ViperEkura
75304d084d
- add skip_no_fp8 (CUDA + fp8_mm kernel + cc 8.9+) to tests/conftest.py - use skip_no_cuda / skip_no_kernel / skip_no_fp8 directly in test modules - drop _GPU alias and tests.extension.conftest re-exports - remove unused imports (Union in hf_adapter, make_grpo_config in data conftest)
69 lines
2.9 KiB
Python
69 lines
2.9 KiB
Python
"""Kernel-level mask dimension support (2D, 3D, 4D)."""
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import torch
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from astrai.extension.ops.attention import attn_prefill
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from tests.conftest import skip_no_kernel
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from tests.extension.conftest import D
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@skip_no_kernel
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def test_kernel_accepts_2d_mask():
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"""Kernel should accept 2D mask [batch, kv_len]."""
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batch, q_len, n_heads, n_kv_heads = 1, 8, 4, 1
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kv_len = 8
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q = torch.randn(batch, q_len, n_heads, D, device="cuda", dtype=torch.bfloat16)
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k = torch.randn(batch, kv_len, n_kv_heads, D, device="cuda", dtype=torch.bfloat16)
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v = torch.randn(batch, kv_len, n_kv_heads, D, device="cuda", dtype=torch.bfloat16)
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mask = torch.ones(batch, kv_len, dtype=torch.bool, device="cuda")
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mask[:, 4:] = False
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out = attn_prefill(q, k, v, mask=mask, is_causal=False)
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assert out.shape == (batch, q_len, n_heads, D)
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@skip_no_kernel
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def test_kernel_accepts_3d_mask():
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"""Kernel should accept 3D mask [batch, q_len, kv_len]."""
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batch, q_len, n_heads, n_kv_heads = 1, 8, 4, 1
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kv_len = 8
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q = torch.randn(batch, q_len, n_heads, D, device="cuda", dtype=torch.bfloat16)
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k = torch.randn(batch, kv_len, n_kv_heads, D, device="cuda", dtype=torch.bfloat16)
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v = torch.randn(batch, kv_len, n_kv_heads, D, device="cuda", dtype=torch.bfloat16)
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mask = torch.ones(batch, q_len, kv_len, dtype=torch.bool, device="cuda")
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out = attn_prefill(q, k, v, mask=mask, is_causal=False)
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assert out.shape == (batch, q_len, n_heads, D)
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@skip_no_kernel
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def test_kernel_accepts_4d_mask():
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"""Kernel should accept 4D mask [batch, n_heads, q_len, kv_len]."""
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batch, q_len, n_heads, n_kv_heads = 1, 8, 4, 1
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kv_len = 8
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q = torch.randn(batch, q_len, n_heads, D, device="cuda", dtype=torch.bfloat16)
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k = torch.randn(batch, kv_len, n_kv_heads, D, device="cuda", dtype=torch.bfloat16)
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v = torch.randn(batch, kv_len, n_kv_heads, D, device="cuda", dtype=torch.bfloat16)
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mask = torch.ones(batch, 1, q_len, kv_len, dtype=torch.bool, device="cuda")
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mask[:, :, :, 4:] = False
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out = attn_prefill(q, k, v, mask=mask, is_causal=False)
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assert out.shape == (batch, q_len, n_heads, D)
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@skip_no_kernel
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def test_4d_mask_matches_no_mask_when_all_true():
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"""A 4D all-True mask should produce the same output as no mask."""
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batch, q_len, n_heads, n_kv_heads = 1, 8, 4, 1
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kv_len = 8
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q = torch.randn(batch, q_len, n_heads, D, device="cuda", dtype=torch.bfloat16)
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k = torch.randn(batch, kv_len, n_kv_heads, D, device="cuda", dtype=torch.bfloat16)
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v = torch.randn(batch, kv_len, n_kv_heads, D, device="cuda", dtype=torch.bfloat16)
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out_no_mask = attn_prefill(q, k, v, mask=None, is_causal=False)
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mask = torch.ones(batch, 1, q_len, kv_len, dtype=torch.bool, device="cuda")
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out_with_mask = attn_prefill(q, k, v, mask=mask, is_causal=False)
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diff = (out_no_mask.float() - out_with_mask.float()).abs().max().item()
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assert diff == 0.0, f"4D all-True mask diff: {diff}"
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