diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 246ed97b14e9a..1c1f8de995873 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -2010,6 +2010,33 @@ def scatter(self, inputs, input_types):
         src = inputs[3]
         return _op.transform.scatter(data, index, src, axis)
 
+    def index_put(self, inputs, input_types):
+        in_tensor = inputs[0]
+        indices = inputs[1]
+        values = inputs[2]
+        accumulate = inputs[3]
+        # accumulate parameter is ignored.
+        # torch.index_put default is False but Relay.scatter_nd accumulates values.
+        # We assume there is no duplicate indices in torch.index_put input
+        if not accumulate:
+            logging.warning(
+                "torch.index_put accumulate parameter is False. "
+                "TVM uses tvm.relay.scatter_nd operator which accumulates values. "
+                "Make sure there is no duplicate indices in torch.index_put input."
+            )
+        # Relay scatter_nd does not support input tensor
+        # We assume that torch.index_put is used with empty zero-values input tensor
+        # scatter_nd will create empty zero-values tensor with a given shape
+        out_shape = self.infer_shape(in_tensor)
+        logging.warning(
+            "tvm.relay.scatter_nd operator does not support input tensor parameter. "
+            "TVM assumes that torch.index_put is used with empty zero-values input tensor"
+        )
+        # Combine array of index tensors into one index tensor with shape (N,_)
+        indices_expdim = [self.unsqueeze((x, 0), None) for x in indices]
+        indices_concat = self.concatenate((indices_expdim, 0), None)
+        return _op.transform.scatter_nd(values, indices_concat, out_shape)
+
     def scalar_tensor(self, inputs, input_types):
         data = inputs[0]
         cast_map = {
@@ -2326,6 +2353,8 @@ def create_convert_map(self):
             "aten::nonzero": self.nonzero,
             "aten::nonzero_numpy": self.nonzero_numpy,
             "aten::scatter": self.scatter,
+            "aten::index_put": self.index_put,
+            "aten::index_put_": self.index_put,
             "aten::scalar_tensor": self.scalar_tensor,
             "aten::__interpolate": self.interpolate,
             "aten::IntImplicit": self.identity,
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index 8d968e9760c9b..aa42b0fb84e45 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -3327,6 +3327,38 @@ def test_fn_scatter_add(dim):
     verify_trace_model(test_fn_scatter_add(1), [in_data, in_index, in_src], targets)
 
 
+def test_forward_index_put():
+    # torch.index_put for 2D tensor and default accumulate (False)
+    def test_fn_index_put2():
+        return lambda data, xidx, yidx, values: torch.index_put(
+            data, indices=[xidx, yidx], values=values
+        )
+
+    # torch.index_put for 3D tensor and accumulate=True
+    def test_fn_index_put3a():
+        return lambda data, xidx, yidx, zidx, values: torch.index_put(
+            data, indices=[xidx, yidx, zidx], values=values, accumulate=True
+        )
+
+    shape = (3, 5)
+    in_data = torch.zeros(shape)
+    xidx = torch.tensor([0, 1, 2, 2])
+    yidx = torch.tensor([0, 1, 3, 4])
+    values = torch.tensor([2.0, 4.0, 7.0, 9.0])
+
+    targets = ["llvm", "cuda"]
+    verify_trace_model(test_fn_index_put2(), [in_data, xidx, yidx, values], targets)
+
+    shape = (3, 5, 3)
+    in_data = torch.zeros(shape)
+    xidx = torch.tensor([0, 1, 2, 2, 0])
+    yidx = torch.tensor([0, 1, 3, 4, 0])
+    zidx = torch.tensor([0, 1, 1, 2, 0])
+    values = torch.tensor([2.0, 4.0, 7.0, 9.0, 1.0])
+
+    verify_trace_model(test_fn_index_put3a(), [in_data, xidx, yidx, zidx, values], targets)
+
+
 def test_numel():
     class Numel(Module):
         def forward(self, data):