num_gpus = 2
partition_len = ((nlayers - 1) // num_gpus) + 1
# Add encoder in the beginning.
tmp_list = [Encoder(ntokens, emsize, dropout).cuda(0)]
module_list = []
# Add all the necessary transformer blocks.
for i in range(nlayers):
transformer_block = TransformerEncoderLayer(emsize, nhead, nhid, dropout)
if i != 0 and i % (partition_len) == 0:
module_list.append(nn.Sequential(*tmp_list))
tmp_list = []
device = i // (partition_len)
tmp_list.append(transformer_block.to(device))
# Add decoder in the end.
tmp_list.append(Decoder(ntokens, emsize).cuda(num_gpus - 1))
module_list.append(nn.Sequential(*tmp_list))
from torch.distributed.pipeline.sync import Pipe
# Build the pipeline.
chunks = 8
model = Pipe(torch.nn.Sequential(*module_list), chunks=chunks)
def get_total_params(module: torch.nn.Module):
total_params = 0
for param in module.parameters():
def run_worker(rank, world_size):
######################################################################
# Load and batch data
# -------------------
#
######################################################################
# The training process uses Wikitext-2 dataset from ``torchtext``. The
# vocab object is built based on the train dataset and is used to numericalize
# tokens into tensors. Starting from sequential data, the ``batchify()``
# function arranges the dataset into columns, trimming off any tokens remaining
# after the data has been divided into batches of size ``batch_size``.
# For instance, with the alphabet as the sequence (total length of 26)
# and a batch size of 4, we would divide the alphabet into 4 sequences of
# length 6:
#
# .. math::
# \begin{bmatrix}
# \text{A} & \text{B} & \text{C} & \ldots & \text{X} & \text{Y} & \text{Z}
# \end{bmatrix}
# \Rightarrow
# \begin{bmatrix}
# \begin{bmatrix}\text{A} \\ \text{B} \\ \text{C} \\ \text{D} \\ \text{E} \\ \text{F}\end{bmatrix} &
# \begin{bmatrix}\text{G} \\ \text{H} \\ \text{I} \\ \text{J} \\ \text{K} \\ \text{L}\end{bmatrix} &
# \begin{bmatrix}\text{M} \\ \text{N} \\ \text{O} \\ \text{P} \\ \text{Q} \\ \text{R}\end{bmatrix} &
# \begin{bmatrix}\text{S} \\ \text{T} \\ \text{U} \\ \text{V} \\ \text{W} \\ \text{X}\end{bmatrix}
# \end{bmatrix}
#
# These columns are treated as independent by the model, which means that
# the dependence of ``G`` and ``F`` can not be learned, but allows more
# efficient batch processing.
#
# In 'run_worker'
def print_with_rank(msg):
print('[RANK {}]: {}'.format(rank, msg))
from torchtext.datasets import WikiText2
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
train_iter = WikiText2(split='train')
tokenizer = get_tokenizer('basic_english')
vocab = build_vocab_from_iterator(map(tokenizer, train_iter),
specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])
def data_process(raw_text_iter):
data = [
torch.tensor(vocab(tokenizer(item)), dtype=torch.long)
for item in raw_text_iter
]
return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))
train_iter, val_iter, test_iter = WikiText2()
train_data = data_process(train_iter)
val_data = data_process(val_iter)
test_data = data_process(test_iter)
device = torch.device(2 * rank)
def batchify(data, bsz, rank, world_size, is_train=False):
# Divide the dataset into bsz parts.
nbatch = data.size(0) // bsz
# Trim off any extra elements that wouldn't cleanly fit (remainders).
data = data.narrow(0, 0, nbatch * bsz)
# Evenly divide the data across the bsz batches.
data = data.view(bsz, -1).t().contiguous()
# Divide the data across the ranks only for training data.
if is_train:
data_per_rank = data.size(0) // world_size
data = data[rank * data_per_rank:(rank + 1) * data_per_rank]
return data.to(device)
batch_size = 20
eval_batch_size = 10
train_data = batchify(train_data, batch_size, rank, world_size, True)
val_data = batchify(val_data, eval_batch_size, rank, world_size)
test_data = batchify(test_data, eval_batch_size, rank, world_size)
######################################################################
# Functions to generate input and target sequence
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
######################################################################
# ``get_batch()`` function generates the input and target sequence for
# the transformer model. It subdivides the source data into chunks of
# length ``bptt``. For the language modeling task, the model needs the
# following words as ``Target``. For example, with a ``bptt`` value of 2,
# we’d get the following two Variables for ``i`` = 0:
#
# .. image:: ../_static/img/transformer_input_target.png
#
# It should be noted that the chunks are along dimension 0, consistent
# with the ``S`` dimension in the Transformer model. The batch dimension
# ``N`` is along dimension 1.
#
# In 'run_worker'
bptt = 35
def get_batch(source, i):
seq_len = min(bptt, len(source) - 1 - i)
data = source[i:i + seq_len]
target = source[i + 1:i + 1 + seq_len].view(-1)
# Need batch dimension first for pipeline parallelism.
return data.t(), target
######################################################################
# Model scale and Pipe initialization
# -----------------------------------
#
######################################################################
# To demonstrate training large Transformer models using pipeline parallelism,
# we scale up the Transformer layers appropriately. We use an embedding
# dimension of 4096, hidden size of 4096, 16 attention heads and 8 total
# transformer layers (``nn.TransformerEncoderLayer``). This creates a model with
# **~1 billion** parameters.
#
# We need to initialize the `RPC Framework <https://pytorch.org/docs/stable/rpc.html>`__
# since Pipe depends on the RPC framework via `RRef <https://pytorch.org/docs/stable/rpc.html#rref>`__
# which allows for future expansion to cross host pipelining. We need to
# initialize the RPC framework with only a single worker since we're using a
# single process to drive multiple GPUs.
#
# The pipeline is then initialized with 8 transformer layers on one GPU and 8
# transformer layers on the other GPU. One pipe is setup across GPUs 0 and 1 and
# another across GPUs 2 and 3. Both pipes are then replicated using DistributedDataParallel.
# In 'run_worker'
ntokens = len(vocab) # the size of vocabulary
emsize = 4096 # embedding dimension
nhid = 4096 # the dimension of the feedforward network model in nn.TransformerEncoder
nlayers = 8 # the number of nn.TransformerEncoderLayer in nn.TransformerEncoder
nhead = 16 # the number of heads in the multiheadattention models
dropout = 0.2 # the dropout value
from torch.distributed import rpc
tmpfile = tempfile.NamedTemporaryFile()
rpc.init_rpc(
name="worker",
rank=0,
world_size=1,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method="file://{}".format(tmpfile.name),
# Specifying _transports and _channels is a workaround and we no longer
# will have to specify _transports and _channels for PyTorch
# versions >= 1.8.1
_transports=["ibv", "uv"],
_channels=["cuda_ipc", "cuda_basic"],
))
# Num gpus for model parallelism.
num_gpus = 2
partition_len = ((nlayers - 1) // num_gpus) + 1
# Add encoder in the beginning.
tmp_list = [Encoder(ntokens, emsize, dropout).cuda(2 * rank)]
module_list = []
# Add all the necessary transformer blocks.
for i in range(nlayers):
transformer_block = TransformerEncoderLayer(emsize, nhead, nhid,
dropout)
if i != 0 and i % (partition_len) == 0:
module_list.append(nn.Sequential(*tmp_list))
tmp_list = []
device = i // (partition_len)
tmp_list.append(transformer_block.to(2 * rank + device))
# Add decoder in the end.
tmp_list.append(Decoder(ntokens, emsize).cuda(2 * rank + num_gpus - 1))
module_list.append(nn.Sequential(*tmp_list))
# Need to use 'checkpoint=never' since as of PyTorch 1.8, Pipe checkpointing
# doesn't work with DDP.
from torch.distributed.pipeline.sync import Pipe
chunks = 8
model = Pipe(torch.nn.Sequential(*module_list),
chunks=chunks,
checkpoint="never")
# Initialize process group and wrap model in DDP.
from torch.nn.parallel import DistributedDataParallel
import torch.distributed as dist
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
model = DistributedDataParallel(model)
def get_total_params(module: torch.nn.Module):
total_params = 0
for param in module.parameters():
total_params += param.numel()
return total_params
print_with_rank('Total parameters in model: {:,}'.format(
get_total_params(model)))
######################################################################
# Run the model
# -------------
#
######################################################################
# `CrossEntropyLoss <https://pytorch.org/docs/master/nn.html?highlight=crossentropyloss#torch.nn.CrossEntropyLoss>`__
# is applied to track the loss and
# `SGD <https://pytorch.org/docs/master/optim.html?highlight=sgd#torch.optim.SGD>`__
# implements stochastic gradient descent method as the optimizer. The initial
# learning rate is set to 5.0. `StepLR <https://pytorch.org/docs/master/optim.html?highlight=steplr#torch.optim.lr_scheduler.StepLR>`__ is
# applied to adjust the learn rate through epochs. During the
# training, we use
# `nn.utils.clip_grad_norm\_ <https://pytorch.org/docs/master/nn.html?highlight=nn%20utils%20clip_grad_norm#torch.nn.utils.clip_grad_norm_>`__
# function to scale all the gradient together to prevent exploding.
#
# In 'run_worker'
criterion = nn.CrossEntropyLoss()
lr = 5.0 # learning rate
optimizer = torch.optim.SGD(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
import time
def train():
model.train() # Turn on the train mode
total_loss = 0.
start_time = time.time()
ntokens = len(vocab)
# Train only for 50 batches to keep script execution time low.
nbatches = min(50 * bptt, train_data.size(0) - 1)
for batch, i in enumerate(range(0, nbatches, bptt)):
data, targets = get_batch(train_data, i)
optimizer.zero_grad()
# Since the Pipe is only within a single host and process the ``RRef``
# returned by forward method is local to this node and can simply
# retrieved via ``RRef.local_value()``.
output = model(data).local_value()
# Need to move targets to the device where the output of the
# pipeline resides.
loss = criterion(output.view(-1, ntokens),
targets.cuda(2 * rank + 1))
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
optimizer.step()
total_loss += loss.item()
log_interval = 10
if batch % log_interval == 0 and batch > 0:
cur_loss = total_loss / log_interval
elapsed = time.time() - start_time
print_with_rank('| epoch {:3d} | {:5d}/{:5d} batches | '
'lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch, nbatches // bptt,
scheduler.get_last_lr()[0],
elapsed * 1000 / log_interval, cur_loss,
math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
def evaluate(eval_model, data_source):
eval_model.eval() # Turn on the evaluation mode
total_loss = 0.
ntokens = len(vocab)
# Evaluate only for 50 batches to keep script execution time low.
nbatches = min(50 * bptt, data_source.size(0) - 1)
with torch.no_grad():
for i in range(0, nbatches, bptt):
data, targets = get_batch(data_source, i)
output = eval_model(data).local_value()
output_flat = output.view(-1, ntokens)
# Need to move targets to the device where the output of the
# pipeline resides.
total_loss += len(data) * criterion(
output_flat, targets.cuda(2 * rank + 1)).item()
return total_loss / (len(data_source) - 1)
######################################################################
# Loop over epochs. Save the model if the validation loss is the best
# we've seen so far. Adjust the learning rate after each epoch.
# In 'run_worker'
best_val_loss = float("inf")
epochs = 3 # The number of epochs
best_model = None
for epoch in range(1, epochs + 1):
epoch_start_time = time.time()
train()
val_loss = evaluate(model, val_data)
print_with_rank('-' * 89)
print_with_rank(
'| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
print_with_rank('-' * 89)
if val_loss < best_val_loss:
best_val_loss = val_loss
best_model = model
scheduler.step()
######################################################################
# Evaluate the model with the test dataset
# -------------------------------------
#
# Apply the best model to check the result with the test dataset.
# In 'run_worker'
test_loss = evaluate(best_model, test_data)
print_with_rank('=' * 89)
print_with_rank(
'| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
test_loss, math.exp(test_loss)))
print_with_rank('=' * 89)
