contrain model

README.md

@@ -263,3 +263,11 @@ litgpt evaluate --tasks 'mmlu_multilingual,mgsm' --out_dir 'evaluate-multilingua
 ```bash
 litgpt evaluate --tasks 'wikitext,qasper' --out_dir 'evaluate-long/' --batch_size 4 --dtype 'bfloat16' out/pretrain/final/
 ```
+
+|     Tasks     |Version|Filter|n-shot|    Metric     |   |  Value   |   |Stderr|
+|---------------|------:|------|-----:|---------------|---|---------:|---|------|
+|qasper_bool    |      1|none  |     0|f1             |↑  |    0.0000|±  |     0|
+|qasper_freeform|      2|none  |     0|f1_abstractive |↑  |    0.0036|±  | 0.001|
+|wikitext       |      2|none  |     0|bits_per_byte  |↓  |    3.0634|±  |   N/A|
+|               |       |none  |     0|byte_perplexity|↓  |    8.3596|±  |   N/A|
+|               |       |none  |     0|word_perplexity|↓  |85375.3002|±  |   N/A|

scripts/TRAIN.md

@@ -54,6 +54,13 @@ state_dict = torch.load('out/converted_model/model.pth', map_location='cpu')
 save_file(state_dict, 'out/converted_model/model.safetensors')
 ```
 
+### Continued Pretraining
+
+```bash
+litgpt convert_pretrained_checkpoint out/pretrain/final/ out/pretrain_checkpoint/final/
+litgpt pretrain --config ./contrain-model.yaml
+```
+
 ## Evaluate
 
 ```bash

scripts/contrain-model.yaml

@@ -0,0 +1,147 @@
+# https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct/blob/main/config.json
+
+# The name of the model to pretrain. Choose from names in ``litgpt.config``. Mutually exclusive with
+# ``model_config``. (type: Optional[str], default: null)
+model_name: "Llama-3.2-1B"
+
+# A ``litgpt.Config`` object to define the model architecture. Mutually exclusive with
+# ``model_config``. (type: Optional[Config], default: null)
+model_config:
+  padded_vocab_size: 32768
+  vocab_size: 32768
+  block_size: 8192
+  n_layer: 8
+  n_head: 32
+  head_size: 64
+  n_embd: 512
+  n_query_groups: 8
+  rotary_percentage: 1.0
+  parallel_residual: true
+  shared_attention_norm: false
+  bias: false
+  norm_class_name: "RMSNorm"
+  norm_eps: 1e-05
+  mlp_class_name: "LLaMAMLP"
+  intermediate_size: 2048
+  rope_base: 500000
+  rope_adjustments:
+    factor: 32.0
+    low_freq_factor: 1.0
+    high_freq_factor: 4.0
+    original_max_seq_len: 8192
+
+# Directory in which to save checkpoints and logs. If running in a Lightning Studio Job, look for it in
+# /teamspace/jobs/<job-name>/share. (type: <class 'Path'>, default: out/pretrain)
+out_dir: "../out/contrain/"
+
+# The precision to use for pretraining. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)
+# precision: bf16-mixed
+precision: bf16-true
+
+# Optional path to a checkpoint directory to initialize the model from.
+# Useful for continued pretraining. Mutually exclusive with ``resume``. (type: Optional[Path], default: null)
+initial_checkpoint_dir: out/pretrain_checkpoint/final/
+
+# Path to a checkpoint directory to resume from in case training was interrupted, or ``True`` to resume
+# from the latest checkpoint in ``out_dir``. An error will be raised if no checkpoint is found. Passing
+# ``'auto'`` will resume from the latest checkpoint but not error if no checkpoint exists.
+# (type: Union[bool, Literal["auto"], Path], default: False)
+resume: false
+# resume: "auto"
+
+# Data-related arguments. If not provided, the default is ``litgpt.data.TinyLlama``.
+data:
+  class_path: LitData
+
+  init_args:
+    data_path: "../contrain-data/"
+    num_workers: 32
+
+# Training-related arguments. See ``litgpt.args.TrainArgs`` for details
+train:
+  # Number of optimizer steps between saving checkpoints (type: Optional[int], default: 1000)
+  save_interval: 200
+
+  # Number of iterations between logging calls (type: int, default: 1)
+  log_interval: 1
+
+  # Number of samples between optimizer steps across data-parallel ranks (type: int, default: 512)
+  global_batch_size: 512
+
+  # Number of samples per data-parallel rank (type: int, default: 4)
+  micro_batch_size: 4
+
+  # Number of iterations with learning rate warmup active (type: int, default: 2000)
+  lr_warmup_steps: 1000
+
+  # Number of epochs to train on (type: Optional[int], default: null)
+  epochs:
+
+  # Total number of tokens to train on (type: Optional[int], default: 3000000000000)
+  # max_tokens: 19626937782 # 1197787 * 8193 * 2
+  max_tokens: 9813468891 # 1197787 * 8193 * 1
+
+  # Limits the number of optimizer steps to run. (type: Optional[int], default: null)
+  max_steps:
+
+  # Limits the length of samples. Off by default (type: Optional[int], default: null)
+  max_seq_length: 8193
+
+  # Whether to tie the embedding weights with the language modeling head weights. (type: Optional[bool], default: False)
+  tie_embeddings: true
+
+  #   (type: Optional[float], default: 1.0)
+  max_norm: 1.0
+
+  #   (type: float, default: 4e-05)
+  min_lr: 1e-05
+
+# Evaluation-related arguments. See ``litgpt.args.EvalArgs`` for details
+eval:
+  # Number of optimizer steps between evaluation calls (type: int, default: 1000)
+  interval: 100
+
+  # Number of tokens to generate (type: Optional[int], default: null)
+  max_new_tokens:
+
+  # Number of iterations (type: int, default: 100)
+  max_iters: 100
+
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
+  # Whether to evaluate on the validation set at the end the training
+  final_validation: true
+
+# Optimizer-related arguments
+optimizer:
+  # class_path: torch.optim.AdamW
+  class_path: grokadamw.GrokAdamW
+
+  init_args:
+    #   (type: float, default: 0.001)
+    lr: 4e-05
+
+    #   (type: float, default: 0.01)
+    weight_decay: 1e-2
+
+    #   (type: tuple, default: (0.9,0.999))
+    betas:
+      - 0.9
+      - 0.999
+
+# How many devices/GPUs to use. Uses all GPUs by default. (type: Union[int, str], default: auto)
+devices: auto
+
+# How many nodes to use. (type: int, default: 1)
+num_nodes: 1
+
+# Optional path to the tokenizer dir that was used for preprocessing the dataset. Only some data
+# module require this. (type: Optional[Path], default: null)
+tokenizer_dir: "../"
+
+# The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: tensorboard)
+logger_name: "wandb"
+
+# The random seed to use for reproducibility. (type: int, default: 42)
+seed: 23

scripts/pretrain-model.yaml

@@ -72,7 +72,6 @@ train:
   micro_batch_size: 16
 
   # Number of iterations with learning rate warmup active (type: int, default: 2000)
-  # lr_warmup_steps: 2000
   lr_warmup_steps: 10
 
   # Number of epochs to train on (type: Optional[int], default: null)
@@ -118,8 +117,6 @@ eval:
 optimizer:
   # class_path: torch.optim.AdamW
   class_path: grokadamw.GrokAdamW
-  # class_path: bitsandbytes.optim.AdamW8bit
-  # class_path: bitsandbytes.optim.PagedAdamW8bit
 
   init_args:
     #   (type: float, default: 0.001)