[Studio] Fix GPU detection for AMD/Intel — add Vulkan VRAM fallback

[HellBoxyz]

Problem

Unsloth Studio doesn't detect GPU on AMD/Intel systems. The VRAM detection (_get_gpu_free_memory()) uses only nvidia-smi, so on non-NVIDIA hardware it returns an empty list. This means:

• Studio thinks there's no GPU at all
• Context length stays at full native (e.g. 128K) without auto-reduction
• KV cache doesn't fit in VRAM and spills into system RAM
• Inference is slow because data transfers between GPU and RAM

Fix

Add a vulkaninfo fallback that kicks in when nvidia-smi is not available:

• Parses Vulkan memory heap budgets (VK_EXT_memory_budget)
• Correctly handles multi-GPU systems and GPUs with multiple DEVICE_LOCAL heaps
• nvidia-smi still has priority — zero impact on NVIDIA setups
• When nvidia-smi succeeds (returncode 0), its result is authoritative — empty list means no visible GPUs, no fallback to vulkan

Before / After

Before (AMD GPU):

GPUs free: [], selected: None, fit: True
→ 128K context, KV cache in RAM, slow

After (AMD GPU):

Vulkan GPU memory detected: GPU0=7382MiB
GPUs free: [(0, 7382)], selected: [0], fit: False
→ Context auto-reduced to fit VRAM, everything on GPU

Tested on

• AMD Radeon RX 5700 XT (8 GB), Windows 11, Vulkan 1.4.341
• Model: gemma-4-E4B-it Q4_K_XL (4.8 GB)
• Context properly auto-reduced, full GPU offload with -ngl -1
• 12 unit tests covering parser + orchestrator edge cases

[gemini-code-assist]

Code Review

This pull request introduces a fallback mechanism for detecting free GPU memory using vulkaninfo, enabling support for AMD, Intel, and other Vulkan-compatible hardware when nvidia-smi is unavailable. The review feedback identifies a logic error in the parsing of vulkaninfo output, where multiple memory heaps are incorrectly treated as distinct GPUs, and provides a more robust implementation that groups heaps by physical device.

[gemini-code-assist]

The current parsing logic for vulkaninfo output is not robust for all systems. It treats every device-local memory heap as a separate GPU, which is incorrect for multi-GPU systems or single GPUs that expose multiple device-local heaps. This can lead to misreporting the number of GPUs and their available memory, causing issues with GPU selection and model offloading.

A more robust approach is to group memory heaps by physical device and report the largest available memory budget for each. This ensures that each physical GPU is represented as a single entry with its correct available VRAM.

# Split output by physical device. vulkaninfo typically separates devices
# with headers like "GPU0", "GPU1", etc. on their own lines.
# The lookahead (?=...) keeps the delimiter.
device_sections = re.split(r"(?=^GPU\\d+\\n)", output, flags=re.MULTILINE)
if len(device_sections) > 1:
    # Filter out any non-GPU sections (like the header before GPU0)
    device_sections = [s for s in device_sections if s.strip().startswith("GPU")]
# If no GPUn headers, device_sections contains the whole output as one element.

budget_re = re.compile(r"budget\\s*=\\s*(\\d+)")
gpus: list[tuple[int, int]] = []

for gpu_idx, device_section in enumerate(device_sections):
    # For each physical device, find the largest device-local memory heap budget.
    # A single GPU can have multiple device-local heaps.
    max_free_mib = 0
    heap_sections = re.split(r"(?=\\tmemoryHeaps\\[\\d+\\]:)", device_section)
    for section in heap_sections:
        if "MEMORY_HEAP_DEVICE_LOCAL_BIT" in section:
            budget_m = budget_re.search(section)
            if budget_m:
                budget_bytes = int(budget_m.group(1))
                free_mib = budget_bytes // (1024 * 1024)
                if free_mib > max_free_mib:
                    max_free_mib = free_mib
    
    if max_free_mib > 0:
        gpus.append((gpu_idx, max_free_mib))

[rolandtannous]

This is duplicate of #4720