Instructions to use Frosty40/hydra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use Frosty40/hydra with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("Frosty40/hydra") - Notebooks
- Google Colab
- Kaggle
| from __future__ import annotations | |
| import pytest | |
| pytest.importorskip("torch") | |
| pytest.importorskip("triton") | |
| def test_dense_causal_csr_cpu_contract(): | |
| from hydra.csr import build_dense_causal_csr | |
| row_ptr, col_idx, seq_lens = build_dense_causal_csr( | |
| batch_size=1, | |
| num_heads=2, | |
| seq_len=128, | |
| block_size=32, | |
| device="cpu", | |
| ) | |
| assert row_ptr.shape == (1, 2, 5) | |
| assert seq_lens.tolist() == [128] | |
| assert row_ptr[0, 0].tolist() == [0, 1, 3, 6, 10] | |
| assert col_idx[0, 0].tolist() == [0, 0, 1, 0, 1, 2, 0, 1, 2, 3] | |
| def test_sliding_window_csr_keeps_diagonal_last(): | |
| from hydra.csr import build_sliding_window_csr | |
| row_ptr, col_idx, _ = build_sliding_window_csr( | |
| window=64, | |
| seq_len=128, | |
| block_size=32, | |
| batch_size=1, | |
| num_heads=1, | |
| device="cpu", | |
| ) | |
| rp = row_ptr[0, 0].tolist() | |
| ci = col_idx[0, 0].tolist() | |
| for q_block in range(4): | |
| lo, hi = rp[q_block], rp[q_block + 1] | |
| assert ci[hi - 1] == q_block | |
| assert all(k < q_block for k in ci[lo : hi - 1]) | |