def test_composite_clone_float32(self):
w = int8()
x = float16()
y = float32()
cz = Composite([x, y], [tanh(x + cast(y, "float16"))])
c = Composite(
[w, x, y],
[
cz(x, y)
- cz(x, y) ** 2
+ cast(x, "int16")
+ cast(x, "float32")
+ cast(w, "float16")
- constant(np.float16(1.0))
],
)
assert has_f16(c)
nc = c.clone_float32()
assert not has_f16(nc)
v = uint8()
w = float16()
x = float16()
y = float16()
z = float16()
c = Composite([v, w, x, y, z], [switch(v, mul(w, x, y), z)])
assert has_f16(c)
nc = c.clone_float32()
assert not has_f16(nc)
def test_composite_clone_float32(self):
def has_f16(comp):
if any(v.type == float16 for v in comp.fgraph.variables):
return True
return False
w = int8()
x = float16()
y = float32()
cz = Composite([x, y], [tanh(x + cast(y, "float16"))])
c = Composite(
[w, x, y],
[
cz(x, y) - cz(x, y)**2 + cast(x, "int16") +
cast(x, "float32") + cast(w, "float16") -
constant(np.float16(1.0))
],
)
assert has_f16(c)
nc = c.clone_float32()
assert not has_f16(nc)
v = uint8()
w = float16()
x = float16()
y = float16()
z = float16()
c = Composite([v, w, x, y, z], [switch(v, mul(w, x, y), z)])
assert has_f16(c)
nc = c.clone_float32()
assert not has_f16(nc)
def _test_binary(binary_op, x_range, y_range):
xi = int8('xi')
yi = int8('yi')
xf = float32('xf')
yf = float32('yf')
ei = binary_op(xi, yi)
fi = theano.function([xi, yi], ei)
ef = binary_op(xf, yf)
ff = theano.function([xf, yf], ef)
for x_val in x_range:
for y_val in y_range:
outi = fi(x_val, y_val)
outf = ff(x_val, y_val)
assert outi.dtype == outf.dtype, 'incorrect dtype'
assert np.allclose(outi, outf), 'insufficient precision'
def _test_binary(binary_op, x_range, y_range):
xi = int8('xi')
yi = int8('yi')
xf = float32('xf')
yf = float32('yf')
ei = binary_op(xi, yi)
fi = theano.function([xi, yi], ei)
ef = binary_op(xf, yf)
ff = theano.function([xf, yf], ef)
for x_val in x_range:
for y_val in y_range:
outi = fi(x_val, y_val)
outf = ff(x_val, y_val)
assert outi.dtype == outf.dtype, 'incorrect dtype'
assert np.allclose(outi, outf), 'insufficient precision'
def test_composite_clone_float32(self):
w = int8()
x = float16()
y = float32()
cz = Composite([x, y], [tanh(x + cast(y, 'float16'))])
c = Composite([w, x, y], [
cz(x, y) - cz(x, y)**2 + cast(x, 'int16') + cast(x, 'float32') +
cast(w, 'float16') - constant(np.float16(1.0))
])
assert has_f16(c)
nc = c.clone_float32()
assert not has_f16(nc)
def test_composite_clone_float32(self):
w = int8()
x = float16()
y = float32()
cz = Composite([x, y], [tanh(x + cast(y, 'float16'))])
c = Composite([w, x, y], [cz(x, y) - cz(x, y)**2 +
cast(x, 'int16') + cast(x, 'float32') +
cast(w, 'float16') -
constant(np.float16(1.0))])
assert has_f16(c)
nc = c.clone_float32()
assert not has_f16(nc)
def test_true_div(self):
# true_div's upcast policy is not exactly "upgrade_to_float",
# so the test is a little bit different
x_range = list(range(-127, 128))
y_range = list(range(-127, 0)) + list(range(1, 127))
xi = int8('xi')
yi = int8('yi')
xf = Scalar(theano.config.floatX)('xf')
yf = Scalar(theano.config.floatX)('yf')
ei = true_div(xi, yi)
fi = theano.function([xi, yi], ei)
ef = true_div(xf, yf)
ff = theano.function([xf, yf], ef)
for x_val in x_range:
for y_val in y_range:
outi = fi(x_val, y_val)
outf = ff(x_val, y_val)
assert outi.dtype == outf.dtype, 'incorrect dtype'
assert np.allclose(outi, outf), 'insufficient precision'
def test_true_div(self):
# true_div's upcast policy is not exactly "upgrade_to_float",
# so the test is a little bit different
x_range = list(range(-127, 128))
y_range = list(range(-127, 0)) + list(range(1, 127))
xi = int8('xi')
yi = int8('yi')
xf = Scalar(theano.config.floatX)('xf')
yf = Scalar(theano.config.floatX)('yf')
ei = true_div(xi, yi)
fi = theano.function([xi, yi], ei)
ef = true_div(xf, yf)
ff = theano.function([xf, yf], ef)
for x_val in x_range:
for y_val in y_range:
outi = fi(x_val, y_val)
outf = ff(x_val, y_val)
assert outi.dtype == outf.dtype, 'incorrect dtype'
assert np.allclose(outi, outf), 'insufficient precision'
def _test_unary(unary_op, x_range):
xi = int8('xi')
xf = float32('xf')
ei = unary_op(xi)
fi = theano.function([xi], ei)
ef = unary_op(xf)
ff = theano.function([xf], ef)
for x_val in x_range:
outi = fi(x_val)
outf = ff(x_val)
assert outi.dtype == outf.dtype, 'incorrect dtype'
assert np.allclose(outi, outf), 'insufficient precision'
def test_0(self):
a = int8()
b = int32()
c = complex64()
d = float64()
f = float32()
assert isinstance((a // b).owner.op, IntDiv)
assert isinstance((b // a).owner.op, IntDiv)
assert isinstance((b / d).owner.op, TrueDiv)
assert isinstance((b / f).owner.op, TrueDiv)
assert isinstance((f / a).owner.op, TrueDiv)
assert isinstance((d / b).owner.op, TrueDiv)
assert isinstance((d / f).owner.op, TrueDiv)
assert isinstance((f / c).owner.op, TrueDiv)
assert isinstance((a / c).owner.op, TrueDiv)
def _test_unary(unary_op, x_range):
xi = int8('xi')
xf = float32('xf')
ei = unary_op(xi)
fi = theano.function([xi], ei)
ef = unary_op(xf)
ff = theano.function([xf], ef)
for x_val in x_range:
outi = fi(x_val)
outf = ff(x_val)
assert outi.dtype == outf.dtype, 'incorrect dtype'
assert np.allclose(outi, outf), 'insufficient precision'
def test_0(self):
a = int8()
b = int32()
c = complex64()
d = float64()
f = float32()
assert isinstance((a // b).owner.op, IntDiv)
assert isinstance((b // a).owner.op, IntDiv)
assert isinstance((b / d).owner.op, TrueDiv)
assert isinstance((b / f).owner.op, TrueDiv)
assert isinstance((f / a).owner.op, TrueDiv)
assert isinstance((d / b).owner.op, TrueDiv)
assert isinstance((d / f).owner.op, TrueDiv)
assert isinstance((f / c).owner.op, TrueDiv)
assert isinstance((a / c).owner.op, TrueDiv)
