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https://github.com/Tencent/ncnn.git
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* runtime cpu dispatch * force thread one * disable openmp for coverage * simplify test layer * print NCNN_TARGET_ARCH * less ci build variants * weight fp16 storage option * test convdw int8 * apple a12 a13 * ncnn_add_layer ncnn_add_shader cmake macro
215 lines
6.6 KiB
C++
215 lines
6.6 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include "layer/lstm.h"
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#include "testutil.h"
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static int test_lstm(const ncnn::Mat& a, int outch, int direction)
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{
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int input_size = a.w * a.h * a.c;
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int num_directions = direction == 2 ? 2 : 1;
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ncnn::ParamDict pd;
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pd.set(0, outch); // num_output
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pd.set(1, outch * input_size * 4 * num_directions);
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pd.set(2, direction); // bias_term
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std::vector<ncnn::Mat> weights(3);
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weights[0] = RandomMat(outch * input_size * 4 * num_directions);
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weights[1] = RandomMat(outch * 4 * num_directions);
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weights[2] = RandomMat(outch * outch * 4 * num_directions);
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int ret = test_layer<ncnn::LSTM>("LSTM", pd, weights, a);
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if (ret != 0)
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{
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fprintf(stderr, "test_lstm failed a.dims=%d a=(%d %d %d) outch=%d, direction = %d \n", a.dims, a.w, a.h, a.c, outch, direction);
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}
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return ret;
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}
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int test_lstm_layer(const ncnn::Mat& a, int outch, int direction, float epsilon = 0.01)
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{
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int input_size = a.w * a.h * a.c;
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ncnn::ParamDict pd;
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pd.set(0, outch); // num_output
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pd.set(1, outch * input_size * 4);
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pd.set(2, direction); // bias_term
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int num_directions = direction == 2 ? 2 : 1;
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std::vector<ncnn::Mat> weights(3);
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weights[0] = RandomMat(outch * input_size * 4 * num_directions);
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weights[1] = RandomMat(outch * 4 * num_directions);
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weights[2] = RandomMat(outch * outch * 4 * num_directions);
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_int8_inference = false;
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ncnn::LSTM* op = (ncnn::LSTM*)ncnn::create_layer(ncnn::layer_to_index("LSTM"));
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if (!op->support_vulkan) opt.use_vulkan_compute = false;
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if (!op->support_packing) opt.use_packing_layout = false;
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if (!op->support_bf16_storage) opt.use_bf16_storage = false;
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if (!op->support_image_storage) opt.use_image_storage = false;
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op->load_param(pd);
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ncnn::ModelBinFromMatArray mb(weights.data());
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op->load_model(mb);
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op->create_pipeline(opt);
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ncnn::Mat b;
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op->LSTM::forward(a, b, opt);
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std::vector<ncnn::Mat> _c1(3);
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std::vector<ncnn::Mat> _c2(3);
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std::vector<ncnn::Mat> a1(3);
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std::vector<ncnn::Mat> a2(3);
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if (direction == 0)
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{
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a1[0] = a.row_range(0, a.h / 2).clone();
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a2[0] = a.row_range(a.h / 2, a.h - a.h / 2).clone();
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}
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else
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{
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a2[0] = a.row_range(0, a.h / 2).clone();
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a1[0] = a.row_range(a.h / 2, a.h - a.h / 2).clone();
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}
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// initial hidden state
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ncnn::Mat hidden(outch);
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if (hidden.empty())
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return -100;
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hidden.fill(0.f);
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ncnn::Mat cell(outch);
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if (cell.empty())
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return -100;
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cell.fill(0.f);
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a1[1] = hidden;
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a1[2] = cell;
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op->forward(a1, _c1, opt);
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a2[1] = _c1[1];
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a2[2] = _c1[2];
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op->forward(a2, _c2, opt);
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ncnn::Mat c1 = _c1[0];
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ncnn::Mat c2 = _c2[0];
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if (direction == 1)
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{
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c2 = _c1[0];
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c1 = _c2[0];
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}
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// total height
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ncnn::Mat c;
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c.create(b.w, b.h, b.elemsize, opt.blob_allocator);
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if (c.empty())
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return -100;
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unsigned char* outptr = c;
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int c1_size = c1.w * c1.h;
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const unsigned char* c1ptr = c1;
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memcpy(outptr, c1ptr, c1_size * c1.elemsize);
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outptr += c1_size * c1.elemsize;
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int c2_size = c2.w * c2.h;
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const unsigned char* c2ptr = c2;
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memcpy(outptr, c2ptr, c2_size * c2.elemsize);
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op->destroy_pipeline(opt);
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delete op;
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if (CompareMat(b, c, epsilon) != 0)
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{
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fprintf(stderr, "test_lstm two step failed a.dims=%d a=(%d %d %d) outch=%d, direction = %d \n", a.dims, a.w, a.h, a.c, outch, direction);
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return -1;
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}
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return 0;
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}
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static int test_lstm_0()
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{
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return 0
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|| test_lstm(RandomMat(4, 1), 2, 2)
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|| test_lstm(RandomMat(8, 2), 2, 2)
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|| test_lstm(RandomMat(16, 8), 7, 2)
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|| test_lstm(RandomMat(17, 8), 8, 2)
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|| test_lstm(RandomMat(19, 15), 8, 2)
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|| test_lstm(RandomMat(5, 16), 16, 2)
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|| test_lstm(RandomMat(3, 16), 8, 2)
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|| test_lstm(RandomMat(8, 16), 16, 2)
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|| test_lstm(RandomMat(2, 5), 17, 2);
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}
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static int test_lstm_1()
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{
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return 0
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|| test_lstm_layer(RandomMat(4, 4), 1, 1)
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|| test_lstm_layer(RandomMat(8, 2), 2, 1)
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|| test_lstm_layer(RandomMat(16, 8), 7, 1)
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|| test_lstm_layer(RandomMat(17, 8), 8, 1)
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|| test_lstm_layer(RandomMat(19, 15), 8, 1)
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|| test_lstm_layer(RandomMat(5, 16), 16, 1)
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|| test_lstm_layer(RandomMat(3, 16), 8, 1)
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|| test_lstm_layer(RandomMat(2, 5), 99, 1)
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|| test_lstm_layer(RandomMat(4, 2), 1, 0)
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|| test_lstm_layer(RandomMat(8, 2), 2, 0)
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|| test_lstm_layer(RandomMat(16, 8), 7, 0)
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|| test_lstm_layer(RandomMat(17, 8), 8, 0)
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|| test_lstm_layer(RandomMat(19, 15), 8, 0)
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|| test_lstm_layer(RandomMat(5, 16), 16, 0)
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|| test_lstm_layer(RandomMat(3, 16), 8, 0)
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|| test_lstm_layer(RandomMat(2, 5), 17, 0);
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}
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static int test_lstm_2()
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{
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return 0
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|| test_lstm(RandomMat(4, 1), 1, 0)
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|| test_lstm(RandomMat(8, 2), 2, 0)
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|| test_lstm(RandomMat(16, 8), 7, 0)
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|| test_lstm(RandomMat(17, 8), 8, 0)
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|| test_lstm(RandomMat(19, 15), 8, 0)
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|| test_lstm(RandomMat(5, 16), 16, 0)
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|| test_lstm(RandomMat(3, 16), 8, 0)
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|| test_lstm(RandomMat(8, 16), 16, 0)
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|| test_lstm(RandomMat(2, 5), 17, 0);
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}
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static int test_lstm_3()
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{
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return 0
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|| test_lstm(RandomMat(4, 1), 1, 1)
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|| test_lstm(RandomMat(8, 2), 2, 1)
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|| test_lstm(RandomMat(16, 8), 7, 1)
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|| test_lstm(RandomMat(17, 8), 8, 1)
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|| test_lstm(RandomMat(19, 15), 8, 1)
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|| test_lstm(RandomMat(5, 16), 16, 1)
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|| test_lstm(RandomMat(3, 16), 8, 1)
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|| test_lstm(RandomMat(8, 16), 16, 1)
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|| test_lstm(RandomMat(2, 5), 17, 1);
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}
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int main()
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{
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SRAND(7767517);
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return 0 || test_lstm_0() || test_lstm_1() || test_lstm_2() || test_lstm_3();
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}
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