feat: add full MPS (Apple Silicon) support

[gruckion]

Summary

This PR enables true MPS GPU acceleration for macOS Apple Silicon users, providing a 44x speedup over CPU fallback.

Performance Benchmarks

Metric	MPS (Apple Silicon GPU)	CPU	Improvement
Generation Time	39.9s	1771.6s (~29.5 min)	44.4x faster
Realtime Factor	0.154x	0.017x	9x better
Tokens/sec	13.2	1.5	8.8x faster

Tested on Apple Silicon with float32 precision, use_torch_compile=False

Background

PR #167 added example/simple-mac.py as a Mac support solution, but it was actually a CPU fallback workaround that avoided MPS issues rather than fixing them. This PR addresses the root causes to enable true MPS GPU acceleration.

---

Changes

1. Guard CUDAGraph Call (Critical)

File: dia/model.py:701-702

# Before (crashes/warns on MPS)
torch.compiler.cudagraph_mark_step_begin()

# After
if self.device.type == "cuda":
    torch.compiler.cudagraph_mark_step_begin()

Why: cudagraph_mark_step_begin() is CUDA-specific and has no MPS equivalent. Called ~860 times per generation in the autoregressive loop.

---

2. Add Device Check to torch.compile (Critical)

File: dia/model.py:657-667

# Before (fails on MPS with "Device mps not supported")
if use_torch_compile and not hasattr(self, "_compiled"):
    self._prepare_generation = torch.compile(...)
    self._decoder_step = torch.compile(..., mode="max-autotune")
    self._compiled = True

# After
if use_torch_compile and not hasattr(self, "_compiled"):
    if self.device.type != "cuda":
        warnings.warn(
            f"torch.compile with max-autotune is only supported on CUDA devices. "
            f"Current device: {self.device.type}. Skipping compilation."
        )
    else:
        # ... compile as before

Why: mode="max-autotune" requires Triton which is CUDA/ROCm only. MPS torch.compile is still experimental (PyTorch #150121).

---

3. Fix CLI Device Auto-Detection

File: cli.py:12-18, 82-83

# Before (skips MPS, falls back to CPU)
default="cuda" if torch.cuda.is_available() else "cpu"

# After
def _get_default_device_str():
    if torch.cuda.is_available():
        return "cuda"
    elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
        return "mps"
    return "cpu"

default=_get_default_device_str()

Why: Mac users running python cli.py "text" --output out.wav now automatically get MPS acceleration instead of slow CPU fallback.

---

4. Fix simple-mac.py dtype

File: example/simple-mac.py:4-6

# Before (potential precision issues)
model = Dia.from_pretrained("nari-labs/Dia-1.6B-0626", compute_dtype="float16")

# After
# Use float32 for better stability on MPS (Apple Silicon)
# float16 may have precision issues on MPS and provides minimal performance benefit
model = Dia.from_pretrained("nari-labs/Dia-1.6B-0626", compute_dtype="float32")

Why: Matches app.py recommendation. MPS float16 has documented precision bugs and provides minimal speedup on Apple Silicon (no dedicated Tensor Cores).

---

5. Guard Triton Config in Benchmark

File: example/benchmark.py:9-13

# Before (AttributeError on macOS)
torch._inductor.config.coordinate_descent_tuning = True
torch._inductor.config.triton.unique_kernel_names = True
torch._inductor.config.fx_graph_cache = True

# After
if sys.platform in ("linux", "win32"):
    torch._inductor.config.coordinate_descent_tuning = True
    torch._inductor.config.triton.unique_kernel_names = True
    torch._inductor.config.fx_graph_cache = True

Why: Triton is only available on Linux/Windows per pyproject.toml. These settings fail on macOS.

---

6. Add MPS Seed Management

Files: cli.py:30-32, app.py:66-68

# Added to set_seed()
if hasattr(torch, "mps") and torch.backends.mps.is_available():
    torch.mps.manual_seed(seed)

Why: Explicit MPS RNG seeding for reproducibility (though MPS reproducibility has known limitations).

---

7. Add ARM64 Linux Dockerfile

File: docker/Dockerfile.arm (new)

Adds support for ARM64 Linux servers (AWS Graviton, Ampere Altra, etc.) with CPU-only inference.

Note: Docker cannot access MPS on macOS (runs in Linux VM). macOS users must install natively for GPU acceleration.

---

Test Plan

• python example/simple-mac.py runs without errors on MPS
• python cli.py "[S1] Hello world." --output test.wav auto-detects MPS
• Audio generation completes successfully on MPS
• No CUDA-specific warnings/errors on MPS device
• Benchmark confirms 44x speedup over CPU

---

References

• PyTorch MPS Backend
• torch.compile MPS Status (#150121)
• Apple Metal PyTorch Guide
• README TODO: "Docker support for ARM architecture and MacOS"

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