// Copyright 2024 Tencent // SPDX-License-Identifier: BSD-3-Clause #include "testutil.h" static int test_embed(int words, int num_output, int input_dim, int bias) { ncnn::ParamDict pd; pd.set(0, num_output); pd.set(1, input_dim); pd.set(2, bias); pd.set(3, num_output * input_dim); std::vector weights(bias ? 2 : 1); weights[0] = RandomMat(num_output * input_dim); if (bias) weights[1] = RandomMat(num_output); ncnn::Mat a(words); RandomizeInt(a, 0, input_dim); int ret = test_layer("Embed", pd, weights, a); if (ret != 0) { fprintf(stderr, "test_embed failed words=%d num_output=%d input_dim=%d bias=%d\n", words, num_output, input_dim, bias); } return ret; } static int test_embed_0() { return 0 || test_embed(128, 128, 128, 0) || test_embed(128, 128, 128, 1) || test_embed(127, 127, 127, 0) || test_embed(127, 127, 127, 1) || test_embed(124, 124, 124, 0) || test_embed(124, 124, 124, 1); } #if NCNN_INT8 static int test_embed_int8(int words, int num_output, int input_dim, int bias) { ncnn::ParamDict pd; pd.set(0, num_output); pd.set(1, input_dim); pd.set(2, bias); pd.set(3, num_output * input_dim); pd.set(18, 2); std::vector weights(bias ? 3 : 2); weights[0] = RandomS8Mat(num_output * input_dim); if (bias) { weights[1] = RandomMat(num_output); weights[2] = RandomMat(1, 100.f, 200.f); } else { weights[1] = RandomMat(1, 100.f, 200.f); } ncnn::Mat a(words); RandomizeInt(a, 0, input_dim); int ret = test_layer("Embed", pd, weights, a); if (ret != 0) { fprintf(stderr, "test_embed_int8 failed words=%d num_output=%d input_dim=%d bias=%d\n", words, num_output, input_dim, bias); } return ret; } static int test_embed_1() { return 0 || test_embed_int8(128, 128, 128, 0) || test_embed_int8(128, 128, 128, 1) || test_embed_int8(127, 127, 127, 0) || test_embed_int8(127, 127, 127, 1) || test_embed_int8(124, 124, 124, 0) || test_embed_int8(124, 124, 124, 1); } #endif // NCNN_INT8 int main() { SRAND(7767517); #if NCNN_INT8 return test_embed_0() || test_embed_1(); #else return test_embed_0(); #endif }