diff options
Diffstat (limited to 'reference_model/test/verify_tests.cpp')
| -rw-r--r-- | reference_model/test/verify_tests.cpp | 58 |
1 files changed, 29 insertions, 29 deletions
diff --git a/reference_model/test/verify_tests.cpp b/reference_model/test/verify_tests.cpp index f92792a..ba18af1 100644 --- a/reference_model/test/verify_tests.cpp +++ b/reference_model/test/verify_tests.cpp @@ -1,4 +1,4 @@ -// Copyright (c) 2023, ARM Limited. +// Copyright (c) 2023-2024, ARM Limited. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -136,13 +136,13 @@ constexpr auto reduceProductError(uint64_t M, uint64_t N) template <typename FP> auto reduceProductTolerance(uint64_t M, uint64_t N, const std::vector<FP>& results) { - const auto error = reduceProductError(M, N); - auto tolerances = std::vector<FP>(results.size()); + const auto error = reduceProductError(M, N); + auto tolerances_fp64 = std::vector<FP>(results.size()); for (unsigned i = 0, end = results.size(); i < end; ++i) { - tolerances[i] = std::abs(results[i]) * error; + tolerances_fp64[i] = std::abs(results[i]) * error; } - return tolerances; + return tolerances_fp64; } } // namespace @@ -299,8 +299,7 @@ TEST_CASE("positive - reduce product") "mode": "REDUCE_PRODUCT", "data_type": "FP32", "reduce_product_info": { - "m": 23, - "n": 8 + "n": 8 } } } @@ -313,48 +312,49 @@ TEST_CASE("positive - reduce product") // Generate some random floats using the full range of fp32. This will be the "result" of our // dot product. Here we "reduced" over the z-axis of our shape. - auto data = generateRandomTensorData<float>(elementCount / reductionSize, false); - // Calculate the tolerances for each element in the result. + auto data_fp32 = generateRandomTensorData<float>(elementCount / reductionSize, false); + std::vector<double> data_fp64(data_fp32.begin(), data_fp32.end()); + // Calculate the tolerances_fp64 for each element in the result. // A float has 23 bit dedicated to the fraction. constexpr uint64_t mantisa_count = 23; - const auto tolerances = reduceProductTolerance(mantisa_count, reductionSize, data); + const auto tolerances_fp64 = reduceProductTolerance(mantisa_count, reductionSize, data_fp64); SUBCASE("same") { - // TODO: Generate some new floats that are as far away as possible from each result without + // Generate some new floats that are as far away as possible from each result without // exceeding the tolerance. - auto otherData = std::vector<float>(elementCount / reductionSize); - for (unsigned i = 0; i < data.size(); ++i) + auto otherData_fp32 = std::vector<float>(elementCount / reductionSize); + for (unsigned i = 0; i < data_fp32.size(); ++i) { - auto newValue = data[i]; - auto oldValue = newValue; - const auto target = tolerances[i] + newValue; + auto newValue = data_fp32[i]; + const double target = tolerances_fp64[i] + newValue; // Here we just increment the value until we exceed the tolerance. For simplicity we go up. + auto previousValue = newValue; while (newValue < target) { - oldValue = newValue; - newValue = std::nextafter(newValue, std::numeric_limits<float>::infinity()); + previousValue = newValue; + newValue = std::nextafter(newValue, std::numeric_limits<float>::infinity()); } - otherData[i] = oldValue; + otherData_fp32[i] = previousValue; } const auto referenceTensor = - TosaTensor("out1", tosa_datatype_fp64_t, outputShape, reinterpret_cast<uint8_t*>(data.data())); + TosaTensor("out1", tosa_datatype_fp64_t, outputShape, reinterpret_cast<uint8_t*>(data_fp64.data())); const auto implementationTensor = - TosaTensor("out1", tosa_datatype_fp32_t, outputShape, reinterpret_cast<uint8_t*>(otherData.data())); + TosaTensor("out1", tosa_datatype_fp32_t, outputShape, reinterpret_cast<uint8_t*>(otherData_fp32.data())); REQUIRE(tvf_verify_data(referenceTensor.cTensor(), nullptr, implementationTensor.cTensor(), jsonCfg.c_str())); } SUBCASE("different") { - // TODO: Generate some new floats that exceed the tolerance. - auto otherData = std::vector<float>(elementCount / reductionSize); - for (unsigned i = 0; i < data.size(); ++i) + // Generate some new floats that exceed the tolerance. + auto otherData_fp32 = std::vector<float>(elementCount / reductionSize); + for (unsigned i = 0; i < data_fp32.size(); ++i) { - auto newValue = data[i]; - const auto target = tolerances[i] + newValue; + auto newValue = data_fp32[i]; + const double target = tolerances_fp64[i] + newValue; // Here we just increment the value until we exceed the tolerance. For simplicity we go up. while (newValue < target) @@ -362,13 +362,13 @@ TEST_CASE("positive - reduce product") newValue = std::nextafter(newValue, std::numeric_limits<float>::infinity()); } - otherData[i] = newValue; + otherData_fp32[i] = newValue; } const auto referenceTensor = - TosaTensor("out1", tosa_datatype_fp64_t, outputShape, reinterpret_cast<uint8_t*>(data.data())); + TosaTensor("out1", tosa_datatype_fp64_t, outputShape, reinterpret_cast<uint8_t*>(data_fp64.data())); const auto implementationTensor = - TosaTensor("out1", tosa_datatype_fp32_t, outputShape, reinterpret_cast<uint8_t*>(otherData.data())); + TosaTensor("out1", tosa_datatype_fp32_t, outputShape, reinterpret_cast<uint8_t*>(otherData_fp32.data())); REQUIRE_FALSE( tvf_verify_data(referenceTensor.cTensor(), nullptr, implementationTensor.cTensor(), jsonCfg.c_str())); } |