Basic forward generation pass with openai dataset and qwen2vl

pdelfin/train/core/config.py

@@ -58,7 +58,7 @@ class GenerateConfig:
 @dataclass
 class WandbConfig:
     entity: str = field(help="The wandb entity to use for logging", default="ai2-llm")
-    project: str = field(help="The wandb project to use for logging", default="refine")
+    project: str = field(help="The wandb project to use for logging", default="pdf-qwen2vl")
     wandb_api_key: Optional[str] = field(help="The wandb api key to use for logging", default=None)
     mode: str = field(help="The wandb mode to use for logging. Set it to `offline`", default="online")
     watch: str = field(help="The wandb watch to use for logging", default="false")

pdelfin/train/train.py

@@ -10,8 +10,10 @@
 # Step 5. Move over from interactive session to gantry launch script
 
 import os
+import base64
 import logging
-import os
+from io import BytesIO
+from PIL import Image
 from functools import partial
 from logging import Logger
 from pathlib import Path
@@ -53,12 +55,10 @@
 )
 
 
-from qwen_vl_utils import process_vision_info
-
 from pdelfin.train.dataloader import build_batch_query_response_vision_dataset
 
 
-def run_train():
+def run_train(config: TrainConfig):
     train_ds = build_batch_query_response_vision_dataset(
                         query_glob_path="s3://ai2-oe-data/jakep/openai_batch_data_v2/*.jsonl",
                         response_glob_path="s3://ai2-oe-data/jakep/openai_batch_done_v2/*.json",
@@ -69,6 +69,46 @@ def run_train():
     )
     processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
 
+    for entry in train_ds:
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": entry["input_prompt_image_base64"]
+                    },
+                    {"type": "text", "text": entry["input_prompt_text"]},
+                ],
+            }
+        ]
+
+        # Preparation for inference
+        text = processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True
+        )
+
+        main_image = Image.open(BytesIO(base64.b64decode(entry["input_prompt_image_base64"])))
+
+        inputs = processor(
+            text=[text],
+            images=[main_image],
+            #videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
+        )
+        #inputs = inputs.to("cuda")
+
+        # Inference: Generation of the output
+        generated_ids = model.generate(**inputs, max_new_tokens=128)
+        generated_ids_trimmed = [
+            out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        output_text = processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+        print(output_text)
+
 
 
 def main():