input_dict["params"][GenParams.TEMPERATURE] = 0.0
-
-        input_dict["params"][GenParams.STOP_SEQUENCES] = ["<|endoftext|>"]
-
-        params = {
-            **input_dict,
-        }
         return params
 
-    async def openai_embeddings(
+    async def check_model_availability(self, model):
-        self,
+        return True
-        model: str,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
-    ) -> OpenAIEmbeddingsResponse:
-        raise NotImplementedError()
 
-    async def openai_completion(
+    async def list_models(self) -> list[Model] | None:
-        self,
+        models = []
-        model: str,
+        for model_spec in self._get_model_specs():
-        prompt: str | list[str] | list[int] | list[list[int]],
+            models.append(
-        best_of: int | None = None,
+                Model(
-        echo: bool | None = None,
+                    identifier=model_spec["model_id"],
-        frequency_penalty: float | None = None,
+                    provider_resource_id=f"{self.__provider_id__}/{model_spec['model_id']}",
-        logit_bias: dict[str, float] | None = None,
+                    provider_id=self.__provider_id__,
-        logprobs: bool | None = None,
+                    metadata={},
-        max_tokens: int | None = None,
+                    model_type=ModelType.llm,
-        n: int | None = None,
+                )
-        presence_penalty: float | None = None,
+            )
-        seed: int | None = None,
+        return models
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-        guided_choice: list[str] | None = None,
-        prompt_logprobs: int | None = None,
-        suffix: str | None = None,
-    ) -> OpenAICompletion:
-        model_obj = await self.model_store.get_model(model)
-        params = await prepare_openai_completion_params(
-            model=model_obj.provider_resource_id,
-            prompt=prompt,
-            best_of=best_of,
-            echo=echo,
-            frequency_penalty=frequency_penalty,
-            logit_bias=logit_bias,
-            logprobs=logprobs,
-            max_tokens=max_tokens,
-            n=n,
-            presence_penalty=presence_penalty,
-            seed=seed,
-            stop=stop,
-            stream=stream,
-            stream_options=stream_options,
-            temperature=temperature,
-            top_p=top_p,
-            user=user,
-        )
-        return await self._get_openai_client().completions.create(**params)  # type: ignore
 
-    async def openai_chat_completion(
+    # LiteLLM provides methods to list models for many providers, but not for watsonx.ai.
-        self,
+    # So we need to implement our own method to list models by calling the watsonx.ai API.
-        model: str,
+    def _get_model_specs(self) -> list[dict[str, Any]]:
-        messages: list[OpenAIMessageParam],
+        """
-        frequency_penalty: float | None = None,
+        Retrieves foundation model specifications from the watsonx.ai API.
-        function_call: str | dict[str, Any] | None = None,
+        """
-        functions: list[dict[str, Any]] | None = None,
+        url = f"{self.config.url}/ml/v1/foundation_model_specs?version=2023-10-25"
-        logit_bias: dict[str, float] | None = None,
+        headers = {
-        logprobs: bool | None = None,
+            # Note that there is no authorization header.  Listing models does not require authentication.
-        max_completion_tokens: int | None = None,
+            "Content-Type": "application/json",
-        max_tokens: int | None = None,
+        }
-        n: int | None = None,
-        parallel_tool_calls: bool | None = None,
-        presence_penalty: float | None = None,
-        response_format: OpenAIResponseFormatParam | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
-        tools: list[dict[str, Any]] | None = None,
-        top_logprobs: int | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        model_obj = await self.model_store.get_model(model)
-        params = await prepare_openai_completion_params(
-            model=model_obj.provider_resource_id,
-            messages=messages,
-            frequency_penalty=frequency_penalty,
-            function_call=function_call,
-            functions=functions,
-            logit_bias=logit_bias,
-            logprobs=logprobs,
-            max_completion_tokens=max_completion_tokens,
-            max_tokens=max_tokens,
-            n=n,
-            parallel_tool_calls=parallel_tool_calls,
-            presence_penalty=presence_penalty,
-            response_format=response_format,
-            seed=seed,
-            stop=stop,
-            stream=stream,
-            stream_options=stream_options,
-            temperature=temperature,
-            tool_choice=tool_choice,
-            tools=tools,
-            top_logprobs=top_logprobs,
-            top_p=top_p,
-            user=user,
-        )
-        if params.get("stream", False):
-            return self._stream_openai_chat_completion(params)
-        return await self._get_openai_client().chat.completions.create(**params)  # type: ignore
 
-    async def _stream_openai_chat_completion(self, params: dict) -> AsyncGenerator:
+        response = requests.get(url, headers=headers)
-        # watsonx.ai sometimes adds usage data to the stream
-        include_usage = False
-        if params.get("stream_options", None):
-            include_usage = params["stream_options"].get("include_usage", False)
-        stream = await self._get_openai_client().chat.completions.create(**params)
 
-        seen_finish_reason = False
+        # --- Process the Response ---
-        async for chunk in stream:
+        # Raise an exception for bad status codes (4xx or 5xx)
-            # Final usage chunk with no choices that the user didn't request, so discard
+        response.raise_for_status()
-            if not include_usage and seen_finish_reason and len(chunk.choices) == 0:
+
-                break
+        # If the request is successful, parse and return the JSON response.
-            yield chunk
+        # The response should contain a list of model specifications
-            for choice in chunk.choices:
+        response_data = response.json()
-                if choice.finish_reason:
+        if "resources" not in response_data:
-                    seen_finish_reason = True
+            raise ValueError("Resources not found in response")
-                    break
+        return response_data["resources"]
+
+
+# TO DO: Delete the test main method.
+if __name__ == "__main__":
+    config = WatsonXConfig(url="https://us-south.ml.cloud.ibm.com", api_key="xxx", project_id="xxx", timeout=60)
+    adapter = WatsonXInferenceAdapter(config)
+    model_specs = adapter._get_model_specs()
+    models = asyncio.run(adapter.list_models())
+    for model in models:
+        print(model.identifier)
+        print(model.provider_resource_id)
+        print(model.provider_id)
+        print(model.metadata)
+        print(model.model_type)
+        print("--------------------------------")

tests/unit/providers/inference/test_inference_client_caching.py

@@ -16,6 +16,8 @@ from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
 from llama_stack.providers.remote.inference.openai.openai import OpenAIInferenceAdapter
 from llama_stack.providers.remote.inference.together.config import TogetherImplConfig
 from llama_stack.providers.remote.inference.together.together import TogetherInferenceAdapter
+from llama_stack.providers.remote.inference.watsonx.config import WatsonXConfig
+from llama_stack.providers.remote.inference.watsonx.watsonx import WatsonXInferenceAdapter
 
 
 def test_groq_provider_openai_client_caching():
@@ -36,6 +38,24 @@ def test_groq_provider_openai_client_caching():
             assert inference_adapter.client.api_key == api_key
 
 
+def test_watsonx_provider_openai_client_caching():
+    """Ensure the WatsonX provider does not cache api keys across client requests"""
+
+    config = WatsonXConfig()
+    inference_adapter = WatsonXInferenceAdapter(config)
+
+    inference_adapter.__provider_spec__ = MagicMock()
+    inference_adapter.__provider_spec__.provider_data_validator = (
+        "llama_stack.providers.remote.inference.watsonx.config.WatsonXProviderDataValidator"
+    )
+
+    for api_key in ["test1", "test2"]:
+        with request_provider_data_context(
+            {"x-llamastack-provider-data": json.dumps({inference_adapter.provider_data_api_key_field: api_key})}
+        ):
+            assert inference_adapter.client.api_key == api_key
+
+
 def test_openai_provider_openai_client_caching():
     """Ensure the OpenAI provider does not cache api keys across client requests"""