tasks registry

llama_stack/apis/evals/client.py

@@ -43,20 +43,20 @@ async def run_main(host: str, port: int):
     client = EvaluationClient(f"http://{host}:{port}")
 
     # CustomDataset
-    # response = await client.run_evals(
-    #     "Llama3.1-8B-Instruct",
-    #     "mmlu-simple-eval-en",
-    #     "mmlu",
-    # )
-    # cprint(f"evaluate response={response}", "green")
-
-    # Eleuther Eval
     response = await client.run_evals(
         "Llama3.1-8B-Instruct",
-        "PLACEHOLDER_DATASET_NAME",
+        "mmlu-simple-eval-en",
         "mmlu",
     )
-    cprint(response.metrics["metrics_table"], "red")
+    cprint(f"evaluate response={response}", "green")
+
+    # Eleuther Eval
+    # response = await client.run_evals(
+    #     "Llama3.1-8B-Instruct",
+    #     "PLACEHOLDER_DATASET_NAME",
+    #     "mmlu",
+    # )
+    # cprint(response.metrics["metrics_table"], "red")
 
 
 def main(host: str, port: int):

llama_stack/distribution/registry/__init__.py

@@ -0,0 +1,5 @@
+# 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.

llama_stack/distribution/registry/tasks/__init__.py

@@ -0,0 +1,13 @@
+# 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.
+# TODO: make these import config based
+from llama_stack.providers.impls.meta_reference.evals.tasks.mmlu_task import MMLUTask
+from .task_registry import TaskRegistry
+
+TaskRegistry.register(
+    "mmlu",
+    MMLUTask,
+)

llama_stack/distribution/registry/tasks/task.py

@@ -8,6 +8,7 @@ from abc import ABC, abstractmethod
 
 class BaseTask(ABC):
     """
+    A task represents a single evaluation benchmark, including it's dataset, preprocessing, postprocessing and scoring methods.
     Base class for all evaluation tasks. Each task needs to implement the following methods:
     - F1: preprocess_sample(self)
     - F2: postprocess_sample(self)

llama_stack/distribution/registry/tasks/task_registry.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.
+from typing import AbstractSet, Dict
+
+from .task import BaseTask
+
+
+class TaskRegistry:
+    _REGISTRY: Dict[str, BaseTask] = {}
+
+    @staticmethod
+    def names() -> AbstractSet[str]:
+        return TaskRegistry._REGISTRY.keys()
+
+    @staticmethod
+    def register(name: str, task: BaseTask) -> None:
+        if name in TaskRegistry._REGISTRY:
+            raise ValueError(f"Task {name} already exists.")
+        TaskRegistry._REGISTRY[name] = task
+
+    @staticmethod
+    def get_task(name: str) -> BaseTask:
+        if name not in TaskRegistry._REGISTRY:
+            raise ValueError(f"Task {name} not found.")
+        return TaskRegistry._REGISTRY[name]
+
+    @staticmethod
+    def reset() -> None:
+        TaskRegistry._REGISTRY = {}

llama_stack/providers/impls/meta_reference/evals/evals.py

@@ -8,12 +8,15 @@ from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.apis.evals import *  # noqa: F403
 from termcolor import cprint
 
+from llama_stack.distribution.registry.tasks.task_registry import TaskRegistry
+
 from llama_stack.providers.impls.meta_reference.evals.datas.dataset_registry import (
     get_dataset,
 )
-from llama_stack.providers.impls.meta_reference.evals.tasks.task_registry import (
+
-    get_task,
+# from llama_stack.providers.impls.meta_reference.evals.tasks.task_registry import (
-)
+#     get_task,
+# )
 
 from .config import MetaReferenceEvalsImplConfig
 
@@ -36,7 +39,8 @@ class MetaReferenceEvalsImpl(Evals):
     ) -> EvaluateResponse:
         cprint(f"model={model}, dataset={dataset}, task={task}", "red")
         dataset = get_dataset(dataset)
-        task_impl = get_task(task, dataset)
+        task_impl = TaskRegistry.get_task(task)(dataset)
+
         x1 = task_impl.preprocess()
 
         # TODO: replace w/ batch inference & async return eval job

llama_stack/providers/impls/meta_reference/evals/tasks/mmlu_task.py

@@ -5,8 +5,8 @@
 # the root directory of this source tree.
 import re
 
-from .task import BaseTask
 from llama_stack.apis.evals import *  # noqa: F403
+from llama_stack.distribution.registry.tasks.task import BaseTask
 
 QUERY_TEMPLATE_MULTICHOICE = """
 Answer the following multiple choice question and make the answer very simple. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD.

llama_stack/providers/impls/meta_reference/evals/tasks/task_registry.py

@@ -1,16 +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 .mmlu_task import MMLUTask
-
-# TODO: make this into a config based registry
-TASKS_REGISTRY = {
-    "mmlu": MMLUTask,
-}
-
-
-def get_task(task_id: str, dataset):
-    task_impl = TASKS_REGISTRY[task_id]
-    return task_impl(dataset)

tests/examples/local-run.yaml

@@ -12,12 +12,12 @@ apis_to_serve:
 - safety
 - evals
 api_providers:
-  evals:
-    provider_type: eleuther
-    config: {}
   # evals:
-  #   provider_type: meta-reference
+  #   provider_type: eleuther
   #   config: {}
+  evals:
+    provider_type: meta-reference
+    config: {}
   inference:
     providers:
     - meta-reference