Experimental Yaml configs

examples/gpt3/gpt_config.yaml

@@ -0,0 +1,303 @@
+# WARNING: Yaml configs is currently an experimental feature
+language_model:
+  # model architecture
+  num_layers: 24
+  hidden_size: 1024
+  num_attention_heads: 16
+  num_query_groups: null
+
+  ffn_hidden_size: null
+  kv_channels: null
+  hidden_dropout: 0.0
+  attention_dropout: 0.0
+  fp32_residual_connection: False
+
+  apply_residual_connection_post_layernorm: False
+  layernorm_epsilon: 1.e-5
+  layernorm_zero_centered_gamma: True
+  add_bias_linear: False
+  bias_activation_fusion: False
+  add_qkv_bias: False
+  gated_linear_unit: False
+  activation_func: swiglu
+  num_moe_experts: null
+  rotary_interleaved: False
+  window_size: null
+
+  # initialization
+  init_method: null
+  init_method_std: 0.02
+  output_layer_init_method: null
+
+  # mixed-precision
+  apply_query_key_layer_scaling: False
+  attention_softmax_in_fp32: False
+
+  # fusion
+  bias_swiglu_fusion: True
+  masked_softmax_fusion: True
+  persist_layer_norm: False
+  memory_efficient_layer_norm: False
+  bias_dropout_fusion: True
+  apply_rope_fusion: True
+
+  # activation recomputation
+  recompute_granularity: null
+  recompute_method: null
+  recompute_num_layers: null
+  distribute_saved_activations: null
+
+  # fp8 related
+  fp8: null
+  fp8_margin: 0
+  fp8_interval: 1
+  fp8_amax_history_len: 1
+  fp8_amax_compute_algo: "most_recent"
+  fp8_wgrad: True
+
+  # miscellaneous
+  clone_scatter_output_in_embedding: True
+
+  normalization: "LayerNorm"  # alt value supported by TE: "RMSNorm"
+
+  # MoE related
+  moe_router_load_balancing_type: "aux_loss"
+  moe_router_topk: 2
+  moe_grouped_gemm: False
+  moe_aux_loss_coeff: 0  # 1e-2 would be a good start value for load balance loss.
+  moe_z_loss_coeff: null  # 1e-3 would be a good start value for z-loss
+  moe_input_jitter_eps: null
+  moe_token_dropping: False
+
+model_parallel:
+  # Model parallelism
+  tensor_model_parallel_size: 1
+  context_parallel_size: 1
+  pipeline_model_parallel_size: 1
+  virtual_pipeline_model_parallel_size: null
+  sequence_parallel: True
+  expert_model_parallel_size: 1
+
+  # Initialization
+  perform_initialization: True
+  use_cpu_initialization: null
+
+  # Training
+  fp16: False
+  bf16: True
+  params_dtype: null # Set from above arguments for core
+  timers: null
+
+  # Optimizations
+  gradient_accumulation_fusion: True
+  async_tensor_model_parallel_allreduce: True
+  tp_comm_overlap: False
+
+  # Debug Options
+  tp_comm_split_ag: True
+  tp_comm_atomic_ag: True
+  tp_comm_split_rs: True
+  tp_comm_atomic_rs: True
+  tp_comm_bulk_wgrad: True
+  tp_comm_bulk_dgrad: True
+
+  # Parallelism
+  finalize_model_grads_func: null
+
+  # Pipeline Parallel
+  pipeline_dtype: null
+  grad_scale_func: null
+  enable_autocast: False
+  autocast_dtype: null
+  variable_seq_lengths: False
+  num_microbatches_with_partial_activation_checkpoints: null
+  overlap_p2p_comm: False
+  batch_p2p_comm: True
+  batch_p2p_sync: True
+  use_ring_exchange_p2p: False
+  deallocate_pipeline_outputs: False
+  no_sync_func: null
+  grad_sync_func: null
+  param_sync_func: null
+  pipeline_model_parallel_split_rank: null
+
+  # CPU Offloading
+  cpu_offloading: False
+  cpu_offloading_num_layers: 0
+  _cpu_offloading_context: null
+  cpu_offloading_weights: False
+  cpu_offloading_activations: True
+
+  # Timing
+  barrier_with_L1_time: True
+
+# training:
+use_mcore_models: True
+spec: null
+micro_batch_size: 2
+global_batch_size: 128
+rampup_batch_size: [32, 32, 65324160] 
+check_for_nan_in_loss_and_grad: True
+num_layers_per_virtual_pipeline_stage: null
+
+encoder_num_layers: null
+decoder_num_layers: null
+rotary_seq_len_interpolation_factor: null
+add_position_embedding: False
+make_vocab_size_divisible_by: 128
+group_query_attention: False
+
+
+exit_signal_handler: False
+exit_duration_in_mins: null
+exit_interval: null
+
+untie_embeddings_and_output_weights: True
+position_embedding_type: rope
+rotary_percent: 0.5
+openai_gelu: False
+squared_relu: False
+swiglu: True
+onnx_safe: null
+bert_binary_head: True
+max_position_embeddings: 4096
+
+transformer_impl: local
+use_flash_attn: False
+seed: 1234
+data_parallel_random_init: False
+
+# Optimizer
+optimizer: adam
+lr: 2.5e-4
+lr_decay_style: cosine
+lr_decay_iters: null
+lr_decay_samples: 255126953
+lr_warmup_fraction: null
+lr_warmup_iters: 0
+lr_warmup_samples: 81381
+lr_warmup_init: 0.0
+min_lr: 2.5e-5
+weight_decay: 0.1
+start_weight_decay: null
+end_weight_decay: null
+weight_decay_incr_style: constant
+clip_grad: 1.0
+adam_beta1: 0.9
+adam_beta2: 0.95
+adam_eps: 1.e-08
+sgd_momentum: 0.9
+override_opt_param_scheduler: False
+use_checkpoint_opt_param_scheduler: False
+
+# checkpointing arguments
+save: null
+save_interval: 20000
+no_save_optim: null
+no_save_rng: null
+load: null
+no_load_optim: null
+no_load_rng: null
+finetune: False
+use_checkpoint_args: False
+exit_on_missing_checkpoint: False
+
+# loss arguments
+loss_scale: null
+initial_loss_scale: 4294967296
+min_loss_scale: 1.0
+loss_scale_window: 1000 
+hysteresis: 2
+accumulate_allreduce_grads_in_fp32: False
+fp16_lm_cross_entropy: False
+
+# distributed arguments
+distributed_backend: nccl
+distributed_timeout_minutes: 10
+overlap_grad_reduce: False
+delay_grad_reduce: True
+overlap_param_gather: False
+delay_param_gather: False
+scatter_gather_tensors_in_pipeline: True
+local_rank: null
+lazy_mpu_init: null
+empty_unused_memory_level: 0
+standalone_embedding_stage: False
+use_distributed_optimizer: False
+nccl_communicator_config_path: null
+
+train_iters: null
+eval_iters: 32
+eval_interval: 2000
+skip_train: False
+
+adlr_autoresume: False
+adlr_autoresume_interval: 1000
+
+# garbage collection
+manual_gc: False
+manual_gc_interval: 0
+manual_gc_eval: True
+
+tp_comm_overlap_cfg: null
+
+#data
+data_path: null
+split: '99,1,0'
+train_data_path: null
+valid_data_path: null
+test_data_path: null
+data_cache_path: null
+mock_data: False
+vocab_size: null
+vocab_file: null
+merge_file: null
+vocab_extra_ids: 0
+seq_length: 4096
+encoder_seq_length: null
+decoder_seq_length: null
+retriever_seq_length: 256
+sample_rate: 1.0
+mask_prob: 0.15
+short_seq_prob: 0.1
+num_workers: 2
+tokenizer_type: GPTSentencePieceTokenizer
+tokenizer_model: null
+reset_position_ids: False
+reset_attention_mask: False
+eod_mask_loss: False
+train_samples: 268554688
+dataloader_type: null
+
+#profile:
+profile: False
+profile_ranks: [0]
+profile_step_end: 12
+profile_step_start: 10
+
+#logging:
+log_params_norm: True
+log_num_zeros_in_grad: True
+log_throughput: False
+log_progress: False
+timing_log_level: 0
+timing_log_option: minmax
+tensorboard_log_interval: 1
+tensorboard_queue_size: 1000
+log_timers_to_tensorboard: False
+log_batch_size_to_tensorboard: False
+log_learning_rate_to_tensorboard: True
+log_learning_rate_to_tensorboard: True
+log_validation_ppl_to_tensorboard: False
+log_memory_to_tensorboard: False
+log_world_size_to_tensorboard: False
+log_loss_scale_to_tensorboard: True
+wandb_project: ''
+wandb_exp_name: ''
+wandb_save_dir: ''
+enable_one_logger: False
+one_logger_project: e2e-tracking
+one_logger_entity: hwinf_dcm
+one_logger_run_name: null
+log_interval: 100
+tensorboard_dir: null

megatron/arguments.py

@@ -46,13 +46,20 @@ def parse_args(extra_args_provider=None, ignore_unknown_args=False):
     # Custom arguments.
     if extra_args_provider is not None:
         parser = extra_args_provider(parser)
-
+    
     # Parse.
     if ignore_unknown_args:
         args, _ = parser.parse_known_args()
     else:
         args = parser.parse_args()
 
+    # Experimental yaml
+    if args.yaml_cfg is not None:
+        from .yaml_arguments import load_yaml
+        assert args.yaml_cfg and args.use_mcore_models, "To use yaml, mcore must be enabled"
+        args = load_yaml(args.yaml_cfg)
+
+
     # Args from environment
     args.rank = int(os.getenv('RANK', '0'))
     args.world_size = int(os.getenv("WORLD_SIZE", '1'))
@@ -1474,5 +1481,7 @@ def _add_experimental_args(parser):
                        'To use local spec specify local as the argument.'
                        'For more details, see the model class, '
                        '`transformer_block.py`, or `transformer_layer.py`')
+    group.add_argument('--yaml-cfg', type=str, default=None, 
+                       help = 'Config file to add additional arguments')
 
     return parser

megatron/global_vars.py

@@ -247,4 +247,3 @@ def _ensure_var_is_not_initialized(var, name):
     assert var is None, '{} is already initialized.'.format(name)
 
 
-

megatron/initialize.py

@@ -16,6 +16,7 @@
 from megatron import get_tensorboard_writer
 from megatron.core import mpu, tensor_parallel
 from megatron.arguments import parse_args, validate_args
+from megatron.yaml_arguments import validate_yaml
 from megatron.checkpointing import load_args_from_checkpoint
 from megatron.global_vars import set_global_variables
 from megatron.model.transformer import bias_dropout_add_fused_train
@@ -47,7 +48,11 @@ def initialize_megatron(
         assert args.load is not None, "--use-checkpoints-args requires --load argument"
         load_args_from_checkpoint(args)
 
-    validate_args(args, args_defaults)
+    if args.yaml_cfg is not None:
+        args = validate_yaml(args, args_defaults)
+    else:
+        validate_args(args, args_defaults)
+
 
     # set global args, build tokenizer, and set adlr-autoresume,
     # tensorboard-writer, and timers.

megatron/training.py

@@ -548,7 +548,7 @@ def train_step(forward_step_func, data_iterator,
         torch.cuda.empty_cache()
 
     # Vision gradients.
-    if args.vision_pretraining and args.vision_pretraining_type == "dino":
+    if getattr(args, 'vision_pretraining', False) and args.vision_pretraining_type == "dino":
         unwrapped_model = unwrap_model(model[0])
         unwrapped_model.cancel_gradients_last_layer(args.curr_iteration)
 
@@ -558,7 +558,7 @@ def train_step(forward_step_func, data_iterator,
     timers('optimizer').stop()
 
     # Vision momentum.
-    if args.vision_pretraining and args.vision_pretraining_type == "dino":
+    if getattr(args, 'vision_pretraining', False) and args.vision_pretraining_type == "dino":
         unwrapped_model = unwrap_model(model[0])
         unwrapped_model.update_momentum(args.curr_iteration)