revive tests. now that we have a tiny stories260K model this only requires a 2MB download. phew

test_all.py

@@ -4,37 +4,65 @@ $ pytest
 """
 import os
 import pytest # pip install pytest
+import requests
 import subprocess
 
+
 import torch
 from model import ModelArgs, Transformer
+from tokenizer import Tokenizer
 
-def test_argmax_inference():
-    """
-    Only the simplest test for now: run inference with temperature 0 
-    (for determinism) in both C and PyTorch, and see that the sampled tokens 
-    are the same.
-    """
-    test_ckpt_dir = "out" # TODO create a dummy test checkpoint for this?
+# -----------------------------------------------------------------------------
+# test utilities
 
-    # run C version
-    model_path = os.path.join(test_ckpt_dir, "model.bin")
-    command = ["./run", model_path, "0.0"]
-    proc = subprocess.Popen(command, stdout=subprocess.PIPE)
-    c_tokens = []
-    for line in proc.stdout:
-        token = int(line.decode('utf-8').strip())
-        c_tokens.append(token)
-    proc.wait()
-    #print(c_tokens)
+test_ckpt_dir = "test"
 
-    # run PyTorch version
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    ckpt_path = os.path.join(test_ckpt_dir, "ckpt.pt")
-    checkpoint = torch.load(ckpt_path, map_location=device)
-    gptconf = ModelArgs(**checkpoint['model_args'])
+def download_file(url, filename):
+    print(f"Downloading {url} to {filename}")
+    response = requests.get(url, stream=True)
+    response.raise_for_status() # Raise an HTTPError on bad status code
+    with open(filename, 'wb') as file:
+        for chunk in response.iter_content(chunk_size=8192):
+            file.write(chunk)
+
+def attempt_download_files():
+    os.makedirs(test_ckpt_dir, exist_ok=True)
+    root_url = "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K"
+    need = ["stories260K.bin", "stories260K.pt", "tok512.bin", "tok512.model"]
+    for file in need:
+        url = os.path.join(root_url, file)
+        filename = os.path.join(test_ckpt_dir, file)
+        if not os.path.exists(filename):
+            download_file(url, filename)
+
+expected_stdout = b'Once upon a time, there was a little girl named Lily. She loved to play outside in the park. One day, she saw a big, red ball. She wanted to play with it, but it was too high.\nLily\'s mom said, "Lily, let\'s go to the park." Lily was sad and didn\'t know what to do. She said, "I want to play with your ball, but I can\'t find it."\nLily was sad and didn\'t know what to do. She said, "I\'m sorry, Lily. I didn\'t know what to do."\nLily didn\'t want to help her mom, so she'
+
+# -----------------------------------------------------------------------------
+# actual tests
+
+def test_runc():
+    """ Forwards a model against a known-good desired outcome in run.c for 200 steps"""
+
+    model_path = os.path.join(test_ckpt_dir, "stories260K.bin")
+    tokenizer_path = os.path.join(test_ckpt_dir, "tok512.bin")
+    command = ["./run", model_path, "-z", tokenizer_path, "-t", "0.0", "-n", "200"]
+    proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+
+    stdout, stderr = proc.communicate()
+
+    # strip the very last \n that is added by run.c for aesthetic reasons
+    stdout = stdout[:-1]
+    assert stdout == expected_stdout
+
+def test_python():
+    """ Forwards a model against a known-good desired outcome in sample.py for 200 steps"""
+
+    device = "cpu" # stories260K is small enough to just breeze through it on CPU
+    checkpoint = os.path.join(test_ckpt_dir, "stories260K.pt")
+    checkpoint_dict = torch.load(checkpoint, map_location=device)
+    gptconf = ModelArgs(**checkpoint_dict['model_args'])
     model = Transformer(gptconf)
-    state_dict = checkpoint['model']
+    state_dict = checkpoint_dict['model']
     unwanted_prefix = '_orig_mod.'
     for k,v in list(state_dict.items()):
         if k.startswith(unwanted_prefix):
@@ -44,10 +72,12 @@ def test_argmax_inference():
     model.to(device)
     x = torch.tensor([[1]], dtype=torch.long, device=device) # 1 is BOS
     with torch.inference_mode():
-        y = model.generate(x, max_new_tokens=gptconf.max_seq_len, temperature=0.0)
+        y = model.generate(x, max_new_tokens=200, temperature=0.0)
     pt_tokens = y[0].tolist()
-    pt_tokens = pt_tokens[1:] # remove BOS
-    #print(pt_tokens)
 
-    # compare
-    assert c_tokens == pt_tokens
+    tokenizer_model = os.path.join(test_ckpt_dir, "tok512.model")
+    enc = Tokenizer(tokenizer_model=tokenizer_model)
+    text = enc.decode(pt_tokens)
+    text = text.encode('ascii') # turn into bytes
+
+    assert text == expected_stdout