blob: d29860bd3ac58d14b7a6fa2e80258d0769aeee57 [file] [log] [blame]
#include <array>
#include <memory>
#include <vector>
#include <gtest/gtest.h>
#include "caffe2/core/blob.h"
#include "caffe2/core/context.h"
#include "caffe2/core/tensor.h"
#include "caffe2/proto/caffe2_pb.h"
#include "caffe2/utils/conversions.h"
#include "caffe2/utils/math.h"
#include <c10/util/irange.h>
namespace caffe2 {
TEST(MathTest, GemmNoTransNoTrans) {
DeviceOption option;
CPUContext cpu_context(option);
Tensor X(std::vector<int>{5, 10}, CPU);
Tensor W(std::vector<int>{10, 6}, CPU);
Tensor Y(std::vector<int>{5, 6}, CPU);
EXPECT_EQ(X.numel(), 50);
EXPECT_EQ(W.numel(), 60);
math::Set<float, CPUContext>(
X.numel(), 1, X.mutable_data<float>(), &cpu_context);
math::Set<float, CPUContext>(
W.numel(), 1, W.mutable_data<float>(), &cpu_context);
EXPECT_EQ(Y.numel(), 30);
for (int i = 0; i < X.numel(); ++i) {
TORCH_CHECK_EQ(X.data<float>()[i], 1);
}
for (int i = 0; i < W.numel(); ++i) {
TORCH_CHECK_EQ(W.data<float>()[i], 1);
}
const float kOne = 1.0;
const float kPointFive = 0.5;
const float kZero = 0.0;
math::Gemm<float, CPUContext>(
CblasNoTrans,
CblasNoTrans,
5,
6,
10,
kOne,
X.data<float>(),
W.data<float>(),
kZero,
Y.mutable_data<float>(),
&cpu_context);
EXPECT_EQ(Y.numel(), 30);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 10) << i;
}
// Test Accumulate
math::Gemm<float, CPUContext>(
CblasNoTrans,
CblasNoTrans,
5,
6,
10,
kOne,
X.data<float>(),
W.data<float>(),
kPointFive,
Y.mutable_data<float>(),
&cpu_context);
EXPECT_EQ(Y.numel(), 30);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 15) << i;
}
// Test Accumulate
math::Gemm<float, CPUContext>(
CblasNoTrans,
CblasNoTrans,
5,
6,
10,
kPointFive,
X.data<float>(),
W.data<float>(),
kOne,
Y.mutable_data<float>(),
&cpu_context);
EXPECT_EQ(Y.numel(), 30);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 20) << i;
}
}
TEST(MathTest, GemmNoTransTrans) {
DeviceOption option;
CPUContext cpu_context(option);
Tensor X(std::vector<int>{5, 10}, CPU);
Tensor W(std::vector<int>{6, 10}, CPU);
Tensor Y(std::vector<int>{5, 6}, CPU);
EXPECT_EQ(X.numel(), 50);
EXPECT_EQ(W.numel(), 60);
math::Set<float, CPUContext>(
X.numel(), 1, X.mutable_data<float>(), &cpu_context);
math::Set<float, CPUContext>(
W.numel(), 1, W.mutable_data<float>(), &cpu_context);
EXPECT_EQ(Y.numel(), 30);
for (int i = 0; i < X.numel(); ++i) {
TORCH_CHECK_EQ(X.data<float>()[i], 1);
}
for (int i = 0; i < W.numel(); ++i) {
TORCH_CHECK_EQ(W.data<float>()[i], 1);
}
const float kOne = 1.0;
const float kPointFive = 0.5;
const float kZero = 0.0;
math::Gemm<float, CPUContext>(
CblasNoTrans,
CblasTrans,
5,
6,
10,
kOne,
X.data<float>(),
W.data<float>(),
kZero,
Y.mutable_data<float>(),
&cpu_context);
EXPECT_EQ(Y.numel(), 30);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 10) << i;
}
// Test Accumulate
math::Gemm<float, CPUContext>(
CblasNoTrans,
CblasTrans,
5,
6,
10,
kOne,
X.data<float>(),
W.data<float>(),
kPointFive,
Y.mutable_data<float>(),
&cpu_context);
EXPECT_EQ(Y.numel(), 30);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 15) << i;
}
math::Gemm<float, CPUContext>(
CblasNoTrans,
CblasTrans,
5,
6,
10,
kPointFive,
X.data<float>(),
W.data<float>(),
kOne,
Y.mutable_data<float>(),
&cpu_context);
EXPECT_EQ(Y.numel(), 30);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 20) << i;
}
}
namespace {
constexpr float kEps = 1e-5;
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
class GemmBatchedTest
: public testing::TestWithParam<testing::tuple<bool, bool>> {
protected:
void SetUp() override {
cpu_context_ = make_unique<CPUContext>(option_);
ReinitializeTensor(
&X_, std::vector<int64_t>{3, 5, 10}, at::dtype<float>().device(CPU));
ReinitializeTensor(
&W_, std::vector<int64_t>{3, 6, 10}, at::dtype<float>().device(CPU));
ReinitializeTensor(
&Y_, std::vector<int64_t>{3, 5, 6}, at::dtype<float>().device(CPU));
math::Set<float, CPUContext>(
X_.numel(), 1, X_.mutable_data<float>(), cpu_context_.get());
math::Set<float, CPUContext>(
W_.numel(), 1, W_.mutable_data<float>(), cpu_context_.get());
trans_X_ = std::get<0>(GetParam());
trans_W_ = std::get<1>(GetParam());
}
void RunGemmBatched(const float alpha, const float beta) {
const float* X_data = X_.template data<float>();
const float* W_data = W_.template data<float>();
float* Y_data = Y_.template mutable_data<float>();
const int X_stride = 5 * 10;
const int W_stride = 6 * 10;
const int Y_stride = 5 * 6;
std::array<const float*, 3> X_array = {
X_data, X_data + X_stride, X_data + 2 * X_stride};
std::array<const float*, 3> W_array = {
W_data, W_data + W_stride, W_data + 2 * W_stride};
std::array<float*, 3> Y_array = {
Y_data, Y_data + Y_stride, Y_data + 2 * Y_stride};
math::GemmBatched(
trans_X_ ? CblasTrans : CblasNoTrans,
trans_W_ ? CblasTrans : CblasNoTrans,
3,
5,
6,
10,
alpha,
X_array.data(),
W_array.data(),
beta,
Y_array.data(),
cpu_context_.get());
}
void RunGemmStridedBatched(const float alpha, const float beta) {
const float* X_data = X_.template data<float>();
const float* W_data = W_.template data<float>();
float* Y_data = Y_.template mutable_data<float>();
const int X_stride = 5 * 10;
const int W_stride = 6 * 10;
const int Y_stride = 5 * 6;
math::GemmStridedBatched<float, CPUContext>(
trans_X_ ? CblasTrans : CblasNoTrans,
trans_W_ ? CblasTrans : CblasNoTrans,
3,
5,
6,
10,
alpha,
X_data,
X_stride,
W_data,
W_stride,
beta,
Y_data,
Y_stride,
cpu_context_.get());
}
void VerifyOutput(const float value) const {
for (int i = 0; i < Y_.numel(); ++i) {
EXPECT_FLOAT_EQ(value, Y_.template data<float>()[i]);
}
}
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
DeviceOption option_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
std::unique_ptr<CPUContext> cpu_context_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
Tensor X_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
Tensor W_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
Tensor Y_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
bool trans_X_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
bool trans_W_;
};
TEST_P(GemmBatchedTest, GemmBatchedFloatTest) {
RunGemmBatched(1.0f, 0.0f);
VerifyOutput(10.0f);
RunGemmBatched(1.0f, 0.5f);
VerifyOutput(15.0f);
RunGemmBatched(0.5f, 1.0f);
VerifyOutput(20.0f);
}
TEST_P(GemmBatchedTest, GemmStridedBatchedFloatTest) {
RunGemmStridedBatched(1.0f, 0.0f);
VerifyOutput(10.0f);
RunGemmStridedBatched(1.0f, 0.5f);
VerifyOutput(15.0f);
RunGemmStridedBatched(0.5f, 1.0f);
VerifyOutput(20.0f);
}
INSTANTIATE_TEST_CASE_P(
GemmBatchedTrans,
GemmBatchedTest,
testing::Combine(testing::Bool(), testing::Bool()));
} // namespace
TEST(MathTest, GemvNoTrans) {
DeviceOption option;
CPUContext cpu_context(option);
Tensor A(std::vector<int>{5, 10}, CPU);
Tensor X(std::vector<int>{10}, CPU);
Tensor Y(std::vector<int>{5}, CPU);
EXPECT_EQ(A.numel(), 50);
EXPECT_EQ(X.numel(), 10);
math::Set<float, CPUContext>(
A.numel(), 1, A.mutable_data<float>(), &cpu_context);
math::Set<float, CPUContext>(
X.numel(), 1, X.mutable_data<float>(), &cpu_context);
EXPECT_EQ(Y.numel(), 5);
for (int i = 0; i < A.numel(); ++i) {
TORCH_CHECK_EQ(A.data<float>()[i], 1);
}
for (int i = 0; i < X.numel(); ++i) {
TORCH_CHECK_EQ(X.data<float>()[i], 1);
}
const float kOne = 1.0;
const float kPointFive = 0.5;
const float kZero = 0.0;
math::Gemv<float, CPUContext>(
CblasNoTrans,
5,
10,
kOne,
A.data<float>(),
X.data<float>(),
kZero,
Y.mutable_data<float>(),
&cpu_context);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 10) << i;
}
// Test Accumulate
math::Gemv<float, CPUContext>(
CblasNoTrans,
5,
10,
kOne,
A.data<float>(),
X.data<float>(),
kPointFive,
Y.mutable_data<float>(),
&cpu_context);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 15) << i;
}
// Test Accumulate
math::Gemv<float, CPUContext>(
CblasNoTrans,
5,
10,
kPointFive,
A.data<float>(),
X.data<float>(),
kOne,
Y.mutable_data<float>(),
&cpu_context);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 20) << i;
}
}
TEST(MathTest, GemvTrans) {
DeviceOption option;
CPUContext cpu_context(option);
Tensor A(std::vector<int>{6, 10}, CPU);
Tensor X(std::vector<int>{6}, CPU);
Tensor Y(std::vector<int>{10}, CPU);
EXPECT_EQ(A.numel(), 60);
EXPECT_EQ(X.numel(), 6);
math::Set<float, CPUContext>(
A.numel(), 1, A.mutable_data<float>(), &cpu_context);
math::Set<float, CPUContext>(
X.numel(), 1, X.mutable_data<float>(), &cpu_context);
EXPECT_EQ(Y.numel(), 10);
for (int i = 0; i < A.numel(); ++i) {
TORCH_CHECK_EQ(A.data<float>()[i], 1);
}
for (int i = 0; i < X.numel(); ++i) {
TORCH_CHECK_EQ(X.data<float>()[i], 1);
}
const float kOne = 1.0;
const float kPointFive = 0.5;
const float kZero = 0.0;
math::Gemv<float, CPUContext>(
CblasTrans,
6,
10,
kOne,
A.data<float>(),
X.data<float>(),
kZero,
Y.mutable_data<float>(),
&cpu_context);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 6) << i;
}
// Test Accumulate
math::Gemv<float, CPUContext>(
CblasTrans,
6,
10,
kOne,
A.data<float>(),
X.data<float>(),
kPointFive,
Y.mutable_data<float>(),
&cpu_context);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 9) << i;
}
// Test Accumulate
math::Gemv<float, CPUContext>(
CblasTrans,
6,
10,
kPointFive,
A.data<float>(),
X.data<float>(),
kOne,
Y.mutable_data<float>(),
&cpu_context);
for (int i = 0; i < Y.numel(); ++i) {
TORCH_CHECK_EQ(Y.data<float>()[i], 12) << i;
}
}
TEST(MathTest, FloatToHalfConversion) {
float a = 1.0f;
float b = 1.75f;
float c = 128.125f;
float converted_a = static_cast<float>(at::Half(a));
float converted_b = static_cast<float>(at::Half(b));
float converted_c = static_cast<float>(at::Half(c));
TORCH_CHECK_EQ(a, converted_a);
TORCH_CHECK_EQ(b, converted_b);
TORCH_CHECK_EQ(c, converted_c);
}
namespace {
class BroadcastTest : public testing::Test {
protected:
void SetUp() override {
cpu_context_ = make_unique<CPUContext>(option_);
}
void RunBroadcastTest(
const std::vector<int>& X_dims,
const std::vector<int>& Y_dims,
const std::vector<float>& X_data,
const std::vector<float>& Y_data) {
std::vector<int64_t> X_dims_64;
std::vector<int64_t> Y_dims_64;
std::copy(X_dims.cbegin(), X_dims.cend(), std::back_inserter(X_dims_64));
std::copy(Y_dims.cbegin(), Y_dims.cend(), std::back_inserter(Y_dims_64));
ReinitializeTensor(&X_, X_dims_64, at::dtype<float>().device(CPU));
ReinitializeTensor(&Y_, Y_dims_64, at::dtype<float>().device(CPU));
ASSERT_EQ(X_data.size(), X_.numel());
cpu_context_->CopyFromCPU<float>(
X_data.size(), X_data.data(), X_.mutable_data<float>());
for (bool allow_broadcast_fastpath : {false, true}) {
math::Broadcast<float, CPUContext>(
X_dims.size(),
X_dims.data(),
Y_dims.size(),
Y_dims.data(),
1.0f,
X_.data<float>(),
Y_.mutable_data<float>(),
cpu_context_.get(),
allow_broadcast_fastpath);
ASSERT_EQ(Y_data.size(), Y_.numel());
for (const auto i : c10::irange(Y_data.size())) {
EXPECT_FLOAT_EQ(Y_data[i], Y_.data<float>()[i]);
}
}
}
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
DeviceOption option_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
std::unique_ptr<CPUContext> cpu_context_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
Tensor X_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
Tensor Y_;
};
TEST_F(BroadcastTest, BroadcastFloatTest) {
RunBroadcastTest({2}, {2}, {1.0f, 2.0f}, {1.0f, 2.0f});
RunBroadcastTest({1}, {2}, {1.0f}, {1.0f, 1.0f});
RunBroadcastTest({1}, {2, 2}, {1.0f}, {1.0f, 1.0f, 1.0f, 1.0f});
RunBroadcastTest({2, 1}, {2, 2}, {1.0f, 2.0f}, {1.0f, 1.0f, 2.0f, 2.0f});
RunBroadcastTest({1, 2}, {2, 2}, {1.0f, 2.0f}, {1.0f, 2.0f, 1.0f, 2.0f});
RunBroadcastTest(
{2, 1},
{2, 2, 2},
{1.0f, 2.0f},
{1.0f, 1.0f, 2.0f, 2.0f, 1.0f, 1.0f, 2.0f, 2.0f});
RunBroadcastTest(
{1, 2},
{2, 2, 2},
{1.0f, 2.0f},
{1.0f, 2.0f, 1.0f, 2.0f, 1.0f, 2.0f, 1.0f, 2.0f});
}
class RandFixedSumTest : public testing::Test {
protected:
void SetUp() override {
cpu_context_ = make_unique<CPUContext>(option_);
}
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
DeviceOption option_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
std::unique_ptr<CPUContext> cpu_context_;
};
TEST_F(RandFixedSumTest, UpperBound) {
std::vector<int> l(20);
math::RandFixedSum<int, CPUContext>(
20, 1, 1000, 1000, l.data(), cpu_context_.get());
}
} // namespace
} // namespace caffe2