Merge e9bf1a5a67 into f068604948

nanochat/common.py

@@ -27,6 +27,12 @@ def _detect_compute_dtype():
         # fp16 training requires GradScaler (not yet implemented), so fall back to fp32.
         # Users can still force fp16 via NANOCHAT_DTYPE=float16 if they know what they're doing.
         return torch.float32, f"auto-detected: CUDA SM {capability[0]}{capability[1]} (pre-Ampere, bf16 not supported, using fp32)"
+    
+    if torch.backends.mps.is_available():
+        # torch.float16
+        # return torch.float16, "auto-detected: mps, float16"
+        return torch.float32, "auto-detected: mps, float16"
+
     return torch.float32, "auto-detected: no CUDA (CPU/MPS)"
 COMPUTE_DTYPE, COMPUTE_DTYPE_REASON = _detect_compute_dtype()
 

nanochat/engine.py

@@ -160,6 +160,8 @@ class RowState:
         self.python_expr_tokens = [] # Tokens of the current python expression
         self.completed = False # Whether this row has completed generation
 
+from nanochat.common import COMPUTE_DTYPE
+
 class Engine:
 
     def __init__(self, model, tokenizer):
@@ -177,7 +179,7 @@ class Engine:
         # As a quick hack, we're making generate() function inherit and know about this repo-wise assumption.
         # I think there has to be a bigger refactor to deal with device/dtype tracking across the codebase.
         # In particular, the KVCache should allocate its tensors lazily
-        dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
+        dtype = COMPUTE_DTYPE #torch.bfloat16 if device.type == "cuda" else torch.float32
         rng = torch.Generator(device=device)
         rng.manual_seed(seed)
 

nanochat/flash_attention.py

@@ -77,7 +77,7 @@ def _sdpa_attention(q, k, v, window_size, enable_gqa):
 
     # Full context, same length
     if (window < 0 or window >= Tq) and Tq == Tk:
-        return F.scaled_dot_product_attention(q, k, v, is_causal=True, enable_gqa=enable_gqa)
+        return F.scaled_dot_product_attention(q, k, v, is_causal=True)#, enable_gqa=enable_gqa)
 
     # Single token generation
     if Tq == 1:
@@ -86,7 +86,7 @@ def _sdpa_attention(q, k, v, window_size, enable_gqa):
             start = max(0, Tk - (window + 1))
             k = k[:, :, start:, :]
             v = v[:, :, start:, :]
-        return F.scaled_dot_product_attention(q, k, v, is_causal=False, enable_gqa=enable_gqa)
+        return F.scaled_dot_product_attention(q, k, v, is_causal=False)#, enable_gqa=enable_gqa)
 
     # Need explicit mask for sliding window/chunk inference
     device = q.device
@@ -99,7 +99,7 @@ def _sdpa_attention(q, k, v, window_size, enable_gqa):
     if window >= 0 and window < Tk:
         mask = mask & ((row_idx - col_idx) <= window)
 
-    return F.scaled_dot_product_attention(q, k, v, attn_mask=mask, enable_gqa=enable_gqa)
+    return F.scaled_dot_product_attention(q, k, v, attn_mask=mask)#, enable_gqa=enable_gqa)
 
 # =============================================================================
 # Public API: Same interface as FA3

nanochat/gpt.py

@@ -40,7 +40,7 @@ class GPTConfig:
 
 
 def norm(x):
-    return F.rms_norm(x, (x.size(-1),)) # note that this will run in bf16, seems ok
+    return F.layer_norm(x, (x.size(-1),)) # note that this will run in bf16, seems ok
 
 class Linear(nn.Linear):
     """nn.Linear that casts weights to match input dtype in forward.
@@ -355,7 +355,7 @@ class GPT(nn.Module):
             'total': total,
         }
 
-    def setup_optimizer(self, unembedding_lr=0.004, embedding_lr=0.2, matrix_lr=0.02, weight_decay=0.0, scalar_lr=0.5):
+    def setup_optimizer(self, unembedding_lr=0.004, embedding_lr=0.2, matrix_lr=0.02, weight_decay=0.0, scalar_lr=0.5, device_type="cuda"):
         model_dim = self.config.n_embd
         ddp, rank, local_rank, world_size = get_dist_info()
 
@@ -390,7 +390,7 @@ class GPT(nn.Module):
             ))
 
         Factory = DistMuonAdamW if ddp else MuonAdamW
-        optimizer = Factory(param_groups)
+        optimizer = Factory(param_groups, device_type)
         for group in optimizer.param_groups:
             group["initial_lr"] = group["lr"]
         return optimizer

nanochat/optim.py

@@ -17,7 +17,7 @@ Good old AdamW optimizer, fused kernel.
 https://arxiv.org/abs/1711.05101
 """
 
-@torch.compile(dynamic=False, fullgraph=True)
+# @torch.compile(dynamic=False, fullgraph=True)
 def adamw_step_fused(
     p: Tensor,              # (32768, 768) - parameter tensor
     grad: Tensor,           # (32768, 768) - gradient, same shape as p
@@ -35,6 +35,7 @@ def adamw_step_fused(
     All in one compiled graph to eliminate Python overhead between ops.
     The 0-D CPU tensors avoid recompilation when hyperparameter values change.
     """
+    p = p.to(grad.device)
     # Weight decay (decoupled, applied before the update)
     p.mul_(1 - lr_t * wd_t)
     # Update running averages (lerp_ is cleaner and fuses well)
@@ -87,7 +88,7 @@ polar_express_coeffs = [
     (2.3465413258596377, -1.7097828382687081, 0.42323551169305323),
 ]
 
-@torch.compile(dynamic=False, fullgraph=True)
+# @torch.compile(dynamic=False, fullgraph=True)
 def muon_step_fused(
     stacked_grads: Tensor,          # (12, 768, 3072) - stacked gradients
     stacked_params: Tensor,         # (12, 768, 3072) - stacked parameters
@@ -112,7 +113,7 @@ def muon_step_fused(
     g = stacked_grads.lerp_(momentum_buffer, momentum)
 
     # Polar express
-    X = g.bfloat16()
+    X = g.bfloat16() if torch.cuda.is_available() else g
     X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.01 + 1e-6)
     if g.size(-2) > g.size(-1): # Tall matrix
         for a, b, c in polar_express_coeffs[:ns_steps]:
@@ -175,40 +176,43 @@ class MuonAdamW(torch.optim.Optimizer):
             - For AdamW groups: 'lr', 'betas', 'eps', 'weight_decay'
             - For Muon groups: 'lr', 'momentum', 'ns_steps', 'beta2', 'weight_decay'
     """
-    def __init__(self, param_groups: list[dict]):
+    def __init__(self, param_groups: list[dict], device_type: str = "cpu"):
         super().__init__(param_groups, defaults={})
         # 0-D CPU tensors to avoid torch.compile recompilation when values change
         # AdamW tensors
-        self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        device=device_type
+        self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device=device)
+        self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device=device)
+        self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device=device)
+        self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device=device)
+        self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device=device)
+        self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device=device)
         # Muon tensors
-        self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device=device)
+        self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device=device)
+        self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device=device)
+        self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device=device)
 
     def _step_adamw(self, group: dict) -> None:
         """
         AdamW update for each param in the group individually.
         Lazy init the state, fill in all 0-D tensors, call the fused kernel.
         """
+
+        device = self._adamw_step_t.device
         for p in group['params']:
             if p.grad is None:
                 continue
-            grad = p.grad
+            grad = p.grad.to(device)
             state = self.state[p]
 
             # State init
             if not state:
                 state['step'] = 0
-                state['exp_avg'] = torch.zeros_like(p)
-                state['exp_avg_sq'] = torch.zeros_like(p)
-            exp_avg = state['exp_avg']
-            exp_avg_sq = state['exp_avg_sq']
+                state['exp_avg'] = torch.zeros_like(p).to(device)
+                state['exp_avg_sq'] = torch.zeros_like(p).to(device)
+            exp_avg = state['exp_avg'].to(device)
+            exp_avg_sq = state['exp_avg_sq'].to(device)
             state['step'] += 1
 
             # Fill 0-D tensors with current values
@@ -244,18 +248,18 @@ class MuonAdamW(torch.optim.Optimizer):
         # Momentum for every individual parameter
         if "momentum_buffer" not in state:
             state["momentum_buffer"] = torch.zeros(num_params, *shape, dtype=dtype, device=device)
-        momentum_buffer = state["momentum_buffer"]
+        momentum_buffer = state["momentum_buffer"].to(self._muon_momentum_t.device)
 
         # Second momentum buffer is factored, either per-row or per-column
         if "second_momentum_buffer" not in state:
             state_shape = (num_params, shape[-2], 1) if shape[-2] >= shape[-1] else (num_params, 1, shape[-1])
             state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device)
-        second_momentum_buffer = state["second_momentum_buffer"]
+        second_momentum_buffer = state["second_momentum_buffer"].to(self._muon_momentum_t.device)
         red_dim = -1 if shape[-2] >= shape[-1] else -2
 
         # Stack grads and params (NOTE: this assumes all params have the same shape)
-        stacked_grads = torch.stack([p.grad for p in params])
-        stacked_params = torch.stack(params)
+        stacked_grads = torch.stack([p.grad for p in params]).to(self._muon_momentum_t.device)
+        stacked_params = torch.stack(params).to(self._muon_momentum_t.device)
 
         # Fill all the 0-D tensors with current values
         self._muon_momentum_t.fill_(group["momentum"])
@@ -278,7 +282,9 @@ class MuonAdamW(torch.optim.Optimizer):
         )
 
         # Copy back to original params
-        torch._foreach_copy_(params, list(stacked_params.unbind(0)))
+        # torch._foreach_copy_(params, list(stacked_params.unbind(0)))
+        for p, sp in zip(params, stacked_params.unbind(0)):
+            p.copy_(sp)
 
     @torch.no_grad()
     def step(self):
@@ -352,19 +358,19 @@ class DistMuonAdamW(torch.optim.Optimizer):
             - For AdamW groups: 'lr', 'betas', 'eps', 'weight_decay'
             - For Muon groups: 'lr', 'momentum', 'ns_steps', 'beta2', 'weight_decay'
     """
-    def __init__(self, param_groups: list[dict]):
+    def __init__(self, param_groups: list[dict], device_type: str = "cpu"):
         super().__init__(param_groups, defaults={})
         # 0-D CPU tensors to avoid torch.compile recompilation when values change
-        self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
-        self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu")
+        self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
+        self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
+        self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
+        self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
+        self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
+        self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
+        self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
+        self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
+        self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
+        self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device=device_type)
 
     def _reduce_adamw(self, group: dict, world_size: int) -> dict:
         """Launch async reduce ops for AdamW group. Returns info dict with per-param infos."""

pyproject.toml

@@ -19,7 +19,6 @@ dependencies = [
     "tabulate>=0.9.0",
     "tiktoken>=0.11.0",
     "tokenizers>=0.22.0",
-    "torch==2.9.1",
     "transformers>=4.57.3",
     "uvicorn>=0.36.0",
     "wandb>=0.21.3",
@@ -42,15 +41,7 @@ python_functions = ["test_*"]
 
 # target torch to cuda 12.8 or CPU
 [tool.uv.sources]
-torch = [
-    { 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"
@@ -58,8 +49,9 @@ url = "https://download.pytorch.org/whl/cu128"
 explicit = true
 
 [project.optional-dependencies]
+
 cpu = [
-    "torch==2.9.1",
+    
 ]
 gpu = [
     "torch==2.9.1",

runs/runcpu.sh

@@ -10,13 +10,22 @@
 # Think of this run as educational/fun demo, not something you should expect to work well.
 # You may also want to run this script manually and one by one, copy pasting commands into your terminal.
 
-# all the setup stuff
+# # all the setup stuff
 export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
 mkdir -p $NANOCHAT_BASE_DIR
 command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
 [ -d ".venv" ] || uv venv
+# ## Fails on macbook, instead remove torch cpu from UV file and do this here
 uv sync --extra cpu
+uv pip install torch torchvision torchaudio --upgrade --index-url https://download.pytorch.org/whl/cpu
+uv pip uninstall numpy
+uv pip install --force-reinstall -v "numpy==1.25.2"
+
 source .venv/bin/activate
+uv pip install torch torchvision torchaudio --upgrade --index-url https://download.pytorch.org/whly/cpu
+uv pip uninstall numpy
+uv pip install --force-reinstall -v "numpy==1.25.2"
+
 if [ -z "$WANDB_RUN" ]; then
     WANDB_RUN=dummy
 fi

scripts/base_train.py

@@ -83,6 +83,8 @@ user_config = vars(args).copy()  # for logging
 # Compute init and wandb logging
 
 device_type = autodetect_device_type() if args.device_type == "" else args.device_type
+
+
 ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
 master_process = ddp_rank == 0 # this process will do logging, checkpointing etc.
 synchronize = torch.cuda.synchronize if device_type == "cuda" else lambda: None
@@ -242,7 +244,7 @@ def disable_fp8(model):
 # Compile the model
 
 orig_model = model # original, uncompiled model, for saving raw model state_dict and for inference/evaluation (because the shapes may change shape)
-model = torch.compile(model, dynamic=False) # the inputs to model will never change shape so dynamic=False is safe
+# model = torch.compile(model, dynamic=False) # the inputs to model will never change shape so dynamic=False is safe
 
 # -----------------------------------------------------------------------------
 # Scaling laws and muP extrapolations to determine the optimal training horizon, batch size, learning rates, weight decay.
@@ -312,15 +314,19 @@ optimizer = model.setup_optimizer(
     # Muon hyperparameters
     matrix_lr=args.matrix_lr * batch_lr_scale,
     weight_decay=weight_decay_scaled,
+    device_type=device_type if device_type != "cuda" else "cpu" # since k keeps optim in cpu 
 )
-
+# optimizer.to(device)
 if resuming:
     optimizer.load_state_dict(optimizer_data)
     del optimizer_data
 
 # -----------------------------------------------------------------------------
 # GradScaler for fp16 training (bf16/fp32 don't need it — bf16 has the same exponent range as fp32)
-scaler = torch.amp.GradScaler() if COMPUTE_DTYPE == torch.float16 else None
+# scaler = torch.amp.GradScaler() if COMPUTE_DTYPE == torch.float16 else None
+scaler = torch.cuda.amp.GradScaler() if COMPUTE_DTYPE == torch.float16 else None
+# scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16'))
+
 if scaler is not None:
     print0("GradScaler enabled for fp16 training")
 

scripts/chat_sft.py

@@ -117,7 +117,7 @@ for name, fallback, source in [
         print0(f"Using {name}={arg_val}")
 
 orig_model = model
-model = torch.compile(model, dynamic=False)
+# model = torch.compile(model, dynamic=False)
 depth = model.config.n_layer
 num_flops_per_token = model.estimate_flops()
 tokens_per_fwdbwd = args.device_batch_size * args.max_seq_len # tokens per iteration for a single rank
@@ -131,7 +131,7 @@ token_bytes = get_token_bytes(device=device)
 
 # Initialize the Optimizer (combined MuonAdamW: Muon for matrix params, AdamW for rest)
 # Note that pretraining ramps weight_decay to zero by end of pretraining, so SFT continues with zero
-optimizer = model.setup_optimizer(unembedding_lr=args.unembedding_lr, embedding_lr=args.embedding_lr, matrix_lr=args.matrix_lr, weight_decay=0.0)
+optimizer = model.setup_optimizer(unembedding_lr=args.unembedding_lr, embedding_lr=args.embedding_lr, matrix_lr=args.matrix_lr, weight_decay=0.0, device_type=device_type)
 
 # Optionally warm-start optimizer from pretrained checkpoint (momentum buffers etc.)
 # Note: load_state_dict overwrites param_group metadata (LRs, betas, etc.) with the

uv.lock

@@ -1505,10 +1505,6 @@ dependencies = [
     { name = "tabulate" },
     { name = "tiktoken" },
     { name = "tokenizers" },
-    { name = "torch", version = "2.9.1", 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 = "2.9.1", 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 = "2.9.1+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 = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "extra == 'extra-8-nanochat-gpu'" },
     { name = "transformers" },
     { name = "uvicorn" },
     { name = "wandb" },
@@ -1516,12 +1512,8 @@ dependencies = [
 ]
 
 [package.optional-dependencies]
-cpu = [
-    { name = "torch", version = "2.9.1", 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 = "2.9.1+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 = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" } },
+    { name = "torch" },
 ]
 
 [package.dev-dependencies]
@@ -1545,9 +1537,7 @@ requires-dist = [
     { name = "tabulate", specifier = ">=0.9.0" },
     { name = "tiktoken", specifier = ">=0.11.0" },
     { name = "tokenizers", specifier = ">=0.22.0" },
-    { name = "torch", specifier = "==2.9.1" },
-    { name = "torch", marker = "extra == 'cpu'", specifier = "==2.9.1", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "nanochat", extra = "cpu" } },
-    { name = "torch", marker = "extra == 'gpu'", specifier = "==2.9.1", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "nanochat", extra = "gpu" } },
+    { name = "torch", marker = "extra == 'gpu'", specifier = "==2.9.1" },
     { name = "transformers", specifier = ">=4.57.3" },
     { name = "uvicorn", specifier = ">=0.36.0" },
     { name = "wandb", specifier = ">=0.21.3" },
@@ -1572,13 +1562,8 @@ name = "networkx"
 version = "3.4.2"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "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'",
+    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux'",
 ]
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