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bigram_model.py
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bigram_model.py
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from argparse import ArgumentParser
import struct
from functools import lru_cache
from itertools import accumulate
import time
import os
import numpy as np
import torch
from tqdm import tqdm
GPTNEOX_TOKENIZER_SIZE=50276
DATASET_PATH="/share/edc/home/alon_albalak/data/pile/preprocessed/{}/{}/{}_text_document"
def print_rank_0(*message):
"""If distributed is initialized print only on rank 0."""
if torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
print(*message, flush=True)
else:
print(*message, flush=True)
dtypes = {
1: np.uint8,
2: np.int8,
3: np.int16,
4: np.int32,
5: np.int64,
6: np.float32,
7: np.float64,
8: np.uint16,
}
def code(dtype):
for k in dtypes.keys():
if dtypes[k] == dtype:
return k
raise ValueError(dtype)
def _warmup_mmap_file(path):
with open(path, "rb") as stream:
while stream.read(100 * 1024 * 1024):
pass
def index_file_path(prefix_path):
return prefix_path + ".idx"
def data_file_path(prefix_path):
return prefix_path + ".bin"
class MMapIndexedDataset(torch.utils.data.Dataset):
class Index(object):
_HDR_MAGIC = b"MMIDIDX\x00\x00"
@classmethod
def writer(cls, path, dtype):
class _Writer(object):
def __enter__(self):
self._file = open(path, "wb")
# Write Magic string so we can check the file format then opening it again.
self._file.write(cls._HDR_MAGIC)
# Write version number
# Little endian unsigned 64 Bit integer
self._file.write(struct.pack("<Q", 1))
# Little endian unsigned 8 Bit integer
self._file.write(struct.pack("<B", code(dtype)))
return self
@staticmethod
def _get_pointers(sizes):
pointers = np.zeros(len(sizes), dtype=np.int64)
sizes = np.array(sizes, dtype=np.int64)
np.cumsum(sizes[:-1], out=pointers[1:])
pointers = pointers * dtype().itemsize
return pointers
def write(self, sizes, doc_idx):
pointers = self._get_pointers(sizes)
# Little endian unsigned 64 Bit integer
self._file.write(struct.pack("<Q", len(sizes)))
# Little endian unsigned 64 Bit integer
self._file.write(struct.pack("<Q", len(doc_idx)))
sizes = np.array(sizes, dtype=np.int32)
self._file.write(sizes.tobytes(order="C"))
del sizes
pointers = np.array(pointers, dtype=np.int64)
self._file.write(pointers.tobytes(order="C"))
del pointers
doc_idx = np.array(doc_idx, dtype=np.int64)
self._file.write(doc_idx.tobytes(order="C"))
def __exit__(self, exc_type, exc_val, exc_tb):
self._file.close()
return _Writer()
def __init__(self, path, skip_warmup=False):
with open(path, "rb") as stream:
magic_test = stream.read(9)
assert self._HDR_MAGIC == magic_test, (
"Index file doesn't match expected format. "
"Make sure that --dataset-impl is configured properly."
)
# Little endian unsigned 64 Bit integer
version = struct.unpack("<Q", stream.read(8))
assert (1,) == version
# Little endian unsigned 8 Bit integer
(dtype_code,) = struct.unpack("<B", stream.read(1))
self._dtype = dtypes[dtype_code]
self._dtype_size = self._dtype().itemsize
self._len = struct.unpack("<Q", stream.read(8))[0]
self._doc_count = struct.unpack("<Q", stream.read(8))[0]
offset = stream.tell()
if not skip_warmup:
print_rank_0(" warming up index mmap file...")
_warmup_mmap_file(path)
self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
self._bin_buffer = memoryview(self._bin_buffer_mmap)
print_rank_0(" reading sizes...")
self._sizes = np.frombuffer(
self._bin_buffer, dtype=np.int32, count=self._len, offset=offset
)
print_rank_0(" reading pointers...")
self._pointers = np.frombuffer(
self._bin_buffer,
dtype=np.int64,
count=self._len,
offset=offset + self._sizes.nbytes,
)
print_rank_0(" reading document index...")
self._doc_idx = np.frombuffer(
self._bin_buffer,
dtype=np.int64,
count=self._doc_count,
offset=offset + self._sizes.nbytes + self._pointers.nbytes,
)
def __del__(self):
self._bin_buffer_mmap._mmap.close()
del self._bin_buffer_mmap
@property
def dtype(self):
return self._dtype
@property
def sizes(self):
return self._sizes
@property
def doc_idx(self):
return self._doc_idx
@lru_cache(maxsize=8)
def __getitem__(self, i):
return self._pointers[i], self._sizes[i]
def __len__(self):
return self._len
def __init__(self, path, skip_warmup=False):
super().__init__()
self._path = None
self._index = None
self._bin_buffer = None
self._do_init(path, skip_warmup)
def __getstate__(self):
return self._path
def __setstate__(self, state):
self._do_init(state)
def _do_init(self, path, skip_warmup):
self._path = path
self._index = self.Index(index_file_path(self._path), skip_warmup)
if not skip_warmup:
print_rank_0(" warming up data mmap file...")
_warmup_mmap_file(data_file_path(self._path))
print_rank_0(" creating numpy buffer of mmap...")
self._bin_buffer_mmap = np.memmap(
data_file_path(self._path), mode="r", order="C"
)
print_rank_0(" creating memory view of numpy buffer...")
self._bin_buffer = memoryview(self._bin_buffer_mmap)
def __del__(self):
self._bin_buffer_mmap._mmap.close()
del self._bin_buffer_mmap
del self._index
def __len__(self):
return len(self._index)
# @lru_cache(maxsize=8)
def __getitem__(self, idx):
if isinstance(idx, int):
ptr, size = self._index[idx]
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
)
return np_array
elif isinstance(idx, slice):
start, stop, step = idx.indices(len(self))
if step != 1:
raise ValueError("Slices into indexed_dataset must be contiguous")
ptr = self._index._pointers[start]
sizes = self._index._sizes[idx]
offsets = list(accumulate(sizes))
total_size = sum(sizes)
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=total_size, offset=ptr
)
sents = np.split(np_array, offsets[:-1])
return sents
def get(self, idx, offset=0, length=None):
"""Retrieves a single item from the dataset with the option to only
return a portion of the item.
get(idx) is the same as [idx] but get() does not support slicing.
"""
ptr, size = self._index[idx]
if length is None:
length = size - offset
ptr += offset * np.dtype(self._index.dtype).itemsize
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=length, offset=ptr
)
return np_array
@property
def sizes(self):
return self._index.sizes
@property
def doc_idx(self):
return self._index.doc_idx
def get_doc_idx(self):
return self._index._doc_idx
def set_doc_idx(self, doc_idx_):
self._index._doc_idx = doc_idx_
@property
def supports_prefetch(self):
return False
@staticmethod
def exists(path):
return os.path.exists(index_file_path(path)) and os.path.exists(
data_file_path(path)
)
class GPT2Dataset(torch.utils.data.Dataset):
def __init__(
self,
name,
data_prefix,
documents,
indexed_dataset,
num_samples,
seq_length,
seed,
build_index_mappings=True,
use_shared_fs=True,
max_samples = None,
name_passthrough = False
):
self.name = name
self.indexed_dataset = indexed_dataset
self.data_prefix=data_prefix
self.documents=documents
self.seq_length=seq_length
self.seed=seed
self.use_shared_fs=use_shared_fs
self.max_samples = max_samples
self.name_passthrough = name_passthrough
if num_samples is None:
self._repeatable=True
self._completed_epochs = 0
# Checks
assert np.min(documents) >= 0
assert np.max(documents) < indexed_dataset.sizes.shape[0]
if build_index_mappings:
# Build index mappings.
self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings_single_epoch(
self.name,
self.data_prefix,
self.documents,
self.indexed_dataset.sizes,
self.seq_length,
self.seed,
self.use_shared_fs,
self._completed_epochs
)
if self.max_samples is not None:
if self.max_samples > self.shuffle_idx.shape[0] - 1:
print_rank_0(f"WARNING: max_samples ({self.max_samples}) is greater than the number of samples ({self.shuffle_idx.shape[0] - 1})")
else:
print_rank_0(f"Resetting number of samples in {self.name} from {len(self.shuffle_idx)} to {self.max_samples}")
self.shuffle_idx = self.shuffle_idx[:self.max_samples+1]
self.shuffle_idx_len = self.shuffle_idx.shape[0] - 1
self.sample_idx_len = self.sample_idx.shape[0] - 1
if self.shuffle_idx_len != self.sample_idx_len:
print_rank_0(
f"WARNING: shuffle index length ({self.shuffle_idx_len}) is not equal to sample index length ({self.sample_idx_len})"
)
def __len__(self):
return min(self.shuffle_idx_len, self.sample_idx_len)
def __getitem__(self, idx):
try:
# Get the shuffled index.
idx = self.shuffle_idx[idx]
# Start and end documents and offsets.
doc_index_f = self.sample_idx[idx][0]
doc_index_l = self.sample_idx[idx + 1][0]
offset_f = self.sample_idx[idx][1]
offset_l = self.sample_idx[idx + 1][1]
# If we are within the same document, just extract the chunk.
if doc_index_f == doc_index_l:
sample = self.indexed_dataset.get(
self.doc_idx[doc_index_f],
offset=offset_f,
length=offset_l - offset_f + 1,
)
else:
# Otherwise, get the rest of the initial document.
sample_list = [
self.indexed_dataset.get(self.doc_idx[doc_index_f], offset=offset_f)
]
# Loop over all in between documents and add the entire document.
for i in range(doc_index_f + 1, doc_index_l):
sample_list.append(self.indexed_dataset.get(self.doc_idx[i]))
# And finally add the relevant portion of last document.
sample_list.append(
self.indexed_dataset.get(
self.doc_idx[doc_index_l], length=offset_l + 1
)
)
sample = np.concatenate(sample_list)
if self.name_passthrough:
return {"text": np.array(sample, dtype=np.int64), "dataset_name": self.name}
else:
return {"text": np.array(sample, dtype=np.int64)}
except IndexError:
new_idx = idx % len(self)
print(
f"WARNING: Got index out of bounds error with index {idx} - taking modulo of index instead ({new_idx})"
)
return self[new_idx]
def _build_index_mappings_single_epoch(
name, data_prefix, documents, sizes, seq_length, seed, use_shared_fs=True, epoch=0
):
"""Build doc-idx, sample-idx, and shuffle-idx.
doc-idx: is an array (ordered) of documents to be used in training.
sample-idx: is the start document index and document offset for each
training sample.
shuffle-idx: maps the sample index into a random index into sample-idx.
"""
# Filename of the index mappings.
_filename = data_prefix
_filename += "_{}_indexmap".format(name)
_filename += "_ep{}".format(epoch)
_filename += "_{}sl".format(seq_length)
_filename += "_{}s".format(seed)
doc_idx_filename = _filename + "_doc_idx.npy"
sample_idx_filename = _filename + "_sample_idx.npy"
shuffle_idx_filename = _filename + "_shuffle_idx.npy"
# Load mappings.
start_time = time.time()
print_rank_0(" > loading doc-idx mapping from {}".format(doc_idx_filename))
doc_idx = np.load(doc_idx_filename, allow_pickle=True, mmap_mode="r")
print_rank_0(" > loading sample-idx mapping from {}".format(sample_idx_filename))
sample_idx = np.load(sample_idx_filename, allow_pickle=True, mmap_mode="r")
print_rank_0(" > loading shuffle-idx mapping from {}".format(shuffle_idx_filename))
shuffle_idx = np.load(shuffle_idx_filename, allow_pickle=True, mmap_mode="r")
print_rank_0(
" loaded indexed file in {:3.3f} seconds".format(time.time() - start_time)
)
print_rank_0(" total number of samples: {}".format(sample_idx.shape[0]))
return doc_idx, sample_idx, shuffle_idx
def _num_tokens(documents, sizes):
"""Total number of tokens in the dataset."""
return np.sum(sizes[documents])
def get_the_dataset(
data_prefix,
name,
num_samples=None,
seq_length=1024,
seed=42,
skip_warmup=True,
build_index_mappings=True,
max_samples=None,
name_passthrough=False,
):
indexed_dataset = MMapIndexedDataset(data_prefix, skip_warmup=skip_warmup)
total_num_of_documents = indexed_dataset.sizes.shape[0]
print_rank_0(" {}:".format(name))
print_rank_0(" no. of documents:{}".format(total_num_of_documents))
dataset = None
documents = np.arange(start=0, stop=total_num_of_documents, step=1, dtype=np.int32)
dataset = GPT2Dataset(
name,
data_prefix,
documents,
indexed_dataset,
num_samples,
seq_length,
seed,
build_index_mappings=build_index_mappings,
max_samples=max_samples,
name_passthrough=name_passthrough
)
return dataset
class bigram:
def __init__(self, vocab_size):
self.counts = np.ones((vocab_size+1, vocab_size+1), dtype=np.uint32)
self.vocab_size = vocab_size
self.total_count = vocab_size ** 2
def update(self, x):
self.counts[x[:-1], x[1:]] += 1
self.total_count += len(x)
def get_prob(self, x):
return self.counts[x[:-1], x[1:]] / self.total_count
def get_log_prob(self, x):
return np.log(self.get_prob(x))
def get_perplexity(self, x):
return np.exp(-np.sum(self.get_log_prob(x)) / len(x))
def get_entropy(self, x):
return -np.sum(self.get_log_prob(x)) / len(x)
def train_bigram(dataset_name, num_samples):
print("**** Loading dataset...")
split="train"
data_path = DATASET_PATH.format(split, dataset_name, dataset_name)
indexed_dataset = get_the_dataset(data_path, f"{split}_{dataset_name}")
print(f"**** Dataset disk size: {len(indexed_dataset)*1025*8/1024/1024/1024} GB")
if len(indexed_dataset) < num_samples:
print(f"**** Dataset has only {len(indexed_dataset)} samples, training on all of them")
num_samples = len(indexed_dataset)
print(f"**** Creating bigram model...")
start_time = time.time()
bigram_model = bigram(GPTNEOX_TOKENIZER_SIZE)
print(f"**** Created bigram model in {time.time() - start_time} seconds")
print("**** Training bigram model...")
for i in tqdm(range(num_samples)):
x = indexed_dataset[i]["text"]
bigram_model.update(x)
print("**** Trained bigram model in {:3.3f} seconds".format(time.time() - start_time))
return bigram_model
def evaluate_bigram(bigram_model, dataset_name, num_samples, split):
print(f"**** Loading evaluation split: {split}...")
data_path = DATASET_PATH.format(split, dataset_name, dataset_name)
if split == "validation":
indexed_dataset = get_the_dataset(data_path, f"valid_{dataset_name}")
else:
indexed_dataset = get_the_dataset(data_path, f"{split}_{dataset_name}")
print(f"**** Dataset disk size: {len(indexed_dataset)*1025*8/1024/1024/1024} GB")
if len(indexed_dataset) < num_samples:
print(f"**** Dataset has only {len(indexed_dataset)} samples, evaluating all of them")
num_samples = len(indexed_dataset)
print("**** Evaluating bigram model...")
start_time = time.time()
lm_loss = 0
perplexity = 0
for i in tqdm(range(num_samples)):
x = indexed_dataset[i]["text"]
lm_loss += bigram_model.get_entropy(x)
perplexity += bigram_model.get_perplexity(x)
print("**** Evaluated bigram model in {:3.3f} seconds".format(time.time() - start_time))
return lm_loss / num_samples, perplexity / num_samples
def main():
parser = ArgumentParser()
parser.add_argument(
"--save_path",
type=str,
default="/share/edc/home/alon_albalak/bigram_models",
help="The path to save the bigram model",
)
parser.add_argument(
"--dataset_name",
type=str,
required=True,
help="The name of the dataset to train on",
)
parser.add_argument(
"--train_samples",
type=int,
required=True,
help="The number of samples to train on",
)
parser.add_argument(
"--train",
action="store_true",
help="Train a bigram model",
)
parser.add_argument(
"--evaluate_samples",
type=int,
default=10000000,
help="The number of samples to evaluate on",
)
parser.add_argument(
"--evaluate",
action="store_true",
help="Evaluate a bigram model",
)
args = parser.parse_args()
save_path = args.save_path
dataset_name = args.dataset_name
train_samples = args.train_samples
evaluate_samples = args.evaluate_samples
train = args.train
evaluate = args.evaluate
if not os.path.exists(save_path):
os.makedirs(save_path)
if train:
if not os.path.exists(os.path.join(save_path,f"{dataset_name}_bigram_model.npy")):
bigram_model = train_bigram(dataset_name, train_samples)
print(f"**** Saving bigram model...")
np.save(os.path.join(save_path,f"{dataset_name}_bigram_model.npy"), bigram_model.counts)
print(f"**** Saved bigram model")
else:
print(f"**** Bigram model already exists in {save_path}, skipping training")
if evaluate:
bigram_model = bigram(GPTNEOX_TOKENIZER_SIZE)
bigram_model.counts = np.load(os.path.join(save_path,f"{dataset_name}_bigram_model.npy"))
print("Evaluation results:")
for split in ["train", "validation", "test"]:
lm_loss, perplexity = evaluate_bigram(bigram_model, dataset_name, evaluate_samples, split)
print(f"lm_loss {split}: {lm_loss}")
print(f"perplexity {split}: {perplexity}")
if __name__ == "__main__":
main()