ViperEkura
d6f757dc13
- delete the warp-tiled kernel and both per-shape (N,K) selector tables; block size is 256 threads everywhere except M=8 with N*K <= 12 MiB, which keeps a 128-thread CTA
- HBM-streaming measurements (weight copies rotated through L2, the real decode regime) show the variants within ~3% on L20 because the kernel is bandwidth-bound; the retired tables were tuned against an L2-resident loop and sometimes picked the slowest variant ((2048,8192) M=8: coop128 6% slower than coop256)
- a shape no longer switches kernels (and accumulation order) with M, removing one shape-dependent nondeterminism source
- remove the stale split-K launcher comment
- move bf16_gemv.cu and bf16_swiglu.cu from csrc/kernels/gemv/ to csrc/kernels/ beside rotary_emb.cu; the family keeps no shared headers
- rename test_bf16_gemv_matches_half_cta_edge_bands to test_bf16_gemv_matches_m8_edge_bands and update docs/developer/cuda_kernels.md
Benchmark: L20 (sm_89), PyTorch 2.11.0+cu128, interleaved CUDA-event timing with rotated weight copies exceeding the 96MB L2; variant spread <=3% across 14 shapes x M in {1,2,4,8}, and the retained rule wins 5-9% at M=8 small weights ((512,3584), (1536,1536), (6912,1536))
224 lines
6.9 KiB
Python
224 lines
6.9 KiB
Python
import pytest
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import torch
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import torch.nn.functional as F
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from astrai.extension import bf16_gemv, is_available
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GEMV_AVAILABLE = (
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torch.cuda.is_available()
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and is_available("bf16_gemv")
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and torch.cuda.get_device_capability() >= (8, 0)
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)
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skip_no_gemv = pytest.mark.skipif(
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not GEMV_AVAILABLE,
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reason="BF16 GEMV requires a built kernel and compute capability 8.0+",
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)
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@skip_no_gemv
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@pytest.mark.parametrize(
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"n,k",
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[(256, 1536), (1536, 1536), (6912, 1536), (1536, 6912), (100000, 1536)],
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)
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def test_bf16_gemv_matches_linear_shape_families(n, k):
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torch.manual_seed(17)
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x = torch.randn(k, device="cuda", dtype=torch.bfloat16)
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weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
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actual = bf16_gemv(x, weight)
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expected = F.linear(x, weight)
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assert actual.shape == (n,)
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
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@skip_no_gemv
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@pytest.mark.parametrize("m", [2, 3, 4, 5, 6, 7, 8])
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@pytest.mark.parametrize("n,k", [(256, 1536), (1536, 1536), (1536, 6912)])
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def test_bf16_gemv_matches_small_decode_batches(m, n, k):
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torch.manual_seed(19 + m)
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x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
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weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
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actual = bf16_gemv(x, weight)
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expected = F.linear(x, weight)
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assert actual.shape == (m, n)
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.5)
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@skip_no_gemv
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@pytest.mark.parametrize("m", [2, 4])
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@pytest.mark.parametrize(
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"n,k",
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[
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(1024, 4096),
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(4096, 4096),
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(11008, 4096),
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(4096, 11008),
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(14336, 4096),
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(4096, 14336),
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(5120, 5120),
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(13824, 5120),
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(5120, 13824),
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(16384, 4096),
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(4096, 16384),
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(512, 3584),
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(3584, 3584),
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(18944, 3584),
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(3584, 18944),
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(1024, 8192),
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(8192, 8192),
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(28672, 8192),
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(8192, 28672),
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(2048, 2048),
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(8192, 2048),
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(2048, 8192),
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],
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)
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def test_bf16_gemv_matches_common_transformer_shapes(m, n, k):
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torch.manual_seed(2026 + m + n + k)
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x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
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weight = torch.empty(n, k, device="cuda", dtype=torch.bfloat16)
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weight.normal_(mean=0.0, std=0.02)
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actual = bf16_gemv(x, weight)
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expected = F.linear(x, weight)
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
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@skip_no_gemv
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@pytest.mark.parametrize(
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"m,n,k",
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[
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(1, 8192, 2048),
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(8, 4096, 11008),
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(8, 512, 3584),
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(8, 1024, 8192),
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(8, 2048, 8192),
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],
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)
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def test_bf16_gemv_matches_m8_edge_bands(m, n, k):
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torch.manual_seed(2026 + m + n + k)
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x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
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weight = torch.empty(n, k, device="cuda", dtype=torch.bfloat16)
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weight.normal_(mean=0.0, std=0.02)
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actual = bf16_gemv(x, weight)
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expected = F.linear(x, weight)
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
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@skip_no_gemv
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def test_bf16_gemv_preserves_singleton_batch_and_fuses_bias():
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torch.manual_seed(23)
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x = torch.randn(1, 1536, device="cuda", dtype=torch.bfloat16)
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weight = torch.randn(1536, 1536, device="cuda", dtype=torch.bfloat16)
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bias = torch.randn(1536, device="cuda", dtype=torch.bfloat16)
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actual = bf16_gemv(x, weight, bias)
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expected = F.linear(x, weight, bias)
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assert actual.shape == (1, 1536)
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
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@skip_no_gemv
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def test_bf16_gemv_small_batch_fuses_bias():
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torch.manual_seed(25)
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x = torch.randn(4, 1536, device="cuda", dtype=torch.bfloat16)
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weight = torch.randn(256, 1536, device="cuda", dtype=torch.bfloat16)
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bias = torch.randn(256, device="cuda", dtype=torch.bfloat16)
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actual = bf16_gemv(x, weight, bias)
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expected = F.linear(x, weight, bias)
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
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@skip_no_gemv
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def test_bf16_gemv_uses_current_stream():
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x = torch.randn(1536, device="cuda", dtype=torch.bfloat16)
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weight = torch.randn(256, 1536, device="cuda", dtype=torch.bfloat16)
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stream = torch.cuda.Stream()
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with torch.cuda.stream(stream):
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actual = bf16_gemv(x, weight)
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expected = F.linear(x, weight)
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stream.synchronize()
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
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@skip_no_gemv
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def test_bf16_gemv_cuda_graph_replay():
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torch.manual_seed(29)
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x = torch.randn(1536, device="cuda", dtype=torch.bfloat16)
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weight = torch.randn(1536, 1536, device="cuda", dtype=torch.bfloat16)
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for _ in range(3):
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bf16_gemv(x, weight)
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graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(graph):
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actual = bf16_gemv(x, weight)
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x.copy_(torch.randn_like(x))
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graph.replay()
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expected = F.linear(x, weight)
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
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@skip_no_gemv
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@pytest.mark.parametrize("n,k", [(64, 7), (64, 12), (33, 100), (256, 1534)])
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def test_bf16_gemv_handles_unaligned_k(n, k):
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torch.manual_seed(29)
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x = torch.randn(k, device="cuda", dtype=torch.bfloat16)
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weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
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actual = bf16_gemv(x, weight)
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expected = F.linear(x, weight)
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
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x3 = torch.randn(3, k, device="cuda", dtype=torch.bfloat16)
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actual3 = bf16_gemv(x3, weight)
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torch.testing.assert_close(actual3, F.linear(x3, weight), rtol=0.02, atol=0.5)
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@skip_no_gemv
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def test_bf16_gemv_small_batch_cuda_graph_replay():
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torch.manual_seed(31)
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x = torch.randn(8, 1536, device="cuda", dtype=torch.bfloat16)
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weight = torch.randn(1536, 1536, device="cuda", dtype=torch.bfloat16)
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for _ in range(3):
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bf16_gemv(x, weight)
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graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(graph):
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actual = bf16_gemv(x, weight)
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x.copy_(torch.randn_like(x))
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graph.replay()
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expected = F.linear(x, weight)
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torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
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@skip_no_gemv
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@pytest.mark.parametrize(
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"make_args,error",
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[
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(
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lambda: (
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torch.randn(9, 16, device="cuda", dtype=torch.bfloat16),
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torch.randn(8, 16, device="cuda", dtype=torch.bfloat16),
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),
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"M must",
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),
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(
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lambda: (
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torch.randn(16, device="cuda", dtype=torch.float16),
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torch.randn(8, 16, device="cuda", dtype=torch.float16),
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),
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"bf16",
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),
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(
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lambda: (
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torch.randn(
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16, device="cuda", dtype=torch.bfloat16, requires_grad=True
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),
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torch.randn(8, 16, device="cuda", dtype=torch.bfloat16),
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),
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"autograd",
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),
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],
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)
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def test_bf16_gemv_rejects_unsupported_inputs(make_args, error):
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with pytest.raises(RuntimeError, match=error):
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bf16_gemv(*make_args())
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