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307 lines
10 KiB
307 lines
10 KiB
# 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 Tensor
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from mindspore import context
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from mindspore.common.api import _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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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 C.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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_executor.compile(net, x, y, b)
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def test_matmul_pow():
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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().set_strategy(strategy1)
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self.pow = P.Pow().set_strategy(strategy2)
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self.matmul2 = P.MatMul().set_strategy(strategy1)
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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.pow(out, 2.0)
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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)
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), ())
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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_exp():
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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().set_strategy(strategy1)
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self.exp = P.Exp().set_strategy(strategy2)
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self.matmul2 = P.MatMul().set_strategy(strategy1)
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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.exp(out)
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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)
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2),)
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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_log():
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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().set_strategy(strategy1)
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self.log = P.Log().set_strategy(strategy2)
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self.matmul2 = P.MatMul().set_strategy(strategy1)
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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.log(out)
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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)
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2),)
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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_logical_not():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.matmul = P.MatMul().set_strategy(strategy1)
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self.logicalnot = P.LogicalNot().set_strategy(strategy2)
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self.equal = P.Equal().set_strategy(strategy3)
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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.equal(out, b)
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out = self.logicalnot(out)
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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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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2),)
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strategy3 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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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([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_cast():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.matmul = P.MatMul().set_strategy(strategy1)
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self.cast = P.Cast().set_strategy(strategy2)
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self.matmul2 = P.MatMul().set_strategy(strategy3)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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b = self.cast(b, ms.float32)
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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)
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2),)
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strategy3 = ((1, 4), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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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.int32)
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compile_net(net, x, y, b)
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def test_cast_before_mirror():
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class Net(nn.Cell):
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def __init__(self, strategy1):
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super().__init__()
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self.matmul = P.MatMul().set_strategy(strategy1)
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self.cast = P.Cast()
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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b = self.cast(b, ms.float32)
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out = self.matmul(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0, cast_before_mirror=True)
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strategy1 = ((2, 2), (2, 2))
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net = GradWrap(NetWithLoss(Net(strategy1)))
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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.float16)
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compile_net(net, x, y, b)
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def test_cast_before_mirror1():
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class Net(nn.Cell):
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def __init__(self, strategy1):
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super().__init__()
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self.matmul = P.MatMul().set_strategy(strategy1)
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self.cast = P.Cast()
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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b = self.cast(b, ms.float16)
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out = self.matmul(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0, cast_before_mirror=True)
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strategy1 = ((2, 2), (2, 2))
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net = GradWrap(NetWithLoss(Net(strategy1)))
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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.float16)
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y = Tensor(np.ones([32, 64]), dtype=ms.float16)
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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_cast_before_mirror2():
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class Net(nn.Cell):
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def __init__(self, strategy1):
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super().__init__()
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self.matmul = P.MatMul().set_strategy(strategy1)
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self.cast = P.Cast()
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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b = self.cast(b, ms.float16)
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out = self.matmul(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0, cast_before_mirror=False)
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strategy1 = ((2, 2), (2, 2))
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net = GradWrap(NetWithLoss(Net(strategy1)))
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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.float16)
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y = Tensor(np.ones([32, 64]), dtype=ms.float16)
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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_cast_before_mirror3():
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class Net(nn.Cell):
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def __init__(self, strategy1):
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super().__init__()
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self.matmul = P.MatMul().set_strategy(strategy1)
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self.cast = P.Cast()
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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b = self.cast(b, ms.float16)
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out = self.matmul(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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strategy1 = ((2, 2), (2, 2))
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net = GradWrap(NetWithLoss(Net(strategy1)))
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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.float16)
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y = Tensor(np.ones([32, 64]), dtype=ms.float16)
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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_mul_two_cast():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.mul = P.Mul().set_strategy(strategy1)
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self.mul2 = P.Mul().set_strategy(strategy2)
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self.cast = P.Cast().set_strategy(strategy3)
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self.cast2 = P.Cast().set_strategy(strategy3)
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def construct(self, x, y, b):
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out = self.mul(x, y)
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out = self.mul2(out, b)
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out = self.cast(out, ms.int32)
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out = self.cast2(out, ms.bool_)
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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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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((8, 1), (8, 1))
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strategy3 = ((8, 1),)
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net = GradWrap(Net(strategy1, strategy2, strategy3))
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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([128, 32]), dtype=ms.float32)
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b = Tensor(np.ones([128, 32]), dtype=ms.float32)
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compile_net(net, x, y, b)
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