Ok, dataloader from start to finish is running, now to write a trainer

olmocr/train/dataloader.py

@@ -12,11 +12,17 @@ from pypdf import PdfReader
 from tqdm import tqdm
 from dataclasses import dataclass, fields
 from abc import ABC, abstractmethod
+import numpy as np
 
 from olmocr.data.renderpdf import render_pdf_to_base64png
 from olmocr.prompts.prompts import PageResponse, build_finetuning_prompt
 from olmocr.prompts.anchor import get_anchor_text
 
+try:
+    import numpy as np
+except ImportError:
+    np = None
+
 # Type alias for samples
 Sample: TypeAlias = Dict[str, Any]
 
@@ -299,6 +305,67 @@ class InstructMessages(PipelineStep):
         return sample
     
 
+@dataclass(frozen=True, slots=True)
+class Tokenizer(PipelineStep):
+    """Tokenizes messages and creates training labels with proper masking."""
+    processor: Any  # The model processor (e.g., AutoProcessor)
+    masking_index: int = -100
+    
+    def __call__(self, sample: Sample) -> Sample:
+        """Tokenize messages and create labels for training."""
+        if np is None:
+            raise ImportError("numpy is required for Tokenizer step")
+            
+        messages = sample["messages"]
+        main_image = sample["image"]
+        
+        # Apply chat template to full conversation
+        text = self.processor.apply_chat_template(
+            messages, 
+            tokenize=False, 
+            add_generation_prompt=False  # Don't add prompt since we have the response
+        )
+        
+        # Process everything together
+        inputs = self.processor(
+            text=[text],
+            images=[main_image],
+            padding=True,
+            return_tensors="np",
+        )
+        
+        # Create labels by copying input_ids and masking the prompt portion
+        labels = inputs.input_ids.copy()
+        
+        # Find where the assistant response starts
+        # This assumes the processor adds some delimiter between user and assistant
+        # You might need to adjust based on your specific chat template
+
+        assistant_token = self.processor.tokenizer.encode("assistant", add_special_tokens=False)[0]
+        assistant_start_idx = np.where(inputs.input_ids[0] == assistant_token)[0]
+        
+        if len(assistant_start_idx) > 0:
+            # Mask everything before the assistant's actual response content
+            # Usually there's a few tokens after "assistant" role marker
+            response_start = assistant_start_idx[-1] + 2  # Adjust offset as needed
+            labels[0, :response_start] = self.masking_index
+        else:
+            raise Exception("Could not find assistant tokens")
+
+        # Add tokenized data to sample
+        sample["input_ids"] = inputs.input_ids[0]
+        sample["attention_mask"] = inputs.attention_mask[0]
+        sample["labels"] = labels[0]
+        
+        # Add image-related tensors if present
+        if hasattr(inputs, 'pixel_values'):
+            sample["pixel_values"] = inputs.pixel_values
+        if hasattr(inputs, 'image_grid_thw'):
+            sample["image_grid_thw"] = inputs.image_grid_thw[0]
+            
+        return sample
+
+
 class MarkdownPDFDocumentDataset(BaseMarkdownPDFDataset):
     """Dataset that includes front matter parsing and PDF rendering by default."""
     
@@ -326,6 +393,7 @@ class MarkdownPDFDocumentDataset(BaseMarkdownPDFDataset):
         super().__init__(root_dir, pipeline_steps)
 
 
+
 if __name__ == "__main__":
     import argparse
     from pathlib import Path
@@ -399,5 +467,94 @@ if __name__ == "__main__":
         print(f"PDF: {Path(first_sample['pdf_path']).name}")
         print(f"Image size: {first_sample['image'].size}")
         print(f"Page data: {first_sample['page_data']}")
+        
+        # Test with actual Qwen2.5-VL tokenization
+        print("\n\n=== Testing with Qwen2.5-VL-7B-Instruct Tokenization ===")
+        
+        try:
+            from transformers import AutoProcessor
+            
+            print("Loading Qwen2.5-VL processor...")
+            processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+            
+            # Create pipeline with real tokenizer
+            tokenized_dataset = BaseMarkdownPDFDataset(
+                args.root_dir,
+                pipeline_steps=[
+                    FrontMatterParser(front_matter_class=PageResponse),
+                    PDFRenderer(target_longest_image_dim=512),
+                    StaticLengthDocumentAnchoring(target_anchor_text_len=1000),
+                    FinetuningPrompt(),
+                    FrontMatterOutputFormat(),
+                    InstructMessages(),
+                    Tokenizer(processor),
+                ]
+            )
+            
+            if len(tokenized_dataset) > 0:
+                print("\nProcessing first sample with Qwen2.5-VL...")
+                tokenized_sample = tokenized_dataset[0]
+                
+                print("\nTokenized output:")
+                print(f"  Keys: {list(tokenized_sample.keys())}")
+                print(f"  Input IDs shape: {tokenized_sample['input_ids'].shape}")
+                print(f"  Labels shape: {tokenized_sample['labels'].shape}")
+                print(f"  Attention mask shape: {tokenized_sample['attention_mask'].shape}")
+                
+                if 'pixel_values' in tokenized_sample:
+                    print(f"  Pixel values shape: {tokenized_sample['pixel_values'].shape}")
+                if 'image_grid_thw' in tokenized_sample:
+                    print(f"  Image grid THW: {tokenized_sample['image_grid_thw']}")
+                
+                # Show label masking
+                print(f"\nLabel masking analysis:")
+                labels = tokenized_sample['labels']
+                masked_count = np.sum(labels == -100)
+                total_count = len(labels)
+                print(f"  Total tokens: {total_count}")
+                print(f"  Masked tokens: {masked_count} ({masked_count/total_count*100:.1f}%)")
+                print(f"  Unmasked tokens: {total_count - masked_count} ({(total_count - masked_count)/total_count*100:.1f}%)")
+                
+                # Find the transition point
+                transition_idx = None
+                for i in range(len(labels) - 1):
+                    if labels[i] == -100 and labels[i + 1] != -100:
+                        transition_idx = i + 1
+                        break
+                
+                if transition_idx:
+                    print(f"  Transition from masked to unmasked at position: {transition_idx}")
+                
+                # Print all tokens
+                input_ids = tokenized_sample['input_ids']
+                print(f"\nAll tokens ({len(input_ids)} total):")
+                print("Format: [index] Token (repr) | Label | Token ID")
+                print("-" * 80)
+                
+                for i in range(len(input_ids)):
+                    token = processor.tokenizer.decode([input_ids[i]])
+                    token_repr = repr(token)
+                    label = labels[i] if i < len(labels) else "N/A"
+                    token_id = input_ids[i]
+                    
+                    # Mark special positions
+                    marker = ""
+                    if transition_idx and i == transition_idx:
+                        marker = " <-- TRANSITION (first unmasked)"
+                    elif i == 0:
+                        marker = " <-- START"
+                    elif label != -100 and i > 0 and labels[i-1] == -100:
+                        marker = " <-- response begins"
+                    
+                    print(f"[{i:4d}] {token_repr:20s} | {str(label):6s} | {token_id:6d}{marker}")
+                
+        except ImportError as e:
+            print(f"\nCould not import transformers: {e}")
+            print("Install with: pip install transformers")
+        except Exception as e:
+            print(f"\nError during tokenization test: {e}")
+            import traceback
+            traceback.print_exc()
+        
     else:
         raise AssertionError("Expected some data to be created at this point")