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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import json
import numpy as np
import os
import torch
import types
from megatron.global_vars import set_global_variables, set_retro_args
from megatron.initialize import (
initialize_megatron,
_initialize_distributed,
_set_random_seed,
_compile_dependencies,
)
from tools.retro.db.utils import (
get_indexed_dataset_infos as get_db_indexed_dataset_infos,
get_merged_train_dataset as get_db_dataset,
)
from tools.retro.main import add_retro_args
from tools.retro.query.retro_dataset import get_retro_datasets
from tools.retro.utils import get_args_path, get_bert_tokenizer, get_gpt_tokenizer
def shorten_str(s, n):
s = "\\n".join(s.splitlines())
return s if len(s) <= n else "%s ... %s" % (s[:n//2], s[-n//2:])
class retro:
args = None
##############################################
# initialize.
##############################################
@classmethod
def parse_dtype_str(cls, dtype_str):
return {
"torch.float16" : torch.float16,
"torch.float32" : torch.float32,
"torch.bfloat16" : torch.bfloat16,
}[dtype_str]
@classmethod
def init_megatron(cls, workdir):
'''Custom initialization of Megatron.'''
# Load args.
args_path = get_args_path(workdir)
assert os.path.exists(args_path), "args.json not found in workdir."
with open(args_path) as f:
cls.args = types.SimpleNamespace(**json.load(f))
cls.args.retro_workdir = workdir # just in case workdir moved
cls.args.rank = 0 # override env
cls.args.world_size = 1 # override env
cls.args.params_dtype = cls.parse_dtype_str(cls.args.params_dtype)
set_global_variables(cls.args)
set_retro_args(cls.args)
_initialize_distributed()
_set_random_seed(cls.args.seed, cls.args.data_parallel_random_init)
_compile_dependencies()
@classmethod
def init(cls, workdir):
'''Initialize Megatron, tokenizers, and datasets.'''
# Load args.
cls.init_megatron(workdir)
cls.tokenizers = types.SimpleNamespace(
gpt=get_gpt_tokenizer(),
bert=get_bert_tokenizer(),
)
# Load data.
cls.db_indexed_dataset_infos = get_db_indexed_dataset_infos()
cls.db_dataset = get_db_dataset()
pt_train_ds, pt_valid_ds, _ = get_retro_datasets(verify_sizes=False)
cls.pt_datasets = types.SimpleNamespace(
train=pt_train_ds,
valid=pt_valid_ds,
)
# Retrieve max saved neighbors.
for key in vars(cls.pt_datasets):
getattr(cls.pt_datasets, key).num_neighbors = \
cls.args.retro_query_num_neighbors_save
# Print usage.
cls.print_usage()
##############################################
# utils.
##############################################
@classmethod
def gpt_to_text(cls, token_ids):
'''GPT tokens to text.'''
return cls.tokenizers.gpt.detokenize(token_ids.tolist()
if isinstance(token_ids, np.ndarray)
else token_ids)
@classmethod
def text_to_bert(cls, text):
'''Text to Bert tokens.'''
return cls.tokenizers.bert.tokenize(text)
##############################################
# chunk db.
##############################################
@classmethod
def get_db_num_indexed_datasets(cls):
'''Number of indexed datasets within blendable dataset.'''
return len(cls.db_indexed_dataset_infos)
@classmethod
def get_db_indexed_dataset_infos(cls):
'''Dataset infos, including number of training & sampled sets.'''
return [(info["ratio"], info["name"])
for info in cls.db_indexed_dataset_infos]
@classmethod
def get_db_dataset(cls):
return cls.db_dataset
@classmethod
def get_db_num_chunks(cls):
'''Number of DB chunks.'''
return len(cls.get_db_dataset())
@classmethod
def get_db_chunk_gpt(cls, idx):
'''Get DB chunk as GPT token ids.'''
return cls.get_db_dataset()[idx]["text"].tolist()
@classmethod
def get_db_chunk_bert(cls, idx):
'''Get DB chunk as Bert token ids.'''
return cls.text_to_bert(cls.get_db_chunk_text(idx))
@classmethod
def get_db_chunk_text(cls, idx):
'''Get DB chunk as text.'''
return cls.gpt_to_text(cls.get_db_chunk_gpt(idx))
@classmethod
def get_db_chunk_and_continuation_text(cls, idx):
'''Get DB chunk along with continuation, as text.'''
# Modulus used here to match original implementation (i.e., last
# chunks continuation wraps around to first chunk).
return [
cls.get_db_chunk_text(idx),
cls.get_db_chunk_text((idx + 1) % len(cls.get_db_dataset())),
]
##############################################
# pretraining corpus.
##############################################
@classmethod
def get_pt_num_samples_and_chunks(cls, data_key):
'''Number of samples & chunks (e.g., 32*n_samples) in corpus.'''
assert hasattr(cls.pt_datasets, data_key), \
"pretraining set '%s' not found (choices: %s)." % (
data_key, ", ".join(vars(cls.pt_datasets).keys()))
chunk_dataset = getattr(cls.pt_datasets, data_key).chunk_dataset
return (
len(chunk_dataset.sample_dataset),
len(chunk_dataset),
)
@classmethod
def get_pt_num_samples(cls, data_key):
'''Number of pretraining samples.'''
return cls.get_pt_num_samples_and_chunks(data_key)[0]
@classmethod
def get_pt_num_chunks(cls, data_key):
'''Number of pretraining chunks (e.g., 32*n_samples).'''
return cls.get_pt_num_samples_and_chunks(data_key)[1]
@classmethod
def get_pt_dataset(cls, data_key):
return getattr(cls.pt_datasets, data_key)
@classmethod
def get_pt_sample(cls, data_key, idx):
return getattr(cls.pt_datasets, data_key)[idx]
@classmethod
def get_neighbor_tokens(cls, sample_id, chunk_id, data_key="train"):
try:
sample = cls.get_pt_sample(data_key, sample_id)
sample_token_ids = sample["text"]
chunk_length = cls.args.retro_gpt_chunk_length
chunk_start_idx = chunk_id * chunk_length
chunk_end_idx = min(sample_token_ids.shape[0],
chunk_start_idx + chunk_length)
chunk_token_ids = sample_token_ids[chunk_start_idx:chunk_end_idx]
neighbor_token_ids = sample["neighbor_tokens"][chunk_id]
return {
"chunk_tokens" : chunk_token_ids,
"neighbor_tokens" : neighbor_token_ids,
}
except:
return None
@classmethod
def print_neighbor_texts(cls, sample_id, chunk_id, data_key="train"):
tokens = cls.get_neighbor_tokens(sample_id, chunk_id, data_key)
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
try:
print("PRETRAINING CHUNK:")
print(" - %s" % shorten_str(cls.gpt_to_text(tokens["chunk_tokens"]), 150))
print("NEIGHBOR_CHUNKS:")
for token_ids in tokens["neighbor_tokens"]:
print(" - %s" % shorten_str(cls.gpt_to_text(token_ids), 150))
except:
print("<no neighbors for sample %d>" % sample_id)
##############################################
# usage.
##############################################
@classmethod
def print_usage(cls):
'''Print usage.'''
print()
print("+++++++++++++++++++++++++++++++++++++++++++++++++++")
print("examples ... [ *note*: 'db' = chunk db; 'pt' = pretraining corpus. ]")
print("+++++++++++++++++++++++++++++++++++++++++++++++++++")
print()
print("~~~~ indexed datasets ~~~~")
print("retro.get_db_num_indexed_datasets() : %s" %
cls.get_db_num_indexed_datasets())
print("retro.get_db_indexed_dataset_infos() :")
for i, (ratio,prefix) in enumerate(cls.get_db_indexed_dataset_infos()):
print(" %s(%f, %s)%s" % (
"[" if i == 0 else " ",
ratio,
prefix,
"]" if i == len(cls.db_indexed_dataset_infos) - 1 else ",",
))
print()
print("~~~~ counts ~~~~")
print("retro.get_db_num_chunks : %d." % cls.get_db_num_chunks())
print()
for sq_key in ("sample", "chunk"):
for data_key in ("train", "valid"): # test?
print("retro.get_pt_num_%ss('%s') : %d." % (
sq_key, data_key,
getattr(cls, f"get_pt_num_{sq_key}s")(data_key)))
print()
print("~~~~ tokens, text ~~~~")
print("retro.get_db_chunk_gpt(chunk_id) : %s" %
shorten_str(str(retro.get_db_chunk_gpt(0)), 50))
print("retro.get_db_chunk_bert(chunk_id) : %s" %
shorten_str(str(retro.get_db_chunk_bert(0)), 50))
print("retro.get_db_chunk_text(chunk_id) : %s" %
shorten_str(retro.get_db_chunk_text(0).strip(), 50))
print("retro.get_db_chunk_and_continuation_text(chunk_id) :")
for i, t in enumerate(retro.get_db_chunk_and_continuation_text(0)):
print(" %s'%s'%s" % (
"[" if i == 0 else " ",
shorten_str(t.strip().replace("\n", " "), 50),
"]" if i == 1 else ",",
))
sample = cls.get_pt_sample("train", 0)
sample_chunk_id = sample["neighbor_tokens"].shape[0] // 2
sample_neighbor_id = 0
print()
print("retro.get_pt_sample('train', sample_id) :")
print(" {")
for k, v in sample.items():
print(" '%s' : %s" % (k, shorten_str(str(v), 50)))
print(" }")
print()
print("(e.g., sample = retro.get_pt_sample(...))")
print()
print(" sample['text'].shape : %s" % str(sample["text"].shape))
print(" sample['neighbor_tokens'].shape : %s" % str(sample["neighbor_tokens"].shape))
print(" sample['text'] : %s" % shorten_str(str(sample["text"]), 50))
print(" sample['neighbor_tokens'][17][1] : %s" % shorten_str(str(sample["neighbor_tokens"][sample_chunk_id][sample_neighbor_id]), 50))
print(" retro.gpt_to_text(sample['text']) : %s" % shorten_str(cls.gpt_to_text(sample["text"]), 50))
print(" retro.gpt_to_text(sample['neighbor_tokens']) : %s" % shorten_str(cls.gpt_to_text(sample["neighbor_tokens"][sample_chunk_id][sample_neighbor_id]), 50))
print("+++++++++++++++++++++++++++++++++++++++++++++++++++")
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