forked from mindspore-Ecosystem/mindspore
97 lines
3.5 KiB
Python
97 lines
3.5 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 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
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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, mul_weight, batch_matmul_weight, transpose_b=False, strategy1=None, strategy2=None):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.batch_matmul = P.BatchMatMul(transpose_b=transpose_b).shard(strategy2)
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self.mul_weight = Parameter(mul_weight, "w1")
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self.batch_matmul_weight = Parameter(batch_matmul_weight, "w2")
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def construct(self, x, b):
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out = self.mul(x, self.mul_weight)
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out = self.batch_matmul(out, self.batch_matmul_weight)
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return out
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_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
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_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
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_w2 = Tensor(np.ones([128, 32, 32]), dtype=ms.float32)
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_b = Tensor(np.ones([128, 64, 16]), dtype=ms.float32)
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def compile_net(net):
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optimizer = 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_batch_matmul_data_parallel():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((16, 1, 1), (16, 1, 1))
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strategy2 = ((16, 1, 1), (16, 1, 1))
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net = Net(_w1, _w2, False, strategy1, strategy2)
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compile_net(net)
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def test_batch_matmul_model_parallel():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((1, 1, 1), (1, 1, 1))
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strategy2 = ((1, 1, 1), (1, 1, 16))
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net = Net(_w1, _w2, False, strategy1, strategy2)
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compile_net(net)
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def test_batch_matmul_hybrid_parallel():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((2, 2, 2), (2, 2, 2))
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net = Net(_w1, _w2, False, strategy1, strategy2)
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compile_net(net)
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def test_batch_matmul_auto_parallel():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
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net = Net(_w1, _w2, False)
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compile_net(net)
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def test_batch_matmul_repeat_calc():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((2, 2, 4), (2, 2, 4))
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strategy2 = ((1, 2, 2), (1, 2, 2))
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net = Net(_w1, _w2, False, strategy1, strategy2)
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compile_net(net)
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def test_batch_matmul_transpose_b():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((2, 2, 4), (2, 2, 4))
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strategy2 = ((1, 2, 2), (1, 2, 2))
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net = Net(_w1, _w2, True, strategy1, strategy2)
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compile_net(net)
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