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import numpy as np
from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase, rand_array
class TestPartitionOps(TestCase):
def test_configs(self):
# (main dims, partitions, main type, [list of (extra dims, type)])
configs = [
((10, ), 3),
((4, ), 10),
((10, 10), 4),
((100, ), 2),
((5, ), 1),
((1, ), 1),
((2, 10), 2),
]
suffixes = [
[],
[((2, 2), np.float32)],
[((3, ), np.int64), ((2, ), np.float32)],
]
return [
(main_dims, parts, main_type, extra, pack)
for main_dims, parts in configs
for main_type in [np.int32, np.int64] for extra in suffixes
for pack in [False, True]
]
def testPartition(self):
for main_dims, parts, main_type, extra_ins, pack in self.test_configs():
ins = ['in' + str(i) for i in range(1 + len(extra_ins))]
outs = [
'in{}_p{}'.format(j, i)
for i in range(parts) for j in range(1 + len(extra_ins))
]
op = core.CreateOperator(
'Partition', ins, outs, pack_first_input=(1 if pack else 0))
x = []
for i, (dims, t) in enumerate([((), main_type)] + extra_ins):
if t in [np.float32, np.float64]:
d = rand_array(*(main_dims + dims))
else:
d = np.random.randint(-100, 100, (main_dims + dims))
d = d.astype(t)
workspace.FeedBlob(ins[i], d)
x.append(d)
def sharding(x):
# numpy has proper modulo op that yields non-negative results
shards = (x[0] % parts).reshape([-1])
out = []
for i in range(parts):
for ind, v in enumerate(x):
suffix_shape = v.shape[len(x[0].shape):]
accum = []
data = v.reshape((-1, ) + suffix_shape)
if pack and ind == 0:
data = data // parts
for j, s in enumerate(shards):
if s == i:
accum.append(data[j])
def join(a):
if not a:
return np.empty(shape=(0, ) + suffix_shape)
return np.stack(a)
out.append(join(accum))
return out
workspace.RunOperatorOnce(op)
ref = sharding(x)
print(x)
print(ref)
for name, expected in zip(outs, ref):
np.testing.assert_array_equal(
expected, workspace.FetchBlob(name)
)
# test inverse operation (GatherByKey)
if len(main_dims) == 1:
# currently only 1D key tensor supported
for i in range(len(extra_ins)):
expected_out = ins[i + 1]
gather_ins = [ins[0]] + [
outs[len(ins) * p + i + 1] for p in range(parts)]
actual_out = expected_out + '_actual'
op = core.CreateOperator(
'GatherByKey', gather_ins, actual_out)
workspace.RunOperatorOnce(op)
expected = workspace.FetchBlob(expected_out)
actual = workspace.FetchBlob(actual_out)
np.testing.assert_array_equal(expected, actual)
def testLengthsPartition(self):
for main_dims, parts, main_type, extra_ins, pack in self.test_configs():
# For LengthsSharding only 1-D tensors supported as a first input
if len(main_dims) > 1:
continue
ins = ['in' + str(i) for i in range(2 + len(extra_ins))]
outs = [
'in{}_p{}'.format(j, i)
for i in range(parts) for j in range(2 + len(extra_ins))
]
op = core.CreateOperator(
'LengthsPartition', ins, outs,
pack_first_input=(1 if pack else 0)
)
x = []
for i, (dims, t) in enumerate([((), main_type)] + extra_ins):
if t in [np.float32, np.float64]:
d = rand_array(*(main_dims + dims))
else:
d = np.random.randint(-100, 100, (main_dims + dims))
d = d.astype(t)
workspace.FeedBlob(ins[i + 1], d)
x.append(d)
# Randomly generate length tensor as well
elements = np.random.randint(2, 10)
lengths = []
total_length = 0
for _ in range(elements - 1):
lengths.append(np.random.randint(main_dims[0] - total_length))
total_length += lengths[-1]
lengths.append(main_dims[0] - total_length)
workspace.FeedBlob(ins[0], np.array(lengths, dtype=np.int32))
def sharding(x):
# numpy has proper modulo op that yields non-negative results
shards = (x[0] % parts).reshape([-1])
out = []
for i in range(parts):
idx = 0
sharded_lengths = np.zeros(elements)
for ind, length in enumerate(lengths):
for _ in range(length):
if shards[idx] == i:
sharded_lengths[ind] += 1
idx += 1
out.append(sharded_lengths)
for ind, v in enumerate(x):
suffix_shape = v.shape[len(x[0].shape):]
accum = []
data = v.reshape((-1, ) + suffix_shape)
if pack and ind == 0:
data = data // parts
for j, s in enumerate(shards):
if s == i:
accum.append(data[j])
def join(a):
if not a:
return np.empty(shape=(0, ) + suffix_shape)
return np.stack(a)
out.append(join(accum))
return out
workspace.RunOperatorOnce(op)
ref = sharding(x)
for name, expected in zip(outs, ref):
np.testing.assert_array_equal(
expected, workspace.FetchBlob(name)
)
if __name__ == "__main__":
import unittest
unittest.main()
|
