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https://github.com/deepseek-ai/DeepInfer.git
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ed49792a0c
Run DeepInfer on the native torch-neuronx device (PrivateUse1 `neuron`, torch-neuronx 2.14, no torch-xla), following the Ascend port's structure. - deep_cluster: Neuron discovery (one DeepInfer device = one logical NeuronCore, NEURON_RT_VISIBLE_CORES, NUMA from neuron-ls). - PlatformNeuron: takes the Ascend (is_npu) kernel layouts, adds is_neuron; DEEPINFER_NEURON_DEVICE=cpu runs the same code on the host for correctness work. - neuron_ops/transfer_to_neuron: map "cuda" devices, torch.device and torch.cuda.* onto neuron:0 (counterpart of torch_npu transfer_to_npu). - C++ extension: -DWITH_NEURON host-only build; torch-allocated buffers and tensor-view slot copies instead of raw device-pointer copies. - Pure-torch drop-in vendor packages (Ascend API semantics): deep_gemm, flash_mla, deep_select, tile_kernels, tilelang (constants only). - tilelang_ops/neuron backend: KV cache / metadata / norm / compress kernels; CP, ZOS and VL entry points raise NotImplementedError. - Basic mode defaults on Neuron: eager, no DSpark/MTP drafting, no zero-overhead scheduling; MoE avoids the GPU-only mega kernel; mHC prenorm GEMM uses the Ascend single-split reduction. Not yet included: deep_ep, the sampling kernels, end-to-end harness. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
"""AWS Neuron (Trainium) discovery using device files and ``neuron-ls``.
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One DeepInfer device is one logical NeuronCore; visibility is controlled by
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``NEURON_RT_VISIBLE_CORES``. A trn2.48xlarge exposes 16 devices x 4 logical
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cores (LNC=2) = 64 devices.
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"""
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from __future__ import annotations
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import functools
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import json
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import re
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import subprocess
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from pathlib import Path
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_DEVICE_PATTERN = re.compile(r"^neuron(\d+)$")
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def _path(root: Path, absolute: str) -> Path:
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return root / absolute.lstrip("/")
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def _device_node_indices(*, _root: Path = Path("/")) -> tuple[int, ...]:
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device_dir = _path(_root, "/dev")
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if not device_dir.is_dir():
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return ()
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try:
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return tuple(sorted(int(m.group(1)) for p in device_dir.iterdir() if (m := _DEVICE_PATTERN.match(p.name)) is not None))
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except OSError as error:
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raise OSError(f"cannot enumerate {device_dir}: {error}") from error
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def is_available(*, _root: Path = Path("/")) -> bool:
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return bool(_device_node_indices(_root=_root))
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@functools.lru_cache(maxsize=1)
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def _neuron_ls() -> tuple[dict, ...]:
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try:
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output = subprocess.check_output(["neuron-ls", "-j"], text=True, stderr=subprocess.DEVNULL)
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except (FileNotFoundError, subprocess.CalledProcessError):
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return ()
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return tuple(json.loads(output))
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def discover_cores(*, _root: Path = Path("/")) -> list[tuple[int, int, int]]:
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"""Logical NeuronCores as ``(core_id, device_id, numa_node)``; NUMA is -1 when unknown."""
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cores: list[tuple[int, int, int]] = []
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if _root == Path("/"):
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for device in _neuron_ls():
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numa = int(device.get("numa_node", -1))
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cores.extend((int(core), int(device["neuron_device"]), numa) for core in device["neuroncore_ids"])
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if cores:
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return sorted(cores)
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# Fallback without neuron-ls: sysfs core_count is physical cores; trn2 defaults to LNC=2.
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lnc = 2
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for index in _device_node_indices(_root=_root):
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count_path = _path(_root, f"/sys/class/neuron_device/neuron{index}/core_count")
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physical = int(count_path.read_text().strip()) if count_path.is_file() else 8
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base = len(cores)
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cores.extend((base + i, index, -1) for i in range(physical // lnc))
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return cores
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def physical_device_count(*, _root: Path = Path("/")) -> int:
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return len(discover_cores(_root=_root))
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def device_numa_node(index: int, *, _root: Path = Path("/")) -> int:
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cores = discover_cores(_root=_root)
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if index >= len(cores):
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raise IndexError(f"no neuron core at index {index}")
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numa = cores[index][2]
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if numa < 0:
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raise RuntimeError(f"NUMA metadata is unavailable for neuron core {index}")
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return numa
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__all__ = ["device_numa_node", "discover_cores", "is_available", "physical_device_count"]
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