forked from mindspore-Ecosystem/mindspore
162 lines
6.2 KiB
Python
162 lines
6.2 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import pytest
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from mindspore import context
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore import Tensor, Parameter
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import mindspore as ms
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import mindspore.common.api as me
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from mindspore.common.initializer import initializer
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from mindspore.common import set_seed
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from hccl_test.manage.api import Hccl
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def setup_function():
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context.set_auto_parallel_context(dataset_strategy="full_batch")
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, weight1, weight2):
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super().__init__()
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self.weight1 = Parameter(weight1, "w1")
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self.weight2 = Parameter(weight2, "w2")
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self.matmul1 = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
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self.matmul2 = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
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self.relu = P.ReLU().shard(strategy2)
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def construct(self, x):
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out = self.matmul1(x, self.weight1)
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out = self.matmul2(out, self.weight2)
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out = self.relu(out)
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return out
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def check_initializer_weight_slice(init_name="Uniform", using_seed=False):
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def get_slice(rank):
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if using_seed:
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set_seed(1)
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hccl = Hccl()
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rank_save = hccl.rank_id
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hccl.rank_id = rank
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(dataset_strategy="full_batch")
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context.set_auto_parallel_context(device_num=8, global_rank=rank)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 1), (4, 1))
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strategy2 = ((2, 4),)
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context.set_context(mode=context.GRAPH_MODE)
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exe = me._cell_graph_executor
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x = Tensor(np.ones([32, 32]), dtype=ms.float32)
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weight1 = initializer(init_name, [32, 32], ms.float32)
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weight2 = initializer(init_name, [32, 32], ms.float32)
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net = Net(strategy1, strategy2, weight1, weight2)
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net.set_train()
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exe.compile(net, x, phase='train')
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hccl.rank_id = rank_save
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return net.parameters_dict()['w1'].data.asnumpy(), net.parameters_dict()['w2'].data.asnumpy()
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Tensor.delta_seed = 0
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w1_slice0, w2_slice0 = get_slice(0)
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Tensor.delta_seed = 0
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w1_slice1, _ = get_slice(1)
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Tensor.delta_seed = 0
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w1_slice4, _ = get_slice(4)
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slice_shape = w1_slice0.shape
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w1_slice0 = w1_slice0.flatten()
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w1_slice1 = w1_slice1.flatten()
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w1_slice4 = w1_slice4.flatten()
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w2_slice0 = w2_slice0.flatten()
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expect_slice_shape = (8, 32)
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assert expect_slice_shape == slice_shape
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assert all(w1_slice0 == w1_slice4)
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if init_name not in ["One", "Zero"]:
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assert any(w1_slice0 != w1_slice1)
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if using_seed:
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assert all(w1_slice0 == w2_slice0)
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else:
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assert any(w1_slice0 != w2_slice0)
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initializers = ["Uniform", "Normal", "TruncatedNormal", "HeUniform", "HeNormal", "XavierUniform", "One", "Zero"]
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def test_initializer_weight_slice():
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"""
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Feature: test initializer in auto parallel with/without using set_seed.
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Description: test initializer in auto parallel with/without using set_seed.
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Expectation: without any assert error.
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"""
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for init_name in initializers:
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check_initializer_weight_slice(init_name)
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for init_name in initializers:
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check_initializer_weight_slice(init_name, using_seed=True)
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def test_wrong_order_set_parallel_mode_with_initializer():
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"""
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Feature: test parameter initialize in auto parallel.
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Description: test parameter initialize in auto parallel applying initializer before setting auto parallel mode.
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Expectation: without any assert error.
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"""
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weight1 = initializer("Normal", [32, 32], ms.float32)
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weight2 = initializer("Normal", [32, 32], ms.float32)
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strategy1 = ((2, 1), (4, 1))
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strategy2 = ((2, 4),)
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net = Net(strategy1, strategy2, weight1, weight2)
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exe = me._cell_graph_executor
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x = Tensor(np.ones([32, 32]), dtype=ms.float32)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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with pytest.raises(RuntimeError):
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exe.compile(net, x, phase='train')
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def test_wrong_order_set_same_parallel_mode_with_initializer():
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"""
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Feature: test parameter initialize in auto parallel.
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Description: test parameter initialize in auto parallel applying initializer after setting auto parallel mode.
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Expectation: without any assert error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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weight1 = initializer("Normal", [32, 32], ms.float32)
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weight2 = initializer("Normal", [32, 32], ms.float32)
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strategy1 = ((2, 1), (4, 1))
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strategy2 = ((2, 4),)
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net = Net(strategy1, strategy2, weight1, weight2)
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exe = me._cell_graph_executor
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x = Tensor(np.ones([32, 32]), dtype=ms.float32)
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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exe.compile(net, x, phase='train')
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def test_wrong_order_set_parallel_mode_without_initializer():
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"""
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Feature: test parameter initialize in auto parallel.
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Description: test parameter initialize in auto parallel not using initializer.
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Expectation: without any assert error.
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"""
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weight1 = Tensor(np.ones([32, 32]), ms.float32)
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weight2 = Tensor(np.ones([32, 32]), ms.float32)
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strategy1 = ((2, 1), (4, 1))
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strategy2 = ((2, 4),)
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net = Net(strategy1, strategy2, weight1, weight2)
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exe = me._cell_graph_executor
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x = Tensor(np.ones([32, 32]), dtype=ms.float32)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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exe.compile(net, x, phase='train')
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