def check_scalar_grads(precision, backend):
cgt.reset_config()
np.random.seed(0)
cgt.set_precision(precision)
cgt.core.update_config(backend=backend)
x = cgt.scalar('x')
y = cgt.scalar('y')
z = cgt.scalar('z')
vars = [x,y,z] #pylint: disable=W0622
vals = nr.rand(len(vars))+1
PROB2RESULT = {}
for ((key,_), cls) in it.chain(
it.izip(core.UNARY_INFO.items(),it.repeat(core.ElwiseUnary)),
it.izip(core.BINARY_INFO.items(),it.repeat(core.ElwiseBinary))
):
if key == "conj":
print "skipping conj"
continue
utils.colorprint(utils.Color.YELLOW, "Testing %s\n"%key)
if cls == core.ElwiseUnary:
n_in = 1
op = cls(key)
else:
n_in = 2
op = cls(key, (True,True))
inputvars = vars[0:n_in]
inputvals = vals[0:n_in]
out = core.Result(op, inputvars)
f = cgt.function(inputvars, out)
try:
grads = cgt.grad(out, inputvars)
except core.NonDifferentiable:
print "nondiff"
continue
if DISPLAY:
print "Function:"
cgt.print_tree(out)
print "Gradient original:"
cgt.print_tree(grads)
print "Gradient simplified:"
grads_simple = core.simplify(grads)
if DISPLAY: cgt.print_tree(grads_simple)
gradf = cgt.function(inputvars, grads)
eps = {"single":1e-4,"double":1e-9}[precision]
nugrad = numeric_grad(lambda li: f(*li), inputvals,eps=eps) #pylint: disable=W0640
cgtgrad = gradf(*inputvals)
np.testing.assert_almost_equal(nugrad,cgtgrad,decimal={"single":3,"double":6}[precision])
grad_count = core.count_nodes(grads_simple)
PROB2RESULT[key] = {}
PROB2RESULT[key]["grad"] = grad_count
if DISPLAY:
from thirdparty.tabulate import tabulate
print tabulate([[key,val["grad"]] for (key,val) in PROB2RESULT.iteritems()],headers=["funcname","gradcount"])
def _print_stats(key2stats, t_total):
rows = []
for (key, (count,t)) in key2stats.iteritems():
rows.append([str(key), count, t, t/t_total])
rows = sorted(rows, key=lambda row: row[2], reverse=True)
cumsum = 0
for row in rows:
cumsum += row[3]
row.append(cumsum)
from thirdparty.tabulate import tabulate
print tabulate(rows, headers=["Instruction","Count","Time","Frac","Frac cumsum"])
def test_scalars():
np.random.seed(0)
x = cgt.scalar('x')
y = cgt.scalar('y')
z = cgt.scalar('z')
vars = [x,y,z] #pylint: disable=W0622
vals = nr.rand(len(vars))+1
PROB2RESULT = {}
for ((key,_), cls) in it.chain(
it.izip(core.UNARY_INFO.items(),it.repeat(core.ElwiseUnary)),
it.izip(core.BINARY_INFO.items(),it.repeat(core.ElwiseBinary))
):
if key == "conj":
print "skipping conj"
continue
utils.colorprint(utils.Color.YELLOW, "Testing %s\n"%key)
if cls == core.ElwiseUnary:
n_in = 1
op = cls(key)
else:
n_in = 2
op = cls(key, (True,True))
inputvars = vars[0:n_in]
inputvals = vals[0:n_in]
out = core.Result(op, inputvars)
f = cgt.function(inputvars, out)
try:
grads = cgt.grad(out, inputvars)
except core.NonDifferentiable:
print "nondiff"
continue
if DISPLAY:
print "Function:"
cgt.print_tree(out)
print "Gradient original:"
cgt.print_tree(grads)
print "Gradient simplified:"
grads_simple = core.simplify(grads)
if DISPLAY: cgt.print_tree(grads_simple)
gradf = cgt.function(inputvars, grads)
eps = {"single":1e-4,"double":1e-9}[cgt.get_precision()]
nugrad = numeric_grad(lambda li: f(*li), inputvals,eps=eps) #pylint: disable=W0640
cgtgrad = gradf(*inputvals)
np.testing.assert_almost_equal(nugrad,cgtgrad,decimal={"single":3,"double":6}[cgt.get_precision()])
grad_count = core.count_nodes(grads_simple)
PROB2RESULT[key] = {}
PROB2RESULT[key]["grad"] = grad_count
if DISPLAY:
from thirdparty.tabulate import tabulate
print tabulate([[key,val["grad"]] for (key,val) in PROB2RESULT.iteritems()],headers=["funcname","gradcount"])
def print_table(matrix):
table = []
headers = sorted(list(reduce(
lambda x, y: x | y, [set(matrix[c].keys()) for c in matrix])))
for klass, counter in sorted(matrix.items()):
row = [klass]
row.extend(counter[header] for header in headers)
row.append(sum(counter.values()))
table.append(row)
last_row = ['Outcomes']
last_row.extend([sum(c[header] for c in matrix.values())
for header in headers])
last_row.append(sum(last_row[1:]))
table.append(last_row)
headers.append('Samples')
print tabulate(table, headers=headers, tablefmt="rst")
def check_affine_funcs(precision, backend):
cgt.reset_config()
np.random.seed(0)
cgt.set_precision(precision)
cgt.core.update_config(backend=backend)
sA = np.array(nr.rand())
sB = np.array(nr.rand())
sC = np.array(nr.rand())
mA = nr.randn(2, 3)
mB = nr.randn(2, 3)
mC = nr.randn(2, 3)
for fn in [xplusx, _2x_plus_3x, xm1, onemx]:
for arg in [sA, mA]:
check_affine(fn, arg)
check_affine(elem_mult2, mA, mB, mC)
check_affine(elem_mult2, sA, sB, sC)
check_affine(pyramid, sA, sB, sC)
check_affine(pyramid, mA, mB, mC)
check_affine(slisum1, mA)
check_affine(slisum2, mA)
check_affine(slisum3, mA)
check_affine(slisum4, mA)
check_affine(max0, mA)
check_affine(max1, mA)
check_affine(max2, mA)
check_affine(fancysli0, mA)
check_affine(sum10, mA)
check_affine(sum01, mA)
check_affine(repeat0, mA[0:1, :], nr.randn(7, 3))
check_affine(repeat1, mA[:, 0:1], nr.randn(2, 7))
M23 = mA
M35 = nr.randn(3, 5)
v3 = nr.randn(3)
v13 = v3.reshape(1, 3) #XXX
v5 = nr.randn(5)
v15 = v5.reshape(1, 5) #XXX
v3b = nr.randn(3)
check_affine(matmat00, M23, M35)
check_affine(matmat01, M23, M35.T)
check_affine(matmat10, M23.T, M35)
check_affine(matmat11, M23.T, M35.T)
check_affine(matmat00a, M23, M35)
check_affine(matmat01a, M23, M35.T)
# check_affine(matmat10a, M23.T, M35)
check_affine(matmat11a, M23.T, M35.T)
check_affine(matvec, M23, v3)
check_affine(vecvec, v3, v3b)
check_affine(bcadd, M23, v13)
check_affine(matmatplusvec, M23, M35, v15)
check_affine(transpose, M23, nr.randn(3, 2))
T235 = nr.randn(2, 3, 5)
T235a = nr.randn(2, 3, 5)
T257 = nr.randn(2, 5, 7)
T2357 = nr.randn(2, 3, 5, 7)
T2357a = nr.randn(2, 3, 5, 7)
check_affine(transpose012, T235, T235a)
check_affine(transpose021, T235, T235a.transpose(0, 2, 1))
check_affine(transpose102, T235, T235a.transpose(1, 0, 2))
check_affine(transpose0312, T2357, T2357a.transpose(0, 3, 1, 2))
check_affine(transpose0231, T2357, T2357a.transpose(0, 2, 3, 1))
check_affine(batchedmatmul, T235, T257)
check_affine(flip0, M23, nr.randn(2, 3))
check_affine(flip1, M23, nr.randn(2, 3))
# check_affine(negsli0, M23, nr.randn(2,3))
# check_affine(negsli1, M23, nr.randn(2,3))
# check_affine(negsli01, M23, nr.randn(2,3))
# check_affine(rfft, M35)
check_affine(convlike, T2357, nr.randn(11, 3 * 5 * 7), nr.randn(2, 11))
if DISPLAY:
from thirdparty.tabulate import tabulate
print tabulate([[key, val["fn"], val["grad"]]
for (key, val) in sorted(PROB2RESULT.items())],
headers=["funcname", "fncount", "gradcount"])
def test_affine():
np.random.seed(0)
sA = np.array(nr.rand())
sB = np.array(nr.rand())
sC = np.array(nr.rand())
mA = nr.randn(2,3)
mB = nr.randn(2,3)
mC = nr.randn(2,3)
for fn in [xplusx, _2x_plus_3x, xm1, onemx]:
for arg in [sA, mA]:
check_affine(fn, arg)
check_affine(elem_mult2, mA, mB, mC)
check_affine(elem_mult2, sA, sB, sC)
check_affine(pyramid, sA, sB, sC)
check_affine(pyramid, mA, mB, mC)
check_affine(slisum1, mA)
check_affine(slisum2, mA)
check_affine(slisum3, mA)
check_affine(slisum4, mA)
check_affine(max0, mA)
check_affine(max1, mA)
check_affine(max2, mA)
check_affine(flatfancysli0, mA)
check_affine(sum10, mA)
check_affine(sum01, mA)
check_affine(repeat0, mA[0:1, :], nr.randn(7,3))
check_affine(repeat1, mA[:, 0:1], nr.randn(2,7))
M23 = mA
M35 = nr.randn(3,5)
v3 = nr.randn(3)
v13 = v3.reshape(1,3) #XXX
v5 = nr.randn(5)
v15 = v5.reshape(1,5) #XXX
v3b = nr.randn(3)
check_affine(matmat00, M23, M35)
check_affine(matmat01, M23, M35.T)
check_affine(matmat10, M23.T, M35)
check_affine(matmat11, M23.T, M35.T)
check_affine(matmat00a, M23, M35)
check_affine(matmat01a, M23, M35.T)
# check_affine(matmat10a, M23.T, M35)
check_affine(matmat11a, M23.T, M35.T)
check_affine(matvec, M23, v3)
check_affine(vecvec, v3, v3b)
check_affine(bcadd, M23, v13)
check_affine(matmatplusvec, M23, M35, v15)
check_affine(transpose, M23, nr.randn(3,2))
T235 = nr.randn(2,3,5)
T235a = nr.randn(2,3,5)
T257 = nr.randn(2,5,7)
T2357 = nr.randn(2,3,5,7)
T2357a = nr.randn(2,3,5,7)
check_affine(transpose012, T235, T235a)
check_affine(transpose021, T235, T235a.transpose(0,2,1))
check_affine(transpose102, T235, T235a.transpose(1,0,2))
check_affine(transpose0312, T2357, T2357a.transpose(0,3,1,2))
check_affine(transpose0231, T2357, T2357a.transpose(0,2,3,1))
check_affine(batchedmatmul, T235, T257)
check_affine(flip0, M23, nr.randn(2,3))
check_affine(flip1, M23, nr.randn(2,3))
check_affine(negsli0, M23, nr.randn(2,3))
check_affine(negsli1, M23, nr.randn(2,3))
check_affine(negsli01, M23, nr.randn(2,3))
check_affine(fancysli0, M23, nr.randn(5,3))
check_affine(fancysli0a, M23, nr.randn(5,3))
check_affine(fancysli1, M23, nr.randn(2,5))
# check_affine(rfft, M35)
check_affine(convlike, T2357, nr.randn(11,3*5*7), nr.randn(2,11))
if DISPLAY:
from thirdparty.tabulate import tabulate
print tabulate([[key,val["fn"],val["grad"]] for (key,val) in sorted(PROB2RESULT.items())],headers=["funcname","fncount","gradcount"])