feat: Add total logging of generations in training (#172)

Signed-off-by: Sahil Jain <[email protected]>

Author: SahilJain314

nemo_reinforcer/algorithms/grpo.py

@@ -290,7 +290,7 @@ def generate_responses(
     tokenizer,
     input_lengths: torch.Tensor,
     include_logprobs: bool = True,
-) -> Tuple[List[torch.Tensor], List[str], torch.Tensor]:
+) -> Tuple[BatchedDataDict[DatumSpec], List[List[int]], Dict[str, float | int]]:
     """Generate responses from policy."""
     # Generate responses
     generation_outputs = policy_generation.generate(generation_input_data)
@@ -452,6 +452,7 @@ def grpo_train(
         logger.log_metrics(validation_timings, step, prefix="timing/validation")
 
     # Run grpo training (single-turn)
+    batch: BatchedDataDict[DatumSpec]
     for batch in dataloader:
         print(
             f"\n{'=' * 25} Step {step + 1}/{min(len(dataloader), master_config['grpo']['max_num_steps'])} {'=' * 25}"
@@ -645,6 +646,14 @@ def grpo_train(
                 policy.offload_after_refit()
 
         # Logging
+        # Log training data
+        log_data = {"content": flat_messages["content"]}
+        log_data["rewards"] = rewards.tolist()
+        log_data["generation_logprobs"] = train_data["generation_logprobs"].tolist()
+        log_data["prev_logprobs"] = train_data["prev_logprobs"].tolist()
+        log_data["input_lengths"] = input_lengths.tolist()
+        logger.log_batched_dict_as_jsonl(log_data, f"train_data_step{step}.jsonl")
+
         print("\n📊 Training Results:")
         metrics = {
             "loss": train_results["loss"].numpy(),

nemo_reinforcer/utils/logger.py

@@ -19,6 +19,7 @@
 import time
 import threading
 import requests
+import json
 from abc import ABC, abstractmethod
 import logging
 from typing import List, Any, Dict, Optional, TypedDict, Union
@@ -27,8 +28,10 @@
 from rich.panel import Panel
 from rich.box import ROUNDED
 from rich.logging import RichHandler
+import torch
 
 from nemo_reinforcer.data.interfaces import LLMMessageLogType
+from nemo_reinforcer.distributed.batched_data_dict import BatchedDataDict
 from torch.utils.tensorboard import SummaryWriter
 
 import ray
@@ -568,6 +571,32 @@ def log_hyperparams(self, params: Dict[str, Any]) -> None:
         for logger in self.loggers:
             logger.log_hyperparams(params)
 
+    def log_batched_dict_as_jsonl(
+        self, to_log: BatchedDataDict | Dict[str, Any], filename: str
+    ) -> None:
+        """Log a list of dictionaries to a JSONL file.
+
+        Args:
+            to_log: BatchedDataDict to log
+            filename: Filename to log to (within the log directory)
+        """
+        if not isinstance(to_log, BatchedDataDict):
+            to_log = BatchedDataDict(to_log)
+
+        # Create full path within log directory
+        filepath = os.path.join(self.base_log_dir, filename)
+        os.makedirs(os.path.dirname(filepath), exist_ok=True)
+
+        # Write to JSONL file
+        with open(filepath, "w") as f:
+            for i, sample in enumerate(to_log.make_microbatch_iterator(1)):
+                for key, value in sample.items():
+                    if isinstance(value, torch.Tensor):
+                        sample[key] = value.tolist()
+                f.write(json.dumps({**sample, "idx": i}) + "\n")
+
+        print(f"Logged data to {filepath}")
+
     def __del__(self):
         """Clean up resources when the logger is destroyed."""
         if self.gpu_monitor: