mirror of
https://github.com/karpathy/nanochat.git
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43c29dd9d5
The new DataLoader ensures that every token sequence in train/val batches has a BOS token at the beginning. Therefore, no token streams start abruptly in the middle of a document, which could be confusing for the model. Note that this changes the loss scale because there are fewer confusing tokens in the train/val batches. The main downside is that we now waste about 35% of tokens due to cropping. This is ok because we have a lot of data. See dev/LOG.md entry for this change for a lot more information.
369 lines
17 KiB
Python
369 lines
17 KiB
Python
"""
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Midtrain the model. Same as pretraining but simpler.
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Run as:
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python -m scripts.mid_train
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Or torchrun for training:
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torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --device_batch_size=16
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"""
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import argparse
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import os
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os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
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import time
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import wandb
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import torch
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from contextlib import nullcontext
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from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, get_base_dir, autodetect_device_type
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from nanochat.tokenizer import get_token_bytes
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from nanochat.checkpoint_manager import save_checkpoint
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from nanochat.loss_eval import evaluate_bpb
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from nanochat.checkpoint_manager import load_model
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import torch.distributed as dist
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from tasks.common import TaskMixture
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from tasks.gsm8k import GSM8K
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from tasks.mmlu import MMLU
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from tasks.smoltalk import SmolTalk
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from tasks.customjson import CustomJSON
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from tasks.spellingbee import SimpleSpelling, SpellingBee
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# -----------------------------------------------------------------------------
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# CLI arguments
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parser = argparse.ArgumentParser(description="Midtrain the model")
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# Logging
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parser.add_argument("--run", type=str, default="dummy", help="wandb run name ('dummy' disables wandb logging)")
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# Runtime
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parser.add_argument("--device_type", type=str, default="", help="cuda|cpu|mps (empty = autodetect)")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="float32|bfloat16")
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# Model loading
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parser.add_argument("--model_tag", type=str, default=None, help="model tag to load from")
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parser.add_argument("--model_step", type=int, default=None, help="model step to load from")
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# Training horizon
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parser.add_argument("--num_iterations", type=int, default=-1, help="number of optimization steps (-1 = full epoch)")
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# Batch sizes
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parser.add_argument("--max_seq_len", type=int, default=2048, help="max context length")
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parser.add_argument("--device_batch_size", type=int, default=32, help="per-device batch size")
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parser.add_argument("--total_batch_size", type=int, default=524288, help="total batch size in tokens")
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# Optimization
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parser.add_argument("--embedding_lr", type=float, default=0.2, help="learning rate for embedding parameters (Adam)")
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parser.add_argument("--unembedding_lr", type=float, default=0.004, help="learning rate for unembedding parameters (Adam)")
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parser.add_argument("--matrix_lr", type=float, default=0.02, help="learning rate for matrix parameters (Muon)")
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parser.add_argument("--weight_decay", type=float, default=0.0, help="weight decay for embedding/unembedding parameters (Adam)")
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parser.add_argument("--init_lr_frac", type=float, default=1.0, help="initial LR as fraction of base LR")
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# Evaluation
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parser.add_argument("--eval_every", type=int, default=150, help="evaluate val bpb every N steps (-1 = disable)")
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parser.add_argument("--eval_tokens", type=int, default=20*524288, help="number of tokens to evaluate val loss on")
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# Output
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parser.add_argument("--dry_run", action="store_true", help="log to wandb but skip checkpoints/report")
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args = parser.parse_args()
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user_config = vars(args).copy()
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# -----------------------------------------------------------------------------
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# Compute init
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device_type = autodetect_device_type() if args.device_type == "" else args.device_type
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ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
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master_process = ddp_rank == 0
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ptdtype = torch.float32 if args.dtype == 'float32' else torch.bfloat16
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autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype) if device_type == "cuda" else nullcontext()
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synchronize = torch.cuda.synchronize if device_type == "cuda" else lambda: None
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get_max_memory = torch.cuda.max_memory_allocated if device_type == "cuda" else lambda: 0
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# wandb logging init
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use_dummy_wandb = args.run == "dummy" or not master_process
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wandb_run = DummyWandb() if use_dummy_wandb else wandb.init(project="nanochat-mid", name=args.run, config=user_config)
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# Load the model and tokenizer
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model, tokenizer, meta = load_model("base", device, phase="train", model_tag=args.model_tag, step=args.model_step)
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pretrain_batch_size = meta.get("device_batch_size", None)
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if pretrain_batch_size is not None and args.device_batch_size > pretrain_batch_size:
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print0(f"FOOTGUN WARNING: base model training used device_batch_size {pretrain_batch_size}, did you pass in a good --device_batch_size to this script?")
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orig_model = model
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model = torch.compile(model, dynamic=False)
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depth = model.config.n_layer
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num_flops_per_token = model.estimate_flops()
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tokens_per_fwdbwd = args.device_batch_size * args.max_seq_len # tokens per iteration for a single rank
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world_tokens_per_fwdbwd = tokens_per_fwdbwd * ddp_world_size # total tokens per iteration for all ranks
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assert args.total_batch_size % world_tokens_per_fwdbwd == 0
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grad_accum_steps = args.total_batch_size // world_tokens_per_fwdbwd
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print0(f"Tokens / micro-batch / rank: {args.device_batch_size} x {args.max_seq_len} = {tokens_per_fwdbwd:,}")
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print0(f"Tokens / micro-batch: {world_tokens_per_fwdbwd:,}")
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print0(f"Total batch size {args.total_batch_size:,} => gradient accumulation steps: {grad_accum_steps}")
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token_bytes = get_token_bytes(device=device)
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# Initialize the Optimizer (Muon for Linear layers, AdamW for embedding and lm_head)
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optimizers = model.setup_optimizers(unembedding_lr=args.unembedding_lr, embedding_lr=args.embedding_lr, matrix_lr=args.matrix_lr, weight_decay=args.weight_decay)
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adamw_optimizer, muon_optimizer = optimizers
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# Override the initial learning rate as a fraction of the base learning rate
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for opt in optimizers:
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for group in opt.param_groups:
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group["lr"] = group["lr"] * args.init_lr_frac
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group["initial_lr"] = group["lr"] # save the initial learning so we can decay easily later
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# Midtraining data mixture and DataLoader
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base_dir = get_base_dir()
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identity_conversations_filepath = os.path.join(base_dir, "identity_conversations.jsonl")
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train_dataset = TaskMixture([
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SmolTalk(split="train"), # 460K rows of general conversations
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MMLU(subset="auxiliary_train", split="train"), # 100K rows of multiple choice problems drawn from ARC, MC_TEST, OBQA, RACE
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GSM8K(subset="main", split="train"), # 8K rows teaching simple math and (calculator) tool use
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CustomJSON(filepath=identity_conversations_filepath), # 1000 rows of synthetic identity conversations
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CustomJSON(filepath=identity_conversations_filepath), # let's do 2 epochs of these
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SimpleSpelling(size=200000, split="train"), # 200K rows of Simple Spelling (e.g. spell the word 'apple')
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SpellingBee(size=80000, split="train"), # 80K rows of Spelling Bee (e.g. how many 'r' are in 'strawberry'?)
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]) # total: 460K + 100K + 8K + 200K + 80K = 848K rows
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val_dataset = TaskMixture([
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SmolTalk(split="test"), # 24K rows in test set
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MMLU(subset="all", split="test", stop=5200), # 14K rows in test set, use only 5.2K to match the train ratios
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GSM8K(subset="main", split="test", stop=420), # 1.32K rows in test set, use only 420 to match the train ratios
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]) # total: 24K + 14K + 1.32K ~= 39K rows
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# DataLoader is defined here, it emits inputs, targets : 2D tensors of shape (device_batch_size, max_seq_len)
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# A big problem is that we don't know the final num_iterations in advance. So we create
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# these two global variables and update them from within the data generator.
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last_step = False # we will toggle this to True when we reach the end of the training dataset
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approx_progress = 0.0 # will go from 0 to 1 over the course of the epoch
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current_epoch = 1 # track epoch for logging
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def mid_data_generator_bos_bestfit(split, buffer_size=100):
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"""
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BOS-aligned dataloader for midtraining with bestfit-crop packing.
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Each row in the batch starts with BOS (beginning of a conversation).
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Conversations are packed using best-fit algorithm to minimize cropping.
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This matches the BOS-aligned approach used in pretraining.
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"""
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global last_step, approx_progress, current_epoch
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assert split in {"train", "val"}, "split must be 'train' or 'val'"
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dataset = train_dataset if split == "train" else val_dataset
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dataset_size = len(dataset)
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assert dataset_size > 0
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row_capacity = args.max_seq_len + 1 # +1 for target at last position
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# Conversation buffer: list of token lists
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conv_buffer = []
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cursor = ddp_rank # Each rank processes different conversations
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epoch = 1
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it = 0 # iteration counter
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def refill_buffer():
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nonlocal cursor, epoch
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while len(conv_buffer) < buffer_size:
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conversation = dataset[cursor]
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ids, _ = tokenizer.render_conversation(conversation)
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conv_buffer.append(ids)
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cursor += ddp_world_size
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if cursor >= dataset_size:
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cursor = cursor % dataset_size
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epoch += 1
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if split == "train":
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last_step = True # toggle last_step to True, which will terminate the training loop
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while True:
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rows = []
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for _ in range(args.device_batch_size):
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row = []
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while len(row) < row_capacity:
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# Ensure buffer has conversations
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while len(conv_buffer) < buffer_size:
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refill_buffer()
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remaining = row_capacity - len(row)
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# Find largest conversation that fits entirely
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best_idx = -1
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best_len = 0
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for i, conv in enumerate(conv_buffer):
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conv_len = len(conv)
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if conv_len <= remaining and conv_len > best_len:
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best_idx = i
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best_len = conv_len
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if best_idx >= 0:
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# Found a conversation that fits - use it entirely
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conv = conv_buffer.pop(best_idx)
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row.extend(conv)
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else:
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# No conversation fits - crop first conversation to fill remaining
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conv = conv_buffer.pop(0)
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row.extend(conv[:remaining])
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rows.append(row[:row_capacity])
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# Stopping condition to respect num_iterations, if given
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it += 1
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if 0 < args.num_iterations <= it and split == "train":
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last_step = True
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# Update progress tracking
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if split == "train":
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current_epoch = epoch
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if args.num_iterations > 0:
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approx_progress = it / args.num_iterations
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else:
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approx_progress = cursor / dataset_size
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# Build tensors
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use_cuda = device_type == "cuda"
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batch_tensor = torch.tensor(rows, dtype=torch.long, pin_memory=use_cuda)
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inputs = batch_tensor[:, :-1].to(device=device, dtype=torch.int32, non_blocking=use_cuda)
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targets = batch_tensor[:, 1:].to(device=device, dtype=torch.int64, non_blocking=use_cuda)
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yield inputs, targets
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train_loader = mid_data_generator_bos_bestfit("train")
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build_val_loader = lambda: mid_data_generator_bos_bestfit("val")
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progress = 0 # will go from 0 to 1 over the course of the epoch
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# Learning rate scheduler
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def get_lr_multiplier(progress):
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# first 80% of training: no decay, then linearly ramp down to 0.
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return 1 if progress < 0.8 else 1 - (progress - 0.8) / 0.2
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# Momentum scheduler for Muon optimizer
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def get_muon_momentum(it):
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frac = min(it / 300, 1)
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momentum = (1 - frac) * 0.85 + frac * 0.95
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return momentum
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# -----------------------------------------------------------------------------
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# Training loop
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x, y = next(train_loader) # prefetch the very first batch of data
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min_val_bpb = float("inf")
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smooth_train_loss = 0 # EMA of training loss
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ema_beta = 0.9 # EMA decay factor
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total_training_time = 0 # total wall-clock time of training
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step = 0
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while True:
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flops_so_far = num_flops_per_token * args.total_batch_size * step
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# Synchronize last_step across all ranks to avoid hangs in the distributed setting
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if ddp:
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last_step_tensor = torch.tensor(last_step, dtype=torch.int32, device=device)
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dist.all_reduce(last_step_tensor, op=dist.ReduceOp.MAX)
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last_step = bool(last_step_tensor.item())
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# once in a while: evaluate the val bpb (all ranks participate)
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if args.eval_every > 0 and (last_step or step % args.eval_every == 0):
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model.eval()
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val_loader = build_val_loader()
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eval_steps = args.eval_tokens // (args.device_batch_size * args.max_seq_len * ddp_world_size)
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with autocast_ctx:
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val_bpb = evaluate_bpb(model, val_loader, eval_steps, token_bytes)
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print0(f"Step {step:05d} | Validation bpb: {val_bpb:.4f}")
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if val_bpb < min_val_bpb:
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min_val_bpb = val_bpb
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wandb_run.log({
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"step": step,
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"total_training_flops": flops_so_far,
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"total_training_time": total_training_time,
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"val/bpb": val_bpb,
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})
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model.train()
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# save checkpoint at the end of the run (only on master process)
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if master_process and last_step and not args.dry_run:
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output_dirname = args.model_tag if args.model_tag else f"d{depth}" # e.g. d12
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checkpoint_dir = os.path.join(base_dir, "mid_checkpoints", output_dirname)
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save_checkpoint(
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checkpoint_dir,
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step,
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orig_model.state_dict(),
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[opt.state_dict() for opt in optimizers], # TODO: make sure saving across ranks is done correctly
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{
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"step": step,
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"val_bpb": val_bpb, # loss at last step
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"model_config": {
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"sequence_len": args.max_seq_len,
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"vocab_size": tokenizer.get_vocab_size(),
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"n_layer": depth,
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"n_head": model.config.n_head,
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"n_kv_head": model.config.n_kv_head,
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"n_embd": model.config.n_embd,
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},
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"user_config": user_config, # inputs to the training script
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}
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)
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if last_step:
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break
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# -------------------------------------------------------------------------
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# single training step
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# evaluate the gradient
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synchronize()
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t0 = time.time()
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for micro_step in range(grad_accum_steps):
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with autocast_ctx:
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loss = model(x, y)
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train_loss = loss.detach() # for logging
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loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
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loss.backward()
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x, y = next(train_loader) # prefetch the next batch while the GPU is busy with forward/backward
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progress = max(progress, approx_progress) # only increase progress monotonically
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# step the optimizers
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lrm = get_lr_multiplier(progress)
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for opt in optimizers:
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for group in opt.param_groups:
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group["lr"] = group["initial_lr"] * lrm
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muon_momentum = get_muon_momentum(step)
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for group in muon_optimizer.param_groups:
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group["momentum"] = muon_momentum
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for opt in optimizers:
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opt.step()
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model.zero_grad(set_to_none=True)
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synchronize()
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t1 = time.time()
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dt = t1 - t0
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# -------------------------------------------------------------------------
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# State
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step += 1
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# logging
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smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss.item() # EMA the training loss
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debiased_smooth_loss = smooth_train_loss / (1 - ema_beta**(step + 1)) # debias the EMA
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pct_done = 100 * progress
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tok_per_sec = int(args.total_batch_size / dt)
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flops_per_sec = num_flops_per_token * args.total_batch_size / dt
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promised_flops_per_sec_h100 = 989e12 * ddp_world_size # bfloat16 H100 SXM and without 2:4 sparsity
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mfu = 100 * flops_per_sec / promised_flops_per_sec_h100 # in %
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if step > 10:
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total_training_time += dt # only count the time after the first 10 steps
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print0(f"step {step:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.2f} | epoch: {current_epoch} | total time: {total_training_time/60:.2f}m")
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if step % 10 == 0:
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wandb_run.log({
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"step": step,
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"total_training_flops": flops_so_far,
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"total_training_time": total_training_time,
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"train/loss": debiased_smooth_loss,
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"train/lrm": lrm,
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"train/dt": dt,
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"train/tok_per_sec": tok_per_sec,
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"train/mfu": mfu,
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"train/epoch": current_epoch,
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})
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# print a few more stats
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print0(f"Peak memory usage: {get_max_memory() / 1024 / 1024:.2f}MiB")
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print0(f"Total training time: {total_training_time/60:.2f}m")
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print0(f"Minimum validation bpb: {min_val_bpb:.4f}")
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# Log to report
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if not args.dry_run:
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from nanochat.report import get_report
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get_report().log(section="Midtraining", data=[
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user_config, # CLI args
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{ # stats about the training setup
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"Number of iterations": step,
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"DDP world size": ddp_world_size,
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},
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{ # stats about training outcomes
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"Minimum validation bpb": min_val_bpb,
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}
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])
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# cleanup
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wandb_run.finish() # wandb run finish
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compute_cleanup()
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