Merge 02b22a5a13 into 6bb92403d5

README.md

@@ -82,10 +82,10 @@ That said, to give a sense, the example changes needed for the [speedrun.sh](spe
 python -m nanochat.dataset -n 450 &
 ...
 # use --depth to increase model size. to not oom, halve device batch size 32 -> 16:
-torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=26 --device-batch-size=16
+torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=26 --device_batch_size=16
 ...
 # make sure to use the same later during midtraining:
-torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --device-batch-size=16
+torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --device_batch_size=16
 ```
 
 That's it! The biggest thing to pay attention to is making sure you have enough data shards to train on (the code will loop and do more epochs over the same training set otherwise, decreasing learning speed a bit), and managing your memory/VRAM, primarily by decreasing the `device_batch_size` until things fit (the scripts automatically compensate by increasing the number of gradient accumulation loops, simply turning parallel compute to sequential compute).

dev/LOG.md

@@ -4,32 +4,6 @@ A running summary documenting some experiments and findings. Started ~Jan 7 2026
 
 ---
 
-## 2026-01-16: Modded-nanogpt Ideas Sweep (Mostly Negative)
-
-Tested several architectural ideas from modded-nanogpt to see if they transfer to nanochat. All of these did not help:
-
-| Idea | Result | Notes |
-|------|--------|-------|
-| Half-truncated RoPE | No improvement | Only first half of head dims get RoPE (base 1024, linspace). Second half "stationary". |
-| Asymmetric softcap | Slightly worse | `23 * sigmoid((x+5)/7.5)` vs our symmetric `15 * tanh(x/15)`. May only help with FP8. |
-| Smear gate | Negligible | Blend each token with predecessor via learned gate. Tiny improvement not worth n_embd² params. |
-| Backout | No improvement | Save activations at ~60% through network, subtract scaled version at end. |
-| Skip connection | Slightly worse | Save at layer ~25%, add at layer ~50%. Also +2GB memory from storing activations. |
-
-Value Embeddings do show promise. I need a more elaborate exploration of a few related ideas, which I leave for tomorrow.
-
----
-
-## 2026-01-15: Olmo pretraining mix (Negative result)
-
-I attempted to train on the Olmo 3 pretraining dataset [allenai/dolma3_mix-6T](https://huggingface.co/datasets/allenai/dolma3_mix-6T) instead of FineWeb-edu. I ran into a number of [errors and issues](https://huggingface.co/datasets/allenai/dolma3_mix-6T/discussions/2) trying to both download and process the dataset and then noticed some quality issues (e.g. some documents seem to be extremely short, like "5".). I managed to work around these with some sensible hacks (e.g. reject documents less than 100 characters in length) and tried to process the dataset exactly as FineWeb, re-trained the tokenizer and trained a d16 model. The CORE score decreased from 15.5 to 13.8, i.e. the result is quite a bit worse.
-
-I am still looking to try the [DCLM dataset](https://arxiv.org/abs/2406.11794), which according to the paper should be better that FineWeb-edu. I do have some concerns that the same group both prepared the DCLM dataset *and* introduced the CORE score so I'm a bit hesitant in case there was some overfitting to CORE score adjacent data distribution.
-
-Classifying as negative result and reverting back to FineWeb-edu for now.
-
----
-
 ## 2026-01-13: Varlen Attention (Negative Result)
 
 Attempted to prevent attention from "leaking" across document boundaries using Flash Attention's `flash_attn_varlen_func`, similar to modded-nanogpt's approach.

nanochat/adamw.py

@@ -1,42 +1,11 @@
 """
-Distributed AdamW optimizer with a fused step function.
-A bunch of ideas (e.g. dist comms in slices) are borrowed from modded-nanogpt.
+Borrowed from modded-nanogpt. By Keller, @vagrawal, et al.
+Not a general optimizer! But works for our specific use.
 """
 import torch
 import torch.distributed as dist
 from torch import Tensor
 
-@torch.compile(dynamic=False, fullgraph=True)
-def adamw_step_fused(
-    p: Tensor,
-    grad: Tensor,
-    exp_avg: Tensor,
-    exp_avg_sq: Tensor,
-    step_t: Tensor,
-    lr_t: Tensor,
-    beta1_t: Tensor,
-    beta2_t: Tensor,
-    eps_t: Tensor,
-    wd_t: Tensor,
-) -> None:
-    """
-    Fused AdamW step: weight_decay -> momentum_update -> bias_correction -> param_update
-    All in one compiled graph to eliminate Python overhead between ops.
-    The 0-D CPU tensors avoid recompilation when hyperparameter values change.
-    """
-    # Weight decay (decoupled, applied before the update)
-    p.mul_(1 - lr_t * wd_t)
-    # Update running averages (lerp_ is cleaner and fuses well)
-    exp_avg.lerp_(grad, 1 - beta1_t)
-    exp_avg_sq.lerp_(grad.square(), 1 - beta2_t)
-    # Bias corrections
-    bias1 = 1 - beta1_t ** step_t
-    bias2 = 1 - beta2_t ** step_t
-    # Compute update and apply
-    denom = (exp_avg_sq / bias2).sqrt() + eps_t
-    step_size = lr_t / bias1
-    p.add_(exp_avg / denom, alpha=-step_size)
-
 
 class DistAdamW(torch.optim.Optimizer):
     """
@@ -45,26 +14,7 @@ class DistAdamW(torch.optim.Optimizer):
     """
     def __init__(self, param_groups, lr: float = 1e-3, betas: tuple[float, float] = (0.9, 0.999), eps: float = 1e-8, weight_decay: float = 0.01):
         defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
-        rank = dist.get_rank()
-        world_size = dist.get_world_size()
-        # Validate
-        if rank == 0:
-            for group in param_groups:
-                assert isinstance(group, dict), "expecting param_groups to be a list of dicts"
-                assert isinstance(group['params'], list), "expecting group['params'] to be a list of tensors"
-                for p in group['params']:
-                    sliced = p.numel() >= 1024
-                    print(f"AdamW: 1 param of shape {p.shape}, sliced={sliced}")
-                    if sliced: # large parameter tensors will be operated on in slices
-                        assert p.shape[0] % world_size == 0, f"First dim of parameter shape {p.shape} must be divisible by world size {world_size}"
         super().__init__(param_groups, defaults)
-        # 0-D CPU tensors to avoid torch.compile recompilation when values change
-        self._step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
 
     @torch.no_grad()
     def step(self):
@@ -86,7 +36,8 @@ class DistAdamW(torch.optim.Optimizer):
                     grad_slices.append(grad)
                 else:
                     is_small.append(False)
-                    rank_size = grad.shape[0] // world_size # p.shape[0] % world_size == 0 is checked in __init__
+                    assert p.shape[0] % world_size == 0, f"First dim of parameter shape {p.shape} must be divisible by world size {world_size}"
+                    rank_size = grad.shape[0] // world_size
                     grad_slice = torch.empty_like(grad[:rank_size])
                     reduce_futures.append(dist.reduce_scatter_tensor(grad_slice, grad, op=dist.ReduceOp.AVG, async_op=True).get_future())
                     grad_slices.append(grad_slice)
@@ -112,27 +63,28 @@ class DistAdamW(torch.optim.Optimizer):
 
                 # State init
                 if not state:
-                    state['step'] = 0
+                    state['step'] = torch.tensor(0, dtype=torch.int64, device=p.device)
                     state['exp_avg'] = torch.zeros_like(p_slice)
                     state['exp_avg_sq'] = torch.zeros_like(p_slice)
                 exp_avg = state['exp_avg']
                 exp_avg_sq = state['exp_avg_sq']
                 state['step'] += 1
-
-                # Fill 0-D tensors with current values
-                eff_wd = wd * getattr(p, "wd_mul", 1.0)
-                self._step_t.fill_(state['step'])
-                self._lr_t.fill_(lr)
-                self._beta1_t.fill_(beta1)
-                self._beta2_t.fill_(beta2)
-                self._eps_t.fill_(eps)
-                self._wd_t.fill_(eff_wd)
-
-                # Fused update: weight_decay -> momentum -> bias_correction -> param_update
-                adamw_step_fused(
-                    p_slice, g_slice, exp_avg, exp_avg_sq,
-                    self._step_t, self._lr_t, self._beta1_t, self._beta2_t, self._eps_t, self._wd_t,
-                )
+                t = state['step']
+                # weight decay
+                if wd != 0:
+                    eff_weight_decay = lr * wd * getattr(p, "wd_mul", 1.0)
+                    p_slice.mul_(1 - eff_weight_decay)
+                # update running averages
+                exp_avg.mul_(beta1).add_(g_slice, alpha=1 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(g_slice, g_slice, value=1 - beta2)
+                # bias corrections
+                bias1 = 1 - beta1 ** t
+                bias2 = 1 - beta2 ** t
+                # compute step
+                denom = (exp_avg_sq / bias2).sqrt().add_(eps)
+                step_size = lr / bias1
+                update = exp_avg.div(denom).mul_(step_size)
+                p_slice.add_(other=update, alpha=-1.0)
 
                 # Only large params need all_gather
                 if not is_small[idx]:

pyproject.toml

@@ -23,7 +23,6 @@ dependencies = [
     "transformers>=4.57.3",
     "uvicorn>=0.36.0",
     "wandb>=0.21.3",
-    "zstandard>=0.25.0",
 ]
 
 [dependency-groups]

scripts/base_train.py

@@ -208,6 +208,7 @@ if resuming:
 
 # -----------------------------------------------------------------------------
 # Initialize the DataLoaders for train/val
+tokens_dir = os.path.join(base_dir, "tokenized_data")
 dataloader_resume_state_dict = None if not resuming else meta_data["dataloader_state_dict"]
 train_loader = tokenizing_distributed_data_loader_with_state_bos_bestfit(tokenizer, args.device_batch_size, args.max_seq_len, split="train", device=device, resume_state_dict=dataloader_resume_state_dict)
 build_val_loader = lambda: tokenizing_distributed_data_loader_bos_bestfit(tokenizer, args.device_batch_size, args.max_seq_len, split="val", device=device)
@@ -369,15 +370,14 @@ while True:
     for opt in optimizers:
         opt.step()
     model.zero_grad(set_to_none=True)
-    train_loss_f = train_loss.item() # .item() is a CPU-GPU sync point
     synchronize()
     t1 = time.time()
     dt = t1 - t0
     # -------------------------------------------------------------------------
 
-    # logging (CPU action only)
+    # logging
     ema_beta = 0.9 # EMA decay factor for some smoothing just for nicer logging
-    smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss_f # EMA the training loss
+    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 * step / num_iterations
     tok_per_sec = int(args.total_batch_size / dt)

scripts/chat_eval.py

@@ -4,8 +4,8 @@ All the generic code lives here, and all the evaluation-specific
 code lives in nanochat directory and is imported from here.
 
 Example runs:
-python -m scripts.chat_eval -i mid -a ARC-Easy
-torchrun --nproc_per_node=8 -m scripts.chat_eval -- -i mid -a ARC-Easy
+python -m scripts.chat_eval -a ARC-Easy
+torchrun --nproc_per_node=8 -m scripts.chat_eval -- -a ARC-Easy
 """
 
 import argparse

scripts/chat_rl.py

@@ -6,7 +6,7 @@ simpler and more similar to just REINFORCE:
 
 1) Delete trust region, so there is no KL regularization to a reference model
 2) We are on policy, so there's no need for PPO ratio+clip.
-3) We use DAPO style normalization that is token-level, not sequence-level.
+3) We use GAPO style normalization that is token-level, not sequence-level.
 4) Instead of z-score normalization (r - mu)/sigma, only use (r - mu) as the advantage.
 
 1 GPU:

tasks/customjson.py

@@ -25,7 +25,7 @@ class CustomJSON(Task):
             print("-" * 80)
             print(f"Warning: File {filepath} does not exist")
             print("HINT (Oct 21 2025)")
-            print("If you recently did a git pull and suddenly see this, it might be due to the new addition of identity conversations")
+            print("If you recently did a git pull and suddely see this, it might be due to the new addition of identity conversations")
             print("See this discussion for more details: https://github.com/karpathy/nanochat/discussions/139")
             print("Quick fix: simply run the following command to download the file and you're done:")
             print(f"curl -L -o {filepath} https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl")

uv.lock

@@ -1513,7 +1513,6 @@ dependencies = [
     { name = "transformers" },
     { name = "uvicorn" },
     { name = "wandb" },
-    { name = "zstandard" },
 ]
 
 [package.optional-dependencies]
@@ -1552,7 +1551,6 @@ requires-dist = [
     { name = "transformers", specifier = ">=4.57.3" },
     { name = "uvicorn", specifier = ">=0.36.0" },
     { name = "wandb", specifier = ">=0.21.3" },
-    { name = "zstandard", specifier = ">=0.25.0" },
 ]
 provides-extras = ["cpu", "gpu"]
 
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 ]
-
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