Remove unused stuffs (meta-pytorch#484)

Author: joecummings

src/forge/data/dataset_metrics/metric_transform.py

@@ -9,8 +9,6 @@
 from enum import Enum
 from typing import Any, Union
 
-from forge.interfaces import Transform
-
 
 @dataclass(frozen=True)
 class Metric:
@@ -35,7 +33,7 @@ class AggregationType(Enum):
     MIN = "min"
 
 
-class MetricTransform(Transform, ABC):
+class MetricTransform(ABC):
     """Applied to each dataset sample to generate per-sample metrics for training tracking.
 
     Creates Metric objects that are later aggregated by MetricsAggregator. This separation

src/forge/data/datasets/hf_dataset.py

@@ -18,7 +18,6 @@
     Metric,
     MetricTransform,
 )
-from forge.interfaces import Transform
 
 from .dataset import DatasetInfo, InfiniteTuneIterableDataset
 
@@ -37,10 +36,10 @@ class HfIterableDataset(InfiniteTuneIterableDataset):
       - Returning an infinite iterator over the dataset
 
     Args:
-        message_transform (Transform | None): Transforms raw data into a `Message`.
-        model_transform (Transform | None): Prepares messages for the model,
+        message_transform (Callable | None): Transforms raw data into a `Message`.
+        model_transform (Callable | None): Prepares messages for the model,
             usually by tokenizing them.
-        output_transform (Transform | None): Prepares tokenized inputs for the
+        output_transform (Callable | None): Prepares tokenized inputs for the
             recipe, often by manipulating labels (e.g., setting an ignore index).
             This transform is recipe-dependent (e.g., SFT, DPO, etc.).
         metric_transform (MetricTransform | None): Computes metrics from a
@@ -64,9 +63,9 @@ class HfIterableDataset(InfiniteTuneIterableDataset):
     def __init__(
         self,
         *,
-        message_transform: Transform | None = None,
-        model_transform: Transform | None = None,
-        output_transform: Transform | None = None,
+        message_transform: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
+        model_transform: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
+        output_transform: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
         metric_transform: MetricTransform | None = None,
         shuffle_buffer_size: int | None = 1000,
         weight: float | None = 1.0,

src/forge/data/datasets/sft_dataset.py

@@ -11,12 +11,11 @@
 from forge.data import CROSS_ENTROPY_IGNORE_IDX
 from forge.data.dataset_metrics import DefaultTrainingMetricTransform
 from forge.data.utils import mask_messages, TuneMessage
-from forge.interfaces import Transform
 
 from .hf_dataset import HfIterableDataset
 
 
-class AlpacaToMessages(Transform):
+class AlpacaToMessages:
     """
     Message transform class for Alpaca-style datasets with "instruction", "input", and "output"
     (or equivalent fields specified in column_map) columns. User messages are formed from the
@@ -153,10 +152,10 @@ def __call__(self, sample: dict[str, Any]) -> dict[str, Any]:
 
 
 def sft_iterable_dataset(
-    model_transform: Transform,
+    model_transform: Callable[[dict[str, Any]], dict[str, Any]],
     *,
     weight: int = 1,
-    message_transform: Transform,
+    message_transform: Callable[[dict[str, Any]], dict[str, Any]],
     shuffle_buffer_size: int | None = 1000,
     seed: int = 42,
     num_shards_per_rank: int = 64,
@@ -169,9 +168,9 @@ def sft_iterable_dataset(
     Creates an SFT-ready iterable dataset with appropriate output transform.
 
     Args:
-        model_transform (Transform): Usually the tokenizer
+        model_transform (Callable): Usually the tokenizer
         weight (int): Weight of the dataset. Used for sampling when interleaving datasets.
-        message_transform (Transform): Transform to convert raw data to messages
+        message_transform (Callable): Transform to convert raw data to messages
         shuffle_buffer_size (int | None): Buffer size for shuffling
         seed (int): Random seed for shuffling
         num_shards_per_rank (int): Target shards per worker

src/forge/data/rewards.py

@@ -6,10 +6,8 @@
 
 import re
 
-from forge.interfaces import Reward
 
-
-class MathReward(Reward):
+class MathReward:
     """Reward class for evaluating math correctness."""
 
     def __init__(self, tolerance: float = 1e-6, partial_credit: float = 0.1):
@@ -58,7 +56,7 @@ def _to_float(self, text: str) -> float | None:
             return None
 
 
-class ThinkingReward(Reward):
+class ThinkingReward:
     """Reward class for evaluating use of <think> tags in reasoning."""
 
     def __init__(self, partial_reward: float = 0.2, full_reward: float = 1.0):

src/forge/data/sharding.py

@@ -7,30 +7,7 @@
 from abc import ABC, abstractmethod
 from typing import Any, Mapping
 
-from forge.types import Message, Observation, Scalar
-
-
-class Transform(ABC):
-    """Abstract base class for observation transforms.
-
-    Transforms are first-class citizens that can modify observations,
-    typically to add rewards, compute metrics, or modify state.
-
-    They follow a functional interface where they take an observation
-    and return a (potentially modified) observation.
-    """
-
-    @abstractmethod
-    def __call__(self, observation: Observation) -> Observation:
-        """Transform an observation.
-
-        Args:
-            observation: The input observation to transform
-
-        Returns:
-            The transformed observation (may be the same instance if no changes)
-        """
-        pass
+from forge.types import Message, Scalar
 
 
 class BaseTokenizer(ABC):
@@ -139,12 +116,3 @@ def close(self) -> None:
         This will automatically be called via __del__ when the instance goes out of scope.
         Logs should not be written after `close` is called.
         """
-
-
-class Reward(ABC):
-    """Abstract base class for reward models."""
-
-    @abstractmethod
-    def __call__(self, observation: Observation) -> float:
-        """Compute a reward for an observation."""
-        pass