add torch.positive

tools/pnnx/src/CMakeLists.txt

@@ -244,6 +244,7 @@ set(pnnx_pass_level2_SRCS
     pass_level2/torch_normal.cpp
     pass_level2/torch_ones.cpp
     pass_level2/torch_ones_like.cpp
+    pass_level2/torch_positive.cpp
     pass_level2/torch_prod.cpp
     pass_level2/torch_quantize_per_tensor.cpp
     pass_level2/torch_randn.cpp

tools/pnnx/src/pass_level2/torch_positive.cpp

@@ -0,0 +1,40 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2023 THL A29 Limited, a Tencent company. All rights reserved.
+//
+// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+// in compliance with the License. You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// Unless required by applicable law or agreed to in writing, software distributed
+// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
+// CONDITIONS OF ANY KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations under the License.
+
+#include "pass_level2.h"
+
+namespace pnnx {
+
+class torch_positive : public GraphRewriterPass
+{
+public:
+    const char* match_pattern_graph() const
+    {
+        return R"PNNXIR(7767517
+3 2
+pnnx.Input              input       0 1 input
+aten::positive          op_0        1 1 input out
+pnnx.Output             output      1 0 out
+)PNNXIR";
+    }
+
+    const char* type_str() const
+    {
+        return "torch.positive";
+    }
+};
+
+REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(torch_positive, 20)
+
+} // namespace pnnx

tools/pnnx/tests/CMakeLists.txt

@@ -328,6 +328,7 @@ if(Torch_VERSION VERSION_GREATER_EQUAL "1.9")
     pnnx_add_test(F_mish)
     pnnx_add_test(nn_Mish)
     pnnx_add_test(ir_complex)
+    pnnx_add_test(torch_positive)
 endif()
 
 if(Torch_VERSION VERSION_GREATER_EQUAL "1.10")

tools/pnnx/tests/test_torch_positive.py

@@ -0,0 +1,61 @@
+# Tencent is pleased to support the open source community by making ncnn available.
+#
+# Copyright (C) 2023 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# https://opensource.org/licenses/BSD-3-Clause
+#
+# Unless required by applicable law or agreed to in writing, software distributed
+# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
+# CONDITIONS OF ANY KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations under the License.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+    def forward(self, x, y, z):
+        x = torch.positive(x)
+        y = torch.positive(y)
+        z = torch.positive(z)
+        return x, y, z
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    x = torch.rand(1, 3, 16)
+    y = torch.rand(1, 5, 9, 11)
+    z = torch.rand(14, 8, 5, 9, 10)
+
+    a = net(x, y, z)
+
+    # export torchscript
+    mod = torch.jit.trace(net, (x, y, z))
+    mod.save("test_torch_positive.pt")
+
+    # torchscript to pnnx
+    import os
+    os.system("../src/pnnx test_torch_positive.pt inputshape=[1,3,16],[1,5,9,11],[14,8,5,9,10]")
+
+    # pnnx inference
+    import test_torch_positive_pnnx
+    b = test_torch_positive_pnnx.test_inference()
+
+    for a0, b0 in zip(a, b):
+        if not torch.equal(a0, b0):
+            return False
+    return True
+
+if __name__ == "__main__":
+    if test():
+        exit(0)
+    else:
+        exit(1)