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Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,786 @@ import os import sys import time import math import pickle from contextlib import nullcontext from pathlib import Path import subprocess from dataclasses import dataclass import inspect import numpy as np import torch import torch.nn as nn from torch.nn import functional as F from torch.nn.parallel import DistributedDataParallel as DDP from torch.distributed import init_process_group, destroy_process_group # Modal imports import modal # ============================================================================ # CONFIGURATION - All settings embedded here, no CLI args needed # ============================================================================ # Modal configuration N_GPUS = 4 # Number of GPUs to use GPU_TYPE = "A100" # GPU type: "A100", "H200", "A10G", etc. # Training configuration for Shakespeare character-level model CONFIG = { # I/O "out_dir": "/data/checkpoints/shakespeare", "eval_interval": 250, # Will be auto-adjusted based on epochs "log_interval": 10, # Will be auto-adjusted based on epochs "eval_iters": 200, "eval_only": False, "always_save_checkpoint": True, "init_from": "scratch", # wandb logging "wandb_log": False, "wandb_project": "nanogpt-shakespeare", "wandb_run_name": "shakespeare-char", # data "dataset": "shakespeare_char", "gradient_accumulation_steps": 4, # Must be divisible by N_GPUS "batch_size": 64, "block_size": 256, # model "n_layer": 6, "n_head": 6, "n_embd": 384, "dropout": 0.2, "bias": False, # training epochs (max_iters will be calculated automatically) "num_epochs": 21.0, # Set the number of epochs you want # adamw optimizer "learning_rate": 1e-3, "max_iters": None, # Will be calculated based on num_epochs "weight_decay": 1e-1, "beta1": 0.9, "beta2": 0.95, "grad_clip": 1.0, # learning rate decay settings "decay_lr": True, # Will be auto-adjusted based on epochs "warmup_iters": None, # Will be calculated as percentage of max_iters "lr_decay_iters": None, # Will be set to max_iters "min_lr": 1e-4, # DDP settings "backend": "nccl", # system "device": "cuda", "dtype": "bfloat16", "compile": True, } # ============================================================================ # MODEL DEFINITION - Embedded from model.py # ============================================================================ class LayerNorm(nn.Module): """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """ def __init__(self, ndim, bias): super().__init__() self.weight = nn.Parameter(torch.ones(ndim)) self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None def forward(self, input): return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5) class CausalSelfAttention(nn.Module): def __init__(self, config): super().__init__() assert config.n_embd % config.n_head == 0 # key, query, value projections for all heads, but in a batch self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias) # output projection self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias) # regularization self.attn_dropout = nn.Dropout(config.dropout) self.resid_dropout = nn.Dropout(config.dropout) self.n_head = config.n_head self.n_embd = config.n_embd self.dropout = config.dropout # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0 self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') if not self.flash: print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") # causal mask to ensure that attention is only applied to the left in the input sequence self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size)) .view(1, 1, config.block_size, config.block_size)) def forward(self, x): B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd) # calculate query, key, values for all heads in batch and move head forward to be the batch dim q, k, v = self.c_attn(x).split(self.n_embd, dim=2) k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T) if self.flash: # efficient attention using Flash Attention CUDA kernels y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True) else: # manual implementation of attention att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf')) att = F.softmax(att, dim=-1) att = self.attn_dropout(att) y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs) y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side # output projection y = self.resid_dropout(self.c_proj(y)) return y class MLP(nn.Module): def __init__(self, config): super().__init__() self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias) self.gelu = nn.GELU() self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias) self.dropout = nn.Dropout(config.dropout) def forward(self, x): x = self.c_fc(x) x = self.gelu(x) x = self.c_proj(x) x = self.dropout(x) return x class Block(nn.Module): def __init__(self, config): super().__init__() self.ln_1 = LayerNorm(config.n_embd, bias=config.bias) self.attn = CausalSelfAttention(config) self.ln_2 = LayerNorm(config.n_embd, bias=config.bias) self.mlp = MLP(config) def forward(self, x): x = x + self.attn(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x @dataclass class GPTConfig: block_size: int = 1024 vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency n_layer: int = 12 n_head: int = 12 n_embd: int = 768 dropout: float = 0.0 bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster class GPT(nn.Module): def __init__(self, config): super().__init__() assert config.vocab_size is not None assert config.block_size is not None self.config = config self.transformer = nn.ModuleDict(dict( wte = nn.Embedding(config.vocab_size, config.n_embd), wpe = nn.Embedding(config.block_size, config.n_embd), drop = nn.Dropout(config.dropout), h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]), ln_f = LayerNorm(config.n_embd, bias=config.bias), )) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # with weight tying when using torch.compile() some warnings get generated: # "UserWarning: functional_call was passed multiple values for tied weights. # This behavior is deprecated and will be an error in future versions" # not 100% sure what this is, so far seems to be harmless. TODO investigate self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying # init all weights self.apply(self._init_weights) # apply special scaled init to the residual projections, per GPT-2 paper for pn, p in self.named_parameters(): if pn.endswith('c_proj.weight'): torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer)) # report number of parameters print("number of parameters: %.2fM" % (self.get_num_params()/1e6,)) def get_num_params(self, non_embedding=True): """ Return the number of parameters in the model. For non-embedding count (default), the position embeddings get subtracted. The token embeddings would too, except due to the parameter sharing these params are actually used as weights in the final layer, so we include them. """ n_params = sum(p.numel() for p in self.parameters()) if non_embedding: n_params -= self.transformer.wpe.weight.numel() return n_params def _init_weights(self, module): if isinstance(module, nn.Linear): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) if module.bias is not None: torch.nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) def forward(self, idx, targets=None): device = idx.device b, t = idx.size() assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}" pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t) # forward the GPT model itself tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd) pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd) x = self.transformer.drop(tok_emb + pos_emb) for block in self.transformer.h: x = block(x) x = self.transformer.ln_f(x) if targets is not None: # if we are given some desired targets also calculate the loss logits = self.lm_head(x) loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1) else: # inference-time mini-optimization: only forward the lm_head on the very last position logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim loss = None return logits, loss def crop_block_size(self, block_size): # model surgery to decrease the block size if necessary # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024) # but want to use a smaller block size for some smaller, simpler model assert block_size <= self.config.block_size self.config.block_size = block_size self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size]) for block in self.transformer.h: if hasattr(block.attn, 'bias'): block.attn.bias = block.attn.bias[:,:,:block_size,:block_size] def configure_optimizers(self, weight_decay, learning_rate, betas, device_type): # start with all of the candidate parameters param_dict = {pn: p for pn, p in self.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. 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}") return optimizer def estimate_mfu(self, fwdbwd_per_iter, dt): """ estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """ # first estimate the number of flops we do per iteration. # see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311 N = self.get_num_params() cfg = self.config L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd//cfg.n_head, cfg.block_size flops_per_token = 6*N + 12*L*H*Q*T flops_per_fwdbwd = flops_per_token * T flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter # express our flops throughput as ratio of A100 bfloat16 peak flops flops_achieved = flops_per_iter * (1.0/dt) # per second flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS mfu = flops_achieved / flops_promised return mfu @torch.no_grad() def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None): """ Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete the sequence max_new_tokens times, feeding the predictions back into the model each time. Most likely you'll want to make sure to be in model.eval() mode of operation for this. """ for _ in range(max_new_tokens): # if the sequence context is growing too long we must crop it at block_size idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:] # forward the model to get the logits for the index in the sequence logits, _ = self(idx_cond) # pluck the logits at the final step and scale by desired temperature logits = logits[:, -1, :] / temperature # optionally crop the logits to only the top k options if top_k is not None: v, _ = torch.topk(logits, min(top_k, logits.size(-1))) logits[logits < v[:, [-1]]] = -float('Inf') # apply softmax to convert logits to (normalized) probabilities probs = F.softmax(logits, dim=-1) # sample from the distribution idx_next = torch.multinomial(probs, num_samples=1) # append sampled index to the running sequence and continue idx = torch.cat((idx, idx_next), dim=1) return idx # ============================================================================ # DATA PREPARATION # ============================================================================ def ensure_shakespeare_data(data_root="/data"): """Download and prepare Shakespeare dataset if not exists""" import requests data_dir = os.path.join(data_root, "shakespeare_char") # Check if prepared data already exists train_path = os.path.join(data_dir, "train.bin") val_path = os.path.join(data_dir, "val.bin") meta_path = os.path.join(data_dir, "meta.pkl") if os.path.exists(train_path) and os.path.exists(val_path) and os.path.exists(meta_path): print(f"Shakespeare data already prepared in {data_dir}") return # Create directory os.makedirs(data_dir, exist_ok=True) # Download the tiny shakespeare dataset input_file_path = os.path.join(data_dir, 'input.txt') if not os.path.exists(input_file_path): print("Downloading Shakespeare dataset...") data_url = 'https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt' with open(input_file_path, 'w') as f: f.write(requests.get(data_url).text) with open(input_file_path, 'r') as f: data = f.read() print(f"length of dataset in characters: {len(data):,}") # get all the unique characters that occur in this text chars = sorted(list(set(data))) vocab_size = len(chars) print("all the unique characters:", ''.join(chars)) print(f"vocab size: {vocab_size:,}") # create a mapping from characters to integers stoi = { ch:i for i,ch in enumerate(chars) } itos = { i:ch for i,ch in enumerate(chars) } def encode(s): return [stoi[c] for c in s] # encoder: take a string, output a list of integers def decode(l): return ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string # create the train and test splits n = len(data) train_data = data[:int(n*0.9)] val_data = data[int(n*0.9):] # encode both to integers train_ids = encode(train_data) val_ids = encode(val_data) print(f"train has {len(train_ids):,} tokens") print(f"val has {len(val_ids):,} tokens") # export to bin files train_ids = np.array(train_ids, dtype=np.uint16) val_ids = np.array(val_ids, dtype=np.uint16) train_ids.tofile(train_path) val_ids.tofile(val_path) # save the meta information as well, to help us encode/decode later meta = { 'vocab_size': vocab_size, 'itos': itos, 'stoi': stoi, } with open(meta_path, 'wb') as f: pickle.dump(meta, f) print("Data preparation complete!") # ============================================================================ # TRAINING SCRIPT # ============================================================================ def train(): """Main training function that runs under torchrun""" # Load configuration cfg = CONFIG # Setup DDP ddp = int(os.environ.get('RANK', -1)) != -1 if ddp: init_process_group(backend=cfg['backend']) 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 assert cfg['gradient_accumulation_steps'] % ddp_world_size == 0 gradient_accumulation_steps = cfg['gradient_accumulation_steps'] // ddp_world_size else: # single gpu master_process = True seed_offset = 0 ddp_world_size = 1 device = cfg['device'] gradient_accumulation_steps = cfg['gradient_accumulation_steps'] tokens_per_iter = gradient_accumulation_steps * ddp_world_size * cfg['batch_size'] * cfg['block_size'] print(f"tokens per iteration will be: {tokens_per_iter:,}") if master_process: os.makedirs(cfg['out_dir'], exist_ok=True) torch.manual_seed(1337 + seed_offset) torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True device_type = 'cuda' if 'cuda' in device else 'cpu' # Data setup data_dir = os.path.join("/data" if os.path.exists("/data") else "data", cfg['dataset']) # Calculate dataset size and iterations needed for requested epochs train_data_path = os.path.join(data_dir, 'train.bin') if os.path.exists(train_data_path): train_data = np.memmap(train_data_path, dtype=np.uint16, mode='r') dataset_tokens = len(train_data) print(f"Training dataset has {dataset_tokens:,} tokens") # Calculate iterations needed for the requested number of epochs if cfg['num_epochs'] is not None: iterations_per_epoch = dataset_tokens / tokens_per_iter cfg['max_iters'] = int(math.ceil(cfg['num_epochs'] * iterations_per_epoch)) print(f"For {cfg['num_epochs']} epochs, need {cfg['max_iters']} iterations") print(f"Each epoch is ~{iterations_per_epoch:.1f} iterations") # Auto-adjust other parameters based on total iterations if cfg['warmup_iters'] is None: # Default 2% warmup cfg['warmup_iters'] = max(1, int(0.02 * cfg['max_iters'])) if cfg['lr_decay_iters'] is None: cfg['lr_decay_iters'] = cfg['max_iters'] # Adjust eval/log intervals for short runs if cfg['max_iters'] < 20: cfg['eval_interval'] = max(1, cfg['max_iters'] // 4) cfg['log_interval'] = 1 cfg['eval_iters'] = min(50, cfg['eval_iters']) print(f"Adjusted for short run: eval_interval={cfg['eval_interval']}, log_interval={cfg['log_interval']}") # Disable learning rate decay for very short runs if cfg['max_iters'] < 10: cfg['decay_lr'] = False cfg['warmup_iters'] = 0 print("Disabled learning rate decay for very short run") del train_data # Free memory else: if cfg['max_iters'] is None: raise ValueError("Cannot calculate max_iters: training data not found and max_iters not specified") def get_batch(split): # We recreate np.memmap every batch to avoid a memory leak 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) - cfg['block_size'], (cfg['batch_size'],)) x = torch.stack([torch.from_numpy((data[i:i+cfg['block_size']]).astype(np.int64)) for i in ix]) y = torch.stack([torch.from_numpy((data[i+1:i+1+cfg['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 # Init these up here iter_num = 0 best_val_loss = 1e9 # Model init 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 configuration model_args = dict( n_layer=cfg['n_layer'], n_head=cfg['n_head'], n_embd=cfg['n_embd'], block_size=cfg['block_size'], bias=cfg['bias'], vocab_size=meta_vocab_size if meta_vocab_size is not None else 50304, dropout=cfg['dropout'] ) if cfg['init_from'] == 'scratch': print("Initializing a new model from scratch") gptconf = GPTConfig(**model_args) model = GPT(gptconf) elif cfg['init_from'] == 'resume': print(f"Resuming training from {cfg['out_dir']}") ckpt_path = os.path.join(cfg['out_dir'], 'ckpt.pt') checkpoint = torch.load(ckpt_path, map_location=device) checkpoint_model_args = checkpoint['model_args'] for k in ['n_layer', 'n_head', 'n_embd', 'block_size', 'bias', 'vocab_size']: model_args[k] = checkpoint_model_args[k] gptconf = GPTConfig(**model_args) model = GPT(gptconf) state_dict = checkpoint['model'] 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'] # Move model to device model.to(device) # Initialize a GradScaler scaler = torch.cuda.amp.GradScaler(enabled=(cfg['dtype'] == 'float16')) # Optimizer optimizer = model.configure_optimizers(cfg['weight_decay'], cfg['learning_rate'], (cfg['beta1'], cfg['beta2']), device_type) if cfg['init_from'] == 'resume' and 'optimizer' in checkpoint: optimizer.load_state_dict(checkpoint['optimizer']) checkpoint = None # free up memory # Compile the model if cfg['compile']: print("compiling the model... (takes a ~minute)") unoptimized_model = model model = torch.compile(model) # Wrap model into DDP container if ddp: model = DDP(model, device_ids=[ddp_local_rank]) # Training helpers ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[cfg['dtype']] ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype) @torch.no_grad() def estimate_loss(): out = {} model.eval() for split in ['train', 'val']: losses = torch.zeros(cfg['eval_iters']) for k in range(cfg['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 # Learning rate decay scheduler (cosine with warmup) def get_lr(it): # Linear warmup if it < cfg['warmup_iters']: return cfg['learning_rate'] * (it + 1) / (cfg['warmup_iters'] + 1) # If it > lr_decay_iters, return min learning rate if it > cfg['lr_decay_iters']: return cfg['min_lr'] # In between, use cosine decay decay_ratio = (it - cfg['warmup_iters']) / (cfg['lr_decay_iters'] - cfg['warmup_iters']) assert 0 <= decay_ratio <= 1 coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) return cfg['min_lr'] + coeff * (cfg['learning_rate'] - cfg['min_lr']) # Logging if cfg['wandb_log'] and master_process: import wandb wandb.init(project=cfg['wandb_project'], name=cfg['wandb_run_name'], config=cfg) # Training loop X, Y = get_batch('train') t0 = time.time() local_iter_num = 0 raw_model = model.module if ddp else model running_mfu = -1.0 while True: # Determine and set the learning rate for this iteration lr = get_lr(iter_num) if cfg['decay_lr'] else cfg['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 % cfg['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 cfg['wandb_log']: wandb.log({ "iter": iter_num, "train/loss": losses['train'], "val/loss": losses['val'], "lr": lr, "mfu": running_mfu*100, }) if losses['val'] < best_val_loss or cfg['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': cfg, } print(f"saving checkpoint to {cfg['out_dir']}") torch.save(checkpoint, os.path.join(cfg['out_dir'], 'ckpt.pt')) if iter_num == 0 and cfg['eval_only']: break # Forward backward update for micro_step in range(gradient_accumulation_steps): if ddp: model.require_backward_grad_sync = (micro_step == gradient_accumulation_steps - 1) with ctx: logits, loss = model(X, Y) loss = loss / gradient_accumulation_steps X, Y = get_batch('train') scaler.scale(loss).backward() # Clip gradients if cfg['grad_clip'] != 0.0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), cfg['grad_clip']) # Step the optimizer scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) # Timing and logging t1 = time.time() dt = t1 - t0 t0 = t1 if iter_num % cfg['log_interval'] == 0 and master_process: lossf = loss.item() * gradient_accumulation_steps if local_iter_num >= 5: mfu = raw_model.estimate_mfu(cfg['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 # Termination conditions if iter_num > cfg['max_iters']: break if ddp: destroy_process_group() # ============================================================================ # MODAL SETUP # ============================================================================ # Create Modal app app = modal.App("nanogpt-training") # Build Modal image with all dependencies image = ( modal.Image.debian_slim(python_version="3.11") .pip_install( "numpy", "torch", "transformers", "wandb", "requests" ) ) # Create Modal volume for persistent storage volume = modal.Volume.from_name("nanogpt-data", create_if_missing=True) # Modal entry point function @app.function( gpu=f"{GPU_TYPE}:{N_GPUS}", volumes={"/data": volume}, timeout=60 * 60 * 6, # 6 hours image=image, secrets=[modal.Secret.from_name("wandb-secret")] if CONFIG.get("wandb_log", False) else [], ) def train_modal(): """Launch distributed training on Modal""" print(f"Starting Modal training with {N_GPUS} {GPU_TYPE} GPUs") print(f"Dataset: {CONFIG['dataset']}") # Prepare data ensure_shakespeare_data("/data") # Copy this script to a temporary location for torchrun script_path = Path(__file__) script_content = script_path.read_text() temp_script = "/tmp/train_modal.py" Path(temp_script).write_text(script_content) # Launch distributed training with torchrun cmd = [ "torchrun", f"--nproc-per-node={N_GPUS}", temp_script, ] print(f"Running command: {' '.join(cmd)}") # Change to temp directory to run os.chdir("/tmp") # Launch distributed training subprocess.run(cmd, check=True) print("Training completed successfully!") return "Training completed" # Main entry point if __name__ == "__main__": # Check if we're running under torchrun if "RANK" in os.environ: # We're running distributed - execute training train() else: # Not running under torchrun print("This script should be run with torchrun or through Modal") print("Examples:") print(" Local: torchrun --nproc-per-node=4 train_modal_standalone.py") print(" Modal: modal run train_modal_standalone.py::train_modal") sys.exit(1)
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