[Lang] Use ti.linalg.SparseMatrixBuilder as sparse matrix builder (ta…

…ichi-dev#3152) * move taichi/lang/linalg to taichi/lang/linalg_impl * change ti.SparseMatrixBuilder to ti.linalg.SparseMatrixBuilder
NoobTitan · Oct 11, 2021 · 9ef9c74 · 9ef9c74
1 parent c3478c7
commit 9ef9c74
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diff --git a/python/taichi/lang/__init__.py b/python/taichi/lang/__init__.py
@@ -6,7 +6,7 @@
 import time
 from copy import deepcopy as _deepcopy
 
-import taichi.lang.linalg
+import taichi.lang.linalg_impl
 import taichi.lang.meta
 from taichi.core.util import locale_encode
 from taichi.core.util import ti_core as _ti_core
@@ -515,7 +515,7 @@ def polar_decompose(A, dt=None):
     """Perform polar decomposition (A=UP) for arbitrary size matrix.
 
     Mathematical concept refers to https://en.wikipedia.org/wiki/Polar_decomposition.
-    This is only a wrapper for :func:`taichi.lang.linalg.polar_decompose`.
+    This is only a wrapper for :func:`taichi.lang.linalg_impl.polar_decompose`.
 
     Args:
         A (ti.Matrix(n, n)): input nxn matrix `A`.
@@ -526,14 +526,14 @@ def polar_decompose(A, dt=None):
     """
     if dt is None:
         dt = impl.get_runtime().default_fp
-    return taichi.lang.linalg.polar_decompose(A, dt)
+    return taichi.lang.linalg_impl.polar_decompose(A, dt)
 
 
 def svd(A, dt=None):
     """Perform singular value decomposition (A=USV^T) for arbitrary size matrix.
 
     Mathematical concept refers to https://en.wikipedia.org/wiki/Singular_value_decomposition.
-    This is only a wrappers for :func:`taichi.lang.linalg.svd`.
+    This is only a wrappers for :func:`taichi.lang.linalg_impl.svd`.
 
     Args:
         A (ti.Matrix(n, n)): input nxn matrix `A`.
@@ -544,14 +544,14 @@ def svd(A, dt=None):
     """
     if dt is None:
         dt = impl.get_runtime().default_fp
-    return taichi.lang.linalg.svd(A, dt)
+    return taichi.lang.linalg_impl.svd(A, dt)
 
 
 def eig(A, dt=None):
     """Compute the eigenvalues and right eigenvectors of a real matrix.
 
     Mathematical concept refers to https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix.
-    2D implementation refers to :func:`taichi.lang.linalg.eig2x2`.
+    2D implementation refers to :func:`taichi.lang.linalg_impl.eig2x2`.
 
     Args:
         A (ti.Matrix(n, n)): 2D Matrix for which the eigenvalues and right eigenvectors will be computed.
@@ -564,15 +564,15 @@ def eig(A, dt=None):
     if dt is None:
         dt = impl.get_runtime().default_fp
     if A.n == 2:
-        return taichi.lang.linalg.eig2x2(A, dt)
+        return taichi.lang.linalg_impl.eig2x2(A, dt)
     raise Exception("Eigen solver only supports 2D matrices.")
 
 
 def sym_eig(A, dt=None):
     """Compute the eigenvalues and right eigenvectors of a real symmetric matrix.
 
     Mathematical concept refers to https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix.
-    2D implementation refers to :func:`taichi.lang.linalg.sym_eig2x2`.
+    2D implementation refers to :func:`taichi.lang.linalg_impl.sym_eig2x2`.
 
     Args:
         A (ti.Matrix(n, n)): Symmetric Matrix for which the eigenvalues and right eigenvectors will be computed.
@@ -586,7 +586,7 @@ def sym_eig(A, dt=None):
     if dt is None:
         dt = impl.get_runtime().default_fp
     if A.n == 2:
-        return taichi.lang.linalg.sym_eig2x2(A, dt)
+        return taichi.lang.linalg_impl.sym_eig2x2(A, dt)
     raise Exception("Symmetric eigen solver only supports 2D matrices.")
 
 

diff --git a/python/taichi/lang/linalg.py → python/taichi/lang/linalg_impl.py b/python/taichi/lang/linalg.py → python/taichi/lang/linalg_impl.py
@@ -218,8 +218,8 @@ def svd(A, dt):
     """Perform singular value decomposition (A=USV^T) for arbitrary size matrix.
 
     Mathematical concept refers to https://en.wikipedia.org/wiki/Singular_value_decomposition.
-    2D implementation refers to :func:`taichi.lang.linalg.svd2d`.
-    3D implementation refers to :func:`taichi.lang.linalg.svd3d`.
+    2D implementation refers to :func:`taichi.lang.linalg_impl.svd2d`.
+    3D implementation refers to :func:`taichi.lang.linalg_impl.svd3d`.
 
     Args:
         A (ti.Matrix(n, n)): input nxn matrix `A`.
@@ -242,8 +242,8 @@ def polar_decompose(A, dt):
     """Perform polar decomposition (A=UP) for arbitrary size matrix.
 
     Mathematical concept refers to https://en.wikipedia.org/wiki/Polar_decomposition.
-    2D implementation refers to :func:`taichi.lang.linalg.polar_decompose2d`.
-    3D implementation refers to :func:`taichi.lang.linalg.polar_decompose3d`.
+    2D implementation refers to :func:`taichi.lang.linalg_impl.polar_decompose2d`.
+    3D implementation refers to :func:`taichi.lang.linalg_impl.polar_decompose3d`.
 
     Args:
         A (ti.Matrix(n, n)): input nxn matrix `A`.

diff --git a/tests/python/test_sparse_linear_solver.py b/tests/python/test_sparse_linear_solver.py
@@ -33,7 +33,7 @@
 @ti.test(arch=ti.cpu)
 def test_sparse_LLT_solver(solver_type):
     n = 4
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
     b = ti.field(ti.f32, shape=n)
 
     @ti.kernel

diff --git a/tests/python/test_sparse_matrix.py b/tests/python/test_sparse_matrix.py
@@ -4,7 +4,7 @@
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_builder():
     n = 8
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder()):
@@ -21,7 +21,7 @@ def fill(Abuilder: ti.sparse_matrix_builder()):
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_element_access():
     n = 8
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder()):
@@ -37,7 +37,7 @@ def fill(Abuilder: ti.sparse_matrix_builder()):
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_element_modify():
     n = 8
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder()):
@@ -53,8 +53,8 @@ def fill(Abuilder: ti.sparse_matrix_builder()):
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_addition():
     n = 8
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
-    Bbuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Bbuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder(),
@@ -75,8 +75,8 @@ def fill(Abuilder: ti.sparse_matrix_builder(),
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_subtraction():
     n = 8
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
-    Bbuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Bbuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder(),
@@ -97,7 +97,7 @@ def fill(Abuilder: ti.sparse_matrix_builder(),
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_scalar_multiplication():
     n = 8
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder()):
@@ -115,7 +115,7 @@ def fill(Abuilder: ti.sparse_matrix_builder()):
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_transpose():
     n = 8
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder()):
@@ -133,8 +133,8 @@ def fill(Abuilder: ti.sparse_matrix_builder()):
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_elementwise_multiplication():
     n = 8
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
-    Bbuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Bbuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder(),
@@ -155,8 +155,8 @@ def fill(Abuilder: ti.sparse_matrix_builder(),
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_multiplication():
     n = 2
-    Abuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
-    Bbuilder = ti.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
+    Bbuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder(),
@@ -178,8 +178,8 @@ def fill(Abuilder: ti.sparse_matrix_builder(),
 @ti.test(arch=ti.cpu)
 def test_sparse_matrix_nonsymmetric_multiplication():
     n, k, m = 2, 3, 4
-    Abuilder = ti.SparseMatrixBuilder(n, k, max_num_triplets=100)
-    Bbuilder = ti.SparseMatrixBuilder(k, m, max_num_triplets=100)
+    Abuilder = ti.linalg.SparseMatrixBuilder(n, k, max_num_triplets=100)
+    Bbuilder = ti.linalg.SparseMatrixBuilder(k, m, max_num_triplets=100)
 
     @ti.kernel
     def fill(Abuilder: ti.sparse_matrix_builder(),