fix: use dynamo path for conversion utils instead of fx

Author: junstar92

py/torch_tensorrt/dynamo/conversion/converter_utils.py

@@ -8,10 +8,11 @@
 import numpy as np
 import tensorrt as trt
 import torch
-import torch_tensorrt.dynamo.conversion.impl as impl
 from torch.fx.experimental.proxy_tensor import unset_fake_temporarily
 from torch.fx.node import Argument, Target
 from torch.fx.passes.shape_prop import TensorMetadata
+
+import torch_tensorrt.dynamo.conversion.impl as impl
 from torch_tensorrt import _enums
 from torch_tensorrt.dynamo._settings import CompilationSettings
 from torch_tensorrt.dynamo._SourceIR import SourceIR
@@ -141,9 +142,9 @@ def cast_trt_tensor(
 ) -> TRTTensor:
     """Given a TRT Tensor, convert that Tensor to the specified dtype
 
-    Adds an Identity layer to the network which performs the conversion
-    if the input's dtype is different from the cast type. Otherwise returns
-    input unchanged
+    Adds a Cast layer to the network to convert the input tensor to the specified dtype.
+    If the input tensor already has the desired dtype, it is returned unchanged.
+    Otherwise, a Cast layer is added to perform the conversion
 
     Args:
         ctx (ConversionContext): A ConversionContext containing the TensorRT network

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

@@ -5,6 +5,7 @@
 import numpy as np
 import tensorrt as trt
 from torch.fx.node import Target
+
 from torch_tensorrt.dynamo._SourceIR import SourceIR
 from torch_tensorrt.dynamo.conversion import impl
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
@@ -13,6 +14,9 @@
     flatten_dims,
     get_positive_dim,
     get_trt_tensor,
+    has_dynamic_shape,
+    prepend_ones,
+    set_layer_name,
 )
 from torch_tensorrt.dynamo.conversion.impl.cat import cat
 from torch_tensorrt.dynamo.conversion.impl.elementwise import floor_divide
@@ -23,11 +27,6 @@
 from torch_tensorrt.dynamo.conversion.impl.shape import shape as get_shape
 from torch_tensorrt.dynamo.conversion.impl.slice.base import slice
 from torch_tensorrt.dynamo.utils import DYNAMIC_DIM
-from torch_tensorrt.fx.converters.converter_utils import (
-    has_dynamic_shape,
-    prepend_ones,
-    set_layer_name,
-)
 from torch_tensorrt.fx.types import Shape, TRTTensor
 
 
@@ -230,7 +229,7 @@ def expand(
     # If the rank of the input tensor is less than the shape's rank, pad with ones
     if initial_tensor_rank < shape_rank:
         input_t = prepend_ones(
-            ctx.net,
+            ctx,
             input_t,
             name + "_expand_broadcast",
             shape_rank - initial_tensor_rank,