def testAdd(self):
x = onp.array([[1, 2, 3], [4, 5, 6]])
expected = x + x
pfun, axis_name = papply(np.add, 2)
ans = serial_pmap(pfun, axis_name)(x, x)
self.assertAllClose(ans, expected, check_dtypes=True)
def testSum(self):
pfun, axis_name = papply(np.sum)
jaxpr = make_jaxpr(pfun)(onp.zeros(5))
expected_jaxpr = make_jaxpr(lambda x: psum(x, axis_name))(onp.zeros(5))
assert repr(jaxpr) == repr(expected_jaxpr)
ans = pmap(pfun, axis_name)(onp.arange(3.))
expected = onp.sum(onp.arange(3.))
self.assertAllClose(ans, expected, check_dtypes=False)
def testAddBroadcasting(self):
def fun(x):
return x + 3
x = onp.array([[1, 2], [3, 4]])
expected = x + 3
pfun, axis_name = papply(fun)
ans = pmap(pfun, axis_name)(x)
self.assertAllClose(ans, expected, check_dtypes=True)
def testTransposeAndAddRank3(self):
def fun(x):
return x + x.T
x = onp.reshape(onp.arange(8., dtype=onp.float32), (2, 2, 2))
expected = x + x.T
pfun, axis_name = papply(fun, 2)
ans = serial_pmap(pfun, axis_name)(x)
self.assertAllClose(ans, expected, check_dtypes=False)
def testSum(self):
pfun, axis_name = papply(lambda x: np.sum(x, axis=0), 5)
jaxpr = make_jaxpr(pfun)(onp.ones(3))
expected_jaxpr = make_jaxpr(lambda x: lax_parallel.psum(x, axis_name))(
onp.zeros((5, 3)))
assert repr(jaxpr) == repr(expected_jaxpr)
arg = onp.arange(15.).reshape((5, 3))
ans = serial_pmap(pfun, axis_name)(arg)[0]
expected = onp.sum(arg, axis=0)
self.assertAllClose(ans, expected, check_dtypes=False)
def testAddBroadcasting(self):
return SkipTest("test doesn't pass yet") # TODO(frostig)
def fun(x):
return x + 3
x = onp.array([[1, 2], [3, 4]])
expected = x + 3
pfun, axis_name = papply(fun, 2)
ans = serial_pmap(pfun, axis_name)(x)
self.assertAllClose(ans, expected, check_dtypes=True)
def testTransposeWithOddPermutation(self):
def fun(x):
return np.transpose(x, (2, 0, 1))
xs = [
onp.reshape(onp.arange(8., dtype=onp.float32), (2, 2, 2)),
onp.reshape(onp.arange(27., dtype=onp.float32), (3, 3, 3)),
]
for x in xs:
expected = np.transpose(x, (2, 0, 1))
pfun, axis_name = papply(fun, x.shape[0])
ans = serial_pmap(pfun, axis_name)(x)
self.assertAllClose(ans, expected, check_dtypes=False)
def testTranspose(self):
def fun(x):
return x.T
xs = [
onp.reshape(onp.arange(4., dtype=onp.float32), (2, 2)),
onp.reshape(onp.arange(9., dtype=onp.float32), (3, 3)),
]
for x in xs:
expected = x.T
pfun, axis_name = papply(fun, x.shape[0])
ans = serial_pmap(pfun, axis_name)(x)
self.assertAllClose(ans, expected, check_dtypes=False)
def testLogSoftmax(self):
def fun(x):
return x - np.log(np.sum(np.exp(x)))
pfun, axis_name = papply(fun)
jaxpr = make_jaxpr(pfun)(onp.zeros(5))
expected_jaxpr = make_jaxpr(
lambda x: x - np.log(lax.psum(np.exp(x), axis_name)))(onp.zeros(5))
assert repr(jaxpr) == repr(expected_jaxpr)
ans = pmap(pfun, axis_name)(onp.arange(1., 5.))
expected = fun(onp.arange(1., 5.))
self.assertAllClose(ans, expected, check_dtypes=False)
def testDot(self):
def fun(x, y):
return lax.dot(x, y)
xs = [
onp.reshape(onp.arange(4., dtype=onp.float32), (2, 2)),
onp.reshape(onp.arange(9., dtype=onp.float32), (3, 3)),
]
in_axes_combos = [(0, 0), (0, 1)] # [(1, 0)]
for in_axes in in_axes_combos:
for x in xs:
expected = fun(x, x)
pfun, axis_name = papply(fun, x.shape[0], in_axes=in_axes)
ans = serial_pmap(pfun, axis_name)(x, x)
self.assertAllClose(ans, expected, check_dtypes=False)
def testLogSoftmax(self):
return SkipTest("test doesn't pass yet") # TODO(frostig)
def fun(x):
return x - np.log(np.sum(np.exp(x)))
pfun, axis_name = papply(fun, 5)
jaxpr = make_jaxpr(pfun)(onp.zeros(5))
expected_jaxpr = make_jaxpr(
lambda x: x - np.log(lax_parallel.psum(np.exp(x), axis_name)))(
onp.zeros(5))
assert repr(jaxpr) == repr(expected_jaxpr)
ans = serial_pmap(pfun, axis_name)(onp.arange(1., 5.))
expected = fun(onp.arange(1., 5.))
self.assertAllClose(ans, expected, check_dtypes=False)
def testSelect(self):
pfun, axis_name = papply(lax.select, 5, in_axes=(None, 0, None))
p = onp.arange(15).reshape((5, 3)) % 4 == 1
t = onp.ones((5, 3))
f = onp.zeros((5, 3))
jaxpr = make_jaxpr(pfun)(p, t[0], f)
def expected_spmd(p, t, f):
return lax.select(lax_parallel.psplit_like(p, t, axis_name), t,
lax_parallel.psplit_like(f, t, axis_name))
expected_jaxpr = make_jaxpr(expected_spmd)(p, t[0], f)
assert repr(jaxpr) == repr(expected_jaxpr)
ans = serial_pmap(pfun, axis_name, in_axes=(None, 0, None))(p, t, f)
expected = lax.select(p, t, f)
self.assertAllClose(ans, expected, check_dtypes=True)
def testMap(self): pfun, axis_name = papply(np.sin, 3) ans = pfun(onp.arange(3.)) expected = onp.sin(onp.arange(3.)) self.assertAllClose(ans, expected, check_dtypes=False)
def testIdentity(self): pfun, axis_name = papply(lambda x: x, 3) ans = pfun(onp.arange(3)) expected = onp.arange(3) self.assertAllClose(ans, expected, check_dtypes=False)
