135 lines
5.3 KiB
Python
135 lines
5.3 KiB
Python
# Copyright 2022 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 mindspore as ms
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from mindspore import context, Tensor, Parameter
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn import Cell, TrainOneStepCell, Momentum, AdaSumByDeltaWeightWrapCell, AdaSumByGradWrapCell
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from mindspore.ops import operations as P
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class Net(Cell):
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def __init__(self, strategy1=None, strategy2=None, strategy3=None):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.matmul = P.MatMul().shard(strategy2)
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self.gather = P.Gather().shard(strategy3)
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self.reduce_sum = P.ReduceSum()
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self.mul_weight = Parameter(Tensor(np.ones([64, 32]), dtype=ms.float32), "w1")
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self.matmul_weight = Parameter(Tensor(np.ones([32, 32]), dtype=ms.float32), "w2")
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self.embedding_table = Parameter(Tensor(np.ones([64, 32]), dtype=ms.float32), "embedding_table")
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def construct(self, x, b):
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out = self.gather(self.embedding_table, x, 0)
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out = self.matmul(out, self.matmul_weight)
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out = self.mul(out, self.mul_weight)
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out = out + b
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return self.reduce_sum(out)
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_x = Tensor(np.ones([64]), dtype=ms.int32)
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_b = Tensor(np.ones([64, 32]), dtype=ms.float32)
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def compile_net(net, by_grad=True):
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if by_grad:
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optimizer = AdaSumByGradWrapCell(Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9))
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else:
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optimizer = AdaSumByDeltaWeightWrapCell(Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9))
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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train_net.set_train()
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_cell_graph_executor.compile(train_net, _x, _b)
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context.reset_auto_parallel_context()
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def test_auto_parallel_adasum1():
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"""
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Feature: adasum in auto parallel.
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Description: verify adasum by mul/matmul/gather, rank0, dp, mp, not_full_dp
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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mul_strategy1 = ((8, 4), (8, 4))
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matmul_strategy2 = ((8, 1), (1, 1))
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gather_strategy3 = ((1, 1), (32,))
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net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
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compile_net(net)
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def test_auto_parallel_adasum2():
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"""
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Feature: adasum in auto parallel.
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Description: verify adasum by mul/matmul/gather, rank0, dp, mp, not_full_dp
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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mul_strategy1 = ((8, 4), (8, 4))
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matmul_strategy2 = ((8, 1), (1, 1))
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gather_strategy3 = ((1, 1), (32,))
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net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
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compile_net(net, by_grad=False)
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def test_auto_parallel_adasum3():
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"""
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Feature: adasum in auto parallel.
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Description: verify adasum by mul/matmul/gather, rank0, mix_dp_mp, mp
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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mul_strategy1 = ((8, 4), (8, 4))
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matmul_strategy2 = ((8, 4), (4, 1))
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gather_strategy3 = ((32, 1), (1,))
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net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
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compile_net(net)
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def test_auto_parallel_adasum4():
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"""
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Feature: adasum in auto parallel.
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Description: verify adasum by mul/matmul/gather, rank0, mix_dp_mp, mp
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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mul_strategy1 = ((8, 4), (8, 4))
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matmul_strategy2 = ((8, 4), (4, 1))
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gather_strategy3 = ((32, 1), (1,))
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net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
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compile_net(net, by_grad=False)
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def test_auto_parallel_adasum5():
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"""
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Feature: adasum in auto parallel.
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Description: verify adasum by mul/matmul/gather, rank16, dp, mp, not_full_dp
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=16)
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mul_strategy1 = ((8, 4), (8, 4))
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matmul_strategy2 = ((8, 1), (1, 1))
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gather_strategy3 = ((1, 1), (32,))
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net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
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compile_net(net)
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def test_auto_parallel_adasum6():
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"""
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Feature: adasum in auto parallel.
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Description: verify adasum by mul/matmul/gather, rank16, dp, mp, not_full_dp
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=16)
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mul_strategy1 = ((8, 4), (8, 4))
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matmul_strategy2 = ((8, 1), (1, 1))
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gather_strategy3 = ((1, 1), (32,))
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net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
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compile_net(net, by_grad=False)
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