From ae804ed5a857e49d27603728960cb1a3e6742277 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 20 Sep 2025 05:05:05 -0400 Subject: [PATCH] feat: (re-)enable Databricks inference adapter Databricks inference adapter was broken, would not start, see #3486 - remove deprecated completion / chat_completion endpoints - enable dynamic model listing w/o refresh, listing is not async - use SecretStr instead of str for token - backward incompatible change: for consistency with databricks docs, env DATABRICKS_URL -> DATABRICKS_HOST and DATABRICKS_API_TOKEN -> DATABRICKS_TOKEN - databricks urls are custom per user/org, add special recorder handling for databricks urls - add integration test --setup databricks - enable chat completions tests - enable embeddings tests - disable n > 1 tests - disable embeddings base64 tests - disable embeddings dimensions tests note: reasoning models, e.g. gpt oss, fail because databricks has a custom, incompatible response format test with: ./scripts/integration-tests.sh --stack-config server:ci-tests --setup databricks --subdirs inference --pattern openai note: databricks needs to be manually added to the ci-tests distro for replay testing --- .../providers/inference/remote_databricks.md | 6 +- llama_stack/providers/registry/inference.py | 2 +- .../remote/inference/databricks/__init__.py | 3 +- .../remote/inference/databricks/config.py | 10 +- .../remote/inference/databricks/databricks.py | 191 +- .../providers/utils/inference/openai_mixin.py | 2 +- llama_stack/testing/inference_recorder.py | 4 + .../inference/test_openai_completion.py | 2 +- .../inference/test_openai_embeddings.py | 4 +- .../recordings/responses/121a72d1c4cf.json | 728 ++++ .../recordings/responses/249b7f0ddde6.json | 56 + .../recordings/responses/28648cf8d421.json | 56 + .../recordings/responses/29585e055e6f.json | 56 + .../recordings/responses/3ef0f9aab128.json | 344 ++ .../recordings/responses/441e2832387f.json | 1061 ++++++ .../recordings/responses/5fa0e98f3d84.json | 1061 ++++++ .../recordings/responses/6d937e5e9233.json | 1061 ++++++ .../recordings/responses/7f53b458dad9.json | 83 + .../recordings/responses/875323ed9913.json | 3125 +++++++++++++++++ .../recordings/responses/9b9e8cf39b15.json | 1062 ++++++ .../recordings/responses/d64ffaa0de6f.json | 1062 ++++++ .../recordings/responses/e509387fc329.json | 168 + .../recordings/responses/ecf6f0c51485.json | 536 +++ .../recordings/responses/ffd7b58fded8.json | 1061 ++++++ tests/integration/suites.py | 8 + 25 files changed, 11650 insertions(+), 102 deletions(-) create mode 100644 tests/integration/recordings/responses/121a72d1c4cf.json create mode 100644 tests/integration/recordings/responses/249b7f0ddde6.json create mode 100644 tests/integration/recordings/responses/28648cf8d421.json create mode 100644 tests/integration/recordings/responses/29585e055e6f.json create mode 100644 tests/integration/recordings/responses/3ef0f9aab128.json create mode 100644 tests/integration/recordings/responses/441e2832387f.json create mode 100644 tests/integration/recordings/responses/5fa0e98f3d84.json create mode 100644 tests/integration/recordings/responses/6d937e5e9233.json create mode 100644 tests/integration/recordings/responses/7f53b458dad9.json create mode 100644 tests/integration/recordings/responses/875323ed9913.json create mode 100644 tests/integration/recordings/responses/9b9e8cf39b15.json create mode 100644 tests/integration/recordings/responses/d64ffaa0de6f.json create mode 100644 tests/integration/recordings/responses/e509387fc329.json create mode 100644 tests/integration/recordings/responses/ecf6f0c51485.json create mode 100644 tests/integration/recordings/responses/ffd7b58fded8.json diff --git a/docs/source/providers/inference/remote_databricks.md b/docs/source/providers/inference/remote_databricks.md index d0ac89055..3b418f7d4 100644 --- a/docs/source/providers/inference/remote_databricks.md +++ b/docs/source/providers/inference/remote_databricks.md @@ -9,13 +9,13 @@ Databricks inference provider for running models on Databricks' unified analytic | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `url` | `` | No | | The URL for the Databricks model serving endpoint | -| `api_token` | `` | No | | The Databricks API token | +| `api_token` | `` | No | | The Databricks API token | ## Sample Configuration ```yaml -url: ${env.DATABRICKS_URL:=} -api_token: ${env.DATABRICKS_API_TOKEN:=} +url: ${env.DATABRICKS_HOST:=} +api_token: ${env.DATABRICKS_TOKEN:=} ``` diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 0eb4cf104..9b70f4f7b 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -152,7 +152,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="databricks", - pip_packages=[], + pip_packages=["databricks-sdk"], module="llama_stack.providers.remote.inference.databricks", config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig", description="Databricks inference provider for running models on Databricks' unified analytics platform.", diff --git a/llama_stack/providers/remote/inference/databricks/__init__.py b/llama_stack/providers/remote/inference/databricks/__init__.py index 89da31130..24f658a2b 100644 --- a/llama_stack/providers/remote/inference/databricks/__init__.py +++ b/llama_stack/providers/remote/inference/databricks/__init__.py @@ -5,10 +5,11 @@ # the root directory of this source tree. from .config import DatabricksImplConfig -from .databricks import DatabricksInferenceAdapter async def get_adapter_impl(config: DatabricksImplConfig, _deps): + from .databricks import DatabricksInferenceAdapter + assert isinstance(config, DatabricksImplConfig), f"Unexpected config type: {type(config)}" impl = DatabricksInferenceAdapter(config) await impl.initialize() diff --git a/llama_stack/providers/remote/inference/databricks/config.py b/llama_stack/providers/remote/inference/databricks/config.py index cc2a2c302..67cd0480c 100644 --- a/llama_stack/providers/remote/inference/databricks/config.py +++ b/llama_stack/providers/remote/inference/databricks/config.py @@ -6,7 +6,7 @@ from typing import Any -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, SecretStr from llama_stack.schema_utils import json_schema_type @@ -17,16 +17,16 @@ class DatabricksImplConfig(BaseModel): default=None, description="The URL for the Databricks model serving endpoint", ) - api_token: str = Field( - default=None, + api_token: SecretStr = Field( + default=SecretStr(None), description="The Databricks API token", ) @classmethod def sample_run_config( cls, - url: str = "${env.DATABRICKS_URL:=}", - api_token: str = "${env.DATABRICKS_API_TOKEN:=}", + url: str = "${env.DATABRICKS_HOST:=}", + api_token: str = "${env.DATABRICKS_TOKEN:=}", **kwargs: Any, ) -> dict[str, Any]: return { diff --git a/llama_stack/providers/remote/inference/databricks/databricks.py b/llama_stack/providers/remote/inference/databricks/databricks.py index 34ee59212..f2dc302e0 100644 --- a/llama_stack/providers/remote/inference/databricks/databricks.py +++ b/llama_stack/providers/remote/inference/databricks/databricks.py @@ -4,23 +4,26 @@ # 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 +from collections.abc import AsyncIterator +from typing import Any -from openai import OpenAI +from databricks.sdk import WorkspaceClient from llama_stack.apis.common.content_types import ( InterleavedContent, InterleavedContentItem, ) from llama_stack.apis.inference import ( - ChatCompletionRequest, ChatCompletionResponse, + ChatCompletionResponseStreamChunk, + CompletionResponse, + CompletionResponseStreamChunk, EmbeddingsResponse, EmbeddingTaskType, Inference, LogProbConfig, Message, - OpenAIEmbeddingsResponse, + OpenAICompletion, ResponseFormat, SamplingParams, TextTruncation, @@ -29,49 +32,50 @@ from llama_stack.apis.inference import ( ToolDefinition, ToolPromptFormat, ) -from llama_stack.models.llama.sku_types import CoreModelId +from llama_stack.apis.models import Model, ModelType +from llama_stack.log import get_logger from llama_stack.providers.utils.inference.model_registry import ( - ModelRegistryHelper, - build_hf_repo_model_entry, -) -from llama_stack.providers.utils.inference.openai_compat import ( - OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, - 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, + ProviderModelEntry, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import DatabricksImplConfig -SAFETY_MODELS_ENTRIES = [] +logger = get_logger(name=__name__, category="inference::databricks") -# https://docs.databricks.com/aws/en/machine-learning/model-serving/foundation-model-overview -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "databricks-meta-llama-3-1-70b-instruct", - CoreModelId.llama3_1_70b_instruct.value, + +# source: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models +EMBEDDING_MODEL_ENTRIES = { + "databricks-gte-large-en": ProviderModelEntry( + provider_model_id="databricks-gte-large-en", + metadata={ + "embedding_dimension": 1024, + "context_length": 8192, + }, ), - build_hf_repo_model_entry( - "databricks-meta-llama-3-1-405b-instruct", - CoreModelId.llama3_1_405b_instruct.value, + "databricks-bge-large-en": ProviderModelEntry( + provider_model_id="databricks-bge-large-en", + metadata={ + "embedding_dimension": 1024, + "context_length": 512, + }, ), -] + SAFETY_MODELS_ENTRIES +} class DatabricksInferenceAdapter( - ModelRegistryHelper, + OpenAIMixin, Inference, - OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, ): def __init__(self, config: DatabricksImplConfig) -> None: - ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) self.config = config + def get_api_key(self) -> str: + return self.config.api_token.get_secret_value() + + def get_base_url(self) -> str: + return f"{self.config.url}/serving-endpoints" + async def initialize(self) -> None: return @@ -80,72 +84,54 @@ class DatabricksInferenceAdapter( async def completion( self, - model: str, + model_id: str, content: InterleavedContent, sampling_params: SamplingParams | None = None, response_format: ResponseFormat | None = None, stream: bool | None = False, logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: + ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]: + raise NotImplementedError() + + async def openai_completion( + self, + model: str, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | 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, + 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: raise NotImplementedError() async def chat_completion( self, - model: str, + model_id: str, messages: list[Message], sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, tools: list[ToolDefinition] | None = None, tool_choice: ToolChoice | None = ToolChoice.auto, tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, stream: bool | None = False, logprobs: LogProbConfig | None = None, tool_config: ToolConfig | None = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - request = ChatCompletionRequest( - model=model, - messages=messages, - sampling_params=sampling_params, - tools=tools or [], - stream=stream, - logprobs=logprobs, - tool_config=tool_config, - ) - - 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, request) - - 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, request): - yield chunk - - def _get_params(self, request: ChatCompletionRequest) -> dict: - return { - "model": request.model, - "prompt": chat_completion_request_to_prompt(request, self.get_llama_model(request.model)), - "stream": request.stream, - **get_sampling_options(request.sampling_params), - } + ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]: + raise NotImplementedError() async def embeddings( self, @@ -157,12 +143,39 @@ class DatabricksInferenceAdapter( ) -> EmbeddingsResponse: raise NotImplementedError() - async def openai_embeddings( - self, - model: str, - input: str | list[str], - encoding_format: str | None = "float", - dimensions: int | None = None, - user: str | None = None, - ) -> OpenAIEmbeddingsResponse: - raise NotImplementedError() + async def list_models(self) -> list[Model] | None: + self._model_cache = {} # from OpenAIMixin + ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key()) # TODO: this is not async + endpoints = ws_client.serving_endpoints.list() + for endpoint in endpoints: + model = Model( + provider_id=self.__provider_id__, + provider_resource_id=endpoint.name, + identifier=endpoint.name, + ) + if endpoint.task == "llm/v1/chat": + model.model_type = ModelType.llm # this is redundant, but informative + elif endpoint.task == "llm/v1/embeddings": + if endpoint.name not in EMBEDDING_MODEL_ENTRIES: + logger.warning(f"No metadata information available for embedding model {endpoint.name}, skipping.") + continue + model.model_type = ModelType.embedding + model.metadata = EMBEDDING_MODEL_ENTRIES[endpoint.name].metadata + else: + logger.warning(f"Unknown model type, skipping: {endpoint}") + continue + + self._model_cache[endpoint.name] = model + + return list(self._model_cache.values()) + + async def register_model(self, model: Model) -> Model: + if not await self.check_model_availability(model.provider_resource_id): + raise ValueError(f"Model {model.provider_resource_id} is not available in Databricks workspace.") + return model + + async def unregister_model(self, model_id: str) -> None: + pass + + async def should_refresh_models(self) -> bool: + return False diff --git a/llama_stack/providers/utils/inference/openai_mixin.py b/llama_stack/providers/utils/inference/openai_mixin.py index c57f65bca..2fe343f63 100644 --- a/llama_stack/providers/utils/inference/openai_mixin.py +++ b/llama_stack/providers/utils/inference/openai_mixin.py @@ -296,7 +296,7 @@ class OpenAIMixin(ABC): return OpenAIEmbeddingsResponse( data=data, - model=response.model, + model=model, usage=usage, ) diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 674016fb1..b3028a66b 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -262,6 +262,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint raise ValueError(f"Unknown client type: {client_type}") url = base_url.rstrip("/") + endpoint + # Special handling for Databricks URLs to avoid leaking workspace info + # e.g. https://adb-1234567890123456.7.cloud.databricks.com -> https://...cloud.databricks.com + if "cloud.databricks.com" in url: + url = "__databricks__" + url.split("cloud.databricks.com")[-1] method = "POST" headers = {} body = kwargs diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index b232f8658..8ca3860c3 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -98,6 +98,7 @@ def skip_if_doesnt_support_n(client_with_models, model_id): # the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'} "remote::tgi", # TGI ignores n param silently "remote::together", # `n` > 1 is not supported when streaming tokens. Please disable `stream` + "remote::databricks", # Bad request: parameter "n" must be equal to 1 for streaming mode ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") @@ -110,7 +111,6 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode "inline::vllm", "remote::bedrock", "remote::cerebras", - "remote::databricks", "remote::runpod", "remote::watsonx", # watsonx returns 404 when hitting the /openai/v1 endpoint ): diff --git a/tests/integration/inference/test_openai_embeddings.py b/tests/integration/inference/test_openai_embeddings.py index 622b97287..bebc7b6d8 100644 --- a/tests/integration/inference/test_openai_embeddings.py +++ b/tests/integration/inference/test_openai_embeddings.py @@ -41,6 +41,7 @@ def skip_if_model_doesnt_support_encoding_format_base64(client, model_id): provider = provider_from_model(client, model_id) if provider.provider_type in ( "remote::together", # param silently ignored, always returns floats + "remote::databricks", # param silently ignored, always returns floats ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support encoding_format='base64'.") @@ -50,6 +51,8 @@ def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_i if provider.provider_type in ( "remote::together", # returns 400 "inline::sentence-transformers", + # Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "dimensions"\n'} + "remote::databricks", ): pytest.skip( f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions." @@ -73,7 +76,6 @@ def skip_if_model_doesnt_support_openai_embeddings(client, model_id): "inline::meta-reference", "remote::bedrock", "remote::cerebras", - "remote::databricks", "remote::runpod", "remote::sambanova", "remote::tgi", diff --git a/tests/integration/recordings/responses/121a72d1c4cf.json b/tests/integration/recordings/responses/121a72d1c4cf.json new file mode 100644 index 000000000..2f4bd7dce --- /dev/null +++ b/tests/integration/recordings/responses/121a72d1c4cf.json @@ -0,0 +1,728 @@ +{ + "request": { + "method": "POST", + "url": "__databricks__/serving-endpoints/v1/chat/completions", + "headers": {}, + "body": { + "model": "databricks-meta-llama-3-3-70b-instruct", + "messages": [ + { + "role": "user", + "content": "Hello, world!" + } + ], + "stream": true + }, + "endpoint": "/v1/chat/completions", + "model": "databricks-meta-llama-3-3-70b-instruct" + }, + "response": { + "body": [ + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d", + "choices": [ + { + "delta": { + "content": "", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1758326500, + "model": "meta-llama-3.3-70b-instruct-121024", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": null, + "usage": { + "completion_tokens": 3, + "prompt_tokens": 14, + "total_tokens": 17, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d", + "choices": [ + { + "delta": { + "content": "Hello! 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"bge-large-en-v1.5", + "object": "list", + "usage": { + "prompt_tokens": 5, + "total_tokens": 5 + }, + "id": "f6acf878-03d6-4245-a55e-e01b68ddd8c8" + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/suites.py b/tests/integration/suites.py index 231480447..321f477ea 100644 --- a/tests/integration/suites.py +++ b/tests/integration/suites.py @@ -108,6 +108,14 @@ SETUP_DEFINITIONS: dict[str, Setup] = { "embedding_model": "together/togethercomputer/m2-bert-80M-32k-retrieval", }, ), + "databricks": Setup( + name="databricks", + description="Databricks models", + defaults={ + "text_model": "databricks/databricks-meta-llama-3-3-70b-instruct", + "embedding_model": "databricks/databricks-bge-large-en", + }, + ), }