Compressor script

olmocr/train/compress_checkpoint.py

@@ -17,14 +17,13 @@ import json
 import os
 import shutil
 import tempfile
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Union
 
 import boto3
 import torch
 from llmcompressor import oneshot
 from llmcompressor.modifiers.quantization import QuantizationModifier
-from smart_open import smart_open
-from transformers import AutoTokenizer, Qwen2VLForConditionalGeneration
+from transformers import AutoTokenizer, Qwen2VLForConditionalGeneration, Qwen2_5_VLForConditionalGeneration
 
 from olmocr.s3_utils import parse_s3_path
 
@@ -74,7 +73,7 @@ def upload_local_to_s3(local_dir: str, bucket: str, prefix: str) -> None:
             print(f"  Uploaded {rel_path}")
 
 
-def load_model_and_tokenizer(source_path: str) -> Tuple[Qwen2VLForConditionalGeneration, AutoTokenizer, Optional[str]]:
+def load_model_and_tokenizer(source_path: str) -> Tuple[Union[Qwen2VLForConditionalGeneration, Qwen2_5_VLForConditionalGeneration], AutoTokenizer, Optional[str]]:
     """Load model and tokenizer from source path (local or S3)."""
     if is_s3_path(source_path):
         # Download from S3 to temporary directory
@@ -86,12 +85,48 @@ def load_model_and_tokenizer(source_path: str) -> Tuple[Qwen2VLForConditionalGen
         model_path = source_path
         temp_dir = None
     
+    # Read config to determine model architecture
+    config_path = os.path.join(model_path, "config.json")
+    with open(config_path, "r") as f:
+        config = json.load(f)
+    
+    # Get model name from config
+    model_name = config.get("name_or_path", "")
+    
     print(f"Loading model from {model_path}...")
-    model = Qwen2VLForConditionalGeneration.from_pretrained(
-        model_path, 
-        device_map="auto", 
-        torch_dtype="auto"
-    )
+    
+    # Load appropriate model class based on name
+    if "Qwen2.5-VL" in model_name:
+        print("Detected Qwen2.5-VL model")
+        model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            model_path, 
+            device_map="auto", 
+            torch_dtype="auto"
+        )
+    elif "Qwen2-VL" in model_name:
+        print("Detected Qwen2-VL model")
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            model_path, 
+            device_map="auto", 
+            torch_dtype="auto"
+        )
+    else:
+        # Default to checking architectures list
+        architectures = config.get("architectures", [])
+        if "Qwen2_5_VLForConditionalGeneration" in architectures:
+            print("Detected Qwen2.5-VL model from architectures")
+            model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+                model_path, 
+                device_map="auto", 
+                torch_dtype="auto"
+            )
+        else:
+            print("Detected Qwen2-VL model from architectures")
+            model = Qwen2VLForConditionalGeneration.from_pretrained(
+                model_path, 
+                device_map="auto", 
+                torch_dtype="auto"
+            )
     
     print(f"Loading tokenizer from {model_path}...")
     tokenizer = AutoTokenizer.from_pretrained(model_path)
@@ -101,11 +136,6 @@ def load_model_and_tokenizer(source_path: str) -> Tuple[Qwen2VLForConditionalGen
 
 def compress_checkpoint(source_path: str, dest_path: str) -> None:
     """Compress OlmOCR checkpoint using FP8 quantization."""
-    # First, validate the source checkpoint
-    config_path = os.path.join(source_path, "config.json")
-    if is_s3_path(source_path):
-        config_path = f"{source_path}/config.json"
-    
     # Load model and tokenizer
     model, tokenizer, temp_source_dir = load_model_and_tokenizer(source_path)