| title | Megatron-LM GPT2 |
|---|
If you haven't already, we advise you to first read through the Getting Started guide before stepping through this tutorial.
In this tutorial we will be adding DeepSpeed to Megatron-LM GPT2 model, which is a large, powerful transformer. Megatron-LM supports model-parallel and multi-node training. Please see the corresponding paper for more details: Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.
First, we discuss data and environment setup and how to train the GPT-2 model with the original Megatron-LM. Next, we proceed step-by-step in enabling this model to run with DeepSpeed. Finally, we demonstrate the performance gains, and memory footprint reduction from using DeepSpeed.
The original model code from Megatron-LM. We've copied this repo under DeepSpeedExamples/Megatron-LM/ and made it available as a submodule. To download, execute:
git submodule update --init --recursive- Follow Megatron's instructions
to download the webtext data and place a symbolic link under
DeepSpeedExamples/Megatron-LM/data:
-
For a single GPU run:
- change
scripts/pretrain_gpt2.sh, set its--train-dataargument as"webtext". - run
bash scripts/pretrain_gpt2.sh
- change
-
For multiple GPUs and/or nodes run:
-
change
scripts/pretrain_gpt2_model_parallel.sh- set its
--train-dataargument as"webtext" GPUS_PER_NODEindicates how many GPUs per node involved in the testingNNODESindicates how many nodes involved in the testing
- set its
-
run
bash scripts/pretrain_gpt2_model_parallel.sh
-
To use DeepSpeed we will modify three files :
arguments.py: Arguments configurationspretrain_gpt2.py: Main entry point for trainingutils.py: Checkpoints saving and loading utilities
The first step is to apply DeepSpeed is adding DeepSpeed arguments to
Megatron-LM GPT2 model, using deepspeed.add_config_arguments() in
arguments.py.
def get_args():
"""Parse all the args."""
parser = argparse.ArgumentParser(description='PyTorch BERT Model')
parser = add_model_config_args(parser)
parser = add_fp16_config_args(parser)
parser = add_training_args(parser)
parser = add_evaluation_args(parser)
parser = add_text_generate_args(parser)
parser = add_data_args(parser)
# Include DeepSpeed configuration arguments
parser = deepspeed.add_config_arguments(parser)We modify pretrain.py to enable training with DeepSpeed.
We use deepspeed.initialize to create model_engine, optimizer and LR
scheduler. Below is its definition:
def initialize(args,
model,
optimizer=None,
model_parameters=None,
training_data=None,
lr_scheduler=None,
mpu=None,
dist_init_required=True,
collate_fn=None):For the Megatron-LM GPT2 model, we initialize DeepSpeed in its
setup_model_and_optimizer() function as below, to pass the raw model,
optimizer, args, lr_scheduler and mpu.
def setup_model_and_optimizer(args):
"""Setup model and optimizer."""
model = get_model(args)
optimizer = get_optimizer(model, args)
lr_scheduler = get_learning_rate_scheduler(optimizer, args)
if args.deepspeed:
import deepspeed
print_rank_0("DeepSpeed is enabled.")
model, optimizer, _, lr_scheduler = deepspeed.initialize(
model=model,
optimizer=optimizer,
args=args,
lr_scheduler=lr_scheduler,
mpu=mpu,
dist_init_required=False
)Note that when FP16 is enabled, Megatron-LM GPT2 adds a wrapper to the Adam
optimizer. DeepSpeed has its own FP16 Optimizer, so we need to pass the Adam
optimizer to DeepSpeed directly without any wrapper. We return the unwrapped
Adam optimizer from get_optimizer() when DeepSpeed is enabled.
def get_optimizer(model, args):
"""Setup the optimizer."""
......
# Use Adam.
optimizer = Adam(param_groups,
lr=args.lr, weight_decay=args.weight_decay)
if args.deepspeed:
# fp16 wrapper is not required for DeepSpeed.
return optimizerThe model returned by deepspeed.initialize is the DeepSpeed Model Engine
that we will use to train the model using the forward, backward and step API.
The forward propagation API is compatible to PyTorch and no change is required.
Backward propagation is done by calling backward(loss) directly on the model engine.
def backward_step(optimizer, model, lm_loss, args, timers):
"""Backward step."""
# Total loss.
loss = lm_loss
# Backward pass.
if args.deepspeed:
model.backward(loss)
else:
optimizer.zero_grad()
if args.fp16:
optimizer.backward(loss, update_master_grads=False)
else:
loss.backward()Zeroing the gradients is handled automatically by DeepSpeed after the weights have been updated using a mini-batch.
Furthermore, DeepSpeed addresses distributed data parallel and FP16 under the hood, simplifying code in multiple places.
(A) DeepSpeed also performs gradient averaging automatically at the gradient accumulation boundaries. So we skip the allreduce communication.
if args.deepspeed:
# DeepSpeed backward propagation already addressed all reduce communication.
# Reset the timer to avoid breaking timer logs below.
timers('allreduce').reset()
else:
torch.distributed.all_reduce(reduced_losses.data)
reduced_losses.data = reduced_losses.data / args.world_size
if not USE_TORCH_DDP:
timers('allreduce').start()
model.allreduce_params(reduce_after=False,
fp32_allreduce=args.fp32_allreduce)
timers('allreduce').stop()(B) We also skip updating master gradients, since DeepSpeed addresses it internally.
# Update master gradients.
if not args.deepspeed:
if args.fp16:
optimizer.update_master_grads()
# Clipping gradients helps prevent the exploding gradient.
if args.clip_grad > 0:
if not args.fp16:
mpu.clip_grad_norm(model.parameters(), args.clip_grad)
else:
optimizer.clip_master_grads(args.clip_grad)
return lm_loss_reducedThe step() function in DeepSpeed engine updates the model parameters as well
as the learning rate.
if args.deepspeed:
model.step()
else:
optimizer.step()
# Update learning rate.
if not (args.fp16 and optimizer.overflow):
lr_scheduler.step()
else:
skipped_iter = 1The GPT2 training script logs the loss scaling value during training. Inside,
the DeepSpeed optimizer, this value is stored as cur_scale instead of
loss_scale in Megatron's optimizer. Therefore, we appropriately replace it in
the logging string.
if args.fp16:
log_string += ' loss scale {:.1f} |'.format(
optimizer.cur_scale if args.deepspeed else optimizer.loss_scale)DeepSpeed engine has flexible APIs for checkpoint saving and loading, to handle the states from both the client model and its own internal.
def save_checkpoint(self, save_dir, tag, client_state={})
def load_checkpoint(self, load_dir, tag)Applying DeepSpeed needs to update utils.py in which Megatron-LM GPT2 saves and loads its checkpoints.
A new function save_ds_checkpoint() is created as below for DeepSpeed, it
collects the client model states and passes to DeepSpeed engine by calling
save_checkpoint() of DeepSpeed.
def save_ds_checkpoint(iteration, model, args):
"""Save a model checkpoint."""
sd = {}
sd['iteration'] = iteration
# rng states.
if not args.no_save_rng:
sd['random_rng_state'] = random.getstate()
sd['np_rng_state'] = np.random.get_state()
sd['torch_rng_state'] = torch.get_rng_state()
sd['cuda_rng_state'] = torch.cuda.get_rng_state()
sd['rng_tracker_states'] = mpu.get_cuda_rng_tracker().get_states()
model.save_checkpoint(args.save, iteration, client_state = sd)In Megatron-LM GPT2 save_checkpoint() function, adds following lines to
invoke the above function for DeepSpeed.
def save_checkpoint(iteration, model, optimizer,
lr_scheduler, args):
"""Save a model checkpoint."""
if args.deepspeed:
save_ds_checkpoint(iteration, model, args)
else:
......In load_checkpoint() function, use DeepSpeed loading checkpoint API as below,
and return the states for the client model.
def load_checkpoint(model, optimizer, lr_scheduler, args):
"""Load a model checkpoint."""
iteration, release = get_checkpoint_iteration(args)
if args.deepspeed:
checkpoint_name, sd = model.load_checkpoint(args.load, iteration)
if checkpoint_name is None:
if mpu.get_data_parallel_rank() == 0:
print("Unable to load checkpoint.")
return iteration
else:
......Assume webtext data was prepared in previous step, to start training Megatron-LM GPT2 model with DeepSpeed applied, execute the following command to start training.
- Single GPU run
- run
bash scripts/ds_pretrain_gpt2.sh
- run
- Multiple GPUs/Nodes run
- run
bash scripts/ds_pretrain_gpt2_model_parallel.sh
- run
DeepSpeed enables training very large models effectively via the advanced ZeRO optimizer. ZeRO significantly reduces the memory footprint for training large models which means large models can be trained with i) less model parallelism and ii) larger batch sizes. A lower model parallelism degree improves training efficiency by increasing the granularity of the computation such as the matrix multiplication where performance is directly related to the size of the matrices. Furthermore, less model parallelism also results in less communication between model parallel GPUs, which further boosts performance. Larger batch size has a similar effect of increasing the computational granularity as well as reducing communication, also resulting in better performance. Therefore, DeepSpeed combines ZeRO-powered data parallelism with Megatron-LM tensor-slicing model parallelism, which is significantly faster than using Megatron-LM alone.
The observed performance improvements depend on several factors such as the memory per GPU, the local GPU interconnect (i.e., PCI-E vs NVLINK vs NVSwitch), the model size, inter node network interconnect, etc. Below, we show some of the performance improvements from using DeepSpeed over Megatron on a 16 GPU Low Bandwidth (40 Gbps) cluster and a 400 GPU DGX-2 High Bandwidth (800 Gbps) cluster. For details please see the ZeRO Paper. We also present performance improvement on a 64 GPU cluster along with detailed configuration analysis to show where the improvements come from.
The figure depicts system throughput improvements of DeepSpeed (combining ZeRO-powered data parallelism with model parallelism of Nvidia Megatron-LM) over using Megatron-LM alone.
The figure above shows that training 1.5B parameter model with DeepSpeed is nearly 4x faster than without DeepSpeed on a cluster with 4 nodes, 4 GPU per node, and 16 GPUs total. These GPUs have 16GB of memory each, and PCI-E interconnects GPUs within a node, and 40 Gbps infiniband across nodes.
The performance improvement comes from lower model parallelism degree and larger batch size as discussed earlier. Training 1.5B parameter model with Megatron-LM alone requires 4-way model parallelism, and can only fit an effective batch size of 32 using all 16 GPUs. On the other hand, DeepSpeed does not require any model-parallelism to train this model, and can support an effective batch size of 128 without running out of memory, resulting in significantly higher performance.
Each GPU on the DGX-2 cluster has 32 GB of memory, and GPUs inside a box is connected via the high-bandwidth NVSwitch. DGX-2 nodes are connected to each other via 800 Gbps (8 x 100Gbps) infiniband interconnect. As such, running a 1.5B model on DGX-2 requires less model parallelism, and the performance improvement from DeepSpeed for this model size is less significant. However, at larger model sizes, Megatron still requires significantly larger model parallelism degree, and can only run much smaller batch sizes than DeepSpeed. Therefore, as the model sizes get larger, DeepSpeed, by coming ZeRO with Megatron model parallelism, starts to significantly outperform using Megatron-LM alone.
The figure below compares DeepSpeed with Megatron on a 64 GPU cluster with 4 DGX-2 nodes. To give the readers a clear idea of source of the performance improvements, we also present the configuration table for both Megatron and DeepSpeed. It shows the smallest model parallelism degree and the largest batch size that can be used to train these models without running out of memory. As discussed above, the tables demonstrate that DeepSpeed runs with smaller model parallelism degree and achieves better performance.
The figure depicts system throughput improvements of DeepSpeed (combining ZeRO-powered data parallelism with model parallelism of Nvidia Megatron-LM) over using Megatron-LM alone.
a ) Megatron-LM GPT2 Baseline
| Model Parallelism | Data Parallelism | #gpus | batch size | layers | hidden size | attention heads | samples / sec | |
|---|---|---|---|---|---|---|---|---|
| 1.5B | 2 | 32 | 64 | 512 | 48 | 1600 | 16 | 128.56 |
| 4B | 4 | 16 | 64 | 128 | 64 | 2304 | 16 | 49.36 |
| 8B | 4 | 16 | 64 | 128 | 72 | 3072 | 24 | 24.57 |
| 20B | 16 | 4 | 64 | 16 | 111 | 3808 | 32 | 3.42 |
b ) Megatron-LM GPT2 with DeepSpeed
| Model Parallelism | Data Parallelism | #gpus | batch size | layers | hidden size | attention heads | samples / sec | |
|---|---|---|---|---|---|---|---|---|
| 1.5B | 1 | 64 | 64 | 2048 | 48 | 1600 | 16 | 151.35 |
| 4B | 1 | 64 | 64 | 512 | 64 | 2304 | 16 | 75.13 |
| 8B | 2 | 32 | 64 | 512 | 72 | 3072 | 24 | 43.52 |
| 20B | 4 | 16 | 64 | 128 | 111 | 3808 | 32 | 12.65 |