Added fallback to preload cudnn dlls from nvidia cudnn venv package or torch venv package (#1135)

### What does this PR do?

Type of change: Bug fix

There was a QA team that was testing the modelopt 0.43 release and
pointed out that we could install nvidia-cudnn pypi packages and use
ort.preload_dlls() to load the dlls from the python venv instead of
trying to search in system path only .

Here is the info about onnxruntime.preload_dlls() function 
<img width="1478" height="414" alt="image"
src="https://github.com/user-attachments/assets/e43ecbe3-ba52-4dd8-b2a2-e825d013205b"
/>

So added fallback to system path cudnn search to preload dlls and if
that also fails then raise exception.



### Testing
Tested quantization by installing nvidia-cudnn-cu12 package and removing
cudnn dlls from system path. Working as expected.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **Bug Fixes**
* Improved startup handling when CUDA/cuDNN libraries are missing: the
app now attempts a conditional preload from installed Python packages
(when supported), logs captured preload output for diagnostics, warns on
preload errors, and only raises an error if preload ultimately fails.
* **Documentation**
* Error messages now better explain missing-library issues, note
platform/version considerations, and recommend installing a cuDNN pip
package (e.g., nvidia-cudnn-cu12) or setting the appropriate environment
variable.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Hrishith Thadicherla <hthadicherla@nvidia.com>

Author: hthadicherla

modelopt/onnx/quantization/ort_utils.py

@@ -16,9 +16,12 @@
 """Provides basic ORT inference utils, should be replaced by modelopt.torch.ort_client."""
 
 import glob
+import io
 import os
 import platform
+import sys
 from collections.abc import Sequence
+from contextlib import redirect_stderr, redirect_stdout
 
 import onnxruntime as ort
 from onnxruntime.quantization.operators.qdq_base_operator import QDQOperatorBase
@@ -70,11 +73,54 @@ def _check_for_libcudnn():
             f" for your ORT version at https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements."
         )
     else:
-        logger.error(f"cuDNN library not found in {env_variable}")
+        # Fallback: ORT >=1.20 ships a preload_dlls() helper that loads CUDA/cuDNN
+        # DLLs bundled inside pip packages (e.g. nvidia-cudnn-cu12) so they don't
+        # need to be on the system PATH / LD_LIBRARY_PATH.
+        # However, preload_dlls() is broken on Python 3.10 (missing os.add_dll_directory
+        # behaviour), so we skip it for that version.
+        if hasattr(ort, "preload_dlls") and sys.version_info[:2] != (3, 10):
+            logger.warning(
+                f"cuDNN not found in {env_variable}. "
+                "Attempting onnxruntime.preload_dlls() to load from site-packages..."
+            )
+            # preload_dlls() does not raise on failure — it silently prints
+            # "Failed to load ..." messages.  Capture its output and check
+            # whether the key cuDNN DLL actually loaded.
+            cudnn_dll = "cudnn" if platform.system() == "Windows" else "libcudnn_adv"
+            captured = io.StringIO()
+            try:
+                with redirect_stdout(captured), redirect_stderr(captured):
+                    ort.preload_dlls()
+            except Exception as e:
+                logger.warning(f"onnxruntime.preload_dlls() raised an exception: {e}")
+
+            preload_output = captured.getvalue()
+            if preload_output:
+                logger.debug(f"preload_dlls() output:\n{preload_output}")
+
+            if f"Failed to load {cudnn_dll}" in preload_output:
+                logger.error(
+                    f"onnxruntime.preload_dlls() was called but {cudnn_dll} failed to load. "
+                    "cuDNN DLLs were NOT successfully loaded from site-packages."
+                )
+            else:
+                logger.info(
+                    "onnxruntime.preload_dlls() succeeded — CUDA/cuDNN DLLs loaded"
+                    " from site-packages. Verify version compatibility at"
+                    " https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements."
+                )
+                return True
+
         raise FileNotFoundError(
-            f"{lib_pattern} is not accessible in {env_variable}! Please make sure that the path to that library"
-            f" is in the env var to use the CUDA or TensorRT EP and ensure that the correct version is available."
-            f" Versioning compatibility can be checked at https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements."
+            f"{lib_pattern} is not accessible via {env_variable} or site-packages.\n"
+            f"To fix this, either:\n"
+            f"  1. Add the directory containing {lib_pattern} to your"
+            f" {env_variable} env var, or\n"
+            f"  2. Install the cuDNN pip package (Python>=3.11 only):"
+            f" pip install nvidia-cudnn-cu12 (or nvidia-cudnn-cu13)\n"
+            f"This is required for the CUDA / TensorRT execution provider.\n"
+            f"Check version compatibility at"
+            f" https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements."
         )
     return found