785 lines
32 KiB
Python
785 lines
32 KiB
Python
# 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 os
|
|
import shutil
|
|
import glob
|
|
|
|
import numpy as np
|
|
import pytest
|
|
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 import context
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops import functional as F
|
|
import mindspore.ops as P
|
|
from mindspore.parallel.nn import TransformerEncoder, TransformerDecoder, Transformer, TransformerOpParallelConfig, \
|
|
VocabEmbedding, CrossEntropyLoss, OpParallelConfig, EmbeddingOpParallelConfig, FixedSparseAttention
|
|
from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
|
|
from mindspore.nn.optim import AdamWeightDecay
|
|
from mindspore.nn.wrap.cell_wrapper import PipelineCell, _VirtualDatasetCell, TrainOneStepCell
|
|
from mindspore.nn.wrap.loss_scale import _TrainPipelineWithLossScaleCell
|
|
from mindspore.train import Model
|
|
from mindspore.parallel import set_algo_parameters
|
|
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
|
|
|
|
|
|
class TransformerNet(nn.Cell):
|
|
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,
|
|
parallel_config=parallel_config)
|
|
self.loss = CrossEntropyLoss(parallel_config=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)
|
|
|
|
|
|
class TransformerEncoderNet(nn.Cell):
|
|
def __init__(self, batch_size, en_layer, de_layer, parallel_config):
|
|
super(TransformerEncoderNet, self).__init__()
|
|
self.embedding = VocabEmbedding(vocab_size=240, embedding_size=64,
|
|
parallel_config=parallel_config.embedding_dp_mp_config)
|
|
self.network = Transformer(encoder_layers=en_layer,
|
|
decoder_layers=de_layer,
|
|
batch_size=batch_size,
|
|
src_seq_length=20,
|
|
tgt_seq_length=10,
|
|
hidden_size=64,
|
|
num_heads=8,
|
|
ffn_hifloat16dden_size=64,
|
|
parallel_config=parallel_config)
|
|
self.loss = CrossEntropyLoss(parallel_config=config.dp_mp_config)
|
|
|
|
def construct(self, x, encoder_mask, label, input_mask):
|
|
embedded, _ = self.embedding(x)
|
|
logits, _, = self.network(embedded, encoder_mask)
|
|
logits = P.Reshape()(logits, (-1, F.shape(logits)[-1]))
|
|
label = P.Reshape()(label, (-1,))
|
|
input_mask = P.Reshape()(input_mask, (-1,))
|
|
return self.loss(logits, label, input_mask)
|
|
|
|
|
|
config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8, vocab_emb_dp=False)
|
|
pipeline_config = TransformerOpParallelConfig(data_parallel=2, model_parallel=8, pipeline_stage=4,
|
|
micro_batch_num=4, vocab_emb_dp=False)
|
|
|
|
|
|
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 run_network_function(dataset, pipeline_net):
|
|
"""
|
|
Feature: Test transformer embedding shared.
|
|
Description: a basic function for test compiling.
|
|
Expectation: success.
|
|
"""
|
|
params = pipeline_net.trainable_params()
|
|
optimizer = nn.Lamb(params, learning_rate=0.01)
|
|
model = Model(pipeline_net, optimizer=optimizer)
|
|
model.train(2, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def run_total_transformer_model_head(e_layer,
|
|
d_layer,
|
|
arg_parallel_config,
|
|
mode=ParallelMode.SEMI_AUTO_PARALLEL):
|
|
dp = arg_parallel_config.data_parallel
|
|
mp = arg_parallel_config.model_parallel
|
|
pp = arg_parallel_config.pipeline_stage
|
|
if dp * mp * pp != 1:
|
|
set_auto_parallel_context(device_num=8,
|
|
full_batch=True,
|
|
global_rank=0, parallel_mode=mode)
|
|
|
|
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)
|
|
seq = 20
|
|
if d_layer > 0:
|
|
seq = 10
|
|
label = Tensor(np.ones((2 * seq,)), mstype.int32)
|
|
input_mask = Tensor(np.ones((2 * seq,)), mstype.float32)
|
|
net = TransformerNet(en_layer=e_layer, de_layer=d_layer, parallel_config=arg_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_transformer_model():
|
|
set_auto_parallel_context(device_num=8, global_rank=0,
|
|
full_batch=True,
|
|
parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
|
|
net = Transformer(encoder_layers=1,
|
|
decoder_layers=2,
|
|
batch_size=2,
|
|
src_seq_length=20,
|
|
tgt_seq_length=10,
|
|
hidden_size=64,
|
|
num_heads=8,
|
|
ffn_hidden_size=64,
|
|
parallel_config=config)
|
|
|
|
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)
|
|
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)
|
|
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_inputs():
|
|
set_auto_parallel_context(device_num=8, global_rank=0,
|
|
full_batch=True,
|
|
parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
|
|
net = Transformer(encoder_layers=1,
|
|
decoder_layers=2,
|
|
batch_size=2,
|
|
src_seq_length=20,
|
|
tgt_seq_length=10,
|
|
hidden_size=64,
|
|
num_heads=8,
|
|
ffn_hidden_size=64,
|
|
parallel_config=config)
|
|
|
|
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)
|
|
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)
|
|
net_with_grad = TrainOneStepCell(net, optimizer=optimizer)
|
|
model = Model(net_with_grad)
|
|
|
|
model.train(1, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
class TestTransformerEmbeddingHead:
|
|
def __init__(self):
|
|
self.output_path = None
|
|
|
|
def setup_method(self):
|
|
self.output_path = './graphs' + self.__str__()
|
|
context.set_context(save_graphs=True,
|
|
save_graphs_path=self.output_path)
|
|
|
|
def teardown_method(self):
|
|
shutil.rmtree(self.output_path)
|
|
|
|
def virtual_assign_add_from_ir(self, pattern, target_count):
|
|
"""
|
|
This function will check the assign aa count with the golden one.
|
|
:param pattern: The match pattern for the specific count
|
|
:param target_count: The gold float16 count in the Ir files
|
|
"""
|
|
ir_files = glob.glob(os.path.join(self.output_path, 'rank_0', '*_validate*.ir'))
|
|
assert len(ir_files) == 1
|
|
appear_count = 0
|
|
with open(ir_files[0], 'r') as fp:
|
|
for line in fp:
|
|
if pattern in line:
|
|
appear_count += 1
|
|
assert appear_count == target_count
|
|
|
|
def test_pipeline_with_embedding(self):
|
|
"""
|
|
Feature: Test Transformer with embedding as shared
|
|
Description: When do pipeline training and applied optimzier shard, the embedding which is model parallel will
|
|
raise the shape error. This test cast is ensure there is no error raised.
|
|
Expectation: The number of AssignAdd is not as expected.
|
|
"""
|
|
bs = 16
|
|
pp = 2
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, pipeline_stages=pp,
|
|
full_batch=True,
|
|
enable_parallel_optimizer=True)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
cf = TransformerOpParallelConfig(data_parallel=1, model_parallel=4, pipeline_stage=pp, vocab_emb_dp=False)
|
|
pipeline_net = TransformerEncoderNet(batch_size=bs // pp,
|
|
en_layer=2, de_layer=0, parallel_config=cf)
|
|
pipeline_net.embedding.pipeline_stage = 0
|
|
pipeline_net.network.encoder.blocks[0].pipeline_stage = 0
|
|
pipeline_net.network.encoder.blocks[1].pipeline_stage = 1
|
|
|
|
pipeline_cell_net = PipelineCell(pipeline_net, 2)
|
|
encoder_input_value = Tensor(np.ones((bs, 20)), mstype.int32)
|
|
encoder_input_mask = Tensor(np.ones((bs, 20, 20)), mstype.float16)
|
|
label = Tensor(np.ones((bs, 20)), mstype.int32)
|
|
mask = Tensor(np.ones((bs, 20)), mstype.float32)
|
|
dataset = Dataset(encoder_input_value, encoder_input_mask, label, mask)
|
|
run_network_function(dataset, pipeline_cell_net)
|
|
self.virtual_assign_add_from_ir(pattern=r'AssignAdd(', target_count=35)
|
|
|
|
|
|
def test_transformer_model_int64_inputs():
|
|
set_auto_parallel_context(device_num=8, global_rank=0,
|
|
full_batch=True,
|
|
parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
|
|
net = Transformer(encoder_layers=1,
|
|
decoder_layers=2,
|
|
batch_size=2,
|
|
src_seq_length=20,
|
|
tgt_seq_length=10,
|
|
hidden_size=64,
|
|
num_heads=8,
|
|
ffn_hidden_size=64,
|
|
parallel_config=config)
|
|
|
|
encoder_input_value = Tensor(np.ones((2, 20, 64)), mstype.int64)
|
|
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)
|
|
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)
|
|
net_with_grad = TrainOneStepCell(net, optimizer=optimizer)
|
|
model = Model(net_with_grad)
|
|
|
|
with pytest.raises(TypeError):
|
|
model.train(1, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_transformer_model_head_parallel_only_encoder():
|
|
local_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8)
|
|
run_total_transformer_model_head(e_layer=2, d_layer=0, arg_parallel_config=local_config)
|
|
|
|
|
|
def test_transformer_model_head_parallel():
|
|
local_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8)
|
|
run_total_transformer_model_head(e_layer=1, d_layer=1, arg_parallel_config=local_config)
|
|
|
|
|
|
def test_transformer_model_head_parallel_decoder():
|
|
local_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8)
|
|
with pytest.raises(ValueError):
|
|
run_total_transformer_model_head(e_layer=0, d_layer=1, arg_parallel_config=local_config)
|
|
|
|
|
|
def test_transformer_model_head_stand_alone():
|
|
local_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=1)
|
|
run_total_transformer_model_head(e_layer=2, d_layer=2, arg_parallel_config=local_config)
|
|
|
|
|
|
def test_transformer_model_auto_parallel_no_support():
|
|
local_config = TransformerOpParallelConfig(data_parallel=8, model_parallel=1)
|
|
with pytest.raises(RuntimeError):
|
|
run_total_transformer_model_head(e_layer=2, d_layer=2, arg_parallel_config=local_config,
|
|
mode=ParallelMode.AUTO_PARALLEL)
|
|
|
|
|
|
def pipeline_single_transformer(grad_accumulation_shard=False):
|
|
"""
|
|
Feature: Gradient Accumulation Shard for Pipeline and Gradient Accumulation
|
|
Description: Test a single transformer model with pipeline parallel with grad_accumulation_shard False
|
|
Expectation: The compile passed
|
|
"""
|
|
set_auto_parallel_context(device_num=64,
|
|
full_batch=True,
|
|
pipeline_stages=pipeline_config.pipeline_stage, global_rank=0,
|
|
parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
|
|
context.set_auto_parallel_context(parallel_optimizer_config=
|
|
{"gradient_accumulation_shard": grad_accumulation_shard})
|
|
|
|
net = Transformer(batch_size=8 // pipeline_config.micro_batch_num,
|
|
src_seq_length=20,
|
|
tgt_seq_length=10,
|
|
encoder_layers=2,
|
|
decoder_layers=2,
|
|
hidden_size=64,
|
|
num_heads=8,
|
|
ffn_hidden_size=64,
|
|
parallel_config=pipeline_config)
|
|
|
|
encoder_input_value = Tensor(np.ones((8, 20, 64)), mstype.float32)
|
|
encoder_input_mask = Tensor(np.ones((8, 20, 20)), mstype.float16)
|
|
decoder_input_value = Tensor(np.ones((8, 10, 64)), mstype.float32)
|
|
decoder_input_mask = Tensor(np.ones((8, 10, 10)), mstype.float16)
|
|
memory_mask = Tensor(np.ones((8, 10, 20)), mstype.float16)
|
|
net = NetWithLossFiveInputs(net)
|
|
net = PipelineCell(net, pipeline_config.micro_batch_num)
|
|
net = _VirtualDatasetCell(net)
|
|
params = net.infer_param_pipeline_stage()
|
|
optimizer = AdamWeightDecay(params)
|
|
dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
|
|
memory_mask)
|
|
update_cell = DynamicLossScaleUpdateCell(loss_scale_value=1024, scale_factor=2, scale_window=1000)
|
|
net_with_grad = _TrainPipelineWithLossScaleCell(net, optimizer=optimizer,
|
|
scale_sense=update_cell)
|
|
model = Model(net_with_grad)
|
|
|
|
model.train(1, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_pipeline_transformer_gradient_shard_true():
|
|
"""
|
|
Feature: Gradient Accumulation Shard for Pipeline and Gradient Accumulation
|
|
Description: Test a single transformer model with pipeline parallel with grad_accumulation_shard True
|
|
Expectation: The compile passed
|
|
"""
|
|
pipeline_single_transformer(grad_accumulation_shard=True)
|
|
|
|
|
|
def test_pipeline_transformer_gradient_shard_false():
|
|
"""
|
|
Feature: Gradient Accumulation Shard for Pipeline and Gradient Accumulation
|
|
Description: Test a single transformer model with pipeline parallel with grad_accumulation_shard False
|
|
Expectation: The compile passed
|
|
"""
|
|
pipeline_single_transformer(grad_accumulation_shard=False)
|
|
|
|
|
|
def test_transformer_wrong_head():
|
|
set_auto_parallel_context(device_num=64,
|
|
full_batch=True,
|
|
pipeline_stages=pipeline_config.pipeline_stage, global_rank=0,
|
|
parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
|
|
error_test_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8, vocab_emb_dp=False)
|
|
with pytest.raises(ValueError):
|
|
net = Transformer(batch_size=4,
|
|
src_seq_length=20,
|
|
tgt_seq_length=10,
|
|
encoder_layers=2,
|
|
decoder_layers=2,
|
|
hidden_size=64,
|
|
num_heads=7,
|
|
ffn_hidden_size=64,
|
|
parallel_config=error_test_config)
|
|
|
|
with pytest.raises(ValueError):
|
|
net = Transformer(batch_size=4,
|
|
src_seq_length=20,
|
|
tgt_seq_length=10,
|
|
encoder_layers=2,
|
|
decoder_layers=2,
|
|
hidden_size=63,
|
|
num_heads=7,
|
|
ffn_hidden_size=64,
|
|
parallel_config=error_test_config)
|
|
del net
|
|
|
|
|
|
def test_transformer_wrong_dp_no_error():
|
|
set_auto_parallel_context(device_num=64, full_batch=False, parallel_mode=ParallelMode.DATA_PARALLEL,
|
|
pipeline_stages=pipeline_config.pipeline_stage, global_rank=0)
|
|
check_config = TransformerOpParallelConfig(data_parallel=8, model_parallel=1, vocab_emb_dp=False)
|
|
net = Transformer(batch_size=4, src_seq_length=20, tgt_seq_length=10, encoder_layers=2,
|
|
decoder_layers=2, hidden_size=64, num_heads=2, ffn_hidden_size=64,
|
|
parallel_config=check_config)
|
|
del net
|
|
|
|
|
|
def test_transformer_wrong_semi_auto_dp_error():
|
|
set_auto_parallel_context(device_num=64, full_batch=False, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL,
|
|
pipeline_stages=pipeline_config.pipeline_stage, global_rank=0)
|
|
check_config = TransformerOpParallelConfig(data_parallel=16, model_parallel=1, vocab_emb_dp=False)
|
|
with pytest.raises(ValueError):
|
|
net = Transformer(batch_size=4, src_seq_length=20, tgt_seq_length=10, encoder_layers=2,
|
|
decoder_layers=2, hidden_size=64, num_heads=2, ffn_hidden_size=64,
|
|
parallel_config=check_config)
|
|
del net
|
|
|
|
|
|
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)
|
|
|
|
set_auto_parallel_context(device_num=8,
|
|
full_batch=True,
|
|
global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
|
|
net = TransformerEncoder(num_layers=2,
|
|
batch_size=2,
|
|
seq_length=16,
|
|
hidden_size=8,
|
|
ffn_hidden_size=64,
|
|
num_heads=8,
|
|
parallel_config=config)
|
|
|
|
encoder_input_value = Tensor(np.ones((2, 16, 8)), mstype.float32)
|
|
encoder_input_mask = Tensor(np.ones((2, 16, 16)), mstype.float16)
|
|
|
|
net = NetWithLoss(net)
|
|
|
|
net = _VirtualDatasetCell(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)
|
|
|
|
set_auto_parallel_context(device_num=8,
|
|
full_batch=True,
|
|
global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
|
|
net = TransformerDecoder(num_layers=1,
|
|
batch_size=8,
|
|
hidden_size=16,
|
|
ffn_hidden_size=8,
|
|
num_heads=8,
|
|
src_seq_length=20,
|
|
tgt_seq_length=10,
|
|
parallel_config=config)
|
|
|
|
encoder_input_value = Tensor(np.ones((8, 20, 16)), mstype.float32)
|
|
decoder_input_value = Tensor(np.ones((8, 10, 16)), mstype.float32)
|
|
decoder_input_mask = Tensor(np.ones((8, 10, 10)), mstype.float16)
|
|
memory_mask = Tensor(np.ones((8, 10, 20)), mstype.float16)
|
|
|
|
net = NetWithLoss(net)
|
|
|
|
net = _VirtualDatasetCell(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():
|
|
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
|