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Remove cublas dependency

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This commit is contained in:
Matúš Námešný
2024-03-05 18:41:35 +01:00
parent 98ad84c659
commit f4257afd5a
16 changed files with 65 additions and 141 deletions
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38
test/layers/test_dense.cu
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@@ -5,9 +5,9 @@
#include "activations.cuh"
#include "dense.cuh"
#include "test_cublas_fixture.cuh"
class DenseLayerTest : public CublasTestFixture {
class DenseLayerTest : public::testing::Test {
protected:
Layers::Dense commonTestSetup(
int inputSize,
@@ -21,7 +21,7 @@ class DenseLayerTest : public CublasTestFixture {
) {
// Create Dense layer
Layers::Dense denseLayer(
inputSize, outputSize, activation, cublasHandle
inputSize, outputSize, activation
);
// Set weights and biases
@@ -36,10 +36,11 @@ class DenseLayerTest : public CublasTestFixture {
EXPECT_EQ(cudaStatus, cudaSuccess);
// Copy input to device
cublasStatus = cublasSetVector(
input.size(), sizeof(float), input.data(), 1, d_input, 1
cudaStatus = cudaMemcpy(
d_input, input.data(), sizeof(float) * input.size(), cudaMemcpyHostToDevice
);
EXPECT_EQ(cublasStatus, CUBLAS_STATUS_SUCCESS);
EXPECT_EQ(cudaStatus, cudaSuccess);
return denseLayer;
}
@@ -51,7 +52,6 @@ class DenseLayerTest : public CublasTestFixture {
}
cudaError_t cudaStatus;
cublasStatus_t cublasStatus;
};
TEST_F(DenseLayerTest, Init) {
@@ -60,10 +60,8 @@ TEST_F(DenseLayerTest, Init) {
int inputSize = i;
int outputSize = j;
// std::cout << "Dense layer: input size = " << inputSize << ",
// output size = " << outputSize << std::endl;
Layers::Dense denseLayer(
inputSize, outputSize, SIGMOID, cublasHandle
inputSize, outputSize, SIGMOID
);
}
}
@@ -81,7 +79,7 @@ TEST_F(DenseLayerTest, setWeights) {
{1.3f, 0.5f, 0.0f, 1.7f}
};
Layers::Dense denseLayer(inputSize, outputSize, SIGMOID, cublasHandle);
Layers::Dense denseLayer(inputSize, outputSize, SIGMOID);
denseLayer.setWeights(weights);
}
@@ -113,10 +111,10 @@ TEST_F(DenseLayerTest, ForwardUnitWeightMatrixLinear) {
denseLayer.forward(d_input, d_output);
std::vector<float> output(outputSize);
cublasStatus = cublasGetVector(
outputSize, sizeof(float), d_output, 1, output.data(), 1
cudaStatus = cudaMemcpy(
output.data(), d_output, sizeof(float) * outputSize, cudaMemcpyDeviceToHost
);
EXPECT_EQ(cublasStatus, CUBLAS_STATUS_SUCCESS);
EXPECT_EQ(cudaStatus, cudaSuccess);
// Check if the output is a zero vector
EXPECT_FLOAT_EQ(output[0], 2.0f);
@@ -150,10 +148,10 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrixRelu) {
denseLayer.forward(d_input, d_output);
std::vector<float> output(outputSize);
cublasStatus = cublasGetVector(
outputSize, sizeof(float), d_output, 1, output.data(), 1
cudaStatus = cudaMemcpy(
output.data(), d_output, sizeof(float) * outputSize, cudaMemcpyDeviceToHost
);
EXPECT_EQ(cublasStatus, CUBLAS_STATUS_SUCCESS);
EXPECT_EQ(cudaStatus, cudaSuccess);
// weights * inputs = 0.1, 12.5, 8.3, -2.2
// + biases = 0.3, 13, 9, -3.3
@@ -193,10 +191,10 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrixSigmoid) {
denseLayer.forward(d_input, d_output);
std::vector<float> output(outputSize);
cublasStatus = cublasGetVector(
outputSize, sizeof(float), d_output, 1, output.data(), 1
cudaStatus = cudaMemcpy(
output.data(), d_output, sizeof(float) * outputSize, cudaMemcpyDeviceToHost
);
EXPECT_EQ(cublasStatus, CUBLAS_STATUS_SUCCESS);
EXPECT_EQ(cudaStatus, cudaSuccess);
// weights * input = 0.95, 0.43, 0.45, 0.93
// + biases = 1.05, 0.63, 0.75, 1.33