PartialState().local_main_process_first() when map in examples

examples/datasets/anthropic_hh.py

@@ -79,11 +79,7 @@ def process(row):
         row["prompt"] = row["chosen"][0]["content"]
         return row
 
-    ds = ds.map(
-        process,
-        load_from_cache_file=False,
-        num_proc=args.dataset_num_proc,
-    )
+    ds = ds.map(process, num_proc=args.dataset_num_proc)
     if args.push_to_hub:
         revisions = ["main"] if args.update_main_revision else []
         revisions.append(args.revision)

examples/datasets/sentiment_descriptiveness.py

@@ -109,7 +109,7 @@ def filter(row):
             return True
 
     print("=== Before filtering ===", ds)
-    ds = ds.filter(filter, load_from_cache_file=False, num_proc=args.dataset_num_proc)
+    ds = ds.filter(filter, num_proc=args.dataset_num_proc)
     print("=== After filtering ===", ds)
 
     # here we simply take the preferred sample as the chosen one and the first non-preferred sample as the rejected one
@@ -146,12 +146,7 @@ def process(row):
             assert chosen_sample != rejected_sample
         return row
 
-    ds = ds.map(
-        process,
-        batched=True,
-        load_from_cache_file=False,
-        num_proc=args.dataset_num_proc,
-    )
+    ds = ds.map(process, batched=True, num_proc=args.dataset_num_proc)
     for key in ds:  # reorder columns
         ds[key] = ds[key].select_columns(["prompt", "chosen", "rejected"])
     if args.push_to_hub:

examples/datasets/tldr_preference.py

@@ -76,11 +76,7 @@ def process(row):
         row["rejected"] = [{"role": "user", "content": row["prompt"]}, {"role": "assistant", "content": rejected}]
         return row
 
-    ds = ds.map(
-        process,
-        load_from_cache_file=False,
-        num_proc=args.dataset_num_proc,
-    )
+    ds = ds.map(process, num_proc=args.dataset_num_proc)
     for key in ds:  # reorder columns
         ds[key] = ds[key].select_columns(
             ["prompt", "chosen", "rejected", "info", "summaries", "choice", "worker", "batch", "split", "extra"]
@@ -145,11 +141,7 @@ def sft_process(row):
         ]
         return row
 
-    sft_ds = sft_ds.map(
-        sft_process,
-        load_from_cache_file=False,
-        num_proc=args.dataset_num_proc,
-    )
+    sft_ds = sft_ds.map(sft_process, num_proc=args.dataset_num_proc)
     for key in sft_ds:  # reorder columns
         sft_ds[key] = sft_ds[key].select_columns(["prompt", "messages", "id", "subreddit", "title", "post", "summary"])
     if args.push_to_hub:

examples/datasets/tokenize_ds.py

@@ -38,9 +38,5 @@ def process(row):
         row["rejected"] = tokenizer.apply_chat_template(row["rejected"], tokenize=False)
         return row
 
-    ds = ds.map(
-        process,
-        load_from_cache_file=False,
-        num_proc=args.dataset_num_proc,
-    )
+    ds = ds.map(process, num_proc=args.dataset_num_proc)
     print(ds["train"][0]["chosen"])

examples/scripts/bco.py

@@ -183,17 +183,18 @@ def mean_pooling(model_output, attention_mask):
     if tokenizer.chat_template is None:
         model, tokenizer = setup_chat_format(model, tokenizer)
 
-    # Load the dataset
-    dataset = build_helpfulness_dataset(script_args.llm_name, num_proc=bco_args.dataset_num_proc)
-
     # Apply chat template
     def format_dataset(example):
         example["prompt"] = tokenizer.apply_chat_template(
             example["prompt"], tokenize=False, add_generation_prompt=True
         )
         return example
 
+    # Compute that only on the main process for faster data processing.
+    # see: https://github.com/huggingface/trl/pull/1255
     with PartialState().local_main_process_first():
+        # Load the dataset
+        dataset = build_helpfulness_dataset(script_args.llm_name, num_proc=bco_args.dataset_num_proc)
         formatted_dataset = dataset.map(format_dataset, batched=False, num_proc=bco_args.dataset_num_proc)
 
     accelerator = Accelerator()

examples/scripts/cpo.py

@@ -54,6 +54,7 @@
 
 from dataclasses import dataclass, field
 
+from accelerate import PartialState
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
 
@@ -99,11 +100,11 @@ def process(row):
         row["rejected"] = tokenizer.apply_chat_template(row["rejected"], tokenize=False)
         return row
 
-    ds = ds.map(
-        process,
-        load_from_cache_file=False,
-        num_proc=cpo_args.dataset_num_proc,
-    )
+    # Compute that only on the main process for faster data processing.
+    # see: https://github.com/huggingface/trl/pull/1255
+    with PartialState().local_main_process_first():
+        ds = ds.map(process, num_proc=cpo_args.dataset_num_proc)
+
     train_dataset = ds["train"]
     eval_dataset = ds["test"]
 

examples/scripts/dpo.py

@@ -70,7 +70,7 @@
 import torch
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer
-
+from accelerate import PartialState
 from trl import (
     DPOConfig,
     DPOTrainer,
@@ -159,11 +159,11 @@ def process(row):
         row["rejected"] = tokenizer.apply_chat_template([row["rejected"][-1]], tokenize=False)
         return row
 
-    ds = ds.map(
-        process,
-        load_from_cache_file=False,
-        num_proc=training_args.dataset_num_proc,
-    )
+    # Compute that only on the main process for faster data processing.
+    # see: https://github.com/huggingface/trl/pull/1255
+    with PartialState().local_main_process_first():
+        ds = ds.map(process, num_proc=training_args.dataset_num_proc)
+
     train_dataset = ds[args.dataset_train_split]
     eval_dataset = ds[args.dataset_test_split]
 

examples/scripts/dpo_visual.py

@@ -149,8 +149,11 @@ def process(row):
         row["rejected"] = processor.apply_chat_template(row["rejected"], tokenize=False)
         return row
 
+    # Compute that only on the main process for faster data processing.
+    # see: https://github.com/huggingface/trl/pull/1255
     with PartialState().local_main_process_first():
         ds = ds.map(process, num_proc=training_args.dataset_num_proc)
+
     train_dataset = ds[args.dataset_train_split]
     eval_dataset = ds[args.dataset_test_split]
 

examples/scripts/kto.py

@@ -55,6 +55,7 @@
 
 from dataclasses import dataclass
 
+from accelerate import PartialState
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
 
@@ -102,7 +103,10 @@ def format_dataset(example):
         example["completion"] = tokenizer.apply_chat_template(example["completion"], tokenize=False)
         return example
 
-    formatted_dataset = dataset.map(format_dataset, num_proc=kto_args.dataset_num_proc)
+    # Compute that only on the main process for faster data processing.
+    # see: https://github.com/huggingface/trl/pull/1255
+    with PartialState().local_main_process_first():
+        formatted_dataset = dataset.map(format_dataset, num_proc=kto_args.dataset_num_proc)
 
     # Initialize the KTO trainer
     kto_trainer = KTOTrainer(

examples/scripts/online_dpo.py

@@ -1,6 +1,7 @@
 from dataclasses import dataclass
 from typing import Optional
 
+from accelerate import PartialState
 from datasets import load_dataset
 from transformers import (
     AutoModelForCausalLM,
@@ -74,7 +75,6 @@ def tokenize(element):
         remove_columns=dataset.column_names,
         batched=True,
         num_proc=num_proc,
-        load_from_cache_file=False,
     )
 
 
@@ -105,13 +105,18 @@ def tokenize(element):
         for key in raw_datasets:
             raw_datasets[key] = raw_datasets[key].select(range(1024))
     train_dataset = raw_datasets[args.dataset_train_split]
-    train_dataset = prepare_dataset(train_dataset, tokenizer, args.dataset_text_field, config.dataset_num_proc)
 
-    if args.dataset_test_split is not None:
-        eval_dataset = raw_datasets[args.dataset_test_split]
-        eval_dataset = prepare_dataset(eval_dataset, tokenizer, args.dataset_text_field, config.dataset_num_proc)
-    else:
-        eval_dataset = None
+    # Compute that only on the main process for faster data processing.
+    # see: https://github.com/huggingface/trl/pull/1255
+    with PartialState().local_main_process_first():
+        train_dataset = prepare_dataset(train_dataset, tokenizer, args.dataset_text_field, config.dataset_num_proc)
+
+        if args.dataset_test_split is not None:
+            eval_dataset = raw_datasets[args.dataset_test_split]
+            eval_dataset = prepare_dataset(eval_dataset, tokenizer, args.dataset_text_field, config.dataset_num_proc)
+        else:
+            eval_dataset = None
+
     ################
     # Training
     ################

examples/scripts/orpo.py

@@ -54,6 +54,7 @@
 
 from dataclasses import dataclass, field
 
+from accelerate import PartialState
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
 
@@ -100,11 +101,11 @@ def process(row):
         row["rejected"] = tokenizer.apply_chat_template([row["rejected"][-1]], tokenize=False)
         return row
 
-    ds = ds.map(
-        process,
-        load_from_cache_file=False,
-        num_prc=orpo_args.dataset_num_proc,
-    )
+    # Compute that only on the main process for faster data processing.
+    # see: https://github.com/huggingface/trl/pull/1255
+    with PartialState().local_main_process_first():
+        ds = ds.map(process, num_prc=orpo_args.dataset_num_proc)
+
     train_dataset = ds["train"]
     eval_dataset = ds["test"]
 

examples/scripts/ppo.py

@@ -19,7 +19,7 @@
 from typing import Optional
 
 import torch
-from accelerate import Accelerator
+from accelerate import Accelerator, PartialState
 from datasets import load_dataset
 from peft import LoraConfig
 from tqdm import tqdm
@@ -53,11 +53,14 @@ class ScriptArguments:
 
 trl_model_class = AutoModelForCausalLMWithValueHead if not args.use_seq2seq else AutoModelForSeq2SeqLMWithValueHead
 
+tokenizer = AutoTokenizer.from_pretrained(ppo_config.model_name)
+tokenizer.pad_token = tokenizer.eos_token
+
 
 # Below is an example function to build the dataset. In our case, we use the IMDB dataset
 # from the `datasets` library. One should customize this function to train the model on
 # its own dataset.
-def build_dataset(config, query_dataset, input_min_text_length=2, input_max_text_length=8):
+def build_dataset(query_dataset, input_min_text_length=2, input_max_text_length=8):
     """
     Build dataset for training. This builds the dataset from `load_dataset`, one should
     customize this function to train the model on its own dataset.
@@ -70,8 +73,6 @@ def build_dataset(config, query_dataset, input_min_text_length=2, input_max_text
         dataloader (`torch.utils.data.DataLoader`):
             The dataloader for the dataset.
     """
-    tokenizer = AutoTokenizer.from_pretrained(config.model_name)
-    tokenizer.pad_token = tokenizer.eos_token
     # load imdb with datasets
     ds = load_dataset(query_dataset, split="train")
     ds = ds.rename_columns({"text": "review"})
@@ -90,7 +91,10 @@ def tokenize(sample):
 
 
 # We retrieve the dataloader by calling the `build_dataset` function.
-dataset = build_dataset(ppo_config, ppo_config.query_dataset)
+# Compute that only on the main process for faster data processing.
+# see: https://github.com/huggingface/trl/pull/1255
+with PartialState().local_main_process_first():
+    dataset = build_dataset(ppo_config, ppo_config.query_dataset)
 
 
 def collator(data):

examples/scripts/ppo/ppo.py

@@ -1,5 +1,6 @@
 import shutil
 
+from accelerate import PartialState
 from datasets import load_dataset
 from transformers import (
     AutoModelForCausalLM,
@@ -93,10 +94,15 @@ def tokenize(element):
             tokenize,
             batched=True,
             remove_columns=dataset.column_names,
-            load_from_cache_file=False,
             num_proc=config.dataset_num_proc,
         )
 
+    # Compute that only on the main process for faster data processing.
+    # see: https://github.com/huggingface/trl/pull/1255
+    with PartialState().local_main_process_first():
+        train_dataset = prepare_dataset(train_dataset, tokenizer)
+        eval_dataset = prepare_dataset(eval_dataset, tokenizer)
+
     ################
     # Training
     ################
@@ -107,8 +113,8 @@ def tokenize(element):
         ref_policy=ref_policy,
         reward_model=reward_model,
         value_model=value_model,
-        train_dataset=prepare_dataset(train_dataset, tokenizer),
-        eval_dataset=prepare_dataset(eval_dataset, tokenizer),
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
     )
     trainer.train()
     trainer.save_model(config.output_dir)

examples/scripts/ppo/ppo_tldr.py

@@ -1,5 +1,6 @@
 import shutil
 
+from accelerate import PartialState
 from datasets import load_dataset
 from transformers import (
     AutoModelForCausalLM,
@@ -101,11 +102,15 @@ def tokenize(element):
             num_proc=config.dataset_num_proc,
         )
 
-    train_dataset = prepare_dataset(train_dataset, tokenizer)
-    eval_dataset = prepare_dataset(eval_dataset, tokenizer)
-    # filtering
-    train_dataset = train_dataset.filter(lambda x: x["lengths"] <= 512, num_proc=config.dataset_num_proc)
-    eval_dataset = eval_dataset.filter(lambda x: x["lengths"] <= 512, num_proc=config.dataset_num_proc)
+    # Compute that only on the main process for faster data processing.
+    # see: https://github.com/huggingface/trl/pull/1255
+    with PartialState().local_main_process_first():
+        train_dataset = prepare_dataset(train_dataset, tokenizer)
+        eval_dataset = prepare_dataset(eval_dataset, tokenizer)
+        # filtering
+        train_dataset = train_dataset.filter(lambda x: x["lengths"] <= 512, num_proc=config.dataset_num_proc)
+        eval_dataset = eval_dataset.filter(lambda x: x["lengths"] <= 512, num_proc=config.dataset_num_proc)
+
     assert train_dataset[0]["input_ids"][-1] != tokenizer.eos_token_id, "The last token should not be an EOS token"
     ################
     # Training

examples/scripts/ppo_multi_adapter.py

@@ -15,6 +15,7 @@
 from typing import Optional
 
 import torch
+from accelerate import PartialState
 from datasets import load_dataset
 from peft import LoraConfig
 from tqdm import tqdm
@@ -95,7 +96,10 @@ def tokenize(example):
 
 tokenizer.pad_token = tokenizer.eos_token
 
-dataset = create_and_prepare_dataset(tokenizer, script_args.dataset_num_proc)
+# Compute that only on the main process for faster data processing.
+# see: https://github.com/huggingface/trl/pull/1255
+with PartialState().local_main_process_first():
+    dataset = create_and_prepare_dataset(tokenizer, script_args.dataset_num_proc)
 
 
 def collator(data):