forked from huawei/mindspore2022
439 lines
16 KiB
Python
439 lines
16 KiB
Python
# Copyright 2021 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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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.common.api import _cell_graph_executor
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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grad_all = C.GradOperation(get_all=True)
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class NetWithLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y, b):
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predict = self.network(x, y, b)
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return self.loss(predict)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x, y, b):
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return grad_all(self.network)(x, y, b)
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def compile_net(net, x, y, b):
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net.set_auto_parallel()
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net.set_train()
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_cell_graph_executor.compile(net, x, y, b)
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# model_parallel test
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def test_two_matmul():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul1 = P.MatMul().shard(strategy1)
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self.matmul2 = P.MatMul().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul1(x, y)
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out = self.matmul2(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0, gradients_mean=True)
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strategy1 = ((4, 2), (2, 1))
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strategy2 = ((2, 4), (4, 1))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_two_matmul_repeated_calculation1():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul1 = P.MatMul().shard(strategy1)
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self.matmul2 = P.MatMul().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul1(x, y)
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out = self.matmul2(out, b)
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return out
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context.set_auto_parallel_context(device_num=64, global_rank=5, gradients_mean=True)
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strategy1 = ((2, 4), (4, 8))
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strategy2 = ((1, 1), (1, 1))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_two_matmul_repeated_calculation2():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul1 = P.MatMul().shard(strategy1)
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self.matmul2 = P.MatMul().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul1(x, y)
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out = self.matmul2(out, b)
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return out
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context.set_auto_parallel_context(device_num=64, global_rank=15)
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strategy1 = ((2, 4), (4, 8))
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strategy2 = ((2, 2), (2, 1))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_output_strategy_reduce_scatter():
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"""
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Feature: test output strategy for matmul operator
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Description: transpose_b is false, set output strategy and use reduce scatter
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul().shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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matmul_in_strategy = ((2, 2), (2, 2))
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matmul_out_strategy = ((4, 2),)
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mul_strategy = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_output_strategy_reduce_scatter_transpose():
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"""
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Feature: test output strategy for matmul operator
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Description: transpose_b is true, set output strategy and use reduce scatter
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul(transpose_b=True).shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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matmul_in_strategy = ((2, 4), (2, 4))
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matmul_out_strategy = ((8, 2),)
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mul_strategy = ((8, 2), (8, 2))
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net = GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 32]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_output_strategy_all_reduce():
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"""
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Feature: test output strategy for matmul operator
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Description: transpose_b is false, set output strategy and use all reduce
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul().shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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matmul_in_strategy = ((2, 2), (2, 2))
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matmul_out_strategy = ((2, 2),)
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mul_strategy = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_output_strategy_all_reduce_transpose():
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"""
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Feature: test output strategy for matmul operator
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Description: transpose_b is true, set output strategy and use all reduce
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul(transpose_b=True).shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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matmul_in_strategy = ((2, 2), (2, 2))
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matmul_out_strategy = ((2, 2),)
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mul_strategy = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 32]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_output_strategy_reduce_scatter_repeat_calc():
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"""
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Feature: test output strategy for matmul operator
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Description: transpose_b is false, set output strategy use reduce scatter and repeated calculation
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul().shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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matmul_in_strategy = ((2, 2), (2, 2))
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matmul_out_strategy = ((4, 2),)
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mul_strategy = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_output_strategy_reduce_scatter_transpose_repeat_calc():
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"""
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Feature: test output strategy for matmul operator
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Description: transpose_b is true, set output strategy use reduce scatter and repeated calculation
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul(transpose_b=True).shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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matmul_in_strategy = ((2, 4), (2, 4))
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matmul_out_strategy = ((8, 2),)
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mul_strategy = ((8, 2), (8, 2))
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net = GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 32]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_output_strategy_all_reduce_repeat_calc():
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"""
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Feature: test output strategy for matmul operator
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Description: transpose_b is false, set output strategy use all reduce and repeated calculation
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul().shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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matmul_in_strategy = ((2, 2), (2, 2))
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matmul_out_strategy = ((2, 2),)
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mul_strategy = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_output_strategy_all_reduce_transpose_repeat_calc():
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"""
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Feature: test output strategy for matmul operator
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Description: transpose_b is true, set output strategy use all reduce and repeated calculation
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul(transpose_b=True).shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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matmul_in_strategy = ((2, 2), (2, 2))
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matmul_out_strategy = ((2, 2),)
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mul_strategy = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 32]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_in_strategy_not_int():
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"""
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Feature: the type of in_strategy's value is not int
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Description:
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Expectation: rasise TypeError
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul(transpose_b=True).shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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matmul_in_strategy = ((2.0, 2), (2, 2))
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matmul_out_strategy = ((2, 2),)
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mul_strategy = ((4, 2), (4, 2))
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with pytest.raises(TypeError):
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GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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def test_matmul_out_strategy_not_int():
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"""
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Feature: the type of out_strategy's value is not int
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Description:
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Expectation: rasise TypeError
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul(transpose_b=True).shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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matmul_in_strategy = ((2, 2), (2, 2))
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matmul_out_strategy = ((2.0, 2),)
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mul_strategy = ((4, 2), (4, 2))
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with pytest.raises(TypeError):
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GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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def test_matmul_in_strategy_is_none_and_out_strategy_is_not_none():
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"""
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Feature: the in_strategy is none and out_strategy is not none
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Description:
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Expectation: rasise ValueError
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"""
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class Net(nn.Cell):
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def __init__(self, matmul_in_strategy, matmul_out_strategy, mul_strategy):
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super().__init__()
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self.matmul = P.MatMul(transpose_b=True).shard(matmul_in_strategy, matmul_out_strategy)
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self.mul = P.Mul().shard(mul_strategy)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.mul(out, b)
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return out
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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matmul_in_strategy = None
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matmul_out_strategy = ((2, 2),)
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mul_strategy = ((4, 2), (4, 2))
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with pytest.raises(ValueError):
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GradWrap(NetWithLoss(Net(matmul_in_strategy, matmul_out_strategy, mul_strategy)))
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