mirror of
https://github.com/wassname/autoresearch_template.git
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wassname
117 lines
2.9 KiB
Python
117 lines
2.9 KiB
Python
"""
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Model + training. NOT frozen -- agents modify freely in worktrees.
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eval.py is frozen (anti-p-hacking). This file is not.
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Everything agents change lives here: architecture, optimizer, loss, data pipeline.
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"""
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import os
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from dataclasses import dataclass
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import torch
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import torch.nn as nn
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import tyro
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import wandb
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from einops import rearrange # noqa: F401 -- available for use
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from jaxtyping import Float
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from loguru import logger
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from torch import Tensor
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if os.environ.get("BEARTYPE"):
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from beartype import beartype as typechecker
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from jaxtyping import jaxtyped
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else:
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def typechecker(f): return f
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def jaxtyped(**_): return lambda f: f
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# --- Config -------------------------------------------------------------------
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@dataclass
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class Config:
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"""All hyperparameters. Edit freely. eval.py imports this."""
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# model
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d_model: int = 256
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n_layers: int = 4
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# {FILL_IN}: add architecture params
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# training
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lr: float = 3e-4
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batch_size: int = 32
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max_steps: int = 1000
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seed: int = 42
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# data
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# {FILL_IN}: add data params
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# logging
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wandb_project: str = "{FILL_IN}"
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# --- Model --------------------------------------------------------------------
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class Model(nn.Module):
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"""
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{FILL_IN}: replace with your architecture.
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Agents: this is the main thing you modify between experiments.
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"""
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def __init__(self, cfg: Config):
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super().__init__()
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self.cfg = cfg
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# {FILL_IN}: define layers
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@jaxtyped(typechecker=typechecker)
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def forward(self, x: Float[Tensor, "b s"]) -> Float[Tensor, "b s d"]:
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# {FILL_IN}: implement forward pass
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raise NotImplementedError("{FILL_IN}: implement forward()")
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def build_model(cfg: Config) -> Model:
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torch.manual_seed(cfg.seed)
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model = Model(cfg)
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n_params = sum(p.numel() for p in model.parameters())
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logger.info(f"Model: {n_params:,} parameters")
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return model
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# --- Training -----------------------------------------------------------------
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def train(cfg: Config):
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torch.manual_seed(cfg.seed)
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wandb.init(
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project=cfg.wandb_project,
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config=vars(cfg),
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# WANDB_RUN_GROUP env var set by justfile sweep recipes
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)
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model = build_model(cfg)
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optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.lr)
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# {FILL_IN}: load training data
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# train_loader = ...
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model.train()
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for step in range(cfg.max_steps):
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# {FILL_IN}: training step
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# batch = next(iter(train_loader))
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# loss = model(batch)
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# optimizer.zero_grad(); loss.backward(); optimizer.step()
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if step % 100 == 0:
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logger.info(f"step={step}")
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# wandb.log({"loss": loss.item(), "step": step})
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# {FILL_IN}: save checkpoint
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# torch.save(model.state_dict(), f"outputs/{wandb.run.id}.pt")
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wandb.finish()
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logger.info("Training complete")
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if __name__ == "__main__":
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cfg = tyro.cli(Config)
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train(cfg)
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