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Paddle/python/paddle/fluid/tests/unittests/column_parallel_linear_api.py

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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
#
# http://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.
from __future__ import print_function
import numpy as np
import argparse
import os
import sys
import signal
import time
import socket
from contextlib import closing
from six import string_types
import math
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
import paddle.fluid.unique_name as nameGen
from paddle.fluid import core
import paddle.distributed.fleet as fleet
from paddle.fluid.incubate.fleet.base import role_maker
import unittest
from multiprocessing import Process
import paddle.fluid.layers as layers
from functools import reduce
from test_collective_api_base import TestCollectiveAPIRunnerBase, runtime_main
paddle.enable_static()
class TestColumnParallelLinearAPI(TestCollectiveAPIRunnerBase):
def __init__(self):
self.global_ring_id = 0
def get_model(self, main_prog, startup_program, rank):
with fluid.program_guard(main_prog, startup_program):
fleet.init(is_collective=True)
np.random.seed(2020)
np_array = np.random.rand(1000, 16)
data = paddle.static.data(
name='tindata', shape=[10, 1000], dtype="float32")
paddle.distributed.broadcast(data, src=0)
if rank == 0:
param_attr = paddle.fluid.ParamAttr(
initializer=paddle.fluid.initializer.NumpyArrayInitializer(
np_array[:, 0:8]), )
else:
param_attr = paddle.fluid.ParamAttr(
initializer=paddle.fluid.initializer.NumpyArrayInitializer(
np_array[:, 8:16]), )
linear_out = paddle.distributed.split(
data,
size=(1000, 16),
operation='linear',
axis=1,
num_partitions=2,
weight_attr=param_attr,
bias_attr=False, )
return [linear_out]
if __name__ == "__main__":
runtime_main(TestColumnParallelLinearAPI, "column_parallel_linear")