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clean up
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xrsrke committed Jun 18, 2024
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4 changes: 4 additions & 0 deletions .gitignore
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Expand Up @@ -166,3 +166,7 @@ checkpoints/
tests/fp8/reference_convergence/cifar-10-batches-py/
tests/fp8/reference_convergence/cifar-10-python.tar.gz
wandb/

*.pt
*.ipynb
tests/.test_cache/*
110 changes: 0 additions & 110 deletions debug_fp8_optim.txt

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107 changes: 0 additions & 107 deletions debug_optim.txt

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169 changes: 169 additions & 0 deletions examples/config_bfloat16_llama.yaml
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checkpoints:
checkpoint_interval: 1000
checkpoints_path: checkpoints
checkpoints_path_is_shared_file_system: false
# resume_checkpoint_path: checkpoints
save_initial_state: false
data_stages:
# - data:
# dataset:
# dataset_overwrite_cache: false
# dataset_processing_num_proc_per_process: 1
# hf_dataset_config_name: null
# hf_dataset_or_datasets: roneneldan/TinyStories
# hf_dataset_splits: train
# text_column_name: text
# num_loading_workers: 1
# seed: 42
# name: Stable Training Stage
# start_training_step: 1
# - data:
# dataset:
# dataset_overwrite_cache: false
# dataset_processing_num_proc_per_process: 1
# hf_dataset_config_name: null
# hf_dataset_or_datasets: stas/openwebtext-10k
# hf_dataset_splits: train
# text_column_name: text
# num_loading_workers: 1
# seed: 42
# name: Annealing Phase
# start_training_step: 10
- data:
dataset:
dataset_overwrite_cache: false
dataset_processing_num_proc_per_process: 1
hf_dataset_config_name: null
hf_dataset_or_datasets: JeanKaddour/minipile
hf_dataset_splits: train
text_column_name: text
num_loading_workers: 1
seed: 42
name: Stable Training Stage
start_training_step: 1
general:
benchmark_csv_path: null
consumed_train_samples: null
ignore_sanity_checks: true
project: fp8_for_nanotron
run: bfloat16_2_layers_and_seq_len_16_and_micro_batch_256_and_lr_6e-4
seed: 42
step: null
lighteval: null
logging:
iteration_step_info_interval: 1
log_level: info
log_level_replica: info
# monitor_model_states: true
model:
ddp_bucket_cap_mb: 25
dtype: bfloat16
init_method:
# std: 0.25 # sqrt(1/16)
# std: 0.125 # sqrt(1/64)
std: 0.04419417382415922 # sqrt(1/512)
make_vocab_size_divisible_by: 1
model_config:
bos_token_id: 1
eos_token_id: 2
# hidden_act: silu
hidden_act: gelu
hidden_size: 512
initializer_range: 0.02
intermediate_size: 2048
is_llama_config: true
max_position_embeddings: 256
num_attention_heads: 4
num_hidden_layers: 2
num_key_value_heads: 4
pad_token_id: null
pretraining_tp: 1
rms_norm_eps: 1.0e-05
rope_scaling: null
tie_word_embeddings: false
use_cache: true
vocab_size: 49152
# optimizer:
# accumulate_grad_in_fp32: false
# clip_grad: 1.0
# # learning_rate_scheduler:
# # learning_rate: 0.01
# # lr_decay_starting_step: null
# # lr_decay_steps: 13
# # lr_decay_style: linear
# # lr_warmup_steps: 2
# # lr_warmup_style: constant
# # min_decay_lr: 1.0e-05

# # learning_rate_scheduler:
# # learning_rate: 0.00015
# # lr_decay_starting_step: null
# # # lr_decay_steps: null
# # lr_decay_style: linear
# # lr_warmup_steps: 60
# # lr_warmup_style: constant
# # min_decay_lr: 1.0e-05

# optimizer_factory:
# adam_beta1: 0.9
# adam_beta2: 0.999
# adam_eps: 1.0e-08
# name: adam
# torch_adam_is_fused: true
# weight_decay: 0.1
# zero_stage: 0


optimizer:
accumulate_grad_in_fp32: false
# clip_grad: 1.0
learning_rate_scheduler:
# learning_rate: 0.0015 # note: 1/2 of pythia use this for a 400m model
learning_rate: 6.0e-4
lr_decay_starting_step: null
lr_decay_steps: null
lr_decay_style: cosine
lr_warmup_steps: 1000 # 10% warm up of total training steps
lr_warmup_style: linear
min_decay_lr: 6.0e-5
# optimizer_factory:
# adam_beta1: 0.9
# adam_beta2: 0.95
# adam_eps: 1.0e-08
# name: adam
# torch_adam_is_fused: true
adam_beta1: 0.9
adam_beta2: 0.95
adam_eps: 1.0e-08
torch_adam_is_fused: true
weight_decay: 0.1
zero_stage: 0

parallelism:
dp: 1
expert_parallel_size: 1
pp: 1
pp_engine: 1f1b
tp: 2
# tp_linear_async_communication: true
# tp_mode: REDUCE_SCATTER

tp_linear_async_communication: false
tp_mode: ALL_REDUCE

profiler: null
tokenizer:
tokenizer_max_length: null
tokenizer_name_or_path: lvwerra/the-tokenizer-v1
tokenizer_revision: null
tokens:
# NOTE: micro_batch_size * sequence_length * batch_accumulation_per_replica
# = 128 * 256 * 1 = 16384
batch_accumulation_per_replica: 1
limit_test_batches: 0
limit_val_batches: 0
micro_batch_size: 256
# micro_batch_size: 1
sequence_length: 256
train_steps: 24376
val_check_interval: -1
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