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github-python

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# )
### End Load and tokenize training data
### Load pretokenized data (15% of openwebtext tokenized for ctx len of 256, ~1.5B tokens)
# You can subset this even further if you want a smaller dataset.
context_length = 256
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
tokenized_data = load_dataset("xz56/openwebtext-tokenized-small")
total_tokens = tokenized_data['train'].num_rows * context_length
print(f"Training on {total_tokens:_} tokens")
### Adjust llama config to make the model tiny
config = AutoConfig.from_pretrained(
"meta-llama/Llama-2-7b-hf",
vocab_size=len(tokenizer),
n_ctx=context_length,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
dim = 768
n_heads = 6
n_layers = 6
intermediate_size = 1536
config.hidden_size = dim
config.max_position_embeddings = dim
config.num_attention_heads = n_heads
config.num_hidden_layers = n_layers
config.num_key_value_heads = n_heads
config.intermediate_size = intermediate_size
### Create the llama model with our custom config. Convert it to bitnet.
# See utils.py for BitLinear and convert_to_bitnet function details.
model = LlamaForCausalLM(config)
convert_to_bitnet(model, copy_weights=False)
### Print number of parameters.
model_size = sum(t.numel() for t in model.parameters())
print(f"Model size: {model_size/1000**2:.1f}M parameters")
### Set up DataCollator for creating batches
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
### Set up training arguments and begin training. Adjust these to your needs.
# Adjust the batch size until you can train on your device. Then increase accumulation steps to satisfy the following:
# tokens per batch = per_device_train_batch_size * gradient_accumulation_steps * 256
# Adjust this until tokens per batch is at least ~100k.
output_path = "<folder_to_save_checkpoints>"
args = TrainingArguments(
output_dir=output_path,
per_device_train_batch_size=200,
per_device_eval_batch_size=200,
evaluation_strategy="steps",
eval_steps=0.05,
logging_steps=100,
gradient_accumulation_steps=2,
num_train_epochs=1,
weight_decay=0.01,
warmup_steps=0.1,
lr_scheduler_type="cosine",
learning_rate=1.5e-3,
save_steps=0.25,
fp16=True,
report_to="wandb"
)
trainer = Trainer(
model=model,
tokenizer=tokenizer,
args=args,
data_collator=data_collator,
train_dataset=tokenized_data["train"],
eval_dataset=tokenized_data["test"],
)
trainer.train()
trainer.save_model(f"{output_path}/final_model")
# <FILESEP>
#!/usr/bin/env python
import click as ck
import numpy as np
import pandas as pd
from deepgo.utils import Ontology, NAMESPACES
from collections import defaultdict
@ck.command()
@ck.option('--data-root', '-d', default='data')
@ck.option('--in-file', '-i', help='Input file', required=True)
@ck.option('--out-file', '-o', help='Output file', required=True)
def main(data_root, in_file, out_file):
go = Ontology(f'{data_root}/go.obo', with_rels=True)
# Dictionary to hold the data for each identifier
data = defaultdict(list)