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be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2021 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_level1.h"
#include "../utils.h"
namespace pnnx {
class LayerNorm : public FuseModulePass
{
public:
const char* match_type_str() const
{
return "__torch__.torch.nn.modules.normalization.LayerNorm";
}
const char* type_str() const
{
return "nn.LayerNorm";
}
void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const
{
const torch::jit::Node* ln = find_node_by_kind(graph, "aten::layer_norm");
op->params["normalized_shape"] = ln->namedInput("normalized_shape");
op->params["eps"] = ln->namedInput("eps");
op->params["elementwise_affine"] = mod.hasattr("weight") && mod.hasattr("bias");
if (mod.hasattr("weight") && mod.hasattr("bias"))
{
op->attrs["weight"] = mod.attr("weight").toTensor();
op->attrs["bias"] = mod.attr("bias").toTensor();
}
}
};
REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(LayerNorm)
} // namespace pnnx
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