code/train_canon_HEAD.py

"""
Training script with qv_variant choices extended for Phase 6 Q+A variants (normed + lowrank).
"""

import os
import time
import math
import pickle
import argparse
from contextlib import nullcontext

import numpy as np
import torch
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import init_process_group, destroy_process_group

from model import GPTConfig, GPT

out_dir = 'out-shakespeare-char'
eval_interval = 250
log_interval = 10
eval_iters = 200
eval_only = False
always_save_checkpoint = False
wandb_log = False
wandb_project = 'shakespeare-char'
wandb_run_name = 'mini-gpt'
dataset = 'shakespeare_char'
gradient_accumulation_steps = 1
batch_size = 64
block_size = 256
n_layer = 6
n_head = 6
n_embd = 384
dropout = 0.2
bias = False
learning_rate = 1e-3
max_iters = 5000
lr_decay_iters = 5000
min_lr = 1e-4
beta1 = 0.9
beta2 = 0.99
warmup_iters = 100
device = 'cuda'
dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16'
compile = False

config_keys = [k for k, v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))]
parser = argparse.ArgumentParser(description='Train a mini-GPT')
parser.add_argument('--out_dir', type=str, default=out_dir)
parser.add_argument('--eval_interval', type=int, default=eval_interval)
parser.add_argument('--log_interval', type=int, default=log_interval)
parser.add_argument('--eval_iters', type=int, default=eval_iters)
parser.add_argument('--eval_only', action='store_true')
parser.add_argument('--always_save_checkpoint', action='store_true')
parser.add_argument('--wandb_log', action='store_true')
parser.add_argument('--wandb_project', type=str, default=wandb_project)
parser.add_argument('--wandb_run_name', type=str, default=wandb_run_name)
parser.add_argument('--dataset', type=str, default=dataset)
parser.add_argument('--gradient_accumulation_steps', type=int, default=gradient_accumulation_steps)
parser.add_argument('--batch_size', type=int, default=batch_size)
parser.add_argument('--block_size', type=int, default=block_size)
parser.add_argument('--n_layer', type=int, default=n_layer)
parser.add_argument('--n_head', type=int, default=n_head)
parser.add_argument('--n_embd', type=int, default=n_embd)
parser.add_argument('--dropout', type=float, default=dropout)
parser.add_argument('--bias', action='store_true')
parser.add_argument('--learning_rate', type=float, default=learning_rate)
parser.add_argument('--max_iters', type=int, default=max_iters)
parser.add_argument('--lr_decay_iters', type=int, default=lr_decay_iters)
parser.add_argument('--min_lr', type=float, default=min_lr)
parser.add_argument('--beta1', type=float, default=beta1)
parser.add_argument('--beta2', type=float, default=beta2)
parser.add_argument('--warmup_iters', type=int, default=warmup_iters)
parser.add_argument('--device', type=str, default=device)
parser.add_argument('--dtype', type=str, default=dtype)
parser.add_argument('--compile', action='store_true')
parser.add_argument('--seed', type=int, default=1337, help='random seed')
parser.add_argument('--qv_variant', type=str, default='none',
                    choices=[
                        # baseline
                        'none',
                        # normalization variants
                        'vnorm',
                        'qvnorm',
                        # static gates
                        'static_gate',
                        'static_gate_prehead',
                        # post-attn normalization
                        'post_rmsnorm_y',
                        # Q-conditioned gates
                        'dynamic',
                        'dynamic_swiglu',
                        'dynamic_qconditioned_mlp128',
                        'dynamic_qconditioned_mlp192',
                        'dynamic_qconditioned_fullwidth',
                        'dynamic_qconditioned_fullwidth_headspecific',
                        'dynamic_q_headshared_elementwise',
                        # X-conditioned gates
                        'dynamic_xconditioned_g1',
                        'dynamic_xconditioned_fullwidth_headspecific',
                        'dynamic_xconditioned_bottleneck',
                        'dynamic_x_g1_headspecific_elementwise',
                        'dynamic_x_g1_headspecific_headwise',
                        'dynamic_x_g1_headshared_elementwise',
                        # Random / ablation gates
                        'dynamic_random_gate',
                        'dynamic_ones_gate',
                        'dynamic_random_normal',
                        'dynamic_bernoulli_gate',
                        # Zero-parameter dot product gates
                        'dynamic_dot_scalar',
                        'dynamic_dot_elementwise',
                        # A-only gate
                        'dynamic_a_conditioned',
                        # Q+A dual-signal gates
                        'dynamic_qa_conditioned',
                        'dynamic_qa_conditioned_headspecific',
                        'dynamic_qa_conditioned_mlp128',
                        'dynamic_qa_headshared_elementwise',
                        'dynamic_qa_bilinear_diag',
                        'dynamic_qa_conditioned_normed',
                        'dynamic_qa_conditioned_normed_yonly',
                        'dynamic_qa_conditioned_normed_qonly',
                        'dynamic_qa_conditioned_softq',
                        'dynamic_qa_conditioned_softqa',
                        'dynamic_qa_conditioned_softqa_perlayer',
                        'dynamic_qa_conditioned_softqa_perdim',
                        'dynamic_qa_conditioned_softqa_perdim_informed',
                        'dynamic_qa_conditioned_softqa_perdim_free',
                        'dynamic_qa_conditioned_lowrank16',
                        # legacy
                        'dynamic_postconcat_matched',
                    ],
                    help='QV experiment variant')

args = parser.parse_args()
for k in config_keys:
    if hasattr(args, k):
        globals()[k] = getattr(args, k)
config = {k: globals()[k] for k in config_keys}

ddp = int(os.environ.get('RANK', -1)) != -1
if ddp:
    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)
    master_process = ddp_rank == 0
    seed_offset = ddp_rank
    gradient_accumulation_steps //= ddp_world_size
else:
    master_process = True
    seed_offset = 0
    ddp_world_size = 1

tokens_per_iter = gradient_accumulation_steps * ddp_world_size * batch_size * block_size
print(f"tokens per iteration will be: {tokens_per_iter:,}")

if master_process:
    os.makedirs(out_dir, exist_ok=True)

torch.manual_seed(args.seed + seed_offset)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True

device_type = 'cuda' if 'cuda' in device else 'cpu'
ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)

data_dir = os.path.join('data', dataset)

# OPEN MEMMAPS ONCE, REUSE FOREVER
train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
val_data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')

def get_batch(split):
    data = train_data if split == 'train' else val_data
    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':
        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

iter_num = 0
best_val_loss = 1e9

meta_path = os.path.join(data_dir, 'meta.pkl')
meta_vocab_size = None
if os.path.exists(meta_path):
    with open(meta_path, 'rb') as f:
        meta = pickle.load(f)
    meta_vocab_size = meta['vocab_size']
    print(f"found vocab_size = {meta_vocab_size} (inside {meta_path})")

model_args = dict(n_layer=n_layer, n_head=n_head, n_embd=n_embd, block_size=block_size,
                  bias=bias, vocab_size=None, dropout=dropout)

if meta_vocab_size is None:
    print("defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency)")
model_args['vocab_size'] = meta_vocab_size if meta_vocab_size is not None else 50304

gptconf = GPTConfig(**model_args)
gptconf.qv_variant = args.qv_variant
model = GPT(gptconf)
model.to(device)

scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16'))
optimizer = model.configure_optimizers(1e-1, learning_rate, (beta1, beta2), device_type)

if compile:
    print("compiling the model... (takes a ~minute)")
    model = torch.compile(model)

@torch.no_grad()
def estimate_loss():
    out = {}
    model.eval()
    for split in ['train', 'val']:
        losses = torch.zeros(eval_iters)
        for k in range(eval_iters):
            X, Y = get_batch(split)
            with ctx:
                logits, loss = model(X, Y)
            losses[k] = loss.item()
        out[split] = losses.mean()
    model.train()
    return out

def get_lr(it):
    if it < warmup_iters:
        return learning_rate * (it + 1) / (warmup_iters + 1)
    if it > lr_decay_iters:
        return min_lr
    decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
    return min_lr + coeff * (learning_rate - min_lr)

if wandb_log and master_process:
    import wandb
    wandb.init(project=wandb_project, name=wandb_run_name, config=config)

X, Y = get_batch('train')
t0 = time.time()
local_iter_num = 0
raw_model = model.module if hasattr(model, 'module') else model
running_mfu = -1.0

while True:
    lr = get_lr(iter_num)
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

    if iter_num % eval_interval == 0 and master_process:
        losses = estimate_loss()
        print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
        if losses['val'] < best_val_loss or always_save_checkpoint:
            best_val_loss = losses['val']
            if iter_num > 0:
                checkpoint = {
                    'model': raw_model.state_dict(),
                    'optimizer': optimizer.state_dict(),
                    'model_args': model_args,
                    'iter_num': iter_num,
                    'best_val_loss': best_val_loss,
                    'config': config,
                }
                print(f"saving checkpoint to {out_dir}")
                torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt'))

    if iter_num == 0 and eval_only:
        break

    for micro_step in range(gradient_accumulation_steps):
        with ctx:
            logits, loss = model(X, Y)
            loss = loss / gradient_accumulation_steps
        X, Y = get_batch('train')
        scaler.scale(loss).backward()

    scaler.unscale_(optimizer)
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
    scaler.step(optimizer)
    scaler.update()
    optimizer.zero_grad(set_to_none=True)

    t1 = time.time()
    dt = t1 - t0
    t0 = t1
    if iter_num % log_interval == 0 and master_process:
        lossf = loss.item() * gradient_accumulation_steps
        if local_iter_num >= 5:
            mfu = raw_model.estimate_mfu(batch_size * gradient_accumulation_steps, dt)
            running_mfu = mfu if running_mfu == -1.0 else 0.9 * running_mfu + 0.1 * mfu
        print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms, mfu {running_mfu*100:.2f}%")
    iter_num += 1
    local_iter_num += 1

    if iter_num > max_iters:
        break

if ddp:
    destroy_process_group()