Fix the aten::mv for pytorch models #22073

src/frontends/pytorch/src/op_table.cpp

@@ -516,6 +516,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::masked_scatter_", op::inplace_op<op::translate_masked_scatter>},
         {"aten::matmul", op::translate_1to1_match_2_inputs<opset10::MatMul>},
         {"aten::max", op::translate_max},
+        {"aten::mv", op::translate_1to1_match_2_inputs<opset10::MatMul>},
         {"aten::maximum", op::translate_maximum},
         {"aten::max_pool1d", op::quantizable_op<op::translate_max_poolnd>},
         {"aten::max_pool1d_with_indices", op::quantizable_op<op::translate_max_poolnd>},

tests/layer_tests/pytorch_tests/test_matmul.py

@@ -0,0 +1,42 @@
+import numpy as np
+import pytest
+import torch
+from pytorch_layer_test_class import PytorchLayerTest
+
+class TestMatMulOperation(PytorchLayerTest):
+    def _prepare_input(self, matrix, vector):
+        matrix_input = torch.tensor(matrix, dtype=torch.float32)
+        vector_input = torch.tensor(vector, dtype=torch.float32)
+        return [matrix_input, vector_input]
+
+    def create_model(self, matrix, vector):
+        class CustomMatMulOperation(torch.nn.Module):
+            def forward(self, matrix, vector):
+                return torch.matmul(matrix, vector)
+
+        model_class = CustomMatMulOperation()
+        ref_net = None
+        return model_class, ref_net, "aten::mm"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("matrix, vector, dtype, device", [
+        (np.array([[1, 2], [3, 4]]), np.array([5, 6]), torch.float64, 'cpu'),
+        (np.array([[0, 0], [0, 0]]), np.array([1, 2]), torch.float32, 'cpu'),
+        # Add more test cases as needed
+    ])
+    def test_matmul_operation(self, matrix, vector, dtype, device, ie_device, precision, ir_version):
+        matrix_input = torch.tensor(matrix, dtype=torch.float32)
+        vector_input = torch.tensor(vector, dtype=torch.float32)
+
+        # Convert tensors to the specified dtype and device
+        matrix_input = matrix_input.to(dtype=dtype, device=device)
+        vector_input = vector_input.to(dtype=dtype, device=device)
+
+        self._test(
+            *self.create_model(matrix_input, vector_input),
+            ie_device,
+            precision,
+            ir_version,
+            kwargs_to_prepare_input={"matrix": matrix_input, "vector": vector_input}
+        )