Merge branch 'main' into ui-compose

llama_stack/apis/datasets/client.py

@@ -78,6 +78,21 @@ class DatasetsClient(Datasets):
 
             return [DatasetDefWithProvider(**x) for x in response.json()]
 
+    async def unregister_dataset(
+        self,
+        dataset_id: str,
+    ) -> None:
+        async with httpx.AsyncClient() as client:
+            response = await client.delete(
+                f"{self.base_url}/datasets/unregister",
+                params={
+                    "dataset_id": dataset_id,
+                },
+                headers={"Content-Type": "application/json"},
+                timeout=60,
+            )
+            response.raise_for_status()
+
 
 async def run_main(host: str, port: int):
     client = DatasetsClient(f"http://{host}:{port}")

llama_stack/apis/datasets/datasets.py

@@ -64,3 +64,9 @@ class Datasets(Protocol):
 
     @webmethod(route="/datasets/list", method="GET")
     async def list_datasets(self) -> List[Dataset]: ...
+
+    @webmethod(route="/datasets/unregister", method="POST")
+    async def unregister_dataset(
+        self,
+        dataset_id: str,
+    ) -> None: ...

llama_stack/distribution/routers/routing_tables.py

@@ -57,6 +57,8 @@ async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None:
         return await p.unregister_memory_bank(obj.identifier)
     elif api == Api.inference:
         return await p.unregister_model(obj.identifier)
+    elif api == Api.datasetio:
+        return await p.unregister_dataset(obj.identifier)
     else:
         raise ValueError(f"Unregister not supported for {api}")
 
@@ -354,6 +356,12 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
         )
         await self.register_object(dataset)
 
+    async def unregister_dataset(self, dataset_id: str) -> None:
+        dataset = await self.get_dataset(dataset_id)
+        if dataset is None:
+            raise ValueError(f"Dataset {dataset_id} not found")
+        await self.unregister_object(dataset)
+
 
 class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions):
     async def list_scoring_functions(self) -> List[ScoringFn]:

llama_stack/providers/datatypes.py

@@ -63,6 +63,8 @@ class MemoryBanksProtocolPrivate(Protocol):
 class DatasetsProtocolPrivate(Protocol):
     async def register_dataset(self, dataset: Dataset) -> None: ...
 
+    async def unregister_dataset(self, dataset_id: str) -> None: ...
+
 
 class ScoringFunctionsProtocolPrivate(Protocol):
     async def list_scoring_functions(self) -> List[ScoringFn]: ...

llama_stack/providers/inline/datasetio/localfs/datasetio.py

@@ -97,6 +97,9 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
             dataset_impl=dataset_impl,
         )
 
+    async def unregister_dataset(self, dataset_id: str) -> None:
+        del self.dataset_infos[dataset_id]
+
     async def get_rows_paginated(
         self,
         dataset_id: str,

llama_stack/providers/registry/inference.py

@@ -61,6 +61,17 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.remote.inference.sample.SampleConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="cerebras",
+                pip_packages=[
+                    "cerebras_cloud_sdk",
+                ],
+                module="llama_stack.providers.remote.inference.cerebras",
+                config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig",
+            ),
+        ),
         remote_provider_spec(
             api=Api.inference,
             adapter=AdapterSpec(

llama_stack/providers/remote/datasetio/huggingface/huggingface.py

@@ -64,6 +64,11 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
         )
         self.dataset_infos[dataset_def.identifier] = dataset_def
 
+    async def unregister_dataset(self, dataset_id: str) -> None:
+        key = f"{DATASETS_PREFIX}{dataset_id}"
+        await self.kvstore.delete(key=key)
+        del self.dataset_infos[dataset_id]
+
     async def get_rows_paginated(
         self,
         dataset_id: str,

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

@@ -0,0 +1,21 @@
+# 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 CerebrasImplConfig
+
+
+async def get_adapter_impl(config: CerebrasImplConfig, _deps):
+    from .cerebras import CerebrasInferenceAdapter
+
+    assert isinstance(
+        config, CerebrasImplConfig
+    ), f"Unexpected config type: {type(config)}"
+
+    impl = CerebrasInferenceAdapter(config)
+
+    await impl.initialize()
+
+    return impl

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -0,0 +1,191 @@
+# 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 cerebras.cloud.sdk import AsyncCerebras
+
+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 llama_stack.apis.inference import *  # noqa: F403
+
+from llama_models.datatypes import CoreModelId
+
+from llama_stack.providers.utils.inference.model_registry import (
+    build_model_alias,
+    ModelRegistryHelper,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    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,
+)
+
+from .config import CerebrasImplConfig
+
+
+model_aliases = [
+    build_model_alias(
+        "llama3.1-8b",
+        CoreModelId.llama3_1_8b_instruct.value,
+    ),
+    build_model_alias(
+        "llama3.1-70b",
+        CoreModelId.llama3_1_70b_instruct.value,
+    ),
+]
+
+
+class CerebrasInferenceAdapter(ModelRegistryHelper, Inference):
+    def __init__(self, config: CerebrasImplConfig) -> None:
+        ModelRegistryHelper.__init__(
+            self,
+            model_aliases=model_aliases,
+        )
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+        self.client = AsyncCerebras(
+            base_url=self.config.base_url, api_key=self.config.api_key
+        )
+
+    async def initialize(self) -> None:
+        return
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def completion(
+        self,
+        model_id: str,
+        content: InterleavedTextMedia,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        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
+    ) -> CompletionResponse:
+        params = self._get_params(request)
+
+        r = await self.client.completions.create(**params)
+
+        return process_completion_response(r, self.formatter)
+
+    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        stream = await self.client.completions.create(**params)
+
+        async for chunk in process_completion_stream_response(stream, self.formatter):
+            yield chunk
+
+    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] = ToolPromptFormat.json,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        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: CompletionRequest
+    ) -> CompletionResponse:
+        params = self._get_params(request)
+
+        r = await self.client.completions.create(**params)
+
+        return process_chat_completion_response(r, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: CompletionRequest
+    ) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        stream = await self.client.completions.create(**params)
+
+        async for chunk in process_chat_completion_stream_response(
+            stream, self.formatter
+        ):
+            yield chunk
+
+    def _get_params(
+        self, request: Union[ChatCompletionRequest, CompletionRequest]
+    ) -> dict:
+        if request.sampling_params and request.sampling_params.top_k:
+            raise ValueError("`top_k` not supported by Cerebras")
+
+        prompt = ""
+        if type(request) == ChatCompletionRequest:
+            prompt = chat_completion_request_to_prompt(
+                request, self.get_llama_model(request.model), self.formatter
+            )
+        elif type(request) == CompletionRequest:
+            prompt = completion_request_to_prompt(request, self.formatter)
+        else:
+            raise ValueError(f"Unknown request type {type(request)}")
+
+        return {
+            "model": request.model,
+            "prompt": prompt,
+            "stream": request.stream,
+            **get_sampling_options(request.sampling_params),
+        }
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()

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

@@ -0,0 +1,32 @@
+# 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.
+
+import os
+from typing import Any, Dict, Optional
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field
+
+DEFAULT_BASE_URL = "https://api.cerebras.ai"
+
+
+@json_schema_type
+class CerebrasImplConfig(BaseModel):
+    base_url: str = Field(
+        default=os.environ.get("CEREBRAS_BASE_URL", DEFAULT_BASE_URL),
+        description="Base URL for the Cerebras API",
+    )
+    api_key: Optional[str] = Field(
+        default=os.environ.get("CEREBRAS_API_KEY"),
+        description="Cerebras API Key",
+    )
+
+    @classmethod
+    def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
+        return {
+            "base_url": DEFAULT_BASE_URL,
+            "api_key": "${env.CEREBRAS_API_KEY}",
+        }

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -180,7 +180,6 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
     async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator:
         params = await self._get_params(request)
         r = await self.client.generate(**params)
-        assert isinstance(r, dict)
 
         choice = OpenAICompatCompletionChoice(
             finish_reason=r["done_reason"] if r["done"] else None,

llama_stack/providers/tests/datasetio/test_datasetio.py

@@ -81,6 +81,18 @@ class TestDatasetIO:
         assert len(response) == 1
         assert response[0].identifier == "test_dataset"
 
+        with pytest.raises(Exception) as exc_info:
+            # unregister a dataset that does not exist
+            await datasets_impl.unregister_dataset("test_dataset2")
+
+        await datasets_impl.unregister_dataset("test_dataset")
+        response = await datasets_impl.list_datasets()
+        assert isinstance(response, list)
+        assert len(response) == 0
+
+        with pytest.raises(Exception) as exc_info:
+            await datasets_impl.unregister_dataset("test_dataset")
+
     @pytest.mark.asyncio
     async def test_get_rows_paginated(self, datasetio_stack):
         datasetio_impl, datasets_impl = datasetio_stack

llama_stack/providers/tests/inference/fixtures.py

@@ -17,6 +17,7 @@ from llama_stack.providers.inline.inference.meta_reference import (
 )
 from llama_stack.providers.remote.inference.bedrock import BedrockConfig
 
+from llama_stack.providers.remote.inference.cerebras import CerebrasImplConfig
 from llama_stack.providers.remote.inference.fireworks import FireworksImplConfig
 from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
 from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
@@ -64,6 +65,21 @@ def inference_meta_reference(inference_model) -> ProviderFixture:
     )
 
 
+@pytest.fixture(scope="session")
+def inference_cerebras() -> ProviderFixture:
+    return ProviderFixture(
+        providers=[
+            Provider(
+                provider_id="cerebras",
+                provider_type="remote::cerebras",
+                config=CerebrasImplConfig(
+                    api_key=get_env_or_fail("CEREBRAS_API_KEY"),
+                ).model_dump(),
+            )
+        ],
+    )
+
+
 @pytest.fixture(scope="session")
 def inference_ollama(inference_model) -> ProviderFixture:
     inference_model = (
@@ -206,6 +222,7 @@ INFERENCE_FIXTURES = [
     "vllm_remote",
     "remote",
     "bedrock",
+    "cerebras",
     "nvidia",
     "tgi",
 ]

llama_stack/providers/tests/inference/test_text_inference.py

@@ -94,6 +94,7 @@ class TestInference:
             "remote::tgi",
             "remote::together",
             "remote::fireworks",
+            "remote::cerebras",
         ):
             pytest.skip("Other inference providers don't support completion() yet")
 
@@ -139,6 +140,7 @@ class TestInference:
             "remote::tgi",
             "remote::together",
             "remote::fireworks",
+            "remote::cerebras",
         ):
             pytest.skip(
                 "Other inference providers don't support structured output in completions yet"

llama_stack/templates/cerebras/__init__.py

@@ -0,0 +1,7 @@
+# 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 .cerebras import get_distribution_template  # noqa: F401

llama_stack/templates/cerebras/build.yaml

@@ -0,0 +1,17 @@
+version: '2'
+name: cerebras
+distribution_spec:
+  description: Use Cerebras for running LLM inference
+  docker_image: null
+  providers:
+    inference:
+    - remote::cerebras
+    safety:
+    - inline::llama-guard
+    memory:
+    - inline::meta-reference
+    agents:
+    - inline::meta-reference
+    telemetry:
+    - inline::meta-reference
+image_type: conda

llama_stack/templates/cerebras/cerebras.py

@@ -0,0 +1,71 @@
+# 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 pathlib import Path
+
+from llama_models.sku_list import all_registered_models
+
+from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
+from llama_stack.providers.remote.inference.cerebras import CerebrasImplConfig
+from llama_stack.providers.remote.inference.cerebras.cerebras import model_aliases
+
+from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
+
+
+def get_distribution_template() -> DistributionTemplate:
+    providers = {
+        "inference": ["remote::cerebras"],
+        "safety": ["inline::llama-guard"],
+        "memory": ["inline::meta-reference"],
+        "agents": ["inline::meta-reference"],
+        "telemetry": ["inline::meta-reference"],
+    }
+
+    inference_provider = Provider(
+        provider_id="cerebras",
+        provider_type="remote::cerebras",
+        config=CerebrasImplConfig.sample_run_config(),
+    )
+
+    core_model_to_hf_repo = {
+        m.descriptor(): m.huggingface_repo for m in all_registered_models()
+    }
+    default_models = [
+        ModelInput(
+            model_id=core_model_to_hf_repo[m.llama_model],
+            provider_model_id=m.provider_model_id,
+        )
+        for m in model_aliases
+    ]
+
+    return DistributionTemplate(
+        name="cerebras",
+        distro_type="self_hosted",
+        description="Use Cerebras for running LLM inference",
+        docker_image=None,
+        template_path=Path(__file__).parent / "doc_template.md",
+        providers=providers,
+        default_models=default_models,
+        run_configs={
+            "run.yaml": RunConfigSettings(
+                provider_overrides={
+                    "inference": [inference_provider],
+                },
+                default_models=default_models,
+                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
+            ),
+        },
+        run_config_env_vars={
+            "LLAMASTACK_PORT": (
+                "5001",
+                "Port for the Llama Stack distribution server",
+            ),
+            "CEREBRAS_API_KEY": (
+                "",
+                "Cerebras API Key",
+            ),
+        },
+    )

llama_stack/templates/cerebras/doc_template.md

@@ -0,0 +1,60 @@
+# Cerebras Distribution
+
+The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
+
+{{ providers_table }}
+
+{% if run_config_env_vars %}
+### Environment Variables
+
+The following environment variables can be configured:
+
+{% for var, (default_value, description) in run_config_env_vars.items() %}
+- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
+{% endfor %}
+{% endif %}
+
+{% if default_models %}
+### Models
+
+The following models are available by default:
+
+{% for model in default_models %}
+- `{{ model.model_id }} ({{ model.provider_model_id }})`
+{% endfor %}
+{% endif %}
+
+
+### Prerequisite: API Keys
+
+Make sure you have access to a Cerebras API Key. You can get one by visiting [cloud.cerebras.ai](https://cloud.cerebras.ai/).
+
+
+## Running Llama Stack with Cerebras
+
+You can do this via Conda (build code) or Docker which has a pre-built image.
+
+### Via Docker
+
+This method allows you to get started quickly without having to build the distribution code.
+
+```bash
+LLAMA_STACK_PORT=5001
+docker run \
+  -it \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  -v ./run.yaml:/root/my-run.yaml \
+  llamastack/distribution-{{ name }} \
+  --yaml-config /root/my-run.yaml \
+  --port $LLAMA_STACK_PORT \
+  --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
+```
+
+### Via Conda
+
+```bash
+llama stack build --template cerebras --image-type conda
+llama stack run ./run.yaml \
+  --port 5001 \
+  --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
+```

llama_stack/templates/cerebras/run.yaml

@@ -0,0 +1,63 @@
+version: '2'
+image_name: cerebras
+docker_image: null
+conda_env: cerebras
+apis:
+- agents
+- inference
+- memory
+- safety
+- telemetry
+providers:
+  inference:
+  - provider_id: cerebras
+    provider_type: remote::cerebras
+    config:
+      base_url: https://api.cerebras.ai
+      api_key: ${env.CEREBRAS_API_KEY}
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config: {}
+  memory:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/faiss_store.db
+  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/cerebras}/agents_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config: {}
+metadata_store:
+  namespace: null
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/registry.db
+models:
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-8B-Instruct
+  provider_id: null
+  provider_model_id: llama3.1-8b
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-70B-Instruct
+  provider_id: null
+  provider_model_id: llama3.1-70b
+shields:
+- params: null
+  shield_id: meta-llama/Llama-Guard-3-8B
+  provider_id: null
+  provider_shield_id: null
+memory_banks: []
+datasets: []
+scoring_fns: []
+eval_tasks: []