====================================================================================================
#loader.py
### canonically
### torchrun --standalone --nproc_per_node=8 loader.py
### but for us, probably
### set USE_LIBUV=0
### set RANK
### set TORCH_CUDNN_SDPA_ENABLED=1
### torchrun --standalone --nproc_per_node=1 loader.py
import os
import sys
with open(sys.argv[0]) as f:
code = f.read() # read the code of this file ASAP, for logging
import uuid
import glob
import time
from dataclasses import dataclass
import numpy as np
import torch
import bitsandbytes as bnb
from torch import nn
import torch.nn.functional as F
import torch.distributed as dist
import torch._inductor.config as config
from torch.nn.parallel import DistributedDataParallel as DDP
import pgptlformer
#wacky env stuff:
#import tritonpathsetter
#tritonpathsetter.set_cuda_paths()
#tritonpathsetter.add_cuda_files()
### modded-nanogpt distributed dataset loader
# -----------------------------------------------------------------------------
# their 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):
self.process_rank = process_rank
self.num_processes = num_processes
self.B = B
self.T = T
# glob files that match the pattern
self.files = sorted(glob.glob(filename_pattern))
assert len(self.files) > 0, f"did not find any files that match the pattern {filename_pattern}"
# load and validate all data shards, count number of tokens in total
ntok_total = 0
for fname in self.files:
shard_ntok = _peek_data_shard(fname)
assert shard_ntok >= num_processes * B * T + 1
ntok_total += int(shard_ntok)
self.ntok_total = ntok_total
# kick things off
self.reset()
def reset(self):
self.current_shard = 0
self.current_position = self.process_rank * self.B * self.T
self.tokens = _load_data_shard(self.files[self.current_shard])
def advance(self): # advance to next data shard
self.current_shard = (self.current_shard + 1) % len(self.files)
self.current_position = self.process_rank * self.B * self.T
self.tokens = _load_data_shard(self.files[self.current_shard])
def next_batch(self):
B = self.B
T = self.T
buf = self.tokens[self.current_position : self.current_position+B*T+1]
buf = torch.tensor(buf.astype(np.int32), dtype=torch.long)
x = (buf[:-1]).view(B, T) # inputs
y = (buf[1:]).view(B, T) # targets
# advance current position and load next shard if necessary
self.current_position += B * T * self.num_processes
if self.current_position + (B * T * self.num_processes + 1) > len(self.tokens):
self.advance()
return x.cuda(), y.cuda()
# -----------------------------------------------------------------------------
# downgrade to poor man's data loader:
# maybe superfluous bc distributed data loader started working
# delete? [ ]
def get_batch(split):
# We recreate np.memmap every batch to avoid a memory leak, as per
# https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-iterate-once/61472122#61472122
block_size = args.sequence_length
batch_size = args.batch_size
if split == 'train':
data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
else:
data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')
ix = torch.randint(len(data) - block_size, (batch_size,))
x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
if device_type == 'cuda':
# pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True)
else:
x, y = x.to(device), y.to(device)
return x, y
### modded-nanogpt
### either 24/16*20=30 batches per 4090 or 24/32*20=15 batches per 4090,
### depending on what kind of v100 tinystories used.
@dataclass
class Hyperparameters:
# data hyperparams
input_bin : str = 'data/tinystories-pqt/tinystories-pqt_train_*.bin' # input .bin to train on
input_val_bin : str = 'data/tinystories-pqt/tinystories-pqt_val_*.bin' # input .bin to eval validation loss on
# optimization hyperparams
batch_size : int = 4*32 # macrobatch size, in sequences, across all devices
device_batch_size : int = 32 # batch size, in sequences, per device. try to increase/decrease by powers of 2
sequence_length : int = 512 # sequence length, in tokens
num_iterations : int = 6250 # number of iterations to run
attack : int = 40 # 2*(1-betas)^-1
release : int = 256 # number of iterations of linear warmup/warmdown for triangular or trapezoidal schedule
weight_decay : float = 0
# evaluation and logging hyperparams
val_loss_every : int = 2000 # every how many steps to evaluate val loss? 0 for only at the end
val_tokens : int = 5242880 # how many tokens of validation data? it's important to keep this fixed for consistent comparisons
save_every : int = 0 # every how many steps to save the checkpoint? 0 for only at the end
run_name : str = "re-pqt-rmsXrms-ATTNII_rev2"
# supercompute boilerplate
ddp_run : bool = False #this stuff is so nyannoying
device = "cuda" # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1' etc., or try 'mps' on macbooks
torch_compile = True #hahahaha
use_z_loss = True
z_loss_coefficient = 1e-4
args = Hyperparameters()
# convenience variables
B, T = args.device_batch_size, args.sequence_length
# set up DDP (distributed data parallel). torchrun sets this env variable
assert torch.cuda.is_available()
if args.ddp_run == True:
dist.init_process_group(backend='nccl')
ddp_rank = int(os.environ['RANK'])
ddp_local_rank = int(os.environ['LOCAL_RANK'])
ddp_world_size = int(os.environ['WORLD_SIZE'])
device = f'cuda:{ddp_local_rank}'
torch.cuda.set_device(device)
print(f"using device: {device}")
master_process = (ddp_rank == 0) # this process will do logging, checkpointing etc.
else:
# if not ddp, we are running on a single gpu, and one process
master_process = True
seed_offset = 0
ddp_world_size = 1
ddp_rank = 0
device = args.device
#tokens_per_iter = train_accumulation_steps * ddp_world_size * batch_size * block_size
#print(f"tokens per iteration will be: {tokens_per_iter:,}")
# calculate the number of steps to take in the val loop.
assert args.val_tokens % (B * T * ddp_world_size) == 0
val_steps = args.val_tokens // (B * T * ddp_world_size)
# calculate the steps of gradient accumulation required to attain the desired global batch size.
assert args.batch_size % (B * ddp_world_size) == 0
train_accumulation_steps = args.batch_size // (B * ddp_world_size)
# load tokens
train_loader = DistributedDataLoader(args.input_bin, B, T, ddp_rank, ddp_world_size)
val_loader = DistributedDataLoader(args.input_val_bin, B, T, ddp_rank, ddp_world_size)
if master_process:
print(f"Training DataLoader: total number of tokens: {train_loader.ntok_total} across {len(train_loader.files)} files")
print(f"Validation DataLoader: total number of tokens: {val_loader.ntok_total} across {len(val_loader.files)} files")
x, y = train_loader.next_batch()
if master_process:
print("Building model...")
#tinystories
#num_vocab=50304 for non-tinystories models
#qknorm="identitynorm" for nonqknorm models
layer_prefab = {"dim":256,"dim_head":32,"headcount":8,"ff_mult":4,
"lambda":True,"layerwisenorm":"rmsnorm","qknorm":"dynamic_shape_rmsnorm",
"attention_deux":True, "training_seqlen":args.sequence_length}
#global_prefab = {"vocab_size":8192, "num_layers":4}
#weird errors
global_prefab = {"vocab_size":50304, "num_layers":4}
config = {}
config.update(layer_prefab)
config.update(global_prefab)
model = pgptlformer.PGPT_Lformer(config)
if hasattr(config, "coordinate_descent_tuning"):
config.coordinate_descent_tuning = True # suggested by @Chillee
model = model.to(device)
if args.torch_compile:
model = torch.compile(model)
# here we wrap model into DDP container
if args.ddp_run:
model = DDP(model, device_ids=[ddp_local_rank])
#raw_model = model.modules() # always contains the "raw" unwrapped model
ctx = torch.amp.autocast(device_type='cuda', dtype=torch.bfloat16)
if master_process:
print("Model built.")
# CUDNN attention is ~4ms faster than Flash, but doesn't get selected by default in PyTorch 2.5.1
from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
enable_cudnn_sdp(True)
enable_flash_sdp(True)
enable_mem_efficient_sdp(True)
enable_math_sdp(False)
# modded-nanogpt optimizer inits
adam1 = torch.optim.Adam([model.lambdaformer.what_the_embedder_doin.weight], lr=0.3, betas=(0.9, 0.95) )
adam2 = torch.optim.Adam([model.tokenpicker_head.weight], lr=0.002, betas=(0.9, 0.95) )
params = list(model.lambdaformer.blocks.parameters())
matrix_params = [p for p in params if p.ndim == 2]
scalar_params = [p for p in params if p.ndim < 2]
adam3 = bnb.optim.Adam8bit(matrix_params, lr=0.02, betas=(0.9, 0.95) ) #tune this, sensitive
adam4 = bnb.optim.Adam8bit(scalar_params, lr=0.02, betas=(0.9, 0.95) ) #???, less sensitive
optim_ensemble = [adam1, adam2, adam3, adam4]
# lr scheduler
def get_ASR_env(it):
assert it <= args.num_iterations
# A) the famous linear warmup back at it again
if it < args.attack:
return (it+1) / args.attack
# S) constant sustain
elif it < args.num_iterations - args.release:
return 1.0
# R) release
else:
release_ratio = (args.num_iterations - it) / args.release
return release_ratio
schedulers = [torch.optim.lr_scheduler.LambdaLR(opt, get_ASR_env) for opt in optim_ensemble]
# begin logging
if master_process:
run_id = str(uuid.uuid4())
if args.run_name is not None:
sep="-"
run_id = sep.join([args.run_name, run_id])
logdir = 'logs/%s/' % run_id
os.makedirs(logdir, exist_ok=True)
logfile = 'logs/%s.txt' % run_id
# create the log file
with open(logfile, "w") as f:
# begin the log by printing this file (the Python code)
f.write('='*100 + '\n')
f.write(code)
f.write('='*100 + '\n')
# log information about the hardware/software environment this is running on
# and print the full `nvidia-smi` to file
f.write(f"Running pytorch {torch.version.__version__} compiled for CUDA {torch.version.cuda}\nnvidia-smi:\n")
import subprocess
result = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
f.write(f'{result.stdout}\n')
f.write('='*100 + '\n')
training_time_ms = 0
# start the clock
torch.cuda.synchronize()
t0 = time.time()
# begin training
train_loader.reset()
for step in range(args.num_iterations + 1):
last_step = (step == args.num_iterations)
# This effectively ignores timing first 10 steps, which are slower for weird reasons.
# Alternately, and slightly more correctly in terms of benchmarking, we could do 10
# steps with dummy data first, and then re-initialize the model and reset the loader.
if step == 10:
training_time_ms = 0
t0 = time.time()
timed_steps = float('nan') if step <= 11 else (step - 10) + 1 # <= 11 to avoid bug in val
# once in a while evaluate the validation dataset
if (last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0)):
# stop the clock
torch.cuda.synchronize()
training_time_ms += 1000 * (time.time() - t0)
# run validation batches
model.eval()
val_loader.reset()
val_loss = 0.0
val_aux_loss = 0.0
for _ in range(val_steps):
x_val, y_val = val_loader.next_batch()
with ctx: # of course, we'd like to use no_grad() here too, but that creates a torch.compile error for some reason
_, loss, z_loss = model(x_val, y_val, return_logits=False, return_zloss=args.use_z_loss)
val_loss += loss.detach()
if z_loss is not None:
val_aux_loss += z_loss.detach()*args.z_loss_coefficient
del loss, z_loss
if args.ddp_run:
dist.all_reduce(val_loss, op=dist.ReduceOp.AVG)
dist.all_reduce(val_aux_loss, op=dist.ReduceOp.AVG)
val_loss /= val_steps
val_aux_loss /= val_steps
# log val loss to console and to logfile
if master_process:
print(f'step:{step}/{args.num_iterations} val_loss:{val_loss:.4f} val_aux_loss:{val_aux_loss:.4f} train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms/(timed_steps-1):.2f}ms')
with open(logfile, "a") as f:
f.write(f'step:{step}/{args.num_iterations} val_loss:{val_loss:.4f} val_aux_loss:{val_aux_loss:.4f} train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms/(timed_steps-1):.2f}ms\n')
# start the clock again
torch.cuda.synchronize()
t0 = time.time()
if master_process and (last_step or (args.save_every > 0 and step % args.save_every == 0)):
# stop the clock
torch.cuda.synchronize()
training_time_ms += 1000 * (time.time() - t0)
# save the state of the training process
log = dict(step=step, code=code, model=model.state_dict(), model_args=config, optim_ensemble=[opt.state_dict() for opt in optim_ensemble])
torch.save(log, 'logs/%s/state_step%06d.pt' % (run_id, step))
# start the clock again
torch.cuda.synchronize()
t0 = time.time()
# bit confusing: we want to make sure to eval on 0th iteration
# but also after the very last iteration. so we loop for step <= num_iterations
# instead of just < num_iterations (one extra due to <=), only to do
# the validation/sampling one last time, and then we break right here as we're done.
if last_step:
break
# --- train time ---
model.train()
for i in range(1, train_accumulation_steps+1):
# forward pass
with ctx:
_, loss, z_loss = model(x, y, return_logits=False, return_zloss=args.use_z_loss)
train_loss = loss.detach()
if z_loss is not None:
train_aux_loss = z_loss.detach()*args.z_loss_coefficient
loss = loss+z_loss*args.z_loss_coefficient
else:
train_aux_loss = 0
# advance the dataset for the next batch
x, y = train_loader.next_batch()
# backward pass
if args.ddp_run:
if i < train_accumulation_steps:
with model.no_sync(): # there's no need to sync gradients every accumulation step
loss.backward()
else:
loss.backward() # just sync on the last step
for p in model.parameters(): #grad accum normalization?
p.grad /= train_accumulation_steps
# skip muon momentum warmup since we're adaming it
#...
# step the optimizers and schedulers
for opt, sched in zip(optim_ensemble, schedulers):
opt.step()
sched.step()
# null the gradients
model.zero_grad(set_to_none=True)
# --- train time is already over ---
#dist.all_reduce(train_loss, op=dist.ReduceOp.AVG) # all-reducing the training loss would be more correct in terms of logging, but slower
if master_process:
approx_time = training_time_ms + 1000 * (time.time() - t0)
print(f"step:{step+1}/{args.num_iterations} train_loss:{train_loss.item():.4f} aux_loss:{train_aux_loss.item():.4f} train_time:{approx_time:.0f}ms step_avg:{approx_time/timed_steps:.2f}ms")
with open(logfile, "a") as f:
f.write(f"step:{step+1}/{args.num_iterations} train_loss:{train_loss.item():.4f} aux_loss:{train_aux_loss.item():.4f} train_time:{approx_time:.0f}ms step_avg:{approx_time/timed_steps:.2f}ms\n")
if master_process:
print(f"peak memory consumption: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB")
# clean up nice
if args.ddp_run:
dist.destroy_process_group()====================================================================================================
Running pytorch 2.5.1+cu124 compiled for CUDA 12.4
nvidia-smi:
Sun Feb 2 02:21:32 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 551.61 Driver Version: 551.61 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4090 WDDM | 00000000:01:00.0 On | Off |
| 0% 47C P2 67W / 350W | 1483MiB / 24564MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 1496 C+G C:\Windows\System32\dwm.exe N/A |
| 0 N/A N/A 2624 C+G ...-desktop\RemoteDesktopCompanion.exe N/A |
| 0 N/A N/A 2976 C+G ...B\system_tray\lghub_system_tray.exe N/A |
| 0 N/A N/A 3556 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 4288 C+G ...CBS_cw5n1h2txyewy\TextInputHost.exe N/A |
| 0 N/A N/A 6564 C+G ...ekyb3d8bbwe\PhoneExperienceHost.exe N/A |
| 0 N/A N/A 7444 C+G C:\Program Files\VSCodium\VSCodium.exe N/A |
| 0 N/A N/A 8884 C+G ...1.0_x64__8wekyb3d8bbwe\Video.UI.exe N/A |
| 0 N/A N/A 9636 C+G ....Search_cw5n1h2txyewy\SearchApp.exe N/A |
| 0 N/A N/A 12332 C+G ...al\Discord\app-1.0.9180\Discord.exe N/A |
| 0 N/A N/A 12688 C+G ...cal\Microsoft\OneDrive\OneDrive.exe N/A |
| 0 N/A N/A 13000 C+G ...64__8wekyb3d8bbwe\CalculatorApp.exe N/A |
| 0 N/A N/A 14168 C ...rograms\Python\Python310\python.exe N/A |
| 0 N/A N/A 16068 C+G ...\cef\cef.win7x64\steamwebhelper.exe N/A |
| 0 N/A N/A 18804 C+G ...a\Local\slack\app-4.41.98\slack.exe N/A |
| 0 N/A N/A 19876 C+G ...a\Local\slack\app-4.41.98\slack.exe N/A |
| 0 N/A N/A 24308 C+G ...5n1h2txyewy\ShellExperienceHost.exe N/A |
+-----------------------------------------------------------------------------------------+
====================================================================================================
step:0/6250 val_loss:10.8259 val_aux_loss:0.0421 train_time:55ms step_avg:nanms
step:1/6250 train_loss:10.8258 aux_loss:0.0421 train_time:9629ms step_avg:nanms
step:2/6250 train_loss:10.8239 aux_loss:0.0421 train_time:9761ms step_avg:nanms
step:3/6250 train_loss:10.8120 aux_loss:0.0421 train_time:9891ms step_avg:nanms
step:4/6250 train_loss:10.8025 aux_loss:0.0421 train_time:10020ms step_avg:nanms
step:5/6250 train_loss:10.7488 aux_loss:0.0420 train_time:10149ms step_avg:nanms
step:6/6250 train_loss:10.6739 aux_loss:0.0418 train_time:10279ms step_avg:nanms
step:7/6250 train_loss:10.5698 aux_loss:0.0416 train_time:10408ms step_avg:nanms
step:8/6250 train_loss:10.4720 aux_loss:0.0414 train_time:10536ms step_avg:nanms
step:9/6250 train_loss:10.3021 aux_loss:0.0410 train_time:10667ms step_avg:nanms
step:10/6250 train_loss:10.1203 aux_loss:0.0406 train_time:10797ms step_avg:nanms
step:11/6250 train_loss:9.9528 aux_loss:0.0403 train_time:131ms step_avg:nanms
step:12/6250 train_loss:9.7262 aux_loss:0.0399 train_time:265ms step_avg:nanms
step:13/6250 train_loss:9.4927 aux_loss:0.0395 train_time:398ms step_avg:132.71ms
step:14/6250 train_loss:9.2378 aux_loss:0.0390 train_time:530ms step_avg:132.53ms
step:15/6250 train_loss:8.9773 aux_loss:0.0386 train_time:659ms step_avg:131.82ms
step:16/6250 train_loss:8.7440 aux_loss:0.0384 train_time:793ms step_avg:132.19ms
step:17/6250 train_loss:8.5183 aux_loss:0.0386 train_time:926ms step_avg:132.30ms
step:18/6250 train_loss:8.2471 aux_loss:0.0384 train_time:1055ms step_avg:131.89ms
step:19/6250 train_loss:7.8890 aux_loss:0.0373 train_time:1189ms step_avg:132.12ms
step:20/6250 train_loss:7.6552 aux_loss:0.0374 train_time:1322ms step_avg:132.21ms
step:21/6250 train_loss:7.4059 aux_loss:0.0372 train_time:1459ms step_avg:132.65ms
step:22/6250 train_loss:7.1355 aux_loss:0.0373 train_time:1598ms step_avg:133.19ms
step:23/6250 train_loss:7.0230 aux_loss:0.0378 train_time:1738ms step_avg:133.72ms
step:24/6250 train_loss:6.7303 aux_loss:0.0375 train_time:1879ms step_avg:134.24ms
step:25/6250 train_loss:6.6080 aux_loss:0.0377 train_time:2020ms step_avg:134.69ms
step:26/6250 train_loss:6.5241 aux_loss:0.0399 train_time:2160ms step_avg:135.02ms
step:27/6250 train_loss:6.3658 aux_loss:0.0394 train_time:2299ms step_avg:135.26ms
step:28/6250 train_loss:6.2951 aux_loss:0.0391 train_time:2440ms step_avg:135.58ms
step:29/6250 train_loss:6.1911 aux_loss:0.0397 train_time:2580ms step_avg:135.81ms
step:30/6250 train_loss:6.1837 aux_loss:0.0390 train_time:2720ms step_avg:136.02ms
step:31/6250 train_loss:6.0650 aux_loss:0.0371 train_time:2862ms step_avg:136.30ms
step:32/6250 train_loss:6.0454 aux_loss:0.0379 train_time:3003ms step_avg:136.51ms
step:33/6250 train_loss:6.0424 aux_loss:0.0381 train_time:3145ms step_avg:136.75ms
step:34/6250 train_loss:5.9355 aux_loss:0.0377 train_time:3286ms step_avg:136.91ms
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step:6250/6250 val_loss:2.0390 val_aux_loss:0.0163 train_time:837798ms step_avg:134.26ms