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Add dtype parameter to layer constructors

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This commit is contained in:
Matúš Námešný
2025-11-26 00:19:33 +01:00
parent 84153ac49c
commit 13d3d38b68
17 changed files with 169 additions and 49 deletions
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4
include/layer.hpp
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@@ -16,6 +16,8 @@ namespace CUDANet {
*
*/
class Layer {
protected:
CUDANet::DType dtype;
public:
virtual ~Layer(){};
@@ -39,4 +41,4 @@ class Layer {
virtual size_t get_biases_size() = 0;
};
} // namespace CUDANet::Layers
} // namespace CUDANet

5
include/layers/activation.hpp
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@@ -20,12 +20,13 @@ enum ActivationType { SIGMOID, RELU, SOFTMAX, NONE };
* @brief Utility class that performs activation
*
*/
class Activation : public Layer {
class Activation : public CUDANet::Layer {
public:
Activation() = default;
Activation(ActivationType activation, const CUDANet::Shape &shape, CUDANet::Backend* backend);
Activation(ActivationType activation, const CUDANet::Shape &shape, CUDANet::DType dtype, CUDANet::Backend* backend);
~Activation() = default;
@@ -50,7 +51,7 @@ class Activation : public Layer {
private:
CUDANet::Backend* backend;
ActivationType activationType;
ActivationType activation_type;
CUDANet::Shape shape;
CUDANet::Tensor softmax_sum;

3
include/layers/add.hpp
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@@ -8,6 +8,7 @@ namespace CUDANet::Layers {
class Add {
public:
Add(CUDANet::Shape a_shape, CUDANet::Shape b_shape, CUDANet::Backend* backend);
Add(CUDANet::Shape a_shape, CUDANet::Shape b_shape, CUDANet::DType dtype, CUDANet::Backend* backend);
~Add();
@@ -19,6 +20,8 @@ class Add {
CUDANet::Tensor output;
CUDANet::Backend *backend;
CUDANet::DType dtype;
};
} // namespace CUDANet::Layers

11
include/layers/avg_pool.hpp
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@@ -4,7 +4,7 @@
namespace CUDANet::Layers {
class AvgPool2d : public Layer {
class AvgPool2d : public CUDANet::Layer {
public:
AvgPool2d(
CUDANet::Shape input_shape,
@@ -13,6 +13,14 @@ class AvgPool2d : public Layer {
CUDANet::Shape padding_shape,
CUDANet::Backend *backend
);
AvgPool2d(
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::DType dtype,
CUDANet::Backend *backend
);
~AvgPool2d();
@@ -50,6 +58,7 @@ class AvgPool2d : public Layer {
class AdaptiveAvgPool2d : public AvgPool2d {
public:
AdaptiveAvgPool2d(CUDANet::Shape input_shape, CUDANet::Shape output_shape, CUDANet::Backend *backend);
AdaptiveAvgPool2d(CUDANet::Shape input_shape, CUDANet::Shape output_shape, CUDANet::DType dtype, CUDANet::Backend *backend);
};
} // namespace CUDANet::Layers

3
include/layers/batch_norm.hpp
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@@ -4,9 +4,10 @@
namespace CUDANet::Layers {
class BatchNorm2d : public Layer {
class BatchNorm2d : public CUDANet::Layer {
public:
BatchNorm2d(CUDANet::Shape input_shape, float epsilon, CUDANet::Backend *backend);
BatchNorm2d(CUDANet::Shape input_shape, float epsilon, CUDANet::DType dtype, CUDANet::Backend *backend);
~BatchNorm2d();

3
include/layers/concat.hpp
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@@ -12,6 +12,7 @@ class Concat {
public:
Concat(const CUDANet::Shape a_shape, const CUDANet::Shape b_shape, CUDANet::Backend *backend);
Concat(const CUDANet::Shape a_shape, const CUDANet::Shape b_shape, CUDANet::DType dtype, CUDANet::Backend *backend);
~Concat();
@@ -27,6 +28,8 @@ class Concat {
CUDANet::Tensor output;
CUDANet::Backend *backend;
CUDANet::DType dtype;
};
} // namespace CUDANet::Layers

10
include/layers/conv2d.hpp
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@@ -8,7 +8,7 @@ namespace CUDANet::Layers {
* @brief 2D convolutional layer
*
*/
class Conv2d : public Layer {
class Conv2d : public CUDANet::Layer {
public:
Conv2d(
CUDANet::Shape input_shape,
@@ -17,6 +17,14 @@ class Conv2d : public Layer {
CUDANet::Shape padding_shape,
CUDANet::Backend* backend
);
Conv2d(
CUDANet::Shape input_shape,
CUDANet::Shape kernel_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::DType dtype,
CUDANet::Backend* backend
);
~Conv2d();

3
include/layers/dense.hpp
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@@ -9,10 +9,11 @@ namespace CUDANet::Layers {
* @brief Dense (fully connected) layer
*
*/
class Dense : public Layer {
class Dense : public CUDANet::Layer {
public:
Dense(CUDANet::Shape input_shape, CUDANet::Shape output_shape, CUDANet::Backend *backend);
Dense(CUDANet::Shape input_shape, CUDANet::Shape output_shape, CUDANet::DType dtype, CUDANet::Backend *backend);
~Dense();

10
include/layers/max_pool.hpp
View File

@@ -4,7 +4,7 @@
namespace CUDANet::Layers {
class MaxPool2d : public Layer {
class MaxPool2d : public CUDANet::Layer {
public:
MaxPool2d(
CUDANet::Shape input_shape,
@@ -13,6 +13,14 @@ class MaxPool2d : public Layer {
CUDANet::Shape padding_shape,
CUDANet::Backend* backend
);
MaxPool2d(
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::DType dtype,
CUDANet::Backend* backend
);
~MaxPool2d();
CUDANet::Tensor& forward(CUDANet::Tensor &input) override;

39
src/layers/activation.cpp
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@@ -1,14 +1,30 @@
#include "activation.hpp"
#include <format>
#include <stdexcept>
#include <vector>
#include "activation.hpp"
#include "tensor.hpp"
using namespace CUDANet::Layers;
Activation::Activation(ActivationType activation, const CUDANet::Shape &shape, CUDANet::Backend* backend)
: backend(backend), activationType(activation), shape(shape) {
Activation::Activation(
ActivationType activation,
const CUDANet::Shape& shape,
CUDANet::Backend* backend
)
: Activation(activation, shape, backend->get_default_dtype(), backend) {}
Activation::Activation(
ActivationType activation,
const CUDANet::Shape& shape,
CUDANet::DType dtype,
CUDANet::Backend* backend
)
: activation_type(activation),
shape(shape),
backend(backend) {
this->dtype = dtype;
if (shape.size() != 1) {
throw InvalidShapeException("input", 1, shape.size());
@@ -16,15 +32,16 @@ Activation::Activation(ActivationType activation, const CUDANet::Shape &shape, C
auto length = shape[0];
if (activationType == SOFTMAX) {
softmax_sum = CUDANet::Tensor({static_cast<size_t>(length)}, CUDANet::DType::FLOAT32, backend);
tensor_max = CUDANet::Tensor({static_cast<size_t>(length)}, CUDANet::DType::FLOAT32, backend);
if (activation_type == SOFTMAX) {
softmax_sum =
CUDANet::Tensor({static_cast<size_t>(length)}, dtype, backend);
tensor_max =
CUDANet::Tensor({static_cast<size_t>(length)}, dtype, backend);
}
}
CUDANet::Tensor& Activation::forward(CUDANet::Tensor &input) {
switch (activationType)
{
CUDANet::Tensor& Activation::forward(CUDANet::Tensor& input) {
switch (activation_type) {
case ActivationType::SIGMOID:
backend->sigmoid(input);
break;
@@ -57,13 +74,13 @@ size_t Activation::output_size() {
return shape[0];
}
void Activation::set_weights(void *input) {}
void Activation::set_weights(void* input) {}
size_t Activation::get_weights_size() {
return 0;
}
void Activation::set_biases(void *input) {}
void Activation::set_biases(void* input) {}
size_t Activation::get_biases_size() {
return 0;

8
src/layers/add.cpp
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@@ -3,7 +3,11 @@
using namespace CUDANet::Layers;
Add::Add(CUDANet::Shape a_shape, CUDANet::Shape b_shape, CUDANet::Backend* backend) : backend(backend) {
Add::Add(CUDANet::Shape a_shape, CUDANet::Shape b_shape, CUDANet::Backend* backend)
: Add(a_shape, b_shape, backend->get_default_dtype(), backend) {}
Add::Add(CUDANet::Shape a_shape, CUDANet::Shape b_shape, CUDANet::DType dtype, CUDANet::Backend* backend)
: backend(backend), dtype(dtype) {
if (a_shape != b_shape) {
throw InvalidShapeException(
"Add requires matching dimensions", a_shape, b_shape
@@ -11,7 +15,7 @@ Add::Add(CUDANet::Shape a_shape, CUDANet::Shape b_shape, CUDANet::Backend* backe
}
out_shape = a_shape;
output = CUDANet::Tensor(out_shape, CUDANet::DType::FLOAT32, backend);
output = CUDANet::Tensor(out_shape, dtype, backend);
}
Add::~Add() {}

25
src/layers/avg_pooling.cpp
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@@ -11,6 +11,16 @@ AvgPool2d::AvgPool2d(
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Backend* backend
)
: AvgPool2d(input_shape, pool_shape, stride_shape, padding_shape, backend->get_default_dtype(), backend) {}
AvgPool2d::AvgPool2d(
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::DType dtype,
CUDANet::Backend* backend
)
: in_shape(input_shape),
pool_shape(pool_shape),
@@ -33,6 +43,8 @@ AvgPool2d::AvgPool2d(
throw InvalidShapeException("padding", 2, padding_shape.size());
}
this->dtype = dtype;
out_shape = {
(in_shape[0] + 2 * padding_shape[0] - pool_shape[0]) / stride_shape[0] +
1,
@@ -43,7 +55,7 @@ AvgPool2d::AvgPool2d(
output = CUDANet::Tensor(
Shape{out_shape[0] * out_shape[1] * out_shape[2]},
CUDANet::DType::FLOAT32, backend
dtype, backend
);
}
@@ -96,6 +108,14 @@ AdaptiveAvgPool2d::AdaptiveAvgPool2d(
CUDANet::Shape input_shape,
CUDANet::Shape output_shape,
CUDANet::Backend *backend
)
: AdaptiveAvgPool2d(input_shape, output_shape, backend->get_default_dtype(), backend) {}
AdaptiveAvgPool2d::AdaptiveAvgPool2d(
CUDANet::Shape input_shape,
CUDANet::Shape output_shape,
CUDANet::DType dtype,
CUDANet::Backend *backend
)
: AvgPool2d(
input_shape,
@@ -114,12 +134,13 @@ AdaptiveAvgPool2d::AdaptiveAvgPool2d(
(input_shape[0] - (output_shape[0] - 1) * (input_shape[0] / output_shape[0]) - 1) / 2,
(input_shape[1] - (output_shape[1] - 1) * (input_shape[1] / output_shape[1]) - 1) / 2
},
dtype,
backend
) {
out_shape = output_shape;
output = CUDANet::Tensor(
Shape{out_shape[0] * out_shape[1] * out_shape[2]},
CUDANet::DType::FLOAT32, backend
dtype, backend
);
}

22
src/layers/batch_norm.cpp
View File

@@ -12,6 +12,14 @@ BatchNorm2d::BatchNorm2d(
CUDANet::Shape input_shape,
float eps,
CUDANet::Backend *backend
)
: BatchNorm2d(input_shape, eps, backend->get_default_dtype(), backend) {}
BatchNorm2d::BatchNorm2d(
CUDANet::Shape input_shape,
float eps,
CUDANet::DType dtype,
CUDANet::Backend *backend
)
: in_shape(input_shape), backend(backend) {
@@ -19,22 +27,24 @@ BatchNorm2d::BatchNorm2d(
throw InvalidShapeException("input", 3, in_shape.size());
}
epsilon = CUDANet::Tensor({1}, CUDANet::DType::FLOAT32, backend);
this->dtype = dtype;
epsilon = CUDANet::Tensor({1}, dtype, backend);
epsilon.set_data<float>(&eps);
running_mean = CUDANet::Tensor({in_shape[2]}, CUDANet::DType::FLOAT32, backend);
running_mean = CUDANet::Tensor({in_shape[2]}, dtype, backend);
running_mean.zero();
running_var = CUDANet::Tensor({in_shape[2]}, CUDANet::DType::FLOAT32, backend);
running_var = CUDANet::Tensor({in_shape[2]}, dtype, backend);
running_var.fill(1);
weights = CUDANet::Tensor({in_shape[2]}, CUDANet::DType::FLOAT32, backend);
weights = CUDANet::Tensor({in_shape[2]}, dtype, backend);
weights.fill(1);
biases = CUDANet::Tensor({in_shape[2]}, CUDANet::DType::FLOAT32, backend);
biases = CUDANet::Tensor({in_shape[2]}, dtype, backend);
biases.zero();
output = CUDANet::Tensor(in_shape, CUDANet::DType::FLOAT32, backend);
output = CUDANet::Tensor(in_shape, dtype, backend);
}
BatchNorm2d::~BatchNorm2d() {}

7
src/layers/concat.cpp
View File

@@ -3,7 +3,10 @@
using namespace CUDANet::Layers;
Concat::Concat(const CUDANet::Shape a_shape, const CUDANet::Shape b_shape, CUDANet::Backend *backend)
: a_shape(a_shape), b_shape(b_shape), backend(backend) {
: Concat(a_shape, b_shape, backend->get_default_dtype(), backend) {}
Concat::Concat(const CUDANet::Shape a_shape, const CUDANet::Shape b_shape, CUDANet::DType dtype, CUDANet::Backend *backend)
: a_shape(a_shape), b_shape(b_shape), backend(backend), dtype(dtype) {
if (a_shape[0] != b_shape[0] || a_shape[1] != b_shape[1]) {
throw InvalidShapeException(
"Concat requires matching height and width dimensions", a_shape,
@@ -12,7 +15,7 @@ Concat::Concat(const CUDANet::Shape a_shape, const CUDANet::Shape b_shape, CUDAN
}
out_shape = {a_shape[0], a_shape[1], a_shape[2] + b_shape[2]};
output = CUDANet::Tensor(out_shape, CUDANet::DType::FLOAT32, backend);
output = CUDANet::Tensor(out_shape, dtype, backend);
}
Concat::~Concat() {}

18
src/layers/conv2d.cpp
View File

@@ -14,6 +14,16 @@ Conv2d::Conv2d(
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Backend* backend
)
: Conv2d(input_shape, kernel_shape, stride_shape, padding_shape, backend->get_default_dtype(), backend) {}
Conv2d::Conv2d(
CUDANet::Shape input_shape,
CUDANet::Shape kernel_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::DType dtype,
CUDANet::Backend* backend
)
: in_shape(input_shape),
kernel_shape(kernel_shape),
@@ -36,6 +46,8 @@ Conv2d::Conv2d(
throw InvalidShapeException("padding", 3, padding_shape.size());
}
this->dtype = dtype;
out_shape = {
(in_shape[0] - kernel_shape[0] + 2 * padding_shape[0]) /
stride_shape[0] +
@@ -48,17 +60,17 @@ Conv2d::Conv2d(
output = CUDANet::Tensor(
Shape{out_shape[0], out_shape[1], out_shape[2]},
CUDANet::DType::FLOAT32, backend
dtype, backend
);
weights = CUDANet::Tensor(
Shape{
kernel_shape[0], kernel_shape[1], kernel_shape[2], in_shape[2]
},
CUDANet::DType::FLOAT32, backend
dtype, backend
);
biases = CUDANet::Tensor(
Shape{kernel_shape[2]}, CUDANet::DType::FLOAT32, backend
Shape{kernel_shape[2]}, dtype, backend
);
weights.zero();

11
src/layers/dense.cpp
View File

@@ -6,6 +6,9 @@
using namespace CUDANet::Layers;
Dense::Dense(CUDANet::Shape in_shape, CUDANet::Shape out_shape, CUDANet::Backend* backend)
: Dense(in_shape, out_shape, backend->get_default_dtype(), backend) {}
Dense::Dense(CUDANet::Shape in_shape, CUDANet::Shape out_shape, CUDANet::DType dtype, CUDANet::Backend* backend)
: backend(backend),
in_shape(in_shape),
out_shape(out_shape) {
@@ -18,9 +21,11 @@ Dense::Dense(CUDANet::Shape in_shape, CUDANet::Shape out_shape, CUDANet::Backend
throw InvalidShapeException("output", 1, out_shape.size());
}
weights = CUDANet::Tensor(Shape{out_shape[0], in_shape[0]}, CUDANet::DType::FLOAT32, backend);
biases = CUDANet::Tensor(Shape{out_shape[0]}, CUDANet::DType::FLOAT32, backend);
output = CUDANet::Tensor(Shape{out_shape[0]}, CUDANet::DType::FLOAT32, backend);
this->dtype = dtype;
weights = CUDANet::Tensor(Shape{out_shape[0], in_shape[0]}, dtype, backend);
biases = CUDANet::Tensor(Shape{out_shape[0]}, dtype, backend);
output = CUDANet::Tensor(Shape{out_shape[0]}, dtype, backend);
weights.zero();
biases.zero();

14
src/layers/max_pool.cpp
View File

@@ -10,6 +10,16 @@ MaxPool2d::MaxPool2d(
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Backend* backend
)
: MaxPool2d(input_shape, pool_shape, stride_shape, padding_shape, backend->get_default_dtype(), backend) {}
MaxPool2d::MaxPool2d(
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::DType dtype,
CUDANet::Backend* backend
)
: in_shape(input_shape),
pool_shape(pool_shape),
@@ -32,6 +42,8 @@ MaxPool2d::MaxPool2d(
throw InvalidShapeException("padding", 2, padding_shape.size());
}
this->dtype = dtype;
out_shape = {
(in_shape[0] + 2 * padding_shape[0] - pool_shape[0]) / stride_shape[0] +
1,
@@ -42,7 +54,7 @@ MaxPool2d::MaxPool2d(
output = CUDANet::Tensor(
Shape{out_shape[0] * out_shape[1] * out_shape[2]},
CUDANet::DType::FLOAT32, backend
dtype, backend
);
}