fix: Fix .numpy() issue on fake tensors

- Add `fake_tensor_unsupported` decorator to helper backend
- Refactor `conversion` implementation to use compilation settings
object as well, to reduce code duplication and encourage reuse
- Improve debugger messages by pre-formatting support string

py/torch_tensorrt/dynamo/backend/__init__.py

@@ -46,6 +46,8 @@ def compile(
     torch_executed_modules=[],
     **kwargs,
 ):
+    if debug:
+        logger.setLevel(logging.DEBUG)
 
     logger.warn(
         "The Dynamo backend is an experimental feature, for which only the "

py/torch_tensorrt/dynamo/backend/backends.py

@@ -52,6 +52,7 @@ def aot_torch_tensorrt_aten_backend(
     )
 
 
+@fake_tensor_unsupported
 def _pretraced_backend(
     gm: torch.fx.GraphModule,
     sample_inputs: Sequence[torch.Tensor],

py/torch_tensorrt/dynamo/backend/conversion.py

@@ -2,45 +2,41 @@
 import torch
 from torch_tensorrt.fx.trt_module import TRTModule
 from torch_tensorrt import TRTModuleNext
+from torch_tensorrt.dynamo.backend._settings import CompilationSettings
 from torch_tensorrt.fx.fx2trt import (
     InputTensorSpec,
     TRTInterpreter,
 )
-from torch_tensorrt.fx.utils import LowerPrecision
 
 import tensorrt as trt
 
 
 def convert_module(
     module: torch.fx.GraphModule,
     inputs: Sequence[torch.Tensor],
-    debug: bool = False,
-    workspace_size: int = 20 << 30,
-    precision: LowerPrecision = LowerPrecision.FP32,
+    settings: CompilationSettings = CompilationSettings(),
 ) -> Union[TRTModuleNext, TRTModule]:
     """Convert an FX module to a TRT module
     Args:
         module: FX GraphModule to convert
         inputs: Sequence of Tensors representing inputs to the module
-        debug: Whether to print out verbose debugging information
-        workspace_size: Maximum workspace TRT is allowed to use for the module
-        precision: Model Layer precision
+        settings: Compilation settings
     Returns:
         TRTModule or TRTModuleNext
     """
     interp = TRTInterpreter(
         module,
         InputTensorSpec.from_tensors(inputs),
         explicit_batch_dimension=True,
-        logger_level=(trt.Logger.VERBOSE if debug else trt.Logger.WARNING),
+        logger_level=(trt.Logger.VERBOSE if settings.debug else trt.Logger.WARNING),
     )
 
     r = interp.run(
-        max_workspace_size=workspace_size,
-        lower_precision=precision,
+        max_workspace_size=settings.workspace_size,
+        lower_precision=settings.precision,
         profiling_verbosity=(
             trt.ProfilingVerbosity.VERBOSE
-            if debug
+            if settings.debug
             else trt.ProfilingVerbosity.LAYER_NAMES_ONLY
         ),
     )

py/torch_tensorrt/dynamo/backend/lowering/_partition.py

@@ -136,15 +136,18 @@ def print_support_overview(self, num_trt_blocks: Optional[int] = None):
                 f"\nNumber of TensorRT-Accelerated Engines Generated: {num_trt_blocks}"
             )
 
-        logger.debug("\nSupported Nodes:")
+        # Reformat support messages for debugger to print node overview as a single string
+        supported_nodes_str = "\nSupported Nodes:\n"
         for node_name in self.supported_operators:
-            logger.debug("-", node_name)
+            supported_nodes_str += f"- {node_name}\n"
+
+        logger.debug(supported_nodes_str)
 
         if len(self.unsupported_operators) != 0:
-            logger.debug("\nUnsupported or Excluded Nodes:")
+            unsupported_nodes_str = "\nUnsupported or Excluded Nodes:\n"
             for node_name in self.unsupported_operators:
-                logger.debug("-", node_name)
-            logger.debug("\n")
+                unsupported_nodes_str += f"- {node_name}\n"
+            logger.debug(unsupported_nodes_str)
         else:
             logger.debug("\nAll Nodes Supported\n")