Cycles: remove prefix from source code file names

Remove prefix of filenames that is the same as the folder name. This used
to help when #includes were using individual files, but now they are always
relative to the cycles root directory and so the prefixes are redundant.

For patches and branches, git merge and rebase should be able to detect the
renames and move over code to the right file.

1 files changed, 454 insertions, 0 deletions

diff --git a/intern/cycles/util/math_matrix.h b/intern/cycles/util/math_matrix.h
new file mode 100644
index 00000000000..bff7ddb4cee
--- /dev/null
+++ b/intern/cycles/util/math_matrix.h
@@ -0,0 +1,454 @@
+/*
+ * 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(ccl_private float *v, int n)
+{
+  for (int i = 0; i < n; i++) {
+    v[i] = 0.0f;
+  }
+}
+
+ccl_device_inline void math_matrix_zero(ccl_private 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(ccl_private float *a,
+                                       ccl_private const float *ccl_restrict b,
+                                       int n)
+{
+  for (int i = 0; i < n; i++) {
+    a[i] += b[i];
+  }
+}
+
+ccl_device_inline void math_vector_mul(ccl_private float *a,
+                                       ccl_private const float *ccl_restrict 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,
+                                               ccl_private const float *ccl_restrict b,
+                                               int astride,
+                                               int n)
+{
+  for (int i = 0; i < n; i++) {
+    a[i * astride] *= b[i];
+  }
+}
+
+ccl_device_inline void math_vector_scale(ccl_private float *a, float b, int n)
+{
+  for (int i = 0; i < n; i++) {
+    a[i] *= b;
+  }
+}
+
+ccl_device_inline void math_vector_max(ccl_private float *a,
+                                       ccl_private const float *ccl_restrict b,
+                                       int n)
+{
+  for (int i = 0; i < n; i++) {
+    a[i] = max(a[i], b[i]);
+  }
+}
+
+ccl_device_inline void math_vec3_add(ccl_private float3 *v, int n, ccl_private 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, ccl_private float *x, float3 w, int stride)
+{
+  for (int i = 0; i < n; i++) {
+    ccl_global float *elem = (ccl_global float *)(v + i * stride);
+    atomic_add_and_fetch_float(elem + 0, w.x * x[i]);
+    atomic_add_and_fetch_float(elem + 1, w.y * x[i]);
+    atomic_add_and_fetch_float(elem + 2, w.z * 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(ccl_private float *A,
+                                               int n,
+                                               ccl_private const float *ccl_restrict 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, ccl_private const float *ccl_restrict v, float weight, int stride)
+{
+  for (int row = 0; row < n; row++) {
+    for (int col = 0; col <= row; col++) {
+      atomic_add_and_fetch_float(&MATHS(A, row, col, stride), v[row] * v[col] * weight);
+    }
+  }
+}
+
+ccl_device_inline void math_trimatrix_add_gramian(ccl_global float *A,
+                                                  int n,
+                                                  ccl_private const float *ccl_restrict v,
+                                                  float weight)
+{
+  for (int row = 0; row < n; row++) {
+    for (int col = 0; col <= row; col++) {
+      MATHS(A, row, col, 1) += v[row] * v[col] * weight;
+    }
+  }
+}
+
+/* Transpose matrix A in place. */
+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-semi-definite 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 Method 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(ccl_private 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(float4 *A, int n)
+{
+  for (int i = 0; i < n; i++) {
+    A[i] = make_float4(0.0f);
+  }
+}
+
+ccl_device_inline void math_matrix_zero_sse(float4 *A, int n)
+{
+  for (int row = 0; row < n; row++) {
+    for (int col = 0; col <= row; col++) {
+      MAT(A, n, row, col) = make_float4(0.0f);
+    }
+  }
+}
+
+/* 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(float4 *A,
+                                                   int n,
+                                                   const float4 *ccl_restrict v,
+                                                   float4 weight)
+{
+  for (int row = 0; row < n; row++) {
+    for (int col = 0; col <= row; col++) {
+      MAT(A, n, row, col) = MAT(A, n, row, col) + v[row] * v[col] * weight;
+    }
+  }
+}
+
+ccl_device_inline void math_vector_add_sse(float4 *V, int n, const float4 *ccl_restrict a)
+{
+  for (int i = 0; i < n; i++) {
+    V[i] += a[i];
+  }
+}
+
+ccl_device_inline void math_vector_mul_sse(float4 *V, int n, const float4 *ccl_restrict a)
+{
+  for (int i = 0; i < n; i++) {
+    V[i] *= a[i];
+  }
+}
+
+ccl_device_inline void math_vector_max_sse(float4 *a, const float4 *ccl_restrict b, int n)
+{
+  for (int i = 0; i < n; i++) {
+    a[i] = max(a[i], b[i]);
+  }
+}
+
+ccl_device_inline void math_matrix_hsum(float *A, int n, const float4 *ccl_restrict B)
+{
+  for (int row = 0; row < n; row++) {
+    for (int col = 0; col <= row; col++) {
+      MAT(A, n, row, col) = reduce_add(MAT(B, n, row, col))[0];
+    }
+  }
+}
+#endif
+
+#undef MAT
+
+CCL_NAMESPACE_END
+
+#endif /* __UTIL_MATH_MATRIX_H__ */