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This commit is contained in:
Alejandro Saucedo 2021-02-28 16:02:37 +00:00
parent 75315db943
commit 63e220a8a4
26 changed files with 667 additions and 624 deletions
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80
test/TestLogisticRegression.cpp
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@ -29,24 +29,27 @@ TEST(TestLogisticRegression, TestMainLogisticRegression)
std::shared_ptr<kp::Tensor> lOut = mgr.tensor({ 0, 0, 0, 0, 0 });
std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
wIn, wOutI, wOutJ,
bIn, bOut, lOut };
wIn, wOutI, wOutJ,
bIn, bOut, lOut };
mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
std::vector<uint32_t> spirv = std::vector<uint32_t>(
(uint32_t*)kp::shader_data::test_shaders_glsl_test_logistic_regression_comp_spv,
(uint32_t*)(kp::shader_data::test_shaders_glsl_test_logistic_regression_comp_spv +
kp::shader_data::test_shaders_glsl_test_logistic_regression_comp_spv_len));
(uint32_t*)kp::shader_data::
test_shaders_glsl_test_logistic_regression_comp_spv,
(uint32_t*)(kp::shader_data::
test_shaders_glsl_test_logistic_regression_comp_spv +
kp::shader_data::
test_shaders_glsl_test_logistic_regression_comp_spv_len));
std::shared_ptr<kp::Algorithm> algorithm =
mgr.algorithm(params, spirv, kp::Workgroup({5}), kp::Constants({5.0}));
std::shared_ptr<kp::Algorithm> algorithm = mgr.algorithm(
params, spirv, kp::Workgroup({ 5 }), kp::Constants({ 5.0 }));
std::shared_ptr<kp::Sequence> sq =
mgr.sequence()
->record<kp::OpTensorSyncDevice>({ wIn, bIn })
->record<kp::OpAlgoDispatch>(algorithm)
->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
mgr.sequence()
->record<kp::OpTensorSyncDevice>({ wIn, bIn })
->record<kp::OpAlgoDispatch>(algorithm)
->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
// Iterate across all expected iterations
for (size_t i = 0; i < ITERATIONS; i++) {
@ -90,37 +93,38 @@ TEST(TestLogisticRegression, TestMainLogisticRegressionManualCopy)
std::shared_ptr<kp::Tensor> y = mgr.tensor({ 0, 0, 0, 1, 1 });
std::shared_ptr<kp::Tensor> wIn = mgr.tensor(
{ 0.001, 0.001 }, kp::Tensor::TensorTypes::eHost);
std::shared_ptr<kp::Tensor> wIn =
mgr.tensor({ 0.001, 0.001 }, kp::Tensor::TensorTypes::eHost);
std::shared_ptr<kp::Tensor> wOutI = mgr.tensor({ 0, 0, 0, 0, 0 });
std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor({ 0, 0, 0, 0, 0 });
std::shared_ptr<kp::Tensor> bIn = mgr.tensor(
{ 0 },
kp::Tensor::TensorTypes::eHost);
std::shared_ptr<kp::Tensor> bIn =
mgr.tensor({ 0 }, kp::Tensor::TensorTypes::eHost);
std::shared_ptr<kp::Tensor> bOut = mgr.tensor({ 0, 0, 0, 0, 0 });
std::shared_ptr<kp::Tensor> lOut = mgr.tensor({ 0, 0, 0, 0, 0 });
std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
wIn, wOutI, wOutJ,
bIn, bOut, lOut };
wIn, wOutI, wOutJ,
bIn, bOut, lOut };
mgr.sequence()->record<kp::OpTensorSyncDevice>(params)->eval();
std::vector<uint32_t> spirv = std::vector<uint32_t>(
(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
(uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv +
kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
(uint32_t*)(kp::shader_data::
shaders_glsl_logisticregression_comp_spv +
kp::shader_data::
shaders_glsl_logisticregression_comp_spv_len));
std::shared_ptr<kp::Algorithm> algorithm =
mgr.algorithm(params, spirv, kp::Workgroup(), kp::Constants({5.0}));
mgr.algorithm(params, spirv, kp::Workgroup(), kp::Constants({ 5.0 }));
std::shared_ptr<kp::Sequence> sq =
mgr.sequence()
->record<kp::OpTensorSyncDevice>({ wIn, bIn })
->record<kp::OpAlgoDispatch>(algorithm)
->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
mgr.sequence()
->record<kp::OpTensorSyncDevice>({ wIn, bIn })
->record<kp::OpAlgoDispatch>(algorithm)
->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
// Iterate across all expected iterations
for (size_t i = 0; i < ITERATIONS; i++) {
@ -136,18 +140,18 @@ TEST(TestLogisticRegression, TestMainLogisticRegressionManualCopy)
bIn->mapDataIntoHostMemory();
}
// Based on the inputs the outputs should be at least:
// * wi < 0.01
// * wj > 1.0
// * b < 0
// TODO: Add EXPECT_DOUBLE_EQ instead
EXPECT_LT(wIn->data()[0], 0.01);
EXPECT_GT(wIn->data()[1], 1.0);
EXPECT_LT(bIn->data()[0], 0.0);
// Based on the inputs the outputs should be at least:
// * wi < 0.01
// * wj > 1.0
// * b < 0
// TODO: Add EXPECT_DOUBLE_EQ instead
EXPECT_LT(wIn->data()[0], 0.01);
EXPECT_GT(wIn->data()[1], 1.0);
EXPECT_LT(bIn->data()[0], 0.0);
KP_LOG_WARN("Result wIn i: {}, wIn j: {}, bIn: {}",
wIn->data()[0],
wIn->data()[1],
bIn->data()[0]);
KP_LOG_WARN("Result wIn i: {}, wIn j: {}, bIn: {}",
wIn->data()[0],
wIn->data()[1],
bIn->data()[0]);
}
}