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Added set and get functions

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This commit is contained in:
Alejandro Saucedo
2020-11-10 09:07:22 +00:00
parent 752c7b74f3
commit 90bc86d0eb
2 changed files with 10 additions and 14 deletions
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10
python/src/main.cpp
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@@ -39,10 +39,14 @@ PYBIND11_MODULE(kp, m) {
return std::unique_ptr<kp::Tensor>(new kp::Tensor(data, tensorTypes));
}), "Initialiser with list of data components and tensor GPU memory type.")
.def("data", &kp::Tensor::data, DOC(kp, Tensor, data))
.def("get", [](kp::Tensor &self, uint32_t index) -> float { return self.data()[index]; },
"When only an index is necessary")
.def("set", [](kp::Tensor &self, uint32_t index, float value) {
self.data()[index] = value; })
.def("set", &kp::Tensor::setData, "Overrides the data in the local Tensor memory.")
.def("size", &kp::Tensor::size, "Retrieves the size of the Tensor data as per the local Tensor memory.")
.def("tensor_type", &kp::Tensor::tensorType, "Retreves the memory type of the tensor.")
.def("is_init", &kp::Tensor::isInit, "Checks whether the tensor GPU memory has been initialised.")
.def("set_data", &kp::Tensor::setData, "Overrides the data in the local Tensor memory.")
.def("map_data_from_host", &kp::Tensor::mapDataFromHostMemory, "Maps data into GPU memory from tensor local data.")
.def("map_data_into_host", &kp::Tensor::mapDataIntoHostMemory, "Maps data from GPU memory into tensor local data.");
@@ -74,12 +78,12 @@ PYBIND11_MODULE(kp, m) {
"Records an operation using a custom shader provided from a shader path")
.def("record_algo_data", [](kp::Sequence &self,
std::vector<std::shared_ptr<kp::Tensor>> tensors,
py::bytes &bytes) {
py::bytes &bytes) -> float {
// Bytes have to be converted into std::vector
py::buffer_info info(py::buffer(bytes).request());
const char *data = reinterpret_cast<const char *>(info.ptr);
size_t length = static_cast<size_t>(info.size);
self.record<kp::OpAlgoBase>(
return self.record<kp::OpAlgoBase>(
tensors,
std::vector<char>(data, data + length));
},

14
python/test/test_kompute.py
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@@ -214,18 +214,10 @@ def test_logistic_regression_pyshader():
sq.eval()
# Calculate the parameters based on the respective derivatives calculated
w_in_i_val = tensor_w_in.data()[0]
w_in_j_val = tensor_w_in.data()[1]
b_in_val = tensor_b_in.data()[0]
for j_iter in range(tensor_b_out.size()):
w_in_i_val -= learning_rate * tensor_w_out_i.data()[j_iter]
w_in_j_val -= learning_rate * tensor_w_out_j.data()[j_iter]
b_in_val -= learning_rate * tensor_b_out.data()[j_iter]
# Update the parameters to process inference again
tensor_w_in.set_data([w_in_i_val, w_in_j_val])
tensor_b_in.set_data([b_in_val])
tensor_w_in.set(0, tensor_w_in.get(0) - learning_rate * tensor_w_out_i.data()[j_iter])
tensor_w_in.set(1, tensor_w_in.get(1) - learning_rate * tensor_w_out_j.data()[j_iter])
tensor_b_in.set(0, tensor_b_in.get(0) - learning_rate * tensor_b_out.data()[j_iter])
assert tensor_w_in.data()[0] < 0.01
assert tensor_w_in.data()[0] > 0.0