[blender.git] / intern / cycles / util / util_math_matrix.h

/*
 * Copyright 2011-2017 Blender Foundation
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#ifndef __UTIL_MATH_MATRIX_H__
#define __UTIL_MATH_MATRIX_H__

CCL_NAMESPACE_BEGIN

#define MAT(A, size, row, col) A[(row)*(size)+(col)]

/* Variants that use a constant stride on GPUS. */
#ifdef __KERNEL_GPU__
#define MATS(A, n, r, c, s) A[((r)*(n)+(c))*(s)]
/* Element access when only the lower-triangular elements are stored. */
#define MATHS(A, r, c, s) A[((r)*((r)+1)/2+(c))*(s)]
#define VECS(V, i, s) V[(i)*(s)]
#else
#define MATS(A, n, r, c, s) MAT(A, n, r, c)
#define MATHS(A, r, c, s) A[(r)*((r)+1)/2+(c)]
#define VECS(V, i, s) V[i]
#endif

/* Zeroing helpers. */

ccl_device_inline void math_vector_zero(float *v, int n)
{
        for(int i = 0; i < n; i++)
                v[i] = 0.0f;
}

ccl_device_inline void math_matrix_zero(float *A, int n)
{
        for(int row = 0; row < n; row++)
                for(int col = 0; col <= row; col++)
                        MAT(A, n, row, col) = 0.0f;
}

/* Elementary vector operations. */

ccl_device_inline void math_vector_add(float *a, float ccl_restrict_ptr b, int n)
{
        for(int i = 0; i < n; i++)
                a[i] += b[i];
}

ccl_device_inline void math_vector_mul(float *a, float ccl_restrict_ptr b, int n)
{
        for(int i = 0; i < n; i++)
                a[i] *= b[i];
}

ccl_device_inline void math_vector_mul_strided(ccl_global float *a, float ccl_restrict_ptr b, int astride, int n)
{
        for(int i = 0; i < n; i++)
                a[i*astride] *= b[i];
}

ccl_device_inline void math_vector_scale(float *a, float b, int n)
{
        for(int i = 0; i < n; i++)
                a[i] *= b;
}

ccl_device_inline void math_vector_max(float *a, float ccl_restrict_ptr b, int n)
{
        for(int i = 0; i < n; i++)
                a[i] = max(a[i], b[i]);
}

ccl_device_inline void math_vec3_add(float3 *v, int n, float *x, float3 w)
{
        for(int i = 0; i < n; i++)
                v[i] += w*x[i];
}

ccl_device_inline void math_vec3_add_strided(ccl_global float3 *v, int n, float *x, float3 w, int stride)
{
        for(int i = 0; i < n; i++)
                v[i*stride] += w*x[i];
}

/* Elementary matrix operations.
 * Note: TriMatrix refers to a square matrix that is symmetric, and therefore its upper-triangular part isn't stored. */

ccl_device_inline void math_trimatrix_add_diagonal(ccl_global float *A, int n, float val, int stride)
{
        for(int row = 0; row < n; row++)
                MATHS(A, row, row, stride) += val;
}

/* Add Gramian matrix of v to A.
 * The Gramian matrix of v is vt*v, so element (i,j) is v[i]*v[j]. */
ccl_device_inline void math_matrix_add_gramian(float *A,
                                                  int n,
                                                  float ccl_restrict_ptr v,
                                                  float weight)
{
        for(int row = 0; row < n; row++)
                for(int col = 0; col <= row; col++)
                        MAT(A, n, row, col) += v[row]*v[col]*weight;
}

/* Add Gramian matrix of v to A.
 * The Gramian matrix of v is vt*v, so element (i,j) is v[i]*v[j]. */
ccl_device_inline void math_trimatrix_add_gramian_strided(ccl_global float *A,
                                                          int n,
                                                          float ccl_restrict_ptr v,
                                                          float weight,
                                                          int stride)
{
        for(int row = 0; row < n; row++)
                for(int col = 0; col <= row; col++)
                        MATHS(A, row, col, stride) += v[row]*v[col]*weight;
}

/* Transpose matrix A inplace. */
ccl_device_inline void math_matrix_transpose(ccl_global float *A, int n, int stride)
{
        for(int i = 0; i < n; i++) {
                for(int j = 0; j < i; j++) {
                        float temp = MATS(A, n, i, j, stride);
                        MATS(A, n, i, j, stride) = MATS(A, n, j, i, stride);
                        MATS(A, n, j, i, stride) = temp;
                }
        }
}




/* Solvers for matrix problems */

/* In-place Cholesky-Banachiewicz decomposition of the square, positive-definite matrix A
 * into a lower triangular matrix L so that A = L*L^T. A is being overwritten by L.
 * Also, only the lower triangular part of A is ever accessed. */
ccl_device void math_trimatrix_cholesky(ccl_global float *A, int n, int stride)
{
        for(int row = 0; row < n; row++) {
                for(int col = 0; col <= row; col++) {
                        float sum_col = MATHS(A, row, col, stride);
                        for(int k = 0; k < col; k++) {
                                sum_col -= MATHS(A, row, k, stride) * MATHS(A, col, k, stride);
                        }
                        if(row == col) {
                                sum_col = sqrtf(max(sum_col, 0.0f));
                        }
                        else {
                                sum_col /= MATHS(A, col, col, stride);
                        }
                        MATHS(A, row, col, stride) = sum_col;
                }
        }
}

/* Solve A*S=y for S given A and y, where A is symmetrical positive-semidefinite and both inputs are destroyed in the process.
 *
 * We can apply Cholesky decomposition to find a lower triangular L so that L*Lt = A.
 * With that we get (L*Lt)*S = L*(Lt*S) = L*b = y, defining b as Lt*S.
 * Since L is lower triangular, finding b is relatively easy since y is known.
 * Then, the remaining problem is Lt*S = b, which again can be solved easily.
 *
 * This is useful for solving the normal equation S=inv(Xt*W*X)*Xt*W*y, since Xt*W*X is
 * symmetrical positive-semidefinite by construction, so we can just use this function with A=Xt*W*X and y=Xt*W*y. */
ccl_device_inline void math_trimatrix_vec3_solve(ccl_global float *A, ccl_global float3 *y, int n, int stride)
{
        /* Since the first entry of the design row is always 1, the upper-left element of XtWX is a good
         * heuristic for the amount of pixels considered (with weighting), therefore the amount of correction
         * is scaled based on it. */
        math_trimatrix_add_diagonal(A, n, 3e-7f*A[0], stride); /* Improve the numerical stability. */
        math_trimatrix_cholesky(A, n, stride); /* Replace A with L so that L*Lt = A. */

        /* Use forward substitution to solve L*b = y, replacing y by b. */
        for(int row = 0; row < n; row++) {
                float3 sum = VECS(y, row, stride);
                for(int col = 0; col < row; col++)
                        sum -= MATHS(A, row, col, stride) * VECS(y, col, stride);
                VECS(y, row, stride) = sum / MATHS(A, row, row, stride);
        }

        /* Use backward substitution to solve Lt*S = b, replacing b by S. */
        for(int row = n-1; row >= 0; row--) {
                float3 sum = VECS(y, row, stride);
                for(int col = row+1; col < n; col++)
                        sum -= MATHS(A, col, row, stride) * VECS(y, col, stride);
                VECS(y, row, stride) = sum / MATHS(A, row, row, stride);
        }
}





/* Perform the Jacobi Eigenvalue Methon on matrix A.
 * A is assumed to be a symmetrical matrix, therefore only the lower-triangular part is ever accessed.
 * The algorithm overwrites the contents of A.
 *
 * After returning, A will be overwritten with D, which is (almost) diagonal,
 * and V will contain the eigenvectors of the original A in its rows (!),
 * so that A = V^T*D*V. Therefore, the diagonal elements of D are the (sorted) eigenvalues of A.
 */
ccl_device void math_matrix_jacobi_eigendecomposition(float *A, ccl_global float *V, int n, int v_stride)
{
        const float singular_epsilon = 1e-9f;

        for (int row = 0; row < n; row++)
                for (int col = 0; col < n; col++)
                        MATS(V, n, row, col, v_stride) = (col == row) ? 1.0f : 0.0f;

        for (int sweep = 0; sweep < 8; sweep++) {
                float off_diagonal = 0.0f;
                for (int row = 1; row < n; row++)
                        for (int col = 0; col < row; col++)
                                off_diagonal += fabsf(MAT(A, n, row, col));
                if (off_diagonal < 1e-7f) {
                        /* The matrix has nearly reached diagonal form.
                         * Since the eigenvalues are only used to determine truncation, their exact values aren't required - a relative error of a few ULPs won't matter at all. */
                        break;
                }

                /* Set the threshold for the small element rotation skip in the first sweep:
                 * Skip all elements that are less than a tenth of the average off-diagonal element. */
                float threshold = 0.2f*off_diagonal / (n*n);

                for(int row = 1; row < n; row++) {
                        for(int col = 0; col < row; col++) {
                                /* Perform a Jacobi rotation on this element that reduces it to zero. */
                                float element = MAT(A, n, row, col);
                                float abs_element = fabsf(element);

                                /* If we're in a later sweep and the element already is very small, just set it to zero and skip the rotation. */
                                if (sweep > 3 && abs_element <= singular_epsilon*fabsf(MAT(A, n, row, row)) && abs_element <= singular_epsilon*fabsf(MAT(A, n, col, col))) {
                                        MAT(A, n, row, col) = 0.0f;
                                        continue;
                                }

                                if(element == 0.0f) {
                                        continue;
                                }

                                /* If we're in one of the first sweeps and the element is smaller than the threshold, skip it. */
                                if(sweep < 3 && (abs_element < threshold)) {
                                        continue;
                                }

                                /* Determine rotation: The rotation is characterized by its angle phi - or, in the actual implementation, sin(phi) and cos(phi).
                                 * To find those, we first compute their ratio - that might be unstable if the angle approaches 90°, so there's a fallback for that case.
                                 * Then, we compute sin(phi) and cos(phi) themselves. */
                                float singular_diff = MAT(A, n, row, row) - MAT(A, n, col, col);
                                float ratio;
                                if (abs_element > singular_epsilon*fabsf(singular_diff)) {
                                        float cot_2phi = 0.5f*singular_diff / element;
                                        ratio = 1.0f / (fabsf(cot_2phi) + sqrtf(1.0f + cot_2phi*cot_2phi));
                                        if (cot_2phi < 0.0f) ratio = -ratio; /* Copy sign. */
                                }
                                else {
                                        ratio = element / singular_diff;
                                }

                                float c = 1.0f / sqrtf(1.0f + ratio*ratio);
                                float s = ratio*c;
                                /* To improve numerical stability by avoiding cancellation, the update equations are reformulized to use sin(phi) and tan(phi/2) instead. */
                                float tan_phi_2 = s / (1.0f + c);

                                /* Update the singular values in the diagonal. */
                                float singular_delta = ratio*element;
                                MAT(A, n, row, row) += singular_delta;
                                MAT(A, n, col, col) -= singular_delta;

                                /* Set the element itself to zero. */
                                MAT(A, n, row, col) = 0.0f;

                                /* Perform the actual rotations on the matrices. */
#define ROT(M, r1, c1, r2, c2, stride)                                   \
                                {                                                        \
                                        float M1 = MATS(M, n, r1, c1, stride);               \
                                        float M2 = MATS(M, n, r2, c2, stride);               \
                                        MATS(M, n, r1, c1, stride) -= s*(M2 + tan_phi_2*M1); \
                                        MATS(M, n, r2, c2, stride) += s*(M1 - tan_phi_2*M2); \
                                }

                                /* Split into three parts to ensure correct accesses since we only store the lower-triangular part of A. */
                                for(int i = 0    ; i < col; i++) ROT(A, col, i, row, i, 1);
                                for(int i = col+1; i < row; i++) ROT(A, i, col, row, i, 1);
                                for(int i = row+1; i < n  ; i++) ROT(A, i, col, i, row, 1);

                                for(int i = 0    ; i < n  ; i++) ROT(V, col, i, row, i, v_stride);
#undef ROT
                        }
                }
        }

        /* Sort eigenvalues and the associated eigenvectors. */
        for (int i = 0; i < n - 1; i++) {
                float v = MAT(A, n, i, i);
                int k = i;
                for (int j = i; j < n; j++) {
                        if (MAT(A, n, j, j) >= v) {
                                v = MAT(A, n, j, j);
                                k = j;
                        }
                }
                if (k != i) {
                        /* Swap eigenvalues. */
                        MAT(A, n, k, k) = MAT(A, n, i, i);
                        MAT(A, n, i, i) = v;
                        /* Swap eigenvectors. */
                        for (int j = 0; j < n; j++) {
                                float v = MATS(V, n, i, j, v_stride);
                                MATS(V, n, i, j, v_stride) = MATS(V, n, k, j, v_stride);
                                MATS(V, n, k, j, v_stride) = v;
                        }
                }
        }
}

#ifdef __KERNEL_SSE3__

ccl_device_inline void math_vector_zero_sse(__m128 *A, int n)
{
        for(int i = 0; i < n; i++)
                A[i] = _mm_setzero_ps();
}
ccl_device_inline void math_matrix_zero_sse(__m128 *A, int n)
{
        for(int row = 0; row < n; row++)
                for(int col = 0; col <= row; col++)
                        MAT(A, n, row, col) = _mm_setzero_ps();
}

/* Add Gramian matrix of v to A.
 * The Gramian matrix of v is v^T*v, so element (i,j) is v[i]*v[j]. */
ccl_device_inline void math_matrix_add_gramian_sse(__m128 *A, int n, __m128 ccl_restrict_ptr v, __m128 weight)
{
        for(int row = 0; row < n; row++)
                for(int col = 0; col <= row; col++)
                        MAT(A, n, row, col) = _mm_add_ps(MAT(A, n, row, col), _mm_mul_ps(_mm_mul_ps(v[row], v[col]), weight));
}

ccl_device_inline void math_vector_add_sse(__m128 *V, int n, __m128 ccl_restrict_ptr a)
{
        for(int i = 0; i < n; i++)
                V[i] = _mm_add_ps(V[i], a[i]);
}

ccl_device_inline void math_vector_mul_sse(__m128 *V, int n, __m128 ccl_restrict_ptr a)
{
        for(int i = 0; i < n; i++)
                V[i] = _mm_mul_ps(V[i], a[i]);
}

ccl_device_inline void math_vector_max_sse(__m128 *a, __m128 ccl_restrict_ptr b, int n)
{
        for(int i = 0; i < n; i++)
                a[i] = _mm_max_ps(a[i], b[i]);
}

ccl_device_inline void math_matrix_hsum(float *A, int n, __m128 ccl_restrict_ptr B)
{
        for(int row = 0; row < n; row++)
                for(int col = 0; col <= row; col++)
                        MAT(A, n, row, col) = _mm_hsum_ss(MAT(B, n, row, col));
}
#endif

#undef MAT

CCL_NAMESPACE_END

#endif  /* __UTIL_MATH_MATRIX_H__ */