[PT FE] Add aten::logcumsumexp

src/frontends/pytorch/src/op/logcumsumexp.cpp

@@ -0,0 +1,40 @@
+// Copyright (C) 2018-2025 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/convert.hpp"
+#include "openvino/op/cum_sum.hpp"
+#include "openvino/op/exp.hpp"
+#include "openvino/op/log.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+using namespace ov::op;
+
+OutputVector translate_logcumsumexp(const NodeContext& context) {
+    // aten::logcumsumexp(Tensor self, int dim) -> Tensor
+    num_inputs_check(context, 2, 2);
+    auto input = context.get_input(0);
+    auto dim = context.get_input(1);
+
+    // First compute exp(input)
+    auto exp = context.mark_node(std::make_shared<v0::Exp>(input));
+
+    // Then compute cumsum of the exponentials
+    auto cumsum = context.mark_node(std::make_shared<v0::CumSum>(exp, dim));
+
+    // Finally take log of the result
+    auto log = context.mark_node(std::make_shared<v0::Log>(cumsum));
+
+    return {log};
+}
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov

src/frontends/pytorch/src/op_table.cpp

@@ -139,6 +139,7 @@ OP_CONVERTER(translate_log_softmax);
 OP_CONVERTER(translate_log2);
 OP_CONVERTER(translate_log10);
 OP_CONVERTER(translate_logsumexp);
+OP_CONVERTER(translate_logcumsumexp);
 OP_CONVERTER(translate_loop);
 OP_CONVERTER(translate_lstm);
 OP_CONVERTER(translate_masked_fill);
@@ -744,6 +745,7 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"torchvision::deform_conv2d", op::translate_deform_conv},
         {"torchvision::nms", op::translate_nms},
         {"torchvision::roi_align", op::translate_roi_align},
+        {"aten::logcumsumexp", op::translate_logcumsumexp},
     };
 };
 

tests/layer_tests/pytorch_tests/test_logcumsumexp.py

@@ -0,0 +1,31 @@
+# Copyright (C) 2018-2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import numpy as np
+import torch
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestLogCumSumExp(PytorchLayerTest):
+    def _prepare_input(self):
+        return (np.random.randn(1, 3, 224, 224).astype(np.float32),)
+
+    def create_model(self, axis):
+        class aten_logcumsumexp(torch.nn.Module):
+            def __init__(self, axis):
+                super(aten_logcumsumexp, self).__init__()
+                self.axis = axis
+
+            def forward(self, x):
+                return torch.logcumsumexp(x, self.axis)
+
+        ref_net = None
+
+        return aten_logcumsumexp(axis), ref_net, "aten::logcumsumexp"
+
+    @pytest.mark.parametrize("axis", [0, 1, 2, 3, -1, -2, -3, -4])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_logcumsumexp(self, axis, ie_device, precision, ir_version):
+        self._test(*self.create_model(axis), ie_device, precision, ir_version)