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Fix algorithms "newton", "gradient_descen" and "proximal_grad" with cupy inputs #87

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merged 6 commits into from
Oct 9, 2020
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Fix algorithms "newton", "gradient_descen" and "proximal_grad" with cupy inputs #87

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fecd27f
Merge pull request #3 from dask/master
daxiongshu Sep 26, 2020
fcb0d4d
change np.zeros to np.zeros_like
daxiongshu Sep 26, 2020
a247c15
remove print
daxiongshu Sep 26, 2020
c16925a
fix flake8
daxiongshu Sep 26, 2020
6a369e7
use da.ones_like in add_intercept
daxiongshu Oct 9, 2020
1dcf6ad
remove extra parans
daxiongshu Oct 9, 2020
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10 changes: 5 additions & 5 deletions dask_glm/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,7 +97,7 @@ def gradient_descent(X, y, max_iter=100, tol=1e-14, family=Logistic, **kwargs):
stepSize = 1.0
recalcRate = 10
backtrackMult = firstBacktrackMult
beta = np.zeros(p)
beta = np.zeros_like(X._meta, shape=(p))
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Suggested change
beta = np.zeros_like(X._meta, shape=(p))
beta = np.zeros_like(X._meta, shape=p)

I think that we probably don't need the extra parens around p here and below


for k in range(max_iter):
# how necessary is this recalculation?
Expand Down Expand Up @@ -161,7 +161,7 @@ def newton(X, y, max_iter=50, tol=1e-8, family=Logistic, **kwargs):
"""
gradient, hessian = family.gradient, family.hessian
n, p = X.shape
beta = np.zeros(p) # always init to zeros?
beta = np.zeros_like(X._meta, shape=(p))
Xbeta = dot(X, beta)

iter_count = 0
Expand Down Expand Up @@ -387,7 +387,7 @@ def proximal_grad(X, y, regularizer='l1', lamduh=0.1, family=Logistic,
stepSize = 1.0
recalcRate = 10
backtrackMult = firstBacktrackMult
beta = np.zeros(p)
beta = np.zeros_like(X._meta, shape=(p))
regularizer = Regularizer.get(regularizer)

for k in range(max_iter):
Expand All @@ -406,8 +406,8 @@ def proximal_grad(X, y, regularizer='l1', lamduh=0.1, family=Logistic,
# Compute the step size
lf = func
for ii in range(100):
beta = regularizer.proximal_operator(obeta - stepSize * gradient, stepSize * lamduh)
step = obeta - beta
beta = regularizer.proximal_operator(- stepSize * gradient + obeta, stepSize * lamduh)
step = - beta + obeta
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I'm curious, why this change?

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@daxiongshu daxiongshu Oct 9, 2020 •
edited
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When obeta is a cupy array, cupy's operators +/- can't handle a dask array as the second operands in those cases. After the changes, dask's operators of +/- will be used and it works with cupy array as the second operands.

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Maybe you could do obeta = da.from_array(obeta) before the computation?

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It doesn't matter either way. Either way is fine. Can we report this upstream to cupy? Presumably they need to return NotImplemented somwhere?

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It doesn't matter either way. Either way is fine.

I know, just providing a more Dask-like solution.

Can we report this upstream to cupy? Presumably they need to return NotImplemented somwhere?

I don't know exactly what error we get, but would reporting it change anything? Would returning NotImplemented really make things much clearer to bother?

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If a Python object doesn't know what to do with another operand in a binary operator, it can return NotIimplemented (or maybe it's raise?) and then Python will try the other order (calling other.__rsub__(self))

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Thanks @mrocklin , I didn't know of that. I filed cupy/cupy#4118 to start the discussion on the CuPy side, I'm not sure but maybe there's a technical reason not to do that, so let's wait to hear from maintainers.

Xbeta = X.dot(beta)

Xbeta, beta = persist(Xbeta, beta)
Expand Down
4 changes: 2 additions & 2 deletions dask_glm/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ def normalize_inputs(X, y, *args, **kwargs):
raise ValueError('Multiple constant columns detected!')
mean[intercept_idx] = 0
std[intercept_idx] = 1
mean = mean if len(intercept_idx[0]) else np.zeros(mean.shape)
mean = mean if len(intercept_idx[0]) else np.zeros_like(X._meta, shape=mean.shape)
Xn = (X - mean) / std
out = algo(Xn, y, *args, **kwargs).copy()
i_adj = np.sum(out * mean / std)
Expand Down Expand Up @@ -140,7 +140,7 @@ def add_intercept(X):
raise NotImplementedError("Can not add intercept to array with "
"unknown chunk shape")
j, k = X.chunks
o = da.ones((X.shape[0], 1), chunks=(j, 1))
o = da.ones_like(X, shape=(X.shape[0], 1), chunks=(j, 1))
if is_dask_array_sparse(X):
o = o.map_blocks(sparse.COO)
# TODO: Needed this `.rechunk` for the solver to work
Expand Down