auto_parallel_parallelizer.py

# Copyright (c) 2021 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 unittest

import paddle
import paddle.nn as nn
import paddle.static as static
import paddle.nn.functional as F
import paddle.utils as utils
from paddle.fluid import layers
from paddle.distributed import fleet
import paddle.distributed.auto_parallel as auto
from paddle.distributed.auto_parallel.utils import print_program_with_distributed_attr
import paddle.fluid.core as core

paddle.enable_static()
_global_parallel_strategy = None
_global_process_mesh = None
ROOT_MESH = auto.ProcessMesh([0, 1])


class MLPLayer(nn.Layer):
    def __init__(self,
                 hidden_size=1024,
                 intermediate_size=4 * 1024,
                 dropout_ratio=0.1,
                 initializer_range=0.02):
        super(MLPLayer, self).__init__()
        d_model = hidden_size
        dim_feedforward = intermediate_size
        weight_attr = paddle.ParamAttr(initializer=nn.initializer.Normal(
            mean=0.0, std=initializer_range))
        bias_attr = None

        self.linear0 = nn.Linear(
            d_model, dim_feedforward, weight_attr, bias_attr=bias_attr)
        self.linear1 = nn.Linear(
            dim_feedforward, d_model, weight_attr, bias_attr=bias_attr)
        self.linear2 = nn.Linear(d_model, 1, weight_attr, bias_attr=bias_attr)
        self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
        self.dropout = nn.Dropout(dropout_ratio, mode="upscale_in_train")

    def forward(self, input):
        out = self.norm(input)
        out = self.linear0(out)
        out = F.gelu(out, approximate=True)
        out = self.linear1(out)
        out = self.dropout(out)
        out = self.linear2(out)

        return out


def mlp_pretrain_forward(train_program, start_program):
    with static.program_guard(train_program,
                              start_program), utils.unique_name.guard():
        batch_size = 4
        hidden_size = 1024
        sequence_len = 512
        input = static.data(
            name="input",
            shape=[batch_size, sequence_len, hidden_size],
            dtype='float32')
        label = static.data(
            name="label", shape=[batch_size, sequence_len, 1], dtype='float32')

        auto.shard_tensor(input, _global_process_mesh, dim_mapping=[-1, -1, -1])
        auto.set_pipeline_stage(1)

        mlp = MLPLayer(
            hidden_size=hidden_size,
            intermediate_size=4 * hidden_size,
            dropout_ratio=0.1,
            initializer_range=0.02)

        predict = mlp(input)

        cost = layers.cross_entropy(input=predict, label=label)
        avg_cost = layers.mean(x=cost)

    return avg_cost, train_program, start_program


class TestMLPAutoParallelizer(unittest.TestCase):
    def test_mlp_serial(self):

        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(mesh=[0, 1], parent=ROOT_MESH)

        dist_strategy = fleet.DistributedStrategy()
        dist_strategy.amp = False
        dist_strategy.pipeline = False
        dist_strategy.recompute = False

        # init parallel optimizer
        dist_strategy.semi_auto = True

        fleet.init(is_collective=True, strategy=dist_strategy)

        train_program = static.Program()
        start_program = static.Program()
        loss, train_program, start_program = mlp_pretrain_forward(train_program,
                                                                  start_program)

        optimizer = paddle.fluid.optimizer.AdamOptimizer(
            learning_rate=0.00001,
            beta1=0.9,
            beta2=0.999,
            epsilon=1e-08,
            grad_clip=None)

        optimizer = fleet.distributed_optimizer(optimizer)
        _, _, distributed_startup_program, distributed_main_program = optimizer.minimize(
            loss, start_program)
        suffix = core.kAutoParallelSuffix()
        for block in distributed_main_program.blocks:
            for op in block.ops:
                for attr_name in op.attr_names:
                    self.assertTrue(suffix not in attr_name)
        # print_program_with_distributed_attr(distributed_main_program)
        self.assertIsNotNone(distributed_startup_program)
        self.assertIsNotNone(distributed_main_program)


if __name__ == "__main__":
    unittest.main()