add tests

tests/layer_tests/pytorch_tests/test_layer_norm.py

@@ -0,0 +1,50 @@
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+import numpy as np
+
+
+class TestLayerNorm(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randn(20, 5, 10, 10).astype(np.float32),)
+
+    def create_model(self, normalized_shape, weight, bias, eps):
+        import torch
+        import torch.nn.functional as F
+
+        if weight == "ones":
+            weight = torch.ones(normalized_shape)
+
+        if weight == "random":
+            weight = torch.randn(normalized_shape)
+
+        if bias == "zeros":
+            bias = torch.zeros(normalized_shape)
+
+        if bias == "random":
+            bias = torch.randn(normalized_shape)
+
+        class aten_layer_norm(torch.nn.Module):
+            def __init__(self, normalized_shape, weight, bias, eps):
+                super(aten_layer_norm, self).__init__()
+                self.normalized_shape = normalized_shape
+                self.weight = weight
+                self.bias = bias
+                self.eps = eps
+
+            def forward(self, x):
+                return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+
+        ref_net = None
+
+        return aten_layer_norm(normalized_shape, weight, bias, eps), ref_net, "aten::layer_norm"
+
+    @pytest.mark.parametrize("normalized_shape", [[10,], [10, 10], [5, 10, 10]])
+    @pytest.mark.parametrize("weight", [None, "ones", "random"])
+    @pytest.mark.parametrize("bias", [None, "zeros", "random"])
+    @pytest.mark.parametrize("eps", [1e-5, 0.005])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_layer_norm(self, normalized_shape, weight, bias, eps, ie_device, precision, ir_version):
+        self._test(*self.create_model(normalized_shape, weight, bias, eps), ie_device, precision, ir_version)