auto_parallel_save_load.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.

import unittest
import random
import numpy as np
import os
import shutil

import paddle
import paddle.nn as nn
import paddle.utils as utils
import paddle.static as static
import paddle.nn.functional as F
from paddle.distributed.fleet import auto

from paddle.distributed import fleet
from paddle.fluid.initializer import NumpyArrayInitializer
from paddle.distributed.auto_parallel.utils import (
    save_distributed_checkpoint,
    load_checkpoint_into_program,
)

paddle.enable_static()
_global_parallel_strategy = None
_global_process_mesh = None
PP_MESH_0 = None
PP_MESH_1 = None


class MLPLayer(nn.Layer):
    def __init__(
        self, hidden_size=64, intermediate_size=4 * 64, initializer_range=0.02
    ):
        super(MLPLayer, self).__init__()
        d_model = hidden_size
        dim_feedforward = intermediate_size
        np.random.seed(2021)
        arr = np.random.normal(0, 0.02, size=(d_model, dim_feedforward))
        weight_attr = paddle.ParamAttr(initializer=NumpyArrayInitializer(arr))
        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.norm = nn.LayerNorm(d_model, epsilon=1e-5)

    def forward(self, input):
        if _global_parallel_strategy == "pp":
            auto.shard_tensor(self.linear0.weight, PP_MESH_0, [None, None])
            auto.shard_tensor(self.linear1.weight, PP_MESH_1, [None, None])
        elif _global_parallel_strategy == "mp":
            auto.shard_tensor(
                self.linear0.weight, _global_process_mesh, [None, "x"]
            )
            auto.shard_tensor(
                self.linear1.weight, _global_process_mesh, ["x", None]
            )
        elif _global_parallel_strategy == "dp":
            auto.shard_tensor(
                self.linear0.weight, _global_process_mesh, [None, None]
            )
            auto.shard_tensor(
                self.linear1.weight, _global_process_mesh, [None, None]
            )

        out = self.norm(input)
        out = self.linear0(out)
        out = F.gelu(out, approximate=True)
        out = self.linear1(out)

        return out


def mlp_forward(train_program, start_program):
    with static.program_guard(
        train_program, start_program
    ), utils.unique_name.guard():

        batch_size = 4
        hidden_size = 64
        input = static.data(
            name="input", shape=[batch_size, hidden_size], dtype='float32'
        )
        label = static.data(
            name="label", shape=[batch_size, 1], dtype='float32'
        )

        if _global_parallel_strategy == "pp":
            auto.shard_tensor(input, PP_MESH_0, [None, None])
            auto.shard_tensor(label, PP_MESH_1, [None, None])
        elif _global_parallel_strategy == "dp":
            auto.shard_tensor(input, _global_process_mesh, ["x", None])
        elif _global_parallel_strategy == "mp":
            auto.shard_tensor(input, _global_process_mesh, [None, None])

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

        predict = mlp(input)
        error_cost = paddle.nn.functional.square_error_cost(predict, label)
        loss = paddle.mean(error_cost)

    return loss, train_program, start_program


def get_distributed_program():
    train_program = static.Program()
    startup_program = static.Program()

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

    loss, train_program, startup_program = mlp_forward(
        train_program, startup_program
    )

    optimizer = paddle.fluid.optimizer.SGDOptimizer(learning_rate=0.01)
    optimizer = fleet.distributed_optimizer(optimizer)
    _, _, dist_startup_prog, dist_main_prog = optimizer.minimize(
        loss, startup_program
    )

    return dist_main_prog, dist_startup_prog, loss


class TestMLPSaveLoad(unittest.TestCase):
    def setUp(self):
        paddle.seed(2021)
        random.seed(2021)
        np.random.seed(2021)

    def test_mlp_dp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "dp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh([0, 1], dim_names=["x"])

        dist_main_prog, dist_start_prog, loss = get_distributed_program()
        place = paddle.set_device("gpu")
        exe = paddle.static.Executor(place)
        exe.run(dist_start_prog)

        input = np.random.random(size=(80, 64)).astype('float32')
        label = np.random.random(size=(80, 1)).astype('float32')
        for step in range(20):
            if step == 10:
                path = "./output_dp{}".format(paddle.distributed.get_rank())
                os.makedirs(path, exist_ok=True)
                save_distributed_checkpoint(dist_main_prog, path, path)

            res = exe.run(
                dist_main_prog,
                feed={
                    "input": input[step * 4 : (step + 1) * 4, :],
                    "label": label[step * 4 : (step + 1) * 4, :],
                },
                fetch_list=[loss],
            )

        last_res = res[0]
        ckpt_path = [
            "./output_dp0/model_state_rank0.pdmodel",
            "./output_dp1/model_state_rank1.pdmodel",
        ]
        dist_attr_path = [
            "./output_dp0/dist_attr_rank0.pdattr",
            "./output_dp1/dist_attr_rank1.pdattr",
        ]
        load_checkpoint_into_program(ckpt_path, dist_attr_path, dist_main_prog)
        for step in range(10, 20):
            res = exe.run(
                dist_main_prog,
                feed={
                    "input": input[step * 4 : (step + 1) * 4, :],
                    "label": label[step * 4 : (step + 1) * 4, :],
                },
                fetch_list=[loss],
            )

        self.assertEqual(last_res, res[0])
        shutil.rmtree("./output_dp{}".format(paddle.distributed.get_rank()))

    def test_mlp_mp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "mp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh([0, 1], dim_names=["x"])

        dist_main_prog, dist_start_prog, loss = get_distributed_program()

        place = paddle.set_device("gpu")
        exe = paddle.static.Executor(place)
        exe.run(dist_start_prog)

        input = np.random.random(size=(80, 64)).astype('float32')
        label = np.random.random(size=(80, 1)).astype('float32')
        for step in range(20):
            if step == 10:
                path = "./output_mp{}".format(paddle.distributed.get_rank())
                os.makedirs(path, exist_ok=True)
                save_distributed_checkpoint(dist_main_prog, path, path)

            res = exe.run(
                dist_main_prog,
                feed={
                    "input": input[step * 4 : (step + 1) * 4, :],
                    "label": label[step * 4 : (step + 1) * 4, :],
                },
                fetch_list=[loss],
            )

        last_res = res[0]
        ckpt_path = [
            "./output_mp0/model_state_rank0.pdmodel",
            "./output_mp1/model_state_rank1.pdmodel",
        ]
        dist_attr_path = [
            "./output_mp0/dist_attr_rank0.pdattr",
            "./output_mp1/dist_attr_rank1.pdattr",
        ]
        load_checkpoint_into_program(ckpt_path, dist_attr_path, dist_main_prog)
        for step in range(10, 20):
            res = exe.run(
                dist_main_prog,
                feed={
                    "input": input[step * 4 : (step + 1) * 4, :],
                    "label": label[step * 4 : (step + 1) * 4, :],
                },
                fetch_list=[loss],
            )

        self.assertEqual(last_res, res[0])
        shutil.rmtree("./output_mp{}".format(paddle.distributed.get_rank()))

    def test_mlp_pp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "pp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh([0, 1], dim_names=["x"])
        global PP_MESH_0
        PP_MESH_0 = auto.ProcessMesh(mesh=[0], dim_names=["x"])
        global PP_MESH_1
        PP_MESH_1 = auto.ProcessMesh(mesh=[1], dim_names=["x"])

        dist_main_prog, dist_start_prog, loss = get_distributed_program()

        place = paddle.set_device("gpu")
        exe = paddle.static.Executor(place)
        exe.run(dist_start_prog)

        input = np.random.random(size=(80, 64)).astype('float32')
        label = np.random.random(size=(80, 1)).astype('float32')
        for step in range(20):
            if step == 10:
                path = "./output_pp{}".format(paddle.distributed.get_rank())
                os.makedirs(path, exist_ok=True)
                save_distributed_checkpoint(dist_main_prog, path, path)

            if paddle.distributed.get_rank() in [0]:
                res = exe.run(
                    dist_main_prog,
                    feed={
                        "input": input[step * 4 : (step + 1) * 4, :],
                        "label": label[step * 4 : (step + 1) * 4, :],
                    },
                )
            else:
                res = exe.run(
                    dist_main_prog,
                    feed={
                        "input": input[step * 4 : (step + 1) * 4, :],
                        "label": label[step * 4 : (step + 1) * 4, :],
                    },
                    fetch_list=[loss],
                )

        if paddle.distributed.get_rank() in [1]:
            last_res = res[0]

        ckpt_path = [
            "./output_pp0/model_state_rank0.pdmodel",
            "./output_pp1/model_state_rank1.pdmodel",
        ]
        dist_attr_path = [
            "./output_pp0/dist_attr_rank0.pdattr",
            "./output_pp1/dist_attr_rank1.pdattr",
        ]
        load_checkpoint_into_program(ckpt_path, dist_attr_path, dist_main_prog)
        for step in range(10, 20):
            if paddle.distributed.get_rank() in [0]:
                res = exe.run(
                    dist_main_prog,
                    feed={
                        "input": input[step * 4 : (step + 1) * 4, :],
                        "label": label[step * 4 : (step + 1) * 4, :],
                    },
                )
            else:
                res = exe.run(
                    dist_main_prog,
                    feed={
                        "input": input[step * 4 : (step + 1) * 4, :],
                        "label": label[step * 4 : (step + 1) * 4, :],
                    },
                    fetch_list=[loss],
                )

        if paddle.distributed.get_rank() in [1]:
            self.assertEqual(last_res, res[0])
        shutil.rmtree("./output_pp{}".format(paddle.distributed.get_rank()))


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