More docs on gpu for paddle

src/paddle_ocr/scripts/test_dynamic_mode.py

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+#!/usr/bin/env python3
+"""
+Test PaddleOCR in dynamic graph mode (not inference mode).
+
+Dynamic mode compiles kernels at runtime, which may work on Blackwell.
+Inference mode uses pre-compiled kernels which fail on sm_121.
+
+Usage:
+    python test_dynamic_mode.py [image_path]
+"""
+
+import os
+import sys
+
+os.environ['DISABLE_MODEL_SOURCE_CHECK'] = 'True'
+# Force dynamic graph mode
+os.environ['FLAGS_enable_pir_api'] = '0'
+
+import numpy as np
+import paddle
+from PIL import Image
+
+
+def check_gpu():
+    """Check GPU status."""
+    print("=" * 60)
+    print("GPU STATUS")
+    print("=" * 60)
+    print(f"Device: {paddle.device.get_device()}")
+    print(f"CUDA compiled: {paddle.device.is_compiled_with_cuda()}")
+
+    if paddle.device.is_compiled_with_cuda() and paddle.device.cuda.device_count() > 0:
+        props = paddle.device.cuda.get_device_properties(0)
+        print(f"GPU: {props.name} (sm_{props.major}{props.minor})")
+        print(f"Memory: {props.total_memory / (1024**3):.1f} GB")
+    print()
+
+
+def test_paddleocr_dynamic(image_path: str):
+    """Test PaddleOCR with dynamic execution."""
+    print("=" * 60)
+    print("PADDLEOCR DYNAMIC MODE TEST")
+    print("=" * 60)
+
+    # Import PaddleOCR
+    from paddleocr import PaddleOCR
+
+    # Try to force dynamic mode by setting use_static=False if available
+    # or by using the model in eval mode directly
+
+    print("Creating PaddleOCR instance...")
+    print("(This may download models on first run)")
+
+    try:
+        # Create OCR instance - this might still use inference internally
+        ocr = PaddleOCR(
+            text_detection_model_name='PP-OCRv4_mobile_det',
+            text_recognition_model_name='PP-OCRv4_mobile_rec',
+            use_angle_cls=False,  # Simplify
+            lang='es',
+        )
+
+        # Load image
+        img = Image.open(image_path)
+        arr = np.array(img)
+        print(f"Image shape: {arr.shape}")
+
+        # Run prediction
+        print("Running OCR prediction...")
+        result = ocr.predict(arr)
+
+        # Parse results
+        res = result[0].json['res']
+        dt_polys = res.get('dt_polys', [])
+        rec_texts = res.get('rec_texts', [])
+
+        print()
+        print("RESULTS:")
+        print(f"  Detected boxes: {len(dt_polys)}")
+        print(f"  Recognized texts: {len(rec_texts)}")
+
+        if rec_texts:
+            print(f"  First 5 texts: {rec_texts[:5]}")
+            return True
+        else:
+            print("  WARNING: No text recognized!")
+            return False
+
+    except Exception as e:
+        print(f"ERROR: {e}")
+        return False
+
+
+def test_paddle_dynamic_model():
+    """Test loading a paddle model in dynamic graph mode."""
+    print()
+    print("=" * 60)
+    print("PADDLE DYNAMIC GRAPH TEST")
+    print("=" * 60)
+
+    # Ensure we're in dynamic mode
+    paddle.disable_static()
+
+    # Test a simple model forward pass
+    print("Testing dynamic graph execution...")
+
+    # Create a simple ResNet-like block
+    x = paddle.randn([1, 3, 224, 224])
+
+    # Conv -> BN -> ReLU
+    conv = paddle.nn.Conv2D(3, 64, 7, stride=2, padding=3)
+    bn = paddle.nn.BatchNorm2D(64)
+
+    # Forward pass (dynamic mode - compiles at runtime)
+    y = conv(x)
+    y = bn(y)
+    y = paddle.nn.functional.relu(y)
+
+    print(f"Input shape: {x.shape}")
+    print(f"Output shape: {y.shape}")
+    print(f"Output min: {y.min().item():.4f}")
+    print(f"Output max: {y.max().item():.4f}")
+    print(f"Output mean: {y.mean().item():.4f}")
+
+    if y.min() != y.max():
+        print("Dynamic graph mode: WORKING")
+        return True
+    else:
+        print("Dynamic graph mode: BROKEN (constant output)")
+        return False
+
+
+def test_ppocr_model_direct():
+    """Try loading PPOCRv4 model directly in dynamic mode."""
+    print()
+    print("=" * 60)
+    print("PPOCR MODEL DIRECT LOAD TEST")
+    print("=" * 60)
+
+    try:
+        # Try to import ppocr modules directly
+        # This bypasses the inference predictor
+        from paddleocr.ppocr.modeling.architectures import build_model
+        from paddleocr.ppocr.postprocess import build_post_process
+        from paddleocr.ppocr.utils.save_load import load_model
+
+        print("Direct model import available")
+
+        # Note: This approach requires model config files
+        # which may or may not be bundled with paddleocr
+
+    except ImportError as e:
+        print(f"Direct model import not available: {e}")
+        print("PaddleOCR may only support inference mode")
+
+    return False
+
+
+def main():
+    # Default test image
+    image_path = '/app/dataset/0/img/page_0001.png'
+    if len(sys.argv) > 1:
+        image_path = sys.argv[1]
+
+    if not os.path.exists(image_path):
+        print(f"Image not found: {image_path}")
+        sys.exit(1)
+
+    print(f"Testing with image: {image_path}")
+    print()
+
+    check_gpu()
+
+    # Test 1: Basic dynamic graph
+    dynamic_works = test_paddle_dynamic_model()
+
+    if not dynamic_works:
+        print("\nDynamic graph mode is broken - GPU likely unsupported")
+        sys.exit(1)
+
+    # Test 2: Direct model load
+    test_ppocr_model_direct()
+
+    # Test 3: PaddleOCR pipeline
+    ocr_works = test_paddleocr_dynamic(image_path)
+
+    print()
+    print("=" * 60)
+    print("SUMMARY")
+    print("=" * 60)
+    print(f"Dynamic graph mode: {'WORKS' if dynamic_works else 'BROKEN'}")
+    print(f"PaddleOCR pipeline: {'WORKS' if ocr_works else 'BROKEN'}")
+
+    if dynamic_works and not ocr_works:
+        print()
+        print("DIAGNOSIS: Dynamic mode works but PaddleOCR fails.")
+        print("This means PaddleOCR internally uses inference predictor")
+        print("which has pre-compiled kernels without Blackwell support.")
+        print()
+        print("Potential solutions:")
+        print("1. Modify PaddleOCR to use dynamic mode")
+        print("2. Use ONNX export + ONNXRuntime")
+        print("3. Wait for PaddlePaddle Blackwell support")
+
+
+if __name__ == '__main__':
+    main()