add strong typing fix

py/torch_tensorrt/dynamo/conversion/impl/activation/base.py

@@ -5,7 +5,6 @@
 from torch_tensorrt.dynamo._SourceIR import SourceIR
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
 from torch_tensorrt.fx.converters.converter_utils import (
-    mark_as_int8_layer,
     set_layer_name,
 )
 from torch_tensorrt.fx.types import TRTTensor
@@ -37,11 +36,4 @@ def convert_activation(
         layer.beta = beta
     set_layer_name(layer, target, name, source_ir)
 
-    if (
-        not ctx.net.get_flag(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED)
-        and input_val.dynamic_range is not None
-        and dyn_range_fn is not None
-    ):
-        dyn_range = dyn_range_fn(input_val.dynamic_range)
-        mark_as_int8_layer(layer, dyn_range)
     return layer.get_output(0)

py/torch_tensorrt/dynamo/conversion/impl/conv.py

@@ -17,10 +17,6 @@
     to_torch,
     to_trt_weights,
 )
-from torch_tensorrt.fx.converters.converter_utils import (
-    get_dyn_range,
-    mark_as_int8_layer,
-)
 from torch_tensorrt.fx.types import TRTTensor
 
 
@@ -172,11 +168,6 @@ def convNd(
     if groups is not None:
         conv_layer.num_groups = groups
 
-    # Handle quantization cases
-    if scale is not None and zero_point is not None:
-        # Assume the dtype of activation is torch.quint8
-        mark_as_int8_layer(conv_layer, get_dyn_range(scale, zero_point, torch.quint8))
-
     result = conv_layer.get_output(0)
 
     if is_conv1d:

py/torch_tensorrt/dynamo/conversion/impl/deconv.py

@@ -16,8 +16,6 @@
     to_trt_weights,
 )
 from torch_tensorrt.fx.converters.converter_utils import (
-    get_dyn_range,
-    mark_as_int8_layer,
     set_layer_name,
 )
 from torch_tensorrt.fx.types import TRTTensor
@@ -174,11 +172,6 @@ def deconvNd(
     deconv_layer.pre_padding = tuple(pre_padding_values)
     deconv_layer.post_padding = tuple(post_padding_values)
 
-    # Handle quantization cases
-    if scale is not None and zero_point is not None:
-        # Assume the dtype of activation is torch.quint8
-        mark_as_int8_layer(deconv_layer, get_dyn_range(scale, zero_point, torch.quint8))
-
     result = deconv_layer.get_output(0)
 
     if is_deconv1d:

py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py

@@ -547,10 +547,19 @@ def pow(
 
     lhs_dtype = None
     rhs_dtype = None
-    if isinstance(lhs_val, int):
-        lhs_dtype = torch.int32
-    if isinstance(rhs_val, int):
-        rhs_dtype = torch.int32
+    if isinstance(lhs_val, (int, float)) and isinstance(rhs_val, (int, float)):
+        raise ValueError(
+            "Both lhs_val and rhs_val are int or float, at least one of them should be a tensor"
+        )
+    elif isinstance(lhs_val, (int, float)):
+        # At this point, rhs_val must be a Tensor since we checked both aren't scalars
+        assert isinstance(rhs_val, (TRTTensor, torch.Tensor))
+        lhs_dtype = rhs_val.dtype
+    elif isinstance(rhs_val, (int, float)):
+        # At this point, lhs_val must be a Tensor since we checked both aren't scalars
+        assert isinstance(lhs_val, (TRTTensor, torch.Tensor))
+        rhs_dtype = lhs_val.dtype
+
     # POW operation supports only float32 and int8 inputs
     lhs_val = get_trt_tensor(ctx, lhs_val, name + "_lhs_val", lhs_dtype)
     rhs_val = get_trt_tensor(ctx, rhs_val, name + "_rhs_val", rhs_dtype)