Hopefully fixing dataloader for now

pdelfin/train/train.py

@@ -47,6 +47,7 @@ from .utils import (
     log_trainable_parameters,
     packing_collator,
     setup_environment,
+    make_dataset
 )
 
 
@@ -113,6 +114,15 @@ def run_train(config: TrainConfig):
     run_name = RunName.get(config)
 
     setup_environment(aws_config=config.aws, wandb_config=config.wandb, WANDB_RUN_GROUP=run_name.group)
+    accelerator = accelerate.Accelerator()
+
+    # Build and download the dataset on process 0
+    if accelerator.is_main_process:
+        make_dataset(config)
+
+    accelerator.wait_for_everyone()
+
+    train_dataset, valid_dataset = make_dataset(config)    
 
     model = Qwen2VLForConditionalGeneration.from_pretrained(
         config.model.name_or_path, torch_dtype=torch.bfloat16,
@@ -132,30 +142,6 @@ def run_train(config: TrainConfig):
         model = get_peft_model(model=model, peft_config=peft_config)
         log_trainable_parameters(model=model, logger=logger)
 
-    random.seed(config.train_data.seed)
-
-    # Training sets get all concatenated and shuffled
-    train_dataset = (
-        concatenate_datasets(
-            [
-                build_batch_query_response_vision_dataset(source.query_glob_path, source.response_glob_path)
-                for source in config.train_data.sources
-            ]
-        )
-        .filter(partial(filter_by_max_seq_len, processor=processor))
-        .with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor))
-    )
-
-    # Validation sets get put into a datasetdict so each can report a loss separately
-    valid_dataset = DatasetDict(
-        **{
-            source.name: build_batch_query_response_vision_dataset(source.query_glob_path, source.response_glob_path)
-            .filter(partial(filter_by_max_seq_len, processor=processor))
-            .with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor))
-            for source in config.valid_data.sources
-        }
-    )
-
     save_path = join_path("", config.save.path, run_name.run)
 
     save_config(config, join_path("", save_path, "config.yaml"))  # pyright: ignore

pdelfin/train/utils.py

@@ -1,6 +1,7 @@
 import json
 import multiprocessing
 import os
+import random
 from contextlib import contextmanager
 from dataclasses import dataclass
 from datetime import datetime
@@ -13,6 +14,7 @@ import torch
 import torch.nn.functional as F
 from accelerate import Accelerator
 from accelerate.utils import PrecisionType
+from datasets import Dataset, concatenate_datasets, DatasetDict
 
 from .core.cli import to_native_types
 from .core.config import AwsConfig, TrainConfig, WandbConfig
@@ -23,6 +25,9 @@ from .core.state import BeakerState
 
 T = TypeVar("T")
 
+from pdelfin.train.dataloader import build_batch_query_response_vision_dataset
+from pdelfin.train.dataprep import batch_prepare_data_for_qwen2_training, filter_by_max_seq_len
+
 
 def accelerator_to_dtype(accelerator: Accelerator) -> torch.dtype:
     pt = PrecisionType(accelerator.mixed_precision)
@@ -34,6 +39,33 @@ def accelerator_to_dtype(accelerator: Accelerator) -> torch.dtype:
         return torch.float8_e4m3fn
     return torch.float32
 
+def make_dataset(config: TrainConfig) -> tuple[Dataset, Dataset]:
+    random.seed(config.train_data.seed)
+
+    # Training sets get all concatenated and shuffled
+    train_dataset = (
+        concatenate_datasets(
+            [
+                build_batch_query_response_vision_dataset(source.query_glob_path, source.response_glob_path)
+                for source in config.train_data.sources
+            ]
+        )
+        .filter(partial(filter_by_max_seq_len, processor=processor))
+        .with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor))
+    )
+
+    # Validation sets get put into a datasetdict so each can report a loss separately
+    valid_dataset = DatasetDict(
+        **{
+            source.name: build_batch_query_response_vision_dataset(source.query_glob_path, source.response_glob_path)
+            .filter(partial(filter_by_max_seq_len, processor=processor))
+            .with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor))
+            for source in config.valid_data.sources
+        }
+    )
+
+    return train_dataset, valid_dataset
+
 
 def setup_environment(
     aws_config: Optional[AwsConfig] = None, wandb_config: Optional[WandbConfig] = None, **kwargs: str

tests/test_dataloader.py

@@ -37,6 +37,21 @@ class TestBatchQueryResponseDataset(unittest.TestCase):
 
         print(ds[0])
 
+    def testLocalDS(self):
+        ds = build_batch_query_response_vision_dataset(
+            query_glob_path="/root/openai_batch_data_v5_1_train/*.jsonl",
+            response_glob_path="/root/openai_batch_data_v5_1_train_done/*.json",
+        )
+
+        print(ds)
+
+        ds.to_parquet("/root/trainds_parquet/bigds.parquet")
+
+        processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
+        from pdelfin.train.dataprep import filter_by_max_seq_len
+        ds = ds.filter(partial(filter_by_max_seq_len, processor=processor, max_prompt_len=1000))
+
+        print(ds[0])
 
     def testPlotSequenceLengthHistogram(self):
         import plotly.express as px