InstructLab Training Library

• Installing
  • Additional Nvidia packages
• Using the library
• Learning about the training arguments
  • TrainingArgs
  • DeepSpeedOptions
  • FSDPOptions
  • loraOptions
• Learning about TorchrunArgs arguments
• Example training run with arguments

To simplify the process of fine-tuning models with the LAB method, this library provides a simple training interface.

Installing the library

To get started with the library, you must clone this repository and install it via pip.

Install the library:

pip install instructlab-training 

You can then install the library for development:

pip install -e ./training

Additional NVIDIA packages

This library uses the flash-attn package as well as other packages, which rely on NVIDIA-specific CUDA tooling to be installed. If you are using NVIDIA hardware with CUDA, you need to install the following additional dependencies.

Basic install

pip install .[cuda]

Editable install (development)

pip install -e .[cuda]

Using the library

You can utilize this training library by importing the necessary items.

from instructlab.training import (
    run_training,
    TorchrunArgs,
    TrainingArgs,
    DeepSpeedOptions
)

You can then define various training arguments. They will serve as the parameters for your training runs. See:

• Learning about the training argument
• Example training run with arguments

Learning about training arguments

The TrainingArgs class provides most of the customization options for training jobs. There are a number of options you can specify, such as setting DeepSpeed config values or running a LoRA training job instead of a full fine-tune.

TrainingArgs

Field	Description
model_path	Either a reference to a HuggingFace repo or a path to a model saved in the HuggingFace format.
data_path	A path to the .jsonl training dataset. This is expected to be in the messages format.
ckpt_output_dir	Directory where trained model checkpoints will be saved.
data_output_dir	Directory where the processed training data is stored (post filtering/tokenization/masking)
max_seq_len	The maximum sequence length to be included in the training set. Samples exceeding this length will be dropped.
max_batch_len	Maximum tokens per gpu for each batch that will be handled in a single step. Used as part of the multipack calculation. If running into out-of-memory errors, try to lower this value, but not below the max_seq_len.
num_epochs	Number of epochs to run through before stopping.
effective_batch_size	The amount of samples in a batch to see before we update the model parameters.
save_samples	Number of samples the model should see before saving a checkpoint. Consider this to be the checkpoint save frequency.
learning_rate	How fast we optimize the weights during gradient descent. Higher values may lead to unstable learning performance. It's generally recommended to have a low learning rate with a high effective batch size.
warmup_steps	The number of steps a model should go through before reaching the full learning rate. We start at 0 and linearly climb up to learning_rate.
is_padding_free	Boolean value to indicate whether or not we're training a padding-free transformer model such as Granite.
random_seed	The random seed PyTorch will use.
mock_data	Whether or not to use mock, randomly generated, data during training. For debug purposes
mock_data_len	Max length of a single mock data sample. Equivalent to max_seq_len but for mock data.
deepspeed_options	Config options to specify for the DeepSpeed optimizer.
lora	Options to specify if you intend to perform a LoRA train instead of a full fine-tune.
chat_tmpl_path	Specifies the chat template / special tokens for training.
checkpoint_at_epoch	Whether or not we should save a checkpoint at the end of each epoch.
fsdp_options	The settings for controlling FSDP when it's selected as the distributed backend.
distributed_backend	Specifies which distributed training backend to use. Supported options are "fsdp" and "deepspeed".
disable_flash_attn	Disables flash attention when set to true. This allows for training on older devices.

DeepSpeedOptions

This library only currently support a few options in DeepSpeedOptions: The default is to run with DeepSpeed, so these options only currently allow you to customize aspects of the ZeRO stage 2 optimizer.

Field	Description
cpu_offload_optimizer	Whether or not to do CPU offloading in DeepSpeed stage 2.
cpu_offload_optimizer_ratio	Floating point between 0 & 1. Specifies the ratio of parameters updating (i.e. optimizer step) on CPU side.
cpu_offload_optimizer_pin_memory	If true, offload to page-locked CPU memory. This could boost throughput at the cost of extra memory overhead.
save_samples	The number of samples to see before saving a DeepSpeed checkpoint.

For more information about DeepSpeed, see deepspeed.ai

DeepSpeed with CPU Offloading

To use DeepSpeed with CPU offloading, you'll usually encounter an issue indicating that the optimizer needed to use the Adam optimizer on CPU doesn't exist. To resolve this, please follow the following steps:

Rebuild DeepSpeed with CPUAdam:

You'll need to rebuild DeepSpeed in order for the optimizer to be present:

# uninstall deepspeed & reinstall with the flags for installing CPUAdam
pip uninstall deepspeed
DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 pip install deepspeed --no-deps

Ensure -lcurand is linked correctly:

A problem that we commonly encounter is that the -lcurand linker will not be present when DeepSpeed recompiles. To resolve this, you will need to find the location of the libcurand.so file in your machine:

find / -name 'libcurand*.so*' 2>/dev/null

If libcurand.so is not present in the /usr/lib64 directory, you'll need to add a symlink. Ex:

sudo ln -s /usr/local/cuda/lib64/libcurand.so.10 /usr/lib64/libcurand.so

FSDPOptions

Like DeepSpeed, we only expose a number of parameters for you to modify with FSDP. They are listed below:

Field	Description
cpu_offload_params	When set to true, offload parameters from the accelerator onto the CPU. This is an all-or-nothing option.
sharding_strategy	Specifies the model sharding strategy that FSDP should use. Valid options are: FULL_SHARD (ZeRO-3), HYBRID_SHARD (ZeRO-3*), SHARD_GRAD_OP (ZeRO-2), and NO_SHARD.

Note

For sharding_strategy - Only SHARD_GRAD_OP has been extensively tested and is actively supported by this library.

loraOptions

LoRA options currently supported:

Field	Description
rank	The rank parameter for LoRA training.
alpha	The alpha parameter for LoRA training.
dropout	The dropout rate for LoRA training.
target_modules	The list of target modules for LoRA training.
quantize_data_type	The data type for quantization in LoRA training. Valid options are None and "nf4"

Example run with LoRa options

If you'd like to do a LoRA train, you can specify a LoRA option to TrainingArgs via the LoraOptions object.

from instructlab.training import LoraOptions, TrainingArgs

training_args = TrainingArgs(
    lora = LoraOptions(
        rank = 4,
        alpha = 32,
        dropout = 0.1,
    ),
    # ...
)

Learning about TorchrunArgs arguments

When running the training script, we always invoke torchrun.

If you are running a single-GPU system or something that doesn't otherwise require distributed training configuration, you can create a default object:

run_training(
    torchrun_args=TorchrunArgs(),
    training_args=TrainingArgs(
        # ...
    ),
)

However, if you want to specify a more complex configuration, the library currently supports all the options that torchrun accepts today.

Note

For more information about the torchrun arguments, please consult the torchrun documentation.

Example training run with TorchrunArgs arguments

For example, in a 8-GPU, 2-machine system, we would specify the following torchrun config:

MASTER_ADDR = os.getenv('MASTER_ADDR')
MASTER_PORT = os.getnev('MASTER_PORT')
RDZV_ENDPOINT = f'{MASTER_ADDR}:{MASTER_PORT}'

# on machine 1
torchrun_args = TorchrunArgs(
    nnodes = 2, # number of machines 
    nproc_per_node = 4, # num GPUs per machine
    node_rank = 0, # node rank for this machine
    rdzv_id = 123,
    rdzv_endpoint = RDZV_ENDPOINT
)

run_training(
    torchrun_args=torchrun_args,
    training_args=training_args
)

MASTER_ADDR = os.getenv('MASTER_ADDR')
MASTER_PORT = os.getnev('MASTER_PORT')
RDZV_ENDPOINT = f'{MASTER_ADDR}:{MASTER_PORT}'

# on machine 2
torchrun_args = TorchrunArgs(
    nnodes = 2, # number of machines 
    nproc_per_node = 4, # num GPUs per machine
    node_rank = 1, # node rank for this machine
    rdzv_id = 123,
    rdzv_endpoint = f'{MASTER_ADDR}:{MASTER_PORT}'
)

run_training(
    torch_args=torchrun_args,
    train_args=training_args
)

Example training run with arguments

Define the training arguments which will serve as the parameters for our training run:

# define training-specific arguments
training_args = TrainingArgs(
    # define data-specific arguments
    model_path = "ibm-granite/granite-7b-base",
    data_path = "path/to/dataset.jsonl",
    ckpt_output_dir = "data/saved_checkpoints",
    data_output_dir = "data/outputs",

    # define model-trianing parameters
    max_seq_len = 4096,
    max_batch_len = 60000,
    num_epochs = 10,
    effective_batch_size = 3840,
    save_samples = 250000,
    learning_rate = 2e-6,
    warmup_steps = 800,
    is_padding_free = True, # set this to true when using Granite-based models
    random_seed = 42,
)

We'll also need to define the settings for running a multi-process job via torchrun. To do this, create a TorchrunArgs object.

Tip

Note, for single-GPU jobs, you can simply set nnodes = 1 and nproc_per_node=1.

torchrun_args = TorchrunArgs(
    nnodes = 1, # number of machines 
    nproc_per_node = 8, # num GPUs per machine
    node_rank = 0, # node rank for this machine
    rdzv_id = 123,
    rdzv_endpoint = '127.0.0.1:12345'
)

Finally, you can just call run_training and this library will handle the rest 🙂.

run_training(
    torchrun_args=torchrun_args,
    training_args=training_args,
)

Example training with separate data pre-processing

If the machines in the example above have shared storage, users can pre-process the training dataset a single time so that it can then be distributed to each machine by making the following updates.

from instructlab.training import (
    run_training,
    TorchrunArgs,
    TrainingArgs,
    DeepSpeedOptions,
    DataProcessArgs,
    data_process as dp
)

training_args = TrainingArgs(
    # define data-specific arguments
    model_path = "ibm-granite/granite-7b-base",
    data_path = "path/to/dataset.jsonl",
    ckpt_output_dir = "data/saved_checkpoints",
    data_output_dir = "data/outputs",

    # define model-trianing parameters
    max_seq_len = 4096,
    max_batch_len = 60000,
    num_epochs = 10,
    effective_batch_size = 3840,
    save_samples = 250000,
    learning_rate = 2e-6,
    warmup_steps = 800,
    is_padding_free = True, # set this to true when using Granite-based models
    random_seed = 42,
    process_data = True,
)
...

data_process_args = DataProcessArgs(
    data_output_path = training_args.data_output_dir,
    model_path = training_args.model_path,
    data_path = training_args.data_path,
    max_seq_len = training_args.max_seq_len,
    chat_tmpl_path =  training_args.chat_tmpl_path
)

dp.main(data_process_args)
run_training(
    torch_args=torchrun_args,
    train_args=training_args,
)