Datasets:

tomg-group-umd
/

gemstones_data_order_parallel

@@ -18,6 +18,12 @@ reproduce the training batches across the gpus is/was the run the training code.
 This repo is the result of an attempt to simulate the way in which the training code loaded the data and
 stream it out to a portable file format for use in downstream analyses of the model suite.
 # Sharding format: worker parallel
 This version of the dataset approximates the specific subsets of the data that each of the distributed
@@ -36,24 +42,18 @@ This recreation assumes the ~1B Gemstones model sizes which were trained on 32 n
 at a microbatch size of 8 over packed sequences of 2048 tokens.
 They were trained for 82998 steps at a batch size of ~4M tokens to reach ~350B tokens.
-At runtime, the 256 workers each received a slice of the total dataset represented by a subset of
 the thousands of raw training format files (for reference, this format is defined by the `packed_cycle_dataset.py` file in this repo).
 The raw files were first shuffled globally, and then each worker's slice was defined by this round-robin
 strided indexing of the shuffled filelist: `filenames[shard_id:max_num_files:num_shards]`. Then, each worker
 loaded 4 files at a time, and shuffled the "blocks" of 2048 tokens each in a temporary buffer so
 that the contents of the 4 packed files were not read in the exact order in which the tokens appeared in them.
-The `train_mock_data_order_file.py` materializes the shuffled file list, and uses a pool of cpu workers
 to mimic a distributed set of gpus, and passes their process ids into the dataset implementation
 so that each worker in the pool receives its subset of the data and loads it as it would have during training.
-Then, the dataset rows are wrapped in dataloaders, and read in microbatches before being written out
 to the parquet file format.
 Each shard named like `worker_{worker_rank}-of-{total_num_workers}_ordered_dataset.parquet` represents the ordered microbatches that one of the 256 gpus would
 have drawn and passed through its copy of the model during training.
-# Loading
-This data should be loadable using `load_dataset` in the standard manner to auto-download the data.
-Alternately, the dataset can be cloned using git to materialize the files locally, and then loaded
-using the default `parquet` builder as described here: https://huggingface.co/docs/datasets/en/loading#parquet

 This repo is the result of an attempt to simulate the way in which the training code loaded the data and
 stream it out to a portable file format for use in downstream analyses of the model suite.
+# Loading
+This data should be loadable using `load_dataset` in the standard manner to auto-download the data.
+Alternately, the dataset can be cloned using git to materialize the files locally, and then loaded
+using the default `parquet` builder as described here: https://huggingface.co/docs/datasets/en/loading#parquet
 # Sharding format: worker parallel
 This version of the dataset approximates the specific subsets of the data that each of the distributed
 at a microbatch size of 8 over packed sequences of 2048 tokens.
 They were trained for 82998 steps at a batch size of ~4M tokens to reach ~350B tokens.
+The 256 workers each received a slice of the total dataset represented by a subset of
 the thousands of raw training format files (for reference, this format is defined by the `packed_cycle_dataset.py` file in this repo).
 The raw files were first shuffled globally, and then each worker's slice was defined by this round-robin
 strided indexing of the shuffled filelist: `filenames[shard_id:max_num_files:num_shards]`. Then, each worker
 loaded 4 files at a time, and shuffled the "blocks" of 2048 tokens each in a temporary buffer so
 that the contents of the 4 packed files were not read in the exact order in which the tokens appeared in them.
+The `train_mock_data_order_file.py` uses a pool of cpu workers
 to mimic a distributed set of gpus, and passes their process ids into the dataset implementation
 so that each worker in the pool receives its subset of the data and loads it as it would have during training.
+Then, the subsets of data are wrapped in dataloaders and read in microbatches before being written out
 to the parquet file format.
 Each shard named like `worker_{worker_rank}-of-{total_num_workers}_ordered_dataset.parquet` represents the ordered microbatches that one of the 256 gpus would
 have drawn and passed through its copy of the model during training.