mindspore2022/tests/ut/python/parallel/test_initializer_weight_slice.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 numpy as np
import pytest
from mindspore import context
import mindspore.nn as nn
from mindspore.ops import operations as P
from mindspore import Tensor, Parameter
import mindspore as ms
import mindspore.common.api as me
from mindspore.common.initializer import initializer
from mindspore.common import set_seed
from hccl_test.manage.api import Hccl

class Net(nn.Cell):
    def __init__(self, strategy1, strategy2, weight):
        super().__init__()
        self.weight = Parameter(weight, "w1")
        self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
        self.relu = P.ReLU().shard(strategy2)

    def construct(self, x):
        out = self.matmul(x, self.weight)
        out = self.relu(out)
        return out

def check_initializer_weight_slice(init_name="Uniform"):
    def get_slice(rank):
        hccl = Hccl()
        rank_save = hccl.rank_id
        hccl.rank_id = rank
        context.reset_auto_parallel_context()
        context.set_auto_parallel_context(device_num=8, global_rank=0)
        context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
        strategy1 = ((2, 1), (4, 1))
        strategy2 = ((2, 4),)
        context.set_context(mode=context.GRAPH_MODE)
        exe = me._executor

        x = Tensor(np.ones([32, 32]), dtype=ms.float32)
        weight = initializer(init_name, [64, 32], ms.float32)
        net = Net(strategy1, strategy2, weight)
        net.set_auto_parallel()
        exe.compile(net, x, auto_parallel_mode=True, phase='train')
        hccl.rank_id = rank_save
        return net.parameters_dict()['w1'].data.asnumpy()

    slice0 = get_slice(0)
    slice1 = get_slice(1)
    slice4 = get_slice(4)
    slice_shape = slice0.shape

    slice0 = slice0.flatten()
    slice1 = slice1.flatten()
    slice4 = slice4.flatten()
    expect_slice_shape = (16, 32)

    assert expect_slice_shape == slice_shape
    assert all(slice0 == slice4)
    if init_name not in ["One", "Zero"]:
        assert any(slice0 != slice1)

initializers = ["Uniform", "Normal", "TruncatedNormal", "HeUniform", "HeNormal", "XavierUniform", "One", "Zero"]

def test_initializer_weight_slice():
    for init_name in initializers:
        check_initializer_weight_slice(init_name)

def test_wrong_order_set_parallel_mode_with_initializer():
    weight = initializer("Normal", [64, 32], ms.float32)
    strategy1 = ((2, 1), (4, 1))
    strategy2 = ((2, 4),)
    net = Net(strategy1, strategy2, weight)
    exe = me._executor
    x = Tensor(np.ones([32, 32]), dtype=ms.float32)
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
    net.set_auto_parallel()
    with pytest.raises(RuntimeError):
        exe.compile(net, x, auto_parallel_mode=True, phase='train')

def test_wrong_order_set_same_parallel_mode_with_initializer():
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
    weight = initializer("Normal", [64, 32], ms.float32)
    strategy1 = ((2, 1), (4, 1))
    strategy2 = ((2, 4),)
    net = Net(strategy1, strategy2, weight)
    exe = me._executor
    x = Tensor(np.ones([32, 32]), dtype=ms.float32)
    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
    net.set_auto_parallel()
    exe.compile(net, x, auto_parallel_mode=True, phase='train')

def test_wrong_order_set_parallel_mode_without_initializer():
    weight = Tensor(np.ones([64, 32]), ms.float32)
    strategy1 = ((2, 1), (4, 1))
    strategy2 = ((2, 4),)
    net = Net(strategy1, strategy2, weight)
    exe = me._executor
    x = Tensor(np.ones([32, 32]), dtype=ms.float32)
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
    net.set_auto_parallel()
    exe.compile(net, x, auto_parallel_mode=True, phase='train')

def test_check_initializer_weight_slice_seed(init_name="Uniform"):
    def get_slice(rank):
        set_seed(1)
        hccl = Hccl()
        rank_save = hccl.rank_id
        hccl.rank_id = rank
        context.reset_auto_parallel_context()
        context.set_auto_parallel_context(device_num=8, global_rank=0)
        context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
        strategy1 = ((2, 1), (4, 1))
        strategy2 = ((2, 4),)
        context.set_context(mode=context.GRAPH_MODE)
        exe = me._executor

        x = Tensor(np.ones([32, 32]), dtype=ms.float32)
        weight = initializer(init_name, [64, 32], ms.float32)
        net = Net(strategy1, strategy2, weight)
        net.set_auto_parallel()
        exe.compile(net, x, auto_parallel_mode=True, phase='train')
        hccl.rank_id = rank_save
        return net.parameters_dict()['w1'].data.asnumpy()


    slice0 = get_slice(0)
    slice1 = get_slice(1)
    slice4 = get_slice(4)
    slice_shape = slice0.shape

    slice0 = slice0.flatten()
    slice1 = slice1.flatten()
    slice4 = slice4.flatten()
    expect_slice_shape = (16, 32)

    assert expect_slice_shape == slice_shape
    assert all(slice0 == slice4)
    assert all(slice0 == slice1)

if __name__ == '__main__':
    test_initializer_weight_slice()