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535 lines
26 KiB
Python
535 lines
26 KiB
Python
"""
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Supervised fine-tuning (SFT) the model.
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Run as:
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python -m scripts.chat_sft
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Or torchrun for training:
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torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --device-batch-size=16
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"""
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import gc
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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 nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, get_base_dir, autodetect_device_type, get_peak_flops, COMPUTE_DTYPE, COMPUTE_DTYPE_REASON, is_ddp_initialized
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from nanochat.tokenizer import get_token_bytes
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from nanochat.checkpoint_manager import save_checkpoint, load_model, load_optimizer_state
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from nanochat.loss_eval import evaluate_bpb
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import torch.distributed as dist
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from nanochat.flash_attention import HAS_FA3
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from nanochat.engine import Engine
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from scripts.chat_eval import run_chat_eval
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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="Supervised fine-tuning (SFT) 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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# 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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parser.add_argument("--load-optimizer", type=int, default=1, help="warm-start optimizer from pretrained checkpoint (0=no, 1=yes)")
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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 (default: inherit from pretrained checkpoint)
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parser.add_argument("--max-seq-len", type=int, default=None, help="max context length (default: inherit from pretrain)")
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parser.add_argument("--device-batch-size", type=int, default=None, help="per-device batch size (default: inherit from pretrain)")
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parser.add_argument("--total-batch-size", type=int, default=None, help="total batch size in tokens (default: inherit from pretrain)")
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# Optimization (default: inherit from pretrained checkpoint)
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parser.add_argument("--embedding-lr", type=float, default=None, help="learning rate for embedding parameters (Adam) (default: inherit from pretrain)")
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parser.add_argument("--unembedding-lr", type=float, default=None, help="learning rate for unembedding parameters (Adam) (default: inherit from pretrain)")
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parser.add_argument("--matrix-lr", type=float, default=None, help="learning rate for matrix parameters (Muon) (default: inherit from pretrain)")
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parser.add_argument("--init-lr-frac", type=float, default=0.8, help="initial LR as fraction of base LR")
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parser.add_argument("--warmup-ratio", type=float, default=0.0, help="ratio of iterations for LR warmup")
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parser.add_argument("--warmdown-ratio", type=float, default=0.5, help="ratio of iterations for LR warmdown")
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parser.add_argument("--final-lr-frac", type=float, default=0.0, help="final LR as fraction of initial LR")
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# Evaluation
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parser.add_argument("--eval-every", type=int, default=200, help="evaluate val bpb every N steps (-1 = disable)")
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parser.add_argument("--eval-tokens", type=int, default=40*524288, help="number of tokens to evaluate val loss on")
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parser.add_argument("--chatcore-every", type=int, default=200, help="evaluate ChatCORE metric every N steps (-1 = disable)")
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parser.add_argument("--chatcore-max-cat", type=int, default=-1, help="max problems per categorical task for ChatCORE")
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parser.add_argument("--chatcore-max-sample", type=int, default=24, help="max problems per generative task for ChatCORE")
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# Data mixture
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parser.add_argument("--mmlu-epochs", type=int, default=3, help="number of epochs of MMLU in training mixture (teaches Multiple Choice)")
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parser.add_argument("--gsm8k-epochs", type=int, default=4, help="number of epochs of GSM8K in training mixture (teaches Math and Tool Use)")
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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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print0(f"COMPUTE_DTYPE: {COMPUTE_DTYPE} ({COMPUTE_DTYPE_REASON})")
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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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if device_type == "cuda":
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gpu_device_name = torch.cuda.get_device_name(0)
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gpu_peak_flops = get_peak_flops(gpu_device_name)
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print0(f"GPU: {gpu_device_name} | Peak FLOPS (BF16): {gpu_peak_flops:.2e}")
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else:
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gpu_peak_flops = float('inf') # MFU not meaningful for CPU/MPS
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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-sft", name=args.run, config=user_config)
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# Flash Attention status
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if not HAS_FA3:
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print0("WARNING: Flash Attention 3 not available, using PyTorch SDPA fallback. Training will be less efficient.")
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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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# Inherit training hyperparameters from pretrained checkpoint (None = inherit, explicit value = override)
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pretrain_user_config = meta.get("user_config", {})
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for name, fallback, source in [
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("max_seq_len", 2048, meta),
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("device_batch_size", 32, meta),
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("total_batch_size", 524288, meta),
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("embedding_lr", 0.3, pretrain_user_config),
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("unembedding_lr", 0.004, pretrain_user_config),
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("matrix_lr", 0.02, pretrain_user_config),
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]:
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arg_val = getattr(args, name)
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pretrain_val = source.get(name)
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if arg_val is None:
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resolved = pretrain_val if pretrain_val is not None else fallback
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setattr(args, name, resolved)
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print0(f"Inherited {name}={resolved} from pretrained checkpoint")
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elif pretrain_val is not None and arg_val != pretrain_val:
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print0(f"NOTE: --{name.replace('_', '-')}={arg_val} overrides pretrained value of {pretrain_val}")
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else:
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print0(f"Using {name}={arg_val}")
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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 (combined MuonAdamW: Muon for matrix params, AdamW for rest)
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# Note that pretraining ramps weight_decay to zero by end of pretraining, so SFT continues with zero
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optimizer = model.setup_optimizer(unembedding_lr=args.unembedding_lr, embedding_lr=args.embedding_lr, matrix_lr=args.matrix_lr, weight_decay=0.0)
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# Optionally warm-start optimizer from pretrained checkpoint (momentum buffers etc.)
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# Note: load_state_dict overwrites param_group metadata (LRs, betas, etc.) with the
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# pretrained values. Since pretraining warmdown brings LRs to ~0, we must save and
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# restore our fresh SFT LRs after loading.
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base_dir = get_base_dir()
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if args.load_optimizer:
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optimizer_data = load_optimizer_state("base", device, rank=ddp_rank, model_tag=args.model_tag, step=args.model_step)
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if optimizer_data is not None:
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base_lrs = [group["lr"] for group in optimizer.param_groups]
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optimizer.load_state_dict(optimizer_data)
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del optimizer_data
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for group, base_lr in zip(optimizer.param_groups, base_lrs):
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group["lr"] = base_lr
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print0("Loaded optimizer state from pretrained checkpoint (momentum buffers only, LRs reset)")
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else:
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print0("WARNING: optimizer checkpoint not found, starting with fresh optimizer (slightly worse)")
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# GradScaler for fp16 training (bf16/fp32 don't need it)
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scaler = torch.amp.GradScaler() if COMPUTE_DTYPE == torch.float16 else None
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if scaler is not None:
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print0("GradScaler enabled for fp16 training")
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# Override the initial learning rate as a fraction of the base learning rate
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for group in optimizer.param_groups:
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group["lr"] = group["lr"] * args.init_lr_frac
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group["initial_lr"] = group["lr"]
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# SFT data mixture and DataLoader
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identity_conversations_filepath = os.path.join(base_dir, "identity_conversations.jsonl")
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train_tasks = [
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SmolTalk(split="train"), # 460K rows of general conversations
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CustomJSON(filepath=identity_conversations_filepath), # 1000 rows of synthetic identity conversations
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CustomJSON(filepath=identity_conversations_filepath), # 2 epochs of these
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*[MMLU(subset="all", split="auxiliary_train") for _ in range(args.mmlu_epochs)], # 100K rows per epoch
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*[GSM8K(subset="main", split="train") for _ in range(args.gsm8k_epochs)], # 8K rows per epoch
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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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]
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train_dataset = TaskMixture(train_tasks)
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print0(f"Training mixture: {len(train_dataset):,} rows (MMLU x{args.mmlu_epochs}, GSM8K x{args.gsm8k_epochs})")
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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 + 5.2K + 0.42K ~= 29.6K 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 sft_data_generator_bos_bestfit(split, buffer_size=100):
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"""
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BOS-aligned dataloader for SFT with bestfit-pad 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. When no conversation fits,
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the row is padded. If the row is still empty, we fall back to truncating one
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conversation to avoid fully padded rows.
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Padding positions have targets masked with -1 (ignore_index for cross-entropy).
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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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bos_token = tokenizer.get_bos_token_id()
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# Conversation buffer: list of (token_ids, loss_mask) tuples
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conv_buffer = []
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cursor = ddp_rank # Each rank processes different conversations (for fetching)
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consumed = ddp_rank # Track actual consumption separately from buffering
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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, mask = tokenizer.render_conversation(conversation)
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conv_buffer.append((ids, mask))
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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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# Note: last_step is now triggered based on consumption, not fetching
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while True:
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rows = []
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mask_rows = []
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row_lengths = [] # Track actual content length (excluding padding) for each row
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for _ in range(args.device_batch_size):
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row = []
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mask_row = []
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padded = False
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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_mask = conv_buffer.pop(best_idx)
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row.extend(conv)
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mask_row.extend(conv_mask)
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consumed += ddp_world_size # Track actual consumption
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else:
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if len(row) == 0:
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# No conversation fits even on an empty row.
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# Truncate the shortest oversized conversation to avoid fully padded rows.
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trunc_idx = -1
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trunc_len = None
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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 (trunc_len is None or conv_len < trunc_len):
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trunc_idx = i
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trunc_len = conv_len
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conv, conv_mask = conv_buffer.pop(trunc_idx)
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row.extend(conv[:row_capacity])
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mask_row.extend(conv_mask[:row_capacity])
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consumed += ddp_world_size
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else:
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# No conversation fits - pad the remainder instead of dropping tokens.
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content_len = len(row)
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row.extend([bos_token] * remaining)
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mask_row.extend([0] * remaining)
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padded = True
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break # Row is now full (with padding or truncation)
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# Track content length: full row if no padding, otherwise the length before padding
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if padded:
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row_lengths.append(content_len)
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else:
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row_lengths.append(row_capacity)
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rows.append(row[:row_capacity])
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mask_rows.append(mask_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 (based on consumed, not cursor, to account for buffering)
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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 = consumed / dataset_size
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# Trigger last_step when we've consumed enough (instead of when cursor wraps)
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if consumed >= dataset_size:
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last_step = True
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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).contiguous()
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targets = batch_tensor[:, 1:].to(device=device, dtype=torch.int64, non_blocking=use_cuda).contiguous()
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# Apply the loss mask from render_conversation (mask=1 for assistant completions,
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# mask=0 for user prompts, BOS, special tokens, tool outputs). mask[1:] aligns
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# with targets (shifted by 1). Unmasked positions get -1 (ignore_index).
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mask_tensor = torch.tensor(mask_rows, dtype=torch.int8)
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mask_targets = mask_tensor[:, 1:].to(device=device)
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targets[mask_targets == 0] = -1
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# Mask out padding positions in targets (set to -1 = ignore_index)
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# For each row, positions >= (content_length - 1) in targets should be masked
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for i, content_len in enumerate(row_lengths):
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if content_len < row_capacity:
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targets[i, content_len-1:] = -1
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yield inputs, targets
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train_loader = sft_data_generator_bos_bestfit("train")
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build_val_loader = lambda: sft_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 schedule (linear warmup, constant, linear warmdown)
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# Same shape as base_train but uses progress (0→1) instead of absolute step counts,
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# because SFT doesn't always know num_iterations in advance (dataset-driven stopping).
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def get_lr_multiplier(progress):
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if progress < args.warmup_ratio:
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return (progress + 1e-8) / args.warmup_ratio
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elif progress <= 1.0 - args.warmdown_ratio:
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return 1.0
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else:
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decay = (progress - (1.0 - args.warmdown_ratio)) / args.warmdown_ratio
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return (1 - decay) * 1.0 + decay * args.final_lr_frac
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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 last_step or (args.eval_every > 0 and 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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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()
|
|
|
|
# once in a while: estimate the ChatCORE metric (all ranks participate)
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|
# use the original uncompiled model because the inputs keep changing shape
|
|
chatcore_results = {}
|
|
if args.chatcore_every > 0 and (last_step or (step > 0 and step % args.chatcore_every == 0)):
|
|
model.eval()
|
|
engine = Engine(orig_model, tokenizer)
|
|
all_tasks = ['ARC-Easy', 'ARC-Challenge', 'MMLU', 'GSM8K', 'HumanEval', 'SpellingBee']
|
|
categorical_tasks = {'ARC-Easy', 'ARC-Challenge', 'MMLU'}
|
|
baseline_accuracies = {
|
|
'ARC-Easy': 0.25, 'ARC-Challenge': 0.25, 'MMLU': 0.25,
|
|
'GSM8K': 0.0, 'HumanEval': 0.0, 'SpellingBee': 0.0,
|
|
}
|
|
task_results = {}
|
|
for task_name in all_tasks:
|
|
limit = args.chatcore_max_cat if task_name in categorical_tasks else args.chatcore_max_sample
|
|
max_problems = None if limit < 0 else limit # -1 means no limit
|
|
acc = run_chat_eval(task_name, orig_model, tokenizer, engine,
|
|
batch_size=args.device_batch_size, max_problems=max_problems)
|
|
task_results[task_name] = acc
|
|
print0(f" {task_name}: {100*acc:.2f}%")
|
|
# Compute ChatCORE metrics (mean centered accuracy, ranges from 0=random to 1=perfect)
|
|
def centered_mean(tasks):
|
|
return sum((task_results[t] - baseline_accuracies[t]) / (1.0 - baseline_accuracies[t]) for t in tasks) / len(tasks)
|
|
chatcore = centered_mean(all_tasks)
|
|
chatcore_cat = centered_mean(categorical_tasks)
|
|
print0(f"Step {step:05d} | ChatCORE: {chatcore:.4f} | ChatCORE_cat: {chatcore_cat:.4f}")
|
|
wandb_run.log({
|
|
"step": step,
|
|
"total_training_flops": flops_so_far,
|
|
"chatcore_metric": chatcore,
|
|
"chatcore_cat": chatcore_cat,
|
|
**{f"chatcore/{task_name}": acc for task_name, acc in task_results.items()},
|
|
})
|
|
model.train()
|
|
|
|
# save checkpoint at the end of the run (all ranks participate so each saves its optimizer shard)
|
|
if last_step:
|
|
output_dirname = args.model_tag if args.model_tag else f"d{depth}" # e.g. d12
|
|
checkpoint_dir = os.path.join(base_dir, "chatsft_checkpoints", output_dirname)
|
|
save_checkpoint(
|
|
checkpoint_dir,
|
|
step,
|
|
orig_model.state_dict(),
|
|
optimizer.state_dict(),
|
|
{
|
|
"step": step,
|
|
"val_bpb": val_bpb, # loss at last step
|
|
"model_config": {
|
|
"sequence_len": args.max_seq_len,
|
|
"vocab_size": tokenizer.get_vocab_size(),
|
|
"n_layer": depth,
|
|
"n_head": model.config.n_head,
|
|
"n_kv_head": model.config.n_kv_head,
|
|
"n_embd": model.config.n_embd,
|
|
"window_pattern": model.config.window_pattern,
|
|
},
|
|
"user_config": user_config, # inputs to the training script
|
|
},
|
|
rank=ddp_rank,
|
|
)
|
|
|
|
if last_step:
|
|
break
|
|
|
|
# -------------------------------------------------------------------------
|
|
# single training step
|
|
# evaluate the gradient
|
|
synchronize()
|
|
t0 = time.time()
|
|
for micro_step in range(grad_accum_steps):
|
|
loss = model(x, y)
|
|
train_loss = loss.detach() # for logging
|
|
loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
|
|
if scaler is not None:
|
|
scaler.scale(loss).backward()
|
|
else:
|
|
loss.backward()
|
|
x, y = next(train_loader) # prefetch the next batch while the GPU is busy with forward/backward
|
|
progress = max(progress, approx_progress) # only increase progress monotonically
|
|
# step the optimizer
|
|
lrm = get_lr_multiplier(progress)
|
|
muon_momentum = get_muon_momentum(step)
|
|
for group in optimizer.param_groups:
|
|
group["lr"] = group["initial_lr"] * lrm
|
|
if group['kind'] == 'muon':
|
|
group["momentum"] = muon_momentum
|
|
if scaler is not None:
|
|
scaler.unscale_(optimizer)
|
|
if is_ddp_initialized():
|
|
for v in scaler._found_inf_per_device(optimizer).values():
|
|
dist.all_reduce(v, op=dist.ReduceOp.MAX)
|
|
scaler.step(optimizer)
|
|
scaler.update()
|
|
else:
|
|
optimizer.step()
|
|
model.zero_grad(set_to_none=True)
|
|
synchronize()
|
|
t1 = time.time()
|
|
dt = t1 - t0
|
|
# -------------------------------------------------------------------------
|
|
|
|
# State
|
|
step += 1
|
|
|
|
# logging
|
|
smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss.item() # EMA the training loss
|
|
debiased_smooth_loss = smooth_train_loss / (1 - ema_beta**(step + 1)) # debias the EMA
|
|
pct_done = 100 * progress
|
|
tok_per_sec = int(args.total_batch_size / dt)
|
|
flops_per_sec = num_flops_per_token * args.total_batch_size / dt
|
|
mfu = 100 * flops_per_sec / (gpu_peak_flops * ddp_world_size)
|
|
if step > 10:
|
|
total_training_time += dt # only count the time after the first 10 steps
|
|
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")
|
|
if step % 10 == 0:
|
|
wandb_run.log({
|
|
"step": step,
|
|
"total_training_flops": flops_so_far,
|
|
"total_training_time": total_training_time,
|
|
"train/loss": debiased_smooth_loss,
|
|
"train/lrm": lrm,
|
|
"train/dt": dt,
|
|
"train/tok_per_sec": tok_per_sec,
|
|
"train/mfu": mfu,
|
|
"train/epoch": current_epoch,
|
|
})
|
|
|
|
# The garbage collector spends ~500ms scanning for cycles quite frequently.
|
|
# We manually manage it to avoid these pauses during training.
|
|
if step == 1:
|
|
gc.collect() # manually collect a lot of garbage from setup
|
|
gc.freeze() # freeze all currently surviving objects and exclude them from GC
|
|
gc.disable() # disable GC entirely except:
|
|
elif step % 5000 == 0: # every 5000 steps...
|
|
gc.collect() # manually collect, just to be safe for very long runs
|
|
|
|
# print a few more stats
|
|
print0(f"Peak memory usage: {get_max_memory() / 1024 / 1024:.2f}MiB")
|
|
print0(f"Total training time: {total_training_time/60:.2f}m")
|
|
print0(f"Minimum validation bpb: {min_val_bpb:.4f}")
|
|
|
|
# Log to report
|
|
from nanochat.report import get_report
|
|
get_report().log(section="SFT", data=[
|
|
user_config, # CLI args
|
|
{ # stats about the training setup
|
|
"Number of iterations": step,
|
|
"DDP world size": ddp_world_size,
|
|
},
|
|
{ # stats about training outcomes
|
|
"Minimum validation bpb": min_val_bpb,
|
|
}
|
|
])
|
|
|
|
# cleanup
|
|
wandb_run.finish() # wandb run finish
|
|
compute_cleanup()
|