DLFW 26.01 changes to main

tests/py/dynamo/models/test_weight_stripped_engine.py

@@ -4,6 +4,7 @@
 import shutil
 import unittest
 
+import tensorrt as trt
 import torch
 import torch_tensorrt as torch_trt
 from torch.testing._internal.common_utils import TestCase
@@ -642,6 +643,10 @@ def test_refit_identical_engine_weights(self):
         not importlib.util.find_spec("torchvision"),
         "torchvision is not installed",
     )
+    @unittest.skipIf(
+        not hasattr(trt.SerializationFlag, "INCLUDE_REFIT"),
+        "Multiple refit requires TensorRT >= 10.14 with INCLUDE_REFIT serialization flag",
+    )
     def test_refit_weight_stripped_engine_multiple_times(self):
         pyt_model = models.resnet18(pretrained=True).eval().to("cuda")
         example_inputs = (torch.randn((100, 3, 224, 224)).to("cuda"),)

tests/py/ts/api/test_classes.py

@@ -7,6 +7,25 @@
 from torch_tensorrt.dynamo.runtime._TorchTensorRTModule import TorchTensorRTModule
 
 
+def is_blackwell():
+    """
+    Check if running on NVIDIA Blackwell architecture (sm_90+).
+
+    Blackwell architecture adds input/output reformat layers in TensorRT engines.
+
+    Returns:
+        bool: True if running on Blackwell (sm_90+), False otherwise
+    """
+    if not torch.cuda.is_available():
+        return False
+
+    device_properties = torch.cuda.get_device_properties(0)
+    compute_capability = device_properties.major * 10 + device_properties.minor
+
+    # Blackwell is sm_90 and above
+    return compute_capability >= 90
+
+
 @unittest.skipIf(
     not torchtrt.ENABLED_FEATURES.torchscript_frontend,
     "TorchScript Frontend is not available",
@@ -332,13 +351,22 @@ def test_get_layer_info(self):
 
         import json
 
+        if is_blackwell():
+            # blackwell has additional layers-
+            # Layer 0: __mye88_myl0_0           ← Input reformat layer
+            # Layer 1: aten__matmul(...) fc1    ← First matmul (fc1)
+            # Layer 2: aten__matmul(...) fc2    ← Second matmul (fc2)
+            # Layer 3: __mye90_myl0_3           ← Output reformat layer
+            num_layers = 4
+        else:
+            num_layers = 2
         for trt_mod in (
             TestTorchTensorRTModule._get_trt_mod(),
             TestTorchTensorRTModule._get_trt_mod(via_ts=True),
         ):
             trt_json = json.loads(trt_mod.get_layer_info())
             [self.assertTrue(k in trt_json.keys()) for k in ["Layers", "Bindings"]]
-            self.assertTrue(len(trt_json["Layers"]) == 2)
+            self.assertTrue(len(trt_json["Layers"]) == num_layers)
             self.assertTrue(len(trt_json["Bindings"]) == 2)