modelopt/onnx/quantization/autotune/benchmark.py (1)

209-228: ⚠️ Potential issue | 🔴 Critical

Remove early return so remote autotuning args are actually applied.

Line 218 returns from __init__ before Line 227 extends self._base_cmd, so the --remoteAutoTuningConfig/--safe args never make it into the executed command when TRT >= 10.15. This breaks remote autotuning despite passing the version gate.

💡 Proposed fix

-        trtexec_args = self.trtexec_args or []
+        trtexec_args = list(self.trtexec_args or [])
         has_remote_config = any("--remoteAutoTuningConfig" in arg for arg in trtexec_args)

         if has_remote_config:
             try:
                 _check_for_tensorrt(min_version="10.15")
                 self.logger.debug("TensorRT Python API version >= 10.15 detected")
                 if "--safe" not in trtexec_args:
                     self.logger.warning(
                         "Remote autotuning requires '--safe' to be set. Adding it to trtexec arguments."
                     )
-                    self.trtexec_args.append("--safe")
-                return
+                    trtexec_args.append("--safe")
             except ImportError:
                 self.logger.warning(
                     "Remote autotuning is not supported with TensorRT version < 10.15. "
                     "Removing --remoteAutoTuningConfig from trtexec arguments"
                 )
                 trtexec_args = [
                     arg for arg in trtexec_args if "--remoteAutoTuningConfig" not in arg
                 ]
         self._base_cmd.extend(trtexec_args)

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@modelopt/onnx/quantization/autotune/benchmark.py` around lines 209 - 228, The
constructor's remote-autotuning branch returns early so trtexec args like
"--safe" or "--remoteAutoTuningConfig" never get appended to the final command;
remove the early return in the block that calls
_check_for_tensorrt(min_version="10.15") and instead ensure any modifications to
self.trtexec_args (and filtered local trtexec_args) are followed by extending
self._base_cmd with those trtexec_args so the arguments are applied; locate the
logic around _check_for_tensorrt, self.trtexec_args, trtexec_args and
self._base_cmd in the __init__ of the Benchmark class and adjust flow so the
successful TensorRT path does not exit before
self._base_cmd.extend(trtexec_args) executes.