276 lines
8.2 KiB
Cython
276 lines
8.2 KiB
Cython
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# cython: infer_types=True
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# cython: cdivision=True
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cimport cython
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from cymem.cymem cimport Pool
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from libc.stdint cimport int32_t
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from libc.string cimport memcpy, memset
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ctypedef float weight_t
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DEF USE_BLAS = False
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DEF EPS = 1e-5
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IF USE_BLAS:
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cimport blis.cy
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cdef extern from "math.h" nogil:
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weight_t exp(weight_t x)
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weight_t sqrt(weight_t x)
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cdef class Matrix:
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cdef readonly Pool mem
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cdef weight_t* data
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cdef readonly int32_t nr_row
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cdef readonly int32_t nr_col
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cdef class Vec:
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@staticmethod
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cdef inline int arg_max(const weight_t* scores, const int n_classes) nogil:
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if n_classes == 2:
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return 0 if scores[0] > scores[1] else 1
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cdef int i
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cdef int best = 0
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cdef weight_t mode = scores[0]
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for i in range(1, n_classes):
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if scores[i] > mode:
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mode = scores[i]
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best = i
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return best
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@staticmethod
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cdef inline weight_t max(const weight_t* x, int32_t nr) nogil:
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if nr == 0:
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return 0
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cdef int i
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cdef weight_t mode = x[0]
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for i in range(1, nr):
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if x[i] > mode:
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mode = x[i]
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return mode
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@staticmethod
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cdef inline weight_t sum(const weight_t* vec, int32_t nr) nogil:
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cdef int i
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cdef weight_t total = 0
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for i in range(nr):
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total += vec[i]
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return total
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@staticmethod
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cdef inline weight_t norm(const weight_t* vec, int32_t nr) nogil:
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cdef weight_t total = 0
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for i in range(nr):
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total += vec[i] ** 2
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return sqrt(total)
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@staticmethod
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cdef inline void add(weight_t* output, const weight_t* x,
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weight_t inc, int32_t nr) nogil:
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memcpy(output, x, sizeof(output[0]) * nr)
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Vec.add_i(output, inc, nr)
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@staticmethod
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cdef inline void add_i(weight_t* vec, weight_t inc, int32_t nr) nogil:
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cdef int i
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for i in range(nr):
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vec[i] += inc
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@staticmethod
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cdef inline void mul(weight_t* output, const weight_t* vec, weight_t scal,
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int32_t nr) nogil:
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memcpy(output, vec, sizeof(output[0]) * nr)
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Vec.mul_i(output, scal, nr)
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@staticmethod
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cdef inline void mul_i(weight_t* vec, weight_t scal, int32_t nr) nogil:
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cdef int i
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IF USE_BLAS:
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blis.cy.scalv(BLIS_NO_CONJUGATE, nr, scal, vec, 1)
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ELSE:
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for i in range(nr):
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vec[i] *= scal
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@staticmethod
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cdef inline void pow(weight_t* output, const weight_t* vec, weight_t scal,
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int32_t nr) nogil:
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memcpy(output, vec, sizeof(output[0]) * nr)
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Vec.pow_i(output, scal, nr)
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@staticmethod
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cdef inline void pow_i(weight_t* vec, const weight_t scal, int32_t nr) nogil:
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cdef int i
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for i in range(nr):
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vec[i] **= scal
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@staticmethod
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@cython.cdivision(True)
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cdef inline void div(weight_t* output, const weight_t* vec, weight_t scal,
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int32_t nr) nogil:
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memcpy(output, vec, sizeof(output[0]) * nr)
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Vec.div_i(output, scal, nr)
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@staticmethod
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@cython.cdivision(True)
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cdef inline void div_i(weight_t* vec, const weight_t scal, int32_t nr) nogil:
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cdef int i
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for i in range(nr):
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vec[i] /= scal
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@staticmethod
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cdef inline void exp(weight_t* output, const weight_t* vec, int32_t nr) nogil:
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memcpy(output, vec, sizeof(output[0]) * nr)
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Vec.exp_i(output, nr)
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@staticmethod
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cdef inline void exp_i(weight_t* vec, int32_t nr) nogil:
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cdef int i
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for i in range(nr):
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vec[i] = exp(vec[i])
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@staticmethod
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cdef inline void reciprocal_i(weight_t* vec, int32_t nr) nogil:
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cdef int i
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for i in range(nr):
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vec[i] = 1.0 / vec[i]
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@staticmethod
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cdef inline weight_t mean(const weight_t* X, int32_t nr_dim) nogil:
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cdef weight_t mean = 0.
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for x in X[:nr_dim]:
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mean += x
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return mean / nr_dim
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@staticmethod
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cdef inline weight_t variance(const weight_t* X, int32_t nr_dim) nogil:
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# See https://www.johndcook.com/blog/standard_deviation/
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cdef double m = X[0]
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cdef double v = 0.
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for i in range(1, nr_dim):
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diff = X[i]-m
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m += diff / (i+1)
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v += diff * (X[i] - m)
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return v / nr_dim
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cdef class VecVec:
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@staticmethod
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cdef inline void add(weight_t* output,
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const weight_t* x,
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const weight_t* y,
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weight_t scale,
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int32_t nr) nogil:
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memcpy(output, x, sizeof(output[0]) * nr)
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VecVec.add_i(output, y, scale, nr)
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@staticmethod
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cdef inline void add_i(weight_t* x,
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const weight_t* y,
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weight_t scale,
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int32_t nr) nogil:
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cdef int i
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IF USE_BLAS:
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blis.cy.axpyv(BLIS_NO_CONJUGATE, nr, scale, y, 1, x, 1)
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ELSE:
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for i in range(nr):
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x[i] += y[i] * scale
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@staticmethod
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cdef inline void batch_add_i(weight_t* x,
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const weight_t* y,
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weight_t scale,
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int32_t nr, int32_t nr_batch) nogil:
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# For fixed x, matrix of y
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cdef int i, _
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for _ in range(nr_batch):
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VecVec.add_i(x,
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y, scale, nr)
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y += nr
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@staticmethod
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cdef inline void add_pow(weight_t* output,
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const weight_t* x, const weight_t* y, weight_t power, int32_t nr) nogil:
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memcpy(output, x, sizeof(output[0]) * nr)
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VecVec.add_pow_i(output, y, power, nr)
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@staticmethod
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cdef inline void add_pow_i(weight_t* x,
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const weight_t* y, weight_t power, int32_t nr) nogil:
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cdef int i
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for i in range(nr):
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x[i] += y[i] ** power
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@staticmethod
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cdef inline void mul(weight_t* output,
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const weight_t* x, const weight_t* y, int32_t nr) nogil:
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memcpy(output, x, sizeof(output[0]) * nr)
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VecVec.mul_i(output, y, nr)
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@staticmethod
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cdef inline void mul_i(weight_t* x,
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const weight_t* y, int32_t nr) nogil:
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cdef int i
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for i in range(nr):
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x[i] *= y[i]
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@staticmethod
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cdef inline weight_t dot(
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const weight_t* x, const weight_t* y, int32_t nr) nogil:
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cdef int i
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cdef weight_t total = 0
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for i in range(nr):
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total += x[i] * y[i]
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return total
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@staticmethod
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cdef inline int arg_max_if_true(
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const weight_t* scores, const int* is_valid, const int n_classes) nogil:
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cdef int i
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cdef int best = -1
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for i in range(n_classes):
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if is_valid[i] and (best == -1 or scores[i] > scores[best]):
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best = i
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return best
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@staticmethod
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cdef inline int arg_max_if_zero(
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const weight_t* scores, const weight_t* costs, const int n_classes) nogil:
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cdef int i
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cdef int best = -1
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for i in range(n_classes):
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if costs[i] == 0 and (best == -1 or scores[i] > scores[best]):
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best = i
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return best
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cdef class Mat:
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@staticmethod
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cdef inline void mean_row(weight_t* Ex,
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const weight_t* mat, int32_t nr_row, int32_t nr_col) nogil:
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memset(Ex, 0, sizeof(Ex[0]) * nr_col)
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for i in range(nr_row):
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VecVec.add_i(Ex, &mat[i * nr_col], 1.0, nr_col)
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Vec.mul_i(Ex, 1.0 / nr_row, nr_col)
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@staticmethod
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cdef inline void var_row(weight_t* Vx,
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const weight_t* mat, const weight_t* Ex,
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int32_t nr_row, int32_t nr_col, weight_t eps) nogil:
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# From https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
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if nr_row == 0 or nr_col == 0:
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return
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cdef weight_t sum_, sum2
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for i in range(nr_col):
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sum_ = 0.0
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sum2 = 0.0
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for j in range(nr_row):
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x = mat[j * nr_col + i]
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sum2 += (x - Ex[i]) ** 2
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sum_ += x - Ex[i]
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Vx[i] = (sum2 - sum_**2 / nr_row) / nr_row
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Vx[i] += eps
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