rl training

model/model_training/configs/config_rl.yaml

@@ -7,17 +7,17 @@ defaults_rlhf:
   epochs: 10
   datasets:
     - oa_private:
-        data_path: .cache
         split: rl
         val_split: 0.0
         fraction: 1
         file: 2023-02-10_oasst_prod.jsonl
   cache_dir: .cache
   quantization: false
   seq2seqmodel: false
+  output_dir: output
+  reward_model_batch_size: 32
 
 debug_rlhf:
-  model_name: gpt2
   rank_model: /local/home/sanagnos/general/Open-Assistant/model/reward/instructor/facebook/galactica-125m-finetuned/checkpoint-500/
   sft_model: /local/home/sanagnos/general/Open-Assistant/model/model_training/EleutherAI/pythia-70m-deduped-base-finetuned/checkpoint-20/
   batch_size: 2

model/model_training/configs/ppo_config.yaml

@@ -2,7 +2,7 @@ train:
   seq_length: 1024
   epochs: 100
   total_steps: 10000
-  batch_size: 128
+  batch_size: 1
 
   checkpoint_interval: 10000
   eval_interval: 100
@@ -34,8 +34,8 @@ scheduler:
 
 method:
   name: "ppoconfig"
-  num_rollouts: 128
-  chunk_size: 128
+  num_rollouts: 16
+  chunk_size: 16
   ppo_epochs: 4
   init_kl_coef: 0.05
   target: 6

model/model_training/trainer_rl.py

@@ -1,9 +1,10 @@
 import argparse
-import itertools
+import random
 
 import torch
 import transformers
 import trlx
+from custom_datasets.formatting import QA_SPECIAL_TOKENS
 from models import get_specific_model
 from trlx.data.configs import TRLConfig
 from utils import _strtobool, get_dataset, read_yamls
@@ -73,22 +74,32 @@ def rank_model_fn(samples, **kwargs):
 
     train, _ = get_dataset(training_conf, mode="rl")
 
-    print([train[i] for i in range(5)])
+    # trlx requires training data to be a list of prompts
+    # iteratore prompts due to the randomness in the dataset generation
+    prompts = [train[i] for i in range(len(train)) for _ in range(training_conf.epochs)][:100]
 
-    # trlx requires training data to be a list of prompts?
-    prompts = list(itertools.chain(*[list(train[i]) for i in range(len(train)) for _ in range(training_conf.epochs)]))
+    random.shuffle(prompts)
 
     model = get_specific_model(
-        training_conf.sft_model, training_conf.cache_dir, training_conf.quantization, training_conf.seq2seqmodel
+        training_conf.sft_model,
+        cache_dir=training_conf.cache_dir,
+        quantization=training_conf.quantization,
+        seq2seqmodel=training_conf.seq2seqmodel,
     )
     tokenizer = transformers.AutoTokenizer.from_pretrained(training_conf.sft_model)
 
-    trlx_config.tokenizer.tokenizer_path = training_conf.model_name
+    trlx_config.tokenizer.tokenizer_path = training_conf.sft_model
+    trlx_config.model.model_path = training_conf.sft_model
     trlx_config.train.batch_size = training_conf.batch_size
 
     trainer = trlx.train(
-        training_conf.model_name,
+        training_conf.sft_model,
         reward_fn=rank_model_fn,
         prompts=prompts,
         config=trlx_config,
+        stop_sequences=[tokenizer.eos_token, QA_SPECIAL_TOKENS["Question"]],
     )
+
+    training_conf.output_dir = training_conf.output_dir if training_conf.output_dir else training_conf.model_name
+
+    trainer.save_pretrained(training_conf.output_dir)