forked from huawei/mindspore2022
691 lines
28 KiB
Python
691 lines
28 KiB
Python
# Copyright 2021 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 pytest
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.context import set_auto_parallel_context, ParallelMode
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from mindspore import context
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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import mindspore.ops as P
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from mindspore.parallel.nn import TransformerEncoder, TransformerDecoder, Transformer, TransformerOpParallelConfig, \
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VocabEmbedding, CrossEntropyLoss, OpParallelConfig, EmbeddingOpParallelConfig, FixedSparseAttention
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from mindspore.nn import Dense as Linear
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from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
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from mindspore.nn.optim import AdamWeightDecay
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from mindspore.nn.wrap.cell_wrapper import PipelineCell, _VirtualDatasetCell, TrainOneStepCell
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from mindspore.nn.wrap.loss_scale import _TrainPipelineWithLossScaleCell
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from mindspore.train import Model
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from mindspore.parallel import set_algo_parameters
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from tests.dataset_mock import MindData
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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 Dataset(MindData):
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def __init__(self, *inputs, length=3):
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super(Dataset, self).__init__(size=length)
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self.inputs = inputs
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self.index = 0
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.inputs
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def reset(self):
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self.index = 0
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class TransformerNet(nn.Cell):
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def __init__(self, en_layer, de_layer, parallel_config):
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super(TransformerNet, self).__init__()
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self.embedding = VocabEmbedding(vocab_size=240, embedding_size=20,
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parallel_config=config.embedding_dp_mp_config)
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self.network = Transformer(encoder_layers=en_layer,
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decoder_layers=de_layer,
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batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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hidden_size=64,
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num_heads=8,
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ffn_hidden_size=64,
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parallel_config=parallel_config)
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self.head = Linear(in_channels=64, out_channels=200)
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self.loss = CrossEntropyLoss(parallel_config=config.dp_mp_config)
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def construct(self, x1, x2, x3, x4, x5, y, mask):
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predict, _, _ = self.network(x1, x2, x3, x4, x5)
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predict = P.Reshape()(predict, (-1, F.shape(predict)[-1]))
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return self.loss(predict, y, mask)
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config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8, vocab_emb_dp=False)
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pipeline_config = TransformerOpParallelConfig(data_parallel=2, model_parallel=8, pipeline_stage=4,
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micro_batch_num=4, vocab_emb_dp=False)
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class NetWithLossFiveInputs(nn.Cell):
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def __init__(self, network):
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super(NetWithLossFiveInputs, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x1, x2, x3, x4, x5):
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predict, _, _ = self.network(x1, x2, x3, x4, x5)
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return self.loss(predict)
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def run_total_transformer_model_head(e_layer,
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d_layer,
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arg_parallel_config,
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mode=ParallelMode.SEMI_AUTO_PARALLEL):
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dp = arg_parallel_config.data_parallel
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mp = arg_parallel_config.model_parallel
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pp = arg_parallel_config.pipeline_stage
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if dp * mp * pp != 1:
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set_auto_parallel_context(device_num=8,
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full_batch=True,
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global_rank=0, parallel_mode=mode)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), mstype.float32)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), mstype.float32)
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decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16)
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memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16)
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seq = 20
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if d_layer > 0:
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seq = 10
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label = Tensor(np.ones((2 * seq,)), mstype.int32)
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input_mask = Tensor(np.ones((2 * seq,)), mstype.float32)
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net = TransformerNet(en_layer=e_layer, de_layer=d_layer, parallel_config=arg_parallel_config)
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net = _VirtualDatasetCell(net)
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params = net.trainable_params()
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optimizer = AdamWeightDecay(params)
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dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
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memory_mask, label, input_mask)
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net_with_grad = TrainOneStepCell(net, optimizer=optimizer)
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model = Model(net_with_grad)
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model.train(1, dataset, dataset_sink_mode=False)
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def test_transformer_model():
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set_auto_parallel_context(device_num=8, global_rank=0,
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full_batch=True,
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parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
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net = Transformer(encoder_layers=1,
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decoder_layers=2,
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batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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hidden_size=64,
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num_heads=8,
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ffn_hidden_size=64,
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parallel_config=config)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), mstype.float32)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), mstype.float32)
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decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16)
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memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16)
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net = NetWithLossFiveInputs(net)
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net = _VirtualDatasetCell(net)
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params = net.trainable_params()
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optimizer = AdamWeightDecay(params)
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dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
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memory_mask)
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net_with_grad = TrainOneStepCell(net, optimizer=optimizer)
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model = Model(net_with_grad)
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model.train(1, dataset, dataset_sink_mode=False)
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def test_transformer_model_2d_inputs():
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set_auto_parallel_context(device_num=8, global_rank=0,
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full_batch=True,
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parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
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net = Transformer(encoder_layers=1,
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decoder_layers=2,
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batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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hidden_size=64,
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num_heads=8,
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ffn_hidden_size=64,
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parallel_config=config)
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encoder_input_value = Tensor(np.ones((40, 64)), mstype.float32)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16)
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decoder_input_value = Tensor(np.ones((20, 64)), mstype.float32)
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decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16)
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memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16)
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net = NetWithLossFiveInputs(net)
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net = _VirtualDatasetCell(net)
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params = net.trainable_params()
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optimizer = AdamWeightDecay(params)
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dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
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memory_mask)
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net_with_grad = TrainOneStepCell(net, optimizer=optimizer)
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model = Model(net_with_grad)
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model.train(1, dataset, dataset_sink_mode=False)
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def test_transformer_model_int64_inputs():
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set_auto_parallel_context(device_num=8, global_rank=0,
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full_batch=True,
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parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
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net = Transformer(encoder_layers=1,
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decoder_layers=2,
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batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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hidden_size=64,
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num_heads=8,
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ffn_hidden_size=64,
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parallel_config=config)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), mstype.int64)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), mstype.float32)
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decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16)
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memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16)
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net = NetWithLossFiveInputs(net)
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net = _VirtualDatasetCell(net)
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params = net.trainable_params()
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optimizer = AdamWeightDecay(params)
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dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
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memory_mask)
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net_with_grad = TrainOneStepCell(net, optimizer=optimizer)
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model = Model(net_with_grad)
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with pytest.raises(TypeError):
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model.train(1, dataset, dataset_sink_mode=False)
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def test_transformer_model_head_parallel_only_encoder():
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local_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8)
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run_total_transformer_model_head(e_layer=2, d_layer=0, arg_parallel_config=local_config)
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def test_transformer_model_head_parallel():
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local_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8)
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run_total_transformer_model_head(e_layer=1, d_layer=1, arg_parallel_config=local_config)
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def test_transformer_model_head_parallel_decoder():
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local_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8)
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with pytest.raises(ValueError):
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run_total_transformer_model_head(e_layer=0, d_layer=1, arg_parallel_config=local_config)
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def test_transformer_model_head_stand_alone():
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local_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=1)
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run_total_transformer_model_head(e_layer=2, d_layer=2, arg_parallel_config=local_config)
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def test_transformer_model_auto_parallel_no_support():
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local_config = TransformerOpParallelConfig(data_parallel=8, model_parallel=1)
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with pytest.raises(RuntimeError):
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run_total_transformer_model_head(e_layer=2, d_layer=2, arg_parallel_config=local_config,
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mode=ParallelMode.AUTO_PARALLEL)
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def pipeline_single_transformer(grad_accumulation_shard=False):
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"""
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Feature: Gradient Accumulation Shard for Pipeline and Gradient Accumulation
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Description: Test a single transformer model with pipeline parallel with grad_accumulation_shard False
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Expectation: The compile passed
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"""
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set_auto_parallel_context(device_num=64,
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full_batch=True,
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pipeline_stages=pipeline_config.pipeline_stage, global_rank=0,
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parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
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context.set_auto_parallel_context(parallel_optimizer_config=
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{"gradient_accumulation_shard": grad_accumulation_shard})
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net = Transformer(batch_size=8 // pipeline_config.micro_batch_num,
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src_seq_length=20,
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tgt_seq_length=10,
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encoder_layers=2,
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decoder_layers=2,
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hidden_size=64,
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num_heads=8,
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ffn_hidden_size=64,
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parallel_config=pipeline_config)
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encoder_input_value = Tensor(np.ones((8, 20, 64)), mstype.float32)
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encoder_input_mask = Tensor(np.ones((8, 20, 20)), mstype.float16)
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decoder_input_value = Tensor(np.ones((8, 10, 64)), mstype.float32)
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decoder_input_mask = Tensor(np.ones((8, 10, 10)), mstype.float16)
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memory_mask = Tensor(np.ones((8, 10, 20)), mstype.float16)
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net = NetWithLossFiveInputs(net)
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net = PipelineCell(net, pipeline_config.micro_batch_num)
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net = _VirtualDatasetCell(net)
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params = net.infer_param_pipeline_stage()
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optimizer = AdamWeightDecay(params)
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dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
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memory_mask)
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update_cell = DynamicLossScaleUpdateCell(loss_scale_value=1024, scale_factor=2, scale_window=1000)
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net_with_grad = _TrainPipelineWithLossScaleCell(net, optimizer=optimizer,
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scale_sense=update_cell)
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model = Model(net_with_grad)
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model.train(1, dataset, dataset_sink_mode=False)
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def test_pipeline_transformer_gradient_shard_true():
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"""
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Feature: Gradient Accumulation Shard for Pipeline and Gradient Accumulation
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Description: Test a single transformer model with pipeline parallel with grad_accumulation_shard True
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Expectation: The compile passed
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"""
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pipeline_single_transformer(grad_accumulation_shard=True)
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def test_pipeline_transformer_gradient_shard_false():
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"""
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Feature: Gradient Accumulation Shard for Pipeline and Gradient Accumulation
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Description: Test a single transformer model with pipeline parallel with grad_accumulation_shard False
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Expectation: The compile passed
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"""
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pipeline_single_transformer(grad_accumulation_shard=False)
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def test_transformer_wrong_head():
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set_auto_parallel_context(device_num=64,
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full_batch=True,
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pipeline_stages=pipeline_config.pipeline_stage, global_rank=0,
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parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
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error_test_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8, vocab_emb_dp=False)
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with pytest.raises(ValueError):
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net = Transformer(batch_size=4,
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src_seq_length=20,
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tgt_seq_length=10,
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encoder_layers=2,
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decoder_layers=2,
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hidden_size=64,
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num_heads=7,
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ffn_hidden_size=64,
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parallel_config=error_test_config)
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with pytest.raises(ValueError):
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net = Transformer(batch_size=4,
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src_seq_length=20,
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tgt_seq_length=10,
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encoder_layers=2,
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decoder_layers=2,
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hidden_size=63,
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num_heads=7,
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ffn_hidden_size=64,
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parallel_config=error_test_config)
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del net
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def test_transformer_wrong_dp_no_error():
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set_auto_parallel_context(device_num=64, full_batch=False, parallel_mode=ParallelMode.DATA_PARALLEL,
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pipeline_stages=pipeline_config.pipeline_stage, global_rank=0)
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check_config = TransformerOpParallelConfig(data_parallel=8, model_parallel=1, vocab_emb_dp=False)
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net = Transformer(batch_size=4, src_seq_length=20, tgt_seq_length=10, encoder_layers=2,
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decoder_layers=2, hidden_size=64, num_heads=2, ffn_hidden_size=64,
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parallel_config=check_config)
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del net
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def test_transformer_wrong_semi_auto_dp_error():
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set_auto_parallel_context(device_num=64, full_batch=False, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL,
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pipeline_stages=pipeline_config.pipeline_stage, global_rank=0)
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check_config = TransformerOpParallelConfig(data_parallel=16, model_parallel=1, vocab_emb_dp=False)
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with pytest.raises(ValueError):
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net = Transformer(batch_size=4, src_seq_length=20, tgt_seq_length=10, encoder_layers=2,
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decoder_layers=2, hidden_size=64, num_heads=2, ffn_hidden_size=64,
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parallel_config=check_config)
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del net
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def test_encoder():
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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, x1, x2):
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predict, _ = self.network(x1, x2)
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return self.loss(predict)
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set_auto_parallel_context(device_num=8,
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full_batch=True,
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global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
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net = TransformerEncoder(num_layers=2,
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batch_size=2,
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seq_length=16,
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hidden_size=8,
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ffn_hidden_size=64,
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num_heads=8,
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parallel_config=config)
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encoder_input_value = Tensor(np.ones((2, 16, 8)), mstype.float32)
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encoder_input_mask = Tensor(np.ones((2, 16, 16)), mstype.float16)
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net = NetWithLoss(net)
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net = _VirtualDatasetCell(net)
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dataset = Dataset(encoder_input_value, encoder_input_mask)
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model = Model(net)
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model.train(1, dataset, dataset_sink_mode=False)
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def test_decoder():
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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, x1, x2, x3, x4):
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predict, _, _ = self.network(x1, x2, x3, x4)
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return self.loss(predict)
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set_auto_parallel_context(device_num=8,
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full_batch=True,
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global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
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net = TransformerDecoder(num_layers=1,
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batch_size=8,
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hidden_size=16,
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ffn_hidden_size=8,
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num_heads=8,
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src_seq_length=20,
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tgt_seq_length=10,
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parallel_config=config)
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encoder_input_value = Tensor(np.ones((8, 20, 16)), mstype.float32)
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decoder_input_value = Tensor(np.ones((8, 10, 16)), mstype.float32)
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decoder_input_mask = Tensor(np.ones((8, 10, 10)), mstype.float16)
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memory_mask = Tensor(np.ones((8, 10, 20)), mstype.float16)
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net = NetWithLoss(net)
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net = _VirtualDatasetCell(net)
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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():
|
|
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)
|
|
|
|
net = VocabEmbedding(vocab_size=160, embedding_size=16, parallel_config=config.embedding_dp_mp_config)
|
|
net = NetWithLoss(net)
|
|
net = _VirtualDatasetCell(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():
|
|
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)
|
|
|
|
net = VocabEmbedding(vocab_size=160, embedding_size=16, parallel_config=config.embedding_dp_mp_config)
|
|
net = NetWithLoss(net)
|
|
net = _VirtualDatasetCell(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_sparse_attention_parallel_mp():
|
|
set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.AUTO_PARALLEL)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
sparse_attention_config = OpParallelConfig(model_parallel=8)
|
|
net = FixedSparseAttention(batch_size=16,
|
|
seq_length=1024,
|
|
size_per_head=64,
|
|
num_heads=8,
|
|
block_size=64,
|
|
parallel_config=sparse_attention_config)
|
|
q = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
k = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
v = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
mask = Tensor(np.ones((2, 1024, 1024)), mstype.float32)
|
|
dataset = Dataset(q, k, v, mask)
|
|
model = Model(net)
|
|
model.train(1, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_sparse_attention_parallel_mix():
|
|
set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.AUTO_PARALLEL)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
sparse_attention_config = OpParallelConfig(data_parallel=2, model_parallel=4)
|
|
net = FixedSparseAttention(batch_size=16,
|
|
seq_length=1024,
|
|
size_per_head=64,
|
|
num_heads=8,
|
|
block_size=64,
|
|
parallel_config=sparse_attention_config)
|
|
q = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
k = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
v = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
mask = Tensor(np.ones((2, 1024, 1024)), mstype.float32)
|
|
dataset = Dataset(q, k, v, mask)
|
|
model = Model(net)
|
|
model.train(1, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_sparse_attention_parallel_mix1():
|
|
set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.AUTO_PARALLEL)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
sparse_attention_config = OpParallelConfig(data_parallel=4, model_parallel=2)
|
|
net = FixedSparseAttention(batch_size=16,
|
|
seq_length=1024,
|
|
size_per_head=64,
|
|
num_heads=8,
|
|
block_size=64,
|
|
parallel_config=sparse_attention_config)
|
|
q = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
k = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
v = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
mask = Tensor(np.ones((2, 1024, 1024)), mstype.float32)
|
|
dataset = Dataset(q, k, v, mask)
|
|
model = Model(net)
|
|
model.train(1, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_sparse_attention_parallel_dp():
|
|
set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.AUTO_PARALLEL)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
sparse_attention_config = OpParallelConfig(data_parallel=8, model_parallel=1)
|
|
net = FixedSparseAttention(batch_size=16,
|
|
seq_length=1024,
|
|
size_per_head=64,
|
|
num_heads=8,
|
|
block_size=64,
|
|
parallel_config=sparse_attention_config)
|
|
net = _VirtualDatasetCell(net)
|
|
q = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
k = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
v = Tensor(np.ones((2, 1024, 512)), mstype.float16)
|
|
mask = Tensor(np.ones((2, 1024, 1024)), mstype.float32)
|
|
dataset = Dataset(q, k, v, mask)
|
|
model = Model(net)
|
|
model.train(1, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_parallel_cross_entroy_loss_semi_auto_parallel():
|
|
set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.AUTO_PARALLEL)
|
|
|
|
class NetWithLoss(nn.Cell):
|
|
def __init__(self, network, config_setting):
|
|
super(NetWithLoss, self).__init__()
|
|
self.loss = CrossEntropyLoss(config_setting)
|
|
self.network = network
|
|
|
|
def construct(self, x1, x2, x3):
|
|
predict, _ = self.network(x1)
|
|
predict = P.Reshape()(predict, (-1, 16))
|
|
return self.loss(predict, x2, x3)
|
|
|
|
net = VocabEmbedding(vocab_size=160, embedding_size=16, parallel_config=config.embedding_dp_mp_config)
|
|
net = NetWithLoss(net, config.dp_mp_config)
|
|
net = _VirtualDatasetCell(net)
|
|
embed_ids = Tensor(np.ones((2, 64)), mstype.int32)
|
|
labels = Tensor(np.ones((2 * 64,)), mstype.int32)
|
|
input_mask = Tensor(np.ones((2 * 64,)), mstype.float32)
|
|
dataset = Dataset(embed_ids, labels, input_mask)
|
|
|
|
model = Model(net)
|
|
model.train(1, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_transformer_args():
|
|
|
|
with pytest.raises(TypeError):
|
|
Transformer(hidden_size=10, batch_size=2, ffn_hidden_size=20, src_seq_length=10,
|
|
tgt_seq_length=20, decoder_layers="aa")
|
|
|
|
with pytest.raises(TypeError):
|
|
Transformer(hidden_size=10, batch_size=2, ffn_hidden_size=20, src_seq_length=10,
|
|
tgt_seq_length="a")
|
|
|
|
with pytest.raises(TypeError):
|
|
Transformer(hidden_size=10, batch_size=2, ffn_hidden_size=20, src_seq_length=10,
|
|
tgt_seq_length=20, softmax_compute_type=mstype.int64)
|
|
|
|
with pytest.raises(TypeError):
|
|
Transformer(hidden_size=10, batch_size=2, ffn_hidden_size=20, src_seq_length=10,
|
|
tgt_seq_length=20, layernorm_compute_type=mstype.int64)
|
|
|
|
with pytest.raises(TypeError):
|
|
Transformer(hidden_size=10, batch_size=2, ffn_hidden_size=20, src_seq_length=10,
|
|
tgt_seq_length=20, param_init_type=mstype.int64)
|
|
|
|
with pytest.raises(TypeError):
|
|
Transformer(hidden_size=10, batch_size=2, ffn_hidden_size=20, src_seq_length=10,
|
|
tgt_seq_length=20, hidden_dropout_rate=mstype.int64)
|
|
|
|
Transformer(hidden_size=10, batch_size=2, ffn_hidden_size=20, src_seq_length=10,
|
|
tgt_seq_length=20, softmax_compute_type=mstype.float16)
|
|
|
|
|
|
def test_transformer_parallel_config():
|
|
parallel_test_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=3)
|
|
|
|
with pytest.raises(TypeError):
|
|
parallel_test_config.data_parallel = False
|
|
|
|
with pytest.raises(ValueError):
|
|
parallel_test_config.data_parallel = 0
|
|
|
|
with pytest.raises(TypeError):
|
|
parallel_test_config.model_parallel = False
|
|
|
|
with pytest.raises(ValueError):
|
|
parallel_test_config.model_parallel = 0
|
|
|
|
with pytest.raises(TypeError):
|
|
parallel_test_config.pipeline_stage = False
|
|
|
|
with pytest.raises(ValueError):
|
|
parallel_test_config.pipeline_stage = 0
|
|
|
|
with pytest.raises(TypeError):
|
|
parallel_test_config.micro_batch_num = False
|
|
|
|
with pytest.raises(ValueError):
|
|
parallel_test_config.micro_batch_num = 0
|
|
|
|
with pytest.raises(TypeError):
|
|
parallel_test_config.gradient_aggregation_group = False
|
|
|
|
with pytest.raises(ValueError):
|
|
parallel_test_config.gradient_aggregation_group = 0
|
|
|
|
with pytest.raises(TypeError):
|
|
parallel_test_config.recompute = 1
|
|
|
|
parallel_test_config.recompute.recompute = False
|
|
|
|
assert not parallel_test_config.recompute.recompute
|
|
|
|
|
|
def test_parallel_config():
|
|
parallel_test_config = OpParallelConfig(data_parallel=1, model_parallel=3)
|
|
|
|
with pytest.raises(ValueError):
|
|
parallel_test_config.data_parallel = 0
|
|
|
|
with pytest.raises(TypeError):
|
|
parallel_test_config.model_parallel = False
|
|
|
|
with pytest.raises(ValueError):
|
|
parallel_test_config.model_parallel = 0
|
|
|
|
assert parallel_test_config.model_parallel == 3
|
|
|
|
|
|
def test_embedding_parallel_config():
|
|
parallel_test_config = EmbeddingOpParallelConfig(data_parallel=1, model_parallel=3, vocab_emb_dp=False)
|
|
|
|
with pytest.raises(ValueError):
|
|
parallel_test_config.data_parallel = 0
|
|
|
|
with pytest.raises(TypeError):
|
|
parallel_test_config.model_parallel = False
|
|
|
|
with pytest.raises(ValueError):
|
|
parallel_test_config.model_parallel = 0
|
|
|
|
with pytest.raises(TypeError):
|
|
parallel_test_config.vocab_emb_dp = 0
|
|
|
|
assert not parallel_test_config.vocab_emb_dp
|