Submitting an interactive notebook to run SFT

Author: Hossein Kavianihamedani

apps/sft_v2/NOTEBOOK_GUIDE.md

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Abstract Actor class for training/inference actors in Forge.
+
+This provides a base class that can be extended for different types of actors
+(e.g., Trainer, Evaluator, Inferencer, etc.)
+"""
+
+import logging
+import math
+import os
+from abc import ABC, abstractmethod
+from typing import Any, Optional
+
+import torch
+from forge.controller import ForgeActor
+from monarch.actor import current_rank, current_size
+from omegaconf import DictConfig, OmegaConf
+from torch import nn
+from torchtitan.components.loss import LossFunction
+from torchtitan.components.lr_scheduler import LRSchedulersContainer
+from torchtitan.components.optimizer import OptimizersContainer
+from torchtitan.distributed import ParallelDims
+from torchtitan.experiments.forge.engine import ForgeEngine
+from torchtitan.experiments.forge.job_config import ForgeJobConfig
+
+Checkpointer = Any
+Dataloader = Any
+MetricLogger = Any
+Profiler = Any
+Tokenizer = Any
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+
+class BaseForgeActor(ForgeActor, ForgeEngine, ABC):
+    """
+    Abstract base class for Forge actors.
+
+    This class handles common initialization, distributed setup, and provides
+    abstract methods that must be implemented by concrete actor classes.
+    """
+
+    job_config: ForgeJobConfig
+    parallel_dims: ParallelDims
+    model: list[nn.Module]
+    loss_fn: Optional[LossFunction]
+    optimizer: Optional[OptimizersContainer]
+    lr_scheduler: Optional[LRSchedulersContainer]
+    checkpointer: Optional[Checkpointer]
+    tokenizer: Optional[Tokenizer]
+    metric_logger: Optional[MetricLogger]
+    profiler: Optional[Profiler]
+    device: torch.device
+
+    def __init__(self, config: DictConfig):
+        """
+        Initialize the base actor with configuration.
+
+        Args:
+            config: Configuration dictionary containing job settings
+        """
+        job_config = ForgeJobConfig().to_dict()
+        job_config = OmegaConf.merge(job_config, config)
+
+        self.current_step = 0
+        self.metric_logger = None
+        self.gradient_accumulation_steps = 1
+        self._rank = current_rank().rank
+        self._size = math.prod(current_size().values())
+
+        self._init_dist()
+        super().__init__(job_config)
+
+    def _init_dist(self):
+        """
+        Initialize torch distributed environment.
+
+        Sets up environment variables required for distributed training
+        in the Monarch actor framework.
+        """
+        env = {
+            "RANK": str(self._rank),
+            "LOCAL_RANK": str(self._rank),
+            "LOCAL_WORLD_SIZE": str(self._size),
+            "GROUP_RANK": str(self._size),
+            "GROUP_WORLD_SIZE": str(self._size),
+            "ROLE_RANK": str(self._rank),
+            "ROLE_WORLD_SIZE": str(self._size),
+            "ROLE_NAME": "rank",
+            "WORLD_SIZE": str(self._size),
+            "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True",
+        }
+        os.environ.update(env)
+        logger.info(f"Initialized distributed environment: {env}")
+
+    @abstractmethod
+    async def setup(self):
+        """
+        Setup the actor (load data, checkpoint, etc.).
+
+        This method must be implemented by concrete actor classes.
+        """
+        pass
+
+    @abstractmethod
+    async def run(self):
+        """
+        Main execution logic for the actor.
+
+        This method must be implemented by concrete actor classes.
+        """
+        pass
+
+    @abstractmethod
+    async def cleanup(self):
+        """
+        Cleanup resources (close checkpointer, logger, etc.).
+
+        This method must be implemented by concrete actor classes.
+        """
+        pass
+
+    @abstractmethod
+    def __repr__(self) -> str:
+        """String representation of the actor."""
+        pass