diff --git a/torch2trt/converters/__init__.py b/torch2trt/converters/__init__.py
index 83465152..ced2eb7c 100644
--- a/torch2trt/converters/__init__.py
+++ b/torch2trt/converters/__init__.py
@@ -67,3 +67,4 @@
 from .transpose import *
 from .unary import *
 from .view import *
+from .cast import *
diff --git a/torch2trt/converters/cast.py b/torch2trt/converters/cast.py
new file mode 100644
index 00000000..200ad955
--- /dev/null
+++ b/torch2trt/converters/cast.py
@@ -0,0 +1,104 @@
+from torch2trt.torch2trt import *
+from torch2trt.module_test import add_module_test
+
+
+def _key_sanity_check(mode_key, torch2trt_properties):
+    """
+    Raise an error if the given key does not exist.
+    This error will be raised as a warning in case
+    in case "mode-related" keys change in the future.
+    Args:
+        mode_key: A string key for the quantization mode.
+        E.g. ("int8_mode", "fp16_mode")
+        torch2trt_properties: A python dictionary containing
+        the torch2trt properties such as "int8_mode".
+    """
+    if mode_key not in torch2trt_properties:
+        raise KeyError("{} is not a valid torch2trt property. "
+                       "Check the torch2trt API for any changes.".format(mode_key))
+
+
+def convert_cast(ctx):
+    """
+    A simple converter for supporting casting operations.
+
+    IMPORTANT: Note that because TensorRT does not support
+    64 bit data types, .long() will not be supported
+    """
+    input_tensor = ctx.method_args[0]
+    layer = ctx.network.add_identity(input_tensor._trt)
+    trt_kwargs = ctx.torch2trt_kwargs
+
+    # Sanity checks for debugging in case torch2trt property keys change
+    int8_mode_key, fp16_mode_key = "int8_mode", "fp16_mode"
+    _key_sanity_check(int8_mode_key, trt_kwargs)
+    _key_sanity_check(fp16_mode_key, trt_kwargs)
+
+    is_int8_mode = trt_kwargs[int8_mode_key]
+    is_fp16_mode = trt_kwargs[fp16_mode_key]
+    if is_int8_mode:
+        layer.precision = trt.int8
+        layer.set_output_type(0, trt.int8)
+    elif is_fp16_mode:
+        layer.precision = trt.float16
+        layer.set_output_type(0, trt.float16)
+
+    output = ctx.method_return
+    output._trt = layer.get_output(0)
+
+
+@tensorrt_converter("torch.Tensor.float")
+def convert_float(ctx):
+    convert_cast(ctx)
+
+
+@tensorrt_converter("torch.Tensor.int")
+def convert_int(ctx):
+    convert_cast(ctx)
+
+
+@tensorrt_converter("torch.Tensor.bool")
+def convert_bool(ctx):
+    convert_cast(ctx)
+
+
+class DotFloat(torch.nn.Module):
+    def __init__(self):
+        super(DotFloat, self).__init__()
+
+    def forward(self, x):
+        return x.float()
+
+
+class DotInt(torch.nn.Module):
+    def __init__(self):
+        super(DotInt, self).__init__()
+
+    def forward(self, x):
+        return x.int()
+
+
+class DotBool(torch.nn.Module):
+    def __init__(self):
+        super(DotBool, self).__init__()
+
+    def forward(self, x):
+        return x.bool()
+
+
+@add_module_test(torch.bool, torch.device('cuda'), [(1, 3, 3)])
+@add_module_test(torch.int32, torch.device('cuda'), [(1, 3, 3)])
+def test_torch_float_cast():
+    return DotFloat()
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 3)])
+@add_module_test(torch.int32, torch.device('cuda'), [(1, 3, 3)])
+def test_torch_int_cast():
+    return DotInt()
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 3)])
+@add_module_test(torch.int32, torch.device('cuda'), [(1, 3, 3)])
+def test_torch_bool_cast():
+    return DotBool()