def test_large(self):
t = 20_000
num_rep = 5
mat = torch.from_numpy(gen_random(t, t, np.float32, F=False, seed=123))
vec = torch.from_numpy(
gen_random(t, 1, np.float32, F=False, seed=124).reshape((-1, )))
mat_cuda = mat.cuda()
vec_cuda = vec.cuda()
cpu_times = []
for i in range(num_rep):
t_s = time.time()
out_cpu = vec_mul_triang(mat, vec, True, 1)
cpu_times.append(time.time() - t_s)
gpu_times = []
for i in range(num_rep):
t_s = time.time()
out_cuda = vec_mul_triang(mat_cuda, vec_cuda, True, 1)
torch.cuda.synchronize()
gpu_times.append(time.time() - t_s)
print("mat size %d - t_cpu: %.4fs -- t_cuda: %.4fs" %
(t, np.min(cpu_times), np.min(gpu_times)))
np.testing.assert_allclose(out_cpu, out_cuda.cpu().numpy())
def test_large(self):
t = 30_000
mat = gen_random(t, t, np.float64, F=False, seed=123)
vec = gen_random(t, 1, np.float64, F=False, seed=124).reshape((-1, ))
t_s = time.time()
vec_mul_triang(mat, vec, upper=True, side=1)
t_tri = time.time() - t_s
t_s = time.time()
mat *= vec
t_full = time.time() - t_s
print("Our took %.2fs -- Full took %.2fs" % (t_tri, t_full))
def test_all_combos(self, mat, vec, order, device, upper, side):
exp_output = self.exp_vec_mul_triang(mat, vec, upper, side)
vec = fix_mat(vec,
order=order,
dtype=np.float64,
numpy=False,
device=device)
mat2 = fix_mat(mat,
order=order,
dtype=np.float64,
numpy=False,
device=device,
copy=True)
out = vec_mul_triang(mat2, upper=upper, side=side,
multipliers=vec).cpu().numpy()
np.testing.assert_allclose(exp_output.numpy(), out)
assert out.flags["%s_CONTIGUOUS" %
order] is True, "Output is not %s-contiguous" % (
order)
# Test with different vec orderings
vec = vec.reshape(1, -1)
mat2 = fix_mat(mat,
order=order,
dtype=np.float64,
numpy=False,
device=device,
copy=True)
out = vec_mul_triang(mat2, upper=upper, side=side,
multipliers=vec).cpu().numpy()
np.testing.assert_allclose(exp_output.numpy(),
out,
err_msg="Vec row ordering failed")
vec = vec.reshape(-1)
mat2 = fix_mat(mat,
order=order,
dtype=np.float64,
numpy=False,
device=device,
copy=True)
out = vec_mul_triang(mat2, upper=upper, side=side,
multipliers=vec).cpu().numpy()
np.testing.assert_allclose(exp_output.numpy(),
out,
err_msg="Vec 1D ordering failed")
def test_upper(self, mat, vec, order):
mat = fix_mat(mat, order=order, dtype=mat.dtype, numpy=True, copy=True)
out = vec_mul_triang(mat.copy(order="K"),
upper=True,
side=0,
multipliers=vec)
exp = np.array([[0, 0, 0], [2, 2, 4], [6, 6, 4]], dtype=np.float32)
np.testing.assert_allclose(exp, out)
assert out.flags["%s_CONTIGUOUS" %
order] is True, "Output is not %s-contiguous" % (
order)
out = vec_mul_triang(mat.copy(order="K"),
upper=True,
side=1,
multipliers=vec)
exp = np.array([[0, 1, 0.5], [2, 2, 2], [6, 6, 4]], dtype=np.float32)
np.testing.assert_allclose(exp, out)
assert out.flags["%s_CONTIGUOUS" %
order] is True, "Output is not %s-contiguous" % (
order)
def init(self, X: Union[torch.Tensor, SparseTensor], Y: torch.Tensor,
alpha: torch.Tensor, penalty: float, N: int) -> None:
"""Initialize the preconditioner matrix.
This method must be called before the preconditioner becomes usable.
Parameters
----------
X : torch.Tensor
(M x D) matrix of Nystroem centers
Y : torch.Tensor
(M x 1) vector of targets corresponding to the Nystroem centers `X`
alpha : torch.Tensor
(M x 1) parameter vector (of the same dimension as `Y`) which gives the current
solution to the optimization problem.
penalty : float
Regularization amount
N : int
Number of points in the full data-set.
Notes
-----
If `debug=True` is present in the options, this method will print a lot of extra
information pertaining timings of the various preconditioner operations. This can be
useful to help understand how the preconditioner works.
"""
if Y.shape[1] != 1:
raise ValueError(
"Logistic preconditioner can only deal with 1D outputs.")
dtype = X.dtype
M = X.size(0)
eps = self.params.pc_epsilon(dtype)
if self.fC is None:
# This is done only at the first iteration of the logistic-falkon algorithm
# It sets the `T` variable from the paper (chol(kMM)) to the upper part of `self.fC`
with TicToc("Kernel", debug=self.params.debug):
if isinstance(X, torch.Tensor):
C = create_same_stride((M, M),
X,
dtype=dtype,
device='cpu',
pin_memory=self._use_cuda)
else: # If sparse tensor we need fortran for kernel calculation
C = create_fortran((M, M),
dtype=dtype,
device='cpu',
pin_memory=self._use_cuda)
self.kernel(X, X, out=C, opt=self.params)
if not is_f_contig(C):
C = C.T
with TicToc("Add diag", debug=self.params.debug):
# Compute T: lower(fC) = T.T
inplace_add_diag_th(C, eps * M)
with TicToc("Cholesky 1", debug=self.params.debug):
C = potrf_wrapper(C,
clean=True,
upper=False,
use_cuda=self._use_cuda,
opt=self.params)
# Save the diagonal which will be overwritten when computing A
self.dT = C.diag()
with TicToc("Copy triangular", debug=self.params.debug):
# Copy lower(fC) to upper(fC): upper(fC) = T.
copy_triang(C, upper=False)
else:
C = self.fC
if not self._use_cuda:
# Copy non-necessary for cuda since LAUUM will do the copying
with TicToc("Copy triangular", debug=self.params.debug):
# Copy upper(fC) to lower(fC): lower(fC) = T.T
copy_triang(C, upper=True) # does not copy the diagonal
# Setting diagonal necessary for trmm
C.diagonal().copy_(self.dT)
# Compute W
with TicToc("TRMM", debug=self.params.debug):
# T is on upper(fC). Compute T.T @ alpha
alpha = self._trmm(C, alpha.clone())
with TicToc("W (ddf)", debug=self.params.debug):
W = self.loss.ddf(Y, alpha)
with TicToc("W-Multiply", debug=self.params.debug):
W.sqrt_()
vec_mul_triang(C, W.numpy().reshape(-1), side=0, upper=False)
# LAUUM side depends on CUDA or CPU version because the matrix is initially symmetric and
# the CUDA version will write the result on the opposite side (i.e. `write_opposite=True`)
# while the CPU version will write on the same side.
if self._use_cuda:
with TicToc("LAUUM", debug=self.params.debug):
# Product upper(fC) @ upper(fC).T : lower(fC) = T @ T.T
C = lauum_wrapper(C,
upper=True,
use_cuda=self._use_cuda,
opt=self.params)
else:
with TicToc("LAUUM", debug=self.params.debug):
# Product lower(fC).T @ lower(fC) : lower(fC) = T @ T.T
C = lauum_wrapper(C,
upper=False,
use_cuda=self._use_cuda,
opt=self.params)
# NOTE: Here the multiplier is 1/N instead of the more common 1/M!
mul_triang(C, upper=False, preserve_diag=False, multiplier=1 / N)
with TicToc("Add diag", debug=self.params.debug):
# lower(fC) = 1/N * [email protected] + lambda * I
inplace_add_diag_th(C, penalty)
with TicToc("Cholesky 2", debug=self.params.debug):
# Cholesky on lower(fC) : lower(fC) = A.T
C = potrf_wrapper(C,
clean=False,
upper=False,
use_cuda=self._use_cuda,
opt=self.params)
self.dA = C.diag()
self.fC = C
def init(self,
X: Union[torch.Tensor, SparseTensor],
weight_vec: Optional[torch.Tensor] = None):
"""Initialize the preconditioner matrix.
This method must be called before the preconditioner can be used.
Parameters
----------
X : torch.Tensor
The (M x D) matrix of Nystroem centers
weight_vec
An optional vector of size (M x 1) which is used for reweighted least-squares.
This vector should contain the weights corresponding to the Nystrom centers.
"""
if X.is_cuda and not self._use_cuda:
raise RuntimeError(
"use_cuda is set to False, but data is CUDA tensor. "
"Check your options.")
if weight_vec is not None and not check_same_device(X, weight_vec):
raise ValueError(f"Weights and data are not on the same device "
f"({weight_vec.device}, {X.device})")
if weight_vec is not None and weight_vec.shape[0] != X.shape[0]:
raise ValueError(
f"Weights and Nystrom centers should have the same first dimension. "
f"Found instead {weight_vec.shape[0]}, {X.shape[0]}.")
dtype = X.dtype
dev = X.device
eps = self.params.pc_epsilon(X.dtype)
M = X.size(0)
with TicToc("Kernel", debug=self.params.debug):
if isinstance(X, torch.Tensor):
C = create_same_stride((M, M),
X,
dtype=dtype,
device=dev,
pin_memory=self._use_cuda)
else: # If sparse tensor we need fortran for kernel calculation
C = create_fortran((M, M),
dtype=dtype,
device=dev,
pin_memory=self._use_cuda)
self.kernel(X, X, out=C, opt=self.params)
if not is_f_contig(C):
C = C.T
with TicToc("Cholesky 1", debug=self.params.debug):
# Compute T: lower(fC) = T.T
inplace_add_diag_th(C, eps * M)
C = potrf_wrapper(C,
clean=False,
upper=False,
use_cuda=self._use_cuda,
opt=self.params)
# Save the diagonal which will be overwritten when computing A
self.dT = C.diag()
with TicToc("Copy triangular", debug=self.params.debug):
# Copy lower(fC) to upper(fC): upper(fC) = T.
copy_triang(C, upper=False)
# Weighted least-squares needs to weight the A matrix. We can weigh once before LAUUM,
# but since CUDA-LAUUM touches both sides of C, weighting before LAUUM will also modify
# the matrix T. Therefore for CUDA inputs we weigh twice after LAUUM!
if weight_vec is not None and not self._use_cuda:
with TicToc("Weighting(CPU)", debug=self.params.debug):
weight_vec.sqrt_()
vec_mul_triang(C, weight_vec, side=1, upper=False)
if self._use_cuda:
with TicToc("LAUUM(CUDA)", debug=self.params.debug):
# Product upper(fC) @ upper(fC).T, store in lower(fC) = T @ T.T
C = lauum_wrapper(C,
upper=True,
use_cuda=self._use_cuda,
opt=self.params)
else:
with TicToc("LAUUM(CPU)", debug=self.params.debug):
# Product lower(fC).T @ lower(fC), store in lower(fC) = T @ T.T
C = lauum_wrapper(C,
upper=False,
use_cuda=self._use_cuda,
opt=self.params)
if weight_vec is not None and self._use_cuda:
with TicToc("Weighting(CUDA)", debug=self.params.debug):
weight_vec.sqrt_()
vec_mul_triang(C, weight_vec, side=0, upper=False)
vec_mul_triang(C, weight_vec, side=1, upper=False)
with TicToc("Cholesky 2", debug=self.params.debug):
# lower(fC) = 1/M * [email protected]
mul_triang(C, upper=False, preserve_diag=False, multiplier=1 / M)
# lower(fC) = 1/M * [email protected] + lambda * I
inplace_add_diag_th(C, self._lambda)
# Cholesky on lower(fC) : lower(fC) = A.T
C = potrf_wrapper(C,
clean=False,
upper=False,
use_cuda=self._use_cuda,
opt=self.params)
self.dA = C.diag()
self.fC = C
