80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
import ast
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from pathlib import Path
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import logging
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import warnings as _warnings
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import torch
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def _find_vision():
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for p in [
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Path(__file__).resolve().parent / "unsloth" / "models" / "vision.py",
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Path(__file__).resolve().parents[1] / "unsloth" / "models" / "vision.py",
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Path("/mnt/disks/unslothai/ubuntu/workspace_25/github_review/unsloth-pr-5053-staging-3/unsloth/models/vision.py"),
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]:
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if p.exists():
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return p
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raise FileNotFoundError("vision.py not found")
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def _load_fns():
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tree = ast.parse(_find_vision().read_text())
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ns = {"torch": torch, "warnings": _warnings, "logger": logging.getLogger("test")}
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for node in tree.body:
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if isinstance(node, ast.FunctionDef) and node.name in {
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"_infer_device_map_from_loaded_model",
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"_attach_bnb_multidevice_hooks",
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}:
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exec(compile(ast.Module(body=[node], type_ignores=[]), str(_find_vision()), "exec"), ns)
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return ns["_infer_device_map_from_loaded_model"], ns["_attach_bnb_multidevice_hooks"]
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class _P:
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def __init__(self, dev):
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self.device = torch.device(dev) if isinstance(dev, str) else dev
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class _FakeMod:
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def __init__(self, params=None, buffers=None, children=None, hf_device_map=None):
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self._p = list(params or [])
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self._b = list(buffers or [])
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self._c = list(children or [])
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self.hf_device_map = hf_device_map
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def named_parameters(self, recurse=True, remove_duplicate=False):
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for n, d in self._p:
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yield n, _P(d)
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if recurse:
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for cn, cm in self._c:
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for pn, pp in cm.named_parameters(recurse=True, remove_duplicate=remove_duplicate):
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yield f"{cn}.{pn}", pp
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def parameters(self, recurse=True):
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for _, p in self.named_parameters(recurse=recurse):
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yield p
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def named_buffers(self, recurse=True):
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for n, d in self._b:
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yield n, _P(d)
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if recurse:
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for cn, cm in self._c:
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for bn, bb in cm.named_buffers(recurse=True):
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yield f"{cn}.{bn}", bb
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def named_children(self):
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yield from self._c
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def test_infer_three_level_deep_mixed():
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"""Split at the third level of nesting: the algorithm must recurse deep
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enough to distinguish grandchildren on different devices."""
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infer, _ = _load_fns()
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g1 = _FakeMod(params=[("w", "cuda:0")])
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g2 = _FakeMod(params=[("w", "cuda:1")])
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level2 = _FakeMod(children=[("g1", g1), ("g2", g2)])
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level1 = _FakeMod(children=[("l2", level2)])
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root = _FakeMod(children=[("l1", level1)])
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dm = infer(root)
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assert dm.get("l1.l2.g1") == torch.device("cuda", 0)
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assert dm.get("l1.l2.g2") == torch.device("cuda", 1)
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# Intermediate levels that are mixed must NOT collapse prematurely
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assert "l1" not in dm or len({dm.get("l1"), dm.get("l1.l2.g1")}) > 1
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