Multi eval dataset logging

scripts/train/train.py

@@ -33,7 +33,16 @@ def validate_config(cfg: DictConfig):
     """Validates compatible model and dataloader selection."""
     loaders = [cfg.train_loader]
     if 'eval_loader' in cfg:
-        loaders.append(cfg.eval_loader)
+        eval_loader = cfg.eval_loader
+        if isinstance(eval_loader, ListConfig):
+            for loader in eval_loader:
+                if loader.label is None:
+                    raise ValueError(
+                        'When specifying multiple evaluation datasets, each one must include the \
+                            `label` attribute.')
+                loaders.append(loader)
+        else:
+            loaders.append(eval_loader)
     for loader in loaders:
         if loader.name == 'text':
             if cfg.model.name in ['hf_prefix_lm', 'hf_t5']:
@@ -245,10 +254,8 @@ def main(cfg: DictConfig) -> Trainer:
                                                        must_exist=False,
                                                        default_value=None,
                                                        convert=True)
-    eval_loader_config: Optional[DictConfig] = pop_config(cfg,
-                                                          'eval_loader',
-                                                          must_exist=False,
-                                                          default_value=None)
+    eval_loader_config: Optional[Union[DictConfig, ListConfig]] = pop_config(
+        cfg, 'eval_loader', must_exist=False, default_value=None)
     icl_tasks_config: Optional[Union[ListConfig,
                                      str]] = pop_config(cfg,
                                                         'icl_tasks',
@@ -466,15 +473,21 @@ def main(cfg: DictConfig) -> Trainer:
     ## Evaluation
     print('Building eval loader...')
     evaluators = []
-    eval_loader = None
+    eval_loaders = []
     if eval_loader_config is not None:
-        eval_dataloader = build_dataloader(eval_loader_config, tokenizer,
-                                           device_eval_batch_size)
-        eval_loader = Evaluator(
-            label='eval',
-            dataloader=eval_dataloader,
-            metric_names=[],  # we will add these after model is created
-        )
+        is_multi_eval = isinstance(eval_loader_config, ListConfig)
+        eval_configs = eval_loader_config if is_multi_eval else [
+            eval_loader_config
+        ]
+        for eval_config in eval_configs:
+            eval_dataloader = build_dataloader(eval_config, tokenizer,
+                                               device_eval_batch_size)
+            eval_loader = Evaluator(
+                label=f'eval/{eval_config.label}' if is_multi_eval else 'eval',
+                dataloader=eval_dataloader,
+                metric_names=[],  # we will add these after model is created
+            )
+            eval_loaders.append(eval_loader)
 
     eval_gauntlet_callback = None
 
@@ -514,11 +527,11 @@ def main(cfg: DictConfig) -> Trainer:
 
     # Now add the eval metrics
     if eval_loader_config is not None:
-        assert eval_loader is not None
         assert model.train_metrics is not None
         eval_metric_names = list(model.train_metrics.keys())
-        eval_loader.metric_names = eval_metric_names
-        evaluators.insert(0, eval_loader)  # Put the base eval_loader first
+        for eval_loader in eval_loaders:
+            eval_loader.metric_names = eval_metric_names
+            evaluators.insert(0, eval_loader)  # Put the base eval_loaders first
 
     # Build the Trainer
     print('Building trainer...')

tests/test_data_prep_scripts.py

@@ -37,13 +37,13 @@ def test_download_script_from_api():
 
 def test_json_script_from_api():
     # test calling it directly
-    path = os.path.join(os.getcwd(), 'my-copy-c4-3')
+    path = os.path.join(os.getcwd(), 'my-copy-arxiv-1')
     shutil.rmtree(path, ignore_errors=True)
     main_json(
         Namespace(
             **{
                 'path': 'scripts/data_prep/example_data/arxiv.jsonl',
-                'out_root': './my-copy-c4-3',
+                'out_root': './my-copy-arxiv-1',
                 'compression': None,
                 'split': 'train',
                 'concat_tokens': None,

tests/test_train_inputs.py

@@ -158,3 +158,23 @@ def test_invalid_name_in_scheduler_cfg_errors(self,
             main(cfg)
         assert str(exception_info.value
                   ) == 'Not sure how to build scheduler: invalid-scheduler'
+
+    def test_no_label_multiple_eval_datasets(self, cfg: DictConfig) -> None:
+        data_local = './my-copy-c4-multi-eval'
+        make_fake_index_file(f'{data_local}/train/index.json')
+        make_fake_index_file(f'{data_local}/val/index.json')
+        cfg.train_loader.dataset.local = data_local
+        # Set up multiple eval datasets
+        first_eval_loader = cfg.eval_loader
+        first_eval_loader.dataset.local = data_local
+        second_eval_loader = copy.deepcopy(first_eval_loader)
+        # Set the first eval dataloader to have no label
+        first_eval_loader.label = None
+        second_eval_loader.label = 'eval_1'
+        cfg.eval_loader = om.create([first_eval_loader, second_eval_loader])
+        with pytest.raises(ValueError) as exception_info:
+            main(cfg)
+        assert str(
+            exception_info.value
+        ) == 'When specifying multiple evaluation datasets, each one must include the \
+                            `label` attribute.'

tests/test_training.py

@@ -1,6 +1,8 @@
 # Copyright 2022 MosaicML LLM Foundry authors
 # SPDX-License-Identifier: Apache-2.0
+import copy
 import os
+import pathlib
 import shutil
 import sys
 from argparse import Namespace
@@ -16,13 +18,14 @@
 sys.path.append(repo_dir)
 
 from scripts.data_prep.convert_dataset_hf import main as main_hf  # noqa: E402
+from scripts.data_prep.convert_dataset_json import \
+    main as main_json  # noqa: E402
 from scripts.train.train import main  # noqa: E402
 
 
-def create_c4_dataset_xsmall(prefix: str) -> str:
+def create_c4_dataset_xsmall(path: pathlib.Path) -> str:
     """Creates a small mocked version of the C4 dataset."""
-    c4_dir = os.path.join(os.getcwd(), f'my-copy-c4-{prefix}')
-    shutil.rmtree(c4_dir, ignore_errors=True)
+    c4_dir = os.path.join(path, f'my-copy-c4')
     downloaded_split = 'val_xsmall'  # very fast to convert
 
     # Hyperparameters from https://github.com/mosaicml/llm-foundry/blob/340a56658560ebceb2a3aa69d6e37813e415acd0/README.md#L188
@@ -52,6 +55,28 @@ def create_c4_dataset_xsmall(prefix: str) -> str:
     return c4_dir
 
 
+def create_arxiv_dataset(path: pathlib.Path) -> str:
+    """Creates an arxiv dataset."""
+    arxiv_dir = os.path.join(path, f'my-copy-arxiv')
+    downloaded_split = 'train'
+
+    main_json(
+        Namespace(
+            **{
+                'path': 'data_prep/example_data/arxiv.jsonl',
+                'out_root': arxiv_dir,
+                'compression': None,
+                'split': downloaded_split,
+                'concat_tokens': None,
+                'bos_text': None,
+                'eos_text': None,
+                'no_wrap': False,
+                'num_workers': None
+            }))
+
+    return arxiv_dir
+
+
 def gpt_tiny_cfg(dataset_name: str, device: str):
     """Create gpt tiny cfg."""
     conf_path: str = os.path.join(repo_dir,
@@ -89,9 +114,9 @@ def set_correct_cwd():
         os.chdir('..')
 
 
-def test_train_gauntlet(set_correct_cwd: Any):
+def test_train_gauntlet(set_correct_cwd: Any, tmp_path: pathlib.Path):
     """Test training run with a small dataset."""
-    dataset_name = create_c4_dataset_xsmall('cpu-gauntlet')
+    dataset_name = create_c4_dataset_xsmall(tmp_path)
     test_cfg = gpt_tiny_cfg(dataset_name, 'cpu')
     test_cfg.icl_tasks = ListConfig([
         DictConfig({
@@ -150,3 +175,52 @@ def test_train_gauntlet(set_correct_cwd: Any):
         inmemorylogger.data['icl/metrics/eval_gauntlet/average'][-1], tuple)
 
     assert inmemorylogger.data['icl/metrics/eval_gauntlet/average'][-1][-1] == 0
+
+
+def test_train_multi_eval(set_correct_cwd: Any, tmp_path: pathlib.Path):
+    """Test training run with multiple eval datasets."""
+    c4_dataset_name = create_c4_dataset_xsmall(tmp_path)
+    test_cfg = gpt_tiny_cfg(c4_dataset_name, 'cpu')
+    # Set up multiple eval dataloaders
+    first_eval_loader = test_cfg.eval_loader
+    first_eval_loader.label = 'c4'
+    # Create second eval dataloader using the arxiv dataset.
+    second_eval_loader = copy.deepcopy(first_eval_loader)
+    arxiv_dataset_name = create_arxiv_dataset(tmp_path)
+    second_eval_loader.data_local = arxiv_dataset_name
+    second_eval_loader.label = 'arxiv'
+    test_cfg.eval_loader = om.create([first_eval_loader, second_eval_loader])
+    test_cfg.eval_subset_num_batches = 1  # -1 to evaluate on all batches
+
+    test_cfg.max_duration = '1ba'
+    test_cfg.eval_interval = '1ba'
+    test_cfg.loggers = DictConfig({'inmemory': DictConfig({})})
+    trainer = main(test_cfg)
+
+    assert isinstance(trainer.logger.destinations, tuple)
+
+    assert len(trainer.logger.destinations) > 0
+    inmemorylogger = trainer.logger.destinations[
+        0]  # pyright: ignore [reportGeneralTypeIssues]
+    assert isinstance(inmemorylogger, InMemoryLogger)
+    print(inmemorylogger.data.keys())
+
+    # Checks for first eval dataloader
+    assert 'metrics/eval/c4/LanguageCrossEntropy' in inmemorylogger.data.keys()
+    assert isinstance(
+        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'], list)
+    assert len(
+        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'][-1]) > 0
+    assert isinstance(
+        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'][-1], tuple)
+
+    # Checks for second eval dataloader
+    assert 'metrics/eval/arxiv/LanguageCrossEntropy' in inmemorylogger.data.keys(
+    )
+    assert isinstance(
+        inmemorylogger.data['metrics/eval/arxiv/LanguageCrossEntropy'], list)
+    assert len(
+        inmemorylogger.data['metrics/eval/arxiv/LanguageCrossEntropy'][-1]) > 0
+    assert isinstance(
+        inmemorylogger.data['metrics/eval/arxiv/LanguageCrossEntropy'][-1],
+        tuple)