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https://github.com/Tencent/ncnn.git
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風的彷徨
236 lines
8.2 KiB
C++
236 lines
8.2 KiB
C++
// Copyright 2024 Tencent
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// SPDX-License-Identifier: BSD-3-Clause
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#include "testutil.h"
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static int test_rmsnorm(const ncnn::Mat& a, int affine_size, float eps, int affine, int flag = 0)
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{
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ncnn::ParamDict pd;
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pd.set(0, affine_size);
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pd.set(1, eps);
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pd.set(2, affine);
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std::vector<ncnn::Mat> weights(1);
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weights[0] = RandomMat(affine_size);
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int ret = test_layer("RMSNorm", pd, weights, a, 1e-4f, flag);
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if (ret != 0)
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{
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fprintf(stderr, "test_rmsnorm failed a.dims=%d a=(%d %d %d %d) affine_size=%d eps=%f affine=%d\n", a.dims, a.w, a.h, a.d, a.c, affine_size, eps, affine);
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}
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return ret;
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}
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static int test_rmsnorm_0()
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{
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return 0
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|| test_rmsnorm(RandomMat(6, 4, 2), 6, 0.01f, 0)
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|| test_rmsnorm(RandomMat(4, 5, 6), 4, 0.01f, 0)
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|| test_rmsnorm(RandomMat(3, 3, 8), 3, 0.002f, 0)
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|| test_rmsnorm(RandomMat(5, 6, 12), 5, 0.02f, 0)
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|| test_rmsnorm(RandomMat(4, 7, 16), 4, 0.02f, 0)
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|| test_rmsnorm(RandomMat(6, 7, 24), 6, 0.001f, 0)
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|| test_rmsnorm(RandomMat(5, 8, 32), 5, 0.001f, 0, TEST_LAYER_DISABLE_GPU_TESTING)
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|| test_rmsnorm(RandomMat(6, 4, 2), 6, 0.01f, 1)
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|| test_rmsnorm(RandomMat(4, 5, 6), 4, 0.01f, 1)
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|| test_rmsnorm(RandomMat(3, 3, 8), 3, 0.002f, 1)
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|| test_rmsnorm(RandomMat(5, 6, 12), 5, 0.02f, 1)
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|| test_rmsnorm(RandomMat(4, 7, 16), 4, 0.02f, 1)
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|| test_rmsnorm(RandomMat(6, 7, 24), 6, 0.001f, 1)
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|| test_rmsnorm(RandomMat(5, 8, 32), 5, 0.001f, 1);
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}
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static int test_rmsnorm_1()
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{
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return 0
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|| test_rmsnorm(RandomMat(6, 4, 2), 24, 0.01f, 0)
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|| test_rmsnorm(RandomMat(4, 5, 6), 20, 0.01f, 0)
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|| test_rmsnorm(RandomMat(3, 3, 8), 9, 0.002f, 0)
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|| test_rmsnorm(RandomMat(5, 6, 12), 30, 0.02f, 0)
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|| test_rmsnorm(RandomMat(4, 7, 16), 28, 0.02f, 0)
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|| test_rmsnorm(RandomMat(6, 7, 24), 42, 0.001f, 0)
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|| test_rmsnorm(RandomMat(5, 8, 32), 40, 0.001f, 0)
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|| test_rmsnorm(RandomMat(6, 4, 2), 24, 0.01f, 1)
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|| test_rmsnorm(RandomMat(4, 5, 6), 20, 0.01f, 1)
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|| test_rmsnorm(RandomMat(3, 3, 8), 9, 0.002f, 1)
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|| test_rmsnorm(RandomMat(5, 6, 12), 30, 0.02f, 1)
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|| test_rmsnorm(RandomMat(4, 7, 16), 28, 0.02f, 1)
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|| test_rmsnorm(RandomMat(6, 7, 24), 42, 0.001f, 1)
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|| test_rmsnorm(RandomMat(5, 8, 32), 40, 0.001f, 1);
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}
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static int test_rmsnorm_2()
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{
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return 0
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|| test_rmsnorm(RandomMat(4, 2), 4, 0.01f, 0)
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|| test_rmsnorm(RandomMat(5, 6), 5, 0.01f, 0)
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|| test_rmsnorm(RandomMat(3, 8), 3, 0.002f, 0)
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|| test_rmsnorm(RandomMat(6, 12), 6, 0.02f, 0)
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|| test_rmsnorm(RandomMat(4, 16), 4, 0.02f, 0)
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|| test_rmsnorm(RandomMat(7, 24), 7, 0.001f, 0)
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|| test_rmsnorm(RandomMat(8, 32), 8, 0.001f, 0)
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|| test_rmsnorm(RandomMat(4, 2), 4, 0.01f, 1)
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|| test_rmsnorm(RandomMat(5, 6), 5, 0.01f, 1)
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|| test_rmsnorm(RandomMat(3, 8), 3, 0.002f, 1)
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|| test_rmsnorm(RandomMat(6, 12), 6, 0.02f, 1)
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|| test_rmsnorm(RandomMat(4, 16), 4, 0.02f, 1)
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|| test_rmsnorm(RandomMat(7, 24), 7, 0.001f, 1)
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|| test_rmsnorm(RandomMat(8, 32), 8, 0.001f, 1);
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}
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static int test_rmsnorm_3()
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{
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return 0
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|| test_rmsnorm(RandomMat(2), 2, 0.01f, 0)
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|| test_rmsnorm(RandomMat(6), 6, 0.01f, 0)
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|| test_rmsnorm(RandomMat(8), 8, 0.002f, 0)
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|| test_rmsnorm(RandomMat(12), 12, 0.02f, 0)
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|| test_rmsnorm(RandomMat(16), 16, 0.02f, 0)
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|| test_rmsnorm(RandomMat(24), 24, 0.001f, 0)
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|| test_rmsnorm(RandomMat(32), 32, 0.001f, 0)
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|| test_rmsnorm(RandomMat(2), 2, 0.01f, 1)
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|| test_rmsnorm(RandomMat(6), 6, 0.01f, 1)
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|| test_rmsnorm(RandomMat(8), 8, 0.002f, 1)
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|| test_rmsnorm(RandomMat(12), 12, 0.02f, 1)
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|| test_rmsnorm(RandomMat(16), 16, 0.02f, 1)
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|| test_rmsnorm(RandomMat(24), 24, 0.001f, 1)
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|| test_rmsnorm(RandomMat(32), 32, 0.001f, 1);
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}
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static int test_rmsnorm_4()
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{
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return 0
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|| test_rmsnorm(RandomMat(5, 3, 2, 8), 5, 0.01f, 0)
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|| test_rmsnorm(RandomMat(4, 3, 2, 5), 4, 0.01f, 1)
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|| test_rmsnorm(RandomMat(5, 3, 2, 8), 15, 0.002f, 0)
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|| test_rmsnorm(RandomMat(4, 3, 2, 5), 12, 0.002f, 1)
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|| test_rmsnorm(RandomMat(5, 3, 2, 8), 30, 0.001f, 0)
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|| test_rmsnorm(RandomMat(4, 3, 2, 5), 24, 0.001f, 1);
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}
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static int test_rmsnorm_5()
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{
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return 0
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|| test_rmsnorm(RandomMat(4095), 4095, 0.001f, 0)
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|| test_rmsnorm(RandomMat(4096), 4096, 0.001f, 0)
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|| test_rmsnorm(RandomMat(4097), 4097, 0.001f, 1)
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|| test_rmsnorm(RandomMat(16384), 16384, 0.001f, 1)
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|| test_rmsnorm(RandomMat(4095, 3), 4095, 0.001f, 1)
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|| test_rmsnorm(RandomMat(4096, 4), 4096, 0.001f, 0)
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|| test_rmsnorm(RandomMat(4097, 4), 4097, 0.001f, 1)
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|| test_rmsnorm(RandomMat(4096, 2, 1), 4096, 0.001f, 0)
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|| test_rmsnorm(RandomMat(4097, 2, 4), 4097, 0.001f, 1)
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|| test_rmsnorm(RandomMat(513, 3, 2, 4), 1539, 0.001f, 1)
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|| test_rmsnorm(RandomMat(64, 65536, 1), 64, 0.001f, 1)
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|| test_rmsnorm(RandomMat(64, 65537, 1), 64, 0.001f, 1)
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|| test_rmsnorm(RandomMat(16, 65537, 4), 16, 0.001f, 1);
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}
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#if NCNN_VULKAN
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static int test_rmsnorm_gpu_fp16a(const ncnn::Mat& a, bool use_subgroup_ops)
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{
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ncnn::ParamDict pd;
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pd.set(0, a.w);
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pd.set(1, 0.001f);
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pd.set(2, 0);
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std::vector<ncnn::Mat> weights(0);
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_vulkan_compute = true;
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opt.use_subgroup_ops = use_subgroup_ops;
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opt.use_fp16_packed = true;
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opt.use_fp16_storage = true;
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opt.use_fp16_arithmetic = true;
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opt.use_int8_storage = false;
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opt.use_int8_arithmetic = false;
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ncnn::VulkanDevice* vkdev = ncnn::get_gpu_device();
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if (!vkdev->info.support_subgroup_ops()) opt.use_subgroup_ops = false;
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ncnn::VkAllocator* blob_vkallocator = vkdev->acquire_blob_allocator();
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ncnn::VkAllocator* staging_vkallocator = vkdev->acquire_staging_allocator();
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opt.blob_vkallocator = blob_vkallocator;
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opt.workspace_vkallocator = blob_vkallocator;
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opt.staging_vkallocator = staging_vkallocator;
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ncnn::Layer* op = ncnn::create_layer_vulkan("RMSNorm");
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op->vkdev = vkdev;
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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 expected;
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test_layer_naive(ncnn::layer_to_index("RMSNorm"), pd, weights, a, expected, 0);
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ncnn::Mat out;
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int ret;
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{
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ncnn::VkCompute cmd(vkdev);
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ncnn::VkMat a_gpu;
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cmd.record_upload(a, a_gpu, opt);
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ret = op->forward_inplace(a_gpu, cmd, opt);
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if (ret == 0)
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{
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cmd.record_download(a_gpu, out, opt);
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ret = cmd.submit_and_wait();
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}
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}
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op->destroy_pipeline(opt);
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delete op;
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vkdev->reclaim_blob_allocator(blob_vkallocator);
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vkdev->reclaim_staging_allocator(staging_vkallocator);
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if (ret != 0 || CompareMat(expected, out, 0.001f) != 0)
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{
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fprintf(stderr, "test_rmsnorm_gpu_fp16a failed a.dims=%d a=(%d %d %d %d) subgroup=%d ret=%d\n", a.dims, a.w, a.h, a.d, a.c, use_subgroup_ops, ret);
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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_rmsnorm_gpu_fp16a()
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{
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if (ncnn::get_gpu_count() == 0)
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return 0;
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const ncnn::GpuInfo& info = ncnn::get_gpu_info();
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if (!info.support_fp16_storage() || !info.support_fp16_arithmetic())
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return 0;
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ncnn::Mat a(4096);
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a.fill(8.f);
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ncnn::Mat b(4096, 1, 4);
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b.fill(8.f);
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return test_rmsnorm_gpu_fp16a(a, false)
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|| test_rmsnorm_gpu_fp16a(a, true)
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|| test_rmsnorm_gpu_fp16a(b, false)
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|| test_rmsnorm_gpu_fp16a(b, true);
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}
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#endif // NCNN_VULKAN
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int main()
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{
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SRAND(7767517);
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return 0
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|| test_rmsnorm_0()
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|| test_rmsnorm_1()
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|| test_rmsnorm_2()
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|| test_rmsnorm_3()
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|| test_rmsnorm_4()
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|| test_rmsnorm_5()
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#if NCNN_VULKAN
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|| test_rmsnorm_gpu_fp16a()
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#endif // NCNN_VULKAN
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;
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}
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