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83 lines
3.5 KiB
83 lines
3.5 KiB
2 years ago
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import torch
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import pytest
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from einops import rearrange
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from colossalai.kernel.cuda_native.flash_attention import flash_attention, triton_flash_attention, TRITON_AVALIABLE
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def baseline_attention(Z, N_CTX, H, q, k, v, sm_scale):
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M = torch.tril(torch.ones((N_CTX, N_CTX), device="cuda"))
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p = torch.matmul(q, k.transpose(2, 3)) * sm_scale
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for z in range(Z):
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for h in range(H):
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p[:, :, M == 0] = float("-inf")
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p = torch.softmax(p.float(), dim=-1).half()
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ref_out = torch.matmul(p, v)
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return ref_out
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@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(3, 2, 16, 8)])
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def test_triton_flash_attention(Z, H, N_CTX, D_HEAD, dtype=torch.float16):
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torch.manual_seed(20)
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q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_()
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k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_()
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v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_()
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sm_scale = 0.3
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dout = torch.randn_like(q)
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ref_out = baseline_attention(Z, N_CTX, H, q, k, v, sm_scale)
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ref_out.backward(dout)
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ref_dv, v.grad = v.grad.clone(), None
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ref_dk, k.grad = k.grad.clone(), None
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ref_dq, q.grad = q.grad.clone(), None
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# triton implementation
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if TRITON_AVALIABLE:
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tri_out = triton_flash_attention(q, k, v, sm_scale)
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tri_out.backward(dout)
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tri_dv, v.grad = v.grad.clone(), None
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tri_dk, k.grad = k.grad.clone(), None
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tri_dq, q.grad = q.grad.clone(), None
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# compare
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assert torch.allclose(ref_out, tri_out, atol=1e-3)
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assert torch.allclose(ref_dv, tri_dv, atol=1e-3)
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assert torch.allclose(ref_dk, tri_dk, atol=1e-3)
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assert torch.allclose(ref_dq, tri_dq, atol=1e-3)
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else:
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try:
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tri_out = flash_attention(q, k, v, sm_scale, Z, N_CTX)
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except RuntimeError:
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pass
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else:
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raise TypeError("Error type not match!")
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@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(3, 2, 16, 8)])
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def test_flash_attention(Z, H, N_CTX, D_HEAD, dtype=torch.float16):
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torch.manual_seed(20)
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q = torch.randn((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_()
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k = torch.randn((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_()
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v = torch.randn((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_()
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sm_scale = 0.3
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dout = torch.randn_like(q)
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# reference implementation
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ref_out = baseline_attention(Z, N_CTX, H, q, k, v, sm_scale)
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ref_out.backward(dout)
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ref_dv, v.grad = v.grad.clone(), None
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ref_dk, k.grad = k.grad.clone(), None
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ref_dq, q.grad = q.grad.clone(), None
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# flash implementation
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q, k, v = map(lambda x: rearrange(x, 'z h n d -> (z n) h d'), [q, k, v])
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tri_out = flash_attention(q, k, v, sm_scale, Z, N_CTX)
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dout = rearrange(dout, 'z h n d -> (z n) h d').detach()
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tri_out.backward(dout, retain_graph=True)
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tri_dq, tri_dk, tri_dv, = torch.autograd.grad(tri_out, (q, k, v), dout)
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tri_out, tri_dq, tri_dk, tri_dv = map(lambda x: rearrange(x, '(z n) h d -> z h n d', z=Z), (tri_out, tri_dq, tri_dk, tri_dv))
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# compare
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assert torch.allclose(ref_out, tri_out, atol=1e-3)
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assert torch.allclose(ref_dv, tri_dv, atol=1e-3)
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assert torch.allclose(ref_dk, tri_dk, atol=1e-3)
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assert torch.allclose(ref_dq, tri_dq, atol=1e-3)
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