tests/python/gpu/test_kvstore_gpu.py - master - githubmirror/incubator-mxnet

tests/python/gpu/test_kvstore_gpu.py
135 строк6 KB
Zhenghui Jin
[API] Standardize MXNet NumPy creation functions (#20572)
04 ноя 2021, 17:28
04 ноя 2021, 17:28683c974
# Licensed to the Apache Software Foundation (ASF) under one
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# to you under the Apache License, Version 2.0 (the
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# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
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# 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.

# pylint: skip-file
import sys
import os
import mxnet as mx
import numpy as np
import pytest
from mxnet.test_utils import assert_almost_equal, default_device, environment
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
sys.path.insert(0, os.path.join(curr_path, '../unittest'))

shape = (4, 4)
keys = [5, 7, 11]
str_keys = ['b', 'c', 'd']

def init_kv_with_str(stype='default', kv_type='local'):
    """init kv """
    kv = mx.kv.create(kv_type)
    # single
    kv.init('a', mx.nd.zeros(shape, stype=stype))
    # list
    kv.init(str_keys, [mx.nd.zeros(shape=shape, stype=stype)] * len(keys))
    return kv

# 1. Test seed 89411477 (module seed 1829754103) resulted in a py3-gpu CI runner core dump.
# 2. Test seed 1155716252 (module seed 1032824746) resulted in py3-dnnl-gpu have error
# src/operator/nn/dnnl/dnnl_base.cc:567: Check failed: similar
# Both of them are not reproducible, so this test is back on random seeds.
@pytest.mark.skipif(mx.device.num_gpus() < 2, reason="test_rsp_push_pull needs more than 1 GPU")
@pytest.mark.skip("Flaky test https://github.com/apache/incubator-mxnet/issues/14189")
@pytest.mark.serial
def test_rsp_push_pull():
    def check_rsp_push_pull(kv_type, sparse_pull, is_push_cpu=True):
        kv = init_kv_with_str('row_sparse', kv_type)
        kv.init('e', mx.nd.ones(shape).tostype('row_sparse'))
        push_ctxs = [mx.cpu(i) if is_push_cpu else mx.gpu(i) for i in range(2)]
        kv.push('e', [mx.nd.ones(shape, ctx=context).tostype('row_sparse') for context in push_ctxs])

        def check_rsp_pull(kv, ctxs, sparse_pull, is_same_rowid=False, use_slice=False):
            count = len(ctxs)
            num_rows = shape[0]
            row_ids = []
            all_row_ids = np.arange(num_rows)
            vals = [mx.nd.sparse.zeros(shape=shape, ctx=ctxs[i], stype='row_sparse') for i in range(count)]
            if is_same_rowid:
                row_id = np.random.randint(num_rows, size=num_rows)
                row_ids = [mx.nd.array(row_id)] * count
            elif use_slice:
                total_row_ids = mx.nd.array(np.random.randint(num_rows, size=count*num_rows))
                row_ids = [total_row_ids[i*num_rows : (i+1)*num_rows] for i in range(count)]
            else:
                for _ in range(count):
                    row_id = np.random.randint(num_rows, size=num_rows)
                    row_ids.append(mx.nd.array(row_id))
            row_ids_to_pull = row_ids[0] if (len(row_ids) == 1 or is_same_rowid) else row_ids
            vals_to_pull = vals[0] if len(vals) == 1 else vals

            kv.row_sparse_pull('e', out=vals_to_pull, row_ids=row_ids_to_pull)
            for val, row_id in zip(vals, row_ids):
                retained = val.asnumpy()
                excluded_row_ids = np.setdiff1d(all_row_ids, row_id.asnumpy())
                for row in range(num_rows):
                    expected_val = np.zeros_like(retained[row])
                    expected_val += 0 if row in excluded_row_ids else 2
                    assert_almost_equal(retained[row], expected_val)

            if sparse_pull is True:
                kv.pull('e', out=vals_to_pull, ignore_sparse=False)
                for val in vals:
                    retained = val.asnumpy()
                    expected_val = np.zeros_like(retained)
                    expected_val[:] = 2
                    assert_almost_equal(retained, expected_val)

        check_rsp_pull(kv, [mx.gpu(0)], sparse_pull)
        check_rsp_pull(kv, [mx.cpu(0)], sparse_pull)
        check_rsp_pull(kv, [mx.gpu(i//2) for i in range(4)], sparse_pull)
        check_rsp_pull(kv, [mx.gpu(i//2) for i in range(4)], sparse_pull, is_same_rowid=True)
        check_rsp_pull(kv, [mx.cpu(i) for i in range(4)], sparse_pull)
        check_rsp_pull(kv, [mx.cpu(i) for i in range(4)], sparse_pull, is_same_rowid=True)
        check_rsp_pull(kv, [mx.gpu(i//2) for i in range(4)], sparse_pull, use_slice=True)
        check_rsp_pull(kv, [mx.cpu(i) for i in range(4)], sparse_pull, use_slice=True)

    envs = [None, '1']
    key  = 'MXNET_KVSTORE_USETREE'
    for val in envs:
        with environment(key, val):
            if val is '1':
                sparse_pull = False
            else:
                sparse_pull = True
            check_rsp_push_pull('local', sparse_pull)
            check_rsp_push_pull('device', sparse_pull)
            check_rsp_push_pull('device', sparse_pull, is_push_cpu=False)

def test_row_sparse_pull_single_device():
    kvstore = mx.kv.create('device')
    copy = mx.nd.random_normal(shape=(4,4), ctx=mx.gpu(0))
    grad = copy.tostype("row_sparse")

    key = 0
    kvstore.init(key, grad)
    idx = grad.indices
    kvstore.push(key, grad)
    kvstore.row_sparse_pull(key, out=grad, row_ids=idx)

    assert_almost_equal(grad.asnumpy(), copy.asnumpy())


@pytest.mark.serial
def test_rsp_push_pull_large_rowid():
    num_rows = 793470
    val = mx.nd.ones((num_rows, 1)).tostype('row_sparse').copyto(mx.gpu())
    kv = mx.kv.create('device')
    kv.init('a', val)
    out = mx.nd.zeros((num_rows,1), stype='row_sparse').copyto(mx.gpu())
    kv.push('a', val)
    kv.row_sparse_pull('a', out=out, row_ids=mx.nd.arange(0, num_rows, dtype='int64'))
    assert(out.indices.shape[0] == num_rows)