Enhance GPT example user experience

.gitignore

@@ -0,0 +1,165 @@
+# Custom
+examples/data/shakespeare/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

README.md

@@ -26,10 +26,38 @@ python examples/data/shakespeare.py
 
 And finally, let's train a GPT:
 ```bash
-python examples/train-gpt.py
+python examples/gpt.py train
 ```
 
-This runs on CPU and should get train loss: 1.65 and test loss: 1.80 after 2000 iterations.
+This runs on CPU and should get train loss: 1.65 and test loss: 1.80 after 2000 iterations (took a few minutes).
+
+The trained weights will be saved to `examples/data/shakespeare/weights.pt`. You can now run inference:
+```bash
+python examples/gpt.py inference "ROMEO:"
+```
+
+You could also generate your own dataset and train your own GPT! See `examples/data/shakespeare.py` and change the
+source text files, then train your new model:
+
+```bash
+python examples/gpt.py train \
+  --train=mydataset/train.bin \
+  --validation=mydataset/val.bin \
+  --weights=mydataset/weights.pt
+```
+
+Now you can run inference with our fresh weights:
+
+```bash
+python examples/gpt.py inference \
+  --weights=mydataset/weights.pt \
+  "JULIET:"
+```
+
+> Note: you may need to change the `chars` in `examples/gpt.py` to match the chars of your dataset.
+> If you want a more generic approach, consider using something like:
+> `chars = list(string.ascii_letters + string.digits + string.punctuation + string.whitespace)`
+
 
 ## Project roadmap
 
@@ -86,7 +114,7 @@ for step in range(steps:=20):
         mlp.normalize(grad := weights.grad())     # normalize the gradient in the modular norm
         weights -= 0.1 * grad
         weights.zero_grad()
-    
+
         mlp.regularize(weights, strength = 0.01)  # regularize the weight vector
 
     print(step, loss.item())

examples/train-gpt.py → examples/gpt.py

@@ -1,15 +1,20 @@
+import time
+
 import torch
 import numpy as np
 
 # Karpathy's smallest GPT config
 
+chars = list("\n !$&',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz")
 vocab_size = 65
 context = 64
 num_heads = 4
 d_embed = 128
 d_query = 32
 d_value = 32
 num_blocks = 4
+assert len(chars) == vocab_size, "`chars` must be aligned to `vocab_size`"
+
 
 # training hparams
 
@@ -20,6 +25,19 @@
 eval_steps = 100
 log_interval = 200
 
+# encoding/decoding
+
+stoi = {ch: i for i, ch in enumerate(chars)}
+itos = {i: ch for i, ch in enumerate(chars)}
+
+def encode(s):
+    global stoi
+    return [stoi[c] for c in s]
+
+def decode(l):
+    global itos
+    return ''.join([itos[i] for i in l])
+
 # let's start by defining our GPT architecture
 # (we could instead just import GPT from modula.compound)
 
@@ -80,23 +98,22 @@ def __len__(self):
 
 # now let's start doing stuff
 
-if __name__ == "__main__":
-
+def train(train_filename, validation_filename):
     # load the data
 
-    trainset = SimpleLLMDataset(np.memmap("examples/data/shakespeare/train.bin", dtype=np.uint16, mode='r'), context)
-    testset  = SimpleLLMDataset(np.memmap("examples/data/shakespeare/val.bin",   dtype=np.uint16, mode='r'), context)
+    trainset = SimpleLLMDataset(np.memmap(train_filename, dtype=np.uint16, mode="r"), context)
+    testset  = SimpleLLMDataset(np.memmap(validation_filename, dtype=np.uint16, mode="r"), context)
 
     train_sampler = RandomSampler(trainset, batch_size)
     test_sampler  = RandomSampler(testset,  batch_size)
 
-    train_loader = torch.utils.data.DataLoader( trainset, num_workers=1, pin_memory=True, batch_sampler=train_sampler)
-    test_loader  = torch.utils.data.DataLoader( testset,  num_workers=1, pin_memory=True, batch_sampler=test_sampler)
+    train_loader = torch.utils.data.DataLoader(trainset, num_workers=1, pin_memory=True, batch_sampler=train_sampler)
+    test_loader  = torch.utils.data.DataLoader(testset,  num_workers=1, pin_memory=True, batch_sampler=test_sampler)
 
     train_iterator = iter(train_loader)
     test_iterator  = iter(test_loader)
 
-    getBatch = lambda train: next(train_iterator if train else test_iterator)
+    get_batch = lambda train: next(train_iterator if train else test_iterator)
 
     # load the model
 
@@ -114,12 +131,13 @@ def __len__(self):
 
     # train the model
 
+    start = time.time()
     for step in range(steps):
 
         if step % log_interval == 0:
             test_loss = test_acc = 0
             for eval_step in range(eval_steps):
-                data, target = getBatch(train = False)
+                data, target = get_batch(train=False)
                 output = gpt.forward(data, weights)
                 output = output.view(-1, output.size(-1))
                 target = target.view(-1)
@@ -131,7 +149,7 @@ def __len__(self):
             test_loss /= eval_steps
             test_acc /= eval_steps
 
-        data, target = getBatch(train = True)
+        data, target = get_batch(train=True)
         output = gpt.forward(data, weights)
         output = output.view(-1, output.size(-1))
         target = target.view(-1)
@@ -160,6 +178,70 @@ def __len__(self):
             weights.zero_grad()
 
         if step % log_interval == 0:
-            print(    "step:", step,
-                    "\t train loss:", "%.2f" % train_loss.item(), 
-                    "\t test loss:",  "%.2f" % test_loss.item()   )
+            print(     "step:", step,
+                    "\t train loss:", "%.2f" % train_loss.item(),
+                    "\t test loss:",  "%.2f" % test_loss.item()   ,
+                   f"\t took: {time.time() - start:.2f}s")
+            start = time.time()
+
+    return weights
+
+
+def inference(weights, input_text, chars_to_generate):
+    gpt = GPT(vocab_size, context, num_heads, d_embed, d_query, d_value, num_blocks)
+    print(input_text, end="", flush=True)
+    context_tokens = torch.tensor(encode(input_text)).unsqueeze(0)
+    for _ in range(chars_to_generate):
+        with torch.no_grad():
+            output = gpt.forward(context_tokens, weights)
+        logits = output[0, -1, :]
+        probs = torch.softmax(logits, dim=-1)
+        next_token = torch.multinomial(probs, num_samples=1).item()
+        print(decode([next_token]), end="", flush=True)
+        context_tokens = torch.cat([context_tokens, torch.tensor([[next_token]])], dim=1)
+        if context_tokens.shape[1] > context:
+            context_tokens = context_tokens[:, -context:]
+
+
+if __name__ == "__main__":
+    import argparse
+    from pathlib import Path
+
+
+    data_path = Path(__file__).parent / "data" / "shakespeare"
+    default_weights_filename = data_path / "weights.pt"
+    default_train_filename = data_path / "train.bin"
+    default_validation_filename = data_path / "val.bin"
+    parser = argparse.ArgumentParser()
+    subparsers = parser.add_subparsers(dest="mode", required=True)
+
+    parser_train = subparsers.add_parser("train")
+    parser_train.add_argument("--weights", "-w", type=Path, default=default_weights_filename, help="Weights filename to save")
+    parser_train.add_argument("--train", "-t", type=Path, default=default_train_filename, help="Train dataset filename")
+    parser_train.add_argument("--validation", "-v", type=Path, default=default_validation_filename, help="Validation dataset filename")
+
+    parser_inference = subparsers.add_parser("inference")
+    parser_inference.add_argument("--weights", "-w", type=Path, default=default_weights_filename, help="Weights filename to load")
+    parser_inference.add_argument("--chars", "-c", type=int, default=1024, help="Number of chars to generate")
+    parser_inference.add_argument("input", type=str, help="Text to be feed into the model")
+
+    args = parser.parse_args()
+
+    if args.mode == "train":
+        weights_filename = args.weights
+        train_filename = args.train
+        validation_filename = args.validation
+
+        weights = train(train_filename, validation_filename)
+        torch.save(weights, weights_filename)
+        print(f"Weights saved to {weights_filename}")
+
+    elif args.mode == "inference":
+        weights_filename = args.weights
+        input_text = args.input
+        chars_to_generate = args.chars
+
+        print(f"Loading weights from {weights_filename}")
+        weights = torch.load(weights_filename)
+        print()
+        inference(weights, input_text, chars_to_generate)