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1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 | # src/trainer.py
"""
FSDP trainer for CodonTranslator.
No frameworks, no sugar. The model computes its own loss.
Batch invariants:
- codon_ids [B, T] (right-padded; EOS already in-sequence)
- species_ids [B] (SpeciesEmbeddingStore provides fixed-size or sequence embeddings)
- protein_seqs: list[str] (ESM tokenization happens inside the model)
Rules:
- If your loader is IterableDataset, you MUST set args.max_steps > 0. We don't guess.
- If you want epoch-based, use a sized dataset; we call len(dataloader).
"""
from __future__ import annotations
import os
import json
import math
import re
import shutil
import logging
import time
from dataclasses import dataclass
import datetime
import warnings
import importlib.util
import inspect
from typing import Any, Callable, Dict, Optional, Tuple, List
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.distributed as dist
from torch.utils.data import DataLoader, IterableDataset
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp import (
ShardingStrategy,
MixedPrecision,
StateDictType,
FullStateDictConfig,
FullOptimStateDictConfig,
)
from safetensors.torch import save_file, load_file
import wandb
logger = logging.getLogger(__name__)
# ------------------------------
# Args
# ------------------------------
@dataclass
class TrainingArguments:
# Output
output_dir: str = "checkpoints"
save_steps: int = 1000
save_total_limit: int = 3
save_safetensors: bool = True
ckpt_recent_window_steps: int = 0
ckpt_recent_interval: int = 0
ckpt_archive_interval: int = 0
# Schedule
num_train_epochs: int = 1
max_steps: int = -1 # required for IterableDataset
gradient_accumulation_steps: int = 1
warmup_ratio: float = 0.0
lr_scheduler_type: str = "cosine" # "linear" | "cosine" | "constant"
# For streaming datasets: if max_steps<0 and steps_per_epoch>0, shape schedule using
# total_steps = num_train_epochs * steps_per_epoch
steps_per_epoch: int = 0
# Optim
learning_rate: float = 5e-4
weight_decay: float = 0.0
adam_beta1: float = 0.9
adam_beta2: float = 0.95
max_grad_norm: float = 1.0
# Data
per_device_train_batch_size: int = 8
per_device_eval_batch_size: int = 8
dataloader_num_workers: int = 0
# Precision / dist
fp16: bool = False
bf16: bool = False
fsdp: Optional[str] = None # "full_shard" or None
gradient_checkpointing: bool = False
# Global hard cap (prefix + start + codon)
max_length: int = 4096
# ESM (metadata only; model owns ESM)
esm_model_name: str = "esmc_300m"
esm_device: str = "cuda"
esm_dtype: str = "bf16"
# Logging / eval
logging_steps: int = 100
eval_steps: int = 0 # streaming eval: limit number of eval batches when eval dataset is Iterable
eval_interval: int = 0 # run evaluation every N optimizer steps (0 disables)
override_lr_on_resume: bool = False
# Minimal data stream resume cursor (stores total samples yielded so far for train dataset).
# When provided, we load 'skip_samples' from this JSON at start and set the dataset
# to skip exactly that many samples on resume. We also update the file in _save_checkpoint().
data_cursor_path: Optional[str] = None
# ------------------------------
# Trainer
# ------------------------------
class Trainer:
def __init__(
self,
model: nn.Module,
args: TrainingArguments,
data_collator: Optional[Callable] = None,
train_dataset: Optional[Any] = None,
eval_dataset: Optional[Any] = None,
tokenizer: Optional[Any] = None,
model_init: Optional[Callable[[], nn.Module]] = None,
compute_metrics: Optional[Callable] = None,
callbacks: Optional[list] = None,
optimizers: Tuple[Optional[torch.optim.Optimizer], Optional[Any]] = (None, None),
preprocess_logits_for_metrics: Optional[Callable] = None,
species_store=None,
resume_from_checkpoint: Optional[str] = None,
):
self.model = model
self.args = args
self.tokenizer = tokenizer
self.optimizer = optimizers[0]
self.lr_scheduler = optimizers[1]
self.species_store = species_store
self.train_dataloader: Optional[DataLoader] = None
self.eval_dataloader: Optional[DataLoader] = None
# Device (robust local rank resolution)
self.local_rank = 0
if torch.cuda.is_available():
lr_env = os.environ.get("LOCAL_RANK")
if lr_env is not None:
self.local_rank = int(lr_env)
else:
r = int(os.environ.get("RANK", "0"))
ng = max(1, torch.cuda.device_count())
self.local_rank = (r % ng)
self.device = torch.device(f"cuda:{self.local_rank}")
torch.cuda.set_device(self.device)
cd = torch.cuda.current_device()
nm = torch.cuda.get_device_name(cd)
logger.info(
f"[dist] RANK={os.environ.get('RANK')} LOCAL_RANK={os.environ.get('LOCAL_RANK')} WORLD_SIZE={os.environ.get('WORLD_SIZE')} "
f"cuda.count={torch.cuda.device_count()} select={self.device} current={cd} name={nm}"
)
else:
self.device = torch.device("cpu")
# Gradient checkpointing toggle (model owns the flag)
base = self._unwrap(self.model)
if self.args.gradient_checkpointing and hasattr(base, "gradient_checkpointing"):
base.gradient_checkpointing = True
# FSDP or single GPU
if self.args.fsdp:
self._setup_fsdp()
else:
self.model = self.model.to(self.device)
# AMP setup (use torch.amp APIs; GradScaler on CUDA only)
self._use_amp = (self.device.type == "cuda") and (self.args.fp16 or self.args.bf16)
self._amp_dtype = torch.float16 if self.args.fp16 else (torch.bfloat16 if self.args.bf16 else None)
use_cuda = (self.device.type == "cuda")
self._scaler = torch.amp.GradScaler(device="cuda", enabled=(use_cuda and self.args.fp16))
self.state = {"epoch": 0, "global_step": 0}
# Defer resume until after dataloaders are attached so scheduler can be shaped.
self._resume_path: Optional[str] = resume_from_checkpoint
# ---- dataloaders ----
def attach_dataloaders(self, train_loader: DataLoader, eval_loader: Optional[DataLoader] = None):
# Your dataset should handle sharding. We don't wrap with DistributedSampler here.
self.train_dataloader = train_loader
self.eval_dataloader = eval_loader
# Apply minimal resume cursor to the training dataset if configured
p = getattr(self.args, "data_cursor_path", None)
if p and os.path.exists(p):
with open(p, "r") as f:
js = json.load(f)
ds = getattr(self.train_dataloader, "dataset", None)
if hasattr(ds, "set_resume_skip"):
distributed = dist.is_available() and dist.is_initialized()
world = dist.get_world_size() if distributed else 1
rank = dist.get_rank() if distributed else 0
# Prefer the total cursor and split evenly across current world size.
# If total is missing, sum any saved per_rank list.
total: int = 0
if isinstance(js, dict):
try:
total = int(js.get("skip_samples", 0) or 0)
except Exception:
total = 0
if total <= 0:
raw = js.get("per_rank")
if isinstance(raw, list) and raw:
try:
total = int(sum(int(x) for x in raw))
except Exception:
total = 0
if total > 0:
if distributed:
per = total // max(world, 1)
rem = total % max(world, 1)
n_rank = per + (1 if rank < rem else 0)
ds.set_resume_skip(int(n_rank))
if self._is_main():
logger.info(
"resume cursor: total=%s split across world=%s → rank=%s skip=%s",
total, world, rank, n_rank,
)
else:
ds.set_resume_skip(int(total))
if self._is_main():
logger.info("resume cursor: total=%s (single-process) skip=%s", total, total)
# ---- optim + scheduler ----
def _create_optimizer_and_scheduler(self):
if self.optimizer is None:
decay, no_decay = [], []
for n, p in self._unwrap(self.model).named_parameters():
if not p.requires_grad:
continue
if n.endswith("bias") or "norm" in n.lower() or "ln_" in n.lower():
no_decay.append(p)
else:
decay.append(p)
opt_kwargs = dict(
lr=self.args.learning_rate,
betas=(self.args.adam_beta1, self.args.adam_beta2),
)
params = [
{"params": decay, "weight_decay": self.args.weight_decay},
{"params": no_decay, "weight_decay": 0.0},
]
sig_adamw = inspect.signature(torch.optim.AdamW)
if torch.cuda.is_available() and "fused" in sig_adamw.parameters:
opt_kwargs["fused"] = True # type: ignore[assignment]
self.optimizer = torch.optim.AdamW(params, **opt_kwargs)
# Report fused/foreach settings (rank0 only)
if self._is_main():
fused_flag = None
foreach_flag = None
if hasattr(self.optimizer, "defaults"):
fused_flag = self.optimizer.defaults.get("fused")
foreach_flag = self.optimizer.defaults.get("foreach")
logger.info(f"AdamW configured: fused={fused_flag} foreach={foreach_flag}")
# total steps and schedule shape
ds = getattr(self.train_dataloader, "dataset", None)
ga = max(1, self.args.gradient_accumulation_steps)
if isinstance(ds, IterableDataset):
if self.args.max_steps > 0:
# Use max_steps to shape the scheduler; allow multiple epochs to re-iterate the stream
steps_per_epoch = self.args.max_steps
total_steps = self.args.max_steps
elif getattr(self.args, "steps_per_epoch", 0) and self.args.steps_per_epoch > 0:
# steps_per_epoch is already expressed in optimizer steps (train.py accounts for grad_accum)
steps_per_epoch = max(1, int(self.args.steps_per_epoch))
total_steps = max(1, self.args.num_train_epochs) * steps_per_epoch
else:
# Unknown epoch size; use constant LR without pre-shaped schedule
self.lr_scheduler = torch.optim.lr_scheduler.LambdaLR(self.optimizer, lambda step: 1.0)
return
else:
# sized dataloader: len(dataloader) is number of batches
steps_per_epoch = max(len(self.train_dataloader) // ga, 1)
total_steps = self.args.max_steps if self.args.max_steps > 0 else self.args.num_train_epochs * steps_per_epoch
warmup = int(self.args.warmup_ratio * total_steps)
if self.args.lr_scheduler_type == "constant":
self.lr_scheduler = torch.optim.lr_scheduler.LambdaLR(self.optimizer, lambda step: 1.0)
return
def lrs_lambda(step: int) -> float:
if step < warmup:
return max(float(step) / max(warmup, 1), 1e-6)
t = (step - warmup) / max(total_steps - warmup, 1)
if self.args.lr_scheduler_type == "linear":
return max(1.0 - t, 0.0)
# cosine default
return 0.5 * (1.0 + math.cos(math.pi * t))
self.lr_scheduler = torch.optim.lr_scheduler.LambdaLR(self.optimizer, lrs_lambda)
# ---- training ----
def train(self) -> Dict[str, float]:
assert self.train_dataloader is not None, "Call attach_dataloaders() first"
# If a resume path was provided, load it now (dataloaders are attached).
if getattr(self, "_resume_path", None):
self._resume_from(self._resume_path) # loads model/optimizer/scheduler/state
self._resume_path = None
if self.optimizer is None:
self._create_optimizer_and_scheduler()
ds = self.train_dataloader.dataset
# Exact step budget for streaming datasets when max_steps<0 and steps_per_epoch>0
target_total_steps: Optional[int] = None
if isinstance(ds, IterableDataset) and int(self.args.max_steps) < 0:
spe = int(getattr(self.args, "steps_per_epoch", 0) or 0)
if spe > 0:
target_total_steps = max(1, int(self.args.num_train_epochs)) * spe
# Determine total steps for progress bar
progress_total: Optional[int] = None
if int(self.args.max_steps) > 0:
progress_total = int(self.args.max_steps)
elif isinstance(ds, IterableDataset):
if target_total_steps is not None:
progress_total = target_total_steps
else:
ga = max(1, self.args.gradient_accumulation_steps)
steps_per_epoch = max(len(self.train_dataloader) // ga, 1)
progress_total = max(1, int(self.args.num_train_epochs)) * steps_per_epoch
# Initialize Weights & Biases (rank0 only)
if self._is_main():
if not hasattr(self, "_wandb"):
proj = os.environ.get("WANDB_PROJECT", "codontranslator")
name = os.environ.get("WANDB_NAME")
run_id = os.environ.get("WANDB_RUN_ID")
resume = os.environ.get("WANDB_RESUME")
wandb_dir = os.environ.get("WANDB_DIR")
world_size = dist.get_world_size() if dist.is_available() and dist.is_initialized() else int(os.environ.get("WORLD_SIZE", "1"))
init_kwargs = {
"project": proj,
"name": name,
"config": {
"lr": self.args.learning_rate,
"warmup_ratio": self.args.warmup_ratio,
"scheduler": self.args.lr_scheduler_type,
"batch_size": self.args.per_device_train_batch_size,
"eval_batch_size": self.args.per_device_eval_batch_size,
"grad_accum": self.args.gradient_accumulation_steps,
"effective_global_batch": self.args.per_device_train_batch_size * max(1, world_size) * max(1, self.args.gradient_accumulation_steps),
"epochs": self.args.num_train_epochs,
"steps_per_epoch": getattr(self.args, "steps_per_epoch", 0),
"max_steps": self.args.max_steps,
"weight_decay": self.args.weight_decay,
"world_size": world_size,
"output_dir": self.args.output_dir,
"fsdp": self.args.fsdp,
"bf16": self.args.bf16,
"fp16": self.args.fp16,
},
}
if run_id:
init_kwargs["id"] = run_id
if resume:
init_kwargs["resume"] = resume
if wandb_dir:
init_kwargs["dir"] = wandb_dir
self._wandb = wandb.init(**init_kwargs)
self.model.train()
grad_accum = max(1, self.args.gradient_accumulation_steps)
progress = None
if self._is_main() and progress_total is not None and progress_total > 0:
progress = tqdm(total=progress_total, initial=int(self.state["global_step"]), desc="Train", dynamic_ncols=True)
if self.device.type == "cuda" and torch.cuda.is_available():
torch.cuda.reset_peak_memory_stats(self.device)
world_size = dist.get_world_size() if dist.is_available() and dist.is_initialized() else int(os.environ.get("WORLD_SIZE", "1"))
seqs_per_optimizer_step = (
int(self.args.per_device_train_batch_size) * max(1, world_size) * grad_accum
)
log_window_start = time.perf_counter()
log_window_optimizer_steps = 0
for epoch in range(self.state["epoch"], max(1, self.args.num_train_epochs)):
self.state["epoch"] = epoch
running_loss = 0.0
running_count = 0
train_iter = iter(self.train_dataloader)
step = 0
batches_this_epoch = 0
optimizer_steps_this_epoch = 0
# If this is a streaming dataset with a shaped schedule, enforce a per-epoch optimizer step budget
enforce_budget = False
epoch_budget = None
ds = self.train_dataloader.dataset
if isinstance(ds, IterableDataset):
spe = int(getattr(self.args, "steps_per_epoch", 0) or 0)
if spe > 0:
enforce_budget = True
epoch_budget = int(spe)
refill_attempts = 0
max_refills = 64 # avoids infinite loops when dataset is empty
while True:
batch, has_batch, local_has_batch = self._next_batch_sync(train_iter)
if not has_batch:
# If budget-enforced, attempt to refill the iterator and continue until budget is met.
if enforce_budget and (epoch_budget is not None) and (optimizer_steps_this_epoch < epoch_budget):
if local_has_batch and self._is_main():
logger.warning("Rank retained extra batch while peers exhausted stream; dropping to stay in sync")
self._barrier()
train_iter = iter(self.train_dataloader)
refill_attempts += 1
if refill_attempts > max_refills:
if self._is_main():
logger.warning(
"Exceeded max refills for epoch %s (steps %s/%s). Ending epoch early.",
epoch, optimizer_steps_this_epoch, epoch_budget,
)
break
continue
else:
if local_has_batch and self._is_main():
logger.warning("Rank retained extra batch while peers exhausted stream; dropping to stay in sync")
break
batch = self._prepare_batch(batch)
batches_this_epoch += 1
codon_ids = batch["codon_ids"].to(self.device)
input_ids = codon_ids[:, :-1]
labels = codon_ids[:, :-1]
# Mask PAD/EOS in labels
pad_id = int(self.tokenizer.pad_token_id) if self.tokenizer is not None else 0
eos_id = int(self.tokenizer.special_ids.eos) if self.tokenizer is not None else -999
labels = labels.clone()
labels[labels == pad_id] = -100
labels[labels == eos_id] = -100
cond = self._build_cond(batch)
# autocast context
use_cuda = (self.device.type == "cuda")
autocast_dtype = self._amp_dtype
if autocast_dtype is not None and use_cuda:
ctx = torch.amp.autocast(device_type="cuda", dtype=autocast_dtype)
else:
from contextlib import nullcontext
ctx = nullcontext()
with ctx:
out = self.model(codon_ids=input_ids, cond=cond, labels=labels, return_dict=True)
loss = out["loss"]
if self._scaler.is_enabled():
self._scaler.scale(loss / grad_accum).backward()
else:
(loss / grad_accum).backward()
running_loss += float(loss.detach().item())
running_count += 1
do_step = ((step + 1) % grad_accum == 0)
if do_step:
# Clip
if self.args.max_grad_norm and self.args.max_grad_norm > 0:
if isinstance(self.model, FSDP):
FSDP.clip_grad_norm_(self.model, self.args.max_grad_norm)
else:
if self._scaler.is_enabled():
self._scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.max_grad_norm)
# Step
if self._scaler.is_enabled():
self._scaler.step(self.optimizer)
self._scaler.update()
else:
self.optimizer.step()
if self.lr_scheduler is not None:
self.lr_scheduler.step()
self.optimizer.zero_grad(set_to_none=True)
self.state["global_step"] += 1
optimizer_steps_this_epoch += 1
log_window_optimizer_steps += 1
# (wandb) Defer logging to the periodic block below
# Log
should_log = (self.state["global_step"] % max(1, self.args.logging_steps) == 0)
peak_alloc_gb = 0.0
peak_reserved_gb = 0.0
if should_log:
peak_alloc_gb, peak_reserved_gb = self._max_cuda_peak_gb()
if self._is_main() and should_log:
avg = running_loss / max(running_count, 1)
lr = float(self.optimizer.param_groups[0]["lr"])
log_epoch = self._epoch_for_logging()
elapsed = max(time.perf_counter() - log_window_start, 1e-9)
step_time_s = elapsed / max(log_window_optimizer_steps, 1)
seq_per_s = (seqs_per_optimizer_step * max(log_window_optimizer_steps, 1)) / elapsed
msg = f"epoch {log_epoch} step {self.state['global_step']}: loss={avg:.4f} lr={lr:.6g}"
if isinstance(out, dict):
pl = out.get("prefix_len")
pc = out.get("per_cap")
if pl is not None and pc is not None:
msg += f" prefix_mean={float(pl.detach().float().mean().item()):.1f} cap_mean={float(pc.detach().float().mean().item()):.1f}"
msg += (
f" step_time_s={step_time_s:.3f} seq_per_s={seq_per_s:.1f}"
f" peak_mem_alloc_gb={peak_alloc_gb:.1f} peak_mem_reserved_gb={peak_reserved_gb:.1f}"
)
logger.info(msg)
if hasattr(self, "_wandb"):
wandb.log({
"train/loss": float(avg),
"train/lr": float(lr),
"perf/step_time_s": float(step_time_s),
"perf/seq_per_s": float(seq_per_s),
"system/peak_mem_alloc_gb": float(peak_alloc_gb),
"system/peak_mem_reserved_gb": float(peak_reserved_gb),
}, step=self.state["global_step"])
running_loss = 0.0
running_count = 0
log_window_start = time.perf_counter()
log_window_optimizer_steps = 0
# Update progress bar
if progress is not None:
progress.update(1)
# Stop when budget is reached for streaming schedule
if target_total_steps is not None and self.state["global_step"] >= target_total_steps:
metrics = {"train_loss": running_loss / max(running_count, 1)}
self._save_checkpoint("final_model")
self._barrier()
return metrics
# Periodic teacher-forced evaluation on the held-out dataset
should_eval = (
self.eval_dataloader is not None and
self.args.eval_interval > 0 and
(self.state["global_step"] % self.args.eval_interval == 0)
)
if should_eval:
eval_metrics = self.evaluate()
if self._is_main():
el = float(eval_metrics.get("eval_loss", 0.0))
ea = eval_metrics.get("eval_codon_acc", None)
aa = eval_metrics.get("eval_aa_acc", None)
if ea is not None and aa is not None:
logger.info(f"eval: loss={el:.4f} codon_acc={float(ea):.3f} aa_acc={float(aa):.3f}")
elif ea is not None:
logger.info(f"eval: loss={el:.4f} codon_acc={float(ea):.3f}")
elif aa is not None:
logger.info(f"eval: loss={el:.4f} aa_acc={float(aa):.3f}")
else:
logger.info(f"eval: loss={el:.4f}")
if hasattr(self, "_wandb"):
log_payload = {"eval/loss": el}
if ea is not None:
log_payload["eval/codon_acc"] = float(ea)
if aa is not None:
log_payload["eval/aa_acc"] = float(aa)
wandb.log(log_payload, step=self.state["global_step"])
# Save by step
if self.args.save_steps > 0 and (self.state["global_step"] % self.args.save_steps == 0):
self._save_checkpoint(f"checkpoint-{self.state['global_step']}")
# Hard horizon for streaming/step-limited runs
if self.args.max_steps > 0 and self.state["global_step"] >= self.args.max_steps:
metrics = {"train_loss": running_loss / max(running_count, 1)}
self._save_checkpoint("final_model")
self._barrier()
if progress is not None:
progress.close()
return metrics
step += 1
# If we enforce a per-epoch budget for streaming datasets, end the epoch once it's reached
if enforce_budget and (epoch_budget is not None) and (optimizer_steps_this_epoch >= epoch_budget):
break
# Epoch summary (rank0 only)
if self._is_main():
try:
eb = int(epoch_budget) if epoch_budget is not None else -1
except Exception:
eb = -1
logger.info(
"epoch %s completed: optimizer_steps=%s%s",
self._epoch_for_logging(),
optimizer_steps_this_epoch,
(f" / budget {eb}" if eb > 0 else ""),
)
if dist.is_available() and dist.is_initialized():
gather_device = self.device if self.device.type == "cuda" else torch.device("cpu")
counts_tensor = torch.tensor(
[batches_this_epoch, optimizer_steps_this_epoch],
dtype=torch.long,
device=gather_device,
)
gathered = [torch.zeros_like(counts_tensor) for _ in range(dist.get_world_size())]
dist.all_gather(gathered, counts_tensor)
batch_counts = [int(t[0].item()) for t in gathered]
step_counts = [int(t[1].item()) for t in gathered]
batch_gap = max(batch_counts) - min(batch_counts)
step_gap = max(step_counts) - min(step_counts)
if self._is_main() and (batch_gap > 0 or step_gap > 0):
logger.warning(
"Epoch %s imbalance detected across ranks: batches min=%s max=%s, optimizer steps min=%s max=%s",
epoch,
min(batch_counts),
max(batch_counts),
min(step_counts),
max(step_counts),
)
# Epoch boundary save for sized datasets
if not isinstance(ds, IterableDataset):
self._save_checkpoint(f"epoch-{epoch}")
metrics = {"train_loss": 0.0}
if progress is not None:
progress.close()
self._barrier()
return metrics
# ---- evaluation ----
def evaluate(self) -> Dict[str, float]:
if self.eval_dataloader is None:
return {"eval_loss": 0.0}
self.model.eval()
loss_sum = 0.0
loss_tokens = 0
codon_correct = 0
codon_total = 0
aa_correct = 0
aa_total = 0
tok = self.tokenizer
pad_id = int(tok.pad_token_id) if tok is not None else 0
eos_id = int(tok.special_ids.eos) if tok is not None and hasattr(tok, "special_ids") else -999
num_special = int(tok.num_special_tokens) if tok is not None else 0
codon2aa = tok.codon2aa_char_map() if tok is not None and hasattr(tok, "codon2aa_char_map") else {}
is_streaming = isinstance(self.eval_dataloader.dataset, IterableDataset)
max_batches = int(self.args.eval_steps) if (is_streaming and self.args.eval_steps > 0) else None
with torch.no_grad():
eval_iter = iter(self.eval_dataloader)
b_idx = 0
while True:
batch, has_batch, local_has_batch = self._next_batch_sync(eval_iter)
if not has_batch:
if local_has_batch and self._is_main():
logger.debug("eval dataloader: discarded tail batch to stay in sync across ranks")
break
if max_batches is not None and b_idx >= max_batches:
break
batch = self._prepare_batch(batch)
codon_ids = batch["codon_ids"].to(self.device)
input_ids = codon_ids[:, :-1]
labels = codon_ids[:, :-1]
labels = labels.clone()
labels[labels == pad_id] = -100
labels[labels == eos_id] = -100
cond = self._build_cond(batch)
use_cuda = (self.device.type == "cuda")
autocast_dtype = self._amp_dtype
if autocast_dtype is not None and use_cuda:
ctx = torch.amp.autocast(device_type="cuda", dtype=autocast_dtype)
else:
from contextlib import nullcontext
ctx = nullcontext()
with ctx:
out = self.model(codon_ids=input_ids, cond=cond, labels=labels, return_dict=True)
loss = out.get("loss")
per_cap = out.get("per_cap")
logits = out.get("logits")
tokens_in_batch = 0
if per_cap is not None:
tokens_in_batch = int(torch.clamp(per_cap.detach(), min=0).sum().item())
loss_tokens += tokens_in_batch
if loss is not None and tokens_in_batch > 0:
loss_sum += float(loss.detach().item()) * tokens_in_batch
if logits is None or logits.size(1) == 0 or per_cap is None:
continue
max_cap = logits.size(1)
batch_size = logits.size(0)
labels_aligned = torch.full((batch_size, max_cap), -100, dtype=labels.dtype, device=labels.device)
common_cols = min(labels.size(1), max_cap)
if common_cols > 0:
labels_aligned[:, :common_cols] = labels[:, :common_cols]
per_cap_int = torch.clamp(per_cap.to(dtype=torch.long), min=0, max=max_cap)
for row in range(batch_size):
cap = int(per_cap_int[row].item())
if cap < max_cap:
labels_aligned[row, cap:] = -100
supervised = labels_aligned != -100
if num_special > 0:
supervised = supervised & (labels_aligned >= num_special)
if not supervised.any():
continue
preds = logits.argmax(dim=-1)
codon_correct += int((preds[supervised] == labels_aligned[supervised]).sum().item())
codon_total += int(supervised.sum().item())
if codon2aa and isinstance(batch, dict) and "protein_seqs" in batch:
prot_list = batch.get("protein_seqs", [])
for row in range(batch_size):
cap = int(per_cap_int[row].item())
if cap <= 0:
continue
mask_row = supervised[row, :cap]
if not mask_row.any():
continue
preds_row = preds[row, :cap][mask_row]
prot = prot_list[row] if (isinstance(prot_list, list) and row < len(prot_list)) else ""
if not prot:
continue
seq_len = min(len(prot), preds_row.size(0))
if seq_len <= 0:
continue
pred_aa = ''.join(codon2aa.get(int(t.item()), 'X') for t in preds_row[:seq_len])
truth_aa = prot[:seq_len]
aa_correct += sum(1 for i in range(seq_len) if pred_aa[i] == truth_aa[i])
aa_total += seq_len
b_idx += 1
totals = torch.tensor(
[loss_sum, loss_tokens, codon_correct, codon_total, aa_correct, aa_total],
dtype=torch.float64,
device=self.device,
)
if dist.is_available() and dist.is_initialized():
# Ensure every rank has finished its forward passes before the final
# metric reduction, otherwise FSDP may still be issuing _all_gather
# collectives on slower ranks.
self._barrier()
dist.all_reduce(totals, op=dist.ReduceOp.SUM)
loss_sum, loss_tokens, codon_correct, codon_total, aa_correct, aa_total = totals.tolist()
self.model.train()
metrics: Dict[str, float] = {"eval_loss": float(loss_sum) / loss_tokens if loss_tokens > 0 else 0.0}
if codon_total > 0:
metrics["eval_codon_acc"] = float(codon_correct) / codon_total
if aa_total > 0:
metrics["eval_aa_acc"] = float(aa_correct) / aa_total
self._barrier()
return metrics
# ---- internals ----
def _setup_fsdp(self):
# Ensure default process group is initialized (required by FSDP)
device = self.device
if dist.is_available() and not dist.is_initialized():
backend = "nccl" if device.type == "cuda" else "gloo"
sig = inspect.signature(dist.init_process_group)
if "timeout" in sig.parameters:
dist.init_process_group(backend=backend, init_method="env://", timeout=datetime.timedelta(minutes=30))
else:
dist.init_process_group(backend=backend, init_method="env://")
mp = MixedPrecision(
param_dtype=(torch.float16 if self.args.fp16 else torch.bfloat16 if self.args.bf16 else torch.float32),
reduce_dtype=(torch.float16 if self.args.fp16 else torch.bfloat16 if self.args.bf16 else torch.float32),
buffer_dtype=torch.float32,
)
logger.info(f"FSDP enabled: sharding={self.args.fsdp} mp_param={mp.param_dtype} mp_reduce={mp.reduce_dtype}")
# Keep frozen ESM off FSDP if present
base = self._unwrap(self.model)
ignored = []
if hasattr(base, "esm") and isinstance(base.esm, nn.Module):
ignored.append(base.esm)
self.model = FSDP(
self.model,
device_id=(self.device if device.type == "cuda" else None),
sharding_strategy=ShardingStrategy.FULL_SHARD,
mixed_precision=mp,
ignored_modules=(ignored if ignored else None),
sync_module_states=True,
)
# Place ignored module on device exactly once
if ignored:
ignored[0].to(device)
def _unwrap(self, module):
return getattr(module, "module", module)
def _prepare_batch(self, batch: Dict[str, Any]) -> Dict[str, Any]:
# Species embeddings (fixed-size or sequence)
if self.species_store is not None and "species_ids" in batch:
sids = batch["species_ids"]
if torch.is_tensor(sids):
sids = sids.detach().cpu().tolist()
result = self.species_store.batch_get(sids)
if isinstance(result, tuple):
sp_tok, _ = result # [B, Ls, Ds]
batch["species_tok_emb"] = sp_tok.to(self.device, non_blocking=True)
else:
sp = result # [B, Ds]
batch["species_emb"] = sp.to(self.device, non_blocking=True)
# Move obvious tensors
if "codon_ids" in batch and hasattr(batch["codon_ids"], "to"):
batch["codon_ids"] = batch["codon_ids"].to(self.device, non_blocking=True)
return batch
def _build_cond(self, batch: Dict[str, Any]) -> Dict[str, Any]:
cond: Dict[str, Any] = {"control_mode": "fixed"}
if "species_tok_emb" in batch:
cond["species_tok_emb_src"] = batch["species_tok_emb"]
cond["species_tok_emb_tgt"] = batch["species_tok_emb"]
elif "species_emb" in batch:
cond["species_emb_src"] = batch["species_emb"]
cond["species_emb_tgt"] = batch["species_emb"]
if "protein_seqs" in batch:
cond["protein_seqs"] = batch["protein_seqs"]
return cond
def _next_batch_sync(self, iterator):
"""Fetch next batch and drop out early if any rank exhausts its loader."""
try:
batch = next(iterator)
local_has_batch = True
except StopIteration:
batch = None
local_has_batch = False
distributed = dist.is_available() and dist.is_initialized()
has_batch = local_has_batch
if distributed:
flag_device = self.device if self.device.type == "cuda" else torch.device("cpu")
flag = torch.tensor([1 if local_has_batch else 0], device=flag_device)
dist.all_reduce(flag, op=dist.ReduceOp.MIN)
has_batch = bool(flag.item())
if not has_batch:
return None, False, local_has_batch
return batch, True, local_has_batch
def _is_main(self) -> bool:
return (not dist.is_available()) or (not dist.is_initialized()) or dist.get_rank() == 0
def _barrier(self):
if dist.is_available() and dist.is_initialized():
# On NCCL, pass device_ids to avoid rank↔GPU mapping ambiguity when supported
if self.device.type == "cuda":
sig = inspect.signature(dist.barrier)
if "device_ids" in sig.parameters:
dist.barrier(device_ids=[self.local_rank])
return
dist.barrier()
def _max_cuda_peak_gb(self) -> Tuple[float, float]:
if self.device.type != "cuda" or not torch.cuda.is_available():
return 0.0, 0.0
vals = torch.tensor(
[
float(torch.cuda.max_memory_allocated(self.device)),
float(torch.cuda.max_memory_reserved(self.device)),
],
dtype=torch.float64,
device=self.device,
)
if dist.is_available() and dist.is_initialized():
dist.all_reduce(vals, op=dist.ReduceOp.MAX)
scale = float(1024 ** 3)
return float(vals[0].item() / scale), float(vals[1].item() / scale)
# (Per-sample quick eval removed; evaluation now uses held-out dataloader.)
def _epoch_for_logging(self) -> int:
steps_per_epoch = int(getattr(self.args, "steps_per_epoch", 0) or 0)
if steps_per_epoch > 0:
est = self.state.get("global_step", 0) // steps_per_epoch
if self.args.num_train_epochs > 0:
max_epoch = max(int(self.args.num_train_epochs) - 1, 0)
if est > max_epoch:
return max_epoch
return int(est)
return int(self.state.get("epoch", 0))
# ---- checkpointing ----
def _save_checkpoint(self, name: str):
self.state["epoch"] = int(self._epoch_for_logging())
# All ranks participate in FSDP state_dict collectives; only rank0 writes files.
out_dir = os.path.join(self.args.output_dir, name)
os.makedirs(out_dir, exist_ok=True)
optim_state = None
if isinstance(self.model, FSDP):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
with FSDP.state_dict_type(
self.model,
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(rank0_only=True, offload_to_cpu=True),
FullOptimStateDictConfig(rank0_only=True, offload_to_cpu=True),
):
state = self.model.state_dict()
# NOTE: Under FSDP, optimizer.state_dict() is sharded per-rank.
# Use FSDP.optim_state_dict() to materialize a full optimizer state dict (rank0_only).
if self.optimizer is not None:
optim_state = FSDP.optim_state_dict(self.model, self.optimizer)
else:
state = self._unwrap(self.model).state_dict()
if self.optimizer is not None:
optim_state = self.optimizer.state_dict()
# Save minimal data cursor (total samples yielded so far) next to output_dir if configured
per_rank_positions: Optional[List[int]] = None
p = getattr(self.args, "data_cursor_path", None)
if p:
ds = getattr(self.train_dataloader, "dataset", None)
if hasattr(ds, "get_stream_position"):
local_pos = int(ds.get_stream_position())
if dist.is_available() and dist.is_initialized():
gather_device = self.device if self.device.type == "cuda" else torch.device("cpu")
tensor = torch.tensor([local_pos], dtype=torch.long, device=gather_device)
gathered = [torch.zeros_like(tensor) for _ in range(dist.get_world_size())]
dist.all_gather(gathered, tensor)
per_rank_positions = [int(t.item()) for t in gathered]
else:
per_rank_positions = [local_pos]
if not self._is_main():
# Non-main ranks skip serialization but stay in lockstep
self._barrier()
return
# Rank 0 writes artifacts
save_file(state, os.path.join(out_dir, "model.safetensors"))
# Optimizer + scheduler
if optim_state is not None:
torch.save(optim_state, os.path.join(out_dir, "optimizer.pt"))
if self.lr_scheduler is not None:
torch.save(self.lr_scheduler.state_dict(), os.path.join(out_dir, "scheduler.pt"))
# Trainer config/state
base = self._unwrap(self.model)
# Infer mlp_ratio from first block if present
mlp_ratio = 4.0
try:
if hasattr(base, "blocks") and len(getattr(base, "blocks", [])) > 0:
w1 = base.blocks[0].ffn.w1.weight # [H*mlp, H]
H = int(getattr(base, "hidden_size", w1.shape[1]))
if H > 0:
mlp_ratio = float(w1.shape[0]) / float(H)
except Exception:
pass
trainer_cfg = {
# capacity / prefixes
"max_length": int(self.args.max_length),
"max_species_prefix": int(getattr(base, "max_species_prefix", 0)),
"max_protein_prefix": int(getattr(base, "max_protein_prefix", 0)),
# architecture hints
"hidden_size": int(getattr(base, "hidden_size", 0)),
"num_hidden_layers": int(getattr(base, "num_layers", 0)),
"num_attention_heads": int(getattr(base, "num_heads", 0)),
"mlp_ratio": float(mlp_ratio),
# conditioning flags
"prepend_species": bool(getattr(base, "prepend_species", True)),
"prepend_protein": bool(getattr(base, "prepend_protein", False)),
"species_embedding_dim": int(getattr(base, "species_embedding_dim", 1024)),
# ESM info (even if prepend_protein=False)
"esm_model_name": str(getattr(self.args, "esm_model_name", "")),
"esm_device": str(getattr(self.args, "esm_device", "cuda")),
"esm_dtype": str(getattr(self.args, "esm_dtype", "fp32")).lower(),
# kernels
# attention impl
"attn_impl": str(getattr(base, "attn_impl", "gqa")),
"num_kv_groups": int(getattr(base, "num_kv_groups", 0)),
}
with open(os.path.join(out_dir, "trainer_config.json"), "w") as f:
json.dump(trainer_cfg, f, indent=2)
with open(os.path.join(out_dir, "trainer_state.json"), "w") as f:
json.dump({"epoch": self.state["epoch"], "global_step": self.state["global_step"]}, f, indent=2)
if p and per_rank_positions is not None:
payload = {
"skip_samples": int(sum(per_rank_positions)),
"per_rank": per_rank_positions,
"world_size": len(per_rank_positions),
}
os.makedirs(os.path.dirname(os.path.abspath(p)), exist_ok=True)
with open(p, "w") as f:
json.dump(payload, f)
# Tokenizer vocab for sampling
try:
if self.tokenizer is not None and hasattr(self.tokenizer, "save_vocabulary"):
self.tokenizer.save_vocabulary(out_dir)
except Exception as e:
logger.warning(f"Failed to save vocabulary to {out_dir}: {e}")
self._prune_checkpoints(self.args.output_dir, self.args.save_total_limit)
logger.info(f"Saved checkpoint → {out_dir}")
# Release other ranks
self._barrier()
def _resume_from(self, ckpt_dir: str):
st_path = os.path.join(ckpt_dir, "model.safetensors")
if not os.path.exists(st_path):
raise FileNotFoundError(f"No model.safetensors in {ckpt_dir}")
state = load_file(st_path)
if isinstance(self.model, FSDP):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
with FSDP.state_dict_type(
self.model,
StateDictType.FULL_STATE_DICT,
FullStateDictConfig(rank0_only=False, offload_to_cpu=True),
):
self.model.load_state_dict(state, strict=False)
else:
self._unwrap(self.model).load_state_dict(state, strict=False)
scheduler_restored = False
opt_path = os.path.join(ckpt_dir, "optimizer.pt")
if os.path.exists(opt_path):
if self.optimizer is None:
self._create_optimizer_and_scheduler()
if not self.args.override_lr_on_resume:
loaded = torch.load(opt_path, map_location="cpu")
# Under FSDP, saved optimizer.pt is a full optimizer state dict produced by
# FSDP.optim_state_dict(). Convert it to a per-rank state dict before loading.
if isinstance(self.model, FSDP):
try:
loaded = FSDP.optim_state_dict_to_load(self.model, self.optimizer, loaded)
except Exception as e:
msg = (
"Failed to convert FSDP optimizer state dict for loading. "
"This checkpoint likely contains an incomplete (rank0-only sharded) optimizer.pt from an older version. "
"Full optimizer resume is not possible from this checkpoint.\n"
f"Underlying error: {e}\n"
"Options:\n"
" 1) Start a fresh run (new --output_dir), or\n"
" 2) Re-run with --override_lr_on_resume to skip optimizer restore (not a full resume)."
)
if self._is_main():
logger.error(msg)
raise RuntimeError(msg) from e
self.optimizer.load_state_dict(loaded)
sch_path = os.path.join(ckpt_dir, "scheduler.pt")
if os.path.exists(sch_path):
if self.lr_scheduler is None:
self._create_optimizer_and_scheduler()
if self.lr_scheduler is not None and not self.args.override_lr_on_resume:
self.lr_scheduler.load_state_dict(torch.load(sch_path, map_location="cpu"))
scheduler_restored = True
ts_path = os.path.join(ckpt_dir, "trainer_state.json")
if os.path.exists(ts_path):
with open(ts_path, "r") as f:
ts = json.load(f)
self.state["epoch"] = int(ts.get("epoch", 0))
self.state["global_step"] = int(ts.get("global_step", 0))
steps_per_epoch = int(getattr(self.args, "steps_per_epoch", 0) or 0)
if steps_per_epoch > 0:
inferred_epoch = self.state.get("global_step", 0) // steps_per_epoch
num_epochs = max(int(self.args.num_train_epochs), 1)
inferred_epoch = min(inferred_epoch, num_epochs - 1)
if inferred_epoch != self.state.get("epoch"):
if self._is_main():
logger.info(
"Adjusting epoch from %s to %s based on global_step %s and steps_per_epoch %s",
self.state.get("epoch"),
inferred_epoch,
self.state.get("global_step"),
steps_per_epoch,
)
self.state["epoch"] = int(inferred_epoch)
# If we skipped loading the scheduler state (e.g., different world size or override),
# fast-forward it to the saved global_step so LR does not restart from warmup.
if self.lr_scheduler is not None and not scheduler_restored:
target_step = int(self.state.get("global_step", 0))
if target_step > 0:
try:
# Most schedulers (LambdaLR, CosineAnnealing, etc.) accept an "epoch" kwarg.
self.lr_scheduler.step(target_step)
except TypeError:
# Fallback: advance manually.
for _ in range(target_step):
self.lr_scheduler.step()
# Ensure optimizer LR reflects the scheduler's current value.
try:
last_lrs = self.lr_scheduler.get_last_lr()
except Exception:
last_lrs = [group.get("lr") for group in self.optimizer.param_groups]
if last_lrs:
for group, lr in zip(self.optimizer.param_groups, last_lrs):
group["lr"] = float(lr)
logger.info(f"Resumed from {ckpt_dir}")
def _checkpoint_step(self, path: str) -> Optional[int]:
m = re.fullmatch(r"checkpoint-(\d+)", os.path.basename(path))
if not m:
return None
return int(m.group(1))
def _prune_checkpoints(self, root: str, keep: int):
if not os.path.isdir(root):
return
try:
subdirs = [
os.path.join(root, d)
for d in os.listdir(root)
if os.path.isdir(os.path.join(root, d))
]
except FileNotFoundError:
return
step_dirs: list[tuple[int, str]] = []
for path in subdirs:
step = self._checkpoint_step(path)
if step is not None:
step_dirs.append((step, path))
if not step_dirs:
return
step_dirs.sort(key=lambda item: item[0])
latest_step = step_dirs[-1][0]
recent_window = max(0, int(getattr(self.args, "ckpt_recent_window_steps", 0) or 0))
recent_interval = max(0, int(getattr(self.args, "ckpt_recent_interval", 0) or 0))
archive_interval = max(0, int(getattr(self.args, "ckpt_archive_interval", 0) or 0))
keep_paths: set[str] = set()
if recent_window > 0 and (recent_interval > 0 or archive_interval > 0):
if recent_interval <= 0:
recent_interval = max(1, int(getattr(self.args, "save_steps", 1) or 1))
for step, path in step_dirs:
age = latest_step - step
if age <= recent_window:
interval = recent_interval
else:
interval = archive_interval
if interval > 0 and (step % interval == 0):
keep_paths.add(path)
if not keep_paths:
# Legacy fallback: keep the most recent N step checkpoints.
if keep <= 0:
return
keep_paths = {path for _, path in step_dirs[-keep:]}
else:
# Always preserve the newest checkpoint, even if the interval math misses it.
keep_paths.add(step_dirs[-1][1])
if keep > 0:
kept = [(step, path) for step, path in step_dirs if path in keep_paths]
if len(kept) > keep:
trim = len(kept) - keep
for _, path in kept[:trim]:
keep_paths.discard(path)
removed = []
for _, path in step_dirs:
if path in keep_paths:
continue
shutil.rmtree(path, ignore_errors=True)
removed.append(os.path.basename(path))
if removed and self._is_main():
logger.info(
"Pruned %s checkpoints (latest_step=%s, recent_window=%s, recent_interval=%s, archive_interval=%s)",
len(removed),
latest_step,
recent_window,
recent_interval,
archive_interval,
)
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