Add CI tests for chapter 7 (rasbt#239)

.gitignore

@@ -1,5 +1,4 @@
 # Configs and keys
-ch07/01_main-chapter-code/gpt2-medium355M-sft-standalone.pth
 ch07/02_dataset-utilities/config.json
 ch07/03_model-evaluation/config.json
 
@@ -36,6 +35,8 @@ ch06/02_bonus_additional-experiments/gpt2
 ch06/03_bonus_imdb-classification/gpt2
 
 ch07/01_main-chapter-code/gpt2-medium355M-sft.pth
+ch07/01_main-chapter-code/gpt2-medium355M-sft-standalone.pth
+ch07/01_main-chapter-code/Smalltestmodel-sft-standalone.pth
 ch07/01_main-chapter-code/gpt2/
 
 # Datasets

ch07/01_main-chapter-code/gpt_instruction_finetuning.py

@@ -147,7 +147,7 @@ def plot_losses(epochs_seen, tokens_seen, train_losses, val_losses):
     # plt.show()
 
 
-def main():
+def main(test_mode=False):
     #######################################
     # Print package versions
     #######################################
@@ -177,6 +177,12 @@ def main():
     test_data = data[train_portion:train_portion + test_portion]
     val_data = data[train_portion + test_portion:]
 
+    # Use very small subset for testing purposes
+    if args.test_mode:
+        train_data = train_data[:10]
+        val_data = val_data[:10]
+        test_data = test_data[:10]
+
     print("Training set length:", len(train_data))
     print("Validation set length:", len(val_data))
     print("Test set length:", len(test_data))
@@ -217,31 +223,50 @@ def main():
     #######################################
     # Load pretrained model
     #######################################
-    BASE_CONFIG = {
-        "vocab_size": 50257,     # Vocabulary size
-        "context_length": 1024,  # Context length
-        "drop_rate": 0.0,        # Dropout rate
-        "qkv_bias": True         # Query-key-value bias
-    }
 
-    model_configs = {
-        "gpt2-small (124M)": {"emb_dim": 768, "n_layers": 12, "n_heads": 12},
-        "gpt2-medium (355M)": {"emb_dim": 1024, "n_layers": 24, "n_heads": 16},
-        "gpt2-large (774M)": {"emb_dim": 1280, "n_layers": 36, "n_heads": 20},
-        "gpt2-xl (1558M)": {"emb_dim": 1600, "n_layers": 48, "n_heads": 25},
-    }
+    # Small GPT model for testing purposes
+    if args.test_mode:
+        BASE_CONFIG = {
+            "vocab_size": 50257,
+            "context_length": 120,
+            "drop_rate": 0.0,
+            "qkv_bias": False,
+            "emb_dim": 12,
+            "n_layers": 1,
+            "n_heads": 2
+        }
+        model = GPTModel(BASE_CONFIG)
+        model.eval()
+        device = "cpu"
+        CHOOSE_MODEL = "Small test model"
+
+    # Code as it is used in the main chapter
+    else:
+        BASE_CONFIG = {
+            "vocab_size": 50257,     # Vocabulary size
+            "context_length": 1024,  # Context length
+            "drop_rate": 0.0,        # Dropout rate
+            "qkv_bias": True         # Query-key-value bias
+        }
+
+        model_configs = {
+            "gpt2-small (124M)": {"emb_dim": 768, "n_layers": 12, "n_heads": 12},
+            "gpt2-medium (355M)": {"emb_dim": 1024, "n_layers": 24, "n_heads": 16},
+            "gpt2-large (774M)": {"emb_dim": 1280, "n_layers": 36, "n_heads": 20},
+            "gpt2-xl (1558M)": {"emb_dim": 1600, "n_layers": 48, "n_heads": 25},
+        }
 
-    CHOOSE_MODEL = "gpt2-medium (355M)"
+        CHOOSE_MODEL = "gpt2-medium (355M)"
 
-    BASE_CONFIG.update(model_configs[CHOOSE_MODEL])
+        BASE_CONFIG.update(model_configs[CHOOSE_MODEL])
 
-    model_size = CHOOSE_MODEL.split(" ")[-1].lstrip("(").rstrip(")")
-    settings, params = download_and_load_gpt2(model_size=model_size, models_dir="gpt2")
+        model_size = CHOOSE_MODEL.split(" ")[-1].lstrip("(").rstrip(")")
+        settings, params = download_and_load_gpt2(model_size=model_size, models_dir="gpt2")
 
-    model = GPTModel(BASE_CONFIG)
-    load_weights_into_gpt(model, params)
-    model.eval()
-    model.to(device)
+        model = GPTModel(BASE_CONFIG)
+        load_weights_into_gpt(model, params)
+        model.eval()
+        model.to(device)
 
     print("Loaded model:", CHOOSE_MODEL)
     print(50*"-")
@@ -259,6 +284,7 @@ def main():
 
     start_time = time.time()
     optimizer = torch.optim.AdamW(model.parameters(), lr=0.00005, weight_decay=0.1)
+
     num_epochs = 2
 
     torch.manual_seed(123)
@@ -307,4 +333,19 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Finetune a GPT model for classification"
+    )
+    parser.add_argument(
+        "--test_mode",
+        default=False,
+        action="store_true",
+        help=("This flag runs the model in test mode for internal testing purposes. "
+              "Otherwise, it runs the model as it is used in the chapter (recommended).")
+    )
+    args = parser.parse_args()
+
+    main(args.test_mode)

ch07/01_main-chapter-code/tests.py

@@ -0,0 +1,16 @@
+# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
+# Source for "Build a Large Language Model From Scratch"
+#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
+# Code: https://github.com/rasbt/LLMs-from-scratch
+
+# File for internal use (unit tests)
+
+
+import subprocess
+
+
+def test_gpt_class_finetune():
+    command = ["python", "ch06/01_main-chapter-code/gpt_class_finetune.py", "--test_mode"]
+
+    result = subprocess.run(command, capture_output=True, text=True)
+    assert result.returncode == 0, f"Script exited with errors: {result.stderr}"