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Megatron-LM

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Megatron-LM

Megatron-LM enables training large transformer language models at scale. It provides efficient tensor, pipeline and sequence based model parallelism for pre-training transformer based Language Models such as GPT (Decoder Only), BERT (Encoder Only) and T5 (Encoder-Decoder). For detailed information and how things work behind the scene please refer the github repo.

What is integrated?

Accelerate integrates following feature of Megatron-LM to enable large scale pre-training/finetuning of BERT (Encoder), GPT (Decoder) or T5 models (Encoder and Decoder):

a. Tensor Parallelism (TP): Reduces memory footprint without much additional communication on intra-node ranks. Each tensor is split into multiple chunks with each shard residing on separate GPU. At each step, the same mini-batch of data is processed independently and in parallel by each shard followed by syncing across all GPUs (all-reduce operation). In a simple transformer layer, this leads to 2 all-reduces in the forward path and 2 in the backward path. For more details, please refer research paper Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism and this section of blogpost The Technology Behind BLOOM Training.

b. Pipeline Parallelism (PP): Reduces memory footprint and enables large scale training via inter-node parallelization. Reduces the bubble of naive PP via PipeDream-Flush schedule/1F1B schedule and Interleaved 1F1B schedule. Layers are distributed uniformly across PP stages. For example, if a model has 24 layers and we have 4 GPUs for pipeline parallelism, each GPU will have 6 layers (24/4). For more details on schedules to reduce the idle time of PP, please refer to the research paper Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM and this section of blogpost The Technology Behind BLOOM Training.

c. Sequence Parallelism (SP): Reduces memory footprint without any additional communication. Only applicable when using TP. It reduces activation memory required as it prevents the same copies to be on the tensor parallel ranks post all-reduce by replacing then with reduce-scatter and no-op operation would be replaced by all-gather. As all-reduce = reduce-scatter + all-gather, this saves a ton of activation memory at no added communication cost. To put it simply, it shards the outputs of each transformer layer along sequence dimension, e.g., if the sequence length is 1024 and the TP size is 4, each GPU will have 256 tokens (1024/4) for each sample. This increases the batch size that can be supported for training. For more details, please refer to the research paper Reducing Activation Recomputation in Large Transformer Models.

d. Data Parallelism (DP) via Distributed Optimizer: Reduces the memory footprint by sharding optimizer states and gradients across DP ranks (versus the traditional method of replicating the optimizer state across data parallel ranks). For example, when using Adam optimizer with mixed-precision training, each parameter accounts for 12 bytes of memory. This gets distributed equally across the GPUs, i.e., each parameter would account for 3 bytes (12/4) if we have 4 GPUs. For more details, please refer the research paper ZeRO: Memory Optimizations Toward Training Trillion Parameter Models and following section of blog The Technology Behind BLOOM Training.

e. Selective Activation Recomputation: Reduces the memory footprint of activations significantly via smart activation checkpointing. It doesn’t store activations occupying large memory while being fast to recompute thereby achieving great tradeoff between memory and recomputation. For example, for GPT-3, this leads to 70% reduction in required memory for activations at the expense of only 2.7% FLOPs overhead for recomputation of activations. For more details, please refer to the research paper Reducing Activation Recomputation in Large Transformer Models.

f. Fused Kernels: Fused Softmax, Mixed Precision Fused Layer Norm and Fused gradient accumulation to weight gradient computation of linear layer. PyTorch JIT compiled Fused GeLU and Fused Bias+Dropout+Residual addition.

g. Support for Indexed datasets: Efficient binary format of datasets for large scale training. Support for the mmap, cached index file and the lazy loader format.

h. Checkpoint reshaping and interoperability: Utility for reshaping Megatron-LM checkpoints of variable tensor and pipeline parallel sizes to the beloved Transformers sharded checkpoints as it has great support with plethora of tools such as Accelerate Big Model Inference, Megatron-DeepSpeed Inference etc. Support is also available for converting Transformers sharded checkpoints to Megatron-LM checkpoint of variable tensor and pipeline parallel sizes for large scale training.

Pre-Requisites

You will need to install the latest pytorch, cuda, nccl, and NVIDIA APEX releases and the nltk library. See documentation for more details. Another way to setup the environment is to pull an NVIDIA PyTorch Container that comes with all the required installations from NGC.

Below is a step-by-step method to set up the conda environment:

Create a virtual environment

conda create --name ml

Assuming that the machine has CUDA 11.3 installed, installing the corresponding PyTorch GPU Version

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

Install Nvidia APEX

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
cd ..

Installing Megatron-LM

git clone https://github.com/NVIDIA/Megatron-LM.git
cd Megatron-LM
git checkout core_r0.5.0
pip install --no-use-pep517 -e .

Accelerate Megatron-LM Plugin

Important features are directly supported via the accelerate config command. An example of the corresponding questions for using Megatron-LM features is shown below:

:~$ accelerate config --config_file "megatron_gpt_config.yaml"
In which compute environment are you running? ([0] This machine, [1] AWS (Amazon SageMaker)): 0
Which type of machine are you using? ([0] No distributed training, [1] multi-CPU, [2] multi-GPU, [3] TPU): 2
How many different machines will you use (use more than 1 for multi-node training)? [1]: 
Do you want to use DeepSpeed? [yes/NO]: 
Do you want to use FullyShardedDataParallel? [yes/NO]: 
Do you want to use Megatron-LM ? [yes/NO]: yes
What is the Tensor Parallelism degree/size? [1]:2
Do you want to enable Sequence Parallelism? [YES/no]: 
What is the Pipeline Parallelism degree/size? [1]:2
What is the number of micro-batches? [1]:2
Do you want to enable selective activation recomputation? [YES/no]: 
Do you want to use distributed optimizer which shards optimizer state and gradients across data parallel ranks? [YES/no]: 
What is the gradient clipping value based on global L2 Norm (0 to disable)? [1.0]: 
How many GPU(s) should be used for distributed training? [1]:4
Do you wish to use FP16 or BF16 (mixed precision)? [NO/fp16/bf16]: bf16

The resulting config is shown below:

~$ cat megatron_gpt_config.yaml 
compute_environment: LOCAL_MACHINE
deepspeed_config: {}
distributed_type: MEGATRON_LM
downcast_bf16: 'no'
fsdp_config: {}
machine_rank: 0
main_process_ip: null
main_process_port: null
main_training_function: main
megatron_lm_config:
  megatron_lm_gradient_clipping: 1.0
  megatron_lm_num_micro_batches: 2
  megatron_lm_pp_degree: 2
  megatron_lm_recompute_activations: true
  megatron_lm_sequence_parallelism: true
  megatron_lm_tp_degree: 2
  megatron_lm_use_distributed_optimizer: true
mixed_precision: bf16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
use_cpu: false

We will take the example of GPT pre-training. The minimal changes required to the official run_clm_no_trainer.py to use Megatron-LM are as follows:

As Megatron-LM uses its own implementation of Optimizer, the corresponding scheduler compatible with it needs to be used. As such, support for only the Megatron-LM’s scheduler is present. User will need to create accelerate.utils.MegatronLMDummyScheduler. Example is given below:

from accelerate.utils import MegatronLMDummyScheduler

if accelerator.distributed_type == DistributedType.MEGATRON_LM:
    lr_scheduler = MegatronLMDummyScheduler(
        optimizer=optimizer,
        total_num_steps=args.max_train_steps,
        warmup_num_steps=args.num_warmup_steps,
    )
else:
    lr_scheduler = get_scheduler(
        name=args.lr_scheduler_type,
        optimizer=optimizer,
        num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps,
        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
    )

Getting the details of the total batch size now needs to be cognization of tensor and pipeline parallel sizes. Example of getting the effective total batch size is shown below:

if accelerator.distributed_type == DistributedType.MEGATRON_LM:
    total_batch_size = accelerator.state.megatron_lm_plugin.global_batch_size
else:
    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

When using Megatron-LM, the losses are already averaged across the data parallel group

if accelerator.distributed_type == DistributedType.MEGATRON_LM:
    losses.append(loss)
else:
    losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size)))

if accelerator.distributed_type == DistributedType.MEGATRON_LM:
    losses = torch.tensor(losses)
else:
    losses = torch.cat(losses)

For Megatron-LM, we need to save the model using accelerator.save_state

if accelerator.distributed_type == DistributedType.MEGATRON_LM:
    accelerator.save_state(args.output_dir)
else:
    unwrapped_model = accelerator.unwrap_model(model)
    unwrapped_model.save_pretrained(
        args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
    )

That’s it! We are good to go 🚀. Please find the example script in the examples folder at the path accelerate/examples/by_feature/megatron_lm_gpt_pretraining.py. Let’s run it for gpt-large model architecture using 4 A100-80GB GPUs.

accelerate launch --config_file megatron_gpt_config.yaml \
examples/by_feature/megatron_lm_gpt_pretraining.py \
--config_name "gpt2-large" \
--tokenizer_name "gpt2-large" \
--dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 \
--block_size 1024 \
--learning_rate 5e-5 \
--per_device_train_batch_size 24 \
--per_device_eval_batch_size 24 \
--num_train_epochs 5 \
--with_tracking \
--report_to "wandb" \
--output_dir "awesome_model"

Below are some important excerpts from the output logs:

Loading extension module fused_dense_cuda...
>>> done with compiling and loading fused kernels. Compilation time: 3.569 seconds
 > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432)
Building gpt model in the pre-training mode.
The Megatron LM model weights are initialized at random in `accelerator.prepare`. Please use `accelerator.load_checkpoint` to load a pre-trained checkpoint matching the distributed setup.
Preparing dataloader
Preparing dataloader
Preparing model
 > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 210753280
 > number of parameters on (tensor, pipeline) model parallel rank (1, 1): 209445120
 > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 210753280
 > number of parameters on (tensor, pipeline) model parallel rank (0, 1): 209445120
Preparing optimizer
Preparing scheduler
> learning rate decay style: linear
10/10/2022 22:57:22 - INFO - __main__ - ***** Running training *****
10/10/2022 22:57:22 - INFO - __main__ -   Num examples = 2318
10/10/2022 22:57:22 - INFO - __main__ -   Num Epochs = 5
10/10/2022 22:57:22 - INFO - __main__ -   Instantaneous batch size per device = 24
10/10/2022 22:57:22 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 48
10/10/2022 22:57:22 - INFO - __main__ -   Gradient Accumulation steps = 1
10/10/2022 22:57:22 - INFO - __main__ -   Total optimization steps = 245
 20%|████████████▍                                                 | 49/245 [01:04<04:09,  1.27s/it]
 10/10/2022 22:58:29 - INFO - __main__ - epoch 0: perplexity: 1222.1594275215962 eval_loss: 7.10837459564209
 40%|████████████████████████▊                                     | 98/245 [02:10<03:07,  1.28s/it]
 10/10/2022 22:59:35 - INFO - __main__ - epoch 1: perplexity: 894.5236583794557 eval_loss: 6.796291351318359
 60%|████████████████████████████████████▌                        | 147/245 [03:16<02:05,  1.28s/it]
 10/10/2022 23:00:40 - INFO - __main__ - epoch 2: perplexity: 702.8458788508042 eval_loss: 6.555137634277344
 80%|████████████████████████████████████████████████▊            | 196/245 [04:22<01:02,  1.28s/it]
 10/10/2022 23:01:46 - INFO - __main__ - epoch 3: perplexity: 600.3220028695281 eval_loss: 6.39746618270874
100%|█████████████████████████████████████████████████████████████| 245/245 [05:27<00:00,  1.28s/it]

There are a large number of other options/features that one can set using accelerate.utils.MegatronLMPlugin.

Advanced features to leverage writing custom train step and Megatron-LM Indexed Datasets

For leveraging more features, please go through below details.

Below is an example of changes required to customize the Train Step while using Megatron-LM. You will implement the accelerate.utils.AbstractTrainStep or inherit from their corresponding children accelerate.utils.GPTTrainStep, accelerate.utils.BertTrainStep or accelerate.utils.T5TrainStep.

from accelerate.utils import MegatronLMDummyScheduler, GPTTrainStep, avg_losses_across_data_parallel_group


# Custom loss function for the Megatron model
class GPTTrainStepWithCustomLoss(GPTTrainStep):
    def __init__(self, megatron_args, **kwargs):
        super().__init__(megatron_args)
        self.kwargs = kwargs

    def get_loss_func(self):
        def loss_func(inputs, loss_mask, output_tensor):
            batch_size, seq_length = output_tensor.shape
            losses = output_tensor.float()
            loss_mask = loss_mask.view(-1).float()
            loss = losses.view(-1) * loss_mask

            # Resize and average loss per sample
            loss_per_sample = loss.view(batch_size, seq_length).sum(axis=1)
            loss_mask_per_sample = loss_mask.view(batch_size, seq_length).sum(axis=1)
            loss_per_sample = loss_per_sample / loss_mask_per_sample

            # Calculate and scale weighting
            weights = torch.stack([(inputs == kt).float() for kt in self.kwargs["keytoken_ids"]]).sum(axis=[0, 2])
            weights = 1.0 + self.kwargs["alpha"] * weights
            # Calculate weighted average
            weighted_loss = (loss_per_sample * weights).mean()

            # Reduce loss across data parallel groups
            averaged_loss = avg_losses_across_data_parallel_group([weighted_loss])

            return weighted_loss, {"lm loss": averaged_loss[0]}

        return loss_func

    def get_forward_step_func(self):
        def forward_step(data_iterator, model):
            """Forward step."""
            # Get the batch.
            tokens, labels, loss_mask, attention_mask, position_ids = self.get_batch(data_iterator)
            output_tensor = model(tokens, position_ids, attention_mask, labels=labels)

            return output_tensor, partial(self.loss_func, tokens, loss_mask)

        return forward_step


def main():
    # Custom loss function for the Megatron model
    keytoken_ids = []
    keywords = ["plt", "pd", "sk", "fit", "predict", " plt", " pd", " sk", " fit", " predict"]
    for keyword in keywords:
        ids = tokenizer([keyword]).input_ids[0]
        if len(ids) == 1:
            keytoken_ids.append(ids[0])
    accelerator.print(f"Keytoken ids: {keytoken_ids}")
    accelerator.state.megatron_lm_plugin.custom_train_step_class = GPTTrainStepWithCustomLoss
    accelerator.state.megatron_lm_plugin.custom_train_step_kwargs = {
        "keytoken_ids": keytoken_ids,
        "alpha": 0.25,
    }

For using the Megatron-LM datasets, a few more changes are required. Dataloaders for these datasets are available only on rank 0 of each tensor parallel group. As such, there are rank where dataloader won’t be available and this requires tweaks to the training loop. Being able to do all this shows how flexible and extensible Accelerate is. The changes required are as follows.

a. For Megatron-LM indexed datasets, we need to use MegatronLMDummyDataLoader and pass the required dataset args to it such as data_path, seq_length etc. See here for the list of available args.

from accelerate.utils import MegatronLMDummyDataLoader

megatron_dataloader_config = {
    "data_path": args.data_path,
    "splits_string": args.splits_string,
    "seq_length": args.block_size,
    "micro_batch_size": args.per_device_train_batch_size,
}
megatron_dataloader = MegatronLMDummyDataLoader(**megatron_dataloader_config)
accelerator.state.megatron_lm_plugin.megatron_dataset_flag = True

b. megatron_dataloader is repeated 3 times to get training, validation and test dataloaders as per the args.splits_string proportions

model, optimizer, lr_scheduler, train_dataloader, eval_dataloader, _ = accelerator.prepare(
    model, optimizer, lr_scheduler, megatron_dataloader, megatron_dataloader, megatron_dataloader
)

c. Changes to training and evaluation loops as dataloader is only available on tensor parallel ranks 0 So, we need to iterate only if the dataloader isn’t None else provide empty dict As such, we loop using while loop and break when completed_steps is equal to args.max_train_steps This is similar to the Megatron-LM setup wherein user has to provide max_train_steps when using Megaton-LM indexed datasets. This displays how flexible and extensible Accelerate is.

while completed_steps < args.max_train_steps:
    model.train()
    batch = next(train_dataloader) if train_dataloader is not None else {}
    outputs = model(**batch)
    loss = outputs.loss
    ...

    if completed_steps % eval_interval == 0:
        eval_completed_steps = 0
        losses = []
        while eval_completed_steps < eval_iters:
            model.eval()
            with torch.no_grad():
                batch = next(eval_dataloader) if eval_dataloader is not None else {}
                outputs = model(**batch)

Utility for Checkpoint reshaping and interoperability

The scripts for these are present in Transformers library under respective models. Currently, it is available for GPT model checkpoint_reshaping_and_interoperability.py
Below is an example of conversion of checkpoint from Megatron-LM to universal Transformers sharded checkpoint.

python checkpoint_reshaping_and_interoperability.py \
--convert_checkpoint_from_megatron_to_transformers \
--load_path "gpt/iter_0005000" \
--save_path "gpt/trfs_checkpoint" \
--max_shard_size "200MB" \
--tokenizer_name "gpt2" \
--print-checkpoint-structure

Conversion of checkpoint from transformers to megatron with tp_size=2, pp_size=2 and dp_size=2.

python checkpoint_utils/megatgron_gpt2/checkpoint_reshaping_and_interoperability.py \
--load_path "gpt/trfs_checkpoint" \
--save_path "gpt/megatron_lm_checkpoint" \
--target_tensor_model_parallel_size 2 \
--target_pipeline_model_parallel_size 2 \
--target_data_parallel_size 2 \
--target_params_dtype "bf16" \
--make_vocab_size_divisible_by 128 \
--use_distributed_optimizer \
--print-checkpoint-structure

Megatron-LM GPT models support returning logits and megatron_generate function for text generation

Returning logits require setting require_logits=True in MegatronLMPlugin as shown below. These would be available on the in the last stage of pipeline.

megatron_lm_plugin = MegatronLMPlugin(return_logits=True)

megatron_generate method for Megatron-LM GPT model: This will use Tensor and Pipeline Parallelism to complete generations for a batch of inputs when using greedy with/without top_k/top_p sampling and for individual prompt inputs when using beam search decoding. Only a subset of features of transformers generate is supported. This will help in using large models via tensor and pipeline parallelism for generation (already does key-value caching and uses fused kernels by default). This requires data parallel size to be 1, sequence parallelism and activation checkpointing to be disabled. It also requires specifying path to tokenizer’s vocab file and merges file. Below example shows how to configure and use megatron_generate method for Megatron-LM GPT model.

# specifying tokenizer's vocab and merges file
vocab_file = os.path.join(args.resume_from_checkpoint, "vocab.json")
merge_file = os.path.join(args.resume_from_checkpoint, "merges.txt")
other_megatron_args = {"vocab_file": vocab_file, "merge_file": merge_file}
megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args)

# inference using `megatron_generate` functionality
tokenizer.pad_token = tokenizer.eos_token
max_new_tokens = 64
batch_texts = [
    "Are you human?",
    "The purpose of life is",
    "The arsenal was constructed at the request of",
    "How are you doing these days?",
]
batch_encodings = tokenizer(batch_texts, return_tensors="pt", padding=True)

# top-p sampling
generated_tokens = model.megatron_generate(
    batch_encodings["input_ids"],
    batch_encodings["attention_mask"],
    max_new_tokens=max_new_tokens,
    top_p=0.8,
    top_p_decay=0.5,
    temperature=0.9,
)
decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy())
accelerator.print(decoded_preds)

# top-k sampling
generated_tokens = model.megatron_generate(
    batch_encodings["input_ids"],
    batch_encodings["attention_mask"],
    max_new_tokens=max_new_tokens,
    top_k=50,
    temperature=0.9,
)
decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy())
accelerator.print(decoded_preds)

# adding `bos` token at the start
generated_tokens = model.megatron_generate(
    batch_encodings["input_ids"], batch_encodings["attention_mask"], max_new_tokens=max_new_tokens, add_BOS=True
)
decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy())
accelerator.print(decoded_preds)

# beam search => only takes single prompt
batch_texts = ["The purpose of life is"]
batch_encodings = tokenizer(batch_texts, return_tensors="pt", padding=True)
generated_tokens = model.megatron_generate(
    batch_encodings["input_ids"],
    batch_encodings["attention_mask"],
    max_new_tokens=max_new_tokens,
    num_beams=20,
    length_penalty=1.5,
)
decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy())
accelerator.print(decoded_preds)

An end-to-end example of using megatron_generate method for Megatron-LM GPT model is available at megatron_gpt2_generation.py with config file megatron_lm_gpt_generate_config.yaml. The bash script with accelerate launch command is available at megatron_lm_gpt_generate.sh. The output logs of the script are available at megatron_lm_gpt_generate.log.

Support for ROPE and ALiBi Positional embeddings and Multi-Query Attention

For ROPE/ALiBi attention, pass position_embedding_type with ("absolute" | "rotary" | "alibi") to MegatronLMPlugin as shown below.

other_megatron_args = {"position_embedding_type": "alibi"}
megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args)

For Multi-Query Attention, pass attention_head_type with ("multihead" | "multiquery") to MegatronLMPlugin as shown below.

other_megatron_args = {"attention_head_type": "multiquery"}
megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args)

Caveats

Supports Transformers GPT2, Megatron-BERT and T5 models. This covers Decoder only, Encode only and Encoder-Decoder model classes.
Only loss is returned from model forward pass as there is quite complex interplay of pipeline, tensor and data parallelism behind the scenes. The model(**batch_data) call return loss(es) averaged across the data parallel ranks. This is fine for most cases wherein pre-training jobs are run using Megatron-LM features and you can easily compute the perplexity using the loss. For GPT model, returning logits in addition to loss(es) is supported. These logits aren’t gathered across data parallel ranks. Use accelerator.utils.gather_across_data_parallel_groups to gather logits across data parallel ranks. These logits along with labels can be used for computing various performance metrics.
The main process is the last rank as the losses/logits are available in the last stage of pipeline. accelerator.is_main_process and accelerator.is_local_main_process return True for last rank when using Megatron-LM integration.
In accelerator.prepare call, a Megatron-LM model corresponding to a given Transformers model is created with random weights. Please use accelerator.load_state to load the Megatron-LM checkpoint with matching TP, PP and DP partitions.
Currently, checkpoint reshaping and interoperability support is only available for GPT. Soon it will be extended to BERT and T5.
gradient_accumulation_steps needs to be 1. When using Megatron-LM, micro batches in pipeline parallelism setting is synonymous with gradient accumulation.
When using Megatron-LM, use accelerator.save_state and accelerator.load_state for saving and loading checkpoints.
Below are the mapping from Megatron-LM model architectures to the the equivalent transformers model architectures. Only these transformers model architectures are supported.

a. Megatron-LM BertModel : transformers models with megatron-bert in config’s model type, e.g., MegatronBERT

b. Megatron-LM GPTModel : transformers models with gpt2 in config’s model type, e.g., OpenAI GPT2

c. Megatron-LM T5Model : transformers models with t5 in config’s model type, e.g., T5 and MT5

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