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test x86 arm convolution oom (#5492)
* skip mips loongarch riscv oom test atm * test softmax oom
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| // Tencent is pleased to support the open source community by making ncnn available. | ||
| // | ||
| // Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. | ||
| // | ||
| // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except | ||
| // in compliance with the License. You may obtain a copy of the License at | ||
| // | ||
| // https://opensource.org/licenses/BSD-3-Clause | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software distributed | ||
| // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR | ||
| // CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
| // specific language governing permissions and limitations under the License. | ||
|
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| #include "testutil.h" | ||
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| static int test_convolution_oom(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias) | ||
| { | ||
| ncnn::Mat a = RandomMat(w, h, c); | ||
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| ncnn::ParamDict pd; | ||
| pd.set(0, outch); | ||
| pd.set(1, kernel); | ||
| pd.set(2, dilation); | ||
| pd.set(3, stride); | ||
| pd.set(4, pad); | ||
| pd.set(5, bias); | ||
| pd.set(6, outch * c * kernel * kernel); | ||
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| int activation_type = RAND() % 7; // 0 1 2 3 4 5 6 | ||
| ncnn::Mat activation_params(2); | ||
| activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha | ||
| activation_params[1] = RandomFloat(0, 1); // beta | ||
| pd.set(9, activation_type); | ||
| pd.set(10, activation_params); | ||
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| std::vector<ncnn::Mat> weights(bias ? 2 : 1); | ||
| weights[0] = RandomMat(outch * c * kernel * kernel); | ||
| if (bias) | ||
| weights[1] = RandomMat(outch); | ||
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| int ret = test_layer_oom("Convolution", pd, weights, a); | ||
| if (ret != 0) | ||
| { | ||
| fprintf(stderr, "test_convolution_oom failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]); | ||
| return ret; | ||
| } | ||
|
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| return ret; | ||
| } | ||
|
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| static int test_convolution_0() | ||
| { | ||
| return 0 | ||
| || test_convolution_oom(9, 7, 31, 63, 1, 1, 1, 0, 1) | ||
| || test_convolution_oom(9, 7, 31, 63, 3, 1, 1, 1, 1); | ||
| } | ||
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| #if NCNN_INT8 | ||
| static int test_convolution_oom_int8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, bool requant = false) | ||
| { | ||
| ncnn::Mat a = RandomMat(w, h, c); | ||
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| ncnn::ParamDict pd; | ||
| pd.set(0, outch); | ||
| pd.set(1, kernel); | ||
| pd.set(2, dilation); | ||
| pd.set(3, stride); | ||
| pd.set(4, pad); | ||
| pd.set(5, bias); | ||
| pd.set(6, outch * c * kernel * kernel); | ||
| pd.set(8, requant ? 101 : 1); // int8_scale_term | ||
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| int activation_type = RAND() % 7; // 0 1 2 3 4 5 6 | ||
| ncnn::Mat activation_params(2); | ||
| activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha | ||
| activation_params[1] = RandomFloat(0, 1); // beta | ||
| pd.set(9, activation_type); | ||
| pd.set(10, activation_params); | ||
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| std::vector<ncnn::Mat> weights(bias ? 5 : 4); | ||
| 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); | ||
| ncnn::Mat input_scales = scales_mat(a, 1, w * h * c, a.cstep); | ||
| ncnn::Mat top_scales = requant ? scales_mat(a, 1, w * h * c, a.cstep) : ncnn::Mat(); | ||
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| if (kernel == 3 && dilation == 1 && stride == 1) | ||
| { | ||
| // test for 6bit quant | ||
| for (int i = 0; i < weight_scales.w; i++) | ||
| weight_scales[i] = weight_scales[i] / 4.f; | ||
| } | ||
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| if (bias) | ||
| { | ||
| weights[1] = RandomMat(outch); | ||
| weights[2] = weight_scales; | ||
| weights[3] = input_scales; | ||
| weights[4] = top_scales; | ||
| } | ||
| else | ||
| { | ||
| weights[1] = weight_scales; | ||
| weights[2] = input_scales; | ||
| weights[3] = top_scales; | ||
| } | ||
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| int flag = TEST_LAYER_DISABLE_GPU_TESTING; | ||
| int ret = test_layer_oom("Convolution", pd, weights, a, flag); | ||
| if (ret != 0) | ||
| { | ||
| fprintf(stderr, "test_convolution_oom_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d requant=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, requant, activation_type, activation_params[0], activation_params[1]); | ||
| return ret; | ||
| } | ||
|
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| return ret; | ||
| } | ||
|
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| static int test_convolution_1() | ||
| { | ||
| return 0 | ||
| || test_convolution_oom_int8(9, 7, 31, 63, 1, 1, 1, 0, 1) | ||
| || test_convolution_oom_int8(9, 7, 31, 63, 3, 1, 1, 1, 1); | ||
| } | ||
|
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| static int test_convolution_2() | ||
| { | ||
| return 0 | ||
| || test_convolution_oom_int8(9, 7, 31, 63, 1, 1, 1, 0, 1, true) | ||
| || test_convolution_oom_int8(9, 7, 31, 63, 3, 1, 1, 1, 1, true); | ||
| } | ||
| #endif // NCNN_INT8 | ||
|
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| int main() | ||
| { | ||
| SRAND(7767517); | ||
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| #if __mips__ || __loongarch64 || __riscv | ||
| // TODO | ||
| return 0; | ||
| #endif | ||
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| #if NCNN_INT8 | ||
| return test_convolution_0() || test_convolution_1() || test_convolution_2(); | ||
| #else | ||
| return test_convolution_0(); | ||
| #endif | ||
| } |
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