wehub-resource-sync
235 lines
10 KiB
Python
235 lines
10 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from paddle.io import Dataset, IterableDataset
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from scipy.linalg import block_diag
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def generate_greedy_packs(examples, max_length):
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left_len = np.zeros([len(examples)]) - 1
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left_len[0] = max_length # At the beginning, only the first pack is valid.
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generate_packs = [[] for i in range(len(examples))]
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index, left_index = 0, 0
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while index < len(examples):
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record = examples[index]
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max_left_index = left_len.argmax()
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# Put the current sequence into the largest left space valid pack.
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if len(record["input_ids"]) <= left_len[max_left_index]:
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generate_packs[max_left_index].append(record)
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left_len[max_left_index] -= len(record["input_ids"])
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index += 1
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else:
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left_index += 1
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left_len[left_index] = max_length
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return generate_packs
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class ZeroPadding:
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required_output_keys = ["input_ids", "labels", "attention_mask"]
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# Only supported the following keys for ZeroPadding. Keys outside of the set will be ignored.
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supported_input_keys = [
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"input_ids",
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"labels",
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"attention_mask",
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"position_ids",
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"response_0_labels",
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"response_1_labels",
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"response_indexs",
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"attn_mask_startend_row_indices",
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]
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@classmethod
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def _pad_batch_records(cls, batch_records):
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# Only consider supported input keys
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input_keys = [key for key in batch_records[0].keys() if key in cls.supported_input_keys]
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if "attn_mask_startend_row_indices" not in input_keys and "attention_mask" not in input_keys:
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input_keys.append("attention_mask")
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batched_features = {key: [] for key in input_keys}
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sequence_sum = 0
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for record in batch_records:
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batched_features["input_ids"].extend(record["input_ids"])
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if "labels" in record:
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batched_features["labels"].extend(record["labels"])
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elif "response_1_labels" in input_keys and "response_0_labels" in input_keys:
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batched_features["response_1_labels"].extend(record["response_1_labels"])
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batched_features["response_0_labels"].extend(record["response_0_labels"])
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response_indexs = [
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ri + sequence_sum if i < 3 else ri for i, ri in enumerate(record["response_indexs"])
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]
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batched_features["response_indexs"].append(response_indexs)
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elif "response_indexs" in input_keys:
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response_indexs = [
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ri + sequence_sum if i < 3 else ri for i, ri in enumerate(record["response_indexs"])
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]
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batched_features["response_indexs"].append(response_indexs)
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else:
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raise ValueError("labels is required for ZeroPadding Dataset")
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seq_length = len(record["input_ids"])
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# If attention_mask is not given, assume it's causal mask
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if "attn_mask_startend_row_indices" in record:
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attn_mask_startend_row_indices = [i + sequence_sum for i in record["attn_mask_startend_row_indices"]]
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batched_features["attn_mask_startend_row_indices"].extend(attn_mask_startend_row_indices)
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else:
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attention_mask = record.get("attention_mask", np.tril(np.ones([seq_length, seq_length], dtype=bool)))
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batched_features["attention_mask"].append(attention_mask)
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# NOTE: position_ids is optional and not required by every model
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# We append instead of extend here to accommodate 2D position ids
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if "position_ids" in record:
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batched_features["position_ids"].append(record["position_ids"])
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sequence_sum += seq_length
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if "attention_mask" in batched_features:
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block_attention_mask = block_diag(*batched_features["attention_mask"])
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# convert to 3-D [batch_size(1), seq_length, seq_length]
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batched_features["attention_mask"] = np.expand_dims(block_attention_mask, axis=0)
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if "position_ids" in batched_features:
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# Accommodate both 1D and 2D position ids
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batched_features["position_ids"] = np.concatenate(batched_features["position_ids"], axis=-1).tolist()
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return batched_features
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class ZeroPaddingMapDataset(ZeroPadding, Dataset):
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def __init__(self, data, tokenizer, max_length, greedy_zero_padding=False):
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self.tokenizer = tokenizer
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self.max_length = max_length
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self.greedy_zero_padding = greedy_zero_padding
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self.new_data = self._create_zero_padding_data(data)
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def _create_zero_padding_data(self, data):
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total_data = []
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if not self.greedy_zero_padding:
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batch_records = []
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cur_len_so_far = 0
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for i in range(len(data)):
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record = data[i]
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if len(record["input_ids"]) > self.max_length:
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continue
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to_append = (cur_len_so_far + len(record["input_ids"])) <= self.max_length
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if to_append:
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batch_records.append(record)
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cur_len_so_far += len(record["input_ids"])
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else:
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# exceed max length
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padded_list = self._pad_batch_records(batch_records)
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total_data.append(padded_list)
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# reset
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batch_records = []
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cur_len_so_far = 0
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# append current data
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batch_records.append(record)
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cur_len_so_far += len(record["input_ids"])
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# remaining data
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if batch_records:
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padded_list = self._pad_batch_records(batch_records)
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total_data.append(padded_list)
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else:
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examples = []
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buffer_size = 500
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i = 0
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for record in data:
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if len(record["input_ids"]) > self.max_length:
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continue
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if i < buffer_size:
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examples.append(record)
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i += 1
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else:
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# Running greedy strategy in examples.
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generate_packs = generate_greedy_packs(examples, self.max_length)
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for batch_records in generate_packs:
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if len(batch_records) > 0:
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padded_list = self._pad_batch_records(batch_records)
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total_data.append(padded_list)
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examples = [record]
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i = 1
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if len(examples) > 0:
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generate_packs = generate_greedy_packs(examples, self.max_length)
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for batch_records in generate_packs:
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if len(batch_records) > 0:
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padded_list = self._pad_batch_records(batch_records)
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total_data.append(padded_list)
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return total_data
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def __getitem__(self, idx):
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return self.new_data[idx]
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def __len__(self):
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return len(self.new_data)
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class ZeroPaddingIterableDataset(ZeroPadding, IterableDataset):
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def __init__(self, data, tokenizer, max_length, greedy_zero_padding=False):
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self.data = data
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self.tokenizer = tokenizer
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self.max_length = max_length
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self.zero_padding_global_step = 0
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self.greedy_zero_padding = greedy_zero_padding
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def __iter__(self):
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if not self.greedy_zero_padding:
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batch_records = []
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cur_len_so_far = 0
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for record in self.data:
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to_append = (cur_len_so_far + len(record["input_ids"])) <= self.max_length
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if to_append:
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batch_records.append(record)
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self.zero_padding_global_step += 1
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cur_len_so_far += len(record["input_ids"])
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else:
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# exceed max length
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padded_list = self._pad_batch_records(batch_records)
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yield padded_list
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# reset
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batch_records = []
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cur_len_so_far = 0
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# append current data
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batch_records.append(record)
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self.zero_padding_global_step += 1
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cur_len_so_far += len(record["input_ids"])
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if batch_records:
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padded_list = self._pad_batch_records(batch_records)
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yield padded_list
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else:
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examples = []
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buffer_size = 500
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i = 0
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for record in self.data:
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if len(record["input_ids"]) > self.max_length:
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continue
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if i < buffer_size:
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examples.append(record)
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self.zero_padding_global_step += 1
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i += 1
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else:
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# Running greedy strategy in examples.
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generate_packs = generate_greedy_packs(examples, self.max_length)
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for batch_records in generate_packs:
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if len(batch_records) > 0:
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padded_list = self._pad_batch_records(batch_records)
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yield padded_list
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examples = [record]
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self.zero_padding_global_step += 1
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i = 1
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if len(examples) > 0:
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generate_packs = generate_greedy_packs(examples, self.max_length)
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for batch_records in generate_packs:
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if len(batch_records) > 0:
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padded_list = self._pad_batch_records(batch_records)
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yield padded_list
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