diff --git a/python/tvm/relay/op/contrib/tensorrt.py b/python/tvm/relay/op/contrib/tensorrt.py
index cbe6a22f4a4d..bd9051716969 100644
--- a/python/tvm/relay/op/contrib/tensorrt.py
+++ b/python/tvm/relay/op/contrib/tensorrt.py
@@ -723,8 +723,12 @@ def pad_annotate_fn(expr):  # pylint: disable=unused-variable
     if attrs.pad_mode != "constant":
         logger.info("nn.pad: pad mode is %s but must be constant.", attrs.pad_mode)
         return False
-    if float(attrs.pad_value) != 0.0:
-        logger.info("nn.pad: pad value is %f but must be 0.0.", float(attrs.pad_value))
+    if (
+        not isinstance(args[1], relay.Constant)
+        or len(args[1].checked_type.shape) != 0
+        or args[1].data.numpy().item() != 0.0
+    ):
+        logger.info("nn.pad: pad value is %s but must be 0.0.", args[1])
         return False
     if len(attrs.pad_width) not in [4, 5]:
         logger.info("nn.pad: can only pad 4D or 5D inputs")
diff --git a/src/runtime/contrib/tensorrt/tensorrt_ops.cc b/src/runtime/contrib/tensorrt/tensorrt_ops.cc
index 7197172d73db..020ef866692a 100644
--- a/src/runtime/contrib/tensorrt/tensorrt_ops.cc
+++ b/src/runtime/contrib/tensorrt/tensorrt_ops.cc
@@ -1057,7 +1057,7 @@ class ReshapeOpConverter : public TensorRTOpConverter {
 
 class PadOpConverter : public TensorRTOpConverter {
  public:
-  PadOpConverter() : TensorRTOpConverter({kTensor}) {}
+  PadOpConverter() : TensorRTOpConverter({kTensor, kWeight}) {}
 
   void Convert(TensorRTOpConverterParams* params) const {
     auto input = params->inputs.at(0).tensor;
diff --git a/tests/python/contrib/test_tensorrt.py b/tests/python/contrib/test_tensorrt.py
index f9912c9674e5..7ff051a4d38b 100644
--- a/tests/python/contrib/test_tensorrt.py
+++ b/tests/python/contrib/test_tensorrt.py
@@ -793,16 +793,25 @@ def get_graph(x_shape=(1, 16)):
 
 
 def test_pad():
-    def get_graph(x_shape, pad_width):
+    def get_graph(x_shape, pad_width, pad_value=0.0):
         x = relay.var("x", shape=(x_shape), dtype="float32")
-        out = relay.nn.pad(x, pad_width=pad_width)
+        out = relay.nn.pad(x, pad_width=pad_width, pad_value=pad_value)
         f = relay.Function([x], out)
         return f, {"x": x_shape}, []
 
     run_and_verify_func(get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [0, 0], [0, 0]]))
     run_and_verify_func(get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [1, 1], [1, 1]]))
+    run_and_verify_func(
+        get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [1, 1], [1, 1]], pad_value=-1.0e30)
+    )
     run_and_verify_func(get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [0, 1], [2, 0]]))
+    run_and_verify_func(
+        get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [0, 1], [2, 0]], pad_value=-1.0e30)
+    )
     run_and_verify_func(get_graph((1, 8, 3, 16, 16), [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]))
+    run_and_verify_func(
+        get_graph((1, 8, 3, 16, 16), [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]], pad_value=-1.0e30)
+    )
 
 
 def test_softmax():