Arguments for gpt-neox. All of the following can be specified in your .yml config file(s):
NeoXArgsLRScheduler
LR Scheduler Arguments
lr_decay_style: typing.Literal['constant', 'linear', 'cosine', 'exponential']
Default = linear
Learning rate decay function. Choose from 'constant', 'linear', 'cosine', 'exponential'.
lr_decay_iters: int
Default = None
Number of iterations to decay learning rate over. If None, defaults to --train-iters or the equivalent inferred value from train_epochs.
lr_decay_fraction: float
Default = None
Effective fraction of training over which to decay lr. Overrides lr_decay_iters. Useful when specifying train_epochs.
min_lr: float
Default = 0.0
Minimum value for learning rate. The scheduler clips values below this threshold.
warmup: float
Default = 0.01
Percentage of total iterations to warmup on (.01 = 1 percent of all training iters).
override_lr_scheduler: bool
Default = False
Reset the values of the scheduler (learning rate,warmup iterations, minimum learning rate, maximum number of iterations, and decay style from input arguments and ignore values from checkpoints. Note that all the above values will be reset.
use_checkpoint_lr_scheduler: bool
Default = False
Use checkpoint to set the values of the scheduler (learning rate, warmup iterations, minimum learning rate, maximum number of iterations, and decay style from checkpoint and ignore input arguments.
NeoXArgsLogging
Logging Arguments
use_wandb: bool
Default = None
Flag indicating if wandb is to be used.
wandb_group: str
Default = None
Weights and Biases group name - used to group together "runs".
wandb_team: str
Default = None
Team name for Weights and Biases.
wandb_project: str
Default = neox
wandb project name
wandb_host: str
Default = https://api.wandb.ai
url of the wandb host
wandb_init_all_ranks: bool
Default = False
Initialize wandb on all ranks.
git_hash: str
Default = 62c9738a
current git hash of repository
log_dir: str
Default = None
Directory to save logs to.
tensorboard_dir: str
Default = None
Write TensorBoard logs to this directory.
use_comet: bool
Default = None
Flag indicating if comet is to be used.
comet_workspace: Optional
Default = None
Comet workspace name, if not configured Comet Experiments will be created in the user configured default workspace.
comet_project: Optional
Default = None
Comet project name, if not configured Comet Experiments will be created in the Uncategorized Experiments project.
comet_experiment_name: Optional
Default = None
Custom name for the Comet experiment. If not provided, a random name is used.
comet_tags: Optional
Default = None
List of tags to attach to the created Comet Experiment.
comet_others: Optional
Default = None
Custom metadata to attach to the created Comet Experiment.
log_interval: int
Default = 100
Interval between logging.
log_grad_pct_zeros: bool
Default = False
Log the percentage of zeros for the gradient of each parameter to wandb / tensorboard (useful for debugging). Needs wandb_init_all_ranks set to True if using pipeline parallelism to log all ranks.
log_param_norm: bool
Default = False
Log the frob norm of the parameters to wandb / tensorboard (useful for debugging). Needs wandb_init_all_ranks set to True if using pipeline parallelism to log all ranks.
log_grad_norm: bool
Default = False
Log the frob norm of the gradients to wandb / tensorboard (useful for debugging). (N.B - this will only work with pp = 0 for now, as we don't have access to the gradients of the model because deepspeed.)
log_optimizer_states: bool
Default = False
Log the frob norm of the optimizer states to wandb / tensorboard (useful for debugging).
log_gradient_noise_scale: bool
Default = False
Whether to log the gradient noise scale when training (cf. https://arxiv.org/abs/1812.06162 for explanation)
gradient_noise_scale_n_batches: int
Default = 5
Number of batches to accumulate gradients for in the gradient noise scale logger.
gradient_noise_scale_cpu_offload: bool
Default = False
Whether to offload the buffered gradients to cpu when measuring gradient noise scale.
memory_profiling: bool
Default = False
Whether to take a memory snapshot of the model. Useful for debugging memory issues.
memory_profiling_path: str
Default = None
Path to save memory snapshot to.
profile: bool
Default = False
Enable nsys profiling. When using this option, nsys options should be specified in commandline. An example nsys commandline is
nsys profile -s none -t nvtx,cuda -o <path/to/output_file> --force-overwrite true --capture-range=cudaProfilerApi --capture-range-end=stopprofile_step_start: int
Default = 10
Step to start profiling at.
profile_step_stop: int
Default = 12
Step to stop profiling at.
NeoXArgsModel
Model Arguments
precision: typing.Literal['fp16', 'fp32', 'bfloat16']
Default = None
description of the used precision, either one of fp16 or fp32 (and in the future bf16).
num_layers: int
Default = None
Number of transformer layers.
hidden_size: int
Default = None
Transformer hidden size.
intermediate_size: int
Default = None
Transformer intermediate size. Default = 4h
mlp_multiple_of: int
Default = 1
force mlp size to be a multiple of this value
expansion_factor: float
Default = None
Transformer intermediate size. Default = 4
num_attention_heads: int
Default = None
Number of transformer attention heads.
If num_kv_heads is set, will control only number of query heads.
num_kv_heads: int
Default = None
Number of transformer key/value attention heads.
If set to None or the same value as num_attention_heads, will perform multi-head attention (MHA). If set to < num_attention_heads but > 1, will perform grouped-query attention (GQA) (https://arxiv.org/pdf/2305.13245.pdf) If set to 1, will perform multi-query attention.
Must be < num_attention_heads and divide num_attention_heads evenly.
seq_length: int
Default = None
Maximum sequence length to process.
sliding_window_width: int
Default = None
Width of the attention sliding window. Only supported with Flash Attention 2.
max_position_embeddings: int
Default = None
Maximum number of position embeddings to use. This is the size of position embedding.
norm: typing.Literal['layernorm', 'rmsnorm', 'scalenorm', 'te_rmsnorm', 'te_layernorm']
Default = layernorm
Normalization layer to use. Choose from "layernorm", "rmsnorm", "scalenorm", "te_rmsnorm", "te_layernorm".
layernorm_fusion: bool
Default = False
Use fused layer norm kernel (if
normislayernorm).rmsnorm_fusion: bool
Default = False
Use fused RMS norm kernel (if
normisrmsnorm).use_qk_layernorm: bool
Default = False
Use QK Normalization
layernorm_epsilon: float
Default = 1e-05
Layer norm epsilon.
rms_norm_epsilon: float
Default = 1e-08
Root mean squared norm epsilon
scalenorm_epsilon: float
Default = 1e-08
Scalenorm epsilon
pos_emb: typing.Literal['learned', 'rotary', 'sinusoidal', 'rpe', 'alibi', 'none']
Default = learned
Type of positional embedding to use - choose from 'learned', 'rotary', 'sinusoidal', 'rpe', 'none'
rpe_num_buckets: int
Default = 32
T5 relative positional encoding number of buckets, default 32.
rpe_max_distance: int
Default = 128
T5 relative positional encoding max distance, default 128.
opt_pos_emb_offset: int
Default = 0
Learned position embedding offset (only used by OPT, where it should be set to 2).
no_weight_tying: bool
Default = False
Disables weight tying between embedding weights and final Linear layer
attention_config: list
Default = None
Attention configuration for gpt-neox
The first item in the list specifies the attention type(s), and should be a list of strings. The second item specifies the number of times to repeat those attention types in the full list.
attention type choices: [global, local, sparse_fixed, sparse_variable, bslongformer, bigbird, "gmlp", "amlp", "flash", "mamba", "rwkv"]
So a 12 layer network with only global attention could be specified like: [[[
global], 12]] or a 12 layer network with alternating global / local like: [[[global,local], 6]] If none is specified, this defaults to [[[global], n_layers]]sparsity_config: dict
Default = None
Sparsity configuration dict as defined in https://www.deepspeed.ai/docs/config-json/#sparse-attention
Note that since neox is autoregressive, attention is always "unidirectional" and
horizontal_global_attentionis always false.The main difference between our sparsity config and deepspeed's is that
modeis ignored - since it is instead specified in attention_config defining each layer.An example config is given below: "sparse_attention": { "block": 16, "different_layout_per_head": true, "num_local_blocks": 4, "num_global_blocks": 1, "num_different_global_patterns": 4, "num_random_blocks": 0, "local_window_blocks": [4], "global_block_indices": [0], "global_block_end_indices": None, "num_sliding_window_blocks": 3 }
num_unique_layers: int
Default = None
Number of unique transformer layers. num-layers should be divisible by this value. Currently only has an effect when pipe_parallel_size=0.
param_sharing_style: str
Default = grouped
Ordering of the shared parameters. For example, for a num-layers=4 and --num-unique-layers=2, we will have the following ordering for two unique layers 1 and 2-: grouped: [1, 2, 1, 2] and spaced: [1, 1, 2, 2].
make_vocab_size_divisible_by: int
Default = 128
Pad the vocab size to be divisible by this value. This is added for computational efficiency reasons.
activation: typing.Literal['gelu', 'geglu', 'relu', 'softsign', 'swish', 'mish', 'silu', 'reglu', 'swiglu', 'bilinear', 'glu']
Default = gelu
Activation function to use - choose from ["gelu", "geglu", "relu", "softsign", "swish", "mish", "silu", "reglu", "swiglu", "bilinear", "glu"]
use_flashattn_swiglu: bool
Default = False
Use flash attention's version of swiglu
scaled_upper_triang_masked_softmax_fusion: bool
Default = False
Enable fusion of query_key_value_scaling time (upper diagonal) masking and softmax.
scaled_masked_softmax_fusion: bool
Default = False
Enable fusion of query_key_value_scaling general masking and softmax.
bias_gelu_fusion: bool
Default = False
Enable bias and gelu fusion.
bias_dropout_fusion: bool
Default = False
Enable bias and dropout fusion.
rope_fusion: bool
Default = False
Enable rotary embedding fusion.
fp16_lm_cross_entropy: bool
Default = False
Move the cross entropy unreduced loss calculation for lm head to fp16.
init_method_std: float
Default = 0.02
Standard deviation of the zero mean normal distribution used for weight initialization.
apply_query_key_layer_scaling: bool
Default = False
Scale Q * K^T by 1 / layer-number. If this flag is set, then it will automatically set attention-softmax-in-fp32 to true
use_cpu_initialization: bool
Default = False
If set, affine parallel weights initialization uses CPU
attention_softmax_in_fp32: bool
Default = False
Run attention masking and softmax in fp32.
rotary_pct: float
Default = 1.0
pct of hidden dims to apply rotary positional embedding to
rotary_emb_base: int
Default = 10000
Base for rotary positional embedding
rotary_save_freqs_buffer: bool
Default = False
Used to control whether the
inv_freqsbuffer in rotary embeddings will be stored in checkpoints (persistent=True) or not.Defaults to false, but is left configurable to maintain backward-compatibility with GPT-NeoX checkpoints that were trained with this flag.
init_method: typing.Literal['normal', 'scaled_normal', 'orthogonal', 'scaled_orthogonal', 'xavier_uniform', 'xavier_normal', 'wang_init', 'small_init', 'single_residual_scaled_normal']
Default = normal
Init function used on all layers except ff residual outputs - choose from ["normal", "scaled_normal", "orthogonal", "scaled_orthogonal", "xavier_uniform", "xavier_normal", "wang_init", "small_init"]
output_layer_init_method: typing.Literal['normal', 'scaled_normal', 'orthogonal', 'scaled_orthogonal', 'xavier_uniform', 'xavier_normal', 'wang_init', 'small_init', 'single_residual_scaled_normal']
Default = scaled_normal
Init function used for ff residual outputs - choose from ["normal", "scaled_normal", "orthogonal", "scaled_orthogonal", "xavier_uniform", "xavier_normal", "wang_init", "small_init"]
gmlp_attn_dim: int
Default = 64
the dimension of the single head self attention in gmlp model (not used in gpt models). If None - gmlp model doesn't use attention.
gpt_j_residual: bool
Default = False
If false, we use the conventional residual path: x = x + attn(ln1(x)) x = x + mlp(ln2(x)) Otherwise, we use the residual path from GPT-J, which offers a slight speedup: x = ln(x) x = x + attn(x) + mlp(x)
gpt_j_tied: bool
Default = False
If false, we use x = x + attn(ln1(x)) + mlp(ln2(x)) Otherwise, we tie the layer norms y = ln(x) x = x + attn(y) + mlp(y)
use_bias_in_norms: bool
Default = True
If false, norms (e.g. LayerNorm) will not have bias terms
use_bias_in_attn_linear: bool
Default = True
If false, attn_linear (e.g. QKVO) will not have bias terms
use_bias_in_mlp: bool
Default = True
If false, mlps will not have bias terms
soft_prompt_tuning: dict
Default = None
Dictionary configuring the soft prompt tuning parameters. If enabled, will train only the soft prompt, and freezes the rest of the model. parameters in the dict are: 'enabled': bool = True # enables soft prompting 'num_tokens': int = 10 # length of the soft prompt in tokens 'init_string': str = '' # if provided, initialize the soft prompt with the word embeddings of this string 'init_range': float = 0.5 # if no init string is provided, initialize the soft prompt with a uniform distribution between -init_range and init_rang
mamba_selective_scan_fusion: bool
Default = False
Enable fused kernels for Mamba selective scan.
mamba_causal_conv_fusion: bool
Default = False
Enable fused kernels for Mamba causal Conv1d.
mamba_inner_func_fusion: bool
Default = False
Enable fused inner operator for Mamba. (Supersedes conv. and selective scan fusion flags, requires each of those kernels to be installed.)
mamba_selective_fp32_params: bool
Default = True
Keep selected parameters in fp32 for Mamba (A and D). Requires https://github.com/EleutherAI/DeeperSpeed/pull/61 .
mamba_use_bias_in_conv: bool
Default = True
If false, conv1d in mamba block will not have bias term
mamba_use_bias_in_linears: bool
Default = False
Enable bias terms in mamba block up- and down- projections (in_proj and out_proj).
output_layer_parallelism: typing.Literal['column']
Default = column
Parameter controlling whether the output layer is parallelized over the hidden dim (row) or the vocab dim (column)
dim_att: int
Default = None
Total dimension of the attention mechanism for RWKV. If not set, defaults to hidden_size.
head_size: int
Default = None
Size of each attention head for RWKV. Calculated as dim_att // num_attention_heads.
ffn_dim: int
Default = None
Dimension of the feed-forward network for RWKV. If not set, calculated based on hidden_size and expansion_factor.
NeoXArgsOptimizer
Optimizer Arguments
optimizer_type: typing.Literal['adam', 'onebitadam', 'cpu_adam', 'cpu_torch_adam', 'sm3', 'madgrad_wd', 'sgd', 'lion']
Default = adam
Type of optimizer to use. Choose from ['adam', 'onebitadam', 'cpu_adam', 'cpu_torch_adam', 'sm3', 'madgrad_wd', 'sgd', 'lion'] NOTE: sgd will use MuSGD from Mup. Mup must be enabled for this optimizer.
use_bnb_optimizer: bool
Default = False
Whether to enable the bitsandbytes optimizers
zero_stage: typing.Union[int, typing.List[int], typing.Literal['all']]
Default = None
Zero Optimizer stage
zero_reduce_scatter: bool
Default = None
Zero: Uses reduce or reduce scatter instead of allreduce to average gradients
zero_contiguous_gradients: bool
Default = None
Zero: Copies the gradients to a contiguous buffer as they are produced. Avoids memory fragmentation during backward pass. Only useful when running very large models.
zero_reduce_bucket_size: int
Default = None
Zero: Number of elements reduced/allreduced at a time. Limits the memory required for the allgather for large model sizes
zero_allgather_bucket_size: int
Default = None
Zero: Number of elements allgathered at a time. Limits the memory required for the allgather for large model sizes
lr: float
Default = None
Max Learning rate during training
NeoXArgsOther
Misc. Arguments
distributed_backend: str
Default = nccl
Which backend to use for distributed training.
local_rank: int
Default = None
local rank passed from distributed launcher.
rank: int
Default = None
global rank of process being run (passed in via distributed launcher)
lazy_mpu_init: bool
Default = False
If set to True, initialize_megatron() skips DDP initialization and returns function to complete it instead. Also turns on use-cpu-initialization flag. This is for external DDP manager.
short_seq_prob: float
Default = 0.1
Probability of producing a short sequence.
eod_mask_loss: bool
Default = False
Mask loss for the end of document tokens.
adlr_autoresume: bool
Default = False
Enable auto-resume on adlr cluster.
adlr_autoresume_interval: int
Default = 1000
Intervals over which check for auto-resume termination signal
seed: int
Default = 1234
Random seed used for python, numpy, pytorch, and cuda.
onnx_safe: bool
Default = False
Use workarounds for known problems with Torch ONNX exporter
deepscale: bool
Default = False
(Deprecated) enable DeepSpeed (helper flag for user code, no impact on DeepSpeed backend)'
deepscale_config: str
Default = None
(Deprecated) deepscale json configuration file.
deepspeed_mpi: bool
Default = False
Run via MPI, this will attempt to discover the necessary variables to initialize torch distributed from the MPI environment
deepspeed_slurm: bool
Default = False
Run via SLURM, this will attempt to discover the necessary variables to initialize torch distributed from the SLURM environment
user_script: str
Default = None
user script to be run
iteration: int
Default = None
Set during training
do_train: bool
Default = None
Set during training
do_valid: bool
Default = None
Set during training
do_test: bool
Default = None
Set during training
save_iters: list
Default = None
Set during training
global_num_gpus: int
Default = None
Set during launching
NeoXArgsParallelism
Parallelism Arguments
pipe_parallel_size: int
Default = 0
Number of pipeline parallel stages. Disable with 0.
model_parallel_size: int
Default = 1
Size of the model parallelism.
pipe_partition_method: str
Default = type:transformer|mlp
method used to distribute model layers across pipeline stages. Choose from "parameters", which balances the number of parameters on each pipeline stage, "uniform", which naively balances the number of layers per stage, or "type:[regex]", which balances layers whose class names match [regex]
world_size: int
Default = None
Total world size (i.e number of gpus in cluster). Configured post-launch using distributed launcher
is_pipe_parallel: bool
Default = False
flag to determine whether pipeline parallelism is on - shouldn't be set by user, is automatically determined according to pipeline parallel size.
sequence_parallel: bool
Default = False
flag to determine whether Megatron-style Sequence Parallelism (https://arxiv.org/abs/2205.05198) (Layernorm inputs and activations are sharded across model parallel group) will be used. Has no effect when model_parallel_size is 1. Set by user, in contrast to neox_args.is_pipe_parallel.
expert_interval: int
Default = 2
Have one MoE layer every expert_interval layers
NeoXArgsTemplate
NeoXArgsTemplate()
NeoXArgsTextgen
Text Generation arguments
text_gen_type: str
Default = None
How to generate text/sample the model. Options:
unconditional,input-file,interactive,precomputeprecompute_model_name: str
Default = None
Model name to use for saving precomputed logprobs
temperature: float
Default = 0.0
exponential scaling output distribution ("higher == more risk")
top_p: float
Default = 0.0
Top-p (nucleus) sampling chooses from the smallest possible set of tokens whose cumulative probability exceeds the probability top_p.
top_k: int
Default = 0
integer between 0 and the models vocab size. Filters out any logits with a probability less than that of the top_kth token.
return_logits: bool
Default = False
Boolean for whether to return the logits for generated tokens
maximum_tokens: int
Default = 64
maximum number of tokens to be generated
prompt_end: str
Default =
a single prompt's end. Defaults to newline
sample_input_file: str
Default = None
Get input from file instead of interactive mode, each line is an input.
sample_output_file: str
Default = samples.txt
Output file
num_samples: int
Default = 1
Number of samples to generate unconditionally, defaults to 1 and interactive conditional sampling
recompute: bool
Default = False
During generation recompute all attention instead of using previously computed keys/values. Should be set to true for sparse attention models
eval_results_prefix: str
Default =
prefix to which to save evaluation results - final fp will be {eval_results_prefix}_eval_results_yy-mm-dd-HH-MM.json
eval_tasks: list
Default = None
Tasks to evaluate on using lm_eval_harness
NOTE: Requires internet connection
moe_top_k: int
Default = 1
Activate top K experts in MoE
use_tutel: bool
Default = False
Use Tutel optimizations in MoE
moe_num_experts: int
Default = 1
Number of MoE experts
moe_loss_coeff: float
Default = 0.1
Coefficient for MoE loss
moe_train_capacity_factor: float
Default = 1.0
The capacity of the expert at train time
moe_eval_capacity_factor: float
Default = 1.0
The capacity of the expert at eval time
moe_min_capacity: int
Default = 4
The minimum capacity per expert regardless of the capacity_factor
moe_token_dropping: bool
Default = False
Whether to drop tokens when exceeding capacity
create_moe_param_group: bool
Default = True
Whether to create a separate parameter group for MoE parameters
moe_use_residual: bool
Default = True
Whether to use residual in MoE
moe_expert_parallel_size: int
Default = 1
Number of parallel experts in MoE
moe_type: str
Default = megablocks
Either
deepspeedormegablocksmoe_glu: bool
Default = False
Use gated linear units in MoE
moe_lbl_in_fp32: bool
Default = False
Whether to compute the load balancing loss in fp32.
moe_jitter_eps: float
Default = None
Coefficient for MoE routing jitter. Jitter is not used if set to None
enable_expert_tensor_parallelism: bool
Default = False
Enable expert tensor parallelism
NeoXArgsTokenizer
Tokenizer Arguments
tokenizer_type: typing.Literal['GPT2BPETokenizer', 'HFTokenizer', 'HFGPT2Tokenizer', 'SPMTokenizer', 'CharLevelTokenizer', 'TiktokenTokenizer']
Default = GPT2BPETokenizer
Type of tokenizer to use - should be one of ["GPT2BPETokenizer", "HFTokenizer", "HFGPT2Tokenizer", "SPMTokenizer", "CharLevelTokenizer", "TiktokenTokenizer"]
padded_vocab_size: int
Default = None
Total (padded) vocabulary size of tokenizer. Configured after launching of training, as it's dependent on the parallelism size.
NeoXArgsTraining
Training Arguments
data_path: str
Default = None
Path to combined dataset to split.
use_shared_fs: bool
Default = True
Whether to use a shared filesystem for data loading. If False, local rank 0 on all nodes will preprocess the data, otherwise only global rank 0 will preprocess the data. This is implemented in megatron/data/gpt2_dataset.py::_build_index_mappings.
train_data_paths: list
Default = None
List of paths to train datasets.
train_label_data_paths: list
Default = None
List of paths to train label datasets (not shifted by 1 yet!).
train_reward_data_paths: list
Default = None
List of paths to train reward datasets
test_data_paths: list
Default = None
List of paths to test datasets.
test_label_data_paths: list
Default = None
List of paths to test label datasets (not shifted by 1 yet!).
test_reward_data_paths: list
Default = None
List of paths to test reward datasets
valid_data_paths: list
Default = None
List of paths to validation datasets.
valid_label_data_paths: list
Default = None
List of paths to validation label datasets (not shifted by 1 yet!).
valid_reward_data_paths: list
Default = None
List of paths to validation reward datasets
pos_train_data_paths: list
Default = None
neg_train_data_paths: list
Default = None
List of paths to positive and negative training datasets.
pos_train_label_data_paths: list
Default = None
neg_train_label_data_paths: list
Default = None
List of paths to positive and negative training label datasets (not shifted by 1 yet!).
pos_valid_data_paths: list
Default = None
neg_valid_data_paths: list
Default = None
List of paths to positive and negative validation datasets.
pos_valid_label_data_paths: list
Default = None
neg_valid_label_data_paths: list
Default = None
List of paths to positive and negative validation label datasets (not shifted by 1 yet!).
pos_test_data_paths: list
Default = None
neg_test_data_paths: list
Default = None
List of paths to positive and negative test datasets.
pos_test_label_data_paths: list
Default = None
neg_test_label_data_paths: list
Default = None
List of paths to positive and negative test label datasets (not shifted by 1 yet!).
train_data_weights: list
Default = None
List of 'weights' that decide how often to sample from each training dataset when blending datasets. If None, defaults to equal weighting. Should be a list the same length as
train_data_pathsvalid_data_weights: list
Default = None
List of 'weights' that decide how often to sample from each validation dataset when blending datasets. If None, defaults to equal weighting. Should be a list the same length as
valid_data_pathstest_data_weights: list
Default = None
List of 'weights' that decide how often to sample from each test dataset when blending datasets. If None, defaults to equal weighting. Should be a list the same length as
test_data_pathsweight_by_num_documents: bool
Default = False
If True, Builds dataset weights from a multinomial distribution over groups of data according to the number of documents in each group.
WARNING: setting this to True will override any user provided weights
We sample from a group according to the probability p(L) ∝ |L| ** α, where p(L) is the probability of sampling from a given group, |L| is the number of examples in that datapoint, and α is a coefficient that acts to upsample data from underrepresented groups Hence α (
alpha) allows us to control how much to 'boost' the probability of training on low-resource groups.See https://arxiv.org/abs/1911.02116 for more details
weighted_sampler_alpha: float
Default = 1.0
Alpha value for
weight_by_num_documents. Only has an effect ifweight_by_num_documents= True.when alpha = 1, the probability of sampling from a given group = n_samples / total_samples as alpha -> 0, the probability of sampling from all groups becomes equal, and number of documents has no effect as alpha -> inf, the probability of sampling from the groups with the most samples -> 1
data_impl: typing.Literal['infer', 'mmap', 'cached']
Default = infer
Implementation of indexed datasets, can be one of "infer", "cached", or "mmap"
pack_impl: typing.Literal['packed', 'pack_until_overflow', 'unpacked']
Default = packed
Packing implementation, can be one of "packed", "pack_until_overflow", or "unpacked".
warning: pack_until_overflow is very naive and will likely have issues with pretraining scale datasets
dataset_impl: typing.Literal['gpt2', 'pairwise']
Default = gpt2
Dataset implementation, can be one of "gpt2" or "pairwise"
train_impl: typing.Literal['normal', 'dpo', 'rm', 'kto']
Default = normal
Training implementation, can be one of "normal", "dpo", "kto", or "rm"
dpo_fp32: bool
Default = True
Whether to cast logits to fp32 for DPO loss calculation.
dpo_reference_free: bool
Default = False
Whether to use reference-free DPO.
dpo_beta: float
Default = 0.1
Beta value for DPO
kto_fp32: bool
Default = True
Whether to cast logits to fp32 for KTO loss calculation.
kto_desirable_weight: float
Default = 1.0
Weight for desirable loss in KTO. Might help if you have unbalanced desirable and undesirable classes.
kto_undesirable_weight: float
Default = 1.0
Weight for undesirable loss in KTO. Might help if you have unbalanced desirable and undesirable classes.
kto_beta: float
Default = 0.1
Beta value for KTO
allow_chopped: bool
Default = True
WARNING: if your packing impl is packed, this is ignored.
Allow chopped samples in the dataset. (e.g if your sequence length is 1024 and you have a sample of length 1026, it will be chopped to 1024)
mmap_warmup: bool
Default = False
Warm up mmap files.
save: str
Default = None
Output directory to save checkpoints to.
s3_path: str
Default = None
Path to s3 bucket for saving checkpoints.
s3_chunk_size: int
Default = 104857600
The number of bytes in each file chunk when uploading to s3. Defaults to 100MiB.
config_files: dict
Default = None
Store of original config files mapping config filename to file contents
load: str
Default = None
Directory containing a model checkpoint.
checkpoint_validation_with_forward_pass: bool
Default = False
save input and output of a forward pass with the checkpoint and validate after load
checkpoint_scale: typing.Literal['linear', 'log']
Default = linear
How step at which checkpoints are saved should scale. "linear" implies 1 checkpoint will be saved at every multiple of
checkpoint-factor, while "log" implies that the number of steps between each checkpoint will be multiplied bycheckpoint-factorat each step, starting from step 1.checkpoint_factor: int
Default = None
Acts as a multiplier on either the "log" or "linear" checkpoint spacing.
With
checkpoint-scale="linear",checkpoint-factor=20, andtrain-iters=100, checkpoints will be saved at steps [20, 40, 60, 80, 100].With
checkpoint-scale="log",checkpoint-factor=2, andtrain-iters=100, checkpoints will be saved at steps [1, 2, 4, 8, 16, 32, 64, 100].Note that the last checkpoint step is always saved.
extra_save_iters: list
Default = None
Additional iterations when a checkpoint should be saved. Must be a list of ints or
None.no_save_optim: bool
Default = False
Do not save current optimizer.
no_save_rng: bool
Default = False
Do not save current rng state.
no_load_optim: bool
Default = False
Do not load optimizer when loading checkpoint.
no_load_rng: bool
Default = False
Do not load rng state when loading checkpoint.
finetune: bool
Default = False
Load model for finetuning. Do not load optimizer or rng state from checkpoint and set iteration to 0. Assumed when loading a release checkpoint.
batch_size: int
Default = None
training microbatch size per gpu
train_iters: int
Default = None
Number of iterations to run for training.
train_epochs: int
Default = None
Number of epochs to run for training. Do not specify both train_epochs and train_iters. Not currently compatible with data reweighing, pairwise datasets, and packing other than 'packed'
eval_iters: int
Default = 100
Number of iterations to run for evaluation validation/test for.
keep_last_n_checkpoints: int
Default = None
Number of last checkpoints to keep
eval_interval: int
Default = 1000
Interval between running evaluation on validation set.
split: str
Default = 969, 30, 1
Comma_separated list of proportions for training, validation, and test split. For example the split 90,5,5 will use 90% of data for training, 5% for validation and 5% for test.
vocab_file: str
Default = None
Path to the vocab file.
merge_file: str
Default = None
Path to the BPE merge file.
num_workers: int
Default = 2
Dataloader number of workers.
exit_interval: int
Default = None
Exit the program after the iteration is divisible by this value.
attention_dropout: float
Default = 0.0
Post attention dropout probability.
hidden_dropout: float
Default = 0.0
Dropout probability for hidden state transformer.
weight_decay: float
Default = 0.1
Weight decay coefficient for L2 regularization.
checkpoint_activations: bool
Default = False
Checkpoint activation to allow for training with larger models, sequences, and batch sizes.
checkpoint_num_layers: int
Default = 1
Chunk size (number of layers) for checkpointing.
deepspeed_activation_checkpointing: bool
Default = True
DEPRECATED - TODO: remove Uses activation checkpointing from deepspeed
contiguous_checkpointing: bool
Default = False
Contiguous memory checkpointing for activations.
checkpoint_in_cpu: bool
Default = False
Move the activation checkpoints to CPU.
synchronize_each_layer: bool
Default = False
does a synchronize at the beginning and end of each checkpointed layer.
profile_backward: bool
Default = False
Enables backward pass profiling for checkpointed layers.
partition_activations: bool
Default = False
Partition Activations across GPUs before checkpointing.
clip_grad: float
Default = 1.0
Gradient clipping based on global L2 norm.
hysteresis: int
Default = 2
hysteresis for dynamic loss scaling
dynamic_loss_scale: bool
Default = None
flag indicating whether dynamic loss scale is used
loss_scale: float
Default = None
Static loss scaling, positive power of 2 values can improve fp16 convergence. If None, dynamic loss scaling is used.
loss_scale_window: float
Default = 1000.0
Window over which to raise/lower dynamic scale.
min_scale: float
Default = 1.0
Minimum loss scale for dynamic loss scale.
char_level_ppl: bool
Default = False
Whether to calculate character level perplexity as well as token level perplexity. (may incur a time cost)
use_mup: bool
Default = False
Whether to use Microsoft's Mup https://github.com/microsoft/mup
coord_check: bool
Default = False
Whether to generate a "coord check" plot to verify mup's implementation in neox
save_base_shapes: bool
Default = False
Whether to save base shapes for mup. This will save the shapes to the path specified in base-shapes-file.
base_shapes_file: str
Default = None
Path to the base shapes to save to/load from
mup_init_scale: float
Default = 1.0
Initialization scale: All the parameters are multiplied by this value
mup_attn_temp: float
Default = 1.0
Attention temperature: Reciprocal of the multiplier applied to the input to attention softmax
mup_output_temp: float
Default = 1.0
Output temperature: Reciprocal of the multiplier applied to the input to softmax that produces the distribution over output tokens.
mup_embedding_mult: float
Default = 1.0
Scalar by which we multiply the output of the embedding layer
mup_rp_embedding_mult: float
Default = 1.0
Scalar by which we multiply vectors representing relative position
mup_width_scale: int
Default = 2
What to scale width by when creating the delta model for mup
NeoXArgsDeepspeedConfig
Args for deepspeed config Every argument included here will be included in deepspeed config json As of Mar 8 2023, up to date compared to https://www.deepspeed.ai/docs/config-json/
deepspeed: bool
Default = True
boolean flag to enable DeepSpeed (Always True)
train_batch_size: int
Default = None
The effective training batch size. This is the amount of data samples that leads to one step of model update. train_batch_size is aggregated by the batch size that a single GPU processes in one forward/backward pass (a.k.a., train_step_batch_size), the gradient accumulation steps (a.k.a., gradient_accumulation_steps), and the number of GPUs.
train_micro_batch_size_per_gpu: int
Default = None
Batch size to be processed by one GPU in one step (without gradient accumulation). When specified, gradient_accumulation_steps is automatically calculated using train_batch_size and number of GPUs. Should not be concurrently specified with gradient_accumulation_steps in the configuration JSON.
gradient_accumulation_steps: int
Default = 1
Number of training steps to accumulate gradients before averaging and applying them. This feature is sometimes useful to improve scalability since it results in less frequent communication of gradients between steps. Another impact of this feature is the ability to train with larger batch sizes per GPU. When specified, train_step_batch_size is automatically calculated using train_batch_size and number of GPUs. Should not be concurrently specified with train_step_batch_size in the configuration JSON.
optimizer: dict
Default = None
dict containing the keys type and params
type: The optimizer name. DeepSpeed natively supports Adam, AdamW, OneBitAdam, Lamb, and OneBitLamb optimizers (See here for details) and will import other optimizers from torch.
params: Dictionary of parameters to instantiate optimizer. The parameter names must match the optimizer constructor signature (e.g., for Adam).
scheduler: dict
Default = None
dict containing the keys type and params
type: The scheduler name. See here (https://deepspeed.readthedocs.io/en/latest/schedulers.html) for list of support schedulers.
params: Dictionary of parameters to instantiate scheduler. The parameter names should match scheduler constructor signature.
fp32_allreduce: bool
Default = False
During gradient averaging perform allreduce with 32 bit values
prescale_gradients: bool
Default = False
Scale gradients before doing allreduce
gradient_predivide_factor: float
Default = 1.0
Before gradient averaging predivide gradients by a specified factor, can sometimes help with fp16 stability when scaling to large numbers of GPUs
sparse_gradients: bool
Default = False
Enable sparse compression of torch.nn.Embedding gradients.
fp16: dict
Default = None
Configuration for using mixed precision/FP16 training that leverages NVIDIA’s Apex package.
Dictionary options as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#fp16-training-options
bf16: dict
Default = None
Configuration for using bfloat16 floating-point format as an alternative to FP16. BFLOAT16 requires hardware support (e.g., NVIDIA A100).
Dictionary options as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#bfloat16-training-options
amp: dict
Default = None
Configuration for using automatic mixed precision (AMP) training that leverages NVIDIA’s Apex AMP package.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#automatic-mixed-precision-amp-training-options
gradient_clipping: float
Default = 1.0
Enable gradient clipping with provided value
zero_optimization: dict
Default = None
Configuration for using ZeRO optimization.
Multi-level dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#zero-optimization-options
curriculum_learning: dict
Default = None
curriculum_seqlen: int
Default = 0
Internal var for tracking the current seqlen
steps_per_print: int
Default = 10
Print train loss every N steps.
wall_clock_breakdown: bool
Default = False
Enable timing of the latency of forward/backward/update training phases.
dump_state: bool
Default = False
Print out state information of DeepSpeed object after initialization.
flops_profiler: dict
Default = None
Configuration for using FLOPS profiler.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#flops-profiler
communication_data_type: bool
Default = None
During gradient averaging, perform communication with selected data type. By default it will be determined by selected regime
autotuning: dict
Default = None
Configuration for using autotuning.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#autotuning
activation_checkpointing: dict
Default = None
Configuration for using activation checkpointing.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#activation-checkpointing
sparse_attention: dict
Default = None
Configuration for using sparse attention.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#sparse-attention
data_efficiency: dict
Default = None
Configuration for using data efficiency.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#data-efficiency
tensorboard: dict
Default = None
Configuration for using tensorboard.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#monitoring-module-tensorboard-wandb-csv
wandb: dict
Default = None
Configuration for using wandb.
csv_monitor: dict
Default = None
Configuration for using csv_monitor.
elasticity: dict
Default = None
Configuration for using elastic training.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#elastic-training-config-v01-and-v02
comms_logger: dict
Default = None
Configuration for using communication logger.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#communication-logging
compression_training: dict
Default = None
Configuration for using compression training.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#compression
checkpoint: dict
Default = None
Configuration for using checkpointing.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#checkpoint-options
data_types: dict
Default = None
Configuration for using data types.
Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#data-type-options
deepspeed_extra_args: dict
Default = None
Dictionary of extra arguments to be included in the yaml config file. This can be used for any argument not included in the above list.
NeoXArgsDeepspeedRunner
Args for deepspeed runner (deepspeed.launcher.runner). Every argument included here will be passed as command line argument to deepspeed.launcher.runner
hostfile: str
Default = None
list of hostnames / ssh aliases and the number of GPUs per host
example file contents: worker-1 slots=4 worker-2 slots=4 127.0.0 slots=4 127.0.1 slots=4
include: str
Default = None
Specify hardware resources to use during execution. String format is
NODE_SPEC[@NODE_SPEC ...]whereNODE_SPEC=NAME[:SLOT[,SLOT ...]]. If:SLOTis omitted, include all slots on that host. Example:"worker-0@worker-1:0,2"will use all slots. onworker-0and slots[0, 2]onworker-1.exclude: str
Default = None
Specify hardware resources to NOT use during execution. Same format as include
num_nodes: int
Default = -1
Total number of worker nodes to run on, this will use the top N hosts from the given hostfile. -1 will use all.
num_gpus: int
Default = None
Max number of GPUs to use on each node, will use [0:N) GPU ids on each node. None / not specifying a value will use all.
master_port: int
Default = 29500
Port used by PyTorch distributed for communication during training.
master_addr: str
Default = None
IP address of node 0, will be inferred via 'hostname -I' if not specified.
launcher: typing.Literal['pdsh', 'openmpi', 'mvapich', 'slurm']
Default = pdsh
Launcher backend for multi-node training. Options currently include PDSH, OpenMPI, MVAPICH.
force_multi: bool
Default = False
Force multi-node training even if only one node is specified.
detect_nvlink_pairs: bool
Default = False
If true, autodetects nvlink pairs and remaps cuda visible devices to place them next to each other. This is an Eleuther addition to deepspeed, and should speed up model parallel training on setups with nvlink pairs when mp=2.
autotuning_run: str
Default = None
Either "tune", "run", or
None.no_ssh_check: bool
Default = False
If true, overrides the default check where DeepSpeed confirms that the headnode is accessible via ssh.
comment: str
Default = None
Adds a
--commentto the DeepSpeed launch command. In DeeperSpeed this is passed on to the SlurmLauncher as well. Sometimes necessary for cluster rules, or so I've heard.account: str
Default = None
Adds a
--accountto the DeepSpeed launch command. In DeeperSpeed this is passed on to the SlurmLauncher as well. Sometimes necessary for cluster rules, or so I've heard.