feat: Add support for output data types in Interpreter

- Add argument for specification of output data types of TRT engines in
the interpreter, to avoid type mismatches at runtime
- Add support for output data type provision in the Dynamo compile path,
which simultaneously tests the feature via the backend testing and e2e
frameworks

Author: gs-olive

py/torch_tensorrt/dynamo/backend/conversion.py

@@ -27,11 +27,21 @@ def convert_module(
     Returns:
         TRTModule or TRTModuleNext
     """
+    # Specify module output data types to ensure TRT output types agree with
+    # that of the equivalent Torch module
+    module_outputs = module(*inputs)
+
+    if not isinstance(module_outputs, (list, tuple)):
+        module_outputs = [module_outputs]
+
+    output_dtypes = list(output.dtype for output in module_outputs)
+
     interpreter = TRTInterpreter(
         module,
         InputTensorSpec.from_tensors(inputs),
         explicit_batch_dimension=True,
         logger_level=(trt.Logger.VERBOSE if settings.debug else trt.Logger.WARNING),
+        output_dtypes=output_dtypes,
     )
 
     interpreter_result = interpreter.run(

py/torch_tensorrt/dynamo/fx_ts_compat/fx2trt.py

@@ -1,6 +1,7 @@
 import logging
 import warnings
 from datetime import datetime
+from packaging import version
 from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence
 
 import numpy
@@ -40,6 +41,7 @@ def __init__(
         explicit_batch_dimension: bool = True,
         explicit_precision: bool = False,
         logger_level=None,
+        output_dtypes=None,
     ):
         super().__init__(module)
 
@@ -78,6 +80,9 @@ def __init__(
             trt.tensorrt.ITensor, TensorMetadata
         ] = dict()
 
+        # Data types for TRT Module output Tensors
+        self.output_dtypes = output_dtypes
+
     def validate_input_specs(self):
         for shape, _, _, shape_ranges, has_batch_dim in self.input_specs:
             if not self.network.has_implicit_batch_dimension:
@@ -178,13 +183,17 @@ def run(
             algorithm_selector: set up algorithm selection for certain layer
             timing_cache: enable timing cache for TensorRT
             profiling_verbosity: TensorRT logging level
+            max_aux_streams: Maximum number of allowed auxiliary TRT streams for each engine
+            version_compatible: Provide version forward-compatibility for engine plan files
+            optimization_level: Builder optimization 0-5, higher levels imply longer build time,
+                searching for more optimization options. TRT defaults to 3
         Return:
             TRTInterpreterResult
         """
         TRT_INTERPRETER_CALL_PRE_OBSERVER.observe(self.module)
 
         # For float outputs, we set their dtype to fp16 only if lower_precision == LowerPrecision.FP16 and
-        # force_fp32_output=False.
+        # force_fp32_output=False. Overriden by specifying output_dtypes
         self.output_fp16 = (
             not force_fp32_output and lower_precision == LowerPrecision.FP16
         )
@@ -224,14 +233,14 @@ def run(
             cache = builder_config.create_timing_cache(b"")
         builder_config.set_timing_cache(cache, False)
 
-        if trt.__version__ >= "8.2":
+        if version.parse(trt.__version__) >= version.parse("8.2"):
             builder_config.profiling_verbosity = (
                 profiling_verbosity
                 if profiling_verbosity
                 else trt.ProfilingVerbosity.LAYER_NAMES_ONLY
             )
 
-        if trt.__version__ >= "8.6":
+        if version.parse(trt.__version__) >= version.parse("8.6"):
             if max_aux_streams is not None:
                 _LOGGER.info(f"Setting max aux streams to {max_aux_streams}")
                 builder_config.max_aux_streams = max_aux_streams
@@ -372,6 +381,11 @@ def output(self, target, args, kwargs):
         if not all(isinstance(output, trt.tensorrt.ITensor) for output in outputs):
             raise RuntimeError("TensorRT requires all outputs to be Tensor!")
 
+        if self.output_dtypes is not None and len(self.output_dtypes) != len(outputs):
+            raise RuntimeError(
+                f"Specified output dtypes ({len(self.output_dtypes)}) differ from number of outputs ({len(outputs)})"
+            )
+
         for i, output in enumerate(outputs):
             if any(
                 op_name in output.name.split("_")
@@ -396,6 +410,8 @@ def output(self, target, args, kwargs):
             self.network.mark_output(output)
             if output_bool:
                 output.dtype = trt.bool
+            elif self.output_dtypes is not None:
+                output.dtype = torch_dtype_to_trt(self.output_dtypes[i])
             elif self.output_fp16 and output.dtype == trt.float32:
                 output.dtype = trt.float16
             self._output_names.append(name)