Variable batch size and LR scheduler

[bm-synth]

Background and rationale

In many use cases, particularly LLMs, one is faced with inputs (sentences) of variable lengths. A common practice is to pack batches by token count (not a fixed batch size), ie by putting together sentences whose given metric (eg sequence lengths) will add up to an user-provided value. As an example, in Attention is all you need, section 5.1:

Sentence pairs were batched together by approximate sequence length. Each training
batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000
target tokens.

Dynamic batch sizes has been requested in DeepSpeed issue 1051, DeepSpeed issue 3455 , Pytorch Lightning issue 16914, huggingface issue 2647 and is available already in many libraries e.g. NVIDIA Triton and Meta FairSeq (implementation here ).

The immediate use case for this is when one needs to maximize GPU utilization. Moreover, this is particularly relevant for curriculum learning where a BxTxE (Batch x Time x Embedding) -shaped input should ideally have high B and low T at the early curriculum steps (many short sentences packed together as a batch), and low B and high T at the late steps (few long sentences in the batch). A dynamic size T is already supported by Deepspeed, e.g. in the documentation for pipeline parallelism's reset_activation_shape():

For curriculum learning that changes the seqlen of each sample, we need to call this whenever the seqlen is going to change.

However, dynamic B is not supported. A dynamic B would require an adequate increase/decrease of learning rate. This technique has been applied previously, and the two most common LR scaling algorithms have been described as:

1. Linear Scaling Rule: "When the minibatch size is multiplied by k, multiply the learning rate by k", as in Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour, Goyal et al.
2. Square Root scaling: "when multiplying the batch size by k, multiply the learning rate by √k, to keep the variance in the gradient expectation constant" by One weird trick for parallelizing convolutional neural networks, A. Krizhevsky et al.

In practice, the user picks the total token count per batch as the metric that drives batching, instead of batching by sentence count. During runtime, the variable batch size is computed and the LR is adjusted respectively, based on the LR and batch size provided by the config.

Illustration of dynamic batch size, sequence length and LR

Imagine we picked a limit of 30 tokens per batch, and have set a reference lr=1e-3 for a train_batch_size=2 (in the deepspeed config). The batching algorithm for curriculum may pack the data into batches of short sentences (left) at the early stages, and batches of long sentences (right) as later stages, e.g.:

Above, we collected samples until we filled up the batch with at most 30 tokens. The batch sizes (number of samples) became then 10 and 4 on the left and right examples, respectively. Using the linear scaling rule, the LR for those batches become 5e-3 and 2e-3.

Pipeline parallelism

Pipeline parallelism requires the same batch size and same sequence length across all micro-batches in a batch, as the activation sizes must be fixed between gradient accumulation steps. Between batches, these may change, and long as engine.reset_activation_shape() is called so that the new shapes are communicated on the first gradient accumulation step in the batch. Enforcing similar BxTxE between batches may lead to smaller micro-batches. As an example, below we can see an illustration of a 2-node 2-gradient-accumulation-step (ie 4 micro-batches) batching for the same dataset, when preparing data for the regular DDP (left) and for the pipeline parallelism use cases (right):

We can see that the pipeline use case (right) has the same BxTxE shape across all the 4 micro-batches in the same batch, and in order to respect that, it packs less samples in the batch, when compared to the standard use case (left hand size)

Attention Head

For an input of size BxTxE the attention has a shape of TxT for a mask of fixed size across samples of same size, or BxTxT for a different mask per sample (when samples have different sizes, as in the dataset above). This 3D attention matrix can be illustrated for the DDP microbatch 1 (picture above top-left, 4 sentences) as:

Note the memory savings: the attention head has a size of BxTxT, i.e. a linear memory dependency on the batch size B and quadratic memory dependency on the largest sequence length T in the (micro-) batch. Thus, supporting a dynamic size T allows for an increase of B.

PR overview

This PRs implements dynamic batching and LR scaling. The dataloader and LR scheduler necessary can be retrieved by calling get_dataloader_and_lr_scheduler_for_variable_batch_size. A small explanation of that function follows:

• The logic behind the algorithms for LR scaling is in scale_lr;
• The partitioning of samples into batches is done by batch_by_seqlen.
• For pipeline parallelism, it is required that all micro-batches in a pipeline pass to have the same activation shapes. This is enabled by setting to True the following parameters:
  • required_microbatches_of_same_sizes that will force the B dimension to be the same across all gradient accumulation steps of all dataloaders on a batch;
  • required_microbatches_of_same_lengths that will force the T dimension to be the same across all gradient accumulation steps. Works by calling the user-provided sample_padding_fn(sentence, len) that pads a given sentence to the argument length;
  • batch_by_seqlen returns microbatch_sample_ids (the list of sample ids per micro-batch), batch_sizes (the size of effective batch sizes, and batch_max_seqlens (longest sequence across all microbatches in a batch)
• dataloader_for_variable_batch_size relies on microbatch_sample_ids and will iterate/collate/pad samples for every batch and return a dataloader that iterates the final (variable-size) batches;
• lr_scheduler_for_variable_batch_size relies on batch_sizes to compute the learning rate for each effective batch, taking into account the batch size and LR in the config file, and scaling the LR based on the size of each effective batch, and the scaling rule mentioned above (Linear, Square root, etc).
  • Special note to the lr_scheduler returned that will either accept either:
    1. an user-provided Optimizer that will scale the learning rates (in param groups) at every batch, or
    2. an user-defined LRScheduler, that in this case will first get the learning rate from the scheduler and then scale it accordingly.

Example

An example for the use case with and without pipelining is provided in deepspeed/runtime/data_pipeline/data_sampling/variable_batch_size_and_lr_example.py. The example shows an attention head with attention of variable-sized BxTxT per batch, followed by a fixed size feed forward network. These are the main blocks on a Large Language Model. The feed-forward (or linear layer) that follows the attention head requires a constant input size, equivalent to the largest sentence in the whole dataset, so the output of the attention must be padded (see feedforward: needs to convert BxTxE to BxMxE by padding extra tokens in the code).

The output of the variable_batch_size_and_lr_example is the following:

Config

The example file also comments the relevant deepspeed config with comments:

config = {
  "train_batch_size": 16,
  # `train_micro_batch_size_per_gpu` tells how many sequence packs of `max_tokens` each will be collated together.
  #  I.e. the number of tokens per micro batch (ie per gpu iteration) is `train_micro_batch_size_per_gpu`*`max_tokens`.
  "train_micro_batch_size_per_gpu": 2,
  "data_efficiency": {
    "enabled": True,
    # seed to be applied to all data efficiency modules, including dynamic batching
    "seed": 42,
    "data_sampling": {
      "num_workers": 0, # dataloader num_workers argument
      "pin_memory": False,  # dataloader pin_memory argument
      "dynamic_batching": {
        # enables or disables dynamic batching
        "enabled": True,
        # how many tokens we need to fill a pack of sequences (that will be collated together as a sample)
        "max_tokens": 100,
        # Input and output write to read from or write the length of every sequence.
        # Sequence lengths will be loaded from: {metrics_path}/seqlen/seqlen_sample_to_metric.bin and *.idx
        # If files dont exist, they'll be computed and saved on the first run, and loaded on subsequent runs.
        "metrics_path": "./curriculum_output/",
        # As batch size increases/decreses, which method to use to scale LR accordingly?
        # Options: linear, sqrt (square root), or None to disable
        "lr_scaling_method": "linear",
        # how to pick sentences to be packed into samples:
        # - dataloader: by same order as they come in with the dataloader
        # - seqlen: by sequence length (shortest to longest)
        # - random: random order using the seed in config['data_efficiency']['seed'
        "sentence_picking_order": "dataloader",  # "random" / "seqlen" / "dataloader"
        # minimum number of sequences required to reach `max_tokens`. If sentence pack is smaller, it's discarded.
        "min_batch_size": 1,
        # maximum number of sequences required to reach `max_tokens`. If sentence pack is larger, it's discarded.
        "max_batch_size": 10,
        # enable the output of microbatching information about sentence packing
        "verbose": True,
      },
    },
  },
}

Future work

A follow-up PR will enable dynamic batching when calling deepspeed.initialize. I.e. instead of this:

engine, _, _, _ = deepspeed.initialize(config=config, model=model)
dataloader, lr_scheduler, _ = get_dataloader_and_lr_scheduler_for_variable_batch_size_deepspeed(...)
engine.lr_scheduler = lr_scheduler

we'd ideally have this:

engine, _, dataloader, lr_scheduler = deepspeed.initialize(config=config, model=model)

where initialize will call internally get_dataloader_and_lr_scheduler_for_variable_batch_size_deepspeed.

[loadams]

Thanks @bm-synth - can you take a look at the formatting and DCO errors?