Reviewing molmo training

pyproject.toml

@@ -85,6 +85,7 @@ train = [
     "omegaconf",
     "s3fs",
     "necessary",
+    "einops",
     "transformers>=4.45.1"
 ]
 

tests/test_molmo.py

@@ -0,0 +1,57 @@
+import unittest
+
+from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer, GenerationConfig
+from PIL import Image
+import requests
+
+class MolmoProcessorTest(unittest.TestCase):
+    def test_molmo_demo(self):
+        # load the processor
+        processor = AutoProcessor.from_pretrained(
+            'allenai/Molmo-7B-O-0924',
+            trust_remote_code=True,
+            torch_dtype='auto',
+        )
+
+        # load the model
+        model = AutoModelForCausalLM.from_pretrained(
+            'allenai/Molmo-7B-O-0924',
+            trust_remote_code=True,
+            torch_dtype='auto',
+        )
+
+        device = "cuda:0"
+
+        model = model.to(device)
+
+        # process the image and text
+        inputs = processor.process(
+            images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)],
+            text="Describe this image."
+        )
+
+        # move inputs to the correct device and make a batch of size 1
+        inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
+
+        print("Raw inputs")
+        print(inputs)
+
+        print("\nShapes")
+        print({(x, y.shape) for x,y in inputs.items()})
+
+        print("\nTokens")
+        print(processor.tokenizer.batch_decode(inputs["input_ids"]))
+
+        # generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
+        output = model.generate_from_batch(
+            inputs,
+            GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
+            tokenizer=processor.tokenizer
+        )
+
+        # only get generated tokens; decode them to text
+        generated_tokens = output[0,inputs['input_ids'].size(1):]
+        generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+        # print the generated text
+        print(generated_text)