【Hackathon 4 No.9】Add pca_lowrank API to Paddle

python/paddle/linalg.py

@@ -29,6 +29,7 @@
 from .tensor.linalg import matrix_rank  # noqa: F401
 from .tensor.linalg import multi_dot  # noqa: F401
 from .tensor.linalg import norm  # noqa: F401
+from .tensor.linalg import pca_lowrank  # noqa: F401
 from .tensor.linalg import pinv  # noqa: F401
 from .tensor.linalg import qr  # noqa: F401
 from .tensor.linalg import slogdet  # noqa: F401
@@ -50,6 +51,7 @@
     'matrix_rank',
     'svd',
     'qr',
+    'pca_lowrank',
     'lu',
     'lu_unpack',
     'matrix_power',

python/paddle/sparse/__init__.py

@@ -28,6 +28,7 @@
 from .unary import log1p
 from .unary import abs
 from .unary import pow
+from .unary import pca_lowrank
 from .unary import cast
 from .unary import neg
 from .unary import coalesce
@@ -69,6 +70,7 @@
     'log1p',
     'abs',
     'pow',
+    'pca_lowrank',
     'cast',
     'neg',
     'deg2rad',

python/paddle/sparse/unary.py

@@ -14,6 +14,7 @@
 
 import numpy as np
 
+import paddle
 from paddle import _C_ops, in_dynamic_mode
 from paddle.common_ops_import import Variable
 from paddle.fluid.data_feeder import check_type, check_variable_and_dtype
@@ -920,3 +921,205 @@ def slice(x, axes, starts, ends, name=None):
             type=op_type, inputs={'x': x}, outputs={'out': out}, attrs=attrs
         )
         return out
+
+
+def pca_lowrank(x, q=None, center=True, niter=2, name=None):
+    r"""
+    Performs linear Principal Component Analysis (PCA) on a sparse matrix.
+
+    Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:
+
+    .. math::
+        X = U * diag(S) * V^{T}
+
+    Args:
+        x (Tensor): The input tensor. Its shape should be `[N, M]`,
+            N and M can be arbitraty positive number.
+            The data type of x should be float32 or float64.
+        q (int, optional): a slightly overestimated rank of :math:`X`.
+            Default value is :math:`q=min(6,N,M)`.
+        center (bool, optional): if True, center the input tensor.
+            Default value is True.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        - Tensor U, is N x q matrix.
+        - Tensor S, is a vector with length q.
+        - Tensor V, is M x q matrix.
+
+        tuple (U, S, V): which is the nearly optimal approximation of a singular value decomposition of a centered matrix :math:`X`.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            format = "coo"
+            dense_x = paddle.randn((5, 5), dtype='float64')
+
+            if format == "coo":
+                sparse_x = dense_x.to_sparse_coo(len(dense_x.shape))
+            else:
+                sparse_x = dense_x.to_sparse_csr()
+
+            print("sparse.pca_lowrank API only support CUDA 11.x")
+            U, S, V = None, None, None
+            # use code blow when your device CUDA version >= 11.0
+            # U, S, V = paddle.sparse.pca_lowrank(sparse_x)
+
+            print(U)
+            # Tensor(shape=[5, 5], dtype=float64, place=Place(gpu:0), stop_gradient=True,
+            #        [[ 0.02206024,  0.53170082, -0.22392168, -0.48450657,  0.65720625],
+            #         [ 0.02206024,  0.53170082, -0.22392168, -0.32690402, -0.74819812],
+            #         [ 0.02206024,  0.53170082, -0.22392168,  0.81141059,  0.09099187],
+            #         [ 0.15045792,  0.37840027,  0.91333217, -0.00000000,  0.00000000],
+            #         [ 0.98787775, -0.09325209, -0.12410317, -0.00000000, -0.00000000]])
+
+            print(S)
+            # Tensor(shape=[5], dtype=float64, place=Place(gpu:0), stop_gradient=True,
+            #        [2.28621761, 0.93618564, 0.53234942, 0.00000000, 0.00000000])
+
+            print(V)
+            # Tensor(shape=[5, 5], dtype=float64, place=Place(gpu:0), stop_gradient=True,
+            #        [[ 0.26828910, -0.57116436, -0.26548201,  0.67342660, -0.27894114],
+            #         [-0.19592125, -0.31629129,  0.02001645, -0.50484498, -0.77865626],
+            #         [-0.82913017, -0.09391036,  0.37975388,  0.39938099, -0.00241046],
+            #         [-0.41163516,  0.27490410, -0.86666276,  0.03382656, -0.05230341],
+            #         [ 0.18092947,  0.69952818,  0.18385126,  0.36190987, -0.55959343]])
+    """
+
+    def get_floating_dtype(x):
+        dtype = x.dtype
+        if dtype in (paddle.float16, paddle.float32, paddle.float64):
+            return dtype
+        return paddle.float32
+
+    def conjugate(x):
+        if x.is_complex():
+            return x.conj()
+        return x
+
+    def transpose(x):
+        shape = x.shape
+        perm = list(range(0, len(shape)))
+        perm = perm[:-2] + [perm[-1]] + [perm[-2]]
+        if x.is_sparse():
+            return paddle.sparse.transpose(x, perm)
+        return paddle.transpose(x, perm)
+
+    def transjugate(x):
+        return conjugate(transpose(x))
+
+    def get_approximate_basis(x, q, niter=2, M=None):
+        niter = 2 if niter is None else niter
+        m, n = x.shape[-2:]
+        qr = paddle.linalg.qr
+
+        R = paddle.randn((n, q), dtype=x.dtype)
+
+        A_t = transpose(x)
+        A_H = conjugate(A_t)
+        if M is None:
+            Q = qr(paddle.sparse.matmul(x, R))[0]
+            for i in range(niter):
+                Q = qr(paddle.sparse.matmul(A_H, Q))[0]
+                Q = qr(paddle.sparse.matmul(x, Q))[0]
+        else:
+            M_H = transjugate(M)
+            Q = qr(paddle.sparse.matmul(x, R) - paddle.matmul(M, R))[0]
+            for i in range(niter):
+                Q = qr(paddle.sparse.matmul(A_H, Q) - paddle.matmul(M_H, Q))[0]
+                Q = qr(paddle.sparse.matmul(x, Q) - paddle.matmul(M, Q))[0]
+
+        return Q
+
+    def svd_lowrank(x, q=6, niter=2, M=None):
+        q = 6 if q is None else q
+        m, n = x.shape[-2:]
+        if M is None:
+            M_t = None
+        else:
+            M_t = transpose(M)
+        A_t = transpose(x)
+
+        if m < n or n > q:
+            Q = get_approximate_basis(A_t, q, niter=niter, M=M_t)
+            Q_c = conjugate(Q)
+            if M is None:
+                B_t = paddle.sparse.matmul(x, Q_c)
+            else:
+                B_t = paddle.sparse.matmul(x, Q_c) - paddle.matmul(M, Q_c)
+            assert B_t.shape[-2] == m, (B_t.shape, m)
+            assert B_t.shape[-1] == q, (B_t.shape, q)
+            assert B_t.shape[-1] <= B_t.shape[-2], B_t.shape
+            U, S, Vh = paddle.linalg.svd(B_t, full_matrices=False)
+            V = transjugate(Vh)
+            V = Q.matmul(V)
+        else:
+            Q = get_approximate_basis(x, q, niter=niter, M=M)
+            Q_c = conjugate(Q)
+            if M is None:
+                B = paddle.sparse.matmul(A_t, Q_c)
+            else:
+                B = paddle.sparse.matmul(A_t, Q_c) - paddle.matmul(M_t, Q_c)
+            B_t = transpose(B)
+            assert B_t.shape[-2] == q, (B_t.shape, q)
+            assert B_t.shape[-1] == n, (B_t.shape, n)
+            assert B_t.shape[-1] <= B_t.shape[-2], B_t.shape
+            U, S, Vh = paddle.linalg.svd(B_t, full_matrices=False)
+            V = transjugate(Vh)
+            U = Q.matmul(U)
+
+        return U, S, V
+
+    if not paddle.is_tensor(x):
+        raise ValueError(f'Input must be tensor, but got {type(x)}')
+
+    if not x.is_sparse():
+        raise ValueError('Input must be sparse, but got dense')
+
+    cuda_version = paddle.version.cuda()
+    if (
+        cuda_version is None
+        or cuda_version == 'False'
+        or int(cuda_version.split('.')[0]) < 11
+    ):
+        raise ValueError('sparse.pca_lowrank API only support CUDA 11.x')
+
+    (m, n) = x.shape[-2:]
+
+    if q is None:
+        q = min(6, m, n)
+    elif not (q >= 0 and q <= min(m, n)):
+        raise ValueError(
+            'q(={}) must be non-negative integer'
+            ' and not greater than min(m, n)={}'.format(q, min(m, n))
+        )
+    if not (niter >= 0):
+        raise ValueError(f'niter(={niter}) must be non-negative integer')
+
+    dtype = get_floating_dtype(x)
+
+    if not center:
+        return svd_lowrank(x, q, niter=niter, M=None)
+
+    if len(x.shape) != 2:
+        raise ValueError('input is expected to be 2-dimensional tensor')
+    # TODO: complement sparse_csr_tensor test
+    # when sparse.sum with axis(-2) is implemented
+    s_sum = paddle.sparse.sum(x, axis=-2)
+    s_val = s_sum.values() / m
+    c = paddle.sparse.sparse_coo_tensor(
+        s_sum.indices(), s_val, dtype=s_sum.dtype, place=s_sum.place
+    )
+    column_indices = c.indices()[0]
+    indices = paddle.zeros((2, len(column_indices)), dtype=column_indices.dtype)
+    indices[0] = column_indices
+    C_t = paddle.sparse.sparse_coo_tensor(
+        indices, c.values(), (n, 1), dtype=dtype, place=x.place
+    )
+
+    ones_m1_t = paddle.ones(x.shape[:-2] + [1, m], dtype=dtype)
+    M = transpose(paddle.matmul(C_t.to_dense(), ones_m1_t))
+    return svd_lowrank(x, q, niter=niter, M=M)

python/paddle/tensor/__init__.py

@@ -46,6 +46,7 @@
 from .linalg import cov  # noqa: F401
 from .linalg import corrcoef  # noqa: F401
 from .linalg import norm  # noqa: F401
+from .linalg import pca_lowrank  # noqa: F401
 from .linalg import cond  # noqa: F401
 from .linalg import transpose  # noqa: F401
 from .linalg import lstsq  # noqa: F401
@@ -329,6 +330,7 @@
     'mv',
     'matrix_power',
     'qr',
+    'pca_lowrank',
     'eigvals',
     'eigvalsh',
     'abs',