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Unverified Commit 1b7cfd5a authored by Gregory Shtrasberg's avatar Gregory Shtrasberg Committed by GitHub
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[ROCm][V0][Attention] Revert to the previous FA triton kernel (#18226) 


Signed-off-by: default avatarGregory Shtrasberg <Gregory.Shtrasberg@amd.com>
parent da4b69d0
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vllm/attention/backends/rocm_flash_attn.py
View file @ 1b7cfd5a
......@@ -770,8 +770,9 @@ class ROCmFlashAttentionImpl(AttentionImpl):
and layer._v_scale and layer._prob_scale
and self.kv_cache_dtype == "fp8")
full_scales = (
layer._q_scale, layer._k_scale, layer._v_scale,
layer._prob_scale) if use_fp8_scales else None
layer._q_scale.item(), layer._k_scale.item(),
layer._v_scale.item(),
layer._prob_scale.item()) if use_fp8_scales else None
self.triton_attn_func(
query,
key,
......
vllm/attention/ops/triton_flash_attention.py
View file @ 1b7cfd5a
#!/usr/bin/env python
# SPDX-License-Identifier: Apache-2.0
"""
Fused Attention
===============
This is a Triton implementation of the Flash Attention v2 algorithm
See https://tridao.me/publications/flash2/flash2.pdf
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
Credits:
AMD Triton kernels team
OpenAI kernel team
Currently only the forward kernel is supported, and contains these features:
Features supported:
1) Fwd with causal masking
2) Arbitrary Q and KV sequence lengths
3) Arbitrary head sizes
4) Multi and grouped query attention
5) Variable sequence lengths
6) ALiBi and matrix bias
7) FP8 support
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:
from typing import Optional
1) Non power of two head dims
"""
import torch
from vllm import _custom_ops as ops
from vllm.platforms import current_platform
from vllm.platforms.rocm import on_gfx1x
from vllm.triton_utils import tl, triton
SUPPORTED_LAYOUTS = ['thd', 'bhsd', 'bshd']
default_eight_bit_dtype_triton = tl.float8e4b8
default_eight_bit_dtype_torch = current_platform.fp8_dtype()
default_float8_info = torch.finfo(default_eight_bit_dtype_torch)
FP8_MIN = triton.language.constexpr(default_float8_info.min)
# According to https://github.com/vllm-project/vllm/blob/main
# /csrc/quantization/utils.cuh#L31,
# need to make the max for the uz datatype be 224.0 for accuracy reasons.
FP8_MAX = triton.language.constexpr(
default_float8_info.max if default_eight_bit_dtype_torch !=
torch.float8_e4m3fnuz else 224.0)
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
eight_bit = False
layout = None
return_encoded_softmax = False
eight_bit_dtype_triton = default_eight_bit_dtype_triton
eight_bit_dtype_torch = default_eight_bit_dtype_torch
output_dtype = None
# Note about layouts:
#
# thd - [num_tokens, num_heads, head_size]
# bshd - [batch_size, seq_len, num_heads, head_size]
# bhsd - [batch_size, num_heads, seq_len, head_size]
#
# This is for each tensor, all tensors must have same layout.
# Q can have num_heads and seq_len differ from from K and V,
# however K and V must agree on this.
#
# Notes about varlen and bias:
# Only one or the other is implemented, meaning can't combine
# both varlen and bias right now.
#
# Note about quantization:
# Only 8-bit quantization supported (for now) and specifically fp8.
# Scales must be tensors.
# o_scale: This is 'output scaling', but comes from parameter called
# 'input_scale', this is applied to the output from the kernel.
# o_scale should be None if none of the other quantization parameters
# are used.
#
# NOTE: Object is in a tentatively good state after initialized, however,
# to verify, call check_args(q,k,v,o) where o is the output tensor.
def __init__(
self,
sm_scale=1.0,
layout=None, # layout can be 'bshd', 'bhsd', or 'thd'
output_dtype=None,
max_seqlens_q=0,
max_seqlens_k=0,
# varlen params
cu_seqlens_q=None, # only 'thd' layout supported for varlen
cu_seqlens_k=None,
# quant params
q_descale=None,
k_descale=None,
v_descale=None,
p_scale=None,
o_scale=None,
# bias params
bias=None, # varlen not implemented for bias
seqlen_q=None,
seqlen_k=None,
# alibi params
alibi_slopes=None,
alibi_batch=None,
alibi_nheads=None,
# causal
causal=None,
):
self.sm_scale = sm_scale
self.output_dtype = output_dtype
self.max_seqlens_q = max_seqlens_q
self.max_seqlens_k = max_seqlens_k
self.layout = layout
if cu_seqlens_q is not None or cu_seqlens_k is not None:
assert cu_seqlens_q is not None and cu_seqlens_k is not None
assert layout is None or layout not in [
'bshd', 'bhsd'
], "Varlen only implemented for thd layout"
self.set_varlen_params(cu_seqlens_q, cu_seqlens_k)
quant_params = [q_descale, k_descale, v_descale, p_scale, o_scale]
if any(x is not None for x in quant_params):
p_descale = 1.0 / p_scale if p_scale is not None else None
self.set_eight_bit_params(q_descale, k_descale, v_descale, p_scale,
p_descale, o_scale)
if bias is not None:
self.need_bias(bias, seqlen_q, seqlen_k)
if alibi_slopes is not None:
self.need_alibi(alibi_slopes, alibi_batch, alibi_nheads)
if causal is not None and causal:
self.need_causal()
def set_varlen_params(self, cu_seqlens_q, cu_seqlens_k):
self.varlen = True
self.layout = 'thd'
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 set_eight_bit_params(self, q_descale, k_descale, v_descale, p_scale,
p_descale, o_scale):
self.eight_bit = True
self.q_descale = q_descale
self.k_descale = k_descale
self.v_descale = v_descale
self.p_scale = p_scale
self.p_descale = p_descale
self.o_scale = o_scale
self.use_p_scale = (p_scale is not None) and (
p_descale is not None) and (v_descale is not None)
self.eight_bit_kv = ((q_descale is None) and (k_descale is not None)
and (v_descale is not None))
self.eight_bit_dtype_torch = default_eight_bit_dtype_torch
def need_bias(self, bias, seqlen_q, seqlen_k):
assert bias is not None
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 check_args(self, q, k, v, o):
assert q.dim() == k.dim() and q.dim() == v.dim()
batch, nheads_q, nheads_k, head_size = get_shape_from_layout(
q, k, self)
if self.varlen:
assert q.dim() == 3
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 is None
assert not self.return_encoded_softmax
else:
assert q.dim() == 4
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
if self.eight_bit:
if self.eight_bit_kv:
assert (v.dtype == k.dtype
and k.dtype == self.eight_bit_dtype_torch)
assert q.dtype != k.dtype
assert (self.v_descale is not None) and (self.k_descale
is not None)
else:
assert (q.dtype == k.dtype and q.dtype == v.dtype
and q.dtype == self.eight_bit_dtype_torch)
assert (self.q_descale
is not None) and (self.k_descale
is not None) and (self.v_descale
is not None)
if self.use_p_scale:
assert (self.p_scale is not None) and (self.p_descale
is not None)
else:
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
assert self.layout is not None
assert self.layout == 'thd' or not self.varlen
torch_dtype: tl.constexpr = torch.float16
@triton.jit
......@@ -243,85 +40,40 @@ def max_fn(x, y):
return tl.math.max(x, y)
# Convenience function to load with optional boundary checks.
# "First" is the major dim, "second" is the minor dim.
@triton.jit
def masked_load(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
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 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
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)
def compute_alibi_tensor(alibi_slopes, seqlen_q, seqlen_k):
q_idx = torch.arange(seqlen_q, dtype=torch.int32,
device="cuda").unsqueeze(-1) # (N_CTX_Q, 1)
k_idx = torch.arange(seqlen_k, dtype=torch.int32,
device="cuda").unsqueeze(0) # (1, N_CTX_K)
relative_pos = torch.abs(q_idx + seqlen_k - seqlen_q -
k_idx) # (N_CTX_Q, N_CTX_K)
return -1 * alibi_slopes.unsqueeze(-1).unsqueeze(
-1) * relative_pos # (Z, H, N_CTX_Q, N_CTX_K)
@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 quant_fp8(x, scale):
x *= scale
x = tl.clamp(x, FP8_MIN, FP8_MAX)
return x
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
......@@ -330,68 +82,61 @@ def _attn_fwd_inner(
l_i,
m_i,
q,
k_ptrs,
v_ptrs,
bias_ptrs,
stride_kn,
stride_vk,
stride_bn,
K_block_ptr,
V_block_ptr,
start_m,
actual_seqlen_k,
actual_seqlen_q,
dropout_p,
philox_seed,
batch_philox_offset,
encoded_sm_ptrs,
encoded_softmax_block_ptr,
block_min,
block_max,
offs_n_causal,
masked_blocks,
n_extra_tokens,
alibi_slope,
q_descale,
k_descale,
v_descale,
p_scale,
bias_ptr,
IS_CAUSAL: tl.constexpr,
BLOCK_M: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
OFFS_M: tl.constexpr,
OFFS_N: tl.constexpr,
SHOULD_PRE_LOAD_V: tl.constexpr,
SHOULD_MASK_STEPS: tl.constexpr,
SHOULD_RETURN_ENCODED_SOFTMAX: tl.constexpr,
USE_PADDED_HEAD: tl.constexpr,
IS_ACTUAL_BLOCK_DMODEL: tl.constexpr,
QK_SCALE: tl.constexpr,
IS_EIGHT_BIT_GEMM: tl.constexpr,
USE_P_SCALE: tl.constexpr,
IS_EIGHT_BIT_KV: tl.constexpr,
QUANT_DTYPE: tl.constexpr = default_eight_bit_dtype_triton,
PRE_LOAD_V: tl.constexpr,
MASK_STEPS: tl.constexpr,
ENABLE_DROPOUT: tl.constexpr,
RETURN_ENCODED_SOFTMAX: tl.constexpr,
PADDED_HEAD: tl.constexpr,
USE_FP8: tl.constexpr,
qk_scale,
p_descale,
):
# 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_offs_n = start_n + tl.arange(0,
BLOCK_N) if SHOULD_MASK_STEPS else None
k_offs_k = None if not USE_PADDED_HEAD else tl.arange(0, BLOCK_DMODEL)
k = masked_load(k_ptrs, k_offs_k, k_offs_n, IS_ACTUAL_BLOCK_DMODEL,
actual_seqlen_k)
if SHOULD_PRE_LOAD_V:
# We can use the same offsets as k, just with dims transposed.
v = masked_load(v_ptrs, k_offs_n, k_offs_k, actual_seqlen_k,
IS_ACTUAL_BLOCK_DMODEL)
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 SHOULD_MASK_STEPS: # noqa: SIM102
if MASK_STEPS: # noqa: SIM102
# 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.
# 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],
......@@ -404,107 +149,112 @@ def _attn_fwd_inner(
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 ----
if IS_EIGHT_BIT_GEMM:
qk += ((((tl.dot(q, k).to(tl.float32) * q_descale)) * k_descale) *
QK_SCALE)
else:
if IS_EIGHT_BIT_KV:
k = (k * k_descale).to(q.type.element_ty)
qk += (tl.dot(q, k) * QK_SCALE)
if bias_ptrs is not None:
bias_offs_n = start_n + tl.arange(
0, BLOCK_N) if SHOULD_MASK_STEPS else None
bias = masked_load(bias_ptrs, OFFS_M, bias_offs_n, actual_seqlen_q,
actual_seqlen_k)
# 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
qk += tl.dot(q, k)
if USE_FP8:
qk *= qk_scale
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
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 SHOULD_RETURN_ENCODED_SOFTMAX:
tl.store(encoded_sm_ptrs, p.to(encoded_sm_ptrs.type.element_ty))
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 SHOULD_PRE_LOAD_V:
v = masked_load(v_ptrs, k_offs_n, k_offs_k, actual_seqlen_k,
IS_ACTUAL_BLOCK_DMODEL)
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
if IS_EIGHT_BIT_GEMM:
if USE_P_SCALE:
p = quant_fp8(p, p_scale).to(QUANT_DTYPE)
acc += tl.dot(p, v)
else:
# v is in eight_bit but p is not, we want the gemm in p's type
acc += tl.dot(p, v.to(p.type.element_ty))
else:
if IS_EIGHT_BIT_KV:
v = (v * v_descale).to(p.type.element_ty)
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 SHOULD_RETURN_ENCODED_SOFTMAX:
encoded_sm_ptrs += BLOCK_N
if USE_FP8:
p *= p_descale
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
def get_cdna_autotune_configs():
return [
triton.Config(
{
'BLOCK_M': 256,
'BLOCK_N': 64,
'waves_per_eu': 2,
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=8),
triton.Config(
{
'BLOCK_M': 128,
'BLOCK_N': 128,
'waves_per_eu': 2,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=4),
triton.Config(
{
'BLOCK_M': 128,
'BLOCK_N': 64,
'BLOCK_M': 256,
'BLOCK_N': 128,
'waves_per_eu': 2,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=4),
num_warps=8),
triton.Config(
{
'BLOCK_M': 128,
'BLOCK_N': 64,
'waves_per_eu': 3,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'waves_per_eu': 1,
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=4),
......@@ -512,168 +262,141 @@ def get_cdna_autotune_configs():
{
'BLOCK_M': 128,
'BLOCK_N': 64,
'waves_per_eu': 1,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'waves_per_eu': 3,
'PRE_LOAD_V': True
},
num_stages=1,
num_warps=4),
triton.Config(
{
'BLOCK_M': 128,
'BLOCK_N': 32,
'waves_per_eu': 2,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'BLOCK_N': 64,
'waves_per_eu': 3,
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=4),
], [
'IS_CAUSAL', 'MAX_SEQLENS_Q', 'MAX_SEQLENS_K',
'IS_ACTUAL_BLOCK_DMODEL', 'VARLEN', 'HQ', 'HK'
]
def get_rdna_autotune_configs():
return [
triton.Config(
{
'BLOCK_M': 32,
'BLOCK_N': 32,
'BLOCK_M': 64,
'BLOCK_N': 64,
'waves_per_eu': 4,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=2),
num_warps=8),
triton.Config(
{
'BLOCK_M': 32,
'BLOCK_N': 32,
'waves_per_eu': 2,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'waves_per_eu': 4,
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=2),
num_warps=8),
# TODO: This config fails with head_size not pow2 with data mismatches.
# triton.Config({'BLOCK_M': 32, 'BLOCK_N': 16, 'waves_per_eu': 1,
# 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
# Fails in AccelerateAMDMatmul (Triton) assert when using FP8:
# 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', 'BLOCK_DMODEL', 'USE_FP8']
def get_rdna_autotune_configs():
return [
triton.Config(
{
'BLOCK_M': 32,
'BLOCK_N': 16,
'BLOCK_N': 32,
'waves_per_eu': 4,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=2),
triton.Config(
{
'BLOCK_M': 32,
'BLOCK_N': 16,
'BLOCK_N': 32,
'waves_per_eu': 2,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=2),
triton.Config(
{
'BLOCK_M': 16,
'BLOCK_M': 32,
'BLOCK_N': 16,
'waves_per_eu': 4,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=2),
triton.Config(
{
'BLOCK_M': 16,
'BLOCK_M': 32,
'BLOCK_N': 16,
'waves_per_eu': 2,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
},
num_stages=1,
num_warps=2),
# Fall-back config.
triton.Config(
{
'BLOCK_M': 16,
'BLOCK_N': 16,
'waves_per_eu': 1,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
'PRE_LOAD_V': False
},
num_stages=1,
num_warps=2),
], [
'IS_CAUSAL', 'MAX_SEQLENS_Q', 'MAX_SEQLENS_K',
'IS_ACTUAL_BLOCK_DMODEL', 'VARLEN', 'HQ', 'HK'
]
def get_general_autotune_configs():
return [
triton.Config(
{
'BLOCK_M': 128,
'BLOCK_N': 128,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
},
num_stages=1,
num_warps=4),
triton.Config(
{
'BLOCK_M': 128,
'BLOCK_N': 64,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
},
num_stages=1,
num_warps=4),
triton.Config(
{
'BLOCK_M': 128,
'BLOCK_N': 32,
'SHOULD_PRE_LOAD_V': False,
'GRID_CU_MULTIP': 2
},
num_stages=1,
num_warps=4),
], [
'IS_CAUSAL', 'MAX_SEQLENS_Q', 'MAX_SEQLENS_K',
'IS_ACTUAL_BLOCK_DMODEL', 'VARLEN', 'HQ', 'HK'
]
def has_cdna_target():
ROCM_CDNA_TARGETS = ["gfx942", "gfx90a", "gfx908"]
return triton.runtime.driver.active.get_current_target(
).arch in ROCM_CDNA_TARGETS
def is_rocm_cdna():
return current_platform.is_rocm() and has_cdna_target()
# Fails in AccelerateAMDMatmul (Triton) assert when using FP8:
# 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', 'BLOCK_DMODEL', 'USE_FP8']
def get_autotune_configs():
if is_rocm_cdna():
return get_cdna_autotune_configs()
elif current_platform.is_rocm():
if on_gfx1x():
return get_rdna_autotune_configs()
else:
return get_general_autotune_configs()
return get_cdna_autotune_configs()
autotune_configs, autotune_keys = get_autotune_configs()
float8_info = torch.finfo(current_platform.fp8_dtype())
@triton.autotune(
configs=autotune_configs,
key=autotune_keys,
use_cuda_graph=True,
)
@triton.jit
def attn_fwd(
......@@ -681,7 +404,13 @@ def attn_fwd(
K,
V,
bias,
SM_SCALE: tl.constexpr,
sm_scale,
q_scale,
k_scale,
v_scale,
p_scale,
p_descale,
o_descale,
L,
Out,
stride_qz: tl.int64,
......@@ -704,70 +433,44 @@ def attn_fwd(
stride_bh: tl.int64,
stride_bm: tl.int64,
stride_bn: tl.int64,
stride_az: tl.int64,
stride_ah: tl.int64,
q_descale_ptr,
k_descale_ptr,
p_scale_ptr,
p_descale_ptr,
o_descale_ptr,
v_descale_ptr,
q_descale_has_singleton: tl.constexpr,
k_descale_has_singleton: tl.constexpr,
p_descale_has_singleton: tl.constexpr,
v_descale_has_singleton: tl.constexpr,
cu_seqlens_q,
cu_seqlens_k,
dropout_p,
philox_seed,
NUM_CU: tl.constexpr,
GRID_CU_MULTIP: tl.constexpr,
B: tl.constexpr,
philox_offset_base,
encoded_softmax,
alibi_slopes,
HQ: tl.constexpr,
HK: tl.constexpr,
IS_ACTUAL_BLOCK_DMODEL: 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,
USE_FP8: tl.constexpr,
USE_FP8_OUT: tl.constexpr,
BLOCK_N: tl.constexpr,
SHOULD_PRE_LOAD_V: tl.constexpr,
USE_BIAS: tl.constexpr,
SHOULD_RETURN_ENCODED_SOFTMAX: tl.constexpr,
USE_ALIBI: tl.constexpr,
IS_EIGHT_BIT: tl.constexpr,
USE_P_SCALE: tl.constexpr,
IS_EIGHT_BIT_KV: tl.constexpr,
QUANT_DTYPE: tl.constexpr = default_eight_bit_dtype_triton,
PRE_LOAD_V: tl.constexpr,
BIAS_TYPE: tl.constexpr,
ENABLE_DROPOUT: tl.constexpr,
RETURN_ENCODED_SOFTMAX: tl.constexpr,
FP8_MIN: tl.constexpr = float8_info.min,
FP8_MAX: tl.constexpr = float8_info.max,
):
if o_descale_ptr is not None:
o_descale = tl.load(o_descale_ptr)
start_m: tl.int64 = tl.program_id(0)
off_h_q: tl.int64 = tl.program_id(1)
off_z: tl.int64 = tl.program_id(2)
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M).to(tl.int64)
offs_n = tl.arange(0, BLOCK_N).to(tl.int64)
offs_d = tl.arange(0, BLOCK_DMODEL).to(tl.int64)
# as we can't have return statements inside while loop in Triton
continue_condition = True
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.
# 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:
continue_condition = False
# return
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
......@@ -777,598 +480,499 @@ def attn_fwd(
seqlen_q = MAX_SEQLENS_Q
seqlen_k = MAX_SEQLENS_K
if continue_condition:
# 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 = (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=tl.float32)
o_ptrs_mask = (offs_m[:, None] < seqlen_q).broadcast_to(
[BLOCK_M, BLOCK_DMODEL])
# 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_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_value = tl.full([BLOCK_M],
value=float("inf"),
dtype=tl.float32)
l_ptrs_mask = offs_m < MAX_SEQLENS_Q
tl.store(l_ptrs, l_value, mask=l_ptrs_mask)
# TODO: Should dropout and return encoded softmax be
# handled here too?
continue_condition = False
# return
if continue_condition:
# If MQA / GQA, set the K and V head offsets appropriately.
GROUP_SIZE: tl.constexpr = HQ // HK
off_h_k = off_h_q // GROUP_SIZE if GROUP_SIZE != 1 else off_h_q
n_extra_tokens = 0
if seqlen_k < BLOCK_N:
n_extra_tokens = BLOCK_N - seqlen_k
elif seqlen_k % BLOCK_N:
n_extra_tokens = seqlen_k % BLOCK_N
USE_PADDED_HEAD: tl.constexpr = (IS_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)
# Compute pointers for all scale tensors used in this kernel.
IS_EIGHT_BIT_GEMM: tl.constexpr = IS_EIGHT_BIT & (
not IS_EIGHT_BIT_KV)
if IS_EIGHT_BIT:
if k_descale_has_singleton:
k_descale_ptrs = k_descale_ptr
else:
k_descale_ptrs = k_descale_ptr + off_h_k
if v_descale_has_singleton:
v_descale_ptrs = v_descale_ptr
else:
v_descale_ptrs = v_descale_ptr + off_h_k
if not IS_EIGHT_BIT_KV:
if q_descale_has_singleton:
q_descale_ptrs = q_descale_ptr
else:
q_descale_ptrs = q_descale_ptr + off_h_q
if USE_P_SCALE:
if p_descale_has_singleton:
p_scale_ptrs = p_scale_ptr
p_descale_ptrs = p_descale_ptr
else:
p_scale_ptrs = p_scale_ptr + off_h_q
p_descale_ptrs = p_descale_ptr + off_h_q
if USE_BIAS:
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
batch_philox_offset = 0
# We can ask to return the dropout mask without doing any
# dropout. In this case, we return an invalid pointer so
# indicate the mask is not valid.
if SHOULD_RETURN_ENCODED_SOFTMAX:
encoded_sm_base = (encoded_softmax +
off_h_q * seqlen_q * seqlen_k)
encoded_sm_ptrs = (encoded_sm_base +
offs_m[:, None] * seqlen_k +
offs_n[None, :])
else:
encoded_sm_ptrs = None
# 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: tl.constexpr = SM_SCALE * 1.44269504089
# Q is loaded once at the beginning and shared by all N blocks.
q_ptrs_mask = offs_m[:, None] < seqlen_q
if USE_PADDED_HEAD:
q_ptrs_mask = q_ptrs_mask & (offs_d[None, :]
< IS_ACTUAL_BLOCK_DMODEL)
q = tl.load(q_ptrs, mask=q_ptrs_mask, other=0.0)
if IS_EIGHT_BIT:
k_descale = tl.load(k_descale_ptrs)
v_descale = tl.load(v_descale_ptrs)
q_descale = None if IS_EIGHT_BIT_KV else tl.load(
q_descale_ptrs)
if USE_P_SCALE:
p_scale = tl.load(p_scale_ptrs)
p_descale = tl.load(p_descale_ptrs)
else:
p_scale = None
p_descale = None
else:
q_descale = None
k_descale = None
v_descale = None
p_scale = None
p_descale = None
# 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,
start_m,
seqlen_k,
seqlen_q,
philox_seed,
batch_philox_offset,
encoded_sm_ptrs,
# _, _, offs_n_causal, masked_blocks, n_extra_tokens, _
block_min,
block_max,
0,
0,
0,
alibi_slope,
q_descale,
k_descale,
v_descale,
p_scale,
# IS_CAUSAL, ....
False,
BLOCK_M,
BLOCK_DMODEL,
BLOCK_N,
offs_m,
offs_n,
# _, SHOULD_MASK_STEPS, ...
SHOULD_PRE_LOAD_V,
False,
SHOULD_RETURN_ENCODED_SOFTMAX,
USE_PADDED_HEAD,
IS_ACTUAL_BLOCK_DMODEL,
QK_SCALE,
IS_EIGHT_BIT_GEMM,
USE_P_SCALE,
IS_EIGHT_BIT_KV,
QUANT_DTYPE)
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 SHOULD_RETURN_ENCODED_SOFTMAX:
encoded_sm_ptrs += n_full_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,
start_m,
seqlen_k,
seqlen_q,
philox_seed,
batch_philox_offset,
encoded_sm_ptrs,
block_min,
block_max,
offs_n_causal,
masked_blocks,
n_extra_tokens,
alibi_slope,
q_descale,
k_descale,
v_descale,
p_scale,
IS_CAUSAL,
BLOCK_M,
BLOCK_DMODEL,
BLOCK_N,
offs_m,
offs_n,
# _, SHOULD_MASK_STEPS, ...
SHOULD_PRE_LOAD_V,
True,
SHOULD_RETURN_ENCODED_SOFTMAX,
USE_PADDED_HEAD,
IS_ACTUAL_BLOCK_DMODEL,
QK_SCALE,
IS_EIGHT_BIT_GEMM,
USE_P_SCALE,
IS_EIGHT_BIT_KV,
QUANT_DTYPE)
if IS_EIGHT_BIT and not IS_EIGHT_BIT_KV:
if USE_P_SCALE:
acc *= p_descale
acc *= v_descale
# 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 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_EIGHT_BIT and not IS_EIGHT_BIT_KV: # noqa: SIM102
if o_descale_ptr is not None:
acc = quant_fp8(acc, o_descale)
acc = acc.to(Out.type.element_ty)
if IS_CAUSAL: # noqa: SIM102
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 = tl.zeros((1, ), tl.float32)
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)
l_ptrs_mask = tl.arange(0, BLOCK_M) < boundary
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 = (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 USE_PADDED_HEAD:
o_ptrs_mask = o_ptrs_mask & (offs_d[None, :]
< IS_ACTUAL_BLOCK_DMODEL)
tl.store(o_ptrs, acc.to(Out.dtype.element_ty), mask=o_ptrs_mask)
def get_shape_from_layout(q, k, metadata):
assert metadata.layout in SUPPORTED_LAYOUTS, "Got unsupported layout."
if metadata.layout == 'thd':
nheads_q, nheads_k = q.shape[1], k.shape[1]
head_size = q.shape[-1]
batch = metadata.num_contexts
elif metadata.layout == 'bhsd':
batch, nheads_q, _, head_size = q.shape
nheads_k = k.shape[1]
elif metadata.layout == 'bshd':
batch, _, nheads_q, head_size = q.shape
nheads_k = k.shape[2]
return batch, nheads_q, nheads_k, head_size
def get_strides_from_layout(q, k, v, o, metadata):
assert metadata.layout in SUPPORTED_LAYOUTS, "Got unsupported layout."
STRIDE_PERMUTATIONS = {
'thd': (None, 1, 0, 2),
'bhsd': (0, 1, 2, 3),
'bshd': (0, 2, 1, 3),
}
perm = STRIDE_PERMUTATIONS[metadata.layout]
stride = lambda x, p: (0 if p is None else x.stride(p))
strides = lambda x: (stride(x, p) for p in perm)
return tuple(strides(x) for x in [q, k, v, o])
# 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?
return
# If MQA / GQA, set the K and V head offsets appropriately.
GROUP_SIZE: tl.constexpr = HQ // HK
off_h_k = off_h_q // GROUP_SIZE if GROUP_SIZE != 1 else off_h_q
n_extra_tokens = 0
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 = 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:
bias_ptr = tl.make_block_ptr(
base=bias + off_h_q * stride_bh,
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 ENABLE_DROPOUT:
batch_philox_offset = philox_offset_base \
+ (off_z * HQ + off_h_q) \
* 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 i
# 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")
if not USE_FP8:
q = (q * qk_scale).to(Q_block_ptr.type.element_ty)
acc_scale = 1.0
else:
qk_scale *= q_scale * k_scale
acc_scale = p_scale * v_scale
# 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
# 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,
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,
# 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,
USE_FP8,
qk_scale,
p_descale,
)
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:
offs_n_causal = offs_n + (seqlen_q - seqlen_k) if IS_CAUSAL else 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,
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,
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,
USE_FP8,
qk_scale,
p_descale,
)
# epilogue
if USE_FP8:
acc *= acc_scale
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
if USE_FP8_OUT:
acc *= o_descale
acc = tl.clamp(acc, FP8_MIN, FP8_MAX)
acc = acc.to(Out.type.element_ty)
if IS_CAUSAL: # noqa: SIM102
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 = tl.zeros((1, ), tl.float32)
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))
def check_args(
q,
k,
v,
o,
varlen=True,
max_seqlens=None,
cu_seqlens_q=None,
cu_seqlens_k=None,
):
assert q.dim() == k.dim() and q.dim() == v.dim()
if varlen:
assert q.dim() == 3
total_q, nheads_q, head_size = q.shape
total_k, nheads_k, _ = k.shape
assert cu_seqlens_q is not None
assert cu_seqlens_k is not None
assert len(cu_seqlens_q) == len(cu_seqlens_k)
else:
assert q.dim() == 4
batch, nheads_q, seqlen_q, head_size = q.shape
_, nheads_k, seqlen_k, _ = k.shape
assert max_seqlens > 0
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
class _attention(torch.autograd.Function):
@staticmethod
def forward(ctx, q, k, v, o, metadata: MetaData):
# NOTE: a large bias tensor leads to overflow during pointer arithmetic
if (metadata.bias is not None):
assert (metadata.bias.numel() < 2**31)
def forward(
ctx,
q,
k,
v,
o,
cu_seqlens_q,
cu_seqlens_k,
max_seqlens_q,
max_seqlens_k,
causal=False,
sm_scale=1.0,
bias=None,
fp8_scales=None,
fp8_out_scale=None,
):
if fp8_scales is not None:
use_fp8 = True
(q_scale, k_scale, v_scale, p_scale) = fp8_scales
float8 = current_platform.fp8_dtype()
def check_and_convert(t, scale):
if t.dtype != float8:
descale = 1.0 / scale
ts = (t * descale).clamp(min=float8_info.min,
max=float8_info.max)
return ts.to(float8)
else:
return t
if o is None:
if metadata.eight_bit:
o = torch.empty_like(
q,
dtype=metadata.output_dtype if metadata.output_dtype
is not None else metadata.eight_bit_dtype_torch)
else:
o = torch.empty_like(q, dtype=q.dtype)
q = check_and_convert(q, q_scale)
k = check_and_convert(k, k_scale)
v = check_and_convert(v, v_scale)
else:
use_fp8 = False
q_scale = k_scale = v_scale = p_scale = 1.0
metadata.check_args(q, k, v, o)
if o is None:
o = torch.empty_like(q, dtype=v.dtype)
batch, nheads_q, nheads_k, head_size = get_shape_from_layout(
q, k, metadata)
q_strides, k_strides, v_strides, o_strides = get_strides_from_layout(
q, k, v, o, metadata)
check_args(
q,
k,
v,
o,
varlen=True,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
)
if True: # varlen
total_q, nheads_q, head_size = q.shape
total_k, nheads_k, _ = k.shape
batch = len(cu_seqlens_q) - 1
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, seqlen_q, nheads_q, head_size = q.shape
_, seqlen_k, nheads_k, _ = k.shape
q_strides = (q.stride(0), q.stride(2), q.stride(1), q.stride(3))
k_strides = (k.stride(0), k.stride(2), k.stride(1), k.stride(3))
v_strides = (v.stride(0), v.stride(2), v.stride(1), v.stride(3))
o_strides = (o.stride(0), o.stride(2), o.stride(1), o.stride(3))
# 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)
# 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)
unpadded_head_dims = {32, 64, 128, 256}
if head_size not in unpadded_head_dims:
padded_d_model = None
for i in unpadded_head_dims:
if i > head_size:
padded_d_model = i
break
assert padded_d_model is not None
else:
encoded_softmax = None
padded_d_model = head_size
grid = lambda META: (
triton.cdiv(max_seqlens_q, META["BLOCK_M"]),
nheads_q,
batch,
)
M = torch.empty((batch, nheads_q, metadata.max_seqlens_q),
device=q.device,
dtype=torch.float32)
encoded_softmax = None
# 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))
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)
if metadata.alibi_slopes is not None:
alibi_strides = (metadata.alibi_slopes.stride(0),
metadata.alibi_slopes.stride(1))
else:
alibi_strides = (0, 0)
p_descale = 1.0 / p_scale
o_descale = 1.0 / fp8_out_scale.item(
) if fp8_out_scale is not None else 1.0
if metadata.eight_bit:
q_descale, k_descale, p_scale, p_descale, v_descale, o_scale = (
metadata.q_descale, metadata.k_descale, metadata.p_scale,
metadata.p_descale, metadata.v_descale, metadata.o_scale)
o_descale = 1.0 / o_scale if o_scale is not None else None
else:
q_descale = k_descale = p_scale = None
p_descale = v_descale = o_descale = None
# number of compute units available
NUM_CU = torch.cuda.get_device_properties("cuda").multi_processor_count
grid = lambda META: (triton.cdiv(metadata.max_seqlens_q, META[
'BLOCK_M']), nheads_q, batch)
arg_max_seqlens_q = 0 if on_gfx1x() else max_seqlens_q
arg_max_seqlens_k = 0 if on_gfx1x() else max_seqlens_k
attn_fwd[grid](
q,
k,
v,
metadata.bias,
metadata.sm_scale,
M,
bias,
sm_scale,
q_scale,
k_scale,
v_scale,
p_scale,
p_descale,
o_descale,
None,
o,
*q_strides,
*k_strides,
*v_strides,
*o_strides,
*bias_strides,
*alibi_strides,
q_descale,
k_descale,
p_scale,
p_descale,
o_descale,
v_descale,
q_descale.numel() == 1 if q_descale is not None else False,
k_descale.numel() == 1 if k_descale is not None else False,
p_descale.numel() == 1 if p_descale is not None else False,
v_descale.numel() == 1 if v_descale is not None else False,
metadata.cu_seqlens_q,
metadata.cu_seqlens_k,
cu_seqlens_q,
cu_seqlens_k,
dropout_p=0.0,
philox_seed=philox_seed,
philox_offset_base=philox_offset,
encoded_softmax=encoded_softmax,
alibi_slopes=metadata.alibi_slopes,
HQ=nheads_q,
HK=nheads_k,
IS_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,
ACTUAL_BLOCK_DMODEL=head_size,
MAX_SEQLENS_Q=arg_max_seqlens_q,
MAX_SEQLENS_K=arg_max_seqlens_k,
IS_CAUSAL=causal,
VARLEN=True,
BLOCK_DMODEL=padded_d_model,
USE_BIAS=metadata.bias is not None,
USE_ALIBI=metadata.alibi_slopes is not None,
SHOULD_RETURN_ENCODED_SOFTMAX=metadata.return_encoded_softmax,
IS_EIGHT_BIT=metadata.eight_bit,
USE_P_SCALE=metadata.eight_bit and metadata.use_p_scale,
IS_EIGHT_BIT_KV=metadata.eight_bit and metadata.eight_bit_kv,
NUM_CU=NUM_CU,
B=batch,
QUANT_DTYPE=metadata.eight_bit_dtype_triton)
BIAS_TYPE=0 if bias is None else 1,
ENABLE_DROPOUT=False,
RETURN_ENCODED_SOFTMAX=False,
USE_FP8=use_fp8,
USE_FP8_OUT=fp8_out_scale is not None,
)
ctx.grid = grid
ctx.sm_scale = metadata.sm_scale
ctx.sm_scale = sm_scale
ctx.BLOCK_DMODEL = head_size
ctx.causal = metadata.causal
ctx.alibi_slopes = metadata.alibi_slopes
ctx.causal = causal
ctx.dropout_p = 0.0
ctx.philox_seed = philox_seed
ctx.philox_offset = philox_offset
ctx.encoded_softmax = encoded_softmax
ctx.return_encoded_softmax = metadata.return_encoded_softmax
ctx.return_encoded_softmax = False
return o, encoded_softmax
triton_attention_rocm = _attention.apply
def scale_fp8(t, scale=None):
t_scaled, scale_out = ops.scaled_fp8_quant(t.reshape(-1, t.shape[-1]),
scale)
return t_scaled.reshape(t.shape), scale_out
def maybe_quantize_fp8(t, scale):
eight_bit_dtype = current_platform.fp8_dtype()
if t.dtype != eight_bit_dtype:
t, _ = scale_fp8(t, scale)
return t
def check_and_maybe_quantize_qkv(q, k, v, fp8_scales):
(q_scale, k_scale, v_scale, p_scale) = fp8_scales
q = maybe_quantize_fp8(q, q_scale)
k = maybe_quantize_fp8(k, k_scale)
v = maybe_quantize_fp8(v, v_scale)
return q, k, v
# query - [num_tokens, num_heads, head_size]
# key - [num_tokens, num_kv_heads, head_size]
# value - [num_tokens, num_kv_heads, head_size
# output - [num_tokens, num_heads, head_size]
def triton_attention(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
o: torch.Tensor,
cu_seqlens_q: torch.Tensor,
cu_seqlens_k: torch.Tensor,
max_seqlens_q: int,
max_seqlens_k: int,
causal: bool = False,
sm_scale: float = 1.0,
bias: Optional[torch.Tensor] = None,
fp8_scales: Optional[tuple[float, ...]] = None,
input_scale: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if fp8_scales is not None:
q_descale, k_descale, v_descale, p_scale = fp8_scales
else:
q_descale = k_descale = v_descale = p_scale = None
attn_metadata = MetaData(sm_scale=sm_scale,
max_seqlens_q=max_seqlens_q,
max_seqlens_k=max_seqlens_k,
causal=causal,
bias=bias,
output_dtype=q.dtype,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
q_descale=q_descale,
k_descale=k_descale,
v_descale=v_descale,
p_scale=p_scale,
o_scale=input_scale)
if fp8_scales is not None:
q, k, v = check_and_maybe_quantize_qkv(q, k, v, fp8_scales)
return triton_attention_rocm(q, k, v, o, attn_metadata)
triton_attention = _attention.apply
vllm/platforms/rocm.py
View file @ 1b7cfd5a
......@@ -98,6 +98,12 @@ def with_amdsmi_context(fn):
return wrapper
@cache
def on_gfx1x() -> bool:
GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
return any(arch in GPU_ARCH for arch in ["gfx11", "gfx12"])
@cache
def on_mi250_mi300() -> bool:
GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
......
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