mirror of
https://github.com/lordmathis/CUDANet.git
synced 2025-12-22 14:24:22 +00:00
115 lines
3.1 KiB
C++
115 lines
3.1 KiB
C++
#pragma once
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#include <memory>
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#include "shape.hpp"
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namespace CUDANet {
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// Forward declaration
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class Tensor;
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class Backend;
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enum BackendType { CUDA_BACKEND, CPU_BACKEND };
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struct BackendConfig {
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int device_id = 0;
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};
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class BackendFactory {
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public:
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static std::unique_ptr<Backend> create(BackendType backend_type, const BackendConfig& config);
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};
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class Backend {
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public:
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// Memory management
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virtual void* allocate(size_t bytes) = 0;
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virtual void deallocate(void* ptr) = 0;
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// Tensor ops
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virtual void print(const CUDANet::Tensor& input) = 0;
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virtual void zero(CUDANet::Tensor& input) = 0;
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virtual void fill(CUDANet::Tensor& input, int data) = 0;
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virtual void
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copy_to_device(CUDANet::Tensor& tensor, void* data, size_t size) = 0;
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virtual void sum(const CUDANet::Tensor& input, CUDANet::Tensor& sum) = 0;
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virtual void max(const CUDANet::Tensor& input, CUDANet::Tensor& max) = 0;
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// Layer ops
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virtual void relu(CUDANet::Tensor& tensor) = 0;
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virtual void sigmoid(CUDANet::Tensor& tensor) = 0;
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virtual void softmax(
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CUDANet::Tensor& tensor,
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CUDANet::Tensor& temp_max,
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CUDANet::Tensor& temp_sum
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) = 0;
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virtual CUDANet::Tensor& dense(
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const CUDANet::Tensor& weights,
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const CUDANet::Tensor& biases,
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const CUDANet::Tensor& input,
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CUDANet::Tensor& output,
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const size_t input_size,
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const size_t output_size
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) = 0;
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virtual CUDANet::Tensor& conv2d(
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const CUDANet::Tensor& weights,
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const CUDANet::Tensor& biases,
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const CUDANet::Tensor& input,
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CUDANet::Tensor& output,
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const CUDANet::Shape in_shape,
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const CUDANet::Shape padding_shape,
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const CUDANet::Shape kernel_shape,
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const CUDANet::Shape stride_shape,
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const CUDANet::Shape out_shape
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) = 0;
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virtual CUDANet::Tensor& max_pool2d(
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const CUDANet::Tensor& input,
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CUDANet::Tensor& output,
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CUDANet::Shape input_shape,
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CUDANet::Shape pool_shape,
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CUDANet::Shape stride_shape,
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CUDANet::Shape padding_shape,
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CUDANet::Shape output_shape
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) = 0;
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virtual CUDANet::Tensor& avg_pool2d(
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const CUDANet::Tensor& input,
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CUDANet::Tensor& output,
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CUDANet::Shape input_shape,
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CUDANet::Shape pool_shape,
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CUDANet::Shape stride_shape,
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CUDANet::Shape padding_shape,
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CUDANet::Shape output_shape
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) = 0;
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virtual CUDANet::Tensor& batch_norm(
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const CUDANet::Tensor& input,
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CUDANet::Tensor& output,
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CUDANet::Shape input_shape,
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CUDANet::Tensor& weights,
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CUDANet::Tensor& biases,
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CUDANet::Tensor& running_mean,
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CUDANet::Tensor& running_var,
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CUDANet::Tensor& epsilon
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) = 0;
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virtual CUDANet::Tensor& concat(
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CUDANet::Tensor& input_a,
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CUDANet::Tensor& input_b,
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CUDANet::Tensor& output
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) = 0;
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virtual CUDANet::Tensor& add(
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CUDANet::Tensor& input_a,
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CUDANet::Tensor& input_b,
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CUDANet::Tensor& output
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) = 0;
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};
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} // namespace CUDANet
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