shuttling the poor mans configurator aside into its own file and adding it to all of train,sample,bench. because i am leaving args in globals() so i can avoid having to prepend every single variable with an args., i have to exec the configurator and the optional configs. so we're left with something very gross by standard convention but also quite simple and functional. *ducks*

README.md

@@ -71,7 +71,7 @@ I briefly tried finetuning gpt2 a bit more on our OWT and didn't notice dramatic
 For an example of how to finetune a GPT on new text go to `data/shakespeare` and look at `prepare.py` to download the tiny shakespeare dataset and render it into a `train.bin` and `val.bin`. Unlike OpenWebText this will run in seconds. Finetuning takes very little time, e.g. on a single GPU just a few minutes. Run an example finetuning like:
 
 ```
-$ python train.py finetune_shakespeare
+$ python train.py config/finetune_shakespeare.py
 ```
 
 This will load the config parameter overrides in `config/finetune_shakespeare.py` (I didn't tune them much though). Basically, we initialize from a GPT2 checkpoint with `init_from` and train as normal, except shorter and with a small learning rate. The best checkpoint (lowest validation loss) will be in the `out_dir` directory, e.g. in `out-shakespeare` by default, per the config file. You can then run the code in `sample.py` to generate infinite Shakespeare. Note that you'll have to edit it to point to the correct `out_dir`.
@@ -102,7 +102,6 @@ Features / APIs
 - Add back fp16 support? (would need to also add back gradient scaler)
 - Add CPU support
 - Finetune the finetuning script, I think the hyperparams are not great
-- Replace poor man's configurator, and make sample.py configurable...
 - Report and track other metrics e.g. perplexity, num_tokens, MFU, ...
 - Eval zero-shot perplexities on PTB, WikiText, other related benchmarks
 

bench.py

@@ -7,16 +7,19 @@ import time
 import torch
 from model import GPTConfig, GPT
 
+# -----------------------------------------------------------------------------
 device = 'cuda'
+batch_size = 8
+block_size = 1024
+compile = True
+exec(open('configurator.py').read()) # overrides from command line or config file
+# -----------------------------------------------------------------------------
+
+dtype = torch.bfloat16 # todo make configurable
 torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
 torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
 torch.manual_seed(1337)
 
-batch_size = 8
-block_size = 1024
-dtype = torch.bfloat16
-compile = True
-
 # data loading init
 real_data = True
 if real_data:

configurator.py

@@ -0,0 +1,47 @@
+"""
+Poor Man's Configurator. Probably a terrible idea. Example usage:
+$ python train.py config/override_file.py --batch_size=32
+this will first run config/override_file.py, then override batch_size to 32
+
+The code in this file will be run as follows from e.g. train.py:
+>>> exec(open('configurator.py').read())
+
+So it's not a Python module, it's just shuttling this code away from train.py
+The code in this script then overrides the globals()
+
+I know people are not going to love this, I just really dislike configuration
+complexity and having to prepend config. to every single variable. If someone
+comes up with a better simple Python solution I am all ears.
+"""
+
+import sys
+from ast import literal_eval
+
+for arg in sys.argv[1:]:
+    if '=' not in arg:
+        # assume it's the name of a config file
+        assert not arg.startswith('--')
+        config_file = arg
+        print(f"Overriding config with {config_file}:")
+        with open(config_file) as f:
+            print(f.read())
+        exec(open(config_file).read())
+    else:
+        # assume it's a --key=value argument
+        assert arg.startswith('--')
+        key, val = arg.split('=')
+        key = key[2:]
+        if key in globals():
+            try:
+                # attempt to eval it it (e.g. if bool, number, or etc)
+                attempt = literal_eval(val)
+            except (SyntaxError, ValueError):
+                # if that goes wrong, just use the string
+                attempt = val
+            # ensure the types match ok
+            assert type(attempt) == type(globals()[key])
+            # cross fingers
+            print(f"Overriding: {key} = {attempt}")
+            globals()[key] = attempt
+        else:
+            raise ValueError(f"Unknown config key: {key}")

sample.py

@@ -7,7 +7,6 @@ import tiktoken
 from model import GPTConfig, GPT
 
 # -----------------------------------------------------------------------------
-# todo make these overridable like in train.py
 out_dir = 'out'
 device = 'cuda:2'
 compile = False
@@ -17,6 +16,7 @@ max_new_tokens = 500 # number of tokens generated in each sample
 temperature = 0.8 # higher temperature (up to 1) is more random, lower (down to 0) means more greedy
 top_k = 200 # retain only the top_k most likely tokens, clamp others to have 0 probability
 seed = 1337
+exec(open('configurator.py').read()) # overrides from command line or config file
 # -----------------------------------------------------------------------------
 
 torch.manual_seed(seed)

train.py

@@ -13,7 +13,6 @@ import os
 import sys
 import time
 import math
-from ast import literal_eval
 
 import wandb
 import numpy as np
@@ -24,7 +23,7 @@ from torch.distributed import init_process_group, destroy_process_group
 from model import GPTConfig, GPT
 
 # -----------------------------------------------------------------------------
-# default config values
+# default config values designed to train a gpt2 (124M) on OpenWebText
 # I/O
 out_dir = 'out'
 eval_interval = 2000
@@ -62,37 +61,7 @@ min_lr = 6e-5 # minimum learning rate, should be ~= learning_rate/10 per Chinchi
 backend = 'nccl' # 'nccl', 'gloo', etc.
 compile = True # use PyTorch 2.0 to compile the model to be faster
 # -----------------------------------------------------------------------------
-# poor man's Configurator. Potentially a bad idea. Example usage:
+exec(open('configurator.py').read()) # overrides from command line or config file
-# $ python train.py override_file --batch_size=32
-# this will first run config/override_file.py, then override batch_size to 32
-for arg in sys.argv[1:]:
-    if '=' not in arg:
-        # assume it's the name of a config file
-        assert not arg.startswith('--')
-        config_file = os.path.join('config', arg + '.py')
-        print(f"Overriding config with {config_file}:")
-        with open(config_file) as f:
-            print(f.read())
-        exec(open(config_file).read())
-    else:
-        # assume it's a --key=value argument
-        assert arg.startswith('--')
-        key, val = arg.split('=')
-        key = key[2:]
-        if key in globals():
-            try:
-                # attempt to eval it it (e.g. if bool, number, or etc)
-                attempt = literal_eval(val)
-            except (SyntaxError, ValueError):
-                # if that goes wrong, just use the string
-                attempt = val
-            # ensure the types match ok
-            assert type(attempt) == type(globals()[key])
-            # cross fingers
-            print(f"Overriding: {key} = {attempt}")
-            globals()[key] = attempt
-        else:
-            raise ValueError(f"Unknown config key: {key}")
 # -----------------------------------------------------------------------------
 ddp = int(os.environ.get('LOCAL_RANK', -1)) != -1 # is this a ddp run?
 if ddp: