Instructions to use SZLHOLDINGS/YARQA-ATTN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use SZLHOLDINGS/YARQA-ATTN with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("SZLHOLDINGS/YARQA-ATTN") - Notebooks
- Google Colab
- Kaggle
File size: 1,486 Bytes
621477d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | # SPDX-FileCopyrightText: 2026 SZL Holdings
# SPDX-License-Identifier: Apache-2.0
"""API edges. No benchmarks."""
from __future__ import annotations
import pytest
import torch
@pytest.mark.kernels_ci
def test_rejects_n_canals_gt_seq(kernel_mod, cpu_device):
q = k = v = torch.randn(1, 1, 4, 8, device=cpu_device)
with pytest.raises(ValueError, match="cannot exceed"):
kernel_mod.yarqa_attn(q, k, v, 8)
@pytest.mark.kernels_ci
def test_rejects_n_canals_zero(kernel_mod, cpu_device):
q = k = v = torch.randn(1, 1, 4, 8, device=cpu_device)
with pytest.raises(ValueError, match="n_canals"):
kernel_mod.yarqa_attn(q, k, v, 0)
@pytest.mark.kernels_ci
def test_rejects_mismatched_shapes(kernel_mod, cpu_device):
q = torch.randn(1, 1, 4, 8, device=cpu_device)
k = torch.randn(1, 1, 8, 8, device=cpu_device)
v = torch.randn(1, 1, 8, 8, device=cpu_device)
with pytest.raises(ValueError, match="shape"):
kernel_mod.yarqa_attn(q, k, v, 2)
@pytest.mark.kernels_ci
def test_rejects_rank_3(kernel_mod, cpu_device):
q = k = v = torch.randn(2, 4, 8, device=cpu_device)
with pytest.raises(ValueError, match="rank-4"):
kernel_mod.yarqa_attn(q, k, v, 2)
@pytest.mark.kernels_ci
def test_shape_preserved(kernel_mod, cpu_device):
q = k = v = torch.randn(2, 3, 12, 16, device=cpu_device)
y = kernel_mod.yarqa_attn(q, k, v, 3)
assert y.shape == q.shape
assert y.dtype == q.dtype
assert y.device == q.device
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