add data structure to tasks

llama_stack/apis/dataset/dataset.py

@@ -9,11 +9,12 @@ from enum import Enum
 from typing import Any, Dict, Generic, Iterator, Literal, Protocol, TypeVar, Union
 
 from llama_models.schema_utils import json_schema_type, webmethod
+from llama_models.llama3.api.datatypes import *  # noqa: F403
 
 from pydantic import BaseModel, Field
 from typing_extensions import Annotated
 
-# A sample (row) from raw dataset
+# A sample (row) from dataset
 TDatasetSample = TypeVar("TDatasetSample")
 
 
@@ -26,46 +27,20 @@ class DictSample(DatasetSample):
     data: Dict[str, Any]
 
 
+# A sample (row) from evals intermediate dataset
+TProcessedSample = TypeVar("TProcessedSample")
+
+
 @json_schema_type
-class ProcessedDictSample(DatasetSample):
-    data: Dict[str, Any]
-    preprocessed: Dict[str, Any]
-    prediction: Dict[str, Any]
-    postprocessed: Dict[str, Any]
+class PredictionSample(BaseModel):
+    completion_message: str
 
 
-# # A sample (row) after preprocessing the raw dataset
-# TPreprocessedSample = TypeVar("TPreprocessedSample")
-
-# @json_schema_type
-# class PreprocessedSample(BaseModel): ...
-
-# @json_schema_type
-# class InferencePreprocessedSample(PreprocessedSample):
-#     # TODO: either keep it generic or specific to inference API
-#     # messages: List[Message]
-#     data: Dict[str, Any]
-
-# # A sample (row) from model prediction output
-# TPredictionSample = TypeVar("TPredictionSample")
-
-# @json_schema_type
-# class PredictionSample(BaseModel): ...
-
-# @json_schema_type
-# class InferencePredictionSample(PredictionSample):
-#     data: Dict[str, Any]
-
-
-# # A sample (row) from post-processed output
-# TPostprocessedSample = TypeVar("TPostprocessedSample")
-
-# @json_schema_type
-# class PostprocessedSample(BaseModel): ...
-
-# @json_schema_type
-# class InferencePostprocessedSample(PredictionSample):
-#     data: Dict[str, Any]
+@json_schema_type
+class ProcessedDictSample(DictSample):
+    preprocessed: Optional[Dict[str, Any]] = None
+    prediction: Optional[PredictionSample] = None
+    postprocessed: Optional[Dict[str, Any]] = None
 
 
 @json_schema_type

llama_stack/apis/evals/evals.py

@@ -71,16 +71,7 @@ class EvaluateTaskConfig(BaseModel):
     sampling_params: SamplingParams = SamplingParams()
 
 
-class BaseTask(
-    ABC,
-    Generic[
-        TDatasetSample,
-        TPreprocessedSample,
-        TPredictionSample,
-        TPostprocessedSample,
-        TSingleEvalResult,
-    ],
-):
+class BaseTask(ABC, Generic[TDatasetSample, TProcessedSample]):
     """
     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:
@@ -94,17 +85,15 @@ class BaseTask(
         self._name = self.__class__.__name__
 
     @abstractmethod
-    def preprocess_sample(self, sample: TDatasetSample) -> TPreprocessedSample:
+    def preprocess_sample(self, sample: TDatasetSample) -> TProcessedSample:
         raise NotImplementedError()
 
     @abstractmethod
-    def postprocess_sample(self, sample: TPredictionSample) -> TPostprocessedSample:
+    def postprocess_sample(self, sample: TProcessedSample) -> TProcessedSample:
         raise NotImplementedError()
 
     @abstractmethod
-    def score_sample(
-        self, sample: TPostprocessedSample, ground_truth: TPreprocessedSample
-    ):
+    def score_sample(self, sample: TProcessedSample) -> SingleEvalResult:
         raise NotImplementedError()
 
     @abstractmethod
@@ -112,24 +101,15 @@ class BaseTask(
         raise NotImplementedError()
 
     def preprocess(
-        self, dataset: BaseDataset[TDatasetSample]
-    ) -> List[TPreprocessedSample]:
-        return [self.preprocess_sample(sample) for sample in self.dataset]
+        self, dataset: BaseDataset[TProcessedSample]
+    ) -> List[TProcessedSample]:
+        return [self.preprocess_sample(sample) for sample in dataset]
 
-    def postprocess(
-        self, generation: List[TPredictionSample]
-    ) -> List[TPostprocessedSample]:
+    def postprocess(self, generation: List[TProcessedSample]) -> List[TProcessedSample]:
         return [self.postprocess_sample(sample) for sample in generation]
 
-    def score(
-        self,
-        postprocessed: List[TPostprocessedSample],
-        preprocessed_dataset: List[TPreprocessedSample],
-    ) -> List[TSingleEvalResult]:
-        return [
-            self.score_sample(sample, ground_truth)
-            for sample, ground_truth in zip(postprocessed, self.preprocessed_dataset)
-        ]
+    def score(self, postprocessed: List[TProcessedSample]) -> List[SingleEvalResult]:
+        return [self.score_sample(sample) for sample in postprocessed]
 
 
 class Evals(Protocol):

llama_stack/distribution/registry/tasks/task.py

@@ -1,49 +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 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)
-    - F3: score_sample(self)
-    """
-
-    def __init__(self, dataset, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._name = self.__class__.__name__
-        self.dataset = dataset
-
-    @abstractmethod
-    def preprocess_sample(self, sample):
-        raise NotImplementedError()
-
-    @abstractmethod
-    def postprocess_sample(self, sample):
-        raise NotImplementedError()
-
-    @abstractmethod
-    def score_sample(self, sample, ground_truth):
-        raise NotImplementedError()
-
-    @abstractmethod
-    def aggregate_results(self, eval_results):
-        raise NotImplementedError()
-
-    def preprocess(self):
-        return [self.preprocess_sample(sample) for sample in self.dataset]
-
-    def postprocess(self, generation):
-        return [self.postprocess_sample(sample) for sample in generation]
-
-    def score(self, postprocessed):
-        return [
-            self.score_sample(sample, ground_truth)
-            for sample, ground_truth in zip(postprocessed, self.dataset)
-        ]

llama_stack/distribution/registry/tasks/task_registry.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 from typing import AbstractSet, Dict
 
-from .task import BaseTask
+from llama_stack.apis.evals import BaseTask
 
 
 class TaskRegistry:

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

@@ -6,6 +6,8 @@
 
 from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.apis.evals import *  # noqa: F403
+from llama_stack.apis.dataset import *  # noqa: F403
+
 from termcolor import cprint
 
 from llama_stack.distribution.registry.datasets.dataset_registry import DatasetRegistry
@@ -42,30 +44,37 @@ class MetaReferenceEvalsImpl(Evals):
         dataset = DatasetRegistry.get_dataset(dataset)
         dataset.load()
 
-        task_impl = TaskRegistry.get_task(task)(dataset)
-        x1 = task_impl.preprocess()
+        task_impl = TaskRegistry.get_task(task)()
+        preprocessed = task_impl.preprocess(dataset)
 
         # 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)
+            eval_task_config = EvaluateTaskConfig(n_samples=len(preprocessed))
+        if eval_task_config.n_samples is None or eval_task_config.n_samples > len(
+            preprocessed
+        ):
+            eval_task_config.n_samples = len(preprocessed)
 
         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)
+        for sample in preprocessed[: eval_task_config.n_samples]:
+            print("generation: ", sample)
             response = await self.inference_api.chat_completion(
                 model=model,
-                messages=[msg],
+                messages=sample.preprocessed["messages"],
                 stream=False,
             )
-            generation_outputs.append(response.completion_message.content)
+            sample.prediction = PredictionSample(
+                completion_message=response.completion_message.content
+            )
+            generation_outputs.append(sample)
 
-        x2 = task_impl.postprocess(generation_outputs)
-        eval_results = task_impl.score(x2)
-        eval_response = task_impl.aggregate_results(eval_results)
-        return eval_response
+        postprocessed = task_impl.postprocess(generation_outputs)
+        eval_results = task_impl.score(postprocessed)
+        aggr_result = task_impl.aggregate_results(eval_results)
+        return EvaluateResponse(
+            eval_result=aggr_result,
+        )

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

@@ -6,7 +6,8 @@
 import re
 
 from llama_stack.apis.evals import *  # noqa: F403
-from llama_stack.distribution.registry.tasks.task import BaseTask
+
+# 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.
@@ -111,48 +112,60 @@ def normalize_extracted_answer(extracted_answer: str) -> str:
     )
 
 
-class MMLUTask(
-    BaseTask[
-        DictSample,
-        InferencePreprocessedSample,
-        InferencePredictionSample,
-        InferencePostprocessedSample,
-    ]
-):
+class MMLUTask(BaseTask[DictSample, ProcessedDictSample]):
     """
     MMLU Task.
     """
 
-    def __init__(self, dataset, *args, **kwargs):
-        super().__init__(dataset, *args, **kwargs)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
-    def preprocess_sample(self, sample):
-        print(sample)
-        content = QUERY_TEMPLATE_MULTICHOICE.format(**sample)
-        return {
-            "role": "user",
-            "content": content,
+    def preprocess_sample(self, sample: ProcessedDictSample) -> ProcessedDictSample:
+        content = QUERY_TEMPLATE_MULTICHOICE.format(**sample.data)
+        preprocessed = {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": content,
+                }
+            ],
         }
+        processed_sample = ProcessedDictSample(
+            data=sample.data,
+            preprocessed=preprocessed,
+        )
+        return processed_sample
 
-    def postprocess_sample(self, sample):
-        normalized = normalize_response(sample)
-        return normalized
+    def postprocess_sample(self, sample: ProcessedDictSample) -> ProcessedDictSample:
+        if not sample.postprocessed:
+            sample.postprocessed = {}
+        sample.postprocessed["postprocessed"] = normalize_response(
+            sample.prediction.completion_message
+        )
+        return sample
+
+    def score_sample(self, sample: ProcessedDictSample) -> SingleEvalResult:
+        postprocessed_output = sample.postprocessed["postprocessed"]
+        expected_answer = sample.data["Answer"]
 
-    def score_sample(self, sample, expected):
         extracted_answer = None
         for answer_regex in MULTILINGUAL_ANSWER_REGEXES:
             regex = MULTILINGUAL_ANSWER_PATTERN_TEMPLATE.format(answer_regex)
-            match = re.search(regex, sample)
+            match = re.search(regex, postprocessed_output)
             if match:
                 extracted_answer = normalize_extracted_answer(match.group(1))
                 break
-        score = (
-            1.0 if extracted_answer and extracted_answer == expected["Answer"] else 0.0
-        )
-        # TODO: generalize this into SingleEvalResult
-        return score
 
-    def aggregate_results(self, eval_results):
-        return EvaluateResponse(
-            metrics={"score": str(sum(eval_results) / len(eval_results))}
+        score = 1.0 if extracted_answer and extracted_answer == expected_answer else 0.0
+
+        return SingleEvalResult(
+            score_data={
+                "score": score,
+            },
         )
+
+    def aggregate_results(self, eval_results: List[SingleEvalResult]) -> EvalResult:
+        print("aggregate_results", eval_results)
+        sum_score = sum([result.score_data["score"] for result in eval_results])
+
+        return EvalResult(metrics={"score": str(sum_score / len(eval_results))})

tests/examples/local-run.yaml

@@ -18,14 +18,18 @@ providers:
     provider_type: meta-reference
     config: {}
   inference:
-  - provider_id: meta-reference
-    provider_type: meta-reference
+  - provider_id: remote::tgi
+    provider_type: remote::tgi
     config:
-      model: Llama3.1-8B-Instruct
-      quantization: null
-      torch_seed: null
-      max_seq_len: 4096
-      max_batch_size: 1
+      url: http://127.0.0.1:5009
+  # - provider_id: meta-reference
+  #   provider_type: meta-reference
+  #   config:
+  #     model: Llama3.1-8B-Instruct
+  #     quantization: null
+  #     torch_seed: null
+  #     max_seq_len: 4096
+  #     max_batch_size: 1
   safety:
   - provider_id: meta-reference
     provider_type: meta-reference