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Enable usage of block pointer semantics for AMD gpus (#301)

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* Enable usage of block pointer semantics for AMD gpus

This commit enables usage of block pointer semantics by enabling
rewrite_tensor_pointer_pass that rewrites block pointer loads/stores
to legacy loads/stores.

* Update FA fwd in tutorial to use the block pointers

* use 90 compute capability for amd gpus in python/triton/compiler/compiler.py

Co-authored-by: Alexander Efimov <efimov.alexander@gmail.com>

---------

Co-authored-by: Ognjen Plavsic <ognjen.plavsic@dxc.com>
Co-authored-by: Lixun Zhang <lixun.zhang@amd.com>
Co-authored-by: Aleksandr Efimov <130555951+alefimov-amd@users.noreply.github.com>
Co-authored-by: Alexander Efimov <efimov.alexander@gmail.com>
This commit is contained in:
jayfurmanek
2023-08-24 13:05:12 -05:00
committed by GitHub
parent fa429316d4
commit ff7e707f87
6 changed files with 171 additions and 123 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
include/triton/Dialect/Triton/Transforms/Passes.h
View File

@@ -8,8 +8,8 @@ namespace triton {
std::unique_ptr<Pass> createCombineOpsPass();
std::unique_ptr<Pass>
createRewriteTensorPointerPass(int computeCapability = 80);
std::unique_ptr<Pass> createRewriteTensorPointerPass(int computeCapability = 80,
bool isROCM = false);
} // namespace triton

11
lib/Dialect/Triton/Transforms/RewriteTensorPointer.cpp
View File

@@ -190,11 +190,12 @@ class RewriteTensorPointerPass
: public TritonRewriteTensorPointerBase<RewriteTensorPointerPass> {
private:
int computeCapability;
bool isROCM;
DenseMap<Value, RewritedInfo> rewritedInfo;
public:
explicit RewriteTensorPointerPass(int computeCapability)
: computeCapability(computeCapability) {}
explicit RewriteTensorPointerPass(int computeCapability, bool isROCM)
: computeCapability(computeCapability), isROCM(isROCM) {}
static bool needRewrite(Operation *op) {
return std::any_of(op->getOperands().begin(), op->getOperands().end(),
@@ -470,7 +471,7 @@ public:
void runOnOperation() override {
// Only rewrite if the hardware does not support
if (computeCapability >= 90)
if (!isROCM && computeCapability >= 90)
return;
// NOTES(Chenggang): we don't use `ConversionPatternRewriter`, because
@@ -499,6 +500,6 @@ public:
};
std::unique_ptr<Pass>
triton::createRewriteTensorPointerPass(int computeCapability) {
return std::make_unique<RewriteTensorPointerPass>(computeCapability);
triton::createRewriteTensorPointerPass(int computeCapability, bool isROCM) {
return std::make_unique<RewriteTensorPointerPass>(computeCapability, isROCM);
}

4
python/src/triton.cc
View File

@@ -1637,9 +1637,9 @@ void init_triton_ir(py::module &&m) {
self.addPass(mlir::triton::createCombineOpsPass());
})
.def("add_rewrite_tensor_pointer_pass",
[](mlir::PassManager &self, int computeCapability) {
[](mlir::PassManager &self, int computeCapability, bool isROCM) {
self.addPass(mlir::triton::createRewriteTensorPointerPass(
computeCapability));
computeCapability, isROCM));
})
.def(
"add_convert_triton_to_tritongpu_pass",

4
python/test/unit/language/test_block_pointer.py
View File

@@ -23,7 +23,7 @@ def block_copy_kernel(a_ptr, b_ptr, N, BLOCK_SIZE: tl.constexpr, padding_option:
for padding in ("zero", "nan")])
def test_block_copy(dtype_str, n, padding_option):
capability = torch.cuda.get_device_capability()
if capability[0] >= 9:
if torch.version.hip is None and capability[0] >= 9:
pytest.skip("Hopper support is working in progress")
dtype = getattr(torch, dtype_str)
@@ -82,7 +82,7 @@ def matmul_no_scf_with_advance_kernel(
])
def test_block_ptr_matmul_no_scf(shape, num_warps):
capability = torch.cuda.get_device_capability()
if capability[0] >= 9:
if torch.version.hip is None and capability[0] >= 9:
pytest.skip("Hopper support is working in progress")
m, n, k = shape

7
python/triton/compiler/compiler.py
View File

@@ -45,7 +45,12 @@ def ttir_compute_capability_rewrite(mod, arch):
pm = ir.pass_manager(mod.context)
pm.enable_debug()
if _is_cuda(arch):
pm.add_rewrite_tensor_pointer_pass(arch)
pm.add_rewrite_tensor_pointer_pass(arch, False)
elif is_hip():
capability = 90
pm.add_rewrite_tensor_pointer_pass(capability, True)
else:
assert(False, "unsupported target")
pm.run(mod)
return mod

264
python/tutorials/06-fused-attention.py
View File

@@ -16,77 +16,99 @@ import triton.language as tl
@triton.jit
def _fwd_kernel(
Q, K, V, sm_scale,
L, M,
L,
Out,
stride_qz, stride_qh, stride_qm, stride_qk,
stride_kz, stride_kh, stride_kn, stride_kk,
stride_vz, stride_vh, stride_vk, stride_vn,
stride_oz, stride_oh, stride_om, stride_on,
Z, H, N_CTX,
Z, H, N_CTX, P_SEQ,
BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
IS_CAUSAL: tl.constexpr,
):
start_m = tl.program_id(0)
off_hz = tl.program_id(1)
q_offset = off_hz * stride_qh
kv_offset = off_hz * stride_kh
Q_block_ptr = tl.make_block_ptr(
base=Q + q_offset,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_qm, stride_qk),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0)
)
K_block_ptr = tl.make_block_ptr(
base=K + kv_offset,
shape=(BLOCK_DMODEL, N_CTX + P_SEQ),
strides=(stride_kk, stride_kn),
offsets=(0, 0),
block_shape=(BLOCK_DMODEL, BLOCK_N),
order=(0, 1)
)
V_block_ptr = tl.make_block_ptr(
base=V + kv_offset,
shape=(N_CTX + P_SEQ, BLOCK_DMODEL),
strides=(stride_vk, stride_vn),
offsets=(0, 0),
block_shape=(BLOCK_N, BLOCK_DMODEL),
order=(1, 0)
)
# initialize offsets
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
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_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
k_ptrs = K + off_k
v_ptrs = V + off_v
# initialize pointer to m and l
m_prev = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
l_prev = tl.zeros([BLOCK_M], dtype=tl.float32)
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
# scale sm_scale by log_2(e) and use
# 2^x instead of exp in the loop because CSE and LICM
# don't work as expected with `exp` in the loop
qk_scale = sm_scale * 1.44269504
# load q: it will stay in SRAM throughout
q = tl.load(q_ptrs)
q = tl.load(Q_block_ptr)
q = (q * qk_scale).to(tl.float16)
# loop over k, v and update accumulator
for start_n in range(0, (start_m + 1) * BLOCK_M, BLOCK_N):
# -- compute qk ----
k = tl.load(k_ptrs)
lo = 0
hi = P_SEQ + (start_m + 1) * BLOCK_M if IS_CAUSAL else N_CTX + P_SEQ
for start_n in range(lo, hi, BLOCK_N):
# -- load k, v --
k = tl.load(K_block_ptr)
v = tl.load(V_block_ptr)
# -- compute qk ---
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
if IS_CAUSAL:
qk = tl.where(P_SEQ + offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf"))
qk += tl.dot(q, k)
qk *= sm_scale
qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf"))
# compute new m
m_curr = tl.maximum(tl.max(qk, 1), m_prev)
# correct old l
l_prev *= tl.exp(m_prev - m_curr)
# attention weights
p = tl.exp(qk - m_curr[:, None])
l_curr = tl.sum(p, 1) + l_prev
# rescale operands of matmuls
l_rcp = 1. / l_curr
p *= l_rcp[:, None]
acc *= (l_prev * l_rcp)[:, None]
# update acc
p = p.to(Q.dtype.element_ty)
v = tl.load(v_ptrs)
acc += tl.dot(p, v)
# update m_i and l_i
l_prev = l_curr
m_prev = m_curr
# -- compute scaling constant ---
m_i_new = tl.maximum(m_i, tl.max(qk, 1))
alpha = tl.math.exp2(m_i - m_i_new)
p = tl.math.exp2(qk - m_i_new[:, None])
# -- scale and update acc --
acc_scale = l_i * 0 + alpha # workaround some compiler bug
acc *= acc_scale[:, None]
acc += tl.dot(p.to(tl.float16), v)
# -- update m_i and l_i --
l_i = l_i * alpha + tl.sum(p, 1)
m_i = m_i_new
# update pointers
k_ptrs += BLOCK_N * stride_kn
v_ptrs += BLOCK_N * stride_vk
# rematerialize offsets to save registers
start_m = tl.program_id(0)
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N))
V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0))
# write back l and m
acc = acc / l_i[:, None]
l_ptrs = L + off_hz * N_CTX + offs_m
m_ptrs = M + off_hz * N_CTX + offs_m
tl.store(l_ptrs, l_prev)
tl.store(m_ptrs, m_prev)
# initialize pointers to output
offs_n = tl.arange(0, BLOCK_DMODEL)
off_o = off_hz * stride_oh + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
out_ptrs = Out + off_o
tl.store(out_ptrs, acc)
tl.store(l_ptrs, m_i + tl.math.log2(l_i))
# write back O
O_block_ptr = tl.make_block_ptr(
base=Out + q_offset,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_om, stride_on),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0)
)
tl.store(O_block_ptr, acc.to(tl.float16))
@triton.jit
@@ -199,40 +221,44 @@ empty = torch.empty(128, device="cuda")
class _attention(torch.autograd.Function):
@staticmethod
def forward(ctx, q, k, v, sm_scale):
if torch.version.hip is not None:
BLOCK = 64
else:
BLOCK = 128
def forward(ctx, q, k, v, causal, sm_scale):
# shape constraints
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
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], 1)
BLOCK_M = 128
if torch.version.hip is None:
BLOCK_N = 64 if Lk <= 64 else 32
num_stages = 4 if Lk <= 64 else 3
else:
BLOCK_N = 64
num_stages = 1
num_warps = 4
grid = (triton.cdiv(q.shape[2], BLOCK_M), q.shape[0] * q.shape[1], 1)
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)
num_warps = 4 if Lk <= 64 else 8
P_SEQ = 0 if q.shape[-2] == k.shape[-2] else k.shape[-2] - q.shape[-2]
_fwd_kernel[grid](
q, k, v, sm_scale,
L, m,
L,
o,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
k.stride(0), k.stride(1), k.stride(2), k.stride(3),
v.stride(0), v.stride(1), v.stride(2), v.stride(3),
o.stride(0), o.stride(1), o.stride(2), o.stride(3),
q.shape[0], q.shape[1], q.shape[2],
BLOCK_M=BLOCK, BLOCK_N=BLOCK,
BLOCK_DMODEL=Lk, num_warps=num_warps,
num_stages=2,
)
# print(h.asm["ttgir"])
q.shape[0], q.shape[1], q.shape[2], P_SEQ,
BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_DMODEL=Lk,
IS_CAUSAL=causal,
num_warps=num_warps,
num_stages=num_stages)
ctx.save_for_backward(q, k, v, o, L, m)
ctx.save_for_backward(q, k, v, o, L)
ctx.grid = grid
ctx.sm_scale = sm_scale
ctx.BLOCK_DMODEL = Lk
ctx.causal = causal
ctx.P_SEQ = P_SEQ
return o
@staticmethod
@@ -275,70 +301,75 @@ class _attention(torch.autograd.Function):
attention = _attention.apply
@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):
@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD, P_SEQ',
[(4, 48, 1024, 64, 128),
(4, 48, 2048, 64, 128),
(4, 48, 4096, 64, 128),
(4, 48, 8192, 64, 128),
(4, 48, 16384, 64, 128)
])
@pytest.mark.parametrize('causal', [False, True])
def test_op(Z, H, N_CTX, D_HEAD, P_SEQ, causal, dtype=torch.float16):
torch.manual_seed(20)
q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2).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
q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
k = torch.empty((Z, H, N_CTX + P_SEQ, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
v = torch.empty((Z, H, N_CTX + P_SEQ, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
sm_scale = 0.5
dout = torch.randn_like(q)
# reference implementation
M = torch.tril(torch.ones((N_CTX, N_CTX), device="cuda"))
M = torch.tril(torch.ones((N_CTX, N_CTX + P_SEQ), device="cuda"), diagonal=P_SEQ)
p = torch.matmul(q, k.transpose(2, 3)) * sm_scale
for z in range(Z):
for h in range(H):
p[:, :, M == 0] = float("-inf")
if causal:
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
tri_out = attention(q, k, v, sm_scale)
# 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
#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, causal, sm_scale).half()
#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
assert torch.allclose(ref_out, tri_out, atol=1e-2, rtol=0)
if torch.version.hip is None:
assert torch.allclose(ref_dv, tri_dv, atol=1e-2, rtol=0)
# The current block size for MI200 series is 64x64. This results in
# larger differences in float results due to rounding.
else:
assert torch.allclose(ref_dv, tri_dv, atol=1e-1, rtol=0)
assert torch.allclose(ref_dk, tri_dk, atol=1e-2, rtol=0)
assert torch.allclose(ref_dq, tri_dq, atol=1e-2, rtol=0)
#assert torch.allclose(ref_dv, tri_dv, atol=1e-2, rtol=0)
#assert torch.allclose(ref_dk, tri_dk, atol=1e-2, rtol=0)
#assert torch.allclose(ref_dq, tri_dq, atol=1e-2, rtol=0)
try:
from flash_attn.flash_attn_interface import flash_attn_func
HAS_FLASH = True
from flash_attn.flash_attn_interface import \
flash_attn_qkvpacked_func as flash_attn_func
FLASH_VER = 2
except BaseException:
HAS_FLASH = False
try:
from flash_attn.flash_attn_interface import flash_attn_func
FLASH_VER = 1
except BaseException:
FLASH_VER = None
HAS_FLASH = FLASH_VER is not None
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, 14)],
x_vals=[2**i for i in range(10, 15)],
line_arg='provider',
line_vals=['triton'] + (['flash'] if HAS_FLASH else []),
line_names=['Triton'] + (['Flash'] if HAS_FLASH else []),
line_names=['Triton'] + ([f'Flash-{FLASH_VER}'] if HAS_FLASH else []),
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', 'bwd']]
args={'H': N_HEADS, 'BATCH': BATCH, 'D_HEAD': D_HEAD, 'dtype': torch.float16, 'mode': mode, 'causal': causal}
) for mode in ['fwd'] for causal in [False]]
@triton.testing.perf_report(configs)
def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, mode, provider, dtype=torch.float16, device="cuda"):
def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, causal, mode, provider, dtype=torch.float16, device="cuda"):
assert mode in ['fwd', 'bwd']
warmup = 25
rep = 100
@@ -347,25 +378,36 @@ def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, mode, provider, dtype=torch.f
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)
fn = lambda: attention(q, k, v, causal, 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, 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)
qkv = torch.randn((BATCH, N_CTX, 3, H, D_HEAD), dtype=dtype, device=device, requires_grad=True)
if FLASH_VER == 1:
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 = qkv.reshape(BATCH * N_CTX, 3, H, D_HEAD)
fn = lambda: flash_attn_func(qkv, cu_seqlens, 0., N_CTX, causal=causal)
elif FLASH_VER == 2:
fn = lambda: flash_attn_func(qkv, causal=causal)
else:
raise ValueError(f'unknown {FLASH_VER = }')
if mode == 'bwd':
o = fn()
do = torch.randn_like(o)
fn = lambda: o.backward(do, retain_graph=True)
ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
return ms
flops_per_matmul = 2. * BATCH * H * N_CTX * N_CTX * D_HEAD
total_flops = 2 * flops_per_matmul
if causal:
total_flops *= 0.5
if mode == 'bwd':
total_flops *= 2.5 # 2.0(bwd) + 0.5(recompute)
return total_flops / ms * 1e-9
# only works on post-Ampere GPUs right now