# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest import numpy as np import mindspore.common.dtype as mstype import mindspore.nn as nn import mindspore.ops as P from mindspore import Tensor from mindspore.context import set_auto_parallel_context, ParallelMode from mindspore.ops import composite as C from mindspore.ops import functional as F from mindspore.parallel.nn import Transformer, TransformerOpParallelConfig, MoEConfig, CrossEntropyLoss from mindspore.nn.optim import AdamWeightDecay from mindspore.nn.wrap.cell_wrapper import TrainOneStepCell, _VirtualDatasetCell from mindspore.train import Model from tests.dataset_mock import MindData from tests.ut.python.ops.test_math_ops import VirtualLoss grad_all = C.GradOperation(get_all=True) class Dataset(MindData): def __init__(self, *inputs, length=3): super(Dataset, self).__init__(size=length) self.inputs = inputs self.index = 0 self.length = length def __iter__(self): return self def __next__(self): if self.index >= self.length: raise StopIteration self.index += 1 return self.inputs def reset(self): self.index = 0 config = TransformerOpParallelConfig(data_parallel=2, model_parallel=8, vocab_emb_dp=False) moe_config = MoEConfig(expert_num=4, num_experts_chosen=3) class NetWithLossFiveInputs(nn.Cell): def __init__(self, network): super(NetWithLossFiveInputs, self).__init__() self.loss = VirtualLoss() self.network = network def construct(self, x1, x2, x3, x4, x5): predict, _, _, _ = self.network(x1, x2, x3, x4, x5) return self.loss(predict) def test_transformer_model(): """ Feature: Test Transformer+MoE, with All2All enabled. Description: 3-dim input. Expectation: Successful graph compilation with All2All included. """ set_auto_parallel_context(device_num=16, global_rank=0, full_batch=True, enable_alltoall=True, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL) net = Transformer(encoder_layers=1, decoder_layers=1, batch_size=2, src_seq_length=20, tgt_seq_length=10, hidden_size=64, num_heads=8, ffn_hidden_size=64, moe_config=moe_config, parallel_config=config) net = _VirtualDatasetCell(net) encoder_input_value = Tensor(np.ones((2, 20, 64)), mstype.float32) encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16) decoder_input_value = Tensor(np.ones((2, 10, 64)), mstype.float32) decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16) memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16) net = NetWithLossFiveInputs(net) params = net.trainable_params() optimizer = AdamWeightDecay(params) dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask, memory_mask) net_with_grad = TrainOneStepCell(net, optimizer=optimizer) model = Model(net_with_grad) model.train(1, dataset, dataset_sink_mode=False) def test_transformer_model_2d(): """ Feature: Test Transformer+MoE, with All2All enabled. Description: 2-dim input. Expectation: Successful graph compilation with All2All included. """ set_auto_parallel_context(device_num=16, global_rank=0, full_batch=True, enable_alltoall=True, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL) net = Transformer(encoder_layers=1, decoder_layers=1, batch_size=2, src_seq_length=20, tgt_seq_length=10, hidden_size=64, num_heads=8, ffn_hidden_size=64, moe_config=moe_config, parallel_config=config) net = _VirtualDatasetCell(net) encoder_input_value = Tensor(np.ones((40, 64)), mstype.float32) encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16) decoder_input_value = Tensor(np.ones((20, 64)), mstype.float32) decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16) memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16) net = NetWithLossFiveInputs(net) params = net.trainable_params() optimizer = AdamWeightDecay(params) dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask, memory_mask) net_with_grad = TrainOneStepCell(net, optimizer=optimizer) model = Model(net_with_grad) model.train(1, dataset, dataset_sink_mode=False) class TransformerNet(nn.Cell): """Transformer with loss""" def __init__(self, en_layer, de_layer, parallel_config): super(TransformerNet, self).__init__() self.network = Transformer(encoder_layers=en_layer, decoder_layers=de_layer, batch_size=2, src_seq_length=20, tgt_seq_length=10, hidden_size=64, num_heads=8, ffn_hidden_size=64, moe_config=moe_config, parallel_config=parallel_config) self.loss = CrossEntropyLoss(parallel_config=parallel_config.dp_mp_config) def construct(self, x1, x2, x3, x4, x5, y, mask): predict, _, _ = self.network(x1, x2, x3, x4, x5) predict = P.Reshape()(predict, (-1, F.shape(predict)[-1])) return self.loss(predict, y, mask) def moe_with_loss_plus_mutiparallel(local_parallel_config): set_auto_parallel_context(device_num=16, enable_alltoall=True, full_batch=True, global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL) encoder_input_value = Tensor(np.ones((2, 20, 64)), mstype.float32) encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16) decoder_input_value = Tensor(np.ones((2, 10, 64)), mstype.float32) decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16) memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16) label = Tensor(np.ones((20,)), mstype.int32) input_mask = Tensor(np.ones((20,)), mstype.float32) net = TransformerNet(en_layer=1, de_layer=1, parallel_config=local_parallel_config) net = _VirtualDatasetCell(net) params = net.trainable_params() optimizer = AdamWeightDecay(params) dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask, memory_mask, label, input_mask) net_with_grad = TrainOneStepCell(net, optimizer=optimizer) model = Model(net_with_grad) model.train(1, dataset, dataset_sink_mode=False) def test_moe_expert_parallel1(): """ Feature: Test Transformer+MoE for data_parallel plus expert_parallel, with All2All enabled. Description: 3-dim input. Expectation: Successful graph compilation with All2All included. """ local_p_config = TransformerOpParallelConfig(data_parallel=2, model_parallel=4, expert_parallel=2) moe_with_loss_plus_mutiparallel(local_p_config) def test_moe_expert_parallel2(): """ Feature: Test Transformer+MoE for data_parallel plus expert_parallel, with All2All enabled. Description: 3-dim input. Expectation: Successful graph compilation with All2All included. """ local_p_config = TransformerOpParallelConfig(data_parallel=2, model_parallel=8, expert_parallel=1) moe_with_loss_plus_mutiparallel(local_p_config) def test_moe_expert_parallel3(): """ Feature: Test Transformer+MoE for data_parallel plus expert_parallel, with All2All enabled. Description: 3-dim input. Expectation: Successful graph compilation. """ local_p_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8, expert_parallel=2) with pytest.raises(ValueError): moe_with_loss_plus_mutiparallel(local_p_config) def test_moe_expert_parallel_exception(): """ Feature: Test Transformer+MoE for data_parallel plus expert_parallel, with All2All enabled. Description: data_parallel*model_parallel*expert_parallel > device_num Expectation: Raise ValueError. """ local_p_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8, expert_parallel=4) with pytest.raises(ValueError): moe_with_loss_plus_mutiparallel(local_p_config)