train_snakes.py

"""

This training script can be run both on a single gpu in debug mode,

and also in a larger training run with distributed data parallel (ddp).



To run on a single GPU small debug run, example:

$ python -m train.py --compile=False --eval_iters=10 --batch_size=8



To run with DDP on 4 gpus on 1 node, example:

$ torchrun --standalone --nproc_per_node=4 train.py



To run with DDP on 4 gpus across 2 nodes, example:

- Run on the first (master) node with example IP 123.456.123.456:

$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py

- Run on the worker node:

$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=123.456.123.456 --master_port=1234 train.py

(If your cluster does not have Infiniband interconnect prepend NCCL_IB_DISABLE=1)

"""

import math
import os
import time
from contextlib import nullcontext
from datetime import datetime
from functools import partial
import inspect
import torch
from torch.distributed import destroy_process_group, init_process_group
from torch.nn.parallel import DistributedDataParallel as DDP

from tinystories import Task

from model import MambaLMHeadModel


# -----------------------------------------------------------------------------
# I/O
out_dir = "out/768-8"
eval_interval = 2000
log_interval = 1
eval_iters = 100
eval_only = False  # if True, script exits right after the first eval
always_save_checkpoint = True  # if True, always save a checkpoint after each eval
init_from = "resume"  # 'scratch' or 'resume'
# wandb logging
wandb_log = True  # disabled by default
wandb_project = "tiny-mambas"
wandb_run_name = "run" + datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
# data
batch_size = 128  # if gradient_accumulation_steps > 1, tshis is the micro-batch size
max_seq_len = 256
vocab_size = 4096 # the Llama 2 tokenizer has 32K tokens
vocab_source = "custom"
# model
d_model = 768
n_layer = 8
vocab_size = 4096
# adamw optimizer
gradient_accumulation_steps = 4  # used to simulate larger batch sizes
learning_rate = 5e-4  # max learning rate
max_iters = 100000  # total number of training iterations
weight_decay = 1e-1
beta1 = 0.9
beta2 = 0.95
grad_clip = 1.0  # clip gradients at this value, or disable if == 0.0
# learning rate decay settings
decay_lr = True  # whether to decay the learning rate
warmup_iters = 1000  # how many steps to warm up for
# system
device = "cuda"  # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1' etc., or try 'mps' on macbooks
dtype = "float16"  # float32|bfloat16|float16
compile = False  # use PyTorch 2.0 to compile the model to be faster


class mambaConfig:

    d_model: int = d_model
    n_layer: int = n_layer
    vocab_size: int = vocab_size
    ssm_cfg: dict = None
    rms_norm: bool = True
    residual_in_fp32: bool = True
    fused_add_norm: bool = True
    pad_vocab_size_multiple: int = 8




config_keys = [
    k
    for k, v in globals().items()
    if not k.startswith("_") and isinstance(v, (int, float, bool, str))
]
config = {k: globals()[k] for k in config_keys}  # will be useful for logging

# fixing some hyperparams to sensible defaults
lr_decay_iters = max_iters  # should be ~= max_iters per Chinchilla
min_lr = 5e-5  # minimum learning rate, should be ~= learning_rate/10 per Chinchilla


torch.cuda.set_device(0)

# -----------------------------------------------------------------------------
torch.manual_seed(1337)
torch.backends.cuda.matmul.allow_tf32 = True  # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True  # allow tf32 on cudnn
device_type = "cuda" if "cuda" in device else "cpu"  # for later use in torch.autocast
# note: float16 data type will automatically use a GradScaler
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)
)

# task-specific setup
iter_batches = partial(
    Task.iter_batches,
    batch_size=batch_size,
    max_seq_len=max_seq_len,
    vocab_size=vocab_size,
    vocab_source=vocab_source,
    device=device,
    num_workers=0,
)

# init these up here, can override if init_from='resume' (i.e. from a checkpoint)
iter_num = 0
best_val_loss = 1e9

# model init
model_args = dict(
    d_model=d_model,
    n_layer=n_layer,
    vocab_size=vocab_size,
    max_seq_len=max_seq_len,
) 
tokens_per_iter = gradient_accumulation_steps * 1 * batch_size * max_seq_len

# start with model_args from command line
if init_from == "scratch":
    # init a new model from scratch
    print("Initializing a new model from scratch")
    
    model = MambaLMHeadModel(mambaConfig)
    model.last_loss = None
elif init_from == "resume":
    print(f"Resuming training from {out_dir}")
    # resume training from a checkpoint.
    ckpt_path = os.path.join(out_dir, "ckpt.pt")
    checkpoint = torch.load(ckpt_path, map_location=device)
    checkpoint_model_args = checkpoint["model_args"]
    # force these config attributes to be equal otherwise we can't even resume training
    # the rest of the attributes (e.g. dropout) can stay as desired from command line
    for k in ["d_model", "n_layer", "vocab_size", "max_seq_len"]:
        model_args[k] = checkpoint_model_args[k]
    # create the model
    model = MambaLMHeadModel(mambaConfig)
    model.last_loss = None
    state_dict = checkpoint["model"]
    # fix the keys of the state dictionary :(
    # honestly no idea how checkpoints sometimes get this prefix, have to debug more
    unwanted_prefix = "_orig_mod."
    for k, v in list(state_dict.items()):
        if k.startswith(unwanted_prefix):
            state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k)
    model.load_state_dict(state_dict)
    iter_num = checkpoint["iter_num"]
    best_val_loss = checkpoint["best_val_loss"]
model.to(device)

# initialize a GradScaler. If enabled=False scaler is a no-op
scaler = torch.cuda.amp.GradScaler(enabled=(dtype == "float16"))

# optimizer
# start with all of the candidate parameters
param_dict = {pn: p for pn, p in model.named_parameters()}
# filter out those that do not require grad
param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
# create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
# i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
betas = (beta1, beta2)
decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
optim_groups = [
    {'params': decay_params, 'weight_decay': weight_decay},
    {'params': nodecay_params, 'weight_decay': 0.0}
]
num_decay_params = sum(p.numel() for p in decay_params)
num_nodecay_params = sum(p.numel() for p in nodecay_params)
print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
# Create AdamW optimizer and use the fused version if it is available
fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
use_fused = fused_available and device_type == 'cuda'
extra_args = dict(fused=True) if use_fused else dict()
optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
print(f"using fused AdamW: {use_fused}")



if init_from == "resume" and "optimizer" in checkpoint:
    optimizer.load_state_dict(checkpoint["optimizer"])
checkpoint = None  # free up memory

# compile the model
if compile:
    print("compiling the model... (takes a ~minute)")
    unoptimized_model = model
    model = torch.compile(model)  # requires PyTorch 2.0

# wrap model into DDP container


# helps estimate an arbitrarily accurate loss over either split using many batches
@torch.no_grad()
def estimate_loss():
    out = {}
    model.eval()
    for split in ["train", "val"]:
        batch_iter = iter_batches(split=split)
        losses = torch.zeros(eval_iters)  # keep on CPU
        for k in range(eval_iters):
            X, Y = next(batch_iter)
            with ctx:
                logits = model(X, Y)
                loss = raw_model.last_loss
            losses[k] = loss.item()
        out[split] = losses.mean()
    model.train()
    return out

# learning rate decay scheduler (cosine with warmup)
def get_lr(it):
    # 1) linear warmup for warmup_iters steps
    if it < warmup_iters:
        return learning_rate * it / warmup_iters
    # 2) if it > lr_decay_iters, return min learning rate
    if it > lr_decay_iters:
        return min_lr
    # 3) in between, use cosine decay down to min learning rate
    decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
    assert 0 <= decay_ratio <= 1
    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))  # coeff ranges 0..1
    return min_lr + coeff * (learning_rate - min_lr)

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

# training loop
train_batch_iter = iter_batches(split="train")
X, Y = next(train_batch_iter)  # fetch the very first batch
t0 = time.time()
local_iter_num = 0  # number of iterations in the lifetime of this process
raw_model = model  # unwrap DDP container if needed
running_mfu = -1.0
while True:
    # determine and set the learning rate for this iteration
    lr = get_lr(iter_num) if decay_lr else learning_rate
    for param_group in optimizer.param_groups:
        param_group["lr"] = lr

    # evaluate the loss on train/val sets and write checkpoints
    if iter_num % eval_interval == 0:
        losses = estimate_loss()
        print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
        if wandb_log:
            try:
                wandb.log(
                    {
                        "iter": iter_num,
                        "tokens": iter_num * tokens_per_iter,
                        "loss/train": losses["train"],
                        "loss/val": losses["val"],
                        "lr": lr,
                
                    }, step = iter_num
                )
            except Exception as e:
                print(f"logging to wandb failed: {e}")
        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"))
                #model_export(raw_model, os.path.join(out_dir, "model.bin"), version=0)
    if iter_num == 0 and eval_only:
        break

    # forward backward update, with optional gradient accumulation to simulate larger batch size
    # and using the GradScaler if data type is float16
    for micro_step in range(gradient_accumulation_steps):
        
        with ctx:
            logits = model(X, Y)
            loss = raw_model.last_loss
            loss = loss / gradient_accumulation_steps
        # immediately async prefetch next batch while model is doing the forward pass on the GPU
        X, Y = next(train_batch_iter)
        # backward pass, with gradient scaling if training in fp16
        scaler.scale(loss).backward()
    # clip the gradient
    if grad_clip != 0.0:
        scaler.unscale_(optimizer)
        torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
    # step the optimizer and scaler if training in fp16
    scaler.step(optimizer)
    scaler.update()
    # flush the gradients as soon as we can, no need for this memory anymore
    optimizer.zero_grad(set_to_none=True)

    # timing and logging
    t1 = time.time()
    dt = t1 - t0
    t0 = t1
    if iter_num % log_interval == 0:
        # get loss as float, scale up due to the divide above. note: this is a CPU-GPU sync point
        lossf = loss.item() * gradient_accumulation_steps
        
        print(
            f"{iter_num} | loss {lossf:.4f} | lr {lr:e} | {dt*1000:.2f}ms |"
        )
    iter_num += 1
    local_iter_num += 1

    # termination conditions
    if iter_num > max_iters:
        break