chore: minor updates related to logging

Author: peri044

py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py

@@ -893,7 +893,7 @@ def get_attr(self, target: str, args: Any, kwargs: Any) -> np.ndarray:
             else:
                 constant_tensor = frozen_attr
 
-        return to_torch(constant_tensor)
+            return to_torch(constant_tensor)
 
     def call_method(self, target: str, args: Any, kwargs: Any) -> Any:
         assert isinstance(target, str)

py/torch_tensorrt/dynamo/conversion/converter_utils.py

@@ -590,42 +590,45 @@ def to_numpy(
     Returns:
         A Numpy array or None, if the input was None.
     """
-    output = None
+    with unset_fake_temporarily():
+        output = None
 
-    if value is None or isinstance(value, np.ndarray):
-        output = value
+        if value is None or isinstance(value, np.ndarray):
+            output = value
 
-    elif isinstance(value, torch.Tensor):
-        if value.is_quantized:
-            value = value.dequantize()
-        elif value.dtype == torch.bfloat16:
-            # TODO: Remove when numpy has a BF16 type
-            _LOGGER.warning(
-                "Requested a conversion of bfloat16 tensor from torch to numpy which isn't supported. Casting this tensor to FP32 precision currently. Please use to_torch() API for better data representation",
-            )
-            value = value.to(torch.float)
-
-        output = value.cpu().detach().contiguous().numpy()
+        elif isinstance(value, torch.Tensor):
+            if value.is_quantized:
+                value = value.dequantize()
+            elif value.dtype == torch.bfloat16:
+                # TODO: Remove when numpy has a BF16 type
+                _LOGGER.warning(
+                    "Requested a conversion of bfloat16 tensor from torch to numpy which isn't supported. Casting this tensor to FP32 precision currently. Please use to_torch() API for better data representation",
+                )
+                value = value.to(torch.float)
 
-    elif isinstance(value, int):
-        output = np.array([value], dtype=np.int32)
+            output = value.cpu().detach().contiguous().numpy()
 
-    elif isinstance(value, float):
-        output = np.array([value], dtype=np.float32)
+        elif isinstance(value, int):
+            output = np.array([value], dtype=np.int32)
 
-    elif isinstance(value, bool):
-        output = np.array([value], dtype=np.bool_)
+        elif isinstance(value, float):
+            output = np.array([value], dtype=np.float32)
 
-    if isinstance(output, np.ndarray) or output is None:
-        return (
-            output
-            if (dtype is None or output is None)
-            else output.astype(_enums.dtype._from(dtype).to(np.dtype, use_default=True))
-        )
-    else:
-        raise AssertionError(
-            f"to_numpy can only be called on None, bool, int, float, np.ndarray, or torch.Tensor, got: {value}"
-        )
+        elif isinstance(value, bool):
+            output = np.array([value], dtype=np.bool_)
+
+        if isinstance(output, np.ndarray) or output is None:
+            return (
+                output
+                if (dtype is None or output is None)
+                else output.astype(
+                    _enums.dtype._from(dtype).to(np.dtype, use_default=True)
+                )
+            )
+        else:
+            raise AssertionError(
+                f"to_numpy can only be called on None, bool, int, float, np.ndarray, or torch.Tensor, got: {value}"
+            )
 
 
 def to_torch(

tests/py/dynamo/backend/test_backend_compiler.py

@@ -2,11 +2,10 @@
 from copy import deepcopy
 
 import torch
+import torch_tensorrt
 from torch.testing._internal.common_utils import TestCase, run_tests
 from torch_tensorrt.dynamo.partitioning import fast_partition
 
-import torch_tensorrt
-
 from ..testing_utilities import DECIMALS_OF_AGREEMENT, lower_graph_testing
 
 
@@ -51,7 +50,6 @@ def forward(self, x, y):
             pass_through_build_failures=True,
             torch_executed_ops={"torch.ops.aten.add.Tensor"},
             use_python_runtime=False,
-            debug=True,
         )
         optimized_model_results = optimized_model(*inputs).detach().cpu()
         torch_model_results = fx_graph(*inputs).detach().cpu()
@@ -132,7 +130,6 @@ def forward(self, x, y):
             pass_through_build_failures=True,
             torch_executed_ops={"torch.ops.aten.add.Tensor"},
             use_python_runtime=False,
-            debug=True,
         )
         optimized_model_results = optimized_model(*inputs).detach().cpu()
         torch_model_results = model(*inputs).detach().cpu()
@@ -177,7 +174,6 @@ def forward(self, x, y):
             optimization_level=4,
             version_compatible=True,
             max_aux_streams=5,
-            debug=True,
         )
         optimized_model_results = optimized_model(*inputs).detach().cpu()
         torch_model_results = fx_graph(*inputs).detach().cpu()
@@ -225,7 +221,6 @@ def forward(self, x, y):
             min_block_size=1,
             pass_through_build_failures=True,
             truncate_double=True,
-            debug=True,
         )
         optimized_model_results = optimized_model(*inputs).detach().cpu()
         torch_model_results = fx_graph(*inputs).detach().cpu()
@@ -298,7 +293,6 @@ def forward(self, x, y):
             min_block_size=1,
             pass_through_build_failures=True,
             truncate_double=False,
-            debug=True,
             torch_executed_ops={"torch.ops.aten.add.Tensor"},
         )
         optimized_model_results = optimized_model(*inputs).detach().cpu()

tests/py/dynamo/conversion/harness.py

@@ -415,7 +415,6 @@ def run_test(
         compilation_settings = CompilationSettings(
             enabled_precisions={dtype._from(precision)},
             truncate_double=True,
-            debug=True,
             immutable_weights=immutable_weights,
         )
 
@@ -507,7 +506,6 @@ def run_test_compare_tensor_attributes_only(
         compilation_settings = CompilationSettings(
             enabled_precisions={dtype._from(precision)},
             truncate_double=True,
-            debug=True,
             immutable_weights=immutable_weights,
         )
 

tests/py/dynamo/models/test_dtype_support.py

@@ -297,7 +297,6 @@ def forward(self, x):
                 ir="torch_compile",
                 inputs=inputs,
                 enabled_precisions={torch.bfloat16},
-                debug=True,
                 min_block_size=1,
                 device=device,
                 cache_built_engines=False,