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Changes to support packaging #238

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14 changes: 13 additions & 1 deletion train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,8 @@
wandb_run_name = 'gpt2' # 'run' + str(time.time())
# data
dataset = 'openwebtext'
train_data_path = '' # explicit path to train dataset instead of dataset name convension
val_data_path = '' # explicit path to valiidation dataset instead of dataset name convension
gradient_accumulation_steps = 5 # used to simulate larger batch sizes
batch_size = 12 # if gradient_accumulation_steps > 1, this is the micro-batch size
block_size = 1024
Expand Down Expand Up @@ -74,7 +76,10 @@
compile = True # use PyTorch 2.0 to compile the model to be faster
# -----------------------------------------------------------------------------
config_keys = [k for k,v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))]
exec(open('configurator.py').read()) # overrides from command line or config file

dir_path = os.path.dirname(os.path.realpath(__file__))
configurator = os.path.join(dir_path, 'configurator.py')
exec(open(configurator).read()) # overrides from command line or config file
config = {k: globals()[k] for k in config_keys} # will be useful for logging
# -----------------------------------------------------------------------------

Expand Down Expand Up @@ -106,6 +111,13 @@

# poor man's data loader
data_dir = os.path.join('data', dataset)
if not train_data_path:
train_data_path = os.path.join(data_dir, 'train.bin')
if not val_data_path:
val_data_path = os.path.join(data_dir, 'val.bin')
train_data = np.memmap(train_data_path, dtype=np.uint16, mode='r')
val_data = np.memmap(val_data_path, dtype=np.uint16, mode='r')

train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
val_data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')
def get_batch(split):
Expand Down