Support Apple Silicon (MPS): --device auto and CUDA-only Triton gate

Apply upstream PR openai#22 by @Berkkirik: "--device auto"
resolves cuda > mps > cpu, and the Triton MoE kernels are auto-enabled
only on CUDA devices (Triton cannot target Metal).

Verified end-to-end on Apple Silicon (M4 Pro, torch 2.12.1) with the
released checkpoint: spans identical to CPU output.

Co-authored-by: berkkirik <berk.kirik@outlook.com>
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Author: 3 people

opf/_cli/common.py

@@ -55,8 +55,13 @@ def add_device_arg(parser: object) -> None:
     parser.add_argument(
         "--device",
         type=str,
-        default="cuda",
-        help="Device to run on",
+        default="auto",
+        help=(
+            "Device to run on. 'auto' (default) picks the best available "
+            "backend: cuda > mps (Apple Silicon) > cpu. Pass an explicit "
+            "value like 'cuda', 'mps', or 'cpu' to override or to get a "
+            "loud error when the requested backend is unavailable."
+        ),
     )
 
 

opf/_common/device.py

@@ -0,0 +1,51 @@
+"""Device-name resolution helpers shared by CLI entrypoints."""
+
+from __future__ import annotations
+
+import sys
+
+import torch
+
+AUTO_DEVICE: str = "auto"
+
+
+def _mps_is_available() -> bool:
+    """Return True when the current PyTorch build supports Apple Metal (MPS)."""
+    backend = getattr(torch.backends, "mps", None)
+    if backend is None:
+        return False
+    is_available = getattr(backend, "is_available", None)
+    if is_available is None:
+        return False
+    try:
+        return bool(is_available())
+    except Exception:
+        return False
+
+
+def resolve_device(device_name: str) -> torch.device:
+    """Resolve a user-supplied device name into a concrete ``torch.device``.
+
+    ``"auto"`` selects the best available device in this order: CUDA (NVIDIA
+    GPU) > MPS (Apple Silicon GPU) > CPU. Any other value is passed through
+    to ``torch.device`` as-is so that explicit requests like ``"cuda"`` or
+    ``"mps"`` still fail loudly when the underlying backend is unavailable.
+    """
+    if device_name == AUTO_DEVICE:
+        if torch.cuda.is_available():
+            return torch.device("cuda")
+        if _mps_is_available():
+            print(
+                "info: no CUDA device detected; using Apple Metal (MPS).",
+                file=sys.stderr,
+                flush=True,
+            )
+            return torch.device("mps")
+        print(
+            "info: no CUDA or MPS device detected; falling back to CPU "
+            "(pass --device cuda or --device mps to override).",
+            file=sys.stderr,
+            flush=True,
+        )
+        return torch.device("cpu")
+    return torch.device(device_name)

opf/_core/runtime.py

@@ -19,6 +19,7 @@
     REDACTED_OUTPUT_LABEL,
     REDACTED_OUTPUT_PLACEHOLDER,
 )
+from .._common.device import resolve_device
 from .._common.env import get_env_bool
 from .decoding import ViterbiCRFDecoder
 from .._common.label_space import resolve_label_space_from_config
@@ -215,7 +216,7 @@ def load_inference_runtime(
     if output_mode not in OUTPUT_MODES:
         raise ValueError(f"Unsupported output_mode: {output_mode!r}")
     _validate_checkpoint_dir(checkpoint)
-    device = torch.device(device_name)
+    device = resolve_device(device_name)
     checkpoint_config = _load_checkpoint_config(checkpoint)
     n_ctx = _resolve_n_ctx(checkpoint_config, n_ctx_override, device)
     encoding_name = checkpoint_config.get("encoding")

opf/_model/model.py

@@ -750,8 +750,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         expert_indices = experts.indices
         expert_weights = expert_weights / self.experts_per_token
         experts_per_token_eff = self.experts_per_token
-        not_running_on_cpu = t.device.type != "cpu"
-        use_triton = get_env_bool("OPF_MOE_TRITON", default=not_running_on_cpu)
+        # Triton kernels are CUDA-only; auto-enable only on CUDA devices. MPS
+        # and CPU fall back to the torch-ops path unless the user explicitly
+        # opts in via OPF_MOE_TRITON=1.
+        is_cuda_device = t.device.type == "cuda"
+        use_triton = get_env_bool("OPF_MOE_TRITON", default=is_cuda_device)
         if use_triton:
             _require_triton()
 

opf/_train/runner.py

@@ -16,6 +16,7 @@
 from .args import parse_args
 from .._api import resolve_checkpoint_path
 from .._common.constants import SCHEMA_VERSION
+from .._common.device import resolve_device
 from .._common.label_space import (
     resolve_checkpoint_label_space,
     resolve_label_space_from_config,
@@ -588,11 +589,12 @@ def main(argv: Sequence[str] | None = None, *, prog: str | None = None) -> int:
                 progress_interval_s = parsed_interval
 
     checkpoint = resolve_checkpoint_path(args.checkpoint)
-    device = torch.device(args.device)
+    device = resolve_device(args.device)
 
-    # Default to Triton-backed MoE kernels on non-CPU devices unless callers
-    # explicitly opt out. CPU uses torch ops by default so Triton stays optional.
-    if device.type != "cpu":
+    # Default to Triton-backed MoE kernels on CUDA devices unless callers
+    # explicitly opt out. CPU and MPS use torch ops by default so Triton
+    # stays CUDA-only (the kernels don't run on Metal).
+    if device.type == "cuda":
         os.environ.setdefault("OPF_MOE_TRITON", "1")
 
     base_config = _load_checkpoint_config(checkpoint)