Initial commit

verl/utils/dataset/rm_dataset.py

@@ -0,0 +1,143 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from typing import List, Union
+
+import pandas as pd
+
+import torch
+from torch.utils.data import Dataset
+from transformers import AutoTokenizer
+
+from verl.utils import hf_tokenizer
+
+
+def download_files_distributed(download_fn):
+    import torch.distributed
+    if torch.distributed.is_initialized():
+        if torch.distributed.get_rank() == 0:
+            # download files
+            download_fn()
+
+        torch.distributed.barrier()
+    else:
+        # download anyway
+        download_fn()
+
+
+class RMDataset(Dataset):
+
+    def __init__(self,
+                 parquet_files: Union[str, List[str]],
+                 tokenizer,
+                 prompt_key='prompt',
+                 chosen_key='chosen',
+                 rejected_key='rejected',
+                 max_length=1024,
+                 add_eos=True,
+                 cache_dir='~/.cache/verl/rm'):
+        if not isinstance(parquet_files, List):
+            parquet_files = [parquet_files]
+
+        self.parquet_files = parquet_files
+        self.cache_dir = os.path.expanduser(cache_dir)
+        if isinstance(tokenizer, str):
+            tokenizer = hf_tokenizer(tokenizer)
+        self.tokenizer = tokenizer
+
+        self.prompt_key = prompt_key
+        self.chosen_key = chosen_key
+        self.rejected_key = rejected_key
+
+        self.add_eos = add_eos
+        self.max_length = max_length
+
+        self._download()
+        self._read_files_and_tokenize()
+
+    def _download(self):
+
+        def _download_files():
+            from verl.utils.fs import copy, _is_non_local
+            os.makedirs(self.cache_dir, exist_ok=True)
+            assert os.path.exists(self.cache_dir)
+            for i, parquet_file in enumerate(self.parquet_files):
+                if _is_non_local(parquet_file):
+                    dst = os.path.join(self.cache_dir, os.path.basename(parquet_file))
+                    if not os.path.exists(dst):
+                        copy(src=parquet_file, dst=dst)
+                    self.parquet_files[i] = dst
+
+        download_files_distributed(_download_files)
+
+    def _read_files_and_tokenize(self):
+        dataframes = []
+        for parquet_file in self.parquet_files:
+            # read parquet files and cache
+            dataframe = pd.read_parquet(parquet_file)
+            dataframes.append(dataframe)
+        self.dataframe = pd.concat(dataframes)
+        self.prompts = self.dataframe[self.prompt_key].tolist()
+        self.chosen_responses = self.dataframe[self.chosen_key].tolist()
+        self.rejected_responses = self.dataframe[self.rejected_key].tolist()
+
+    def __len__(self):
+        return len(self.prompts)
+
+    def _pad_to_length(self, input_ids, attention_mask):
+        curr_length = input_ids.shape[-1]
+
+        if curr_length < self.max_length:
+            input_ids = torch.cat(
+                (input_ids, torch.zeros(size=(self.max_length - curr_length,), dtype=input_ids.dtype)), dim=-1)
+            attention_mask = torch.cat(
+                (attention_mask, torch.zeros(size=(self.max_length - curr_length,), dtype=attention_mask.dtype)),
+                dim=-1)
+        elif curr_length > self.max_length:
+            input_ids = input_ids[:self.max_length]
+            attention_mask = attention_mask[:self.max_length]
+
+        return input_ids, attention_mask
+
+    def __getitem__(self, item):
+        prompt = self.prompts[item]
+        chosen_response = self.chosen_responses[item]
+        rejected_response = self.rejected_responses[item]
+
+        prompt_ids = self.tokenizer(prompt, return_tensors='pt')['input_ids'][0]
+        chosen_response_ids = self.tokenizer(chosen_response, return_tensors='pt')['input_ids'][0]
+        rejected_response_ids = self.tokenizer(rejected_response, return_tensors='pt')['input_ids'][0]
+
+        if self.add_eos:
+            chosen_response_ids = torch.cat((chosen_response_ids, torch.tensor([self.tokenizer.eos_token_id])), dim=-1)
+            rejected_response_ids = torch.cat((rejected_response_ids, torch.tensor([self.tokenizer.eos_token_id])),
+                                              dim=-1)
+
+        chosen_input_ids = torch.cat((prompt_ids, chosen_response_ids), dim=-1)
+        chosen_attention_mask = torch.ones_like(chosen_input_ids)
+
+        rejected_input_ids = torch.cat((prompt_ids, rejected_response_ids), dim=-1)
+        rejected_attention_mask = torch.ones_like(rejected_input_ids)
+
+        chosen_input_ids, chosen_attention_mask = self._pad_to_length(chosen_input_ids, chosen_attention_mask)
+        rejected_input_ids, rejected_attention_mask = self._pad_to_length(rejected_input_ids, rejected_attention_mask)
+
+        input_ids = torch.stack((chosen_input_ids, rejected_input_ids), dim=0)
+        attention_mask = torch.stack((rejected_input_ids, rejected_attention_mask), dim=0)
+
+        return {
+            'input_ids': input_ids,
+            'attention_mask': attention_mask,
+        }