tests/stats_tests.cpp - master - vuron/adept

tests/stats_tests.cpp
70 строк3 KB
kolkir
Refactor simd algos to use map reduce
16 мар 2025, 23:09
16 мар 2025, 23:095f536e1
#include <adept/tensor.hpp>
#include <vector>

#include "adept/types.hpp"
#include "catch.hpp"

using namespace adept;

TEMPLATE_TEST_CASE("Tensors max less lane", "[stats]", float32_t, float64_t, int32_t) {
  std::vector<TestType> dx = {1, 2, 3, 4};
  auto x = Tensor::from_blob(
      dx.data(), {.shape = {1, 4}, .device = device_t::CPU, .dtype = to_dtype<TestType>()});

  Tensor s = x.max();
  REQUIRE_THAT(s.at<TestType>({0, 0}), Catch::WithinRel(4.0f, 0.001f));
}

TEMPLATE_TEST_CASE("Tensors max", "[stats]", float32_t, float64_t, int32_t) {
  std::vector<TestType> dx = {1, 2, 3, 4, 5, 6, 7, 8, 9};
  auto x = Tensor::from_blob(
      dx.data(), {.shape = {3, 3}, .device = device_t::CPU, .dtype = to_dtype<TestType>()});

  Tensor s = x.max();
  REQUIRE_THAT(s.at<TestType>({0, 0}), Catch::WithinRel(9.0f, 0.001f));
}

TEMPLATE_TEST_CASE("Tensors sum less lane", "[stats]", float32_t, float64_t, int32_t) {
  std::vector<TestType> dx = {1, 2, 3, 4};
  auto x = Tensor::from_blob(
      dx.data(), {.shape = {1, 4}, .device = device_t::CPU, .dtype = to_dtype<TestType>()});

  Tensor s = x.sum();
  REQUIRE_THAT(s.at<TestType>({0, 0}), Catch::WithinRel(10.0f, 0.001f));
}

TEMPLATE_TEST_CASE("Tensors sum", "[stats]", float32_t, float64_t, int32_t) {
  std::vector<TestType> dx = {1, 2, 3, 4, 5, 6, 7, 8, 9};
  auto x = Tensor::from_blob(
      dx.data(), {.shape = {3, 3}, .device = device_t::CPU, .dtype = to_dtype<TestType>()});

  Tensor s = x.sum();
  REQUIRE_THAT(s.at<TestType>({0, 0}), Catch::WithinRel(45.0f, 0.001f));
}

TEMPLATE_TEST_CASE("Tensors mean", "[stats]", float32_t, float64_t, int32_t) {
  std::vector<TestType> dx = {1, 2, 3, 4, 5, 6, 7, 8, 9};
  auto x = Tensor::from_blob(
      dx.data(), {.shape = {3, 3}, .device = device_t::CPU, .dtype = to_dtype<TestType>()});

  Tensor s = x.mean();
  REQUIRE_THAT(s.at<TestType>({0, 0}), Catch::WithinRel(5.0f, 0.001f));
}

TEST_CASE("Tensors sum with zero", "[stats]") {
  std::vector<float32_t> dx = {0.f, .2f, .3f, 0.4f, 0.1f};
  auto x = Tensor::from_blob(dx.data(),
                             {.shape = {1, 5}, .device = device_t::CPU, .dtype = dtype_t::Float32});

  Tensor s = x.sum();
  REQUIRE_THAT(s.at<float32_t>({0, 0}), Catch::WithinRel(1.0f, 0.001f));
}

TEST_CASE("Tensors mean with zero", "[stats]") {
  std::vector<float32_t> dx = {0.f, .2f, .3f, 0.4f, 0.1f};
  auto x = Tensor::from_blob(dx.data(),
                             {.shape = {1, 5}, .device = device_t::CPU, .dtype = dtype_t::Float32});

  Tensor s = x.mean();
  REQUIRE_THAT(s.at<float32_t>({0, 0}), Catch::WithinRel(0.2f, 0.001f));
}