Process reward models (#2241)

* adding model_cfg to set num_labels * using a num_labels field instead * linting * WIP stepwise prompt tokenizer * this should work? * trainer working? * pushing to runpod * fixing saving * updating conf * updating config, adding docs * adding stepwise supervision docpage * updating tests * adding test for dataset * fixing tests * linting * addressing some comments * adding additional cfg fields support * updating tests, fixing cfg * fixing tests * updating loss * Update test_process_reward_model_smollm2.py * updating loss values and seed * dumb pre-commit
2025-01-29 05:08:33 +00:00
parent c071a530f7
commit 54dd7abfc1
17 changed files with 542 additions and 25 deletions
--- a/tests/e2e/test_process_reward_model_smollm2.py
+++ b/tests/e2e/test_process_reward_model_smollm2.py
@@ -0,0 +1,69 @@
+"""
+E2E tests for process reward model w/ lora llama
+"""
+
+import logging
+import os
+import unittest
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import check_model_output_exists, check_tensorboard, with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestProcessRewardSmolLM2(unittest.TestCase):
+    """
+    Test case for Llama process reward models using LoRA
+    """
+
+    @with_temp_dir
+    def test_prm(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "model_type": "AutoModelForTokenClassification",
+                "num_labels": 2,
+                "process_reward_model": True,
+                "sequence_len": 512,
+                "val_set_size": 0.0,
+                "datasets": [
+                    {
+                        "path": "trl-lib/math_shepherd",
+                        "type": "stepwise_supervised",
+                        "step_separator": "\n",
+                        "split": "train[:10%]",
+                    },
+                ],
+                "max_steps": 100,
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.0005,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "gradient_checkpointing": True,
+                "warmup_ratio": 0.1,
+                "use_tensorboard": True,
+                "special_tokens": {"pad_token": "<|endoftext|>"},
+                "seed": 42,
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.5, "Train Loss is too high"
+        )
+
+        check_model_output_exists(temp_dir, cfg)