# 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 numpy as np import mindspore.common.dtype as mstype import mindspore.nn as nn from mindspore import Tensor from mindspore.context import set_auto_parallel_context, ParallelMode from mindspore.ops import composite as C from mindspore.nn.parallel import TransformerEncoder, TransformerDecoder, Transformer, TransformerParallelConfig,\ VocabEmbedding 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 def test_transformer_model(): class NetWithLoss(nn.Cell): def __init__(self, network): super(NetWithLoss, 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) config = TransformerParallelConfig(dp=1, mp=8) set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL) net = Transformer(encoder_layers=1, decoder_layers=2, hidden_size=64, num_heads=8, ffn_hidden_size=64, src_seq_length=20, tgt_seq_length=20, parallel_config=config) encoder_input_value = Tensor(np.ones((2, 20, 64)), mstype.float32) encoder_input_mask = Tensor(np.ones((2, 1, 20, 20)), mstype.float16) decoder_input_value = Tensor(np.ones((2, 10, 64)), mstype.float32) decoder_input_mask = Tensor(np.ones((2, 1, 10, 10)), mstype.float16) memory_mask = Tensor(np.ones((2, 1, 10, 20)), mstype.float16) net = NetWithLoss(net) dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask, memory_mask) model = Model(net) model.train(1, dataset, dataset_sink_mode=False) def test_encoder(): class NetWithLoss(nn.Cell): def __init__(self, network): super(NetWithLoss, self).__init__() self.loss = VirtualLoss() self.network = network def construct(self, x1, x2): predict, _ = self.network(x1, x2) return self.loss(predict) config = TransformerParallelConfig(dp=1, mp=8) set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL) net = TransformerEncoder(num_layers=2, hidden_size=8, ffn_hidden_size=64, seq_length=16, num_heads=8, parallel_config=config) encoder_input_value = Tensor(np.ones((2, 16, 8)), mstype.float32) encoder_input_mask = Tensor(np.ones((2, 1, 16, 16)), mstype.float16) net = NetWithLoss(net) dataset = Dataset(encoder_input_value, encoder_input_mask) model = Model(net) model.train(1, dataset, dataset_sink_mode=False) def test_decoder(): class NetWithLoss(nn.Cell): def __init__(self, network): super(NetWithLoss, self).__init__() self.loss = VirtualLoss() self.network = network def construct(self, x1, x2, x3, x4): predict, _, _ = self.network(x1, x2, x3, x4) return self.loss(predict) config = TransformerParallelConfig(dp=1, mp=8) set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL) net = TransformerDecoder(num_layers=1, hidden_size=16, ffn_hidden_size=8, num_heads=8, seq_length=10, parallel_config=config) encoder_input_value = Tensor(np.ones((2, 20, 16)), mstype.float32) decoder_input_value = Tensor(np.ones((2, 10, 16)), mstype.float32) decoder_input_mask = Tensor(np.ones((2, 1, 10, 10)), mstype.float16) memory_mask = Tensor(np.ones((2, 1, 10, 20)), mstype.float16) net = NetWithLoss(net) dataset = Dataset(decoder_input_value, decoder_input_mask, encoder_input_value, memory_mask) model = Model(net) model.train(1, dataset, dataset_sink_mode=False) def test_vocabembedding_dp_true(): config = TransformerParallelConfig(dp=1, mp=8) set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL) class NetWithLoss(nn.Cell): def __init__(self, network): super(NetWithLoss, self).__init__() self.loss = VirtualLoss() self.network = network def construct(self, x1): predict, _ = self.network(x1) return self.loss(predict) class GradWrap(nn.Cell): def __init__(self, network): super(GradWrap, self).__init__() self.network = network def construct(self, x1): return grad_all(self.network)(x1) net = VocabEmbedding(vocab_size=100, embedding_size=16, parallel_config=config) net = NetWithLoss(net) encoder_input_value = Tensor(np.ones((2, 64)), mstype.int32) dataset = Dataset(encoder_input_value) model = Model(net) model.train(1, dataset, dataset_sink_mode=False) def test_vocabembedding_dp_false(): config = TransformerParallelConfig(dp=1, mp=8, vocab_emb_dp=False) set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL) class NetWithLoss(nn.Cell): def __init__(self, network): super(NetWithLoss, self).__init__() self.loss = VirtualLoss() self.network = network def construct(self, x1): predict, _ = self.network(x1) return self.loss(predict) class GradWrap(nn.Cell): def __init__(self, network): super(GradWrap, self).__init__() self.network = network def construct(self, x1): return grad_all(self.network)(x1) net = VocabEmbedding(vocab_size=160, embedding_size=16, parallel_config=config) net = NetWithLoss(net) encoder_input_value = Tensor(np.ones((2, 64)), mstype.int32) dataset = Dataset(encoder_input_value) model = Model(net) model.train(1, dataset, dataset_sink_mode=False)