diff --git a/mmdeploy/pytorch/functions/__init__.py b/mmdeploy/pytorch/functions/__init__.py
index 5ee8ef348..19515408a 100644
--- a/mmdeploy/pytorch/functions/__init__.py
+++ b/mmdeploy/pytorch/functions/__init__.py
@@ -2,6 +2,7 @@
 from . import adaptive_pool  # noqa: F401,F403
 from . import any  # noqa: F401,F403
 from . import atan2  # noqa: F401,F403
+from . import cat  # noqa: F401,F403
 from . import chunk  # noqa: F401,F403
 from . import clip  # noqa: F401,F403
 from . import expand  # noqa: F401,F403
diff --git a/mmdeploy/pytorch/functions/cat.py b/mmdeploy/pytorch/functions/cat.py
new file mode 100644
index 000000000..ea1f623bd
--- /dev/null
+++ b/mmdeploy/pytorch/functions/cat.py
@@ -0,0 +1,24 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Sequence
+
+import torch
+from torch import Tensor
+
+from mmdeploy.core import FUNCTION_REWRITER
+from mmdeploy.utils import get_dynamic_axes
+
+
+@FUNCTION_REWRITER.register_rewriter(func_name='torch.cat', backend='tensorrt')
+def cat__tensorrt(tensors: Sequence[Tensor], *args, **kwargs) -> torch.Tensor:
+    """Rewrite `cat` for TensorRT backend.
+
+    cat in TensorRT does not support bool or uint8 type when input is dynamic.
+    """
+    ctx = FUNCTION_REWRITER.get_context()
+    if get_dynamic_axes(ctx.cfg) is None:
+        return ctx.origin_func(tensors, *args, **kwargs)
+    if len(tensors) > 0 and (tensors[0].dtype in [torch.bool, torch.uint8]):
+        original_dtype = tensors[0].dtype
+        tensors = [i.to(torch.int32) for i in tensors]
+        return ctx.origin_func(tensors, *args, **kwargs).to(original_dtype)
+    return ctx.origin_func(tensors, *args, **kwargs)
diff --git a/tests/test_pytorch/test_pytorch_functions.py b/tests/test_pytorch/test_pytorch_functions.py
index 245bfba9d..638e8b62c 100644
--- a/tests/test_pytorch/test_pytorch_functions.py
+++ b/tests/test_pytorch/test_pytorch_functions.py
@@ -19,10 +19,13 @@ deploy_cfg_ncnn = Config(
         codebase_config=dict(type='mmdet', task='ObjectDetection')))
 
 
-def get_trt_config(output_names, shape):
+def get_trt_config(output_names, shape, dynamic_axes=None):
     deploy_cfg_tensorrt = Config(
         dict(
-            onnx_config=dict(input_shape=None, output_names=output_names),
+            onnx_config=dict(
+                input_shape=None,
+                output_names=output_names,
+                dynamic_axes=dynamic_axes),
             backend_config=dict(
                 type='tensorrt',
                 common_config=dict(
@@ -615,3 +618,30 @@ def test_linspace__default():
 
         assert np.allclose(
             model_output, rewrite_outputs, rtol=1e-03, atol=1e-05)
+
+
+@backend_checker(Backend.TENSORRT)
+@pytest.mark.parametrize('dtype', [torch.bool, torch.float32])
+@pytest.mark.parametrize('dynamic_axes',
+                         [None, dict(input=dict({
+                             0: 'dim0',
+                             1: 'dim1'
+                         }))])
+def test_cat__tensorrt(dtype, dynamic_axes):
+    input = torch.rand(2, 4)
+    model = WrapFunction(lambda input: torch.cat(
+        [input.to(dtype), input.to(dtype)], -1))
+    pytorch_output = model(input)
+    rewrite_output, _ = get_rewrite_outputs(
+        model,
+        model_inputs={'input': input},
+        deploy_cfg=get_trt_config(['output'],
+                                  shape=[2, 4],
+                                  dynamic_axes=dynamic_axes),
+        run_with_backend=True)
+    assert pytorch_output.dtype == rewrite_output[0].dtype
+    assert torch.allclose(
+        pytorch_output.cpu().float(),
+        rewrite_output[0].cpu().float(),
+        rtol=1e-3,
+        atol=1e-5)