fix KV cache dtype mismatch on CPU: use COMPUTE_DTYPE instead of hardcoded logic

The KV cache was hardcoded to float32 on non-CUDA devices, but the model
weights are loaded in bfloat16 via NANOCHAT_DTYPE env var. This caused a
RuntimeError in scaled_dot_product_attention. Now uses COMPUTE_DTYPE from
common.py which respects the env var.

Also broadened CI/CD path triggers to nanochat/**.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.github/workflows/deploy.yml

@@ -4,8 +4,7 @@ on:
   push:
     branches: [master]
     paths:
-      - 'nanochat/ui.html'
-      - 'nanochat/logo.svg'
+      - 'nanochat/**'
       - 'scripts/chat_web.py'
       - 'scripts/chat_cli.py'
 

nanochat/engine.py

@@ -17,7 +17,7 @@ import signal
 import warnings
 from contextlib import contextmanager
 from collections import deque
-from nanochat.common import compute_init, autodetect_device_type
+from nanochat.common import compute_init, autodetect_device_type, COMPUTE_DTYPE
 from nanochat.checkpoint_manager import load_model
 
 # -----------------------------------------------------------------------------
@@ -183,7 +183,7 @@ 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
         rng = torch.Generator(device=device)
         rng.manual_seed(seed)