From 562628eb714a66aec2e97718426c2495ed187af7 Mon Sep 17 00:00:00 2001 From: Varun Jaiswal <96684656+varun-jaiswal17@users.noreply.github.com> Date: Wed, 22 Apr 2026 12:30:51 +0530 Subject: [PATCH] Merge pull request #28836 from varun-jaiswal17:fix-dnn-nan-bugs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fix dnn NaN bugs in GELU SIMD and softmax for large inputs #28836 ### Bug Description - **GELU SIMD bug:** `exp(2*inner)` overflows to `inf` for large inputs (e.g. `x=10.6` → `inner≈51`), causing `inf/inf = NaN`; fixed by clamping `inner` to [-9, 9] as `tanh` already saturates to ±1.0 beyond this range. - **Softmax bug:** All `-inf` inputs (masked attention rows) produce `sum=0`, then `1/0 = inf` and `0*inf = NaN`; fixed by outputting zeros when `sum == 0`. - Add regression tests for both fixes Depends on : https://github.com/opencv/opencv/pull/28837 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake --- .../cpu_kernels/activation_kernels.simd.hpp | 3 + .../dnn/src/layers/cpu_kernels/softmax.cpp | 9 ++- modules/dnn/test/test_layers.cpp | 56 +++++++++++++++++++ 3 files changed, 66 insertions(+), 2 deletions(-) diff --git a/modules/dnn/src/layers/cpu_kernels/activation_kernels.simd.hpp b/modules/dnn/src/layers/cpu_kernels/activation_kernels.simd.hpp index 161c1e0479..a28e57c441 100644 --- a/modules/dnn/src/layers/cpu_kernels/activation_kernels.simd.hpp +++ b/modules/dnn/src/layers/cpu_kernels/activation_kernels.simd.hpp @@ -232,10 +232,13 @@ static void activationGELUApprox(const void* input, void* output, v_float32 half = vx_setall_f32(0.5f), one = vx_setall_f32(1.f); v_float32 v_s2pi = vx_setall_f32(sqrt2_pi), v_coeff = vx_setall_f32(coeff); v_float32 two = vx_setall_f32(2.f); + // Clamp to [-9, 9] to prevent overflow in exp(2*inner); tanh saturates here anyway + v_float32 clamp_hi = vx_setall_f32(9.f), clamp_lo = vx_setall_f32(-9.f); for (; i + vlanes <= len; i += vlanes) { v_float32 x = vx_load(inp + i); // inner = sqrt(2/pi) * x + coeff * x^3 = x * (sqrt(2/pi) + coeff * x^2) v_float32 inner = v_mul(x, v_add(v_s2pi, v_mul(v_coeff, v_mul(x, x)))); + inner = v_min(v_max(inner, clamp_lo), clamp_hi); // tanh via exp: (exp(2*inner)-1)/(exp(2*inner)+1) v_float32 e2 = v_exp(v_mul(two, inner)); v_float32 t = v_div(v_sub(e2, one), v_add(e2, one)); diff --git a/modules/dnn/src/layers/cpu_kernels/softmax.cpp b/modules/dnn/src/layers/cpu_kernels/softmax.cpp index aa5f683be6..e07be85e51 100644 --- a/modules/dnn/src/layers/cpu_kernels/softmax.cpp +++ b/modules/dnn/src/layers/cpu_kernels/softmax.cpp @@ -11,6 +11,7 @@ #include "../../precomp.hpp" #include "softmax.hpp" +#include "opencv2/core/fast_math.hpp" namespace cv { namespace dnn { @@ -101,10 +102,13 @@ void softmax(Mat &dst, const Mat &src, int axis, int axisBias, int axisStep){ s += axisBuf[cnDim]; } - s = 1.f / s; - // copy back the result to src _cnDim = 0; + if (s == 0.f || cvIsInf(1.f / s)) { + for (; _cnDim < axisStep; _cnDim++) + dstPtr[srcOffset + (_cnDim + axisBias) * cnStep] = 0.f; + } else { + s = 1.f / s; #if CV_ENABLE_UNROLLED && defined(_M_ARM64) for (; _cnDim + 3 < axisStep; _cnDim += 4) { dstPtr[srcOffset + (_cnDim + 0 + axisBias) * cnStep] = axisBuf[_cnDim + 0] * s; @@ -115,6 +119,7 @@ void softmax(Mat &dst, const Mat &src, int axis, int axisBias, int axisStep){ #endif for (; _cnDim < axisStep; _cnDim++) dstPtr[srcOffset + (_cnDim + axisBias) * cnStep] = axisBuf[_cnDim] * s; + } } }, nstripes); } diff --git a/modules/dnn/test/test_layers.cpp b/modules/dnn/test/test_layers.cpp index fba5f4ad78..5be5b4eee9 100644 --- a/modules/dnn/test/test_layers.cpp +++ b/modules/dnn/test/test_layers.cpp @@ -41,6 +41,7 @@ #include "test_precomp.hpp" #include +#include #include "npy_blob.hpp" #include #include @@ -2959,4 +2960,59 @@ TEST(ConvolutionWinograd, Accuracy) normAssert(outLarge, refLarge, "Large input after small", 0.0, 0.0); } +TEST(Layer_Test_GeluApprox, NoNaN_LargeInput) +{ + LayerParams lp; + lp.type = "GeluApproximation"; + lp.name = "test_gelu_approx"; + Ptr layer = LayerFactory::createLayerInstance("GeluApproximation", lp); + ASSERT_TRUE(layer != nullptr); + + float data[] = {-15.f, -10.f, -7.4f, -1.f, 0.f, 1.f, 5.f, 10.6f, 15.f, 20.f}; + int dims[] = {1, 1, 10}; + Mat inp(3, dims, CV_32F, data); + std::vector inpVec = {inp}; + std::vector outVec; + + runLayer(layer, inpVec, outVec); + ASSERT_EQ(outVec.size(), (size_t)1); + + Mat& out = outVec[0]; + for (int i = 0; i < 10; i++) { + float val = out.ptr()[i]; + EXPECT_FALSE(cvIsNaN(val)) << "NaN at index " << i << " (input=" << data[i] << ")"; + EXPECT_FALSE(cvIsInf(val)) << "Inf at index " << i << " (input=" << data[i] << ")"; + } + + EXPECT_NEAR(out.ptr()[9], 20.f, 0.01f); + EXPECT_NEAR(out.ptr()[0], 0.f, 1e-6f); + EXPECT_NEAR(out.ptr()[4], 0.f, 1e-6f); +} + +TEST(Layer_Test_Softmax, NoNaN_AllNegInf) +{ + LayerParams lp; + lp.type = "Softmax"; + lp.name = "test_softmax"; + lp.set("axis", 1); + Ptr layer = LayerFactory::createLayerInstance("Softmax", lp); + ASSERT_TRUE(layer != nullptr); + + int dims[] = {1, 8}; + Mat inp(2, dims, CV_32F, Scalar(-std::numeric_limits::infinity())); + std::vector inpVec = {inp}; + std::vector outVec; + + runLayer(layer, inpVec, outVec); + ASSERT_EQ(outVec.size(), (size_t)1); + + Mat& out = outVec[0]; + for (int i = 0; i < 8; i++) { + float val = out.ptr()[i]; + EXPECT_FALSE(cvIsNaN(val)) << "NaN at index " << i; + EXPECT_FALSE(cvIsInf(val)) << "Inf at index " << i; + EXPECT_EQ(val, 0.f) << "Expected 0 at index " << i; + } +} + }} // namespace