vuron/adept: Платформа для обучения и вывода нейронных сетей, с API приближенным к PyTorch.

tests/dot_tests.cpp
120 строк5 KB
kolkir
Refactor Shape class
19 янв 2025, 13:34
19 янв 2025, 13:3458ce2e9
#include <adept/tensor.hpp>
#include <adept/tensor_print.hpp>
#include <vector>

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

using namespace adept;

namespace {

template <typename T>
using Data = std::vector<T>;
}

TEST_CASE("Tensor dot fail sizes", "[tensor]") {
  Data<float32_t> dx = {1, 2, 3, 4, 5, 6, 7, 8};
  Data<float32_t> dy = {7, 8, 1, 3, 9, 4, 5, 2};

  auto x = Tensor::from_blob(
      dx.data(), {.shape = {2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto y = Tensor::from_blob(
      dy.data(), {.shape = {2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});

  REQUIRE_THROWS(x.matmul(y));
}

TEST_CASE("Tensor dot1", "[tensor]") {
  Data<float32_t> dx = {1, 2, 3, 4, 5, 6, 7, 8};
  Data<float32_t> dy = {7, 8, 1, 3, 9, 4, 5, 2};

  auto x = Tensor::from_blob(
      dx.data(), {.shape = {2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto y = Tensor::from_blob(
      dy.data(), {.shape = {4, 2}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto z = x.matmul(y);
  REQUIRE(z.properties().shape.dim(0) == 2);
  REQUIRE(z.properties().shape.dim(1) == 2);

  REQUIRE_THAT(z.at<float32_t>({0, 0}), Catch::WithinRel(56.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({0, 1}), Catch::WithinRel(34.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({1, 0}), Catch::WithinRel(144.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({1, 1}), Catch::WithinRel(102.f, 0.001f));
}

TEST_CASE("Tensor dot2", "[tensor]") {
  Data<float32_t> dx = {1, 2, 5, 6};
  Data<float32_t> dy = {7, 8, 1, 3, 9, 4, 5, 2};

  auto x = Tensor::from_blob(
      dx.data(), {.shape = {2, 2}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto y = Tensor::from_blob(
      dy.data(), {.shape = {2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto z = x.matmul(y);
  REQUIRE(z.properties().shape.dim(0) == 2);
  REQUIRE(z.properties().shape.dim(1) == 4);

  REQUIRE_THAT(z.at<float32_t>({0, 0}), Catch::WithinRel(25.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({0, 1}), Catch::WithinRel(16.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({0, 2}), Catch::WithinRel(11.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({0, 3}), Catch::WithinRel(7.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({1, 0}), Catch::WithinRel(89.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({1, 1}), Catch::WithinRel(64.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({1, 2}), Catch::WithinRel(35.f, 0.001f));
  REQUIRE_THAT(z.at<float32_t>({1, 3}), Catch::WithinRel(27.f, 0.001f));
}

TEST_CASE("Tensor batch dot fail sizes", "[tensor]") {
  Data<float32_t> dx = {1, 2, 3, 4, 5, 6, 7, 8};
  Data<float32_t> dy = {7, 8, 1, 3, 9, 4, 5, 2, 7, 8, 1, 3, 9, 4, 5, 2};

  auto x = Tensor::from_blob(
      dx.data(), {.shape = {1, 2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto y = Tensor::from_blob(
      dy.data(), {.shape = {2, 2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});

  REQUIRE_THROWS(x.matmul(y));
}

TEST_CASE("Tensor batch dot", "[tensor]") {
  Data<float32_t> dx = {1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8};
  Data<float32_t> dy = {7, 8, 1, 3, 9, 4, 5, 2, 7, 8, 1, 3, 9, 4, 5, 2};

  auto x = Tensor::from_blob(
      dx.data(), {.shape = {2, 2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto y = Tensor::from_blob(
      dy.data(), {.shape = {2, 4, 2}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto z = x.matmul(y);
  REQUIRE(z.properties().shape.dim(0) == 2);
  REQUIRE(z.properties().shape.dim(1) == 2);
  REQUIRE(z.properties().shape.dim(2) == 2);

  for (index_t b = 0; b < 2; ++b) {
    REQUIRE_THAT(z.at<float32_t>({b, 0, 0}), Catch::WithinRel(56.f, 0.001f));
    REQUIRE_THAT(z.at<float32_t>({b, 0, 1}), Catch::WithinRel(34.f, 0.001f));
    REQUIRE_THAT(z.at<float32_t>({b, 1, 0}), Catch::WithinRel(144.f, 0.001f));
    REQUIRE_THAT(z.at<float32_t>({b, 1, 1}), Catch::WithinRel(102.f, 0.001f));
  }
}

TEST_CASE("Tensor batch dot2", "[tensor]") {
  Data<float32_t> dx = {1, 2, 3, 4, 5, 6, 7, 8};
  Data<float32_t> dy = {7, 8, 1, 3, 9, 4, 5, 2, 7, 8, 1, 3, 9, 4, 5, 2};

  auto x = Tensor::from_blob(
      dx.data(), {.shape = {2, 4}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto y = Tensor::from_blob(
      dy.data(), {.shape = {2, 4, 2}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()});
  auto z = x.matmul(y);
  REQUIRE(z.properties().shape.dim(0) == 2);
  REQUIRE(z.properties().shape.dim(1) == 2);
  REQUIRE(z.properties().shape.dim(2) == 2);

  for (index_t b = 0; b < 2; ++b) {
    REQUIRE_THAT(z.at<float32_t>({b, 0, 0}), Catch::WithinRel(56.f, 0.001f));
    REQUIRE_THAT(z.at<float32_t>({b, 0, 1}), Catch::WithinRel(34.f, 0.001f));
    REQUIRE_THAT(z.at<float32_t>({b, 1, 0}), Catch::WithinRel(144.f, 0.001f));
    REQUIRE_THAT(z.at<float32_t>({b, 1, 1}), Catch::WithinRel(102.f, 0.001f));
  }
}