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nihui
41f031e045
* adjust tests * opt shader coverage op * update gpu_coverage.cpp atomicor coalesce
395 lines
12 KiB
C++
395 lines
12 KiB
C++
// Copyright 2019 Tencent
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// SPDX-License-Identifier: BSD-3-Clause
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#include "testutil.h"
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#if NCNN_INT8
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static int test_convolution_int8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, bool requant = false, int int8_scale_term = 0, bool sgemm = false, bool input_int8 = false, bool test_winograd43 = false)
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{
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ncnn::Mat a = RandomMat(w, h, c);
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if (int8_scale_term == 0)
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int8_scale_term = requant ? 101 : 1;
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const bool use_requant = int8_scale_term > 100;
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ncnn::ParamDict pd;
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pd.set(0, outch);
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pd.set(1, kernel);
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pd.set(2, dilation);
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pd.set(3, stride);
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pd.set(4, pad);
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pd.set(5, bias);
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pd.set(6, outch * c * kernel * kernel);
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pd.set(8, int8_scale_term); // int8_scale_term
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int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
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ncnn::Mat activation_params(2);
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activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
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activation_params[1] = RandomFloat(0, 1); // beta
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pd.set(9, activation_type);
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pd.set(10, activation_params);
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std::vector<ncnn::Mat> weights(bias ? 5 : 4);
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weights[0] = RandomMat(outch * c * kernel * kernel);
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ncnn::Mat weight_scales = scales_mat(weights[0], outch, c * kernel * kernel, c * kernel * kernel);
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ncnn::Mat input_scales = scales_mat(a, 1, w * h * c, a.cstep);
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ncnn::Mat top_scales = use_requant ? scales_mat(a, 1, w * h * c, a.cstep) : ncnn::Mat();
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ncnn::Mat a_int8 = a;
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if (input_int8)
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = false;
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ncnn::quantize_to_int8(a, a_int8, input_scales, opt);
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}
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if (kernel == 3 && dilation == 1 && stride == 1)
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{
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// test for 6bit quant
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for (int i = 0; i < weight_scales.w; i++)
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weight_scales[i] = weight_scales[i] / 4.f;
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}
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if (bias)
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{
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weights[1] = RandomMat(outch);
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weights[2] = weight_scales;
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weights[3] = input_scales;
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weights[4] = top_scales;
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}
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else
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{
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weights[1] = weight_scales;
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weights[2] = input_scales;
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weights[3] = top_scales;
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}
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int flag = input_int8 ? TEST_LAYER_DISABLE_AUTO_INPUT_CASTING : 0;
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int ret = 0;
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if (input_int8)
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = true;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_bf16_packed = false;
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opt.use_bf16_storage = false;
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opt.use_sgemm_convolution = sgemm;
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opt.use_winograd_convolution = false;
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ret = test_layer_opt("Convolution", pd, weights, opt, a_int8, use_requant ? 1.0f : 0.001f, flag);
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}
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else
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{
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ret = test_layer("Convolution", pd, weights, a_int8, use_requant ? 1.0f : 0.001f, flag);
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}
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d int8_scale_term=%d sgemm=%d input_int8=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, int8_scale_term, sgemm, input_int8, activation_type, activation_params[0], activation_params[1]);
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return ret;
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}
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if (input_int8)
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return ret;
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if (kernel == 3 && dilation == 1 && stride == 1)
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = true;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_bf16_packed = false;
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opt.use_bf16_storage = false;
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opt.use_sgemm_convolution = false;
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opt.use_winograd_convolution = true;
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for (int i = 0; i < (test_winograd43 ? 2 : 1); i++)
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{
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opt.use_winograd23_convolution = i == 0;
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opt.use_winograd43_convolution = i == 1;
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ret = test_layer_opt("Convolution", pd, weights, opt, a, use_requant ? 1.0f : 0.001f, flag);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d int8_scale_term=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, int8_scale_term, activation_type, activation_params[0], activation_params[1]);
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return ret;
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}
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}
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}
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = false;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_bf16_packed = false;
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opt.use_bf16_storage = false;
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opt.use_sgemm_convolution = false;
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opt.use_winograd_convolution = false;
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ret = test_layer_opt("Convolution", pd, weights, opt, a, use_requant ? 1.0f : 0.001f, flag);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d int8_scale_term=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, int8_scale_term, activation_type, activation_params[0], activation_params[1]);
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return ret;
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}
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}
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = true;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_bf16_packed = false;
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opt.use_bf16_storage = false;
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opt.use_sgemm_convolution = false;
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opt.use_winograd_convolution = false;
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ret = test_layer_opt("Convolution", pd, weights, opt, a, use_requant ? 1.0f : 0.001f, flag);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d int8_scale_term=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, int8_scale_term, activation_type, activation_params[0], activation_params[1]);
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return ret;
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}
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}
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = true;
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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_bf16_packed = true;
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opt.use_bf16_storage = true;
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opt.use_sgemm_convolution = false;
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opt.use_winograd_convolution = false;
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ret = test_layer_opt("Convolution", pd, weights, opt, a, use_requant ? 1.0f : 0.001f, flag);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d int8_scale_term=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, int8_scale_term, activation_type, activation_params[0], activation_params[1]);
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return ret;
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}
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}
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if (sgemm)
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = true;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_bf16_packed = false;
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opt.use_bf16_storage = false;
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opt.use_sgemm_convolution = true;
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opt.use_winograd_convolution = false;
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ret = test_layer_opt("Convolution", pd, weights, opt, a, use_requant ? 1.0f : 0.001f, flag);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d int8_scale_term=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, int8_scale_term, activation_type, activation_params[0], activation_params[1]);
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return ret;
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}
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}
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return ret;
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}
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static int test_convolution_1()
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{
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// exercise each packing direction on the pointwise and 3x3 paths
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static const int kdsp_packing[2][4] = {
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{1, 1, 2, 0},
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{3, 1, 2, 1},
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};
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static const int channels_packing[10][2] = {
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{1, 1},
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{2, 2},
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{3, 3},
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{4, 4},
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{7, 7},
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{8, 8},
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{15, 15},
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{16, 15},
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{15, 16},
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{16, 16},
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};
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for (int q = 0; q < 2; q++)
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{
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for (int i = 0; i < 2; i++)
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{
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const int k = kdsp_packing[i][0];
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const int d = kdsp_packing[i][1];
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const int s = kdsp_packing[i][2];
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const int p = kdsp_packing[i][3];
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for (int j = 0; j < 10; j++)
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{
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const int c = channels_packing[j][0];
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const int outch = channels_packing[j][1];
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if (test_convolution_int8(9, 7, c, outch, k, d, s, p, 1, q != 0) != 0)
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return -1;
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}
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}
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}
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// vary kernel geometry with packed, unpacked and mixed channel directions
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static const int kdsp_geometry[4][4] = {
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{2, 1, 2, -233},
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{4, 1, 2, -233},
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{5, 1, 2, 2},
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{5, 2, 2, 2},
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};
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static const int channels_geometry[6][2] = {
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{1, 1},
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{4, 4},
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{8, 8},
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{16, 15},
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{15, 16},
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{16, 16},
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};
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for (int q = 0; q < 2; q++)
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{
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for (int i = 0; i < 4; i++)
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{
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const int k = kdsp_geometry[i][0];
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const int d = kdsp_geometry[i][1];
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const int s = kdsp_geometry[i][2];
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const int p = kdsp_geometry[i][3];
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for (int j = 0; j < 6; j++)
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{
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const int c = channels_geometry[j][0];
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const int outch = channels_geometry[j][1];
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if (test_convolution_int8(9, 7, c, outch, k, d, s, p, 1, q != 0) != 0)
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return -1;
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}
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}
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}
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// vary large kernels separately from channel packing transitions
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static const int kdsp_7x7[1][4] = {
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{7, 1, 2, 3},
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};
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static const int channels_7x7[2][2] = {
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{1, 1},
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{8, 8},
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};
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for (int q = 0; q < 2; q++)
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{
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for (int i = 0; i < 1; i++)
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{
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const int k = kdsp_7x7[i][0];
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const int d = kdsp_7x7[i][1];
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const int s = kdsp_7x7[i][2];
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const int p = kdsp_7x7[i][3];
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for (int j = 0; j < 2; j++)
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{
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const int c = channels_7x7[j][0];
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const int outch = channels_7x7[j][1];
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if (test_convolution_int8(9, 7, c, outch, k, d, s, p, 1, q != 0) != 0)
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return -1;
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}
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}
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}
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return 0
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|| test_convolution_int8(6, 7, 64, 64, 3, 1, 2, 0, 1)
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|| test_convolution_int8(5, 7, 32, 8, 5, 1, 2, 0, 1)
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|| test_convolution_int8(16, 10, 31, 32, 2, 1, 3, 0, 0)
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|| test_convolution_int8(33, 5, 15, 5, 2, 1, 3, 0, 1)
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|| test_convolution_int8(3, 63, 2, 28, 2, 1, 2, 0, 0)
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|| test_convolution_int8(9, 7, 8, 12, 2, 1, 2, 1, 1, false, 1, true)
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|| test_convolution_int8(9, 7, 8, 12, 2, 1, 2, 1, 1, true, 101, true);
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}
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static int test_convolution_1_2()
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{
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static const int channels_3x3[8][2] = {
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{1, 1},
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{2, 0},
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{7, 1},
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{8, 0},
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{15, 1},
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{16, 0},
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{31, 1},
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{32, 0},
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};
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for (int i = 0; i < 8; i++)
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{
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const int c = channels_3x3[i][0];
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const int bias = channels_3x3[i][1];
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if (test_convolution_int8(19, 17, c, 1, 3, 2, 2, 0, bias) != 0)
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return -1;
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}
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// sweep input and output tails with both dilation and stride
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static const int channels_5x5[19][3] = {
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{1, 32, 1},
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{2, 32, 0},
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{7, 32, 1},
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{8, 8, 0},
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{15, 32, 1},
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{16, 32, 0},
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{31, 32, 1},
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{32, 32, 0},
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{32, 2, 0},
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{32, 7, 0},
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{32, 15, 0},
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{32, 16, 0},
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{32, 31, 0},
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{7, 7, 1},
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{1, 2, 1},
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{1, 7, 1},
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{1, 15, 1},
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{8, 2, 0},
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{8, 31, 0},
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};
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for (int s = 2; s <= 3; s++)
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{
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for (int i = 0; i < 19; i++)
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{
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const int c = channels_5x5[i][0];
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const int outch = channels_5x5[i][1];
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const int bias = channels_5x5[i][2];
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if (test_convolution_int8(19, 17, c, outch, 5, 2, s, 0, bias) != 0)
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return -1;
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}
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}
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return 0;
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}
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#endif // NCNN_INT8
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int main()
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{
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SRAND(7767517);
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#if NCNN_INT8
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return 0
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|| test_convolution_1()
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|| test_convolution_1_2();
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#endif
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return 0;
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}
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