tests/linear_tests.cpp - master - vuron/adept

tests/linear_tests.cpp
83 строки4 KB
kolkir
Add tests for Linear layer and update matmul usage
28 мар 2025, 23:53
28 мар 2025, 23:53bc788d8
#include <adept/nn/linear.hpp>

#include "catch.hpp"

using namespace adept;

TEST_CASE("Linear construction", "[linear]") {
  Linear conv(32, 16);
}

TEST_CASE("Linear 4x2", "[linear]") {
  std::vector<float32_t> in_data = {1, 2, 3, 4};
  std::vector<float32_t> weights_data = {7, 8, 1, 3, 7, 8, 1, 3};
  std::vector<float32_t> bias_data = {3, 2};

  auto in = Tensor::from_blob(
      in_data.data(), {.shape = {1, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto weights =
      Tensor::from_blob(weights_data.data(),
                        {.shape = {2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto bias = Tensor::from_blob(
      bias_data.data(), {.shape = {2}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});

  Linear fc(4, 2);
  fc->set_weights(weights);
  fc->set_bias(bias);
  auto res = fc(in);

  REQUIRE_THAT(res.data().at<float32_t>({0, 0}), Catch::WithinRel(41.f, 0.001f));
  REQUIRE_THAT(res.data().at<float32_t>({0, 1}), Catch::WithinRel(40.f, 0.001f));

  mean(res).backward();

  REQUIRE_THAT(fc->weights().grad().at<float32_t>({0, 0}), Catch::WithinRel(0.5f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({0, 1}), Catch::WithinRel(1.f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({0, 2}), Catch::WithinRel(1.5f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({0, 3}), Catch::WithinRel(2.f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({1, 0}), Catch::WithinRel(0.5f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({1, 1}), Catch::WithinRel(1.f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({1, 2}), Catch::WithinRel(1.5f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({1, 3}), Catch::WithinRel(2.f, 0.001f));

  REQUIRE_THAT(fc->bias().grad().at<float32_t>({0}), Catch::WithinRel(0.5f, 0.001f));
  REQUIRE_THAT(fc->bias().grad().at<float32_t>({1}), Catch::WithinRel(0.5f, 0.001f));
}

TEST_CASE("Linear batched 4x2", "[linear]") {
  std::vector<float32_t> in_data = {1, 2, 3, 4, 5, 6, 7, 8};
  std::vector<float32_t> weights_data = {7, 8, 1, 3, 7, 8, 1, 3};
  std::vector<float32_t> bias_data = {3, 2};

  auto in = Tensor::from_blob(
      in_data.data(), {.shape = {2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto weights =
      Tensor::from_blob(weights_data.data(),
                        {.shape = {2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto bias = Tensor::from_blob(
      bias_data.data(), {.shape = {2}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});

  Linear fc(4, 2);
  fc->set_weights(weights);
  fc->set_bias(bias);
  auto res = fc(in);

  REQUIRE_THAT(res.data().at<float32_t>({0, 0}), Catch::WithinRel(41.f, 0.001f));
  REQUIRE_THAT(res.data().at<float32_t>({0, 1}), Catch::WithinRel(40.f, 0.001f));
  REQUIRE_THAT(res.data().at<float32_t>({1, 0}), Catch::WithinRel(117.f, 0.001f));
  REQUIRE_THAT(res.data().at<float32_t>({1, 1}), Catch::WithinRel(116.f, 0.001f));

  mean(res).backward();

  REQUIRE_THAT(fc->weights().grad().at<float32_t>({0, 0}), Catch::WithinRel(1.5f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({0, 1}), Catch::WithinRel(2.f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({0, 2}), Catch::WithinRel(2.5f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({0, 3}), Catch::WithinRel(3.f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({1, 0}), Catch::WithinRel(1.5f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({1, 1}), Catch::WithinRel(2.f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({1, 2}), Catch::WithinRel(2.5f, 0.001f));
  REQUIRE_THAT(fc->weights().grad().at<float32_t>({1, 3}), Catch::WithinRel(3.f, 0.001f));

  REQUIRE_THAT(fc->bias().grad().at<float32_t>({0}), Catch::WithinRel(0.5f, 0.001f));
  REQUIRE_THAT(fc->bias().grad().at<float32_t>({1}), Catch::WithinRel(0.5f, 0.001f));
}