cf5f23996f
- apply_attention_backend now restores the native default when no backend is requested or a
kernel fails. diffusers keeps a process-wide active attention backend that
set_attention_backend updates, and a fresh transformer's processors follow it, so a load
that wanted native could silently inherit a backend (e.g. cuDNN) an earlier speed-profile
load pinned, breaking the bit-identical/off guarantee.
- select_attention_backend drops flash3/flash4 up front when the CUDA capability is below
Hopper/Blackwell. diffusers only checks the kernels package at set time, so an explicit
request on the wrong card set fine then crashed mid-generation; it now falls back to native.
- Add the sdpa alias to the attention_backend Literal so an API request with sdpa (already a
valid alias of native) is accepted instead of 422-rejected by Pydantic.
- Drop the dead replace('-','_') normalization (no alias uses dashes/underscores).
- perf_levers_probe.py output dir is now relative to the script, not a hardcoded path.
167 lines
7.2 KiB
Python
167 lines
7.2 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""Hermetic CPU tests for attention-backend selection. No torch/diffusers needed:
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``_is_cuda_nvidia`` is monkeypatched for the policy tests, and the apply path uses a fake
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transformer that records / raises on ``set_attention_backend``.
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"""
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from __future__ import annotations
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import types
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import pytest
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import core.inference.diffusion_attention as att
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from core.inference.diffusion_attention import (
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ATTN_AUTO,
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apply_attention_backend,
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normalize_attention_backend,
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select_attention_backend,
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)
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def _target(device = "cuda"):
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return types.SimpleNamespace(device = device)
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# ── normalize ────────────────────────────────────────────────────────────────────
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def test_normalize_defaults_and_aliases():
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assert normalize_attention_backend(None) == ATTN_AUTO
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assert normalize_attention_backend("") == ATTN_AUTO
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assert normalize_attention_backend("auto") == ATTN_AUTO
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assert normalize_attention_backend("CuDNN") == "cudnn"
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assert normalize_attention_backend("FLASH3") == "flash3"
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assert normalize_attention_backend("sdpa") == "sdpa"
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def test_normalize_rejects_unknown():
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with pytest.raises(ValueError):
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normalize_attention_backend("bogus")
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# dashes are no longer silently rewritten to underscores -> a dashed alias is rejected.
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with pytest.raises(ValueError):
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normalize_attention_backend("flash-3")
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def test_sdpa_alias_maps_to_native():
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# sdpa is an alias for native -> nothing to set on the dispatcher.
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assert select_attention_backend(_target(), "sdpa", speed_active = True) is None
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# ── select policy ─────────────────────────────────────────────────────────────────
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def test_auto_upgrades_to_cudnn_on_nvidia_when_speed_active(monkeypatch):
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monkeypatch.setattr(att, "_is_cuda_nvidia", lambda target: True)
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assert select_attention_backend(_target(), "auto", speed_active = True) == "_native_cudnn"
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def test_auto_stays_native_when_speed_off(monkeypatch):
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# off must stay bit-identical -> no backend change even on NVIDIA.
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monkeypatch.setattr(att, "_is_cuda_nvidia", lambda target: True)
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assert select_attention_backend(_target(), "auto", speed_active = False) is None
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def test_auto_stays_native_off_nvidia(monkeypatch):
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monkeypatch.setattr(att, "_is_cuda_nvidia", lambda target: False)
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assert select_attention_backend(_target(device = "mps"), "auto", speed_active = True) is None
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def test_explicit_backend_honored_regardless_of_speed(monkeypatch):
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monkeypatch.setattr(att, "_is_cuda_nvidia", lambda target: False)
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# Pin a high capability so the arch-gated flash4 isn't dropped by the runtime check.
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monkeypatch.setattr(att, "_cuda_capability", lambda: (10, 0))
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assert select_attention_backend(_target(), "sage", speed_active = False) == "sage"
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assert select_attention_backend(_target(), "flash4", speed_active = False) == "flash_4_hub"
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assert select_attention_backend(_target(), "cudnn", speed_active = False) == "_native_cudnn"
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def test_explicit_native_returns_none():
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# native is the default -> nothing to set.
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assert select_attention_backend(_target(), "native", speed_active = True) is None
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# ── arch gating (flash3/flash4 need a specific CUDA capability) ─────────────────────
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def test_flash3_dropped_below_hopper(monkeypatch):
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monkeypatch.setattr(att, "_cuda_capability", lambda: (8, 9)) # Ada / consumer
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assert select_attention_backend(_target(), "flash3", speed_active = False) is None
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def test_flash4_dropped_below_blackwell(monkeypatch):
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monkeypatch.setattr(att, "_cuda_capability", lambda: (9, 0)) # Hopper, but FA4 needs SM100
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assert select_attention_backend(_target(), "flash4", speed_active = False) is None
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# flash3 still allowed on Hopper.
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assert select_attention_backend(_target(), "flash3", speed_active = False) == "_flash_3_hub"
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def test_arch_gate_does_not_block_when_capability_unknown(monkeypatch):
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# Unknown capability (e.g. no CUDA) must not block -> diffusers' set-time check still guards.
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monkeypatch.setattr(att, "_cuda_capability", lambda: None)
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assert select_attention_backend(_target(), "flash4", speed_active = False) == "flash_4_hub"
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# ── apply ─────────────────────────────────────────────────────────────────────────
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class _FakeTransformer:
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def __init__(self, *, fail = False):
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self.fail = fail
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self.set_to = None
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def set_attention_backend(self, name):
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if self.fail:
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raise RuntimeError(f"{name} kernel unavailable")
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self.set_to = name
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def _pipe(transformer):
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return types.SimpleNamespace(transformer = transformer)
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def test_apply_none_leaves_native_when_global_already_native(monkeypatch):
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# Global already native -> no redundant set call, returns None.
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monkeypatch.setattr(att, "_active_attention_backend", lambda: "native")
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t = _FakeTransformer()
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assert apply_attention_backend(_pipe(t), None) is None
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assert t.set_to is None
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def test_apply_none_restores_native_when_global_polluted(monkeypatch):
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# A previous load pinned cuDNN process-wide; a native load must reset it so it can't
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# silently inherit cuDNN (the bit-identical/off guarantee).
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monkeypatch.setattr(att, "_active_attention_backend", lambda: "_native_cudnn")
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t = _FakeTransformer()
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assert apply_attention_backend(_pipe(t), None) is None
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assert t.set_to == "native"
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def test_apply_sets_backend():
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t = _FakeTransformer()
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engaged = apply_attention_backend(_pipe(t), "_native_cudnn")
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assert engaged == "_native_cudnn" and t.set_to == "_native_cudnn"
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def test_apply_falls_back_on_unavailable_kernel(monkeypatch):
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# an unavailable kernel must not fail the load -> returns None (diffusers default).
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monkeypatch.setattr(att, "_active_attention_backend", lambda: "native")
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t = _FakeTransformer(fail = True)
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assert apply_attention_backend(_pipe(t), "sage") is None
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def test_apply_failed_kernel_restores_native_when_polluted(monkeypatch):
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# Requested kernel fails AND the global is polluted: restore native before returning.
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monkeypatch.setattr(att, "_active_attention_backend", lambda: "_native_cudnn")
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class _FailOnceTransformer:
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def __init__(self):
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self.calls = []
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def set_attention_backend(self, name):
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self.calls.append(name)
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if name != "native":
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raise RuntimeError(f"{name} kernel unavailable")
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t = _FailOnceTransformer()
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assert apply_attention_backend(_pipe(t), "sage") is None
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assert t.calls == ["sage", "native"]
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def test_apply_handles_missing_method():
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pipe = types.SimpleNamespace(transformer = types.SimpleNamespace())
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assert apply_attention_backend(pipe, "_native_cudnn") is None
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