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""" |
Reference code for GPT-2 training and inference with Sharpness Analysis. |
Will save the model weights into files, to be read from C as initialization. |
References: |
1) the official GPT-2 TensorFlow implementation released by OpenAI: |
https://github.com/openai/gpt-2/blob/master/src/model.py |
2) huggingface/transformers PyTorch implementation: |
https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py |
Example launches to only benchmark the speed of bfloat16 compiled GPU training: |
1 GPU: |
python train_gpt2.py --write_tensors=0 --num_iterations=50 --sequence_length=1024 --compile=1 --tensorcores=1 --dtype=bfloat16 |
you can also turn on flash-attention by appending --flash=1 |
4 GPU: |
torchrun --standalone --nproc_per_node=4 train_gpt2.py --write_tensors=0 --num_iterations=50 --sequence_length=1024 --compile=1 --tensorcores=1 --dtype=bfloat16 |
""" |
import sys |
with open(sys.argv[0]) as f: |
code = f.read() # read the code of this file ASAP, for logging |
import os |
import math |
import glob |
import struct |
import inspect |
from contextlib import nullcontext |
from dataclasses import dataclass |
import random |
import numpy as np |
import torch |
from torch import Tensor |
import torch.nn as nn |
from torch.nn import functional as F |
import torch._inductor.config as config |
from torch.nn.parallel import DistributedDataParallel as DDP |
from torch.distributed import init_process_group, destroy_process_group |
from torch.distributed.optim import ZeroRedundancyOptimizer |
import torch.distributed as dist |
from torch.amp import autocast |
import copy |
import gc |
import uuid |
import json |
from pathlib import Path |
# Import Muon optimizer |
import sys |
sys.path.append("/home/aiops/zhangfz/MUON_LLM/modded-nanogpt/optimizers") |
from MUON_fix import Muon |
# Import GPT model |
sys.path.append("/home/aiops/zhangfz/MUON_LLM/modded-nanogpt/models") |
import nano_GPT_qkvonorm_pure |
from nano_GPT_qkvonorm_pure import GPT, GPTConfig |
# Import debug utilities |
# from debug_utils import setup_debugpy |
# ----------------------------------------------------------------------------- |
# Our own simple Distributed Data Loader |
def _peek_data_shard(filename): |
# only reads the header, returns header data |
with open(filename, "rb") as f: |
# first read the header, which is 256 int32 integers (4 bytes each) |
header = np.frombuffer(f.read(256*4), dtype=np.int32) |
if header[0] != 20240520: |
print("ERROR: magic number mismatch in the data .bin file!") |
print("---> HINT: Are you passing in a correct file with --input_bin?") |
print("---> HINT: Dataset encoding changed recently, re-run data prepro or refer again to README") |
print("---> HINT: For example re-run: `python dev/data/tinyshakespeare.py`, then re-try") |
exit(1) |
assert header[1] == 1, "unsupported version" |
ntok = header[2] # number of tokens (claimed) |
return ntok # for now just return the number of tokens |
def _load_data_shard(filename): |
with open(filename, "rb") as f: |
# first read the header, which is 256 int32 integers (4 bytes each) |
header = np.frombuffer(f.read(256*4), dtype=np.int32) |
assert header[0] == 20240520, "magic number mismatch in the data .bin file" |
assert header[1] == 1, "unsupported version" |
ntok = header[2] # number of tokens (claimed) |
# the rest of it are tokens, stored as uint16 |
tokens = np.frombuffer(f.read(), dtype=np.uint16) |
assert len(tokens) == ntok, "number of tokens read does not match header?" |
return tokens |
class DistributedDataLoader: |
def __init__(self, filename_pattern, B, T, process_rank, num_processes, |
shuffle_files=False, random_seed=None): |
self.process_rank = process_rank |
self.num_processes = num_processes |
self.B = B |
self.T = T |
self.shuffle_files = shuffle_files |
self.random_seed = random_seed |
self._rng = random.Random(random_seed) if shuffle_files and random_seed is not None else None |
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