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Unverified 提交 f772066a authored 作者: Jesse Grabowski's avatar Jesse Grabowski 提交者： GitHub
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Remove `sparse.sandbox` (#1664)

* Delete `sparse.sandbox` * delete test_sp2.py
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pytensor/sparse/sandbox/sp.py deleted 100644 → 0
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pytensor/sparse/sandbox/sp2.py deleted 100644 → 0
浏览文件 @ 95acdb3d
import numpy as np
import scipy.sparse
import pytensor
from pytensor import tensor as pt
from pytensor.graph.basic import Apply
from pytensor.graph.op import Op
from pytensor.sparse.basic import (
Remove0,
SparseTensorType,
_is_sparse,
as_sparse_variable,
remove0,
)
from pytensor.tensor.type import discrete_dtypes, float_dtypes
# Probability Ops are currently back in sandbox, because they do not respect
# PyTensor's Op contract, as their behaviour is not reproducible: calling
# the perform() method twice with the same argument will yield different
# results.
# from pytensor.sparse.basic import (
# Multinomial, multinomial, Poisson, poisson,
# Binomial, csr_fbinomial, csc_fbinomial, csr_dbinomial, csc_dbinomial)
# Alias to maintain compatibility
EliminateZeros = Remove0
eliminate_zeros = remove0
# Probability
class Poisson(Op):
"""Return a sparse having random values from a Poisson density
with mean from the input.
WARNING: This Op is NOT deterministic, as calling it twice with the
same inputs will NOT give the same result. This is a violation of
PyTensor's contract for Ops
:param x: Sparse matrix.
:return: A sparse matrix of random integers of a Poisson density
with mean of `x` element wise.
"""
__props__ = ()
def make_node(self, x):
x = as_sparse_variable(x)
return Apply(self, [x], [x.type()])
def perform(self, node, inputs, outputs):
(x,) = inputs
(out,) = outputs
assert _is_sparse(x)
assert x.format in ("csr", "csc")
out[0] = x.copy()
out[0].data = np.asarray(np.random.poisson(out[0].data), dtype=x.dtype)
out[0].eliminate_zeros()
def grad(self, inputs, outputs_gradients):
comment = "No gradient exists for class Poisson in\
pytensor/sparse/sandbox/sp2.py"
return [
pytensor.gradient.grad_undefined(
op=self, x_pos=0, x=inputs[0], comment=comment
)
]
def infer_shape(self, fgraph, node, ins_shapes):
return ins_shapes
poisson = Poisson()
class Binomial(Op):
"""Return a sparse matrix having random values from a binomial
density having number of experiment `n` and probability of success
`p`.
WARNING: This Op is NOT deterministic, as calling it twice with the
same inputs will NOT give the same result. This is a violation of
PyTensor's contract for Ops
:param n: Tensor scalar representing the number of experiment.
:param p: Tensor scalar representing the probability of success.
:param shape: Tensor vector for the output shape.
:return: A sparse matrix of integers representing the number
of success.
"""
__props__ = ("format", "dtype")
def __init__(self, format, dtype):
self.format = format
self.dtype = np.dtype(dtype).name
def make_node(self, n, p, shape):
n = pt.as_tensor_variable(n)
p = pt.as_tensor_variable(p)
shape = pt.as_tensor_variable(shape)
assert n.dtype in discrete_dtypes
assert p.dtype in float_dtypes
assert shape.dtype in discrete_dtypes
return Apply(
self,
[n, p, shape],
[SparseTensorType(dtype=self.dtype, format=self.format)()],
)
def perform(self, node, inputs, outputs):
(n, p, shape) = inputs
(out,) = outputs
binomial = np.random.binomial(n, p, size=shape)
csx_matrix = getattr(scipy.sparse, self.format + "_matrix")
out[0] = csx_matrix(binomial, dtype=self.dtype)
def connection_pattern(self, node):
return [[True], [True], [False]]
def grad(self, inputs, gout):
(n, p, _shape) = inputs
(_gz,) = gout
comment_n = "No gradient exists for the number of samples in class\
Binomial of pytensor/sparse/sandbox/sp2.py"
comment_p = "No gradient exists for the prob of success in class\
Binomial of pytensor/sparse/sandbox/sp2.py"
return [
pytensor.gradient.grad_undefined(op=self, x_pos=0, x=n, comment=comment_n),
pytensor.gradient.grad_undefined(op=self, x_pos=1, x=p, comment=comment_p),
pytensor.gradient.disconnected_type(),
]
def infer_shape(self, fgraph, node, ins_shapes):
return [(node.inputs[2][0], node.inputs[2][1])]
csr_fbinomial = Binomial("csr", "float32")
csc_fbinomial = Binomial("csc", "float32")
csr_dbinomial = Binomial("csr", "float64")
csc_dbinomial = Binomial("csc", "float64")
class Multinomial(Op):
"""Return a sparse matrix having random values from a multinomial
density having number of experiment `n` and probability of success
`p`.
WARNING: This Op is NOT deterministic, as calling it twice with the
same inputs will NOT give the same result. This is a violation of
PyTensor's contract for Ops
:param n: Tensor type vector or scalar representing the number of
experiment for each row. If `n` is a scalar, it will be
used for each row.
:param p: Sparse matrix of probability where each row is a probability
vector representing the probability of success. N.B. Each row
must sum to one.
:return: A sparse matrix of random integers from a multinomial density
for each row.
:note: It will works only if `p` have csr format.
"""
__props__ = ()
def make_node(self, n, p):
n = pt.as_tensor_variable(n)
p = as_sparse_variable(p)
assert p.format in ("csr", "csc")
return Apply(self, [n, p], [p.type()])
def perform(self, node, inputs, outputs):
(n, p) = inputs
(out,) = outputs
assert _is_sparse(p)
if p.format != "csr":
raise NotImplementedError
out[0] = p.copy()
if n.ndim == 0:
for i in range(p.shape[0]):
k, l = p.indptr[i], p.indptr[i + 1]
out[0].data[k:l] = np.random.multinomial(n, p.data[k:l])
elif n.ndim == 1:
if n.shape[0] != p.shape[0]:
raise ValueError(
"The number of element of n must be "
"the same as the number of row of p."
)
for i in range(p.shape[0]):
k, l = p.indptr[i], p.indptr[i + 1]
out[0].data[k:l] = np.random.multinomial(n[i], p.data[k:l])
def grad(self, inputs, outputs_gradients):
comment_n = "No gradient exists for the number of samples in class\
Multinomial of pytensor/sparse/sandbox/sp2.py"
comment_p = "No gradient exists for the prob of success in class\
Multinomial of pytensor/sparse/sandbox/sp2.py"
return [
pytensor.gradient.grad_undefined(
op=self, x_pos=0, x=inputs[0], comment=comment_n
),
pytensor.gradient.grad_undefined(
op=self, x_pos=1, x=inputs[1], comment=comment_p
),
]
def infer_shape(self, fgraph, node, ins_shapes):
return [ins_shapes[1]]
multinomial = Multinomial()
tests/sparse/sandbox/test_sp.py deleted 100644 → 0
浏览文件 @ 95acdb3d
import time
import pytest
pytest.importorskip("scipy", minversion="0.7.0")
import numpy as np
from scipy.signal import convolve2d
from pytensor import function
from pytensor.sparse.sandbox import sp
from pytensor.tensor.type import dmatrix, dvector
from tests import unittest_tools as utt
class TestSP:
@pytest.mark.slow
def test_convolution(self):
# print '\n\n*************************************************'
# print ' TEST CONVOLUTION'
# print '*************************************************'
# fixed parameters
bsize = 10 # batch size
imshp = (28, 28)
kshp = (5, 5)
nkern = 5
ssizes = ((1, 1), (2, 2), (3, 3), (4, 4))
convmodes = ("full", "valid")
# symbolic stuff
bias = dvector()
kerns = dmatrix()
input = dmatrix()
rng = np.random.default_rng(3423489)
filters = rng.standard_normal((nkern, np.prod(kshp)))
biasvals = rng.standard_normal(nkern)
for mode in ("FAST_COMPILE", "FAST_RUN"):
ttot, ntot = 0, 0
for conv_mode in convmodes:
for ss in ssizes:
output, _outshp = sp.convolve(
kerns, kshp, nkern, input, imshp, ss, bias=bias, mode=conv_mode
)
f = function([kerns, bias, input], output, mode=mode)
# now test with real values
img2d = np.arange(bsize * np.prod(imshp)).reshape((bsize, *imshp))
img1d = img2d.reshape(bsize, -1)
# create filters (need to be flipped to use convolve2d)
filtersflipped = np.zeros((nkern, *kshp))
for k in range(nkern):
it = reversed(filters[k, :])
for i in range(kshp[0]):
for j in range(kshp[1]):
filtersflipped[k, i, j] = next(it)
# compute output with convolve2d
if conv_mode == "valid":
fulloutshp = np.array(imshp) - np.array(kshp) + 1
else:
fulloutshp = np.array(imshp) + np.array(kshp) - 1
ntime1 = time.perf_counter()
refout = np.zeros((bsize, *fulloutshp, nkern))
for b in range(bsize):
for n in range(nkern):
refout[b, ..., n] = convolve2d(
img2d[b, :, :], filtersflipped[n, ...], conv_mode
)
ntot += time.perf_counter() - ntime1
# need to flatten images
bench1 = refout[:, 0 :: ss[0], 0 :: ss[1], :].reshape(
bsize, -1, nkern
)
bench1 += biasvals.reshape(1, 1, nkern)
# swap the last two dimensions (output needs to be nkern x outshp)
bench1 = np.swapaxes(bench1, 1, 2)
ttime1 = time.perf_counter()
out1 = f(filters, biasvals, img1d)
ttot += time.perf_counter() - ttime1
temp = bench1.flatten() - out1.flatten()
assert (temp < 1e-5).all()
# test downward propagation -- symbolic stuff
# vis = pytensor.gradient.grad(output, input, output)
# downprop = function([kerns,input], vis, mode=mode)
# visval = downprop(filters,img1d)
# test downward propagation -- reference implementation
# pshape = (img1d.shape[0],np.prod(outshp[1:]),np.prod(kshp))
# patchstack = np.zeros(pshape)
# for bi in np.arange(pshape[0]): # batch index
# abspos = 0
# for outy in np.arange(outshp[1]):
# for outx in np.arange(outshp[2]):
# for ni in np.arange(nkern):
# print 'filters[n,:].shape = ', filters[n,:].shape
# print 'out1[bi,abspos].shape =',out1[bi,abspos].shape
# patchstack[bi,abspos,:] = filters[n,:]*out1[bi,abspos]
# abspos+=1
# patchstack = patchstack.reshape(1,-1)
# indices, indptr, spmat_shape, sptype, outshp = \
# sp.convolution_indices.conv_eval(imshp,kshp,ss,conv_mode)
# spmat = sparse.csc_matrix((np.ones_like(indices),indices,indptr),spmat_shape)
# visref = np.dot(patchstack, spmat.todense())
# print 'visval = ', visval
# print 'visref = ', visref
# assert np.all(visref==visval)
# print '**** Convolution Profiling Results (',mode,') ****'
# print 'Numpy processing time: ', ntot
# print 'PyTensor processing time: ', ttot
# this doesn't compare the output of anything... but I manually verified that the patches
# are properly generated
def test_multilayer_conv(self):
# fixed parameters
bsize = 10 # batch size
imshp = (5, 5)
kshp = ((3, 3), (2, 2))
nkerns = (3, 6) # per output pixel
ssizes = (((1, 1), (2, 2)),)
convmodes = ("full",) # 'valid',)
# symbolic stuff
kerns = [dmatrix(), dmatrix()]
input = dmatrix()
# build actual input images
img2d = np.arange(bsize * np.prod(imshp)).reshape((bsize, *imshp))
img1d = img2d.reshape(bsize, -1)
for mode in ("FAST_COMPILE", "FAST_RUN"):
for conv_mode in convmodes:
for ss in ssizes:
l1hid, l1shp = sp.convolve(
kerns[0],
kshp[0],
nkerns[0],
input,
imshp,
ss[0],
mode=conv_mode,
)
l1propup = function([kerns[0], input], l1hid, mode=mode)
l1kernvals = np.arange(nkerns[0] * np.prod(kshp[0])).reshape(
nkerns[0], np.prod(kshp[0])
)
l1hidval = l1propup(l1kernvals, img1d)
# actual values
l2hid, _l2shp = sp.convolve(
kerns[1],
kshp[1],
nkerns[1],
l1hid,
l1shp,
ss[1],
mode=conv_mode,
)
l2propup = function([kerns[1], l1hid], l2hid, mode=mode)
l2kernvals = np.arange(
nkerns[1] * np.prod(kshp[1]) * nkerns[0]
).reshape(nkerns[1], np.prod(kshp[1]) * nkerns[0])
# for debugging, we bring things back to integers
l1hidval = np.arange(np.size(l1hidval)).reshape(l1hidval.shape)
l2propup(l2kernvals, l1hidval)
def test_maxpool(self):
# generate flatted images
maxpoolshps = ((2, 2), (3, 3), (4, 4), (5, 5), (6, 6))
rng = np.random.default_rng(2938)
imval = rng.random((4, 5, 10, 10))
images = dmatrix()
for maxpoolshp in maxpoolshps:
# symbolic stuff
output, outshp = sp.max_pool(images, imval.shape[1:], maxpoolshp)
f = function(
[
images,
],
[
output,
],
)
output_val = f(imval.reshape(imval.shape[0], -1))
# numeric verification
my_output_val = np.zeros(
(
imval.shape[0],
imval.shape[1],
imval.shape[2] // maxpoolshp[0],
imval.shape[3] // maxpoolshp[1],
)
)
assert np.prod(my_output_val.shape[1:]) == np.prod(
np.r_[imval.shape[1], outshp]
)
for n in range(imval.shape[0]):
for k in range(imval.shape[1]):
for i in range(imval.shape[2] // maxpoolshp[0]):
for j in range(imval.shape[3] // maxpoolshp[1]):
ii, jj = i * maxpoolshp[0], j * maxpoolshp[1]
patch = imval[
n, k, ii : ii + maxpoolshp[0], jj : jj + maxpoolshp[1]
]
my_output_val[n, k, i, j] = np.max(patch)
my_output_val = my_output_val.reshape(imval.shape[0], -1)
assert np.all(output_val == my_output_val)
def mp(input):
output, _outshp = sp.max_pool(input, imval.shape[1:], maxpoolshp)
return output
utt.verify_grad(mp, [imval.reshape(imval.shape[0], -1)])
tests/sparse/test_sp2.py deleted 100644 → 0
浏览文件 @ 95acdb3d
import pytest
sp = pytest.importorskip("scipy", minversion="0.7.0")
import numpy as np
import pytensor
from pytensor import sparse
from pytensor.configdefaults import config
from pytensor.sparse.sandbox.sp2 import (
Binomial,
Multinomial,
Poisson,
multinomial,
poisson,
)
from pytensor.tensor.type import lscalar, lvector, scalar
from tests import unittest_tools as utt
from tests.sparse.test_basic import as_sparse_format
class TestPoisson(utt.InferShapeTester):
x = {}
a = {}
for format in sparse.sparse_formats:
variable = getattr(pytensor.sparse, format + "_matrix")
a_val = np.array(
np.random.default_rng(utt.fetch_seed()).integers(1, 4, size=(3, 4)) - 1,
dtype=pytensor.config.floatX,
)
x[format] = variable()
a[format] = as_sparse_format(a_val, format)
def setup_method(self):
super().setup_method()
self.op_class = Poisson
def test_op(self):
for format in sparse.sparse_formats:
f = pytensor.function([self.x[format]], poisson(self.x[format]))
tested = f(self.a[format])
assert tested.format == format
assert tested.dtype == self.a[format].dtype
assert np.allclose(np.floor(tested.data), tested.data)
assert tested.shape == self.a[format].shape
def test_infer_shape(self):
for format in sparse.sparse_formats:
self._compile_and_check(
[self.x[format]],
[poisson(self.x[format])],
[self.a[format]],
self.op_class,
)
class TestBinomial(utt.InferShapeTester):
n = scalar(dtype="int64")
p = scalar()
shape = lvector()
_n = 5
_p = 0.25
_shape = np.asarray([3, 5], dtype="int64")
inputs = [n, p, shape]
_inputs = [_n, _p, _shape]
def setup_method(self):
super().setup_method()
self.op_class = Binomial
def test_op(self):
for sp_format in sparse.sparse_formats:
for o_type in sparse.float_dtypes:
f = pytensor.function(
self.inputs, Binomial(sp_format, o_type)(*self.inputs)
)
tested = f(*self._inputs)
assert tested.shape == tuple(self._shape)
assert tested.format == sp_format
assert tested.dtype == o_type
assert np.allclose(np.floor(tested.todense()), tested.todense())
def test_infer_shape(self):
for sp_format in sparse.sparse_formats:
for o_type in sparse.float_dtypes:
self._compile_and_check(
self.inputs,
[Binomial(sp_format, o_type)(*self.inputs)],
self._inputs,
self.op_class,
)
class TestMultinomial(utt.InferShapeTester):
p = sparse.csr_matrix()
_p = sp.sparse.csr_matrix(
np.asarray(
[
[0.0, 0.5, 0.0, 0.5],
[0.1, 0.2, 0.3, 0.4],
[0.0, 1.0, 0.0, 0.0],
[0.3, 0.3, 0.0, 0.4],
],
dtype=config.floatX,
)
)
def setup_method(self):
super().setup_method()
self.op_class = Multinomial
def test_op(self):
n = lscalar()
f = pytensor.function([self.p, n], multinomial(n, self.p))
_n = 5
tested = f(self._p, _n)
assert tested.shape == self._p.shape
assert np.allclose(np.floor(tested.todense()), tested.todense())
assert tested[2, 1] == _n
n = lvector()
f = pytensor.function([self.p, n], multinomial(n, self.p))
_n = np.asarray([1, 2, 3, 4], dtype="int64")
tested = f(self._p, _n)
assert tested.shape == self._p.shape
assert np.allclose(np.floor(tested.todense()), tested.todense())
assert tested[2, 1] == _n[2]
def test_infer_shape(self):
self._compile_and_check(
[self.p], [multinomial(5, self.p)], [self._p], self.op_class, warn=False
)
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