From 4a4cec1a0cc2738fd939aece6b36b5e8f2aa1941 Mon Sep 17 00:00:00 2001
From: apbose <apbose694@gmail.com>
Date: Fri, 26 Sep 2025 18:56:00 -0700
Subject: [PATCH] addresses the case when shape of upsample tensor contains
 ITensor

---
 .../dynamo/conversion/impl/shape.py           | 38 +++++++++++++++++++
 .../dynamo/conversion/impl/upsample.py        | 12 ++++--
 2 files changed, 47 insertions(+), 3 deletions(-)

diff --git a/py/torch_tensorrt/dynamo/conversion/impl/shape.py b/py/torch_tensorrt/dynamo/conversion/impl/shape.py
index 27af02e5bb..c487dfe598 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/shape.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/shape.py
@@ -123,3 +123,41 @@ def get_shape_with_dynamic_shape(
     select_layer = ctx.net.add_select(condition_val, input_shape, scale_res)
     set_layer_name(select_layer, target, f"{name}_select")
     return select_layer.get_output(0)
+
+
+def to_trt_shape_tensor(
+    ctx: ConversionContext, target: Target, name: str, shape_list: List[int | TRTTensor]
+) -> TRTTensor:
+    """
+    Convert a mixed shape list (ints + ITensors) into a single ITensor.
+
+    Args:
+        ctx: ConversionContext
+        target: fx node target (used for naming).
+        name (str): base name for layer naming.
+        shape_list (list[int | ITensor]): list containing static ints and/or ITensors.
+
+    Returns:
+        ITensor if shape_list contains any ITensors, else plain Python list of ints.
+    """
+    trt_tensors = []
+
+    for i, s in enumerate(shape_list):
+        if isinstance(s, int):
+            const = ctx.net.add_constant((1,), np.array([s], dtype=np.int32))
+            set_layer_name(const, target, f"{name}_dim{i}_const")
+            trt_tensors.append(const.get_output(0))
+        else:
+            # Assume it's already an ITensor
+            trt_tensors.append(s)
+
+    if trt_tensors:
+        if any(not isinstance(s, int) for s in shape_list):
+            # Concatenate everything into a single ITensor
+            concat_layer = ctx.net.add_concatenation(trt_tensors)
+            concat_layer.axis = 0
+            set_layer_name(concat_layer, target, f"{name}_shape_concat")
+            return concat_layer.get_output(0)
+
+    # If no ITensor found, return plain list of ints
+    return shape_list
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/upsample.py b/py/torch_tensorrt/dynamo/conversion/impl/upsample.py
index 4b47ca5dec..4cedb396d1 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/upsample.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/upsample.py
@@ -9,7 +9,10 @@
     has_dynamic_shape,
     set_layer_name,
 )
-from torch_tensorrt.dynamo.conversion.impl.shape import get_shape_with_dynamic_shape
+from torch_tensorrt.dynamo.conversion.impl.shape import (
+    get_shape_with_dynamic_shape,
+    to_trt_shape_tensor,
+)
 
 
 def upsample(
@@ -28,14 +31,17 @@ def upsample(
     if scale_factor is not None:
         layer.scales = [1.0, 1.0] + list(scale_factor)
     else:
-        shape = list(input.shape)[:2] + list(size)
+        shape = list(input.shape)[:2]
+    if size is not None:
+        shape += list(size)
         if has_dynamic_shape(shape):
             shape = get_shape_with_dynamic_shape(
                 ctx, target, source_ir, name, shape, input
             )
             layer.set_input(1, shape)
         else:
-            layer.shape = shape
+            layer.shape = to_trt_shape_tensor(ctx, target, name, shape)
+            layer.set_input(1, layer.shape)
 
     if mode == "nearest":
         layer.resize_mode = trt.InterpolationMode.NEAREST