Use relative bias and MSE on fp values.

Fixes: 134099258
Test: NeuralNetworksTest_static_fuzzing
Change-Id: I2350d3b117836f8d6de9a3e816edbad61588107c

2 files changed, 10 insertions, 4 deletions

diff --git a/nn/runtime/test/fuzzing/RandomGraphGenerator.cpp b/nn/runtime/test/fuzzing/RandomGraphGenerator.cpp
index 18295e49f..2ad183b48 100644
--- a/nn/runtime/test/fuzzing/RandomGraphGenerator.cpp
+++ b/nn/runtime/test/fuzzing/RandomGraphGenerator.cpp
@@ -277,6 +277,7 @@ constexpr uint32_t kMaxNumberOfPrintedErrors = 5;
 template <typename T>
 void expectNear(const RandomOperand& op, const OperandBuffer& test,
                 const AccuracyCriterion& criterion) {
+    constexpr uint32_t kMinNumberOfElementsToTestBiasMSE = 10;
     const T* actualBuffer = reinterpret_cast<const T*>(test.data());
     const T* expectedBuffer = reinterpret_cast<const T*>(op.buffer.data());
     uint32_t len = op.getNumberOfElements();
@@ -297,8 +298,11 @@ void expectNear(const RandomOperand& op, const OperandBuffer& test,
             continue;
         }
 
-        // Accumulate bias and MSE.
+        // Accumulate bias and MSE. Use relative bias and MSE for floating point values.
         double diff = actual - expected;
+        if constexpr (NN_IS_FLOAT(T)) {
+            diff /= std::max(1.0, std::abs(expected));
+        }
         bias += diff;
         mse += diff * diff;
 
@@ -309,7 +313,7 @@ void expectNear(const RandomOperand& op, const OperandBuffer& test,
     EXPECT_EQ(numErrors, 0u);
 
     // Test bias and MSE.
-    if (len == numSkip) return;
+    if (len < numSkip + kMinNumberOfElementsToTestBiasMSE) return;
     bias /= static_cast<double>(len - numSkip);
     mse /= static_cast<double>(len - numSkip);
     EXPECT_LE(std::fabs(bias), criterion.bias);
diff --git a/nn/runtime/test/fuzzing/RandomGraphGenerator.h b/nn/runtime/test/fuzzing/RandomGraphGenerator.h
index 5599f0810..a0c41968b 100644
--- a/nn/runtime/test/fuzzing/RandomGraphGenerator.h
+++ b/nn/runtime/test/fuzzing/RandomGraphGenerator.h
@@ -103,11 +103,13 @@ struct RandomOperation {
 // TODO: Consider relative bias and mse on floating point data types?
 struct AccuracyCriterion {
     // We expect the driver results to be unbiased.
-    // Formula: abs(sum_{i}(actual - expected)) <= bias
+    // Formula: abs(sum_{i}(diff)) <= bias, where
+    // * fixed point: diff = actual - expected
+    // * floating point: diff = (actual - expected) / max(1, abs(expected))
     float bias = std::numeric_limits<float>::max();
 
     // Set the threshold on Mean Square Error (MSE).
-    // Formula: sum_{i}((actual - expected) ^ 2) / sum(1) <= mse
+    // Formula: sum_{i}(diff ^ 2) / sum(1) <= mse
     float mse = std::numeric_limits<float>::max();
 
     // We also set accuracy thresholds on each element to detect any particular edge cases that may