fix: get_padded_shape_tensors can now handle dynamic pads (#3123)

py/torch_tensorrt/dynamo/conversion/impl/pad.py

@@ -26,7 +26,7 @@ def get_padded_shape_tensors(
     source_ir: Optional[SourceIR],
     name: str,
     input: TRTTensor,
-    pad: Sequence[int],
+    pad: Sequence[Union[int, TRTTensor]],
 ) -> TRTTensor:
     rank = len(input.shape)
     if len(pad) // 2 > rank:
@@ -47,11 +47,11 @@ def get_padded_shape_tensors(
     start_list = [0] * rank
     for i in range(len(pad) // 2):
         dim_index = rank - (i + 1)
-        pad_before = pad[i * 2]
-        pad_after = pad[i * 2 + 1]
+        pad_before = get_trt_tensor(ctx, pad[i * 2], f"{name}_pad_before_{i}")
+        pad_after = get_trt_tensor(ctx, pad[i * 2 + 1], f"{name}_pad_after_{i}")
 
-        pad_sum = get_trt_tensor(
-            ctx, pad_before + pad_after, f"{name}_pad_sum_{i}", dtype=np.int32
+        pad_sum = impl.elementwise.add(
+            ctx, target, source_ir, f"{name}_pad_sum_{i}", pad_before, pad_after
         )
         dim_shape = ctx.net.add_slice(
             input_shape_tensor,
@@ -63,7 +63,9 @@ def get_padded_shape_tensors(
         new_dim_shape = impl.elementwise.add(
             ctx, target, source_ir, f"{name}_shape_dim_{i}", dim_shape, pad_sum
         )
-        start_list[dim_index] = -pad_before
+        start_list[dim_index] = impl.elementwise.sub(
+            ctx, target, source_ir, f"{name}_pad_before_neg_{i}", 0, pad_before
+        )
 
         slices = []
         for j in range(rank):
@@ -79,14 +81,23 @@ def get_padded_shape_tensors(
                     ).get_output(0)
                 )
         padded_shape_tensor = impl.cat.cat(
-            ctx, target, source_ir, f"{name}_cat_dim_{i}", slices, 0
+            ctx,
+            target,
+            source_ir,
+            f"{name}_cat_dim_{i}",
+            slices,
+            0,
+            cast_dtype=padded_shape_tensor.dtype,
         )
 
-    start_indices_tensor = get_trt_tensor(
+    start_indices_tensor = impl.cat.cat(
         ctx,
-        np.array(start_list, dtype=np.int32),
+        target,
+        source_ir,
         f"{name}_start_indices_tensor",
-        dtype=np.int32,
+        start_list,
+        0,
+        cast_dtype=padded_shape_tensor.dtype,
     )
 
     return start_indices_tensor, padded_shape_tensor
@@ -98,7 +109,7 @@ def constant_padNd(
     source_ir: Optional[SourceIR],
     name: str,
     input: TRTTensor,
-    pad: Sequence[int],
+    pad: Sequence[Union[int, TRTTensor]],
     value: Union[int, float] = 0,
 ) -> TRTTensor: