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 - **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 = 7ed7bc6 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. - **log_interval**: int Default = None 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. ## 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. - **num_attention_heads**: int Default = None Number of transformer attention heads. - **seq_length**: int Default = None Maximum sequence length to process. - **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'] Default = layernorm Normalization layer to use. Choose from "layernorm", "rmsnorm", "scalenorm". - **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] 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_attention` is always false. The main difference between our sparsity config and deepspeed's is that `mode` is 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'] Default = gelu Activation function to use - choose from ["gelu", "geglu", "relu", "softsign", "swish", "mish"] - **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. - **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 - **init_method**: typing.Literal['normal', 'scaled_normal', 'orthogonal', 'scaled_orthogonal', 'xavier_uniform', 'xavier_normal', 'wang_init', 'small_init'] 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'] 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) - **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 - **output_layer_parallelism**: typing.Literal['row', 'column'] Default = row Parameter controlling whether the output layer is parallelized over the hidden dim (row) or the vocab dim (column) ## NeoXArgsOptimizer Optimizer Arguments - **optimizer_type**: typing.Literal['adam', 'onebitadam', 'cpu_adam', 'cpu_torch_adam', 'sm3', 'madgrad_wd'] Default = adam Type of optimizer to use. Choose from ['adam', 'onebitadam', 'cpu_adam', 'cpu_torch_adam', 'sm3', 'madgrad_wd] - **use_bnb_optimizer**: bool Default = False Whether to enable the bitsandbytes optimizers - **zero_stage**: int 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**: int Default = None Set during training - **do_valid**: int Default = None Set during training - **do_test**: int 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. ## NeoXArgsTemplate NeoXArgsTemplate() ## NeoXArgsTextgen Text Generation arguments - **text_gen_type**: str Default = None How to generate text/sample the model. Options: `unconditional`, `input-file`, `interactive` - **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 ## 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. - **test_data_paths**: list Default = None List of paths to test datasets. - **valid_data_paths**: list Default = None List of paths to validation datasets. - **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_paths` - **valid_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_paths` - **test_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_paths` - **weight_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 = 0.3 Alpha value for `weight_by_num_documents`. Only has an effect if `weight_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**: str Default = infer Implementation of indexed datasets. - **mmap_warmup**: bool Default = False Warm up mmap files. - **save**: str Default = None Output directory to save checkpoints to. - **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 by `checkpoint-factor` at 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`, and `train-iters=100`, checkpoints will be saved at steps [20, 40, 60, 80, 100]. With `checkpoint-scale="log"`, `checkpoint-factor=2`, and `train-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. - **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 = 0.1 Post attention dropout probability. - **hidden_dropout**: float Default = 0.1 Dropout probability for hidden state transformer. - **weight_decay**: float Default = 0.01 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. - **gas**: int Default = None gradient_accumulation_steps - **clip_grad**: float Default = None 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) ## NeoXArgsDeepspeedConfig Args for deepspeed config Every argument included here will be included in deepspeed config json #TODO this list is not complete as 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. - **amp**: dict Default = None Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#automatic-mixed-precision-amp-training-options - **gradient_clipping**: float Default = 0.0 Enable gradient clipping with provided value - **zero_optimization**: dict Default = None - **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 Dictionary as described in Deepspeed documentation: https://www.deepspeed.ai/docs/config-json/#flops-profiler - **zero_allow_untested_optimizer**: bool Default = False Whether Deepspeed Zero Optimizer will allow an optimizer that hasn't been tested by the deepspeed team ## 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 ...]` where `NODE_SPEC=NAME[:SLOT[,SLOT ...]]`. If `:SLOT` is omitted, include all slots on that host. Example: `"worker-0@worker-1:0,2"` will use all slots. on `worker-0` and slots `[0, 2]` on `worker-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**: str Default = pdsh Launcher backend for multi-node training. Options currently include PDSH, OpenMPI, MVAPICH. - **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. - **comment**: str Default = None Adds a `--comment` to the DeepSpeed launch command. In DeeperSpeed this is passed on to the SlurmLauncher as well. Sometime necessary for cluster rules, or so I've heard.