[PT FE] Align types for aten::clamp_max and aten::clamp_min (openvino…

…toolkit#21137) * Align types for aten::clamp_max and aten::clamp_min * Add tests
ababushk · Nov 17, 2023 · 6bdc159 · 6bdc159
1 parent 8b5b7a6
commit 6bdc159
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Showing 3 changed files with 34 additions and 10 deletions.
diff --git a/src/frontends/pytorch/src/op_table.cpp b/src/frontends/pytorch/src/op_table.cpp
@@ -296,8 +296,8 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::channel_shuffle", op::translate_channel_shuffle},
         {"aten::clamp", op::translate_clamp},
         {"aten::clamp_", op::inplace_op<op::translate_clamp>},
-        {"aten::clamp_max", op::translate_1to1_match_2_inputs<opset10::Minimum>},
-        {"aten::clamp_min", op::translate_1to1_match_2_inputs<opset10::Maximum>},
+        {"aten::clamp_max", op::translate_1to1_match_2_inputs_align_types<opset10::Minimum>},
+        {"aten::clamp_min", op::translate_1to1_match_2_inputs_align_types<opset10::Maximum>},
         {"aten::clip", op::translate_clamp},
         {"aten::clip_", op::inplace_op<op::translate_clamp>},
         {"aten::clone", op::skip_node},       // ignore clone operators that are inserted by PyTorch autograd

diff --git a/tests/layer_tests/pytorch_tests/test_clamp.py b/tests/layer_tests/pytorch_tests/test_clamp.py
@@ -47,11 +47,13 @@ def forward_clip_(self, x):
     @pytest.mark.parametrize("op_type", ["clamp", "clamp_"])
     @pytest.mark.nightly
     def test_clamp(self, minimum, maximum, as_tensors, op_type, ie_device, precision, ir_version):
-        self._test(*self.create_model(minimum, maximum, as_tensors, op_type), ie_device, precision, ir_version)
+        self._test(*self.create_model(minimum, maximum, as_tensors,
+                   op_type), ie_device, precision, ir_version)
 
     @pytest.mark.xfail(reason='OpenVINO clamp does not support min > max')
     def test_clamp_min_greater(self, ie_device, precision, ir_version):
-        self._test(*self.create_model(1.0, 0.0), ie_device, precision, ir_version)
+        self._test(*self.create_model(1.0, 0.0),
+                   ie_device, precision, ir_version)
 
 
 class TestClampMin(PytorchLayerTest):
@@ -74,11 +76,12 @@ def forward(self, x):
         op_name = "aten::clamp_min"
         return aten_clamp_min(minimum, as_tensor), ref_net, op_name
 
-    @pytest.mark.parametrize("minimum", [0., 1., -1., 0.5])
+    @pytest.mark.parametrize("minimum", [0., 1., -1., 0.5, 2])
     @pytest.mark.parametrize("as_tensor", [True, False])
     @pytest.mark.nightly
     def test_clamp_min(self, minimum, as_tensor, ie_device, precision, ir_version):
-        self._test(*self.create_model(minimum, as_tensor), ie_device, precision, ir_version)
+        self._test(*self.create_model(minimum, as_tensor), ie_device,
+                   precision, ir_version, use_convert_model=True, trace_model=True)
 
 
 class TestClampMax(PytorchLayerTest):
@@ -101,9 +104,10 @@ def forward(self, x):
         op_name = "aten::clamp_max"
         return aten_clamp_max(maximum, as_tensor), ref_net, op_name
 
-    @pytest.mark.parametrize("maximum", [0., 1., -1., 0.5])
+    @pytest.mark.parametrize("maximum", [0., 1., -1., 0.5, 2])
     @pytest.mark.parametrize("as_tensor", [True, False])
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_clamp(self, maximum, as_tensor, ie_device, precision, ir_version):
-        self._test(*self.create_model(maximum, as_tensor), ie_device, precision, ir_version)
+        self._test(*self.create_model(maximum, as_tensor), ie_device,
+                   precision, ir_version, use_convert_model=True, trace_model=True)
diff --git a/tests/layer_tests/pytorch_tests/test_norm.py b/tests/layer_tests/pytorch_tests/test_norm.py
@@ -327,7 +327,27 @@ def test_linalg_norm(self, p, dim, keepdim, dtype, out, prim_dtype, input_shape,
         self._test(*self.create_model(p, dim, keepdim, dtype, out, prim_dtype),
                    ie_device, precision, ir_version,
                    kwargs_to_prepare_input={
-                       "out": out or prim_dtype, 
+                       "out": out or prim_dtype,
                        "out_dtype": dtype if prim_dtype else None,
                        "input_shape": input_shape
-                       })
+        })
+
+
+class TestTrickyNorm(PytorchLayerTest):
+
+    def _prepare_input(self, input_shape=(3, 3)):
+        return (np.random.randn(*input_shape).astype(np.float32),)
+
+    def create_model(self):
+        class aten_norm(torch.nn.Module):
+            def forward(self, x):
+                return torch.nn.functional.normalize(x, eps=2)
+
+        return aten_norm(), None, ["aten::linalg_vector_norm", "aten::clamp_min"]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("input_shape", [[15, 15, 17]])
+    def test_tricky_norm(self, input_shape, ie_device, precision, ir_version):
+        self._test(*self.create_model(), ie_device, precision, ir_version,
+                   kwargs_to_prepare_input={"input_shape": input_shape}, use_convert_model=True, trace_model=True)