Formatting fixes no functionality changes. Remove unneeded namespace qualifer in model builder. (0699f971) · Commits · Cianciosa, Mark / ml_model_embedder

ml_model_embedder.xcodeproj/project.xcworkspace/xcuserdata/m4c.xcuserdatad/UserInterfaceState.xcuserstate

+15.7 KiB (172 KiB)

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ml_model_embedder.xcodeproj/xcuserdata/m4c.xcuserdatad/xcschemes/xcschememanagement.plist

+5 −0

Original line number	Diff line number	Diff line
		@@ -4,6 +4,11 @@
		<dict>
		<key>SchemeUserState</key>
		<dict>
		<key>ml_embedder_test.xcscheme_^#shared#^_</key>
		<dict>
		<key>orderHint</key>
		<integer>0</integer>
		</dict>
		<key>ml_embeder_test.xcscheme_^#shared#^_</key>
		<dict>
		<key>orderHint</key>

source/hdf5.hpp

+4 −2

Original line number	Diff line number	Diff line
		@@ -100,9 +100,11 @@ namespace ml_embedder {
		buffer<T> read(const size_t size) {
		buffer<T> b(size);
		if constexpr (std::is_same<T, float>::value) {
		check_error(H5Dread(d, H5T_NATIVE_FLOAT, H5S_ALL, H5S_ALL, 0, b.data()));
		check_error(H5Dread(d, H5T_NATIVE_FLOAT, H5S_ALL,
		H5S_ALL, 0, b.data()));
		} else {
		check_error(H5Dread(d, H5T_NATIVE_DOUBLE, H5S_ALL, H5S_ALL, 0, b.data()));
		check_error(H5Dread(d, H5T_NATIVE_DOUBLE, H5S_ALL,
		H5S_ALL, 0, b.data()));
		}
		return b;
		}

source/model_builder.hpp

+15 −13

Original line number	Diff line number	Diff line
		@@ -87,31 +87,33 @@ namespace ml_embedder {
		const int input_size,
		function &last) {
		const int width = config["units"];
		ml_embedder::hdf5::file::group vars = layer.open_group("vars");
		ml_embedder::hdf5::file::group::dataset weigths = vars.open_dataset("0");
		hdf5::file::group vars = layer.open_group("vars");
		hdf5::file::group::dataset weigths = vars.open_dataset("0");

		mlx::core::array weight = mlx::core::zeros({0});
		if (config["dtype"]["config"]["name"] == "float32") {
		ml_embedder::hdf5::file::group::dataset::buffer<float> wb = weigths.template read<float> (input_size*width);
		hdf5::file::group::dataset::buffer<float> wb = weigths.template read<float> (input_size*width);
		weight = mlx::core::array(wb.data(), {width, input_size});
		} else {
		ml_embedder::hdf5::file::group::dataset::buffer<double> wb = weigths.template read<double> (input_size*width);
		weight = mlx::core::array(wb.data(), {width, input_size}, mlx::core::float64);
		hdf5::file::group::dataset::buffer<double> wb = weigths.template read<double> (input_size*width);
		weight = mlx::core::array(wb.data(), {width, input_size},
		mlx::core::float64);
		}

		last = mlx::core::compile([last, weight](const std::vector<mlx::core::array> &in) {
		return std::vector<mlx::core::array> ({mlx::core::einsum("ij,...j->...i", {weight, last({in[0]})[0]})});
		return std::vector<mlx::core::array> ({mlx::core::einsum("ij,...j->...i",
		{weight, last({in[0]})[0]})});
		});

		if (config["use_bias"]) {
		ml_embedder::hdf5::file::group::dataset biass = vars.open_dataset("1");
		hdf5::file::group::dataset biass = vars.open_dataset("1");

		mlx::core::array bias = mlx::core::zeros({0});
		if (config["dtype"]["config"]["name"] == "float32") {
		ml_embedder::hdf5::file::group::dataset::buffer<float> bb = biass.template read<float> (width);
		hdf5::file::group::dataset::buffer<float> bb = biass.template read<float> (width);
		bias = mlx::core::array(bb.data(), {width});
		} else {
		ml_embedder::hdf5::file::group::dataset::buffer<double> bb = biass.template read<double> (width);
		hdf5::file::group::dataset::buffer<double> bb = biass.template read<double> (width);
		bias = mlx::core::array(bb.data(), {width}, mlx::core::float64);
		}

		@@ -139,16 +141,16 @@ namespace ml_embedder {
		keras(const std::string model_file) {
		initalize();

		ml_embedder::zip zip(model_file);
		zip zip(model_file);
		const nlohmann::json config =
		nlohmann::json::parse(zip.get_file("config.json").get_buffer());

		if (config["class_name"] == "Sequential") {
		ml_embedder::zip::file::buffer weights_file =
		zip::file::buffer weights_file =
		zip.get_file("model.weights.h5").get_buffer();
		ml_embedder::hdf5::file hf(weights_file.data(),
		hdf5::file hf(weights_file.data(),
		weights_file.size());
		ml_embedder::hdf5::file::group layers = hf.open_group("layers");
		hdf5::file::group layers = hf.open_group("layers");

		// Start with a place holder layer.
		func = mlx::core::compile([](inputs in) {

tests/ml_embedder_c_test.c

+2 −1

Original line number	Diff line number	Diff line
		@@ -27,7 +27,8 @@ int main(int argc, const char * argv[]) {
		totalsize *= in_shape[i];
		}
		float input_buffer = (float )malloc(sizeof(float)*totalsize);
		for (float pos = input_buffer, end = input_buffer + totalsize; pos < end; pos++) {
		for (float pos = input_buffer, end = input_buffer + totalsize;
		pos < end; pos++) {
		pos[0] = 1.0;
		}

fortran_binding/ml_f_embedder.f90

+1 −1

File changed.

Contains only whitespace changes.