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Copy path1_8b.yaml
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system:
#skip_iters_range: [10, 20]
distributed_backend: flagcx
reset_position_ids: True
reset_attention_mask: True
add_qkv_bias: True
tensor_model_parallel_size: 1
pipeline_model_parallel_size: 2
disable_bias_linear: True
use_distributed_optimizer: True
precision:
bf16: True
min_loss_scale: 1.0
attention_softmax_in_fp32: True
accumulate_allreduce_grads_in_fp32: True
logging:
log_interval: 1
log_throughput: True
tensorboard_log_interval: 1
wandb_project: ${experiment.exp_name}
wandb_exp_name: ${experiment.exp_name}
checkpoint:
ckpt_format: torch
save_interval: 5
hetero:
enable_hetero: True
hetero_use_cpu_communication: False
use_partial_reduce_for_shared_embedding: True
# mesh format [tp1,cp1,ep1,dp1,pp1,(tp2,cp2...)]
hetero_pipeline_layer_split: [12,12]
hetero_process_meshes: [1,1,1,2,1, 1,1,1,2,1]
hetero_device_types: ["A800", "mlu590"]
standalone_embedding_stage: False
hetero_current_device_type: "A800"
model:
transformer_impl: transformer_engine
num_layers: 24
hidden_size: 2048
num_attention_heads: 16
seq_length: 4096
max_position_embeddings: 4096 # only for adding position embeddings
norm_epsilon: 1e-6
norm_init_weight: 0.8441
use_rotary_position_embeddings: true
no_position_embedding: true
rotary_base: 1000000
swiglu: true
multiple_of: 128
#hidden_dim_multiplier: 2 # ffn_hidden_size 11008 / 5504
normalization: RMSNorm
position_embedding_type: rope
untie_embeddings_and_output_weights: True
init_method_std: 0.02
attention_dropout: 0.0
hidden_dropout: 0.0
weight_decay: 0.1
clip_grad: 1.0
train_samples: 8192 #8192 #29297664 #120B tokens 100step
eval_iters: 0
eval_interval: 1000
micro_batch_size: 8
global_batch_size: 1024
seed: 42
optimizer:
weight_decay: 0.1
adam_beta1: 0.9
adam_beta2: 0.95
lr_scheduler:
lr: 3.0e-4
min_lr: 3.0e-5
lr_warmup_samples: 2048000
lr_wsd_decay_style: cosine
data:
data_path: xxx # Please replace with your actual data path
split: 1
no_mmap_bin_files: true
tokenizer:
tokenizer_type: QwenTokenizerFS
tokenizer_path: xxx xxxx # Please replace with your actual tokenizer path
vocab_size: xxx # Please replace with your actual vocab size
make_vocab_size_divisible_by: 64