Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add unit-tests for dotCL function, N=1, M=1 case #2968

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
+91 −0
Open

Add unit-tests for dotCL function, N=1, M=1 case #2968

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
91 changes: 91 additions & 0 deletions test/unittest/unittest_blas_kernels_cl.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -459,6 +459,97 @@ TEST(blas_kernels, dotCL_sgemv_n_fp16) {
EXPECT_THROW(dotCl(A_fp16, B_fp16, transA, transB), std::runtime_error);
}

TEST(blas_kernels, dotCL_sgemv_N_1_M_1_1) {
setUpGpuContext();
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Do you know if we need to call this for every test? I see most of the test cases do not, but only two test cases call it. (dotCL_sgemv_M_1_1, and dotCL_sgemv_M_1_1_fp16)

int batch = 1;
int channel = 1;
int height = 1;
int width = 768;

int height_b = 1;
int width_b = 768;

bool transA = false;
bool transB = true;

const float alpha = 1e-1;
const int MOD = 10;

nntrainer::TensorDim::TensorType t_type_nchw_fp32 = {
nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP32};

nntrainer::Tensor A_fp32(batch, channel, height, width, t_type_nchw_fp32);
nntrainer::Tensor B_fp32(batch, channel, height_b, width_b, t_type_nchw_fp32);

auto gen_data = [](nntrainer::Tensor x) {
auto ptr = x.getData();
for (int i = 0; i < x.size(); ++i) {
ptr[i] = static_cast<float>(rand()) / static_cast<float>(RAND_MAX);
}
};

gen_data(A_fp32), gen_data(B_fp32);

nntrainer::Tensor C = dotCl(A_fp32, B_fp32, transA, transB);
nntrainer::Tensor C_fp32 = A_fp32.dot(B_fp32, transA, transB);

float err = mse<float>(C.getData<float>(), C_fp32.getData<float>(), C.size());

double cosSimNeon = cosine_similarity<float>(
C.getData<float>(), C_fp32.getData<float>(), C.size());

const float epsilon = 1e-5 * width;

EXPECT_IN_RANGE(err, 0, epsilon);
EXPECT_IN_RANGE((float)cosSimNeon, 0.99, 1);
}

TEST(blas_kernels, dotCL_sgemv_N_1_M_1_2) {
setUpGpuContext();
int batch = 1;
int channel = 1;
int height = 1;
int width = 768;

int height_b = 1;
int width_b = 768;

bool transA = false;
bool transB = true;

const float alpha = 1e-1;
const int MOD = 10;

nntrainer::TensorDim::TensorType t_type_nchw_fp32 = {
nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP32};

nntrainer::Tensor A_fp32(batch, channel, height, width, t_type_nchw_fp32);
nntrainer::Tensor B_fp32(batch, channel, height_b, width_b, t_type_nchw_fp32);

GEN_TEST_INPUT(A_fp32, ((i * (batch * height * channel) +
j * (batch * height) + k * (width) + l + 1) %
MOD) *
alpha);
GEN_TEST_INPUT_B(B_fp32, ((i * (batch * height_b * channel) +
j * (batch * height_b) + k * (width_b) + l + 1) %
MOD) *
alpha);

nntrainer::Tensor C = dotCl(A_fp32, B_fp32, transA, transB);
nntrainer::Tensor C_fp32 = A_fp32.dot(B_fp32, transA, transB);

float mseErrorNeon =
mse<float>(C.getData<float>(), C_fp32.getData<float>(), C.size());

double cosSimNeon = cosine_similarity<float>(
C.getData<float>(), C_fp32.getData<float>(), C.size());

const float epsilon = 1e-5 * width;

EXPECT_IN_RANGE(mseErrorNeon, 0, epsilon);
EXPECT_IN_RANGE((float)cosSimNeon, 0.99, 1);
}

TEST(nntrainer_Tensor, multiply_i) {

int batch = 1;
Expand Down
Loading