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enginex-hygon-vllm/vllm/attention/ops/flash_attn_triton_mqa_gqa.py

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init src 0.9.2
2026-01-09 15:09:53 +08:00
#!/usr/bin/env python
"""
Fused Attention
===============
This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao (https://tridao.me/publications/flash2/flash2.pdf)
Credits: OpenAI kernel team, AMD ML Frameworks Triton team
Features supported:
1) Fwd with causal masking
2) Any sequence lengths without padding (currently fwd kernel only)
3) Support for different sequence lengths for q and k
4) Nested tensor API currently does not support dropout or bias.
Not currently supported:
1) Non power of two head dims
"""
import argparse
import pytest
import random
import sys
import torch
import triton
import triton.language as tl
torch_dtype:tl.constexpr = torch.float16
TORCH_HAS_FP8E5 = hasattr(torch, 'float8_e5m2fnuz')
if TORCH_HAS_FP8E5:
torch_dtype:tl.constexpr = torch.float8_e5m2fnuz
class MetaData():
cu_seqlens_q = None
cu_seqlens_k = None
max_seqlens_q = 0
max_seqlens_k = 0
bias = None
alibi_slopes = None
causal = False
num_contexts = 0
varlen = False
dropout_p, return_encoded_softmax = 0.0, False
def __init__(self, sm_scale=1.0, causal=False, dropout_p=0.0, return_encoded_softmax=False):
self.sm_scale = sm_scale
self.causal = causal
self.dropout_p = dropout_p
self.return_encoded_softmax = return_encoded_softmax
def set_varlen_params(self, cu_seqlens_q, cu_seqlens_k):
self.varlen = True
self.cu_seqlens_q = cu_seqlens_q
self.cu_seqlens_k = cu_seqlens_k
# Without "varlen", there should still be one sequence.
assert len(cu_seqlens_q) >= 2
assert len(cu_seqlens_q) == len(cu_seqlens_k)
self.num_contexts = len(cu_seqlens_q) - 1
for i in range (0, self.num_contexts):
self.max_seqlens_q = max(cu_seqlens_q[i+1].item() - cu_seqlens_q[i].item(), self.max_seqlens_q)
self.max_seqlens_k = max(cu_seqlens_k[i+1].item() - cu_seqlens_k[i].item(), self.max_seqlens_k)
def need_bias(self, bias, batch, nheads, seqlen_q, seqlen_k):
assert bias.is_cuda
assert bias.dim() == 4
assert bias.shape[0] == 1
assert bias.shape[2:] == (seqlen_q, seqlen_k)
self.bias = bias
def need_alibi(self, alibi_slopes, batch, nheads):
assert alibi_slopes.is_cuda
assert alibi_slopes.dim() == 2
assert alibi_slopes.shape[0] == batch
assert alibi_slopes.shape[1] == nheads
self.alibi_slopes = alibi_slopes
def need_causal(self):
self.causal = True
def need_dropout(dropout_p, return_encoded_softmax):
self.dropout_p = dropout_p
self.return_encoded_softmax = return_encoded_softmax
def check_args(self, q, k, v, o):
assert q.dim() == k.dim() and q.dim() == v.dim()
if self.varlen:
assert q.dim() == 3
total_q, nheads_q, head_size = q.shape
total_k, nheads_k, _ = k.shape
assert self.cu_seqlens_q is not None
assert self.cu_seqlens_k is not None
assert len(self.cu_seqlens_q) == len(self.cu_seqlens_k)
# TODO: Remove once bias is supported with varlen
assert self.bias == None
# TODO:Remove once dropout is supported with varlen
assert self.dropout_p == 0.0
assert not self.return_encoded_softmax
else:
assert q.dim() == 4
batch, nheads_q, seqlen_q, head_size = q.shape
_, nheads_k, seqlen_k, _ = k.shape
assert self.max_seqlens_q > 0 and self.max_seqlens_k > 0
assert self.cu_seqlens_q is None and self.cu_seqlens_k is None
assert k.shape == v.shape
assert q.shape[-1] == k.shape[-1] and q.shape[-1] == v.shape[-1]
# TODO: Change assert if we support qkl f8 and v f16
assert q.dtype == k.dtype and q.dtype == v.dtype
assert head_size <= 256
assert o.shape == q.shape
assert (nheads_q % nheads_k) == 0
@triton.jit
def cdiv_fn(x,y):
return (x + y - 1) // y
@triton.jit
def max_fn(x, y):
return tl.math.max(x, y)
@triton.jit
def dropout_offsets(philox_seed, philox_offset, dropout_p, m, n, stride):
ms = tl.arange(0, m)
ns = tl.arange(0, n)
return philox_offset + ms[:, None] * stride + ns[None, :]
@triton.jit
def dropout_rng(philox_seed, philox_offset, dropout_p, m, n, stride):
rng_offsets = dropout_offsets(philox_seed, philox_offset, dropout_p, m, n, stride).to(tl.uint32)
# TODO: use tl.randint for better performance
return tl.rand(philox_seed, rng_offsets)
@triton.jit
def dropout_mask(philox_seed, philox_offset, dropout_p, m, n, stride):
rng_output = dropout_rng(philox_seed, philox_offset, dropout_p, m, n, stride)
rng_keep = rng_output > dropout_p
return rng_keep
@triton.jit
def load_fn(block_ptr, first, second, pad):
if first and second:
tensor = tl.load(block_ptr, boundary_check=(0,1), padding_option=pad)
elif first:
tensor = tl.load(block_ptr, boundary_check=(0,), padding_option=pad)
elif second:
tensor = tl.load(block_ptr, boundary_check=(1,), padding_option=pad)
else:
tensor = tl.load(block_ptr)
return tensor
@triton.jit
def print_gpu(prefix, val=None):
if (tl.program_id(0) == 0) and ((tl.program_id(1) == 0) and (tl.program_id(2) == 0)):
if val is not None:
tl.device_print(prefix, val)
else:
tl.device_print(prefix)
@triton.jit
def compute_alibi_block(alibi_slope, seqlen_q, seqlen_k, offs_m, offs_n, transpose = False):
# when seqlen_k and seqlen_q are different we want the diagonal to stick to the bottom right of the attention matrix
# for casual mask we want something like this where (1 is kept and 0 is masked)
# seqlen_q = 2 and seqlen_k = 5
# 1 1 1 1 0
# 1 1 1 1 1
# seqlen_q = 5 and seqlen_k = 2
# 0 0
# 0 0
# 0 0
# 1 0
# 1 1
# for alibi the diagonal is 0 indicating no penalty for attending to that spot and increasing penalty for attending further from the diagonal
# e.g. alibi_slope = 1, seqlen_q = 2, seqlen_k = 5, offs_m = [0, 1, 2, 3], offs_n = [0, 1, 2, 3, 4], transpose = False
# 1. offs_m[:,None] = [[0],
# [1],
# 2. offs_m[:,None] + seqlen_k = [[5],
# [6],
# 3. offs_m[:,None] + seqlen_k - seqlen_q = [[3],
# [4],
# 4. offs_m[:,None] + seqlen_k - seqlen_q - offs_n[None,:] = [[3], - [[0, 1, 2, 3, 4]] = [[ 3, 2, 1, 0,-1],
# [4], [ 4, 3, 2, 1, 0]]
# 5. -1 * alibi_slope * tl.abs(relative_pos_block) = [[ -3, -2, -1, 0,-1],
# [ -4, -3, -2, -1, 0]],
relative_pos_block = offs_m[:,None] + seqlen_k - seqlen_q - offs_n[None,:]
alibi_block = -1 * alibi_slope * tl.abs(relative_pos_block)
if transpose:
return alibi_block.T
else:
return alibi_block
@triton.jit
def _attn_fwd_inner(
acc, l_i, m_i, q,
K_block_ptr, V_block_ptr,
start_m,
actual_seqlen_k,
actual_seqlen_q,
dropout_p,
philox_seed,
batch_philox_offset,
encoded_softmax_block_ptr,
block_min, block_max,
offs_n_causal,
masked_blocks,
n_extra_tokens,
bias_ptr,
alibi_slope,
IS_CAUSAL: tl.constexpr,
BLOCK_M: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
OFFS_M: tl.constexpr,
OFFS_N: tl.constexpr,
PRE_LOAD_V: tl.constexpr,
MASK_STEPS: tl.constexpr,
ENABLE_DROPOUT: tl.constexpr,
RETURN_ENCODED_SOFTMAX: tl.constexpr,
PADDED_HEAD: tl.constexpr
):
# loop over k, v, and update accumulator
for start_n in range (block_min, block_max, BLOCK_N):
# For padded blocks, we will overrun the tensor size if
# we load all BLOCK_N. For others, the blocks are all within range.
k = load_fn(K_block_ptr, PADDED_HEAD, MASK_STEPS and (n_extra_tokens != 0), "zero")
if PRE_LOAD_V:
v = load_fn(V_block_ptr, MASK_STEPS and (n_extra_tokens != 0), PADDED_HEAD, "zero")
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
# We start from end of seqlen_k so only the first iteration would need
# to be checked for padding if it is not a multiple of block_n
# TODO: This can be optimized to only be true for the padded block.
if MASK_STEPS:
# If this is the last block / iteration, we want to
# mask if the sequence length is not a multiple of block size
# a solution is to always do BLOCK_M // BLOCK_N + 1 steps if not is_modulo_mn.
# last step might get wasted but that is okay. check if this masking works For
# that case.
if (start_n + BLOCK_N == block_max) and (n_extra_tokens != 0):
boundary_m = tl.full([BLOCK_M], actual_seqlen_k, dtype=tl.int32)
size_n = start_n + OFFS_N[None,:]
mask = size_n < boundary_m[:,None]
qk = tl.where(mask, qk, float("-inf"))
if IS_CAUSAL:
causal_boundary = start_n + offs_n_causal
causal_mask = OFFS_M[:, None] >= causal_boundary[None, :]
qk = tl.where(causal_mask, qk, float("-inf"))
# -- compute qk ----
qk += tl.dot(q, k)
if bias_ptr is not None:
bias = load_fn(bias_ptr, False, MASK_STEPS and (n_extra_tokens != 0), "zero")
# While bias is added after multiplying qk with sm_scale,
# our optimization to use 2^x instead of e^x results in an additional
# scale factor of log2(e) which we must also multiply the bias with.
qk += (bias * 1.44269504089)
if alibi_slope is not None:
# Compute the global position of each token within the sequence
global_m_positions = start_m*BLOCK_M + tl.arange(0, BLOCK_M)
global_n_positions = start_n + tl.arange(0, BLOCK_N)
alibi_block = compute_alibi_block(alibi_slope, actual_seqlen_q, actual_seqlen_k, global_m_positions, global_n_positions)
qk += (alibi_block * 1.44269504089) # scale factor of log2(e)
# softmax
m_ij = tl.maximum(m_i, tl.max(qk,1))
qk = qk - m_ij[:, None]
p = tl.math.exp2(qk)
# CAVEAT: Must update l_ij before applying dropout
l_ij = tl.sum(p, 1)
if ENABLE_DROPOUT:
philox_offset = batch_philox_offset + start_m * BLOCK_M * actual_seqlen_k + start_n - BLOCK_N
keep = dropout_mask(philox_seed, philox_offset, dropout_p, BLOCK_M, BLOCK_N, actual_seqlen_k)
if RETURN_ENCODED_SOFTMAX:
tl.store(encoded_softmax_block_ptr, tl.where(keep, p, -p).to(encoded_softmax_block_ptr.type.element_ty))
p = tl.where(keep, p, 0.0)
elif RETURN_ENCODED_SOFTMAX:
tl.store(encoded_softmax_block_ptr, p.to(encoded_softmax_block_ptr.type.element_ty))
# -- update output accumulator --
alpha = tl.math.exp2(m_i - m_ij)
acc = acc * alpha[:, None]
if not PRE_LOAD_V:
v = load_fn(V_block_ptr, MASK_STEPS and (n_extra_tokens != 0), PADDED_HEAD, "zero")
# -- update m_i and l_i
l_i = l_i * alpha + l_ij
# update m_i and l_i
m_i = m_ij
acc += tl.dot(p.to(V_block_ptr.type.element_ty), v)
V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0))
K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N))
if bias_ptr is not None:
bias_ptr = tl.advance(bias_ptr, (0, BLOCK_N))
if RETURN_ENCODED_SOFTMAX:
encoded_softmax_block_ptr = tl.advance(encoded_softmax_block_ptr, (0, BLOCK_N))
return acc, l_i, m_i
@triton.autotune(
configs=[
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'waves_per_eu': 0, 'PRE_LOAD_V': False}, num_stages=1, num_warps=8),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'waves_per_eu': 0, 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'waves_per_eu': 0, 'PRE_LOAD_V': False}, num_stages=1, num_warps=8),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 0, 'PRE_LOAD_V': True}, num_stages=1, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 0, 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'waves_per_eu': 0, 'PRE_LOAD_V': True}, num_stages=2, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'waves_per_eu': 0, 'PRE_LOAD_V': False}, num_stages=2, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 16, 'waves_per_eu': 0, 'PRE_LOAD_V': False}, num_stages=1, num_warps=8),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 64, 'waves_per_eu': 0, 'PRE_LOAD_V': False}, num_stages=1, num_warps=8),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 64, 'waves_per_eu': 0, 'PRE_LOAD_V': True}, num_stages=1, num_warps=4),
triton.Config({'BLOCK_M': 32, 'BLOCK_N': 32, 'waves_per_eu': 0, 'PRE_LOAD_V': False}, num_stages=1, num_warps=8),
# TODO: This config fails with head_size not pow2 with data mismatches. Check why.
# triton.Config({'BLOCK_M': 32, 'BLOCK_N': 16, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
# triton.Config({'BLOCK_M': 16, 'BLOCK_N': 16, 'waves_per_eu': 0, 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
],
key=['IS_CAUSAL', 'dropout_p', 'BLOCK_DMODEL'],
# use_cuda_graph=True,
)
@triton.jit
def attn_fwd(
Q, K, V, bias, sm_scale, 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,
stride_bz, stride_bh, stride_bm, stride_bn,
stride_az, stride_ah,
cu_seqlens_q, cu_seqlens_k,
dropout_p, philox_seed, philox_offset_base, encoded_softmax,
alibi_slopes,
HQ: tl.constexpr, HK:tl.constexpr,
ACTUAL_BLOCK_DMODEL:tl.constexpr,
MAX_SEQLENS_Q:tl.constexpr, MAX_SEQLENS_K:tl.constexpr,
VARLEN: tl.constexpr,
IS_CAUSAL: tl.constexpr,
BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr,
PRE_LOAD_V: tl.constexpr,
BIAS_TYPE: tl.constexpr,
ENABLE_DROPOUT: tl.constexpr,
RETURN_ENCODED_SOFTMAX: tl.constexpr,
USE_ALIBI: tl.constexpr,
BATCH_SIZE: tl.constexpr,
):
start_m = tl.program_id(0)
off_h_q = tl.program_id(1)
off_z = tl.program_id(2)
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
if VARLEN:
cu_seqlens_q_start = tl.load(cu_seqlens_q + off_z)
cu_seqlens_q_end = tl.load(cu_seqlens_q + off_z + 1)
seqlen_q = cu_seqlens_q_end - cu_seqlens_q_start
# We have a one-size-fits-all grid in id(0). Some seqlens might be too
# small for all start_m so for those we return early.
if start_m * BLOCK_M > seqlen_q:
return
cu_seqlens_k_start = tl.load(cu_seqlens_k + off_z)
cu_seqlens_k_end = tl.load(cu_seqlens_k + off_z + 1)
seqlen_k = cu_seqlens_k_end - cu_seqlens_k_start
else:
cu_seqlens_q_start = 0
cu_seqlens_k_start = 0
seqlen_q = MAX_SEQLENS_Q
seqlen_k = MAX_SEQLENS_K
# Now we compute whether we need to exit early due to causal masking.
# This is because for seqlen_q > seqlen_k, M rows of the attn scores
# are completely masked, resulting in 0s written to the output, and
# inf written to LSE. We don't need to do any GEMMs in this case.
# This block of code determines what N is, and if this WG is operating
# on those M rows.
n_blocks = cdiv_fn(seqlen_k, BLOCK_N)
if (IS_CAUSAL):
# If seqlen_q == seqlen_k, the attn scores are a square matrix.
# If seqlen_q != seqlen_k, attn scores are rectangular which means
# the causal mask boundary is bottom right aligned, and ends at either
# the top edge (seqlen_q < seqlen_k) or left edge.
# This captures the decrease in n_blocks if we have a rectangular attn matrix
n_blocks_seqlen = cdiv_fn(
(start_m + 1) * BLOCK_M + seqlen_k - seqlen_q,
BLOCK_N
)
# This is what adjusts the block_max for the current WG, only
# if IS_CAUSAL. Otherwise we want to always iterate through all n_blocks
n_blocks = min(n_blocks, n_blocks_seqlen)
# If we have no blocks after adjusting for seqlen deltas, this WG is part of
# the blocks that are all 0. We exit early.
if n_blocks <= 0:
o_offset = off_z * stride_oz + cu_seqlens_q_start * stride_om + off_h_q * stride_oh
O_block_ptr = tl.make_block_ptr(
base=Out + o_offset,
shape=(seqlen_q, BLOCK_DMODEL),
strides=(stride_om, stride_on),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0)
)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=Out.type.element_ty)
# We still need to write 0s to the result
tl.store(O_block_ptr, acc.to(Out.type.element_ty), boundary_check=(0,1))
l_ptrs = L + off_z * HQ * MAX_SEQLENS_Q + off_h_q * MAX_SEQLENS_Q + offs_m
# We store inf to LSE, not -inf because in the bwd pass, we subtract this
# from qk which makes it -inf, such that exp(qk - inf) = 0 for these masked blocks.
l = tl.full([BLOCK_M], value=float("inf"), dtype=tl.float32)
tl.store(l_ptrs, l)
# TODO: Should dropout and return encoded softmax be handled here too?
return
# If MQA / GQA, set the K and V head offsets appropriately.
GROUP_SIZE: tl.constexpr = HQ // HK
if GROUP_SIZE != 1:
off_h_k = off_h_q // GROUP_SIZE
else:
off_h_k = off_h_q
need_padding = False
n_extra_tokens = 0
if seqlen_k < BLOCK_N:
need_padding = True
n_extra_tokens = BLOCK_N - seqlen_k
elif seqlen_k % BLOCK_N:
need_padding = True
n_extra_tokens = seqlen_k % BLOCK_N
PADDED_HEAD:tl.constexpr = (ACTUAL_BLOCK_DMODEL != BLOCK_DMODEL)
# Compute pointers for all the tensors used in this kernel.
q_offset = off_z * stride_qz + off_h_q * stride_qh + cu_seqlens_q_start * stride_qm
Q_block_ptr = tl.make_block_ptr(
base=Q + q_offset,
shape=(seqlen_q, ACTUAL_BLOCK_DMODEL),
strides=(stride_qm, stride_qk),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0)
)
k_offset = off_z * stride_kz + off_h_k * stride_kh + cu_seqlens_k_start * stride_kn
K_block_ptr = tl.make_block_ptr(
base=K + k_offset,
shape=(ACTUAL_BLOCK_DMODEL, seqlen_k),
strides=(stride_kk, stride_kn),
offsets=(0, 0),
block_shape=(BLOCK_DMODEL, BLOCK_N),
order=(0, 1)
)
v_offset = off_z * stride_vz + off_h_k * stride_vh + cu_seqlens_k_start * stride_vk
V_block_ptr = tl.make_block_ptr(
base=V + v_offset,
shape=(seqlen_k, ACTUAL_BLOCK_DMODEL),
strides=(stride_vk, stride_vn),
offsets=(0, 0),
block_shape=(BLOCK_N, BLOCK_DMODEL),
order=(1, 0)
)
if BIAS_TYPE != 0:
b_offset = off_h_q * stride_bh # Note: this might get large enough to overflow on some configs
bias_ptr = tl.make_block_ptr(
base=bias + b_offset,
shape=(seqlen_q, seqlen_k),
strides=(stride_bm, stride_bn),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_N),
order=(1, 0),
)
else:
bias_ptr = None
if USE_ALIBI:
a_offset = off_z * stride_az + off_h_q * stride_ah
alibi_slope = tl.load(alibi_slopes + a_offset)
else:
alibi_slope = None
if ENABLE_DROPOUT:
batch_philox_offset = philox_offset_base + off_hz * seqlen_q * seqlen_k
else:
batch_philox_offset = 0
# We can ask to return the dropout mask without actually doing any dropout. In
# this case, we return an invalid pointer so indicate the mask is not valid.
# TODO: Fix encoded softmax. It currently uses just h_q in the base offset.
if RETURN_ENCODED_SOFTMAX:
encoded_softmax_block_ptr = tl.make_block_ptr(
base=encoded_softmax + off_h_q * seqlen_q * seqlen_k,
shape=(seqlen_q, seqlen_k),
strides=(seqlen_k, 1),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_N),
order=(1, 0)
)
else:
encoded_softmax_block_ptr = 0
# initialize pointer to m and l
m_i = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
l_i = tl.full([BLOCK_M], 1.0, dtype=tl.float32)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
# scale sm_scale by log_2(e) and use 2^x in the loop as we do not
# have native e^x support in HW.
qk_scale = sm_scale * 1.44269504089
# Q is loaded once at the beginning and shared by all N blocks.
q = load_fn(Q_block_ptr, True, PADDED_HEAD, "zero")
q = (q * qk_scale).to(Q_block_ptr.type.element_ty)
# Here we compute how many full and masked blocks we have.
padded_block_k = n_extra_tokens != 0
is_modulo_mn = not padded_block_k and (seqlen_q % BLOCK_M == 0)
if IS_CAUSAL:
# There are always at least BLOCK_M // BLOCK_N masked blocks.
# Additionally there might be one more due to dissimilar seqlens.
masked_blocks = BLOCK_M // BLOCK_N + (not is_modulo_mn)
else:
# Padding on Q does not need to be masked in the FA loop.
masked_blocks = padded_block_k
# if IS_CAUSAL, not is_modulo_mn does not always result in an additional block.
# In this case we might exceed n_blocks so pick the min.
masked_blocks = min(masked_blocks, n_blocks)
n_full_blocks = n_blocks - masked_blocks
block_min = 0
block_max = n_blocks * BLOCK_N
# Compute for full blocks. Here we set causal to false regardless of its actual
# value because there is no masking. Similarly we do not need padding.
if n_full_blocks > 0:
block_max = (n_blocks - masked_blocks) * BLOCK_N
acc, l_i, m_i = _attn_fwd_inner(
acc, l_i, m_i, q, K_block_ptr, V_block_ptr,
start_m, seqlen_k, seqlen_q,
dropout_p, philox_seed, batch_philox_offset, encoded_softmax_block_ptr,
# _, _, offs_n_causal, masked_blocks, n_extra_tokens, _
block_min, block_max, 0, 0, 0, bias_ptr, alibi_slope,
# IS_CAUSAL, ....
False, BLOCK_M, BLOCK_DMODEL, BLOCK_N, offs_m, offs_n,
# _, MASK_STEPS, ...
PRE_LOAD_V, False, ENABLE_DROPOUT, RETURN_ENCODED_SOFTMAX, PADDED_HEAD
)
block_min = block_max
block_max = n_blocks * BLOCK_N
tl.debug_barrier()
# Remaining blocks, if any, are full / not masked.
if (masked_blocks > 0):
if IS_CAUSAL:
offs_n_causal = offs_n + (seqlen_q - seqlen_k)
else:
offs_n_causal = 0
K_block_ptr = tl.advance(K_block_ptr, (0, n_full_blocks*BLOCK_N))
V_block_ptr = tl.advance(V_block_ptr, (n_full_blocks*BLOCK_N, 0))
if bias_ptr is not None:
bias_ptr = tl.advance(bias_ptr, (0, n_full_blocks*BLOCK_N))
if RETURN_ENCODED_SOFTMAX:
encoded_softmax_block_ptr = tl.advance(encoded_softmax_block_ptr,
(0, n_full_blocks))
acc, l_i, m_i = _attn_fwd_inner(
acc, l_i, m_i, q, K_block_ptr, V_block_ptr,
start_m, seqlen_k, seqlen_q,
dropout_p, philox_seed, batch_philox_offset, encoded_softmax_block_ptr,
block_min, block_max, offs_n_causal, masked_blocks, n_extra_tokens, bias_ptr, alibi_slope,
IS_CAUSAL, BLOCK_M, BLOCK_DMODEL, BLOCK_N, offs_m, offs_n,
# _, MASK_STEPS, ...
PRE_LOAD_V, True, ENABLE_DROPOUT, RETURN_ENCODED_SOFTMAX, PADDED_HEAD
)
# epilogue
acc = acc / l_i[:, None]
if ENABLE_DROPOUT:
acc = acc / (1 - dropout_p)
# If seqlen_q > seqlen_k but the delta is not a multiple of BLOCK_M,
# then we have one block with a row of all NaNs which come from computing
# softmax over a row of all -infs (-inf - inf = NaN). We check for that here
# and store 0s where there are NaNs as these rows should've been zeroed out.
end_m_idx = (start_m + 1) * BLOCK_M
start_m_idx = start_m * BLOCK_M
causal_start_idx = seqlen_q - seqlen_k
acc = acc.to(Out.type.element_ty)
if IS_CAUSAL:
if causal_start_idx > start_m_idx and causal_start_idx < end_m_idx:
out_mask_boundary = tl.full((BLOCK_DMODEL,), causal_start_idx, dtype=tl.int32)
mask_m_offsets = start_m_idx + tl.arange(0, BLOCK_M)
out_ptrs_mask = mask_m_offsets[:, None] >= out_mask_boundary[None, :]
z = 0.0
acc = tl.where(out_ptrs_mask, acc, z.to(acc.type.element_ty))
# write back LSE
l_ptrs = L + off_z * HQ * MAX_SEQLENS_Q + off_h_q * MAX_SEQLENS_Q + offs_m
# If seqlen_q not multiple of BLOCK_M, we need to mask out the last few rows.
# This is only true for the last M block. For others, overflow_size will be -ve
overflow_size = end_m_idx - seqlen_q
if overflow_size > 0:
boundary = tl.full((BLOCK_M,), BLOCK_M - overflow_size, dtype=tl.int32)
# This is a > check because mask being 0 blocks the store.
l_ptrs_mask = boundary > tl.arange(0, BLOCK_M)
tl.store(l_ptrs, m_i + tl.math.log2(l_i), mask=l_ptrs_mask)
else:
tl.store(l_ptrs, m_i + tl.math.log2(l_i))
# write back O
o_offset = off_z * stride_oz + cu_seqlens_q_start * stride_om + off_h_q * stride_oh
O_block_ptr = tl.make_block_ptr(
base=Out + o_offset,
shape=(seqlen_q, ACTUAL_BLOCK_DMODEL),
strides=(stride_om, stride_on),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0)
)
# Need boundary check on this to make sure the padding from the
# Q and KV tensors in both dims are not part of what we store back.
# TODO: Do the boundary check optionally.
tl.store(O_block_ptr, acc, boundary_check=(0,1))
@triton.jit
def _attn_bwd_preprocess(
Out, DO,
Delta,
stride_oz, stride_oh, stride_om, stride_on,
stride_doz, stride_doh, stride_dom, stride_don,
seqlen_q,
head_dim,
BLOCK_M: tl.constexpr,
D_HEAD: tl.constexpr,
):
# off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
# off_n = tl.arange(0, D_HEAD)
off_m = tl.program_id(0) * BLOCK_M
off_h = tl.program_id(1) # head index
off_z = tl.program_id(2) # batch index
num_h = tl.num_programs(1)
o_offset = off_h * stride_oh + off_z * stride_oz
O_block_ptr = tl.make_block_ptr(
base=Out + o_offset,
shape=(seqlen_q, head_dim),
strides=(stride_om, stride_on),
offsets=(off_m, 0),
block_shape=(BLOCK_M, D_HEAD),
order=(1, 0)
)
do_offset = off_h * stride_doh + off_z * stride_doz
DO_block_ptr = tl.make_block_ptr(
base=DO + do_offset,
shape=(seqlen_q, head_dim),
strides=(stride_dom, stride_don),
offsets=(off_m, 0),
block_shape=(BLOCK_M, D_HEAD),
order=(1, 0)
)
# load
# o = tl.load(Out + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
# do = tl.load(DO + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
o = tl.load(O_block_ptr, boundary_check=(0,1), padding_option="zero").to(tl.float32)
do = tl.load(DO_block_ptr, boundary_check=(0,1), padding_option="zero").to(tl.float32)
# compute
delta = tl.sum(o * do, axis=1)
# write-back, shape (q.shape[0] * q.shape[1], q.shape[2])
off_zh = off_z * num_h + off_h * 1
# Check for OOB accesses
delta_ptrs = Delta + off_zh * seqlen_q + off_m + tl.arange(0, BLOCK_M)
overflow = off_m + BLOCK_M - seqlen_q
if overflow > 0:
boundary = tl.full((BLOCK_M, ), BLOCK_M - overflow, dtype=tl.int32)
mask = boundary > tl.arange(0, BLOCK_M)
tl.store(delta_ptrs, delta, mask=mask)
else:
tl.store(delta_ptrs, delta)
@triton.jit
def _bwd_kernel_dk_dv(
dk, dv,
Q, k, v, sm_scale, alibi_slope,
DO,
M, D,
# shared by Q/K/V/DO.
stride_tok, stride_d,
H, N_CTX, BLOCK_M1: tl.constexpr,
BLOCK_N1: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
# Filled in by the wrapper.
start_n, start_m, num_steps,
MASK: tl.constexpr):
offs_m = start_m + tl.arange(0, BLOCK_M1)
offs_n = start_n + tl.arange(0, BLOCK_N1)
offs_k = tl.arange(0, BLOCK_DMODEL)
QT_block_ptr = tl.make_block_ptr(
base=Q,
shape=(BLOCK_DMODEL, N_CTX),
strides=(stride_d, stride_tok),
offsets=(0, start_m),
block_shape=(BLOCK_DMODEL, BLOCK_M1),
order=(0,1)
)
DO_block_ptr = tl.make_block_ptr(
base=DO,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_tok, stride_d),
offsets=(start_m, 0),
block_shape=(BLOCK_M1, BLOCK_DMODEL),
order=(1,0)
)
# BLOCK_N1 must be a multiple of BLOCK_M1, otherwise the code wouldn't work.
tl.static_assert(BLOCK_N1 % BLOCK_M1 == 0)
curr_m = start_m
step_m = BLOCK_M1
for blk_idx in range(num_steps):
qT = tl.load(QT_block_ptr)
# Load m before computing qk to reduce pipeline stall.
offs_m = curr_m + tl.arange(0, BLOCK_M1)
m = tl.load(M + offs_m)
kqT = tl.dot(k, qT)
if alibi_slope is not None:
alibi_block = compute_alibi_block(alibi_slope, N_CTX, N_CTX, offs_m, offs_n, True)
kqT += alibi_block * 1.44269504089
pT = tl.math.exp2(kqT - m[None, :])
# Autoregressive masking.
if MASK:
mask = (offs_m[None, :] >= offs_n[:, None])
pT = tl.where(mask, pT, 0.0)
do = tl.load(DO_block_ptr)
# Compute dV.
ppT = pT
ppT = ppT.to(tl.float16)
dv += tl.dot(ppT, do)
# D (= delta) is pre-divided by ds_scale.
Di = tl.load(D + offs_m)
# Compute dP and dS.
dpT = tl.dot(v, tl.trans(do))
dsT = pT * (dpT - Di[None, :])
dsT = dsT.to(tl.float16)
dk += tl.dot(dsT, tl.trans(qT))
# Increment pointers.
curr_m += step_m
QT_block_ptr = tl.advance(QT_block_ptr, (0, step_m))
DO_block_ptr = tl.advance(DO_block_ptr, (step_m, 0))
return dk, dv
@triton.jit
def _bwd_kernel_dq(dq, q, K, V,
do, m, D, alibi_slope,
# shared by Q/K/V/DO.
stride_tok, stride_d,
H, N_CTX,
BLOCK_M2: tl.constexpr,
BLOCK_N2: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
# Filled in by the wrapper.
start_m, start_n, num_steps,
MASK: tl.constexpr):
offs_m = start_m + tl.arange(0, BLOCK_M2)
offs_n = start_n + tl.arange(0, BLOCK_N2)
offs_k = tl.arange(0, BLOCK_DMODEL)
KT_block_ptr = tl.make_block_ptr(
base=K,
shape=(BLOCK_DMODEL, N_CTX),
strides=(stride_d, stride_tok),
offsets=(0, start_n),
block_shape=(BLOCK_DMODEL, BLOCK_N2),
order=(0, 1)
)
VT_block_ptr = tl.make_block_ptr(
base=V,
shape=(BLOCK_DMODEL, N_CTX),
strides=(stride_d, stride_tok),
offsets=(0, start_n),
block_shape=(BLOCK_DMODEL, BLOCK_N2),
order=(0, 1)
)
# D (= delta) is pre-divided by ds_scale.
Di = tl.load(D + offs_m)
# BLOCK_M2 must be a multiple of BLOCK_N2, otherwise the code wouldn't work.
tl.static_assert(BLOCK_M2 % BLOCK_N2 == 0)
curr_n = start_n
step_n = BLOCK_N2
for blk_idx in range(num_steps):
kT = tl.load(KT_block_ptr)
qk = tl.dot(q, kT)
if alibi_slope is not None:
alibi_block = compute_alibi_block(alibi_slope, N_CTX, N_CTX, offs_m, offs_n)
qk += alibi_block * 1.44269504089
p = tl.math.exp2(qk - m)
# Autoregressive masking.
if MASK:
offs_n = curr_n + tl.arange(0, BLOCK_N2)
mask = (offs_m[:, None] >= offs_n[None, :])
p = tl.where(mask, p, 0.0)
# Compute dP and dS.
vT = tl.load(VT_block_ptr)
dp = tl.dot(do, vT).to(tl.float32)
ds = p * (dp - Di[:, None])
ds = ds.to(tl.float16)
# Compute dQ.0.
# NOTE: We need to de-scale dq in the end, because kT was pre-scaled.
dq += tl.dot(ds, tl.trans(kT))
# Increment pointers.
curr_n += step_n
KT_block_ptr = tl.advance(KT_block_ptr, (0, step_n))
VT_block_ptr = tl.advance(VT_block_ptr, (0, step_n))
return dq
@triton.jit
def _attn_bwd(Q, K, V, sm_scale, alibi_slopes,
DO,
DQ, DK, DV,
M, D,
# shared by Q/K/V/DO.
stride_z, stride_h, stride_tok, stride_d,
# H = 16, N_CTX = 1024
H, N_CTX,
BLOCK_DMODEL: tl.constexpr,
BLOCK_M1: tl.constexpr,
BLOCK_N1: tl.constexpr,
BLOCK_M2: tl.constexpr,
BLOCK_N2: tl.constexpr,
BLK_SLICE_FACTOR: tl.constexpr,
USE_ALIBI: tl.constexpr):
LN2: tl.constexpr = 0.6931471824645996 # = ln(2)
bhid = tl.program_id(2)
off_chz = (bhid * N_CTX).to(tl.int64)
adj = (stride_h * (bhid % H) + stride_z * (bhid // H)).to(tl.int64)
pid = tl.program_id(0)
# offset pointers for batch/head
Q += adj
K += adj
V += adj
DO += adj
DQ += adj
DK += adj
DV += adj
M += off_chz
D += off_chz
offs_k = tl.arange(0, BLOCK_DMODEL)
start_n = pid * BLOCK_N1
# This assignment is important. It is what allows us to pick the diagonal
# blocks. Later, when we want to do the lower triangular, we update start_m
# after the first dkdv call.
start_m = start_n
MASK_BLOCK_M1: tl.constexpr = BLOCK_M1 // BLK_SLICE_FACTOR
offs_n = start_n + tl.arange(0, BLOCK_N1)
dv = tl.zeros([BLOCK_N1, BLOCK_DMODEL], dtype=tl.float32)
dk = tl.zeros([BLOCK_N1, BLOCK_DMODEL], dtype=tl.float32)
K_block_ptr = tl.make_block_ptr(
base=K,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_tok, stride_d),
offsets=(start_n, 0),
block_shape=(BLOCK_N1, BLOCK_DMODEL),
order=(1, 0),
)
V_block_ptr = tl.make_block_ptr(
base=V,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_tok, stride_d),
offsets=(start_n, 0),
block_shape=(BLOCK_N1, BLOCK_DMODEL),
order=(1, 0),
)
# load K and V: they stay in SRAM throughout the inner loop for dkdv.
k = tl.load(K_block_ptr)
v = tl.load(V_block_ptr)
if USE_ALIBI:
a_offset = bhid
alibi_slope = tl.load(alibi_slopes + a_offset)
else:
alibi_slope = None
# compute dK and dV for blocks close to the diagonal that need to be masked
num_steps = BLOCK_N1 // MASK_BLOCK_M1
dk, dv = _bwd_kernel_dk_dv(
dk, dv,
Q, k, v, sm_scale, alibi_slope,
DO,
M, D,
stride_tok, stride_d,
H, N_CTX,
MASK_BLOCK_M1, BLOCK_N1, BLOCK_DMODEL,
start_n, start_m, num_steps,
MASK=True
)
# compute dK and dV for blocks that don't need masking further from the diagonal
start_m += num_steps * MASK_BLOCK_M1
num_steps = (N_CTX - start_m) // BLOCK_M1
dk, dv = _bwd_kernel_dk_dv(
dk, dv,
Q, k, v, sm_scale, alibi_slope,
DO,
M, D,
stride_tok, stride_d,
H, N_CTX,
BLOCK_M1, BLOCK_N1, BLOCK_DMODEL,
start_n, start_m, num_steps,
MASK=False
)
DV_block_ptrs = tl.make_block_ptr(
base=DV,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_tok, stride_d),
offsets=(start_n, 0),
block_shape=(BLOCK_N1, BLOCK_DMODEL),
order=(1,0)
)
tl.store(DV_block_ptrs, dv.to(v.dtype))
# Write back dK.
dk *= sm_scale
DK_block_ptrs = tl.make_block_ptr(
base=DK,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_tok, stride_d),
offsets=(start_n, 0),
block_shape=(BLOCK_N1, BLOCK_DMODEL),
order=(1,0)
)
tl.store(DK_block_ptrs, dk.to(k.dtype))
# THIS BLOCK DOES DQ:
start_m = pid * BLOCK_M2
end_n = start_m + BLOCK_M2
MASK_BLOCK_N2: tl.constexpr = BLOCK_N2 // BLK_SLICE_FACTOR
offs_m = start_m + tl.arange(0, BLOCK_M2)
Q_block_ptr = tl.make_block_ptr(
base=Q,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_tok, stride_d),
offsets=(start_m, 0),
block_shape=(BLOCK_M2, BLOCK_DMODEL),
order=(1, 0)
)
DO_block_ptr = tl.make_block_ptr(
base=DO,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_tok, stride_d),
offsets=(start_m, 0),
block_shape=(BLOCK_M2, BLOCK_DMODEL),
order=(1, 0)
)
q = tl.load(Q_block_ptr)
do = tl.load(DO_block_ptr)
dq = tl.zeros([BLOCK_M2, BLOCK_DMODEL], dtype=tl.float32)
m = tl.load(M + offs_m)
m = m[:, None]
# Compute dQ for masked (diagonal) blocks.
# NOTE: This code scans each row of QK^T backward (from right to left,
# but inside each call to _attn_bwd_dq, from left to right), but that's
# not due to anything important. I just wanted to reuse the loop
# structure for dK & dV above as much as possible.
num_steps = BLOCK_M2 // MASK_BLOCK_N2
dq = _bwd_kernel_dq(dq, q, K, V,
do, m, D, alibi_slope,
stride_tok, stride_d,
H, N_CTX,
BLOCK_M2, MASK_BLOCK_N2, BLOCK_DMODEL,
start_m, end_n - num_steps * MASK_BLOCK_N2, num_steps,
MASK=True
)
end_n -= num_steps * MASK_BLOCK_N2
# stage 2
num_steps = end_n // BLOCK_N2
dq = _bwd_kernel_dq(dq, q, K, V,
do, m, D, alibi_slope,
stride_tok, stride_d,
H, N_CTX,
BLOCK_M2, BLOCK_N2, BLOCK_DMODEL,
start_m, end_n - num_steps * BLOCK_N2, num_steps,
MASK=False
)
# Write back dQ.
DQ_block_ptr = tl.make_block_ptr(
base=DQ,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_tok, stride_d),
offsets=(start_m, 0),
block_shape=(BLOCK_M2, BLOCK_DMODEL),
order=(1, 0)
)
dq *= LN2
tl.store(DQ_block_ptr, dq.to(q.dtype))
class _attention(torch.autograd.Function):
@staticmethod
def forward(ctx, q, k, v, o, metadata):
# NOTE: a large bias tensor leads to overflow during pointer arithmetic
if (metadata.bias is not None):
assert(metadata.bias.numel() < 2 ** 31)
if o is None:
o = torch.empty_like(q, dtype=v.dtype)
import os
if os.environ.get("FLASH_ATTENTION_PRINT_PARAM", "0") == "1":
print(f"triton flash attention: {q.shape=}, {k.shape=}, {v.shape}, {o.shape=}")
print(f"triton flash attention: {q.stride()=}, {k.stride()=}, {v.stride()=}, {o.stride()=}")
print(f"triton flash attention: {metadata=}")
metadata.check_args(q, k, v, o)
if metadata.varlen:
total_q, nheads_q, head_size = q.shape
total_k, nheads_k, _ = k.shape
batch = metadata.num_contexts
q_strides = (0, q.stride(1), q.stride(0), q.stride(2))
k_strides = (0, k.stride(1), k.stride(0), k.stride(2))
v_strides = (0, v.stride(1), v.stride(0), v.stride(2))
o_strides = (0, o.stride(1), o.stride(0), o.stride(2))
else:
batch, nheads_q, seqlen_q, head_size = q.shape
_, nheads_k, seqlen_k, _ = k.shape
q_strides = (q.stride(0), q.stride(1), q.stride(2), q.stride(3))
k_strides = (k.stride(0), k.stride(1), k.stride(2), k.stride(3))
v_strides = (v.stride(0), v.stride(1), v.stride(2), v.stride(3))
o_strides = (o.stride(0), o.stride(1), o.stride(2), o.stride(3))
# Get closest power of 2 over or equal to 32.
padded_d_model = 1 << (head_size - 1).bit_length()
padded_d_model = max(padded_d_model, 16)
grid = lambda META: (
triton.cdiv(metadata.max_seqlens_q, META['BLOCK_M']),
nheads_q,
batch
)
# encoded_softmax is used to validate dropout behavior vs the PyTorch SDPA math backend reference. We zero this out
# to give a consistent starting point and then populate it with the output of softmax with the sign bit set according
# to the dropout mask. The resulting return allows this mask to be fed into the reference implementation for testing
# only. This return holds no useful output aside from debugging.
if metadata.return_encoded_softmax:
encoded_softmax = torch.zeros((q.shape[0], q.shape[1], q.shape[2], k.shape[2]), device=q.device, dtype=torch.float32)
else:
encoded_softmax = None
M = torch.empty((batch, nheads_q, metadata.max_seqlens_q), device=q.device, dtype=torch.float32)
# Seed the RNG so we get reproducible results for testing.
philox_seed = 0x1BF52
philox_offset = 0x1D4B42
if metadata.bias is not None:
bias_strides = (metadata.bias.stride(0), metadata.bias.stride(1),
metadata.bias.stride(2), metadata.bias.stride(3))
else:
bias_strides = (0,0,0,0)
if metadata.alibi_slopes is not None:
alibi_strides = (metadata.alibi_slopes.stride(0), metadata.alibi_slopes.stride(1))
else:
alibi_strides = (0, 0)
attn_fwd[grid](
q, k, v, metadata.bias, metadata.sm_scale, M, o,
*q_strides, *k_strides, *v_strides, *o_strides, *bias_strides, *alibi_strides,
metadata.cu_seqlens_q, metadata.cu_seqlens_k,
dropout_p=metadata.dropout_p,
philox_seed=philox_seed,
philox_offset_base=philox_offset,
encoded_softmax=encoded_softmax,
alibi_slopes = metadata.alibi_slopes,
HQ=nheads_q, HK=nheads_k,
ACTUAL_BLOCK_DMODEL=head_size,
MAX_SEQLENS_Q=metadata.max_seqlens_q,
MAX_SEQLENS_K=metadata.max_seqlens_k,
IS_CAUSAL=metadata.causal,
VARLEN=metadata.varlen,
BLOCK_DMODEL=padded_d_model,
BIAS_TYPE=0 if metadata.bias is None else 1,
USE_ALIBI=False if metadata.alibi_slopes is None else True,
ENABLE_DROPOUT=metadata.dropout_p > 0.0,
RETURN_ENCODED_SOFTMAX=metadata.return_encoded_softmax,
BATCH_SIZE= q.shape[0]
)
if os.environ.get("FLASH_ATTENTION_PRINT_PARAM", "0") == "1":
best_config = attn_fwd.get_best_config()
print(f"{best_config.kwargs=}, {best_config.num_stages=}, {best_config.num_warps=}")
ctx.save_for_backward(q, k, v, o, M)
ctx.grid = grid
ctx.sm_scale = metadata.sm_scale
ctx.BLOCK_DMODEL = head_size
ctx.causal = metadata.causal
ctx.alibi_slopes = metadata.alibi_slopes
ctx.dropout_p = metadata.dropout_p
ctx.philox_seed = philox_seed
ctx.philox_offset = philox_offset
ctx.encoded_softmax = encoded_softmax
ctx.return_encoded_softmax = metadata.return_encoded_softmax
return o if not metadata.return_encoded_softmax else (o, encoded_softmax, ) # S_dmask
@staticmethod
def backward(ctx, do, *args):
if torch.version.hip is not None:
BLOCK = 64
else:
BLOCK = 128
q, k, v, o, M = ctx.saved_tensors
import os
if os.environ.get("TRITON_FLASHATTN_DEBUG", "0") == "1":
print(f"triton flash attention: {q.shape=}, {k.shape=}, {v.shape}, {o.shape=}, {do.shape=}")
print(f"triton flash attention: {q.stride()=}, {k.stride()=}, {v.stride()=}, {o.stride()=}, {do.stride()}")
# assert do.is_contiguous()
assert q.stride() == k.stride() == v.stride() == o.stride() == do.stride()
seqlen_q = q.shape[2]
dq = torch.empty_like(q)
dk = torch.empty_like(k)
dv = torch.empty_like(v)
BATCH, N_HEAD, N_CTX = q.shape[:3]
PRE_BLOCK = 128
NUM_WARPS, NUM_STAGES = 4, 1
BLOCK_M1, BLOCK_N1, BLOCK_M2, BLOCK_N2 = 32, 64, 64, 32
BLK_SLICE_FACTOR = 2
RCP_LN2 = 1.4426950408889634 # = 1.0 / ln(2)
arg_k = k
arg_k = arg_k * (ctx.sm_scale * RCP_LN2)
assert N_CTX % PRE_BLOCK == 0
delta = torch.empty_like(M)
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
padded_head = (Lk != ctx.BLOCK_DMODEL)
grid_preprocess = (triton.cdiv(do.shape[2], BLOCK), do.shape[1], do.shape[0])
_attn_bwd_preprocess[grid_preprocess](
o, do, delta,
o.stride(0), o.stride(1), o.stride(2), o.stride(3),
do.stride(0), do.stride(1), do.stride(2), do.stride(3),
seqlen_q,
head_dim=Lk,
BLOCK_M=BLOCK, D_HEAD=ctx.BLOCK_DMODEL,
)
grid = lambda META: (
triton.cdiv(N_CTX, META['BLOCK_N1']),
1,
BATCH * N_HEAD
)
_attn_bwd[grid](
q, arg_k, v, ctx.sm_scale, ctx.alibi_slopes, do, dq, dk, dv,
M, delta,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
N_HEAD, N_CTX,
BLOCK_DMODEL=ctx.BLOCK_DMODEL,
BLOCK_M1=BLOCK_M1, BLOCK_N1=BLOCK_N1, BLOCK_M2=BLOCK_M2, BLOCK_N2=BLOCK_N2,
BLK_SLICE_FACTOR=BLK_SLICE_FACTOR,
USE_ALIBI= False if ctx.alibi_slopes is None else True,
)
return dq, dk, dv, None, None
attention = _attention.apply
# flash_attn wrapper
def input_helper(Z, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype):
torch.manual_seed(20)
# Initialize q, k, v
q = torch.randn((Z, HQ, N_CTX_Q, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
k = torch.randn((Z, HK, N_CTX_K, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
v = torch.randn((Z, HK, N_CTX_K, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
sm_scale = D_HEAD**-0.5
input_metadata = MetaData(sm_scale=sm_scale)
input_metadata.max_seqlens_q = N_CTX_Q
input_metadata.max_seqlens_k = N_CTX_K
return q, k, v, input_metadata
def padding_bshd(t): # BSHD
batch, seqlen, nheads, dim = t.shape
t = torch.nn.functional.pad(t.reshape(batch, seqlen, nheads*dim), (0, 32), 'constant', 0)[:,:,:-32].reshape(batch, seqlen, nheads, dim) # pad: nheads*dim+32
# t = torch.nn.functional.pad(t.reshape(batch, seqlen, nheads, dim), (0, 32), 'constant', 0)[:,:,:,:-32] # pad: dim+32
return t
def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False,
return_attn_probs=False, padding_input=False):
if padding_input:
k, v = (padding_bshd(t) for t in (k, v))
q, k, v = (t.transpose(1, 2) for t in (q, k, v))
softmax_scale = softmax_scale if softmax_scale else q.shape[-1]**-0.5
input_metadata = MetaData(sm_scale=softmax_scale, causal=causal, dropout_p=dropout_p, return_encoded_softmax=return_attn_probs)
input_metadata.max_seqlens_q = q.shape[2]
input_metadata.max_seqlens_k = k.shape[2]
return _attention.apply(q, k, v, None, input_metadata).transpose(1, 2)
def flash_attn_kvpacked_func(q, kv, dropout_p=0.0, softmax_scale=None, causal=False,
return_attn_probs=False, padding_input=False):
k, v = kv[:, :, 0], kv[:, :, 1] # batch_size, seqlen, 2, nheads_k, headdim
if padding_input:
k, v = (padding_bshd(t) for t in (k, v)) # pad
q, k, v = (t.transpose(1, 2) for t in (q, k, v)) # trans bshd to bhsd
softmax_scale = softmax_scale if softmax_scale else q.shape[-1]**-0.5
input_metadata = MetaData(sm_scale=softmax_scale, causal=causal, dropout_p=dropout_p, return_encoded_softmax=return_attn_probs)
input_metadata.max_seqlens_q = q.shape[2]
input_metadata.max_seqlens_k = k.shape[2]
return _attention.apply(q, k, v, None, input_metadata).transpose(1, 2) # trans bhsd to bshd
def flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False,
return_attn_probs=False, padding_input=False):
q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
if padding_input:
k, v = (padding_bshd(t) for t in (k, v))
q, k, v = (t.transpose(1, 2) for t in (q, k, v))
softmax_scale = softmax_scale if softmax_scale else q.shape[-1]**-0.5
input_metadata = MetaData(sm_scale=softmax_scale, causal=causal, dropout_p=dropout_p, return_encoded_softmax=return_attn_probs)
input_metadata.max_seqlens_q = q.shape[2]
input_metadata.max_seqlens_k = k.shape[2]
return _attention.apply(q, k, v, None, input_metadata).transpose(1, 2)
# varlen flash_attn
def varlen_input_helper(Z, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype, causal):
torch.manual_seed(20)
# Random sequence lengths. Using N_CTX as kind of max of sum of individual seqs
max_seqlens_q = N_CTX_Q // Z
max_seqlens_k = N_CTX_K // Z
seqlens_q = torch.randint(1, max_seqlens_q + 1, (Z,), dtype=torch.int32)
seqlens_k = torch.randint(1, max_seqlens_k + 1, (Z,), dtype=torch.int32)
max_seqlens_q = torch.max(seqlens_q).item()
max_seqlens_k = torch.max(seqlens_k).item()
# Calculate cumulative sequence lengths
cu_seqlens_q = torch.cat([torch.tensor([0], dtype=torch.int32), seqlens_q.cumsum(dim=0, dtype=torch.int32)])
cu_seqlens_k = torch.cat([torch.tensor([0], dtype=torch.int32), seqlens_k.cumsum(dim=0, dtype=torch.int32)])
cu_seqlens_q = cu_seqlens_q.to(device="cuda")
cu_seqlens_k = cu_seqlens_k.to(device="cuda")
# -1 because the last entry of cu_seqlens_q specifies the end of the last seq
num_ctxs = len(cu_seqlens_q) - 1
# Initialize q, k, v with variable lengths
total_q = cu_seqlens_q[-1].item()
total_k = cu_seqlens_k[-1].item()
q = torch.randn((total_q, HQ, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
k = torch.randn((total_k, HK, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
v = torch.randn((total_k, HK, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
sm_scale = D_HEAD**-0.5
input_metadata = MetaData(sm_scale=sm_scale)
input_metadata.set_varlen_params(cu_seqlens_q, cu_seqlens_k)
input_metadata.max_seqlens_q = max_seqlens_q
input_metadata.max_seqlens_k = max_seqlens_k
if causal:
input_metadata.need_causal()
return q, k, v, input_metadata
def padding_thd(t): # THD
total_seqlen, nheads, dim = t.shape
t = torch.nn.functional.pad(t.reshape(total_seqlen, nheads*dim), (0, 32), 'constant', 0)[:,:-32].reshape(total_seqlen, nheads, dim) # pad: nheads*dim+32
# t = torch.nn.functional.pad(t.reshape(total_seqlen, nheads, dim), (0, 32), 'constant', 0)[:,:-32] # pad: dim+32
return t
def flash_attn_varlen_qkvpacked_func(qkv, cu_seqlens, max_seqlens, dropout_p=0.0, softmax_scale=None,
causal=False, return_attn_probs=False, padding_input=False):
q, k, v = qkv[:, 0], qkv[:, 1], qkv[:, 2] # total_seqlen, 3, nheads, dim
if padding_input:
k, v = (padding_thd(t) for t in (k, v)) # pad
softmax_scale = softmax_scale if softmax_scale else q.shape[-1]**-0.5
input_metadata = MetaData(sm_scale=softmax_scale, causal=causal, dropout_p=dropout_p, return_encoded_softmax=return_attn_probs)
input_metadata.set_varlen_params(cu_seqlens, cu_seqlens)
input_metadata.max_seqlens_q = max_seqlens
input_metadata.max_seqlens_k = max_seqlens
return _attention.apply(q, k, v, None, input_metadata)
def flash_attn_varlen_kvpacked_func(q, kv, cu_seqlens_q, cu_seqlens_k, max_seqlens_q, max_seqlens_k,
dropout_p=0.0, softmax_scale=None, causal=False,
return_attn_probs=False, padding_input=False):
k, v = kv[:, 0], kv[:, 1] # total_seqlen, 2, nheads, dim
if padding_input:
k, v = (padding_thd(t) for t in (k, v))
softmax_scale = softmax_scale if softmax_scale else q.shape[-1]**-0.5
input_metadata = MetaData(sm_scale=softmax_scale, causal=causal, dropout_p=dropout_p, return_encoded_softmax=return_attn_probs)
input_metadata.set_varlen_params(cu_seqlens_q, cu_seqlens_k)
input_metadata.max_seqlens_q = max_seqlens_q
input_metadata.max_seqlens_k = max_seqlens_k
return _attention.apply(q, k, v, None, input_metadata)
def flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlens_q, max_seqlens_k,
dropout_p=0.0, softmax_scale=None, causal=False,
return_attn_probs=False, padding_input=False):
if padding_input:
k, v = (padding_thd(t) for t in (k, v))
softmax_scale = softmax_scale if softmax_scale else q.shape[-1]**-0.5
input_metadata = MetaData(sm_scale=softmax_scale, causal=causal, dropout_p=dropout_p, return_encoded_softmax=return_attn_probs)
input_metadata.set_varlen_params(cu_seqlens_q, cu_seqlens_k)
input_metadata.max_seqlens_q = max_seqlens_q
input_metadata.max_seqlens_k = max_seqlens_k
return _attention.apply(q, k, v, None, input_metadata)
# legacy interface
def flash_attn_unpadded_qkvpacked_func(qkv, cu_seqlens, max_seqlens, dropout_p, softmax_scale=None,
causal=False, return_attn_probs=False, padding_input=False):
return flash_attn_varlen_qkvpacked_func(qkv, cu_seqlens, max_seqlens, dropout_p, softmax_scale,
causal, return_attn_probs, padding_input)
def flash_attn_unpadded_kvpacked_func(q, kv, cu_seqlens_q, cu_seqlens_k, max_seqlens_q, max_seqlens_k,
dropout_p, softmax_scale=None, causal=False, return_attn_probs=False, padding_input=False):
return flash_attn_varlen_kvpacked_func(q, kv, cu_seqlens_q, cu_seqlens_k, max_seqlens_q, max_seqlens_k,
dropout_p, softmax_scale, causal, return_attn_probs, padding_input)
def flash_attn_unpadded_func(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlens_q, max_seqlens_k,
dropout_p, softmax_scale=None,
causal=False, return_attn_probs=False, padding_input=False):
return flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlens_q, max_seqlens_k,
dropout_p, softmax_scale, causal, return_attn_probs, padding_input)
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