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automatic/modules/flash_attn_triton_amd/fwd_prefill.py

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from typing import Literal, Optional, Union
import torch
import triton
import triton.language as tl
from modules.flash_attn_triton_amd.utils import AUTOTUNE, compute_alibi_block, get_shapes_from_layout, get_strides_from_layout, is_cdna, is_rdna
# Convenience function to load with optional boundary checks.
# "First" is the major dim, "second" is the minor dim.
@triton.jit
def load_fn(ptrs, offset_first, offset_second, boundary_first, boundary_second):
if offset_first is not None and offset_second is not None:
mask = (offset_first[:, None] < boundary_first) & \
(offset_second[None, :] < boundary_second)
tensor = tl.load(ptrs, mask=mask, other=0.0)
elif offset_first is not None:
mask = offset_first[:, None] < boundary_first
tensor = tl.load(ptrs, mask=mask, other=0.0)
elif offset_second is not None:
mask = offset_second[None, :] < boundary_second
tensor = tl.load(ptrs, mask=mask, other=0.0)
else:
tensor = tl.load(ptrs)
return tensor
@triton.jit
def _attn_fwd_inner(acc, l_i, m_i, q, k_ptrs, v_ptrs, bias_ptrs, stride_kn, stride_vk, stride_bn, stride_sn, start_m,
actual_seqlen_k, actual_seqlen_q, dropout_p, philox_seed, philox_ptrs, sd_mask_ptrs, dropout_mask_ptrs,
block_min, block_max, offs_n_causal, masked_blocks, n_extra_tokens, alibi_slope, # pylint: disable=unused-argument
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, PADDED_HEAD: tl.constexpr,
ACTUAL_BLOCK_DMODEL: tl.constexpr, SM_SCALE: tl.constexpr, USE_ALIBI: tl.constexpr, USE_EXP2: tl.constexpr,
RETURN_SCORES: tl.constexpr, ACCUMULATOR_TYPE):
if USE_EXP2:
RCP_LN2: tl.constexpr = 1.4426950408889634
# 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.
if MASK_STEPS:
k_offs_n = start_n + tl.arange(0, BLOCK_N)
else:
k_offs_n = None
k_offs_k = None if not PADDED_HEAD else tl.arange(0, BLOCK_DMODEL)
k = load_fn(k_ptrs, k_offs_k, k_offs_n, ACTUAL_BLOCK_DMODEL, actual_seqlen_k)
if PRE_LOAD_V:
# We can use the same offsets as k, just with dims transposed.
v = load_fn(v_ptrs, k_offs_n, k_offs_k, actual_seqlen_k, ACTUAL_BLOCK_DMODEL)
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=ACCUMULATOR_TYPE)
# 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"))
# compute masks
q_mask = OFFS_M[:, None] < actual_seqlen_q
k_mask = (start_n + tl.arange(0, BLOCK_N))[None, :] < actual_seqlen_k
p_mask = q_mask & k_mask
# -- compute qk ----
qk += tl.dot(q, k)
qk_scaled = qk * SM_SCALE
if IS_CAUSAL:
causal_boundary = start_n + offs_n_causal
causal_mask = OFFS_M[:, None] >= causal_boundary[None, :]
qk_scaled = tl.where(causal_mask, qk_scaled, float("-inf"))
if bias_ptrs is not None:
bias_offs_n = start_n + tl.arange(0, BLOCK_N) if MASK_STEPS else None
bias = load_fn(bias_ptrs, OFFS_M, bias_offs_n, actual_seqlen_q, actual_seqlen_k)
qk_scaled += bias
if USE_ALIBI:
# 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_scaled += alibi_block
# get max scores so far
m_ij = tl.maximum(m_i, tl.max(qk_scaled, 1))
# scale and subtract max
q_shifted = qk_scaled - m_ij[:, None]
# Compute scaled QK and softmax probabilities
if USE_EXP2:
p = tl.math.exp2(q_shifted * RCP_LN2)
else:
p = tl.math.exp(q_shifted)
# CAVEAT: Must update l_ij before applying dropout
l_ij = tl.sum(p, 1)
if ENABLE_DROPOUT:
rng_output = tl.rand(philox_seed, philox_ptrs) # TODO: use tl.randint for better performance
dropout_mask = rng_output > dropout_p
# return scores with negative values for dropped vals
sd_mask = tl.where(dropout_mask, p, -p)
tl.store(sd_mask_ptrs, sd_mask, mask=p_mask)
# apply dropout mask in place
p = tl.where(dropout_mask, p, 0.0)
elif RETURN_SCORES:
# NOTE: the returned score is not the same as the reference because we need to adjust as we find new maxes per block. We are not doing that
tl.store(sd_mask_ptrs, p, mask=p_mask)
# -- update output accumulator --
# alpha is an adjustment factor for acc and li as we loop and find new maxes
# store the diff in maxes to adjust acc and li as we discover new maxes
m_diff = m_i - m_ij
if USE_EXP2:
alpha = tl.math.exp2(m_diff * RCP_LN2)
else:
alpha = tl.math.exp(m_diff)
acc = acc * alpha[:, None]
if not PRE_LOAD_V:
v = load_fn(v_ptrs, k_offs_n, k_offs_k, actual_seqlen_k, ACTUAL_BLOCK_DMODEL)
# -- 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.type.element_ty), v)
k_ptrs += BLOCK_N * stride_kn
v_ptrs += BLOCK_N * stride_vk
if bias_ptrs is not None:
bias_ptrs += BLOCK_N * stride_bn
if RETURN_SCORES:
sd_mask_ptrs += BLOCK_N * stride_sn
if ENABLE_DROPOUT:
dropout_mask_ptrs += BLOCK_N * stride_sn
philox_ptrs += BLOCK_N * stride_sn
return acc, l_i, m_i
def get_cdna_autotune_configs():
return [
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 64, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=4),
# Fall-back config.
triton.Config({'BLOCK_M': 16, 'BLOCK_N': 16, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=4),
], ['IS_CAUSAL', 'dropout_p', 'MAX_SEQLENS_Q', 'MAX_SEQLENS_K', 'ACTUAL_BLOCK_DMODEL', 'IS_VARLEN', 'HQ', 'HK']
def get_rdna_autotune_configs():
return [
triton.Config({'BLOCK_M': 32, 'BLOCK_N': 32, 'waves_per_eu': 4, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=2),
triton.Config({'BLOCK_M': 32, 'BLOCK_N': 32, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=2),
triton.Config({'BLOCK_M': 32, 'BLOCK_N': 16, 'waves_per_eu': 4, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=2),
triton.Config({'BLOCK_M': 32, 'BLOCK_N': 16, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=2),
triton.Config({'BLOCK_M': 16, 'BLOCK_N': 16, 'waves_per_eu': 4, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=2),
triton.Config({'BLOCK_M': 16, 'BLOCK_N': 16, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=2),
# Fall-back config.
triton.Config({'BLOCK_M': 16, 'BLOCK_N': 16, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1,
num_warps=2),
], ['IS_CAUSAL', 'dropout_p', 'MAX_SEQLENS_Q', 'MAX_SEQLENS_K', 'ACTUAL_BLOCK_DMODEL', 'IS_VARLEN', 'HQ', 'HK']
def get_autotune_configs():
if AUTOTUNE:
if is_rdna():
return get_rdna_autotune_configs()
elif is_cdna():
return get_cdna_autotune_configs()
else:
raise ValueError("Unknown Device Type")
else:
return [
triton.Config(
{"BLOCK_M": 64, "BLOCK_N": 64, "waves_per_eu": 1, "PRE_LOAD_V": False},
num_stages=1,
num_warps=4,
),
], [
"IS_CAUSAL",
"dropout_p",
"MAX_SEQLENS_Q",
"MAX_SEQLENS_K",
"ACTUAL_BLOCK_DMODEL",
"IS_VARLEN",
"HQ",
"HK",
]
autotune_configs, autotune_keys = get_autotune_configs()
@triton.autotune(
configs=autotune_configs,
key=autotune_keys,
# use_cuda_graph=True,
)
@triton.jit
def attn_fwd(Q, K, V, bias, Cache_seqlens, Cache_batch_idx, # pylint: disable=unused-argument
SM_SCALE: tl.constexpr, LSE, 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, # pylint: disable=unused-argument
stride_sz, stride_sh, stride_sm, stride_sn, stride_lse_z, stride_lse_h, stride_lse_m, cu_seqlens_q, cu_seqlens_k,
dropout_p, philox_seed, philox_offset_base, sd_mask, dropout_mask, alibi_slopes, HQ: tl.constexpr,
HK: tl.constexpr, ACTUAL_BLOCK_DMODEL: tl.constexpr, MAX_SEQLENS_Q: tl.constexpr,
MAX_SEQLENS_K: tl.constexpr, IS_VARLEN: tl.constexpr, IS_INFERENCE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_M: tl.constexpr,
BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr, PRE_LOAD_V: tl.constexpr, USE_BIAS: tl.constexpr,
ENABLE_DROPOUT: tl.constexpr, RETURN_SCORES: tl.constexpr, USE_ALIBI: tl.constexpr, USE_EXP2: tl.constexpr):
# set params
ACCUMULATOR_TYPE = tl.float32
# compute offsets
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)
offs_d = tl.arange(0, BLOCK_DMODEL)
# handle seqlen
if IS_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
elif IS_INFERENCE:
cu_seqlens_q_start = 0
cu_seqlens_k_start = 0
seqlen_q = MAX_SEQLENS_Q
seqlen_k = tl.load(Cache_seqlens + off_z)
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 = tl.cdiv(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 = tl.cdiv((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 = Out + off_z * stride_oz + off_h_q * stride_oh + cu_seqlens_q_start * stride_om
o_ptrs = o_offset + offs_m[:, None] * stride_om + offs_d[None, :] * stride_on
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=Out.type.element_ty)
o_ptrs_mask = offs_m[:, None] < seqlen_q
# We still need to write 0s to the result
tl.store(o_ptrs, acc, mask=o_ptrs_mask)
# The tensor allocated for L is based on MAX_SEQLENS_Q as that is
# statically known.
l_offset = LSE + off_z * stride_lse_z + off_h_q * stride_lse_h + cu_seqlens_q_start * stride_lse_m
l_ptrs = l_offset + offs_m * stride_lse_m
l = tl.full([BLOCK_M], value=0.0, dtype=ACCUMULATOR_TYPE)
# mask_m_offsets = start_m + tl.arange(0, BLOCK_M)
# lse_mask = mask_m_offsets < causal_start_idx
# softmax_lse = tl.where(lse_mask, 0.0, softmax_lse)
l_ptrs_mask = offs_m < MAX_SEQLENS_Q
tl.store(l_ptrs, l, mask=l_ptrs_mask)
# 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
n_extra_tokens = 0
# print("n_extra_tokens:", n_extra_tokens)
# print("seqlen_k:", seqlen_k)
# print("BLOCK_N:", BLOCK_N)
# return
if seqlen_k < BLOCK_N:
n_extra_tokens = BLOCK_N - seqlen_k
elif seqlen_k % BLOCK_N:
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 = Q + off_z * stride_qz + off_h_q * stride_qh + cu_seqlens_q_start * stride_qm
q_ptrs = q_offset + offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qk
k_offset = K + off_z * stride_kz + off_h_k * stride_kh + cu_seqlens_k_start * stride_kn
k_ptrs = k_offset + offs_d[:, None] * stride_kk + offs_n[None, :] * stride_kn
v_offset = V + off_z * stride_vz + off_h_k * stride_vh + cu_seqlens_k_start * stride_vk
v_ptrs = v_offset + offs_n[:, None] * stride_vk + offs_d[None, :] * stride_vn
if USE_BIAS:
# Note: this might get large enough to overflow on some configs
bias_offset = off_h_q * stride_bh
bias_ptrs = bias + bias_offset + offs_m[:, None] * stride_bm + offs_n[None, :] * stride_bn
else:
bias_ptrs = 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 RETURN_SCORES:
sd_mask_offset = sd_mask + off_z * stride_sz + off_h_q * stride_sh #+ cu_seqlens_q_start * stride_sm
sd_mask_ptrs = sd_mask_offset + offs_m[:, None] * stride_sm + offs_n[None, :] * stride_sn
else:
sd_mask_ptrs = None
if ENABLE_DROPOUT:
dropout_mask_offset = dropout_mask + off_z * stride_sz + off_h_q * stride_sh #+ cu_seqlens_q_start * stride_sm
dropout_mask_ptrs = dropout_mask_offset + offs_m[:, None] * stride_sm + offs_n[None, :] * stride_sn
batch_philox_offset = philox_offset_base + off_z * stride_sz + off_h_q * stride_sh #+ cu_seqlens_q_start * stride_sm
philox_ptrs = batch_philox_offset + offs_m[:, None] * stride_sm + offs_n[None, :] * stride_sn
else:
dropout_mask_ptrs = None
philox_ptrs = 0
# initialize pointer to m and l
m_i = tl.full([BLOCK_M], float("-inf"), dtype=ACCUMULATOR_TYPE)
l_i = tl.full([BLOCK_M], 1.0, dtype=ACCUMULATOR_TYPE)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=ACCUMULATOR_TYPE)
# Q is loaded once at the beginning and shared by all N blocks.
q_ptrs_mask = offs_m[:, None] < seqlen_q
if PADDED_HEAD:
q_ptrs_mask = q_ptrs_mask & (offs_d[None, :] < ACTUAL_BLOCK_DMODEL)
q = tl.load(q_ptrs, mask=q_ptrs_mask, other=0.0)
# 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_ptrs, v_ptrs, bias_ptrs, stride_kn, stride_vk, stride_bn, stride_sn,
start_m, seqlen_k, seqlen_q, dropout_p, philox_seed, philox_ptrs,
sd_mask_ptrs, dropout_mask_ptrs,
# _, _, offs_n_causal, masked_blocks, n_extra_tokens, _
block_min, block_max, 0, 0, 0, alibi_slope,
# IS_CAUSAL, ....
False, BLOCK_M, BLOCK_DMODEL, BLOCK_N, offs_m, offs_n,
# _, MASK_STEPS, ...
PRE_LOAD_V, False, ENABLE_DROPOUT, PADDED_HEAD,
ACTUAL_BLOCK_DMODEL, SM_SCALE, USE_ALIBI=USE_ALIBI, USE_EXP2=USE_EXP2, RETURN_SCORES=RETURN_SCORES, ACCUMULATOR_TYPE=ACCUMULATOR_TYPE)
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_ptrs += n_full_blocks * BLOCK_N * stride_kn
v_ptrs += n_full_blocks * BLOCK_N * stride_vk
if USE_BIAS:
bias_ptrs += n_full_blocks * BLOCK_N * stride_bn
if RETURN_SCORES:
sd_mask_ptrs += n_full_blocks * BLOCK_N * stride_sn
if ENABLE_DROPOUT:
dropout_mask_ptrs += n_full_blocks * BLOCK_N * stride_sn
philox_ptrs += n_full_blocks * BLOCK_N * stride_sn
acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q, k_ptrs, v_ptrs, bias_ptrs, stride_kn, stride_vk, stride_bn, stride_sn,
start_m, seqlen_k, seqlen_q, dropout_p, philox_seed, philox_ptrs,
sd_mask_ptrs, dropout_mask_ptrs, block_min, block_max, offs_n_causal, masked_blocks,
n_extra_tokens, alibi_slope,
IS_CAUSAL, BLOCK_M, BLOCK_DMODEL, BLOCK_N, offs_m, offs_n,
# _, MASK_STEPS, ...
PRE_LOAD_V, True, ENABLE_DROPOUT, PADDED_HEAD,
ACTUAL_BLOCK_DMODEL, SM_SCALE, USE_ALIBI=USE_ALIBI, USE_EXP2=USE_EXP2, RETURN_SCORES=RETURN_SCORES, ACCUMULATOR_TYPE=ACCUMULATOR_TYPE)
# epilogue
# This helps the compiler do Newton Raphson on l_i vs on acc which is much larger.
l_recip = 1 / l_i[:, None]
acc = acc * l_recip
if ENABLE_DROPOUT:
dropout_scale = 1 / (1 - dropout_p)
acc = acc * dropout_scale
# 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
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(Log Sum Exponents), the log of the normalization constant
l_offset = LSE + off_z * stride_lse_z + off_h_q * stride_lse_h + cu_seqlens_q_start * stride_lse_m
l_ptrs = l_offset + offs_m * stride_lse_m
if USE_EXP2:
RCP_LN2: tl.constexpr = 1.4426950408889634
LN2: tl.constexpr = 0.6931471824645996
# compute log-sum-exp in base 2 units
mi_base2 = m_i * RCP_LN2
softmax_lse = mi_base2 + tl.math.log2(l_i)
# convert back to natural units
softmax_lse *= LN2
else:
softmax_lse = m_i + tl.math.log(l_i)
if IS_CAUSAL:
# zero out nans caused by -infs when doing causal
lse_mask = (start_m_idx + tl.arange(0, BLOCK_M)) < causal_start_idx
softmax_lse = tl.where(lse_mask, 0.0, softmax_lse)
# 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)
l_ptrs_mask = tl.arange(0, BLOCK_M) < boundary
tl.store(l_ptrs, softmax_lse, mask=l_ptrs_mask) # the log of the normalization constant
else:
tl.store(l_ptrs, softmax_lse) # the log of the normalization constant
# write back O
o_offset = Out + off_z * stride_oz + off_h_q * stride_oh + cu_seqlens_q_start * stride_om
o_ptrs = o_offset + offs_m[:, None] * stride_om + offs_d[None, :] * stride_on
o_ptrs_mask = tl.full([BLOCK_M, BLOCK_DMODEL], 1, dtype=tl.int1)
if overflow_size > 0:
o_ptrs_mask = o_ptrs_mask & (offs_m[:, None] < seqlen_q)
if PADDED_HEAD:
o_ptrs_mask = o_ptrs_mask & (offs_d[None, :] < ACTUAL_BLOCK_DMODEL)
tl.store(o_ptrs, acc.to(Out.dtype.element_ty), mask=o_ptrs_mask)
def attention_prefill_forward_triton_impl(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
o: torch.Tensor,
sm_scale: float,
alibi_slopes: Optional[torch.Tensor],
causal: bool,
bias: Optional[torch.Tensor],
layout: Literal["bshd", "bhsd", "thd"],
# varlen
cu_seqlens_q: Optional[torch.Tensor],
cu_seqlens_k: Optional[torch.Tensor],
max_seqlens_q: int,
max_seqlens_k: int,
# inference
cache_seqlens: Optional[Union[(int, torch.Tensor)]],
cache_batch_idx: Optional[torch.Tensor],
# dropout
dropout_p: float,
philox_seed: Optional[int],
philox_offset: Optional[int],
# misc
return_softmax: bool,
use_exp2: bool,
):
# check flags
is_varlen = layout == "thd"
use_alibi, (stride_az, stride_ah) = (True, alibi_slopes.stride()) if alibi_slopes is not None else (False, (0, 0))
is_inference = cache_seqlens is not None
if is_inference:
assert layout == "bshd", f"{layout} layout is not supported with inference. Use bshd layout"
# NOTE: a large bias tensor leads to overflow during pointer arithmetic
if (bias is not None):
assert (bias.numel() < 2**31)
batch, nheads_q, nheads_k, head_size, _, _ = get_shapes_from_layout(q, k, layout, cu_seqlens_q, cu_seqlens_k, max_seqlens_q, max_seqlens_k)
q_strides, k_strides, v_strides, o_strides = get_strides_from_layout(q, k, v, o, layout)
# Get closest power of 2 over or equal to 32.
padded_d_model = 1 << (head_size - 1).bit_length()
# Smallest head_dim supported is 16. If smaller, the tile in the
# kernel is padded - there is no padding in memory for any dims.
padded_d_model = max(padded_d_model, 16)
grid = lambda META: (triton.cdiv(max_seqlens_q, META['BLOCK_M']), nheads_q, batch)
# sd_mask 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.
use_dropout = (dropout_p > 0.0)
if use_dropout or return_softmax:
sd_mask = torch.zeros((batch, nheads_q, max_seqlens_q, max_seqlens_k), device=q.device,
dtype=torch.float32)
dropout_mask = torch.zeros((batch, nheads_q, max_seqlens_q, max_seqlens_k), device=q.device,
dtype=torch.float32)
scores_strides = (sd_mask.stride(0), sd_mask.stride(1), sd_mask.stride(2), sd_mask.stride(3))
else:
sd_mask = None
dropout_mask = None
scores_strides = (0, 0, 0, 0)
# stores LSE the log of the normalization constant / sum of expoential score(unnormalzied probablities)
if is_varlen:
softmax_lse = torch.zeros((q.shape[0], nheads_q), device=q.device, dtype=torch.float32)
stride_lse_m, stride_lse_h = softmax_lse.stride()
stride_lse_z = 0
else:
softmax_lse = torch.zeros((batch, nheads_q, max_seqlens_q), device=q.device, dtype=torch.float32)
stride_lse_z, stride_lse_h, stride_lse_m = softmax_lse.stride()
if bias is not None:
bias_strides = (bias.stride(0), bias.stride(1),bias.stride(2),
bias.stride(3))
else:
bias_strides = (0, 0, 0, 0)
attn_fwd[grid](q, k, v, bias, cache_seqlens, cache_batch_idx,
sm_scale, softmax_lse, o, *q_strides, *k_strides, *v_strides, *o_strides,
*bias_strides, stride_az, stride_ah, *scores_strides, stride_lse_z, stride_lse_h, stride_lse_m, cu_seqlens_q, cu_seqlens_k,
dropout_p=dropout_p, philox_seed=philox_seed, philox_offset_base=philox_offset, sd_mask=sd_mask, dropout_mask=dropout_mask, alibi_slopes=alibi_slopes,
HQ=nheads_q, HK=nheads_k, ACTUAL_BLOCK_DMODEL=head_size, MAX_SEQLENS_Q=max_seqlens_q,
MAX_SEQLENS_K=max_seqlens_k, IS_CAUSAL=causal, IS_VARLEN=is_varlen, IS_INFERENCE=is_inference,
BLOCK_DMODEL=padded_d_model, USE_BIAS=False if bias is None else True,
USE_ALIBI=use_alibi, ENABLE_DROPOUT=dropout_p
> 0.0, USE_EXP2=use_exp2, RETURN_SCORES=return_softmax)
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