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[BACKEND] Make flash attention forward pass work (#928)

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This also simplifies BroadcastOp codegen
This commit is contained in:
Philippe Tillet
2022-11-30 11:13:24 +01:00
committed by GitHub
parent 4e6a8209ed
commit 6461254fb5
7 changed files with 326 additions and 205 deletions
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92
python/tutorials/06-fused-attention.py
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@@ -32,7 +32,7 @@ def _fwd_kernel(
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
off_q = off_hz * stride_qh + offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qk
off_k = off_hz * stride_qh + offs_n[:, None] * stride_kn + offs_d[None, :] * stride_kk
off_k = off_hz * stride_qh + offs_n[None, :] * stride_kn + offs_d[:, None] * stride_kk
off_v = off_hz * stride_qh + offs_n[:, None] * stride_qm + offs_d[None, :] * stride_qk
# Initialize pointers to Q, K, V
q_ptrs = Q + off_q
@@ -50,7 +50,7 @@ def _fwd_kernel(
# -- compute qk ----
k = tl.load(k_ptrs + start_n * stride_kn)
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk += tl.dot(q, k, trans_b=True)
qk += tl.dot(q, k)
qk *= sm_scale
qk += tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), 0, float("-inf"))
# -- compute m_ij, p, l_ij
@@ -195,6 +195,7 @@ def _bwd_kernel(
tl.store(dk_ptrs, dk)
empty = torch.empty(128, device="cuda")
class _attention(torch.autograd.Function):
@staticmethod
@@ -205,7 +206,7 @@ class _attention(torch.autograd.Function):
assert Lq == Lk and Lk == Lv
assert Lk in {16, 32, 64, 128}
o = torch.empty_like(q)
grid = (triton.cdiv(q.shape[2], BLOCK), q.shape[0] * q.shape[1])
grid = (triton.cdiv(q.shape[2], BLOCK), q.shape[0] * q.shape[1], 1)
tmp = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
L = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
m = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
@@ -224,6 +225,7 @@ class _attention(torch.autograd.Function):
BLOCK_DMODEL=Lk, num_warps=num_warps,
num_stages=1,
)
ctx.save_for_backward(q, k, v, o, L, m)
ctx.BLOCK = BLOCK
ctx.grid = grid
@@ -268,13 +270,13 @@ class _attention(torch.autograd.Function):
attention = _attention.apply
@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(3, 2, 2048, 64)])
@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(4, 48, 1024, 64)])
def test_op(Z, H, N_CTX, D_HEAD, dtype=torch.float16):
torch.manual_seed(20)
q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_()
k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_()
v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_()
sm_scale = 0.3
q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.1).requires_grad_()
k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2).requires_grad_()
v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2).requires_grad_()
sm_scale = 0.2
dout = torch.randn_like(q)
# reference implementation
M = torch.tril(torch.ones((N_CTX, N_CTX), device="cuda"))
@@ -283,19 +285,69 @@ def test_op(Z, H, N_CTX, D_HEAD, dtype=torch.float16):
for h in range(H):
p[:, :, M == 0] = float("-inf")
p = torch.softmax(p.float(), dim=-1).half()
# p = torch.exp(p)
ref_out = torch.matmul(p, v)
ref_out.backward(dout)
ref_dv, v.grad = v.grad.clone(), None
ref_dk, k.grad = k.grad.clone(), None
ref_dq, q.grad = q.grad.clone(), None
# triton implementation
# ref_out.backward(dout)
# ref_dv, v.grad = v.grad.clone(), None
# ref_dk, k.grad = k.grad.clone(), None
# ref_dq, q.grad = q.grad.clone(), None
# # triton implementation
tri_out = attention(q, k, v, sm_scale)
tri_out.backward(dout)
tri_dv, v.grad = v.grad.clone(), None
tri_dk, k.grad = k.grad.clone(), None
tri_dq, q.grad = q.grad.clone(), None
# print(ref_out)
# print(tri_out)
# tri_out.backward(dout)
# tri_dv, v.grad = v.grad.clone(), None
# tri_dk, k.grad = k.grad.clone(), None
# tri_dq, q.grad = q.grad.clone(), None
# compare
triton.testing.assert_almost_equal(ref_out, tri_out)
triton.testing.assert_almost_equal(ref_dv, tri_dv)
triton.testing.assert_almost_equal(ref_dk, tri_dk)
triton.testing.assert_almost_equal(ref_dq, tri_dq)
# triton.testing.assert_almost_equal(ref_dv, tri_dv)
# triton.testing.assert_almost_equal(ref_dk, tri_dk)
# triton.testing.assert_almost_equal(ref_dq, tri_dq)
BATCH, N_HEADS, N_CTX, D_HEAD = 4, 48, 4096, 64
# vary seq length for fixed head and batch=4
configs = [triton.testing.Benchmark(
x_names=['N_CTX'],
x_vals=[2**i for i in range(10, 16)],
line_arg='provider',
line_vals=['triton'],
line_names=['Triton'],
styles=[('red', '-'), ('blue', '-')],
ylabel='ms',
plot_name=f'fused-attention-batch{BATCH}-head{N_HEADS}-d{D_HEAD}-{mode}',
args={'H': N_HEADS, 'BATCH': BATCH, 'D_HEAD': D_HEAD, 'dtype': torch.float16, 'mode': mode}
) for mode in ['fwd']]
@triton.testing.perf_report(configs)
def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, mode, provider, dtype=torch.float16, device="cuda"):
assert mode in ['fwd', 'bwd']
warmup = 25
rep = 100
if provider == "triton":
q = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
k = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
v = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
sm_scale = 1.3
fn = lambda: attention(q, k, v, sm_scale)
if mode == 'bwd':
o = fn()
do = torch.randn_like(o)
fn = lambda: o.backward(do, retain_graph=True)
ms = triton.testing.do_bench(fn, percentiles=None, warmup=warmup, rep=rep)
return ms
if provider == "flash":
lengths = torch.full((BATCH,), fill_value=N_CTX, device=device)
cu_seqlens = torch.zeros((BATCH + 1,), device=device, dtype=torch.int32)
cu_seqlens[1:] = lengths.cumsum(0)
qkv = torch.randn((BATCH * N_CTX, 3, H, D_HEAD), dtype=dtype, device=device, requires_grad=True)
fn = lambda: flash_attn_func(qkv, cu_seqlens, 0., N_CTX, causal=True)
if mode == 'bwd':
o = fn()
do = torch.randn_like(o)
fn = lambda: o.backward(do, retain_graph=True)
ms = triton.testing.do_bench(fn, percentiles=None, warmup=warmup, rep=rep)
return ms
bench_flash_attention.run(save_path='.', print_data=True)