From 39c865b3aa8587d426e726380e5e6f70ac4b6f6d Mon Sep 17 00:00:00 2001
From: Xusong Wang <xusongw@google.com>
Date: Tue, 28 Aug 2018 16:50:01 -0700
Subject: [PATCH] Fix logics for floating-point comparision in VTS test.

Set the acceptable error range based on both absolute tolerance and
relative tolerance.

Currently, absolute tolerance is set to 1e-5 for FP32 and 5 epsilon
(~5e-3) for FP16 relaxed computation. The relative tolerance is set to
5ULP of the corresponding precision. Add a TODO mark for potential
future adjustment on error limit based on testing.

Bug: 111768023

Test: none
Change-Id: Idedcec3e09fd7de9696811b93c81d0f180e896ef
---
 .../vts/functional/GeneratedTestHarness.cpp   | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
index 0682ab95b2..64495cf763 100644
--- a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -66,8 +66,8 @@ void copy_back(MixedTyped* dst, const std::vector<RequestArgument>& ra, char* sr
 // Top level driver for models and examples generated by test_generator.py
 // Test driver for those generated from ml/nn/runtime/test/spec
 void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored,
-                           const std::vector<MixedTypedExampleType>& examples,
-                           float fpRange = 1e-5f) {
+                           const std::vector<MixedTypedExampleType>& examples, float fpAtol = 1e-5f,
+                           float fpRtol = 1e-5f) {
     const uint32_t INPUT = 0;
     const uint32_t OUTPUT = 1;
 
@@ -175,7 +175,7 @@ void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool
         MixedTyped filtered_test = filter(test, is_ignored);
 
         // We want "close-enough" results for float
-        compare(filtered_golden, filtered_test, fpRange);
+        compare(filtered_golden, filtered_test, fpAtol, fpRtol);
     }
 }
 
@@ -220,7 +220,8 @@ void Execute(const sp<V1_0::IDevice>& device, std::function<V1_0::Model(void)> c
     EXPECT_EQ(ErrorStatus::NONE, prepareReturnStatus);
     ASSERT_NE(nullptr, preparedModel.get());
 
-    EvaluatePreparedModel(preparedModel, is_ignored, examples);
+    float fpAtol = 1e-5f, fpRtol = 5.0f * 1.1920928955078125e-7f;
+    EvaluatePreparedModel(preparedModel, is_ignored, examples, fpAtol, fpRtol);
 }
 
 void Execute(const sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> create_model,
@@ -265,9 +266,13 @@ void Execute(const sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> c
     EXPECT_EQ(ErrorStatus::NONE, prepareReturnStatus);
     ASSERT_NE(nullptr, preparedModel.get());
 
-    // If in relaxed mode, set the error range to be 5ULP of FP16.
-    float fpRange = !model.relaxComputationFloat32toFloat16 ? 1e-5f : 5.0f * 0.0009765625f;
-    EvaluatePreparedModel(preparedModel, is_ignored, examples, fpRange);
+    // TODO: Adjust the error limit based on testing.
+    // If in relaxed mode, set the absolute tolerance to be 5ULP of FP16.
+    float fpAtol = !model.relaxComputationFloat32toFloat16 ? 1e-5f : 5.0f * 0.0009765625f;
+    // Set the relative tolerance to be 5ULP of the corresponding FP precision.
+    float fpRtol = !model.relaxComputationFloat32toFloat16 ? 5.0f * 1.1920928955078125e-7f
+                                                           : 5.0f * 0.0009765625f;
+    EvaluatePreparedModel(preparedModel, is_ignored, examples, fpAtol, fpRtol);
 }
 
 }  // namespace generated_tests