evals new rebase

llama_stack/providers/impls/meta_reference/evals/evals.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 llama_stack.apis.inference import *  # noqa: F403
+from llama_stack.apis.evals import *  # noqa: F403
+from termcolor import cprint
+
+from llama_stack.distribution.registry.datasets.dataset_registry import DatasetRegistry
+
+from llama_stack.distribution.registry.tasks.task_registry import TaskRegistry
+
+from .config import MetaReferenceEvalsImplConfig
+
+
+class MetaReferenceEvalsImpl(Evals):
+    def __init__(self, config: MetaReferenceEvalsImplConfig, inference_api: Inference):
+        self.inference_api = inference_api
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def run_evals(
+        self,
+        model: str,
+        task: str,
+        dataset: Optional[str] = None,
+        eval_task_config: Optional[EvaluateTaskConfig] = None,
+    ) -> EvaluateResponse:
+        cprint(
+            f"model={model}, dataset={dataset}, task={task}, eval_task_config={eval_task_config}",
+            "red",
+        )
+        if not dataset:
+            raise ValueError("dataset must be specified for mete-reference evals")
+
+        dataset = DatasetRegistry.get_dataset(dataset)
+        dataset.load()
+
+        task_impl = TaskRegistry.get_task(task)(dataset)
+        x1 = task_impl.preprocess()
+
+        # TODO: replace w/ batch inference & async return eval job
+        generation_outputs = []
+        if eval_task_config is None:
+            eval_task_config = EvaluateTaskConfig(n_samples=len(x1))
+        if eval_task_config.n_samples is None or eval_task_config.n_samples > len(x1):
+            eval_task_config.n_samples = len(x1)
+
+        print(
+            f"Eval generation start, generate on {eval_task_config.n_samples} samples"
+        )
+
+        for msg in x1[: eval_task_config.n_samples]:
+            print("generation for msg: ", msg)
+            response = await self.inference_api.chat_completion(
+                model=model,
+                messages=[msg],
+                stream=False,
+            )
+            generation_outputs.append(response.completion_message.content)
+
+        x2 = task_impl.postprocess(generation_outputs)
+        eval_results = task_impl.score(x2)
+        eval_response = task_impl.aggregate_results(eval_results)
+        return eval_response