support pytorch efrc op (#21567)

* support pytorch efrc op

* allow dynamic case & fix code format

* fix code style, use space 4

src/frontends/pytorch/src/op/erfc.cpp

@@ -0,0 +1,45 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/convert.hpp"
+#include "openvino/op/erf.hpp"
+#include "openvino/op/subtract.hpp"
+#include "utils.hpp"
+
+using namespace std;
+using namespace ov::op;
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+OutputVector translate_erfc(const NodeContext& context) {
+    // aten::erf(Tensor self) -> Tensor
+    // aten::erf.out(Tensor self, Tensor(!a) out) -> Tensor(!a)
+    num_inputs_check(context, 1, 2);
+    auto x = context.get_input(0);
+
+    // create 'ones' to use to calculate complementary of Erf output
+    auto ones = context.mark_node(make_shared<v0::Constant>(element::f32, Shape{}, 1.0f))->output(0);
+
+    // align data types of input 'x' and ones
+    align_eltwise_input_types(context, x, ones);
+
+    // apply Erf to the input tensor 'x'
+    auto y = context.mark_node(make_shared<v0::Erf>(x));
+
+    y = context.mark_node(make_shared<v1::Subtract>(ones, y));
+
+    if (!context.input_is_none(1)) {
+        context.mutate_input(1, y);
+    }
+    return {y};
+};
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov

src/frontends/pytorch/src/op_table.cpp

@@ -68,6 +68,7 @@ OP_CONVERTER(translate_embedding_bag);
 OP_CONVERTER(translate_empty);
 OP_CONVERTER(translate_empty_like);
 OP_CONVERTER(translate_erf);
+OP_CONVERTER(translate_erfc);
 OP_CONVERTER(translate_expand);
 OP_CONVERTER(translate_expand_as);
 OP_CONVERTER(translate_eye);
@@ -348,6 +349,8 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::eq", op::translate_1to1_match_2_inputs_align_types<opset10::Equal>},
         {"aten::erf", op::translate_erf},
         {"aten::erf_", op::inplace_op<op::translate_erf>},
+        {"aten::erfc", op::translate_erfc},
+        {"aten::erfc_", op::inplace_op<op::translate_erfc>},
         {"aten::exp", op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Exp>},
         {"aten::exp_", op::inplace_op<op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Exp>>},
         {"aten::expand", op::translate_expand},

tests/layer_tests/pytorch_tests/test_erfc.py

@@ -0,0 +1,57 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestErfc(PytorchLayerTest):
+    def _prepare_input(self, input_dtype, out=False):
+        import numpy as np
+        x = np.linspace(-3, 3).astype(input_dtype)
+        if not out:
+            return (x, )
+        return (x, np.zeros_like(x).astype(input_dtype))
+
+    def create_model(self, mode="", input_dtype="float32"):
+        import torch
+        dtypes = {
+            "float32": torch.float32,
+            "float64": torch.float64,
+            "int32": torch.int32
+        }
+
+        dtype = dtypes[input_dtype]
+        class aten_erfc(torch.nn.Module):
+            def __init__(self, mode, dtype):
+                super(aten_erfc, self).__init__()
+                self.dtype = dtype
+                if mode == "out":
+                    self.forward = self.forward_out
+                elif mode == "inplace":
+                    self.forward = self.forward_inplace
+
+            def forward(self, x):
+                return torch.special.erfc(x.to(self.dtype))
+
+            def forward_out(self, x, y):
+                return torch.special.erfc(x.to(self.dtype), out=y), y
+
+            def forward_inplace(self, x):
+                x = x.to(self.dtype)
+                return x.erfc_(), x
+
+        ref_net = None
+
+        return aten_erfc(mode, dtype), ref_net, "aten::erfc" if mode != "inplace" else "aten::erfc_"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("mode,input_dtype", [
+        ("", "float32"), ("", "float64"), ("", "int32"),
+        ("out", "float32"), ("out", "float64"),
+        ("inplace", "float32"), ("inplace", "float64")])
+    def test_erfc(self, mode, input_dtype, ie_device, precision, ir_version):
+        self._test(*self.create_model(mode, input_dtype), ie_device, precision, ir_version,
+                   kwargs_to_prepare_input={"input_dtype": input_dtype, "out": mode == "out"} )