File size: 3,460 Bytes
26d5b81 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | #ifndef NEUROFLOW_TENSOR_OPS_HPP
#define NEUROFLOW_TENSOR_OPS_HPP
namespace neuroflow {
class TensorOps {
public:
static void gemm(const Tensor& A, const Tensor& B, Tensor& C,
bool transA = false, bool transB = false,
float alpha = 1.0f, float beta = 0.0f);
static void quantized_gemm(const Tensor& A, const Tensor& B, Tensor& C);
static void layer_norm(Tensor& x, Tensor& weight, Tensor& bias, float eps = 1e-5f);
static void gelu(Tensor& x);
static void softmax(Tensor& x, int axis = -1);
static void dropout(Tensor& x, float rate, bool training = true);
static void quantize_int8(const Tensor& src, Tensor& dst, Tensor& scale);
static void dequantize_int8(const Tensor& src, Tensor& dst, const Tensor& scale);
static void add(Tensor& a, const Tensor& b);
static void mul(Tensor& a, float scalar);
static void parallel_gemm(const Tensor& A, const Tensor& B, Tensor& C,
bool transA = false, bool transB = false,
float alpha = 1.0f, float beta = 0.0f,
size_t num_threads = 4);
static Tensor matmul(const Tensor& A, const Tensor& B);
static Tensor elementwise_add(const Tensor& A, const Tensor& B);
static Tensor elementwise_sub(const Tensor& A, const Tensor& B);
static Tensor elementwise_mul(const Tensor& A, const Tensor& B);
static Tensor scalar_mul(const Tensor& A, float s);
static Tensor broadcast_add(const Tensor& A, const Tensor& B);
static Tensor transpose2d(const Tensor& A);
static void fill(Tensor& A, float value);
static void copy_data(Tensor& dst, const Tensor& src);
static float dot(const Tensor& A, const Tensor& B);
static float norm2(const Tensor& A);
static Tensor reduce_sum(const Tensor& A, int axis = -1);
static Tensor slice(const Tensor& A, size_t dim, size_t start, size_t end);
static Tensor pad1d(const Tensor& A, size_t left, size_t right, float value = 0.0f);
static void apply_inplace(Tensor& A, std::function<float(float)> fn);
static Tensor apply(const Tensor& A, std::function<float(float)> fn);
static Tensor relu(const Tensor& A);
static Tensor sigmoid(const Tensor& A);
static Tensor tanh_act(const Tensor& A);
static Tensor log(const Tensor& A);
static Tensor exp(const Tensor& A);
static void clip_inplace(Tensor& A, float min_val, float max_val);
static Tensor concat(const std::vector<Tensor>& tensors, size_t axis = 0);
private:
static void gemm_scalar(const float* a, const float* b, float* c,
size_t M, size_t K, size_t N,
bool transA, bool transB,
float alpha, float beta);
#ifdef HAS_AVX2
static void gemm_avx2(const float* a, const float* b, float* c,
size_t M, size_t K, size_t N,
bool transA, bool transB,
float alpha, float beta);
#endif
#ifdef HAS_NEON
static void gemm_neon(const float* a, const float* b, float* c,
size_t M, size_t K, size_t N,
bool transA, bool transB,
float alpha, float beta);
#endif
};
} // namespace neuroflow
#endif // NEUROFLOW_TENSOR_OPS_HPP |