1 // Tencent is pleased to support the open source community by making ncnn available. 2 // 3 // Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved. 4 // 5 // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except 6 // in compliance with the License. You may obtain a copy of the License at 7 // 8 // https://opensource.org/licenses/BSD-3-Clause 9 // 10 // Unless required by applicable law or agreed to in writing, software distributed 11 // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR 12 // CONDITIONS OF ANY KIND, either express or implied. See the License for the 13 // specific language governing permissions and limitations under the License. 14 15 #include "pass_level1.h" 16 17 #include "../utils.h" 18 19 namespace pnnx { 20 21 class LSTM : public FuseModulePass 22 { 23 public: match_type_str() const24 const char* match_type_str() const 25 { 26 return "__torch__.torch.nn.modules.rnn.LSTM"; 27 } 28 type_str() const29 const char* type_str() const 30 { 31 return "nn.LSTM"; 32 } 33 write(Operator * op,const std::shared_ptr<torch::jit::Graph> & graph,const torch::jit::Module & mod) const34 void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const 35 { 36 // mod.dump(true, true, true); 37 38 // graph->dump(); 39 40 const torch::jit::Node* lstm = find_node_by_kind(graph, "aten::lstm"); 41 42 const torch::jit::Node* return_tuple = find_node_by_kind(graph, "prim::TupleConstruct"); 43 if (return_tuple && return_tuple->inputs().size() == 3 && lstm->outputs().size() == 3 44 && return_tuple->inputs()[0] == lstm->outputs()[1] && return_tuple->inputs()[1] == lstm->outputs()[2] && return_tuple->inputs()[2] == lstm->outputs()[0]) 45 { 46 // mark the swapped output tuple 47 // we would restore the fine order in pass_level3/fuse_rnn_unpack 48 fprintf(stderr, "swapped detected !\n"); 49 op->params["pnnx_rnn_output_swapped"] = 1; 50 } 51 52 // for (auto aa : lstm->schema().arguments()) 53 // { 54 // fprintf(stderr, "arg %s\n", aa.name().c_str()); 55 // } 56 57 const auto& weight_ih_l0 = mod.attr("weight_ih_l0").toTensor(); 58 59 op->params["input_size"] = weight_ih_l0.size(1); 60 op->params["hidden_size"] = weight_ih_l0.size(0) / 4; 61 op->params["num_layers"] = lstm->namedInput("num_layers"); 62 op->params["bias"] = lstm->namedInput("has_biases"); 63 op->params["batch_first"] = lstm->namedInput("batch_first"); 64 op->params["bidirectional"] = lstm->namedInput("bidirectional"); 65 66 const int num_layers = op->params["num_layers"].i; 67 const bool bias = op->params["bias"].b; 68 const bool bidirectional = op->params["bidirectional"].b; 69 70 for (int k = 0; k < num_layers; k++) 71 { 72 std::string weight_ih_lk_key = std::string("weight_ih_l") + std::to_string(k); 73 std::string weight_hh_lk_key = std::string("weight_hh_l") + std::to_string(k); 74 75 op->attrs[weight_ih_lk_key] = mod.attr(weight_ih_lk_key).toTensor(); 76 op->attrs[weight_hh_lk_key] = mod.attr(weight_hh_lk_key).toTensor(); 77 78 if (bias) 79 { 80 std::string bias_ih_lk_key = std::string("bias_ih_l") + std::to_string(k); 81 std::string bias_hh_lk_key = std::string("bias_hh_l") + std::to_string(k); 82 83 op->attrs[bias_ih_lk_key] = mod.attr(bias_ih_lk_key).toTensor(); 84 op->attrs[bias_hh_lk_key] = mod.attr(bias_hh_lk_key).toTensor(); 85 } 86 87 if (bidirectional) 88 { 89 std::string weight_ih_lk_reverse_key = std::string("weight_ih_l") + std::to_string(k) + "_reverse"; 90 std::string weight_hh_lk_reverse_key = std::string("weight_hh_l") + std::to_string(k) + "_reverse"; 91 92 op->attrs[weight_ih_lk_reverse_key] = mod.attr(weight_ih_lk_reverse_key).toTensor(); 93 op->attrs[weight_hh_lk_reverse_key] = mod.attr(weight_hh_lk_reverse_key).toTensor(); 94 95 if (bias) 96 { 97 std::string bias_ih_lk_reverse_key = std::string("bias_ih_l") + std::to_string(k) + "_reverse"; 98 std::string bias_hh_lk_reverse_key = std::string("bias_hh_l") + std::to_string(k) + "_reverse"; 99 100 op->attrs[bias_ih_lk_reverse_key] = mod.attr(bias_ih_lk_reverse_key).toTensor(); 101 op->attrs[bias_hh_lk_reverse_key] = mod.attr(bias_hh_lk_reverse_key).toTensor(); 102 } 103 } 104 } 105 } 106 }; 107 108 REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(LSTM) 109 110 } // namespace pnnx 111