!698 [Auto parallel] Support multi-subgraphs in auto-parallel
Merge pull request !698 from Xiaoda/support-wide-deep-in-auto-parallelpull/698/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
ef71ae941f
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import numpy as np
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from mindspore import context
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.nn.optim import Adam, FTRL
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore import Tensor, Parameter, ParameterTuple
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from mindspore.ops import composite as C
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from mindspore.parallel import _cost_model_context as cost_model_context
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from mindspore.common.api import _executor
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from mindspore.parallel import set_algo_parameters, get_algo_parameters, reset_algo_parameters
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from mindspore.parallel._utils import _reset_op_id as reset_op_id
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.mul = P.Mul()
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self.relu = P.ReLU()
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self.wd = Parameter(Tensor(np.ones([8, 8, 8, 8]).astype(np.float32)), name="wide")
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self.wt = Parameter(Tensor(np.ones([8, 8, 8, 8]).astype(np.float32)), name="l")
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def construct(self, x):
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out = self.mul(x, self.wd)
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out = self.mul(out, self.wt)
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out = self.relu(out)
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return out
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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.sum = P.ReduceSum()
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self.mean = P.ReduceMean()
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self.net = network
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def construct(self, x):
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predict = self.net(x)
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loss1 = self.sum(predict, -1)
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loss2 = self.mean(predict, -1)
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return loss1, loss2
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class IthOutputCell(nn.Cell):
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def __init__(self, network, output_index):
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super(IthOutputCell, self).__init__()
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self.network = network
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self.output_index = output_index
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def construct(self, x):
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predict = self.network(x)[self.output_index]
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return predict
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class TrainStepWarp(nn.Cell):
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def __init__(self, network, sens=1000.0):
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super(TrainStepWarp, self).__init__()
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self.network = network
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self.network.set_train()
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self.trainable_params = network.trainable_params()
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weights_w = []
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weights_d = []
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for params in self.trainable_params:
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weights_w.append(params)
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weights_d.append(params)
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self.weights_w = ParameterTuple(weights_w)
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self.weights_d = ParameterTuple(weights_d)
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self.optimizer_w = FTRL(learning_rate=1e-2, params=self.weights_w, l1=1e-8,
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l2=1e-8, initial_accum=1.0)
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self.optimizer_d = Adam(self.weights_d, learning_rate=3.5e-4, eps=1e-8,
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loss_scale=sens)
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self.hyper_map = C.HyperMap()
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self.grad_w = C.GradOperation('grad_w', get_by_list=True, sens_param=True)
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self.grad_d = C.GradOperation('grad_d', get_by_list=True, sens_param=True)
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self.sens = sens
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self.loss_net_w = IthOutputCell(network, output_index=0)
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self.loss_net_d = IthOutputCell(network, output_index=1)
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def construct(self, x):
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weights_w = self.weights_w
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weights_d = self.weights_d
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loss_w, loss_d = self.network(x)
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sens_w = P.Fill()(P.DType()(loss_w), P.Shape()(loss_w), self.sens)
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sens_d = P.Fill()(P.DType()(loss_d), P.Shape()(loss_d), self.sens)
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grads_w = self.grad_w(self.loss_net_w, weights_w)(x, sens_w)
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grads_d = self.grad_d(self.loss_net_d, weights_d)(x, sens_d)
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return F.depend(loss_w, self.optimizer_w(grads_w)), F.depend(loss_d, self.optimizer_d(grads_d))
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def test_double_subgraphs():
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cost_model_context.set_cost_model_context(multi_subgraphs=True)
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context.set_context(save_graphs=True)
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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net = TrainStepWarp(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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x = Tensor(np.ones([8, 8, 8, 8]), dtype=ms.float32)
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reset_op_id()
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_executor.compile(net, x, phase='train')
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strategies = _executor._get_strategy(net)
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expected_strategies = {'Default/network-NetWithLoss/ReduceMean-op0': [[8, 1, 1, 1]],
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'Default/network-NetWithLoss/net-Net/ReLU-op1': [[8, 1, 1, 1]],
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'Default/network-NetWithLoss/net-Net/Mul-op2': [[8, 1, 1, 1], [8, 1, 1, 1]],
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'Default/network-NetWithLoss/net-Net/Mul-op3': [[8, 1, 1, 1], [8, 1, 1, 1]],
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'Default/network-NetWithLoss/ReduceSum-op4': [[8, 1, 1, 1]]}
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assert strategies == expected_strategies
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@ -0,0 +1,70 @@
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import numpy as np
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from mindspore import context
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore import Tensor
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from mindspore.common.api import _executor
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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from mindspore.parallel import set_algo_parameters
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from mindspore.parallel._utils import _reset_op_id as reset_op_id
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import re
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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):
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predict = self.network(x)
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return self.loss(predict)
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class Blockcell(nn.Cell):
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def __init__(self):
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super(Blockcell, self).__init__()
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self.bn = nn.BatchNorm2d(64, momentum=0.9)
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def construct(self, x):
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out = self.bn(x)
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return out
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def getBlock():
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return Blockcell()
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def test_two_bn():
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.block1 = getBlock()
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self.block2 = getBlock()
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self.relu = P.ReLU()
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self.add = P.TensorAdd()
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self.bias = Tensor(np.ones([64, 64]), dtype=ms.float32)
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def construct(self, x):
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out = self.block1(x)
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out = self.relu(out)
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out = self.add(out, self.bias)
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out = self.block2(out)
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return out
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net = NetWithLoss(Net())
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x = Tensor(np.ones([64, 64]), dtype=ms.float32)
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context.set_context(save_graphs=True)
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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="auto_parallel")
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set_algo_parameters(elementwise_op_strategy_follow=True)
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reset_op_id()
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_executor.compile(net, x, phase='train')
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strategies = _executor._get_strategy(net)
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assert len(strategies) == 4
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for (k, v) in strategies.items():
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if re.search('BatchNorm-op', k) is not None:
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assert v == [[8, 1], [1], [1], [1], [1]]
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elif re.search('TensorAdd-op', k) is not None:
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assert v == [[8, 1], [8, 1]]
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elif re.search('ReLU-op', k) is not None:
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assert v == [[8, 1]]
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