forked from huawei/mindspore2022
1199 lines
41 KiB
Python
1199 lines
41 KiB
Python
# Copyright 2019 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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import mindspore.nn as nn
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from mindspore import Parameter, Tensor, 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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def test_matmul_sub():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-sub net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.sub = P.Sub().shard(strategy2)
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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.sub(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_add():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-add net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.add = P.Add().shard(strategy2)
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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.add(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_mul():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-mul net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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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(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_mod():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-mod net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.mod = P.Mod().shard(strategy2)
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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.mod(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_floormod():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-floormod net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.floormod = P.FloorMod().shard(strategy2)
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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.floormod(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_atan2():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-atan2 net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.atan2 = P.Atan2().shard(strategy2)
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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.atan2(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_divNoNan():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-divNoNan net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.divNoNan = P.DivNoNan().shard(strategy2)
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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.divNoNan(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_logicaland():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-logical_and net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.equal = P.Equal().shard(strategy2)
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self.notequal = P.NotEqual().shard(strategy2)
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self.logical = P.LogicalAnd().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out1 = self.equal(out, b)
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out = self.matmul(x, y)
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out2 = self.notequal(out, b)
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out = self.logical(out1, out2)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_logicalor():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-logical_or net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.equal = P.Equal().shard(strategy2)
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self.notequal = P.NotEqual().shard(strategy2)
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self.logical = P.LogicalOr().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out1 = self.equal(out, b)
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out = self.matmul(x, y)
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out2 = self.notequal(out, b)
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out = self.logical(out1, out2)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_div():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-div net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.div = P.Div().shard(strategy2)
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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.div(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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_add_broadcast():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-add broadcast net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.add = P.Add().shard(strategy2)
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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.add(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (2,))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_add_broadcast2():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-add broadcast net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.add = P.Add().shard(strategy2)
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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.add(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 4), (4, 1))
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strategy2 = ((4, 1), (1, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 1]), dtype=ms.float32)
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b = Tensor(np.ones([1, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_sub_broadcast():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-sub broadcast net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.sub = P.Sub().shard(strategy2)
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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.sub(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (2,))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 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]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_sub_broadcast2():
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"""
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Feature: distribute operator sub in auto parallel.
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Description: matmul-sub broadcast net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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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.matmul = P.MatMul().shard(strategy1)
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self.sub = P.Sub().shard(strategy2)
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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.sub(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 4), (4, 1))
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strategy2 = ((4, 1), (1, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_mul_broadcast():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: matmul-mul broadcast net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.mul = P.Mul().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.mul(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 2), (2, 2))
|
|
strategy2 = ((4, 2), (2,))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
b = Tensor(np.ones([64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_mul_broadcast2():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: matmul-mul broadcast net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.mul = P.Mul().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.mul(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_div_broadcast():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: matmul-div broadcast net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.div = P.Div().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.div(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 2), (2, 2))
|
|
strategy2 = ((4, 2), (2,))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
b = Tensor(np.ones([64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_div_broadcast2():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: matmul-div broadcast net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.div = P.Div().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.div(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_greater_broadcast():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: matmul-greater broadcast net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.greater = P.Greater().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.greater(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 2), (2, 2))
|
|
strategy2 = ((4, 2), (2,))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
b = Tensor(np.ones([64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_greater_broadcast2():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: matmul-greater broadcast net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.greater = P.Greater().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.greater(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_floordiv():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: matmul-floordiv net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.floordiv = P.FloorDiv().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.floordiv(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 2), (2, 2))
|
|
strategy2 = ((4, 2), (4, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
b = Tensor(np.ones([64, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_floordiv_broadcast():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: matmul-floordiv broadcast net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.floordiv = P.FloorDiv().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.floordiv(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 2), (2, 2))
|
|
strategy2 = ((4, 2), (2,))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
b = Tensor(np.ones([64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_floordiv_broadcast2():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: matmul-floordiv broadcast net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.floordiv = P.FloorDiv().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.floordiv(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_assign_sub():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: mul-assign_sub net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.assign_sub = P.AssignSub()
|
|
self.mul = P.Mul()
|
|
self.mul_weight = Parameter(Tensor(np.full([128, 32],
|
|
0.5, dtype=np.float32)),
|
|
name="mul_weight")
|
|
self.assignsub_weight = Parameter(Tensor(np.full([128, 32],
|
|
1.1, dtype=np.float32)),
|
|
name="assignsub_weight")
|
|
|
|
def construct(self, x):
|
|
out = self.mul(x, self.mul_weight)
|
|
out = self.assign_sub(self.assignsub_weight, out)
|
|
return out
|
|
|
|
class SubNetWithLoss(nn.Cell):
|
|
def __init__(self, network):
|
|
super(SubNetWithLoss, self).__init__()
|
|
self.loss = VirtualLoss()
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
predict = self.network(x,)
|
|
return self.loss(predict)
|
|
|
|
class SubGradWrap(nn.Cell):
|
|
def __init__(self, network):
|
|
super(SubGradWrap, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
return grad_all(self.network)(x)
|
|
|
|
def compile_sub_net(net, x):
|
|
net.set_auto_parallel()
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x)
|
|
|
|
context.set_auto_parallel_context(device_num=64, global_rank=15)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
net = SubGradWrap(SubNetWithLoss(Net()))
|
|
x = Tensor(np.ones([128, 32]), dtype=ms.float32)
|
|
compile_sub_net(net, x)
|
|
|
|
|
|
def test_assign_add():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: mul-assign_add net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.assign_sub = P.AssignAdd()
|
|
self.mul = P.Mul()
|
|
self.mul_weight = Parameter(Tensor(np.full([128, 32],
|
|
0.5, dtype=np.float32)),
|
|
name="mul_weight")
|
|
self.assignsub_weight = Parameter(Tensor(np.full([128, 32],
|
|
1.1, dtype=np.float32)),
|
|
name="assignsub_weight")
|
|
|
|
def construct(self, x):
|
|
out = self.mul(x, self.mul_weight)
|
|
out = self.assign_sub(self.assignsub_weight, out)
|
|
return out
|
|
|
|
class SubNetWithLoss(nn.Cell):
|
|
def __init__(self, network):
|
|
super(SubNetWithLoss, self).__init__()
|
|
self.loss = VirtualLoss()
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
predict = self.network(x,)
|
|
return self.loss(predict)
|
|
|
|
class SubGradWrap(nn.Cell):
|
|
def __init__(self, network):
|
|
super(SubGradWrap, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
return grad_all(self.network)(x)
|
|
|
|
def compile_sub_net(net, x):
|
|
net.set_auto_parallel()
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x)
|
|
|
|
context.set_auto_parallel_context(device_num=64, global_rank=15)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
net = SubGradWrap(SubNetWithLoss(Net()))
|
|
x = Tensor(np.ones([128, 32]), dtype=ms.float32)
|
|
compile_sub_net(net, x)
|
|
|
|
|
|
def test_assign():
|
|
"""
|
|
Feature: distribute operator sub in auto parallel.
|
|
Description: mul-assign_sub net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.assign_sub = P.Assign()
|
|
self.mul = P.Mul()
|
|
self.mul_weight = Parameter(Tensor(np.full([128, 32],
|
|
0.5, dtype=np.float32)),
|
|
name="mul_weight")
|
|
self.assignsub_weight = Parameter(Tensor(np.full([128, 32],
|
|
1.1, dtype=np.float32)),
|
|
name="assignsub_weight")
|
|
|
|
def construct(self, x):
|
|
out = self.mul(x, self.mul_weight)
|
|
out = self.assign_sub(self.assignsub_weight, out)
|
|
return out
|
|
|
|
class SubNetWithLoss(nn.Cell):
|
|
def __init__(self, network):
|
|
super(SubNetWithLoss, self).__init__()
|
|
self.loss = VirtualLoss()
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
predict = self.network(x,)
|
|
return self.loss(predict)
|
|
|
|
class SubGradWrap(nn.Cell):
|
|
def __init__(self, network):
|
|
super(SubGradWrap, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
return grad_all(self.network)(x)
|
|
|
|
def compile_sub_net(net, x):
|
|
net.set_auto_parallel()
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x)
|
|
|
|
context.set_auto_parallel_context(device_num=64, global_rank=15)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
net = SubGradWrap(SubNetWithLoss(Net()))
|
|
x = Tensor(np.ones([128, 32]), dtype=ms.float32)
|
|
compile_sub_net(net, x)
|
|
|
|
|
|
def test_matmul_bitwise_and_broadcast():
|
|
"""
|
|
Feature: distribute operator BitwiseAnd in auto parallel.
|
|
Description: mul-BitwiseAnd net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.bitwise_and = P.BitwiseAnd().shard(strategy1)
|
|
self.matmul = P.MatMul().shard(strategy2)
|
|
|
|
|
|
def construct(self, x, y, z):
|
|
out = self.bitwise_and(x, y)
|
|
out = self.matmul(out, z)
|
|
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 1), (1, 4))
|
|
strategy2 = ((1, 4), (4, 2))
|
|
net = Net(strategy1, strategy2)
|
|
|
|
x = Tensor(np.ones([64, 1]), dtype=ms.int32)
|
|
y = Tensor(np.ones([1, 64]), dtype=ms.int32)
|
|
z = Tensor(np.ones([64, 32]), dtype=ms.int32)
|
|
compile_net(net, x, y, z)
|
|
|
|
|
|
def test_matmul_bitwise_or_broadcast():
|
|
"""
|
|
Feature: distribute operator BitwiseOr in auto parallel.
|
|
Description: mul-BitwiseOr net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.bitwise_or = P.BitwiseOr().shard(strategy1)
|
|
self.matmul = P.MatMul().shard(strategy2)
|
|
|
|
def construct(self, x, y, z):
|
|
out = self.bitwise_or(x, y)
|
|
out = self.matmul(out, z)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 1), (1, 4))
|
|
strategy2 = ((1, 4), (4, 2))
|
|
net = Net(strategy1, strategy2)
|
|
|
|
x = Tensor(np.ones([64, 1]), dtype=ms.int32)
|
|
y = Tensor(np.ones([1, 64]), dtype=ms.int32)
|
|
z = Tensor(np.ones([64, 32]), dtype=ms.int32)
|
|
compile_net(net, x, y, z)
|
|
|
|
|
|
def test_matmul_bitwise_xor_broadcast():
|
|
"""
|
|
Feature: distribute operator BitwiseXor in auto parallel.
|
|
Description: mul-BitwiseXor net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.bitwise_xor = P.BitwiseXor().shard(strategy1)
|
|
self.matmul = P.MatMul().shard(strategy2)
|
|
|
|
def construct(self, x, y, z):
|
|
out = self.bitwise_xor(x, y)
|
|
out = self.matmul(out, z)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 1), (1, 4))
|
|
strategy2 = ((1, 4), (4, 2))
|
|
net = Net(strategy1, strategy2)
|
|
|
|
x = Tensor(np.ones([64, 1]), dtype=ms.int32)
|
|
y = Tensor(np.ones([1, 64]), dtype=ms.int32)
|
|
z = Tensor(np.ones([64, 32]), dtype=ms.int32)
|
|
compile_net(net, x, y, z)
|
|
|
|
|
|
def test_matmul_mul_no_nan_broadcast():
|
|
"""
|
|
Feature: distribute operator MulNoNan in auto parallel.
|
|
Description: mul-MulNoNan net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.mul_no_nan = P.MulNoNan().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.mul_no_nan(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_truncate_div_broadcast():
|
|
"""
|
|
Feature: distribute operator TruncateDiv in auto parallel.
|
|
Description: mul-TruncateDiv net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.truncate_div = P.TruncateDiv().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.truncate_div(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_truncate_mod_broadcast():
|
|
"""
|
|
Feature: distribute operator TruncateMod in auto parallel.
|
|
Description: mul-TruncateMod net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.truncate_mod = P.TruncateMod().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.truncate_mod(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_xdivy_broadcast():
|
|
"""
|
|
Feature: distribute operator Xdivy in auto parallel.
|
|
Description: mul-Xdivy net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.xdivy = P.Xdivy().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.xdivy(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_xlogy_broadcast():
|
|
"""
|
|
Feature: distribute operator Xlogy in auto parallel.
|
|
Description: mul-Xlogy net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.xlogy = P.Xlogy().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.xlogy(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_squared_difference_broadcast():
|
|
"""
|
|
Feature: distribute operator SquaredDifference in auto parallel.
|
|
Description: mul-SquaredDifference net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.squared_difference = P.SquaredDifference().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.squared_difference(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.float32)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_masked_fill_broadcast_with_value_float():
|
|
"""
|
|
Feature: distribute operator MaskedFill in auto parallel.
|
|
Description: mul-MaskedFill net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.masked_fill = P.MaskedFill().shard(strategy2)
|
|
self.value = 1.0
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.masked_fill(out, b, self.value)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2))
|
|
net = Net(strategy1, strategy2)
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.bool_)
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_matmul_masked_fill_broadcast_with_value_tensor():
|
|
"""
|
|
Feature: distribute operator MaskedFill in auto parallel.
|
|
Description: mul-MaskedFill net with strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul = P.MatMul().shard(strategy1)
|
|
self.masked_fill = P.MaskedFill().shard(strategy2)
|
|
self.value = Tensor(1.0, ms.float32)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul(x, y)
|
|
out = self.masked_fill(out, b, self.value)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (4, 1))
|
|
strategy2 = ((4, 1), (1, 2), ())
|
|
net = Net(strategy1, strategy2)
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 1]), dtype=ms.float32)
|
|
b = Tensor(np.ones([1, 64]), dtype=ms.bool_)
|
|
compile_net(net, x, y, b)
|