Merge 837b43a504 into 67aaca98f5

.gitignore

@@ -3,3 +3,4 @@ __pycache__/
 *.pyc
 rustbpe/target/
 dev-ignore/
+.idea

nanochat/common.py

@@ -79,6 +79,75 @@ def is_ddp():
     # TODO is there a proper way
     return int(os.environ.get('RANK', -1)) != -1
 
+def is_macos():
+    """Check if running on macOS."""
+    import platform
+    return platform.system() == "Darwin"
+
+def get_device_type():
+    """Get the device type string for autocast: 'cuda', 'mps', or 'cpu'."""
+    if torch.cuda.is_available():
+        return "cuda"
+    # Check for MPS (Metal Performance Shaders on Apple Silicon)
+    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+        return "mps"
+    return "cpu"
+
+def get_safe_autocast_dtype(device_type: str, preferred_dtype=None) -> torch.dtype:
+    """
+    Return a safe dtype for autocast on the given device.
+
+    Args:
+        device_type: "cuda", "mps", or "cpu"
+        preferred_dtype: Preferred dtype (torch.dtype, str, or None)
+
+    Returns:
+        A dtype that is safe for autocast on the device
+    """
+    # Parse the preferred dtype
+    if isinstance(preferred_dtype, torch.dtype):
+        dtype = preferred_dtype
+    elif isinstance(preferred_dtype, str):
+        dtype_map = {
+            "bfloat16": torch.bfloat16, "bf16": torch.bfloat16,
+            "float16": torch.float16, "fp16": torch.float16, "half": torch.float16,
+            "float32": torch.float32, "fp32": torch.float32,
+        }
+        dtype = dtype_map.get(preferred_dtype.lower())
+        if dtype is None:
+            raise ValueError(f"Unknown dtype string: {preferred_dtype}")
+    elif preferred_dtype is None:
+        # Default: bfloat16 on CUDA, float16 on MPS, bfloat16 on CPU
+        if device_type == "cuda":
+            dtype = torch.bfloat16
+        elif device_type == "mps":
+            dtype = torch.float16  # MPS works best with float16
+        else:
+            dtype = torch.bfloat16
+    else:
+        raise TypeError(f"Invalid dtype type: {type(preferred_dtype)}")
+
+    # Validate dtype compatibility with device
+    if device_type == "cpu" and dtype == torch.float32:
+        logger.warning(
+            f"CPU autocast doesn't support {dtype}, using bfloat16 instead"
+        )
+        dtype = torch.bfloat16
+    elif device_type == "mps":
+        # MPS has limited dtype support, prefer float16
+        if dtype not in {torch.float16, torch.float32}:
+            logger.warning(
+                f"MPS autocast works best with float16, converting from {dtype}"
+            )
+            dtype = torch.float16
+
+    return dtype
+
+def get_default_dtype():
+    """Get the default dtype for training based on available hardware."""
+    device_type = get_device_type()
+    return get_safe_autocast_dtype(device_type)
+
 def get_dist_info():
     if is_ddp():
         assert all(var in os.environ for var in ['RANK', 'LOCAL_RANK', 'WORLD_SIZE'])
@@ -92,12 +161,15 @@ def get_dist_info():
 def compute_init():
     """Basic initialization that we keep doing over and over, so make common."""
 
-    # CUDA is currently required
-    assert torch.cuda.is_available(), "CUDA is needed for a distributed run atm"
+    # Check available hardware
+    has_cuda = torch.cuda.is_available()
+    has_mps = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()
+    on_macos = is_macos()
 
     # Reproducibility
     torch.manual_seed(42)
-    torch.cuda.manual_seed(42)
+    if has_cuda:
+        torch.cuda.manual_seed(42)
     # skipping full reproducibility for now, possibly investigate slowdown later
     # torch.use_deterministic_algorithms(True)
     # torch.backends.cudnn.deterministic = True
@@ -108,13 +180,27 @@ def compute_init():
 
     # Distributed setup: Distributed Data Parallel (DDP), optional
     ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
-    if ddp:
+
+    # Determine device
+    if has_cuda and ddp:
         device = torch.device("cuda", ddp_local_rank)
-        torch.cuda.set_device(device) # make "cuda" default to this device
+        torch.cuda.set_device(device)
         dist.init_process_group(backend="nccl", device_id=device)
         dist.barrier()
-    else:
+        logger.info(f"Running on CUDA with DDP (rank {ddp_rank}/{ddp_world_size})")
+    elif has_cuda:
         device = torch.device("cuda")
+        logger.info("Running on CUDA (single GPU)")
+    elif has_mps:
+        device = torch.device("mps")
+        logger.info("Running on MPS (Apple Silicon GPU)")
+        if ddp:
+            logger.warning("DDP not supported on MPS, running single process")
+    else:
+        device = torch.device("cpu")
+        logger.info("Running on CPU")
+        if ddp:
+            logger.warning("DDP requested but will run on CPU")
 
     if ddp_rank == 0:
         logger.info(f"Distributed world size: {ddp_world_size}")

nanochat/dataloader.py

@@ -2,7 +2,7 @@ from collections import deque
 
 import torch
 
-from nanochat.common import get_dist_info
+from nanochat.common import get_dist_info, get_device_type
 from nanochat.dataset import parquets_iter_batched
 from nanochat.tokenizer import get_tokenizer
 
@@ -16,7 +16,11 @@ def tokenizing_distributed_data_loader(B, T, split, tokenizer_threads=4, tokeniz
     bos_token = tokenizer.get_bos_token_id()
     # scratch buffer holds the tokens for one iteration
     token_buffer = deque() # we stream tokens on the right and pop from the left
-    scratch = torch.empty(needed_tokens, dtype=torch.int64, pin_memory=True)
+
+    # Check if we're using MPS - it doesn't support pin_memory
+    device_type = get_device_type()
+    use_pin_memory = device_type == "cuda"  # Only pin memory for CUDA
+    scratch = torch.empty(needed_tokens, dtype=torch.int64, pin_memory=use_pin_memory)
 
     # infinite iterator over document batches
     def document_batches():
@@ -38,12 +42,16 @@ def tokenizing_distributed_data_loader(B, T, split, tokenizer_threads=4, tokeniz
                 token_buffer.extend(tokens)
             batch_index += 1
         # Move tokens from the deque into the scratch buffer
-        for i in range(needed_tokens):
-            scratch[i] = token_buffer.popleft()
+        # Build a list first to avoid MPS compatibility issues
+        tokens_list = [token_buffer.popleft() for _ in range(needed_tokens)]
+        scratch[:] = torch.tensor(tokens_list, dtype=torch.int64)
+
         # Create the inputs/targets as 1D tensors
         inputs_cpu = scratch[:-1].to(dtype=torch.int32)
         targets_cpu = scratch[1:]
-        # Reshape to 2D and move to GPU async
-        inputs = inputs_cpu.view(B, T).to(device="cuda", dtype=torch.int32, non_blocking=True)
-        targets = targets_cpu.view(B, T).to(device="cuda", dtype=torch.int64, non_blocking=True)
+        # Reshape to 2D and move to device
+        # For MPS, non_blocking doesn't apply; for CUDA it helps performance
+        non_blocking = (device_type == "cuda")
+        inputs = inputs_cpu.view(B, T).to(device=device_type, dtype=torch.int32, non_blocking=non_blocking)
+        targets = targets_cpu.view(B, T).to(device=device_type, dtype=torch.int64, non_blocking=non_blocking)
         yield inputs, targets

nanochat/engine.py

@@ -308,7 +308,8 @@ if __name__ == "__main__":
     prompt_tokens = tokenizer.encode("The chemical formula of water is", prepend=bos_token_id)
     # generate the reference sequence using the model.generate() function
     generated_tokens = []
-    torch.cuda.synchronize()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
     t0 = time.time()
     stream = model.generate(prompt_tokens, **kwargs)
     for token in stream:
@@ -316,7 +317,8 @@ if __name__ == "__main__":
         chunk = tokenizer.decode([token])
         print(chunk, end="", flush=True)
     print()
-    torch.cuda.synchronize()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
     t1 = time.time()
     print(f"Reference time: {t1 - t0:.2f}s")
     reference_ids = generated_tokens
@@ -324,7 +326,8 @@ if __name__ == "__main__":
     generated_tokens = []
     engine = Engine(model, tokenizer)
     stream = engine.generate(prompt_tokens, num_samples=1, **kwargs) # note: runs in fp32
-    torch.cuda.synchronize()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
     t0 = time.time()
     for token_column, token_masks in stream:
         token = token_column[0] # only print out the first row
@@ -332,7 +335,8 @@ if __name__ == "__main__":
         chunk = tokenizer.decode([token])
         print(chunk, end="", flush=True)
     print()
-    torch.cuda.synchronize()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
     t1 = time.time()
     print(f"Engine time: {t1 - t0:.2f}s")
     # compare the two sequences

nanochat/gpt.py

@@ -19,7 +19,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from nanochat.common import get_dist_info, print0
+from nanochat.common import get_dist_info, print0, get_default_dtype
 from nanochat.muon import Muon, DistMuon
 from nanochat.adamw import DistAdamW
 
@@ -169,8 +169,9 @@ class GPT(nn.Module):
         cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim)
         self.register_buffer("cos", cos, persistent=False) # persistent=False means it's not saved to the checkpoint
         self.register_buffer("sin", sin, persistent=False)
-        # Cast the embeddings from fp32 to bf16: optim can tolerate it and it saves memory: both in the model and the activations
-        self.transformer.wte.to(dtype=torch.bfloat16)
+        # Cast the embeddings to the default dtype: optim can tolerate it and it saves memory: both in the model and the activations
+        default_dtype = get_default_dtype()
+        self.transformer.wte.to(dtype=default_dtype)
 
     def init_weights(self):
         self.apply(self._init_weights)
@@ -210,7 +211,9 @@ class GPT(nn.Module):
         # calculate the rotation frequencies at each (time, channel) pair
         freqs = torch.outer(t, inv_freq)
         cos, sin = freqs.cos(), freqs.sin()
-        cos, sin = cos.bfloat16(), sin.bfloat16() # keep them in bfloat16
+        # keep them in the default dtype (bfloat16 on GPU, float32 on CPU)
+        default_dtype = get_default_dtype()
+        cos, sin = cos.to(default_dtype), sin.to(default_dtype)
         cos, sin = cos[None, :, None, :], sin[None, :, None, :] # add batch and head dims for later broadcasting
         return cos, sin
 
@@ -262,7 +265,9 @@ class GPT(nn.Module):
         # Grab the rotary embeddings for the current sequence length (they are of shape (1, seq_len, 1, head_dim))
         assert T <= self.cos.size(1), f"Sequence length grew beyond the rotary embeddings cache: {T} > {self.cos.size(1)}"
         assert idx.device == self.cos.device, f"Rotary embeddings and idx are on different devices: {idx.device} != {self.cos.device}"
-        assert self.cos.dtype == torch.bfloat16, "Rotary embeddings must be in bfloat16"
+        # Rotary embeddings should match the default dtype for the platform
+        expected_dtype = get_default_dtype()
+        assert self.cos.dtype == expected_dtype, f"Rotary embeddings must be in {expected_dtype}, but got {self.cos.dtype}"
         # if kv cache exists, we need to offset the rotary embeddings to the current position in the cache
         T0 = 0 if kv_cache is None else kv_cache.get_pos()
         cos_sin = self.cos[:, T0:T0+T], self.sin[:, T0:T0+T] # truncate cache to current sequence length

pyproject.toml

@@ -22,12 +22,34 @@ dependencies = [
 requires = ["maturin>=1.7,<2.0"]
 build-backend = "maturin"
 
-# target torch to cuda 12.8
+# target torch to cuda 12.8 or CPU
+[project.optional-dependencies]
+cpu = [
+    "torch>=2.8.0",
+]
+gpu = [
+    "torch>=2.8.0",
+]
+
+[tool.uv]
+conflicts = [
+    [
+        { extra = "cpu" },
+        { extra = "gpu" },
+    ],
+]
+
 [tool.uv.sources]
 torch = [
-    { index = "pytorch-cu128" },
+    { index = "pytorch-cpu", extra = "cpu" },
+    { index = "pytorch-cu128", extra = "gpu" },
 ]
 
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
 [[tool.uv.index]]
 name = "pytorch-cu128"
 url = "https://download.pytorch.org/whl/cu128"

scripts/base_eval.py

@@ -19,7 +19,7 @@ import yaml
 import pandas as pd
 import torch
 
-from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir
+from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, get_device_type, get_default_dtype
 from nanochat.tokenizer import HuggingFaceTokenizer
 from nanochat.checkpoint_manager import load_model
 from nanochat.core_eval import evaluate_task
@@ -122,7 +122,7 @@ def main():
 
     # distributed / precision setup
     ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
-    autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16)
+    autocast_ctx = torch.amp.autocast(device_type=get_device_type(), dtype=get_default_dtype())
 
     # Load model and tokenizer from command line or from file system
     if len(sys.argv) >= 2:

scripts/base_loss.py

@@ -9,7 +9,7 @@ torchrun --standalone --nproc_per_node=8 -m scripts.base_loss
 import os
 import torch
 from nanochat.checkpoint_manager import load_model
-from nanochat.common import compute_init, print0, compute_cleanup
+from nanochat.common import compute_init, print0, compute_cleanup, get_device_type, get_default_dtype
 from nanochat.dataloader import tokenizing_distributed_data_loader
 from nanochat.tokenizer import get_token_bytes
 from nanochat.loss_eval import evaluate_bpb
@@ -28,7 +28,7 @@ model, tokenizer, meta = load_model("base", device, phase="eval", model_tag=mode
 sequence_len = meta["model_config"]["sequence_len"] # could be arbitrary really
 
 # Set up the precision we'll run with
-autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16)
+autocast_ctx = torch.amp.autocast(device_type=get_device_type(), dtype=get_default_dtype())
 
 # Evaluate the loss on each split
 tokens_per_step = device_batch_size * sequence_len * ddp_world_size

scripts/base_train.py

@@ -16,7 +16,7 @@ import torch
 
 from nanochat.gpt import GPT, GPTConfig
 from nanochat.dataloader import tokenizing_distributed_data_loader
-from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, print_banner, get_base_dir
+from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, print_banner, get_base_dir, get_device_type, get_default_dtype
 from nanochat.tokenizer import get_tokenizer, get_token_bytes
 from nanochat.checkpoint_manager import save_checkpoint
 from nanochat.loss_eval import evaluate_bpb
@@ -59,7 +59,7 @@ user_config = {k: globals()[k] for k in config_keys} # will be useful for loggin
 # Compute init
 ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
 master_process = ddp_rank == 0 # this process will do logging, checkpointing etc.
-autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16)
+autocast_ctx = torch.amp.autocast(device_type=get_device_type(), dtype=get_default_dtype())
 
 # wandb logging init
 use_dummy_wandb = run == "dummy" or not master_process
@@ -96,7 +96,7 @@ model_config_kwargs = dict(sequence_len=max_seq_len, vocab_size=vocab_size, n_la
 with torch.device("meta"):
     model_config = GPTConfig(**model_config_kwargs)
     model = GPT(model_config)
-model.to_empty(device="cuda")
+model.to_empty(device=device)
 model.init_weights()
 orig_model = model # original, uncompiled model, for saving raw model state_dict
 model = torch.compile(model, dynamic=False) # TODO: dynamic True/False think through
@@ -252,7 +252,8 @@ for step in range(num_iterations + 1):
     # -------------------------------------------------------------------------
     # single training step
     # evaluate the gradient
-    torch.cuda.synchronize()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
     t0 = time.time()
     for micro_step in range(grad_accum_steps):
         with autocast_ctx:
@@ -275,7 +276,8 @@ for step in range(num_iterations + 1):
     for opt in optimizers:
         opt.step()
     model.zero_grad(set_to_none=True)
-    torch.cuda.synchronize()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
     t1 = time.time()
     dt = t1 - t0
     # -------------------------------------------------------------------------
@@ -304,7 +306,8 @@ for step in range(num_iterations + 1):
         })
 
 # print a few more stats
-print0(f"Peak memory usage: {torch.cuda.max_memory_allocated() / 1024 / 1024:.2f}MiB")
+peak_mem = torch.cuda.max_memory_allocated() / 1024 / 1024 if torch.cuda.is_available() else 0
+print0(f"Peak memory usage: {peak_mem:.2f}MiB")
 print0(f"Total training time: {total_training_time/60:.2f}m")
 print0(f"Minimum validation bpb: {min_val_bpb:.4f}")
 
@@ -330,7 +333,7 @@ get_report().log(section="Base model training", data=[
         "MFU %": f"{mfu:.2f}%",
         "Total training flops": f"{flops_so_far:e}",
         "Total training time": f"{total_training_time/60:.2f}m",
-        "Peak memory usage": f"{torch.cuda.max_memory_allocated() / 1024 / 1024:.2f}MiB",
+        "Peak memory usage": f"{peak_mem:.2f}MiB",
     }
 ])
 

scripts/chat_cli.py

@@ -6,7 +6,7 @@ python -m scripts.chat_cli -i mid
 """
 import argparse
 import torch
-from nanochat.common import compute_init
+from nanochat.common import compute_init, get_device_type, get_default_dtype
 from nanochat.engine import Engine
 from nanochat.checkpoint_manager import load_model
 
@@ -21,7 +21,7 @@ args = parser.parse_args()
 
 # Init the model and tokenizer
 ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
-autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16)
+autocast_ctx = torch.amp.autocast(device_type=get_device_type(), dtype=get_default_dtype())
 model, tokenizer, meta = load_model(args.source, device, phase="eval", model_tag=args.model_tag, step=args.step)
 
 # Special tokens for the chat state machine

scripts/chat_eval.py

@@ -14,7 +14,7 @@ from functools import partial
 import torch
 import torch.distributed as dist
 
-from nanochat.common import compute_init, compute_cleanup, get_dist_info, print0
+from nanochat.common import compute_init, compute_cleanup, get_dist_info, print0, get_device_type
 from nanochat.checkpoint_manager import load_model
 from nanochat.engine import Engine
 
@@ -195,7 +195,7 @@ if __name__ == "__main__":
 
     ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
     ptdtype = torch.float32 if args.dtype == 'float32' else torch.bfloat16
-    autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=ptdtype)
+    autocast_ctx = torch.amp.autocast(device_type=get_device_type(), dtype=ptdtype)
 
     model, tokenizer, meta = load_model(args.source, device, phase="eval", model_tag=args.model_tag, step=args.step)
     engine = Engine(model, tokenizer)

scripts/chat_rl.py

@@ -23,7 +23,7 @@ import wandb
 import torch
 import torch.distributed as dist
 
-from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, DummyWandb
+from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, DummyWandb, get_device_type
 from nanochat.checkpoint_manager import save_checkpoint, load_model
 from nanochat.engine import Engine
 from tasks.gsm8k import GSM8K
@@ -57,7 +57,7 @@ user_config = {k: globals()[k] for k in config_keys} # will be useful for loggin
 ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
 master_process = ddp_rank == 0 # this process will do logging, checkpointing etc.
 dtype = torch.float32 if dtype == 'float32' else torch.bfloat16
-autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=dtype)
+autocast_ctx = torch.amp.autocast(device_type=get_device_type(), dtype=dtype)
 
 # wandb logging init
 use_dummy_wandb = run == "dummy" or not master_process

scripts/chat_sft.py

@@ -17,7 +17,7 @@ import wandb
 import torch
 import torch.distributed as dist
 
-from nanochat.common import compute_init, compute_cleanup, get_base_dir, print0, DummyWandb
+from nanochat.common import compute_init, compute_cleanup, get_base_dir, print0, DummyWandb, get_device_type
 from nanochat.checkpoint_manager import load_model
 from nanochat.checkpoint_manager import save_checkpoint
 from nanochat.engine import Engine
@@ -63,7 +63,7 @@ user_config = {k: globals()[k] for k in config_keys} # possibly useful for loggi
 ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
 master_process = ddp_rank == 0
 dtype = torch.float32 if dtype == 'float32' else torch.bfloat16
-autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=dtype)
+autocast_ctx = torch.amp.autocast(device_type=get_device_type(), dtype=dtype)
 
 # wandb logging init
 use_dummy_wandb = run == "dummy" or not master_process

scripts/chat_web.py

@@ -16,7 +16,7 @@ from fastapi.responses import StreamingResponse, HTMLResponse, FileResponse
 from pydantic import BaseModel
 from typing import List, Optional, AsyncGenerator
 
-from nanochat.common import compute_init
+from nanochat.common import compute_init, get_device_type, get_default_dtype
 from nanochat.checkpoint_manager import load_model
 from nanochat.engine import Engine
 
@@ -32,7 +32,7 @@ parser.add_argument('--host', type=str, default='0.0.0.0', help='Host to bind th
 args = parser.parse_args()
 
 ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
-autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16)
+autocast_ctx = torch.amp.autocast(device_type=get_device_type(), dtype=get_default_dtype())
 
 class ChatMessage(BaseModel):
     role: str

scripts/mid_train.py

@@ -16,7 +16,7 @@ import time
 import wandb
 import torch
 
-from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, get_base_dir
+from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, get_base_dir, get_device_type
 from nanochat.tokenizer import get_token_bytes
 from nanochat.checkpoint_manager import save_checkpoint
 from nanochat.loss_eval import evaluate_bpb
@@ -53,7 +53,7 @@ user_config = {k: globals()[k] for k in config_keys} # possibly useful for loggi
 ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
 master_process = ddp_rank == 0
 dtype = torch.float32 if dtype == 'float32' else torch.bfloat16
-autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=dtype)
+autocast_ctx = torch.amp.autocast(device_type=get_device_type(), dtype=dtype)
 
 # wandb logging init
 use_dummy_wandb = run == "dummy" or not master_process
@@ -214,7 +214,8 @@ while True:
     # -------------------------------------------------------------------------
     # single training step
     # evaluate the gradient
-    torch.cuda.synchronize()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
     t0 = time.time()
     for micro_step in range(grad_accum_steps):
         with autocast_ctx:
@@ -235,7 +236,8 @@ while True:
     for opt in optimizers:
         opt.step()
     model.zero_grad(set_to_none=True)
-    torch.cuda.synchronize()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
     t1 = time.time()
     dt = t1 - t0
     # -------------------------------------------------------------------------
@@ -267,7 +269,8 @@ while True:
         })
 
 # print a few more stats
-print0(f"Peak memory usage: {torch.cuda.max_memory_allocated() / 1024 / 1024:.2f}MiB")
+peak_mem = torch.cuda.max_memory_allocated() / 1024 / 1024 if torch.cuda.is_available() else 0
+print0(f"Peak memory usage: {peak_mem:.2f}MiB")
 print0(f"Total training time: {total_training_time/60:.2f}m")
 print0(f"Minimum validation bpb: {min_val_bpb:.4f}")
 

speedrun.sh

@@ -22,8 +22,19 @@ mkdir -p $NANOCHAT_BASE_DIR
 command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
 # create a .venv local virtual environment (if it doesn't exist)
 [ -d ".venv" ] || uv venv
-# install the repo dependencies
-uv sync
+
+# Detect hardware and install appropriate PyTorch version
+if [[ "$OSTYPE" == "darwin"* ]]; then
+    echo "Detected macOS - installing CPU version of PyTorch"
+    uv sync --extra cpu
+elif command -v nvidia-smi &> /dev/null && nvidia-smi &> /dev/null; then
+    echo "Detected NVIDIA GPU(s) - installing CUDA version of PyTorch"
+    uv sync --extra gpu
+else
+    echo "No GPU detected - installing CPU version of PyTorch"
+    uv sync --extra cpu
+fi
+
 # activate venv so that `python` uses the project's venv instead of system python
 source .venv/bin/activate
 
@@ -70,6 +81,26 @@ python -m scripts.tok_train --max_chars=2000000000
 # evaluate the tokenizer (report compression ratio etc.)
 python -m scripts.tok_eval
 
+# -----------------------------------------------------------------------------
+# Platform detection for compute configuration
+
+# Check if running on macOS
+if [[ "$OSTYPE" == "darwin"* ]]; then
+    echo "Detected macOS - running in CPU mode (single process)"
+    TORCHRUN_CMD="python"
+    ARGS_SEP=""  # No separator needed for python
+# Check if CUDA/GPUs are available
+elif command -v nvidia-smi &> /dev/null && nvidia-smi &> /dev/null; then
+    GPU_COUNT=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
+    echo "Detected $GPU_COUNT GPU(s) - running in GPU mode"
+    TORCHRUN_CMD="torchrun --standalone --nproc_per_node=$GPU_COUNT"
+    ARGS_SEP="--"  # Separator needed for torchrun
+else
+    echo "No GPUs detected - running in CPU mode (single process)"
+    TORCHRUN_CMD="python"
+    ARGS_SEP=""  # No separator needed for python
+fi
+
 # -----------------------------------------------------------------------------
 # Base model (pretraining)
 
@@ -92,25 +123,25 @@ echo "Waiting for dataset download to complete..."
 wait $DATASET_DOWNLOAD_PID
 
 # pretrain the d20 model
-torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=20 --run=$WANDB_RUN
+$TORCHRUN_CMD -m scripts.base_train $ARGS_SEP --depth=20 --run=$WANDB_RUN
 # evaluate the model on a larger chunk of train/val data and draw some samples
-torchrun --standalone --nproc_per_node=8 -m scripts.base_loss
+$TORCHRUN_CMD -m scripts.base_loss
 # evaluate the model on CORE tasks
-torchrun --standalone --nproc_per_node=8 -m scripts.base_eval
+$TORCHRUN_CMD -m scripts.base_eval
 
 # -----------------------------------------------------------------------------
 # Midtraining (teach the model conversation special tokens, tool use, multiple choice)
 
 # run midtraining and eval the model
-torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --run=$WANDB_RUN
-torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i mid
+$TORCHRUN_CMD -m scripts.mid_train $ARGS_SEP --run=$WANDB_RUN
+$TORCHRUN_CMD -m scripts.chat_eval $ARGS_SEP -i mid
 
 # -----------------------------------------------------------------------------
 # Supervised Finetuning (domain adaptation to each sequence all by itself per row)
 
 # train sft and re-eval right away (should see a small bump)
-torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --run=$WANDB_RUN
-torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft
+$TORCHRUN_CMD -m scripts.chat_sft $ARGS_SEP --run=$WANDB_RUN
+$TORCHRUN_CMD -m scripts.chat_eval $ARGS_SEP -i sft
 
 # chat with the model over CLI! Leave out the -p to chat interactively
 # python -m scripts.chat_cli -p "Why is the sky blue?"
@@ -123,9 +154,9 @@ torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft
 # (optional)
 
 # run reinforcement learning
-# torchrun --standalone --nproc_per_node=8 -m scripts.chat_rl -- --run=$WANDB_RUN
+# $TORCHRUN_CMD -m scripts.chat_rl $ARGS_SEP --run=$WANDB_RUN
 # eval the RL model only on GSM8K
-# torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i rl -a GSM8K
+# $TORCHRUN_CMD -m scripts.chat_eval $ARGS_SEP -i rl -a GSM8K
 
 # -----------------------------------------------------------------------------
 # Generate the full report by putting together all the sections

uv.lock

@@ -2,13 +2,32 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.12' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and sys_platform != 'linux'",
-    "python_full_version == '3.11.*' and sys_platform == 'linux'",
-    "python_full_version == '3.11.*' and sys_platform != 'linux'",
-    "python_full_version < '3.11' and sys_platform == 'linux'",
-    "python_full_version < '3.11' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
 ]
+conflicts = [[
+    { package = "nanochat", extra = "cpu" },
+    { package = "nanochat", extra = "gpu" },
+]]
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -26,7 +45,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
     { name = "aiosignal" },
-    { name = "async-timeout", marker = "python_full_version < '3.11'" },
+    { name = "async-timeout", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
     { name = "attrs" },
     { name = "frozenlist" },
     { name = "multidict" },
@@ -111,7 +130,7 @@ version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "frozenlist" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007, upload-time = "2025-07-03T22:54:43.528Z" }
 wheels = [
@@ -132,10 +151,10 @@ name = "anyio"
 version = "4.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
+    { name = "exceptiongroup", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
     { name = "idna" },
     { name = "sniffio" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
 wheels = [
@@ -238,7 +257,7 @@ name = "click"
 version = "8.2.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/60/6c/8ca2efa64cf75a977a0d7fac081354553ebe483345c734fb6b6515d96bbc/click-8.2.1.tar.gz", hash = "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202", size = 286342, upload-time = "2025-05-20T23:19:49.832Z" }
 wheels = [
@@ -292,7 +311,7 @@ name = "exceptiongroup"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.12' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
 wheels = [
@@ -497,7 +516,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "filelock" },
     { name = "fsspec" },
-    { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" },
+    { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
     { name = "packaging" },
     { name = "pyyaml" },
     { name = "requests" },
@@ -602,7 +621,7 @@ name = "maturin"
 version = "1.9.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "tomli", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/13/7c/b11b870fc4fd84de2099906314ce45488ae17be32ff5493519a6cddc518a/maturin-1.9.4.tar.gz", hash = "sha256:235163a0c99bc6f380fb8786c04fd14dcf6cd622ff295ea3de525015e6ac40cf", size = 213647, upload-time = "2025-08-27T11:37:57.079Z" }
 wheels = [
@@ -635,7 +654,7 @@ name = "multidict"
 version = "6.6.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/69/7f/0652e6ed47ab288e3756ea9c0df8b14950781184d4bd7883f4d87dd41245/multidict-6.6.4.tar.gz", hash = "sha256:d2d4e4787672911b48350df02ed3fa3fffdc2f2e8ca06dd6afdf34189b76a9dd", size = 101843, upload-time = "2025-08-11T12:08:48.217Z" }
 wheels = [
@@ -763,11 +782,23 @@ dependencies = [
     { name = "regex" },
     { name = "tiktoken" },
     { name = "tokenizers" },
-    { name = "torch" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu') or (extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu')" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
+    { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "extra == 'extra-8-nanochat-gpu'" },
     { name = "uvicorn" },
     { name = "wandb" },
 ]
 
+[package.optional-dependencies]
+cpu = [
+    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
+]
+gpu = [
+    { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" } },
+]
+
 [package.dev-dependencies]
 dev = [
     { name = "maturin" },
@@ -784,10 +815,13 @@ requires-dist = [
     { name = "regex", specifier = ">=2025.9.1" },
     { name = "tiktoken", specifier = ">=0.11.0" },
     { name = "tokenizers", specifier = ">=0.22.0" },
-    { name = "torch", specifier = ">=2.8.0", index = "https://download.pytorch.org/whl/cu128" },
+    { name = "torch", specifier = ">=2.8.0" },
+    { name = "torch", marker = "extra == 'cpu'", specifier = ">=2.8.0", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "nanochat", extra = "cpu" } },
+    { name = "torch", marker = "extra == 'gpu'", specifier = ">=2.8.0", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "nanochat", extra = "gpu" } },
     { name = "uvicorn", specifier = ">=0.36.0" },
     { name = "wandb", specifier = ">=0.21.3" },
 ]
+provides-extras = ["cpu", "gpu"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -800,8 +834,13 @@ name = "networkx"
 version = "3.4.2"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.11' and sys_platform == 'linux'",
-    "python_full_version < '3.11' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/fd/1d/06475e1cd5264c0b870ea2cc6fdb3e37177c1e565c43f56ff17a10e3937f/networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1", size = 2151368, upload-time = "2024-10-21T12:39:38.695Z" }
 wheels = [
@@ -813,10 +852,20 @@ name = "networkx"
 version = "3.5"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version >= '3.12' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and sys_platform != 'linux'",
-    "python_full_version == '3.11.*' and sys_platform == 'linux'",
-    "python_full_version == '3.11.*' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
+    "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
 ]
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