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[Fix] Use current Paddle device for Triton cache allocation #6

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[Fix] Use current Paddle device for Triton cache allocation #6

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17 changes: 17 additions & 0 deletions fla/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,26 @@

from pkgutil import extend_path

import paddle

@chatgpt-codex-connector chatgpt-codex-connector Bot Aug 4, 2026

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P1 Badge Guard the Paddle-only compatibility hook

When Paddle is not separately installed—as with every documented [cuda], [rocm], [xpu], [npu], and [cpu] installation, since pyproject.toml does not declare it—this top-level import raises ModuleNotFoundError before any FLA API can load; this is reproducible with a plain import fla. Install the compatibility override only when running through Paddle compatibility rather than making Paddle mandatory for every backend.

AGENTS.md reference: AGENTS.md:L40-L41

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__path__ = extend_path(__path__, __name__)
__version__ = "0.5.2"


def _torch_compat_empty(*args, **kwargs):
if kwargs.get("device") == "cuda":
del kwargs["device"]
return paddle.empty(*args, **kwargs)
Comment on lines +16 to +19


paddle.compat.proxy._extend_torch_proxy_overrides(
{
"torch.empty": paddle.compat.proxy.RawOverriddenAttribute(
_torch_compat_empty
),
}
)

from fla import modules, ops # noqa: E402

__all__ = ["modules", "ops"]