wehub-resource-sync
185 lines
7.8 KiB
Python
185 lines
7.8 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 hashlib
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import importlib.metadata
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import os
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import time
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import numpy as np
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import paddle
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local_rank = int(os.getenv("PADDLE_RANK_IN_NODE", 0))
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def print_rank_0(*args, **kwargs):
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if paddle.distributed.get_rank() == 0:
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print(*args, **kwargs)
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class BlendableDataset(paddle.io.Dataset):
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def __init__(self, datasets, weights, size, share_folder, *, data_cache_path=None):
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self.datasets = datasets
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num_datasets = len(datasets)
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assert num_datasets == len(weights)
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self.size = size
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# Normalize weights.
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weights = np.array(weights, dtype=np.float64)
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sum_weights = np.sum(weights)
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assert sum_weights > 0.0
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weights /= sum_weights
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# Build indices.
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def _build_indices():
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start_time = time.time()
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fast_dataindex_version = importlib.metadata.version("fast_dataindex")
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if fast_dataindex_version > "0.1.1":
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assert (
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num_datasets < 32767
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), f"Detect num_datasets({num_datasets})>=32767. Currently, num_datasets should be less than 32767."
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dataset_index = np.zeros(self.size, dtype=np.int16)
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else:
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assert (
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num_datasets < 255
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), f"Detect num_datasets:({num_datasets})>=255. When 'fast_dataindex<=0.1.1', num_datasets should be less than 255. To support num_datasets greater than 255, please upgrade `fast_dataindex>=0.1.2`."
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dataset_index = np.zeros(self.size, dtype=np.uint8)
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dataset_sample_index = np.zeros(self.size, dtype=np.int64)
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from fast_dataindex import helpers
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helpers.build_blending_indices(
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dataset_index,
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dataset_sample_index,
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weights,
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num_datasets,
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self.size,
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local_rank == 0,
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# paddle.distributed.get_rank() == 0,
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)
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print_rank_0(
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"> elapsed time for building blendable dataset indices: "
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"{:.2f} (sec)".format(time.time() - start_time)
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)
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return dataset_index, dataset_sample_index
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desc = "Blendable dataset\n\n"
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desc += "Datasets:\n"
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for dataset in datasets:
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desc += dataset.desc + "\n\n"
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desc += f"Weights: {weights}\n"
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desc += f"Size: {size}\n"
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self.desc = desc
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if data_cache_path:
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desc_hash = hashlib.md5(desc.encode("utf-8")).hexdigest()
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desc_path = os.path.join(data_cache_path, desc_hash + ".dsc")
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index_path = os.path.join(data_cache_path, desc_hash + "_index.npy")
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sample_index_path = os.path.join(data_cache_path, desc_hash + "_sample_index.npy")
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cache_hit = os.path.isfile(index_path) and os.path.isfile(sample_index_path)
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# cache_success = True
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# if paddle.distributed.get_rank() == 0 and not cache_hit:
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check_rank_flag = not cache_hit and local_rank == 0
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if share_folder:
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check_rank_flag = not cache_hit and paddle.distributed.get_rank() == 0
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print(
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f"searching for blendable dataset, cache_hit={cache_hit}, share_folder {share_folder}, check_rank_flag {check_rank_flag}",
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flush=True,
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)
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if check_rank_flag:
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print(
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" > WARNING: could not find index map files for blendable"
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" dataset, building indices on rank 0 ...",
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flush=True,
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)
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dataset_index, dataset_sample_index = _build_indices()
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try:
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os.makedirs(os.path.dirname(index_path), exist_ok=True)
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with open(desc_path, "wt") as fd:
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fd.write(desc)
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np.save(index_path, dataset_index, allow_pickle=True)
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np.save(sample_index_path, dataset_sample_index, allow_pickle=True)
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except OSError:
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print(f"There was an error trying to create the data cache directory ({data_cache_path})")
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print("or a file in it. This is set with the --data-cache-path argument. Please")
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print("ensure you have write access to this directory or specify one that you do have")
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print("write access to.")
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# cache_success = False
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# hcg = paddle.distributed.fleet.get_hybrid_communicate_group()
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# counts = paddle.to_tensor([cache_success], dtype="int64")
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# paddle.distributed.all_reduce(counts, group=hcg.get_data_parallel_group())
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# paddle.distributed.all_reduce(counts, group=hcg.get_pipeline_model_parallel_group())
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# if counts[0].item() != (
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# paddle.distributed.get_world_size()
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# // paddle.distributed.get_world_size(group=hcg.get_tensor_model_parallel_group())
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# ):
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# print_rank_0("Data index creation unsuccessful, exiting.")
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# exit()
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else:
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while True:
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if (not os.path.isfile(index_path)) or (not os.path.isfile(sample_index_path)):
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print("building indices on rank 0 ...", flush=True)
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time.sleep(3)
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else:
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try:
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np.load(index_path, allow_pickle=True, mmap_mode="r")
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print("build success", flush=True)
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break
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except Exception:
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print("%s file is still writing or damaged, please wait for a moment." % index_path)
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time.sleep(3)
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# paddle.distributed.barrier()
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# Load on all ranks.
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print_rank_0(f"> loading blendable dataset index: {index_path}")
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self.dataset_index = np.load(index_path, allow_pickle=True, mmap_mode="r")
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assert self.dataset_index.size == self.size
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print_rank_0(f"> loading blendable dataset sample index: {sample_index_path}")
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self.dataset_sample_index = np.load(sample_index_path, allow_pickle=True, mmap_mode="r")
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assert self.dataset_sample_index.size == self.size
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else:
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print_rank_0(
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"building indices for the blendable dataset, Since --data_cache is not specified, the index file will not be stored.",
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flush=True,
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)
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self.dataset_index, self.dataset_sample_index = _build_indices()
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# Check size
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_ = self.__getitem__(self.size - 1)
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try:
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_ = self.__getitem__(self.size)
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raise RuntimeError("BlendedDataset size is improperly bounded")
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except IndexError:
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pass
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print_rank_0("> size of blendable dataset: " "{} samples".format(self.size))
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def __len__(self):
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return self.size
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def __getitem__(self, idx):
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dataset_idx = self.dataset_index[idx]
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sample_idx = self.dataset_sample_index[idx]
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return {
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"dataset_idx": dataset_idx,
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**self.datasets[dataset_idx][sample_idx],
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}
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