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xmap_test.py
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xmap_test.py
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# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import itertools as it
import os
import re
from itertools import product, permutations
from typing import (Tuple, List, Dict, Generator, Iterator, Union, Optional)
from unittest import SkipTest
import numpy as np
from absl.testing import absltest
from absl.testing import parameterized
from functools import partial
import jax
import jax.numpy as jnp
import jax.scipy as jscipy
from jax._src import test_util as jtu
from jax import vmap
from jax import lax
from jax import core
from jax.core import NamedShape
from jax.experimental import maps
from jax.experimental import global_device_array
from jax.experimental import array
from jax.experimental.sharding import MeshPspecSharding
from jax.experimental.pjit import pjit, with_sharding_constraint
from jax.experimental.pjit import PartitionSpec as P
from jax.experimental.maps import xmap, serial_loop, SerialLoop
from jax.errors import JAXTypeError
from jax._src.lib import xla_bridge
from jax._src.lib import xla_client
from jax._src.util import curry, unzip2, prod, safe_zip
from jax._src.lax.parallel import pgather
from jax.interpreters import batching, pxla
from jax.ad_checkpoint import checkpoint
from jax.config import config
config.parse_flags_with_absl()
# TODO(mattjj): de-duplicate setUpModule and tearDownModule with pmap_test.py
# Run all tests with 8 CPU devices.
def setUpModule():
global prev_xla_flags
prev_xla_flags = os.getenv("XLA_FLAGS")
flags_str = prev_xla_flags or ""
# Don't override user-specified device count, or other XLA flags.
if "xla_force_host_platform_device_count" not in flags_str:
os.environ["XLA_FLAGS"] = (flags_str +
" --xla_force_host_platform_device_count=8")
# Clear any cached backends so new CPU backend will pick up the env var.
xla_bridge.get_backend.cache_clear()
# Reset to previous configuration in case other test modules will be run.
def tearDownModule():
if prev_xla_flags is None:
del os.environ["XLA_FLAGS"]
else:
os.environ["XLA_FLAGS"] = prev_xla_flags
xla_bridge.get_backend.cache_clear()
def create_array(global_shape, global_mesh, mesh_axes, global_data=None):
if global_data is None:
global_data = np.arange(
prod(global_shape), dtype=np.float32).reshape(global_shape)
sharding = MeshPspecSharding(global_mesh, mesh_axes)
return array.make_array_from_callback(
global_shape, sharding, lambda idx: global_data[idx]), global_data
# -------------------- Itertools helpers --------------------
def partitions(s, k):
for indices in product(range(k), repeat=len(s)):
outs = [[] for _ in range(k)]
for i, elt in zip(indices, s):
outs[i].append(elt)
yield outs
def powerset(s):
s = list(s)
return it.chain.from_iterable(it.combinations(s, r) for r in range(len(s)+1))
# -------------------- vmap test helpers --------------------
ensure_bdim_p = core.Primitive('ensure_bdim')
ensure_bdim_p.def_abstract_eval(lambda x, **kwargs: core.raise_to_shaped(x))
def _ensure_bdim_batcher(axis_size, frame_name, main_type, vals_in, dims_in, axis_name, bdim):
v, = vals_in
d, = dims_in
assert d is not batching.not_mapped
return jnp.moveaxis(v, d, bdim), bdim
batching.axis_primitive_batchers[ensure_bdim_p] = _ensure_bdim_batcher
batching.primitive_batchers[ensure_bdim_p] = lambda v, d: (v[0], d[0])
core.axis_substitution_rules[ensure_bdim_p] = partial(jax._src.lax.parallel._subst_all_names_in_param,
'axis_name')
def ensure_bdim(x, axis_name, bdim):
return ensure_bdim_p.bind(x, axis_name=(axis_name,), bdim=bdim)
# -------------------- Axis resources generation --------------------
AxisResources = Dict[str, Union[str, Tuple[str, ...]]]
def schedules(sizes: Dict[str, int]
) -> Generator[Tuple[AxisResources, jtu.MeshSpec], None, None]:
"""Test utility generating xmap parallel schedules from logical names & sizes.
Args:
sizes: dict mapping logical axis name to its corresponding size.
Returns:
A generator producing finitely many values, where each value is a pair in
which the first element is a value suitable for xmap's axis_resources
argument and the second element is a list of pairs with the first element
representing a generated physical mesh axis name and the second element
representing a corresponding generated mesh axis size. The generated mesh
names/sizes can be used to define a physical mesh in tests.
This function doesn't generate schedules which map distinct logical axis names
to the same parallel resource name. It only generates parallel resources; the
rest are implicitly left for vectorization. Parallel resource names are
generated by prepending an 'r', 'r1', or 'r2' to the corresponding logical
name.
Examples:
>>> for sched in schedules({'i': 2, 'j': 4}):
... print(sched)
({}, [])
({'i': 'ri'}, [('ri', 1)])
({'i': 'ri'}, [('ri', 2)])
({'i': ('r1i', 'r2i')}, [('r1i', 1), ('r2i', 1)])
({'i': ('r1i', 'r2i')}, [('r1i', 1), ('r2i', 2)])
({'i': ('r1i', 'r2i')}, [('r1i', 2), ('r2i', 1)])
({'j': 'rj'}, [('rj', 1)])
({'j': 'rj'}, [('rj', 2)])
({'j': 'rj'}, [('rj', 4)])
({'j': ('r1j', 'r2j')}, [('r1j', 1), ('r2j', 1)])
({'j': ('r1j', 'r2j')}, [('r1j', 1), ('r2j', 2)])
({'j': ('r1j', 'r2j')}, [('r1j', 1), ('r2j', 4)])
({'j': ('r1j', 'r2j')}, [('r1j', 2), ('r2j', 1)])
({'j': ('r1j', 'r2j')}, [('r1j', 2), ('r2j', 2)])
({'j': ('r1j', 'r2j')}, [('r1j', 4), ('r2j', 1)])
({'i': 'ri', 'j': 'rj'}, [('ri', 1), ('rj', 1)])
({'i': 'ri', 'j': 'rj'}, [('ri', 1), ('rj', 2)])
({'i': 'ri', 'j': 'rj'}, [('ri', 1), ('rj', 4)])
({'i': 'ri', 'j': 'rj'}, [('ri', 2), ('rj', 1)])
({'i': 'ri', 'j': 'rj'}, [('ri', 2), ('rj', 2)])
({'i': 'ri', 'j': 'rj'}, [('ri', 2), ('rj', 4)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 1), ('r1j', 1), ('r2j', 1)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 1), ('r1j', 1), ('r2j', 2)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 1), ('r1j', 1), ('r2j', 4)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 1), ('r1j', 2), ('r2j', 1)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 1), ('r1j', 2), ('r2j', 2)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 1), ('r1j', 4), ('r2j', 1)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 2), ('r1j', 1), ('r2j', 1)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 2), ('r1j', 1), ('r2j', 2)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 2), ('r1j', 1), ('r2j', 4)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 2), ('r1j', 2), ('r2j', 1)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 2), ('r1j', 2), ('r2j', 2)])
({'i': 'ri', 'j': ('r1j', 'r2j')}, [('ri', 2), ('r1j', 4), ('r2j', 1)])
({'j': 'rj', 'i': ('r1i', 'r2i')}, [('rj', 1), ('r1i', 1), ('r2i', 1)])
({'j': 'rj', 'i': ('r1i', 'r2i')}, [('rj', 1), ('r1i', 1), ('r2i', 2)])
({'j': 'rj', 'i': ('r1i', 'r2i')}, [('rj', 1), ('r1i', 2), ('r2i', 1)])
({'j': 'rj', 'i': ('r1i', 'r2i')}, [('rj', 2), ('r1i', 1), ('r2i', 1)])
({'j': 'rj', 'i': ('r1i', 'r2i')}, [('rj', 2), ('r1i', 1), ('r2i', 2)])
({'j': 'rj', 'i': ('r1i', 'r2i')}, [('rj', 2), ('r1i', 2), ('r2i', 1)])
({'j': 'rj', 'i': ('r1i', 'r2i')}, [('rj', 4), ('r1i', 1), ('r2i', 1)])
({'j': 'rj', 'i': ('r1i', 'r2i')}, [('rj', 4), ('r1i', 1), ('r2i', 2)])
({'j': 'rj', 'i': ('r1i', 'r2i')}, [('rj', 4), ('r1i', 2), ('r2i', 1)])
"""
def divisors(n: int) -> List[int]:
return [m for m in range(1, n + 1) if not n % m]
def divisors2(n: int) -> Iterator[Tuple[int, int]]:
for k1 in divisors(n):
for k2 in divisors(n // k1):
yield (k1, k2)
# choose a subset of logical axis names to map to parallel resources
for names in powerset(sizes):
# partition that set of logical axis names into two subsets: one subset to
# map to one parallel resource axis and a second subset to map to two
# parallel resource axes.
for names1, names2 in partitions(names, 2):
# to avoid generating too many complex cases, we skip generating cases
# where more than one logical axis name is to be mapped to two parallel
# resource axes. comment out this line to generate more complex tests.
if len(names2) > 1: continue
# make up parallel resource axis names for each logical axis
axis_resources1 = ((name, 'r' + name) for name in names1)
axis_resources2 = ((name, ('r1' + name, 'r2' + name)) for name in names2)
axis_resources = dict(it.chain(axis_resources1, axis_resources2))
# make up sizes for each resource axis, where the size must divide the
# corresponding logical axis
for mesh_sizes1 in product(*(divisors(sizes[n]) for n in names1)):
for mesh_sizes2 in product(*(divisors2(sizes[n]) for n in names2)):
mesh_data1 = (('r' + name, size) for name, size in zip(names1, mesh_sizes1))
mesh_data2 = (pair for name, (size1, size2) in zip(names2, mesh_sizes2)
for pair in [('r1' + name, size1), ('r2' + name, size2)])
mesh_data = list(it.chain(mesh_data1, mesh_data2))
yield axis_resources, mesh_data
class XMapTestCase(jtu.BufferDonationTestCase):
pass
# A mixin that enables SPMD lowering tests
class SPMDTestMixin:
def setUp(self):
super().setUp()
jtu.set_spmd_lowering_flag(True)
def tearDown(self):
jtu.restore_spmd_lowering_flag()
class ManualSPMDTestMixin:
def setUp(self):
if not hasattr(xla_client.OpSharding.Type, "MANUAL"):
raise SkipTest
super().setUp()
jtu.set_spmd_lowering_flag(True)
jtu.set_spmd_manual_lowering_flag(True)
def tearDown(self):
jtu.restore_spmd_manual_lowering_flag()
jtu.restore_spmd_lowering_flag()
class XMapTest(XMapTestCase):
def testBasic(self):
local_devices = list(jax.local_devices())
if len(local_devices) < 4:
raise SkipTest("Test requires at least 4 local devices")
def f(a, b):
return a * 2, b * 4
devices = np.array(local_devices[:4]).reshape((2, 2))
with maps.Mesh(devices, ('x', 'y')):
fm = xmap(f,
in_axes=({0: 'a', 1: 'b'}, ['c', ...]),
out_axes=({0: 'a', 1: 'b'}, ['c', ...]),
axis_resources={'a': 'x', 'b': 'y', 'c': 'x'})
ashape = (16, 8, 5)
a = jnp.arange(np.prod(ashape)).reshape(ashape)
bshape = (2, 7)
b = jnp.arange(np.prod(bshape)).reshape(bshape)
c, d = fm(a, b)
self.assertAllClose(c, a * 2)
self.assertAllClose(d, b * 4)
@jtu.with_mesh([('x', 2), ('y', 2)])
def testCollectiveReduce(self):
fm = xmap(lambda a, b: (lax.psum(a * 2, 'a'), b * 4),
in_axes=(['a', 'b', ...], {0: 'c'}),
out_axes=(['b', ...], {0: 'c'}),
axis_resources={'a': 'x', 'b': 'y', 'c': 'x'})
ashape = (16, 8, 5)
a = jnp.arange(np.prod(ashape)).reshape(ashape)
bshape = (2, 7)
b = jnp.arange(np.prod(bshape)).reshape(bshape)
c, d = fm(a, b)
self.assertAllClose(c, (a * 2).sum(0))
self.assertAllClose(d, b * 4)
@jtu.with_mesh([('x', 2), ('y', 2)])
def testCollectivePermute2D(self):
perm = np.array([3, 1, 2, 0])
x = jnp.arange(4).reshape((2, 2))
result = xmap(lambda x: lax.pshuffle(x, ('i', 'j'), perm),
in_axes=['i', 'j', ...],
out_axes=['i', 'j', ...],
axis_resources={'i': 'x', 'j': 'y'})(x).reshape((-1,))
self.assertAllClose(result, perm)
def testCollectivePermute1D(self):
perm = np.array([3, 1, 2, 0])
x = jnp.arange(4)
result = xmap(lambda x: lax.pshuffle(x, 'i', perm),
in_axes=['i', ...],
out_axes=['i', ...])(x)
self.assertAllClose(result, perm)
def testCollectiveAllGather(self):
x = jnp.arange(4, dtype='int32')
result = xmap(lambda x: lax.all_gather(x, 'i') + lax.axis_index('i'),
in_axes=['i', ...], out_axes=['i', ...])(x)
self.assertAllClose(result, x[jnp.newaxis] + x[jnp.newaxis].T)
@jtu.with_mesh([('x', 2), ('y', 2)])
def testOneLogicalTwoMeshAxesBasic(self):
def f(v):
return lax.psum(v * 2, 'a'), v * 4
fm = xmap(f, in_axes=['a', ...], out_axes=({}, {1: 'a'}),
axis_resources={'a': ('x', 'y')})
vshape = (4, 5)
v = jnp.arange(np.prod(vshape)).reshape(vshape)
ans, ans2 = fm(v)
self.assertAllClose(ans, (v * 2).sum(0))
self.assertAllClose(ans2, v.T * 4)
@jtu.with_mesh([('x', 2), ('y', 2)])
def testOneLogicalTwoMeshAxesSharding(self):
def f(v):
return v * 4
fxy = xmap(f, in_axes=['a', ...], out_axes={1: 'a'},
axis_resources={'a': ('x', 'y')})
fyx = xmap(f, in_axes=['a', ...], out_axes={1: 'a'},
axis_resources={'a': ('y', 'x')})
vshape = (4, 5)
v = jnp.arange(np.prod(vshape)).reshape(vshape)
zxy = fxy(v)
self.assertEqual(
zxy.sharding_spec,
pxla.ShardingSpec((pxla.NoSharding(), pxla.Chunked((2, 2))),
(pxla.ShardedAxis(0), pxla.ShardedAxis(1))))
zyx = fyx(v)
self.assertEqual(
zyx.sharding_spec,
pxla.ShardingSpec((pxla.NoSharding(), pxla.Chunked((2, 2))),
(pxla.ShardedAxis(1), pxla.ShardedAxis(0))))
@jtu.with_mesh([('x', 2), ('y', 2)])
def testSkipFirstMeshDim(self):
def run(axis_resources):
return xmap(lambda x: x * 2, in_axes=['i', ...], out_axes=['i', ...],
axis_resources=axis_resources)(jnp.ones((4,)))
self.assertAllClose(run({'i': 'x'}), run({'i': 'y'}))
def testCaching(self):
def f(x):
assert python_should_be_executing
return x * 2
devices = np.array(jax.local_devices()[:2])
if devices.size < 2:
raise SkipTest("Test requires 2 devices")
x = np.arange(8).reshape((2, 2, 2))
with maps.Mesh(devices, ('x',)):
python_should_be_executing = True
xmap(f, in_axes=['a', ...], out_axes=['a', ...],
axis_resources={'a': 'x'})(x)
python_should_be_executing = False
xmap(f, in_axes=['a', ...], out_axes=['a', ...],
axis_resources={'a': 'x'})(x)
with maps.Mesh(devices, ('x',)):
python_should_be_executing = False
xmap(f, in_axes=['a', ...], out_axes=['a', ...],
axis_resources={'a': 'x'})(x)
@parameterized.named_parameters(
{"testcase_name": name, "mesh": mesh, "axis_resources": axis_resources}
for name, mesh, axis_resources in (
('OneToOne', (('x', 2), ('y', 2)), (('a', 'y'), ('b', 'x'))),
('Multiple', (('x', 2), ('y', 2), ('z', 2)), (('a', 'y'), ('b', ('x', 'z')))),
))
@jtu.with_mesh_from_kwargs
def testNestedMesh(self, mesh, axis_resources):
@partial(xmap, in_axes={1: 'a'}, out_axes=({0: 'a'}, {}),
axis_resources=dict([axis_resources[0]]))
def f(x):
y = x * 2
@partial(xmap, in_axes={0: 'b'}, out_axes=({1: 'b'}, {}),
axis_resources=dict([axis_resources[1]]))
def h(y):
# Multiply by a constant array to better exercise the partial_eval rule
return jnp.sin(y) * np.arange(y.size, dtype=float), lax.psum(y, ('a', 'b'))
return h(y)
xshape = (4, 2, 5)
x = jnp.arange(np.prod(xshape), dtype=float).reshape(xshape)
y = f(x)
self.assertAllClose(y, ((jnp.sin(x * 2) * np.arange(xshape[-1], dtype=float)[None, None]).transpose((1, 2, 0)), (x * 2).sum((0, 1))))
self.assertEqual(y[0].sharding_spec.sharding,
(pxla.Chunked([2]), pxla.NoSharding(), pxla.NoSharding()))
self.assertEqual(y[0].sharding_spec.mesh_mapping,
(pxla.Replicated(2), pxla.ShardedAxis(0)) + (pxla.Replicated(2),) * (len(mesh) - 2))
if config.experimental_xmap_spmd_lowering:
hlo = f.lower(x).compiler_ir(dialect="hlo").as_hlo_text()
# Make sure that there are non-partial sharding specs in the HLO
self.assertRegex(hlo, r"sharding={devices=\[[0-9,]+\][0-9,]+}")
@jtu.with_and_without_mesh
def testMultipleCalls(self, mesh, axis_resources):
def f(x, y):
assert x.shape == y.shape == (3, 5)
return jnp.tensordot(x, y, axes=([1], [1]))
f_mapped = xmap(f,
in_axes=(['i', ...], ['j', ...]),
out_axes=['i', 'j', ...],
axis_resources=dict(axis_resources))
x = jnp.arange(30).reshape(2, 3, 5)
expected = jnp.einsum('imk,jnk->ijmn', x, x)
for i in range(10):
self.assertAllClose(f_mapped(x, x), expected)
@jtu.with_and_without_mesh
@jtu.skip_on_devices("cpu") # In/out aliasing not supported on CPU.
def testBufferDonation(self, mesh, axis_resources):
shard = lambda x: x
if axis_resources:
shard = xmap(lambda x: x, in_axes=['i', ...], out_axes=['i', ...],
axis_resources=dict(axis_resources))
f = xmap(lambda x, y: x + y * 4,
in_axes=['i', ...], out_axes=['i', ...],
axis_resources=dict(axis_resources),
donate_argnums=0)
# The multiplications below disable some optimizations that prevent reuse
x = shard(jnp.zeros((2, 5)) * 4)
y = shard(jnp.ones((2, 5)) * 2)
f(x, y)
self.assertNotDeleted(y)
self.assertDeleted(x)
@jtu.skip_on_devices("cpu") # In/out aliasing not supported on CPU.
@jtu.with_mesh([('x', 2)])
@jtu.ignore_warning(category=UserWarning, # SPMD test generates warning.
message="Some donated buffers were not usable*")
def testBufferDonationNamedShape(self):
axis_resources = {'i': 'x'}
# named in_aval, unnamed out_aval
f = xmap(lambda _: jnp.ones((2, 5)),
in_axes=['i', ...], out_axes=[...],
axis_resources=axis_resources,
donate_argnums=0)
shard = xmap(lambda x: x, in_axes=['i', ...], out_axes=['i', ...],
axis_resources=dict(axis_resources))
x = shard(jnp.zeros((4, 5)))
f(x)
self.assertDeleted(x)
def testControlFlow(self):
x = jnp.arange(5)
xmap(lambda x: lax.fori_loop(0, 10, lambda _, x: lax.psum(x, 'i'), x),
in_axes=['i', ...], out_axes=['i', ...])(x)
@jtu.with_and_without_mesh
def testAxisSizes(self, mesh, axis_resources):
result = xmap(lambda: lax.axis_index('i'),
in_axes=(), out_axes=['i', ...],
axis_sizes={'i': 6},
axis_resources=dict(axis_resources))()
self.assertAllClose(result, jnp.arange(6, dtype=result.dtype))
def testCollectiveOverNoName(self):
result = xmap(lambda: lax.psum(jnp.array(2) ** 2, 'i'),
in_axes={}, out_axes={}, axis_sizes={'i': 4})()
self.assertEqual(result, 16)
def VmapOfXmapCases(s):
xmap_in_axes = ([{}] +
[{i: 'x'} for i in range(3)] +
[{i: 'x', j: 'y'} for i in range(4) for j in range(4) if i != j])
for xmap_dim_x, xmap_dim_y in s(product(xmap_in_axes, repeat=2)):
xmap_axes = sorted(set(xmap_dim_x.values()) | set(xmap_dim_y.values()))
num_axes = len(xmap_axes)
if xmap_axes is None:
continue
xmap_out_axes = [dict(zip(dims, xmap_axes))
for dims in permutations(range(2 + num_axes), num_axes)]
for xmap_dim_z in s(xmap_out_axes):
for vmap_dim_x in s([*range(2 + len(xmap_dim_x)), None]):
for vmap_dim_y in s([*range(2 + len(xmap_dim_y)), None]):
if vmap_dim_x is None and vmap_dim_y is None:
continue
for vmap_dim_result in s(range(3)):
for vmap_dim_z in s(range(2 + len(xmap_axes))):
for vmap_as_xmap in s([False, True]):
yield {"testcase_name":
f"_xin={(sorted(xmap_dim_x.items()), sorted(xmap_dim_y.items()))}_"
f"xout={sorted(xmap_dim_z.items())}_vin={(vmap_dim_x, vmap_dim_y)}_"
f"vout={vmap_dim_z}_vresult={vmap_dim_result}_vmap_as_xmap={vmap_as_xmap}",
"xmap_in_axes": (xmap_dim_x, xmap_dim_y),
"xmap_out_axes": xmap_dim_z,
"vmap_in_axes": (vmap_dim_x, vmap_dim_y),
"vmap_out_axes": vmap_dim_z,
"vmap_result_axis": vmap_dim_result,
"vmap_as_xmap": vmap_as_xmap}
@parameterized.named_parameters(jtu.named_cases_from_sampler(VmapOfXmapCases))
def testNestedMap(self,
xmap_in_axes, xmap_out_axes,
vmap_in_axes, vmap_out_axes, vmap_result_axis,
vmap_as_xmap):
"""Test various vmap(xmap) and xmap(xmap) combinations.
The outer map always introduces a single dimension, the inner map introduces one or two.
"""
(xin_x, xin_y) = xmap_in_axes
(vin_x, vin_y) = vmap_in_axes
vmap_size = 7
xmap_sizes = {'x': 11, 'y': 13}
xshape = [2, 3]
yshape = [3, 5]
zshape = [2, 5]
xind = ['n', 'k']
yind = ['k', 'm']
zind = ['n', 'm']
f = lambda x, y: ensure_bdim(jnp.einsum('nk,km->nm', x, y), 'v', vmap_result_axis)
for pos, name in sorted(xin_x.items()):
xshape.insert(pos, xmap_sizes[name])
xind.insert(pos, name)
for pos, name in sorted(xin_y.items()):
yshape.insert(pos, xmap_sizes[name])
yind.insert(pos, name)
for pos, name in sorted(xmap_out_axes.items()):
zshape.insert(pos, xmap_sizes[name])
zind.insert(pos, name)
if vin_x is not None:
xshape.insert(vin_x, vmap_size)
xind.insert(vin_x, 'v')
if vin_y is not None:
yshape.insert(vin_y, vmap_size)
yind.insert(vin_y, 'v')
zshape.insert(vmap_out_axes, vmap_size)
zind.insert(vmap_out_axes, 'v')
if vmap_as_xmap:
do_vmap = partial(xmap,
in_axes=({vin_x: 'v'} if vin_x is not None else {},
{vin_y: 'v'} if vin_y is not None else {}),
out_axes={vmap_out_axes: 'v'})
else:
do_vmap = partial(vmap, in_axes=vmap_in_axes, out_axes=vmap_out_axes, axis_name='v')
fm = do_vmap(xmap(f, in_axes=xmap_in_axes, out_axes=xmap_out_axes))
fref = partial(jnp.einsum, f"{''.join(xind)},{''.join(yind)}->{''.join(zind)}")
rng = self.rng()
x = rng.randn(*xshape)
y = rng.randn(*yshape)
self.assertAllClose(fm(x, y), fref(x, y))
def testBatchingPostProcess(self):
x = jnp.arange(10).reshape(5, 2)
f = jax.vmap(lambda y: xmap(lambda x: x + y, in_axes=['i', ...], out_axes=['i', ...])(x))
ref = jax.vmap(lambda y: jax.vmap(lambda x: x + y)(x))
self.assertAllClose(f(x * 2), ref(x * 2))
def testAutodiffBroadcast(self):
f = xmap(lambda x, y: jnp.cos(lax.dot(x, jnp.sin(y),
precision=lax.Precision.HIGHEST)),
in_axes=(['i', ...], {}), out_axes=['i', ...])
x = jnp.arange(12, dtype=jnp.float32).reshape((3, 4)) / 100
y = jnp.arange(20, dtype=jnp.float32).reshape((4, 5)) / 100
jtu.check_grads(f, (x, y), order=2, modes=['fwd'])
jtu.check_grads(f, (x, y), order=1, modes=['rev'])
with self.assertRaises(AssertionError):
# Second order reverse-mode differentiations seems to be broken,
# likely due to the transpose of psum being defined incorrectly.
jtu.check_grads(f, (x, y), order=2, modes=['rev'])
def testAutodiffNoBroadcast(self):
f = xmap(lambda x, y: jnp.cos(lax.dot(x, jnp.sin(y),
precision=lax.Precision.HIGHEST)),
in_axes=(['i', ...], [None, 'i']), out_axes=['i'])
x = jnp.arange(12, dtype=jnp.float32).reshape((3, 4)) / 100
y = jnp.arange(12, dtype=jnp.float32).reshape((4, 3)) / 100
jtu.check_grads(f, (x, y), order=2)
@jtu.with_and_without_mesh
def testNamedShape(self, mesh, axis_resources):
x = np.arange(4,)
y = 2
f = xmap(lambda x, y: (x + y, y * lax.axis_index('i')),
in_axes=(['i', ...], {}),
out_axes=(['i', ...], ['i', ...]),
axis_resources=dict(axis_resources))
z, w = f(x, y)
self.assertEqual(z.aval.named_shape, {})
self.assertEqual(w.aval.named_shape, {})
@jtu.with_and_without_mesh
def testBroadcast(self, mesh, axis_resources):
x = jnp.asarray(2.0)
f = xmap(lambda x: x, in_axes={}, out_axes=['i'],
axis_sizes={'i': 4}, axis_resources=dict(axis_resources))
self.assertAllClose(f(x), jnp.asarray([2.0, 2.0, 2.0, 2.0]))
def testNestedBroadcast(self):
x = jnp.asarray(2.0)
f = xmap(lambda x: x, in_axes={}, out_axes=['i'], axis_sizes={'i': 4})
g = xmap(f, in_axes={}, out_axes=['j', ...], axis_sizes={'j': 7})
self.assertAllClose(g(x), jnp.tile(x.reshape((1, 1)), (7, 4)))
@serial_loop('l', 4)
def testLoopBasic(self):
x = jnp.arange(16)
y = xmap(lambda x: x + 4, in_axes=['i'], out_axes=['i'],
axis_resources={'i': 'l'})(x)
self.assertAllClose(y, x + 4)
@jtu.with_mesh([('x', 2)])
@serial_loop('l', 4)
def testLoopWithMesh(self):
x = jnp.arange(16)
y = xmap(lambda x: x + 4, in_axes=['i'], out_axes=['i'],
axis_resources={'i': ('x', 'l')})(x)
self.assertAllClose(y, x + 4)
def testLoopAnonBasic(self):
x = jnp.arange(16)
y = xmap(lambda x: x + 4, in_axes=['i'], out_axes=['i'],
axis_resources={'i': SerialLoop(4)})(x)
self.assertAllClose(y, x + 4)
@jtu.with_mesh([('x', 2)])
def testLoopAnonWithMesh(self):
x = jnp.arange(16)
y = xmap(lambda x: x + 4, in_axes=['i'], out_axes=['i'],
axis_resources={'i': ('x', SerialLoop(4))})(x)
self.assertAllClose(y, x + 4)
def testLowerWithAbstractArgs(self):
x = jax.ShapeDtypeStruct((2, 2), jnp.float32)
# Make sure this doesn't crash
xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...]).lower(x)
def testLowerCompile(self):
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
f_exe = f.lower(x).compile()
self.assertAllClose(f_exe(x), f(x))
def testLowerCompileInTreeMismatch(self):
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
f_exe = f.lower(x).compile()
self.assertRaisesRegex(
TypeError, "function compiled for .*, called with .*",
lambda: f_exe([x]))
def testLowerCompileArgTypeMismatch(self):
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
x_f32 = x.astype(jnp.float32)
x_i32 = x.astype(jnp.int32)
f_exe = f.lower(x_f32).compile()
self.assertRaisesRegex(
TypeError,
"Computation compiled for input types:\n.*float32.*\n"
"called with:\n.*int32.*",
lambda: f_exe(x_i32))
def testLowerAsText(self):
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
f = f.lower(x)
self.assertIsInstance(f.as_text(), str)
self.assertIsInstance(f.as_text(dialect='hlo'), str)
self.assertIsInstance(f.as_text(dialect='mhlo'), str)
def testLowerCompilerIR(self):
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
f = f.lower(x)
self.assertIsNotNone(f.compiler_ir())
self.assertIsNotNone(f.compiler_ir(dialect='hlo'))
self.assertIsNotNone(f.compiler_ir(dialect='mhlo'))
@jtu.ignore_warning(category=DeprecationWarning)
def testLowerCompileCompilerIR(self):
# TODO(frostig): remove (deprecated)
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
f = f.lower(x).compile()
self.assertIsNotNone(f.compiler_ir())
def testLowerCompileAsText(self):
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
f = f.lower(x).compile()
self.assertIsInstance(f.as_text(), (str, type(None)))
def testLowerCompileCostAnalysis(self):
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
f = f.lower(x).compile()
f.cost_analysis() # doesn't raise
def testLowerCompileMemoryAnalysis(self):
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
f = f.lower(x).compile()
f.memory_analysis() # doesn't raise
def testLowerCompileExecutable(self):
f = xmap(lambda x: x + 4, in_axes=['i', ...], out_axes=['i', ...])
x = jnp.arange(4, dtype=jnp.float32).reshape((2, 2))
f = f.lower(x).compile()
self.assertIsNotNone(f.runtime_executable())
def testNewCheckpoint(self):
f = checkpoint(xmap(lambda x: x, in_axes=['i', ...], out_axes=['i', ...]))
self.assertAllClose(jax.grad(lambda x: f(x).sum())(jnp.arange(3.)), jnp.ones(3))
class XMapTestSPMD(SPMDTestMixin, XMapTest):
"""Re-executes all basic tests with the SPMD partitioner enabled"""
skipped_tests = {
"CollectivePermute2D" # vmap of multidimensional permute not implemented yet
}
def setUp(self):
for skipped_name in self.skipped_tests:
if skipped_name in self._testMethodName:
raise SkipTest
super().setUp()
@jtu.with_mesh([('x', 2), ('y', 2), ('z', 2)])
def testNestedMeshSPMD(self):
h = xmap(lambda y: (jnp.sin(y) * np.arange(y.size, dtype=float),
lax.psum(y, ('a', 'b', 'c'))),
in_axes={0: 'c'}, out_axes=({1: 'c'}, {}),
axis_resources={'c': 'z'})
f = xmap(lambda x: h(x * 2),
in_axes=[None, 'a', 'b', ...], out_axes=(['a', 'b', ...], {}),
axis_resources={'a': 'x', 'b': 'y'})
xshape = (8, 2, 4, 5)
x = jnp.arange(np.prod(xshape), dtype=float).reshape(xshape)
hlo = f.lower(x).compiler_ir(dialect="hlo").as_hlo_text()
match = re.search(r"sharding={devices=\[([0-9,]+)\][0-9,]+}", hlo)
self.assertIsNot(match, None)
tile_factors = [int(s) for s in match.group(1).split(',')]
self.assertEqual(set(tile_factors), {1, 2})
@jtu.with_mesh([('x', 2)])
def testFixedSharding(self):
# TODO(apaszke): Add support for extracting XLA computations generated by
# xmap and make this less of a smoke test.
try:
config.update("experimental_xmap_ensure_fixed_sharding", True)
f = xmap(lambda x: jnp.sin(2 * jnp.sum(jnp.cos(x) + 4, 'i')),
in_axes=['i'], out_axes={}, axis_resources={'i': 'x'})
x = jnp.arange(20, dtype=jnp.float32)
f(x)
finally:
config.update("experimental_xmap_ensure_fixed_sharding", False)
class XMapTestManualSPMD(ManualSPMDTestMixin, XMapTestCase):
@jtu.with_mesh([('x', 2)])
def testBasic(self):
f = lambda x: jnp.sin(jnp.cos(x) + x) * x
fx = xmap(f, in_axes=['i'], out_axes=['i'], axis_resources={'i': 'x'})
x = jnp.arange(20, dtype=jnp.float32)
self.assertAllClose(fx(x), f(x))
@jtu.with_mesh([('x', 2)])
def testReplicated(self):
# TODO(apaszke): This seems to be failing if I try to have a replicated and a mapped argument?
f = lambda x: jnp.sin(jnp.cos(x) + x) * x
fx = xmap(f, in_axes=[...], out_axes=[...], axis_sizes={'i': 4}, axis_resources={'i': 'x'})
x = jnp.arange(20, dtype=jnp.float32)
self.assertAllClose(fx(x), f(x))
@jtu.with_mesh([('x', 2), ('y', 1)])
def testInPJit(self):
f = xmap(lambda x: jnp.sin(x) + x, in_axes=['i'], out_axes=['i'], axis_resources={'i': 'x'})
h = pjit(lambda x: f(x * x) + x, in_axis_resources=P('y'), out_axis_resources=None)
x = jnp.arange(20, dtype=jnp.float32)
self.assertAllClose(h(x), jnp.sin(x * x) + x * x + x)
@jtu.with_mesh([('x', 2), ('y', 1)])
def testInPJitReplicated(self):
f = xmap(lambda x: jnp.sin(x) + x, in_axes={}, out_axes={}, axis_sizes={'i': 4}, axis_resources={'i': 'x'})
h = pjit(lambda x: f(x * x) + x, in_axis_resources=P('y'), out_axis_resources=None)
x = jnp.arange(20, dtype=jnp.float32)
self.assertAllClose(h(x), jnp.sin(x * x) + x * x + x)
@jtu.with_mesh([('x', 2), ('y', 1)])
def testNestedConstraint(self):
# TODO(b/219691408): Using P('y') instead of P() causes an XLA crash!
fimpl = lambda x: with_sharding_constraint(jnp.sin(x), P()) + x
f = xmap(fimpl, in_axes=['i', ...], out_axes=['i', ...], axis_resources={'i': 'x'})
h = pjit(lambda x: f(x * x) + x, in_axis_resources=P('y'), out_axis_resources=None)
x = jnp.arange(20, dtype=jnp.float32).reshape(4, 5)
self.assertAllClose(h(x), jnp.sin(x * x) + x * x + x)
class NamedNumPyTest(XMapTestCase):
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": f"_{reduction.__name__}_axes={axes}_i={mapped_axis}",
"reduction": reduction, "axes": axes, "mapped_axis": mapped_axis}
for reduction in (jnp.sum, jnp.max, jnp.min, jnp.mean, jnp.var, jnp.std,
jscipy.special.logsumexp)
for axes in (0, 'i', (1,), ('i',), (0, 1), (0, 'i'), ('i', 0))
for mapped_axis in range(3)))
def testReductions(self, reduction, axes, mapped_axis):
axes_t = axes if isinstance(axes, tuple) else (axes,)
ref_red = partial(reduction,
axis=tuple(mapped_axis if a == 'i' else a + (a >= mapped_axis)
for a in axes_t))
mapped_axis_after_red = mapped_axis - sum(axis < mapped_axis if axis != 'i' else 0
for axis in axes_t)
xmap_red = xmap(lambda x: reduction(x, axes),
in_axes={mapped_axis: 'i'},
out_axes=({} if 'i' in axes_t else {mapped_axis_after_red: 'i'}))
rng = self.rng()
x = rng.randn(2, 5, 6)
self.assertAllClose(ref_red(x), xmap_red(x))
class NamedRandomTest(XMapTestCase):
@curry
def parameterize_by_sampler(extra, f, subset):
if extra is None:
extra = [("", {})]
else:
extra = list(extra)
subset_fn = jtu.cases_from_list if subset else lambda x: x
return parameterized.named_parameters(subset_fn(
{"testcase_name": name + extra_name, "distr_sample": sample, **extra_kwargs}
for name, sample in [
("Uniform", jax.random.uniform),
("Normal", jax.random.normal),
("Bernoulli", partial(jax.random.bernoulli, p=0.5)),
("TruncatedNormal", partial(jax.random.truncated_normal, lower=-2, upper=2)),
]
for extra_name, extra_kwargs in extra))(f)
@parameterize_by_sampler(None, subset=False)
def testSamplerSharding(self, distr_sample):
def sample(shape, map_size):
return xmap(lambda: distr_sample(jax.random.PRNGKey(0), shape=shape),
in_axes=(), out_axes=[None, 'i', ...], axis_sizes={'i': map_size})()
replicated = sample((3,), 4)
self.assertTrue((replicated[:,[0]] == replicated).all())
sharded = sample(NamedShape(3, i=4), 4)
self.assertFalse((sharded[:,[0]] == sharded[:,1:]).all(1).any())
error = "The shape of axis i was specified as 4, but it really is 5"
with self.assertRaisesRegex(ValueError, error):
sample(NamedShape(3, i=4), 5)
@parameterize_by_sampler(
((f"_mesh={mesh}_resources={sorted(axis_resources.items())}",
{"axis_resources": tuple(axis_resources.items()), "mesh": tuple(mesh)})
for axis_resources, mesh in schedules({'i': 4, 'j': 6})), subset=True)
@jtu.with_mesh_from_kwargs
def testSamplerResourceIndependence(self, distr_sample, axis_resources, mesh):
def sample(axis_resources):
return xmap(lambda: distr_sample(jax.random.PRNGKey(0), shape=NamedShape(3, i=4, j=6)),
in_axes=(), out_axes=['i', 'j', ...], axis_sizes={'i': 4, 'j': 6},
axis_resources=axis_resources)()
self.assertAllClose(sample({}), sample(dict(axis_resources)))
class NamedNNTest(XMapTestCase):
def testOneHot(self):
f = xmap(lambda x: jax.nn.one_hot(jnp.array([1, 2, 0], dtype='int32'), 3, axis='i'),
in_axes=['i', ...], out_axes=['i', ...])
expected = jnp.array([[0., 1., 0.],
[0., 0., 1.],
[1., 0., 0.]]).T
self.assertAllClose(f(jnp.ones(3, dtype='int32')), expected)
def testOneHotOutOfBound(self):
f = xmap(lambda x: jax.nn.one_hot(jnp.array([-1, 3], dtype='int32'), 3, axis='i'),
in_axes=['i', ...], out_axes=['i', ...])
self.assertAllClose(f(jnp.ones(3, dtype='int32')), jnp.zeros((3, 2)))
def testOneHotAxisSizeMismatch(self):
f = xmap(lambda x: jax.nn.one_hot(jnp.array([-1, 3], dtype='int32'), 3, axis='i'),
in_axes=['i', ...], out_axes=['i', ...])
with self.assertRaisesRegex(ValueError, "to match the size of axis i, but 3 != 5"):
f(jnp.ones(5, dtype='int32'))
@parameterized.named_parameters(jtu.cases_from_list(
{"testcase_name": f"_map_in={map_in}_map_out={map_out}_fan={fan}_distr={distr}",
"map_in": map_in, "map_out": map_out, "fan": fan,
"distr": distr}
for map_in, map_out in [(True, False), (False, True), (True, True)]
for fan in ['fan_in', 'fan_out', 'fan_avg']
for distr in ['uniform', 'normal', 'truncated_normal']))
def testVarianceScaling(self, map_in, map_out, fan, distr):
shape = (80, 50, 7)
fan_in, fan_out = jax._src.nn.initializers._compute_fans(
NamedShape(*shape), 0, 1)
key = jax.random.PRNGKey(1)
base_scaling = partial(jax.nn.initializers.variance_scaling, 100, fan, distr)
ref_sampler = lambda: base_scaling(in_axis=0, out_axis=1)(key, shape)
if map_in and map_out:
out_axes = ['i', 'o', ...]
named_shape = NamedShape(shape[2], i=shape[0], o=shape[1])
xmap_sampler = lambda: base_scaling(in_axis='i', out_axis='o')(key, named_shape)
elif map_in:
out_axes = ['i', ...]
named_shape = NamedShape(shape[1], shape[2], i=shape[0])
xmap_sampler = lambda: base_scaling(in_axis='i', out_axis=0)(key, named_shape)
elif map_out:
out_axes = [None, 'o', ...]
named_shape = NamedShape(shape[0], shape[2], o=shape[1])
xmap_sampler = lambda: base_scaling(in_axis=0, out_axis='o')(key, named_shape)
mapped_sampler = xmap(xmap_sampler,
in_axes=(), out_axes=out_axes,
axis_sizes={'i': shape[0], 'o': shape[1]})
self.assertAllClose(jnp.var(mapped_sampler()), jnp.var(ref_sampler()),
atol=1e-4, rtol=2e-2)
class XMapGDATest(XMapTestCase):
@jtu.with_mesh([('x', 4), ('y', 2)])
def test_basic(self):
global_mesh = jtu.create_global_mesh((4, 2), ('x', 'y'))
global_input_shape = (8, 2)
mesh_axes = P('x', 'y')
input_data = np.arange(
prod(global_input_shape)).reshape(global_input_shape)
def cb(index):
return input_data[index]
gda_obj = global_device_array.GlobalDeviceArray.from_callback(
global_input_shape, global_mesh, mesh_axes, cb)
with jax._src.config.parallel_functions_output_gda(True):
f = maps.xmap(
lambda x: x,
in_axes=({0: "a", 1: "b"}),
out_axes=({0: "a", 1: "b"}),
axis_resources={"a": "x", "b": "y"})
out = f(gda_obj)
self.assertIsInstance(out, global_device_array.GlobalDeviceArray)
self.assertEqual(out.shape, (8, 2))
self.assertEqual(out.local_shards[0].data.shape, (2, 1))
self.assertDictEqual(out.mesh.shape, {'x': 4, 'y': 2})
for s in out.local_shards:
self.assertArraysEqual(s.data, input_data[s.index])
@jtu.with_mesh([('x', 4), ('y', 2)])
def test_xmap_gda_mixed_inputs(self):
global_mesh = jtu.create_global_mesh((4, 2), ('x', 'y'))
global_input_shape = (8, 2)
mesh_axes = P('x')
input_data = np.arange(
prod(global_input_shape)).reshape(global_input_shape)
def cb(index):
return input_data[index]
gda_obj = global_device_array.GlobalDeviceArray.from_callback(
global_input_shape, global_mesh, mesh_axes, cb)
with jax._src.config.parallel_functions_output_gda(True):
f = maps.xmap(
lambda x, y: (x @ x.T, y @ y.T),
in_axes=({0: "a"}, ["c", ...]),
out_axes=({0: "a"}, ["c", ...]),
axis_resources={"a": "x", "c": "x"})
expected_matrix_mul = np.diagonal(input_data @ input_data.T)
out1, out2 = f(gda_obj, input_data)
self.assertIsInstance(out1, global_device_array.GlobalDeviceArray)
self.assertEqual(out1.shape, (8,))
self.assertEqual(out1.local_shards[0].data.shape, (2,))
self.assertDictEqual(out1.mesh.shape, {'x': 4, 'y': 2})
for s in out1.local_shards:
self.assertArraysEqual(s.data, expected_matrix_mul[s.index])
self.assertIsInstance(out2, global_device_array.GlobalDeviceArray)
self.assertEqual(out2.shape, (8,))
self.assertEqual(out2.local_shards[0].data.shape, (2,))
self.assertDictEqual(out2.mesh.shape, {'x': 4, 'y': 2})
for s in out2.local_shards:
self.assertArraysEqual(s.data, expected_matrix_mul[s.index])
for i, j in safe_zip(out1.local_shards, out2.local_shards):
self.assertArraysEqual(i.data, j.data)
@jtu.with_mesh([('x', 4), ('y', 2)])
def test_xmap_gda_double_input(self):
global_mesh = jtu.create_global_mesh((4, 2), ('x', 'y'))
global_input_shape = (8, 2)