chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
@@ -0,0 +1,125 @@
+//
+//  ShapeLSTM.cpp
+//  MNN
+//
+//  Created by MNN on 2019/01/10.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "shape/SizeComputer.hpp"
+#include "core/Macro.h"
+#include "core/TensorUtils.hpp"
+
+namespace MNN {
+
+// Size Computer
+class LSTMComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op *op, const std::vector<Tensor *> &inputs,
+                               const std::vector<Tensor *> &outputs) const override {
+        if (1 == outputs.size()) {
+            // For compability for old version model
+            MNN_ASSERT(1 == outputs.size());
+
+            // copy dims
+            auto &input  = inputs[0]->buffer();
+            auto &output = outputs[0]->buffer();
+            memcpy(output.dim, input.dim, sizeof(halide_dimension_t) * input.dimensions);
+
+            auto LSTM            = op->main_as_LSTM();
+            output.dimensions = 4;
+            output.dim[3].extent = LSTM->outputCount();
+            output.dim[2].extent = 1;
+            output.type = halide_type_of<float>();
+            TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+            return true;
+        }
+        // Onnx's LSTM
+        MNN_ASSERT(inputs.size() >= 4);
+        MNN_ASSERT(outputs.size() == 3);
+        auto X = inputs[0];
+        auto seqLength = X->length(0);
+        auto batchSize = X->length(1);
+        auto hiddenSize = op->main_as_LSTM()->outputCount();
+
+        auto Y = outputs[0];
+        auto ht = outputs[1];
+        auto ct = outputs[2];
+        Y->buffer().dimensions = 4;
+        ht->buffer().dimensions = 3;
+        ct->buffer().dimensions = 3;
+        Y->setLength(0, seqLength);
+        
+        int direction = inputs[1]->length(0);
+        MNN_ASSERT(1 == direction || 2 == direction);
+        Y->setLength(1, direction);
+        Y->setLength(2, batchSize);
+        Y->setLength(3, hiddenSize);
+        
+        ht->setLength(0, direction);
+        ht->setLength(1, batchSize);
+        ht->setLength(2, hiddenSize);
+
+        ct->setLength(0, direction);
+        ct->setLength(1, batchSize);
+        ct->setLength(2, hiddenSize);
+
+        TensorUtils::getDescribe(Y)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
+        TensorUtils::getDescribe(ht)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
+        TensorUtils::getDescribe(ct)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
+
+        return true;
+    }
+};
+
+REGISTER_SHAPE(LSTMComputer, OpType_LSTM);
+
+// LSTMCellBlock Size Computer
+class LSTMBlockCellComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op *op, const std::vector<Tensor *> &inputs,
+                               const std::vector<Tensor *> &outputs) const override {
+        MNN_ASSERT(inputs.size() == 8);
+        MNN_ASSERT(outputs.size() == 7);
+        for (int i = 0; i < outputs.size(); i++) {
+            TensorUtils::copyShape(inputs[1], outputs[i]);
+        }
+        return true;
+    }
+};
+
+REGISTER_SHAPE(LSTMBlockCellComputer, OpType_LSTMBlockCell);
+
+// Size Computer
+class RNNComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op *op, const std::vector<Tensor *> &inputs,
+                               const std::vector<Tensor *> &outputs) const override {
+        MNN_ASSERT(inputs.size() >= 4 && outputs.size() == 2);
+        
+        auto X = inputs[0];
+        auto seqLength = X->length(0), batchSize = X->length(1);
+        auto hiddenSize = op->main_as_LSTM()->outputCount();
+
+        auto Y = outputs[0], ht = outputs[1];
+        Y->buffer().dimensions = 4;
+        ht->buffer().dimensions = 3;
+        Y->setLength(0, seqLength);
+        
+        int direction = inputs[1]->length(0);
+        MNN_ASSERT(1 == direction || 2 == direction);
+        Y->setLength(1, direction);
+        Y->setLength(2, batchSize);
+        Y->setLength(3, hiddenSize);
+        
+        ht->setLength(0, direction);
+        ht->setLength(1, batchSize);
+        ht->setLength(2, hiddenSize);
+
+        TensorUtils::getDescribe(Y)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
+        TensorUtils::getDescribe(ht)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
+
+        return true;
+    }
+};
+
+REGISTER_SHAPE(RNNComputer, OpType_RNN);
+
+} // namespace MNN