e2182cc21d
* Integrate ck branch of ck_tile/fa_bwd_opt * Assume dq and q share the same stride * update ck * Integrate more stride of dq_acc * Revert fwd dropout * Fix paremeter order * Integrate ck with more stride * update the limit of hdim of bwd * Check argument * Add test_flash_attn_causal * Support unpad lse * Add test_flash_attn_varlen_causal, test_flash_attn_race_condition, test_flash_attn_bwd_overflow, test_flash_attn_bwd_transpose, test_flash_attn_bwd_varlen_overflow, test_flash_attn_deterministic, test_flash_attn_varlen_deterministic * Fix stride and Kn0 * Fix CK sync issue * Fix typo * Update CK for changing of fmha_fwd_args * Add kvcache tmp * Add kvcache * Fix comment * Sync behavior with ck * Update CK to develop * remove large test case * Add kvcache test * Fix page_block_size in arg * Minor fix * Fix stride error * Update seqlen of kvcache before splitkv * Fix compile error * Fix bug of hdim is not 8x * Fit ck arg * support adaptive num_splits * add more tests * Refine test tolerance * update CK * Move override_num_splits_if_necessary into cpp * update ck * Update ck * Support different flag for different version of hip * remove coerce-illegal, becasue this is not required in FA * Update ck to fix xcratch memory * Add coerce-illegal in some version * Add compile flag for rtn rounding * remove redundant init * Using env var to switch rounding mode * update ck
346 lines
15 KiB
C++
346 lines
15 KiB
C++
/******************************************************************************
|
|
* Copyright (c) 2024, Tri Dao.
|
|
******************************************************************************/
|
|
|
|
#include "flash_common.hpp"
|
|
|
|
#include "fmha_fwd.hpp"
|
|
#include "mask.hpp"
|
|
|
|
fmha_fwd_traits get_ck_fmha_fwd_traits(const mask_info &mask,
|
|
std::string dtype,
|
|
int head_size,
|
|
bool has_dropout,
|
|
bool has_lse,
|
|
bool enable_alibi)
|
|
{
|
|
return fmha_fwd_traits{head_size,
|
|
head_size,
|
|
dtype,
|
|
false, // is_group_mode
|
|
true, // is_v_rowmajor
|
|
mask.type,
|
|
enable_alibi ? bias_enum::alibi : bias_enum::no_bias,
|
|
has_lse,
|
|
has_dropout,
|
|
false}; // do_fp8_static_quant
|
|
}
|
|
|
|
fmha_fwd_args get_ck_fmha_fwd_args(bool has_lse,
|
|
bool has_dropout_randval,
|
|
const mask_info &mask,
|
|
// sizes
|
|
const int b,
|
|
const int seqlen_q,
|
|
const int seqlen_k,
|
|
const int h,
|
|
const int h_k,
|
|
const int d,
|
|
// device pointers
|
|
const at::Tensor q,
|
|
const at::Tensor k,
|
|
const at::Tensor v,
|
|
c10::optional<at::Tensor> &alibi_slopes_,
|
|
at::Tensor out,
|
|
at::Tensor softmax_lse,
|
|
at::Tensor dropout_randval,
|
|
float softmax_scale,
|
|
float p_dropout,
|
|
uint64_t drop_seed,
|
|
uint64_t drop_offset)
|
|
{
|
|
// q: (batch_size, seqlen_q, nheads, d)
|
|
// k: (batch_size, seqlen_k, nheads_k, d)
|
|
// v: (batch_size, seqlen_k, nheads_k, d)
|
|
// o: (batch_size, seqlen_q, nheads, d)
|
|
|
|
// alibi_slopes:(batch_size, nheads) or (nhead)
|
|
// lse: (batch_size, nheads, seqlen_q)
|
|
// randval: (batch_size, nheads, seqlen_q, seqlen_k)
|
|
|
|
ck_tile::index_t stride_q = q.stride(1);
|
|
ck_tile::index_t stride_k = k.stride(1);
|
|
ck_tile::index_t stride_v = v.stride(1);
|
|
ck_tile::index_t stride_o = out.stride(1);
|
|
ck_tile::index_t stride_randval = has_dropout_randval ? dropout_randval.stride(2) : 0;
|
|
|
|
ck_tile::index_t nhead_stride_q = q.stride(2);
|
|
ck_tile::index_t nhead_stride_k = k.stride(2);
|
|
ck_tile::index_t nhead_stride_v = v.stride(2);
|
|
ck_tile::index_t nhead_stride_o = out.stride(2);
|
|
ck_tile::index_t nhead_stride_lse = has_lse ? softmax_lse.stride(1) : 0;
|
|
ck_tile::index_t nhead_stride_randval = has_dropout_randval ? dropout_randval.stride(1) : 0;
|
|
|
|
ck_tile::index_t batch_stride_q = q.stride(0);
|
|
ck_tile::index_t batch_stride_k = k.stride(0);
|
|
ck_tile::index_t batch_stride_v = v.stride(0);
|
|
ck_tile::index_t batch_stride_o = out.stride(0);
|
|
|
|
ck_tile::index_t batch_stride_lse = has_lse ? softmax_lse.stride(0) : 0;
|
|
ck_tile::index_t batch_stride_randval = has_dropout_randval ? dropout_randval.stride(0) : 0;
|
|
|
|
void *alibi_slopes_ptr = nullptr;
|
|
ck_tile::index_t stride_alibi_slopes = 0;
|
|
|
|
if (alibi_slopes_.has_value()) {
|
|
auto alibi_slopes = alibi_slopes_.value();
|
|
CHECK_DEVICE(alibi_slopes);
|
|
TORCH_CHECK(alibi_slopes.stride(-1) == 1, "ALiBi slopes tensor must have contiguous last dimension");
|
|
TORCH_CHECK(alibi_slopes.sizes() == torch::IntArrayRef({h}) || alibi_slopes.sizes() == torch::IntArrayRef({b, h}));
|
|
alibi_slopes_ptr = alibi_slopes.data_ptr();
|
|
stride_alibi_slopes = alibi_slopes.dim() == 2 ? alibi_slopes.stride(0) : 0;
|
|
}
|
|
|
|
return fmha_fwd_args{q.data_ptr(),
|
|
k.data_ptr(),
|
|
v.data_ptr(),
|
|
alibi_slopes_ptr, // bias
|
|
has_dropout_randval ? dropout_randval.data_ptr() : nullptr,
|
|
has_lse ? softmax_lse.data_ptr() : nullptr,
|
|
out.data_ptr(),
|
|
nullptr, // seqstart_q
|
|
nullptr, // seqstart_k
|
|
nullptr,
|
|
seqlen_q,
|
|
seqlen_k,
|
|
b,
|
|
seqlen_q, // max_seqlen_q
|
|
d, // hdim_q
|
|
d, // hdim_v
|
|
h, // nhead
|
|
h_k, // nhead_k
|
|
softmax_scale, // scale_s
|
|
1, // scale_p
|
|
1, // scale_o
|
|
stride_q,
|
|
stride_k,
|
|
stride_v,
|
|
stride_alibi_slopes,
|
|
stride_randval,
|
|
stride_o,
|
|
nhead_stride_q,
|
|
nhead_stride_k,
|
|
nhead_stride_v,
|
|
0, // nhead_stride_bias, FA without bias
|
|
nhead_stride_randval,
|
|
nhead_stride_lse,
|
|
nhead_stride_o,
|
|
batch_stride_q,
|
|
batch_stride_k,
|
|
batch_stride_v,
|
|
0, // batch_stride_bias, FA without bias
|
|
batch_stride_randval,
|
|
batch_stride_lse,
|
|
batch_stride_o,
|
|
mask.left,
|
|
mask.right,
|
|
static_cast<ck_tile::index_t>(mask.type),
|
|
p_dropout,
|
|
has_dropout_randval,
|
|
{drop_seed, drop_offset}};
|
|
}
|
|
|
|
std::vector<at::Tensor>
|
|
mha_fwd(at::Tensor &q, // batch_size x seqlen_q x num_heads x head_size
|
|
const at::Tensor &k, // batch_size x seqlen_k x num_heads_k x head_size
|
|
const at::Tensor &v, // batch_size x seqlen_k x num_heads_k x head_size
|
|
c10::optional<at::Tensor> &out_, // batch_size x seqlen_q x num_heads x head_size
|
|
c10::optional<at::Tensor> &alibi_slopes_, // num_heads or batch_size x num_heads
|
|
const float p_dropout,
|
|
const float softmax_scale,
|
|
bool is_causal,
|
|
int window_size_left,
|
|
int window_size_right,
|
|
const float /*softcap*/,
|
|
const bool return_dropout_randval,
|
|
c10::optional<at::Generator> gen_)
|
|
{
|
|
auto q_dtype = q.dtype();
|
|
TORCH_CHECK(q_dtype == torch::kFloat16 || q_dtype == torch::kBFloat16,
|
|
"FlashAttention only support fp16 and bf16 data type");
|
|
|
|
TORCH_CHECK(k.dtype() == q_dtype, "query and key must have the same dtype");
|
|
TORCH_CHECK(v.dtype() == q_dtype, "query and value must have the same dtype");
|
|
|
|
std::string q_dtype_str = q_dtype == torch::kFloat16 ? "fp16" : "bf16";
|
|
|
|
CHECK_DEVICE(q); CHECK_DEVICE(k); CHECK_DEVICE(v);
|
|
|
|
TORCH_CHECK(q.stride(-1) == 1, "Input tensor must have contiguous last dimension");
|
|
TORCH_CHECK(k.stride(-1) == 1, "Input tensor must have contiguous last dimension");
|
|
TORCH_CHECK(v.stride(-1) == 1, "Input tensor must have contiguous last dimension");
|
|
|
|
const auto sizes = q.sizes();
|
|
|
|
const int batch_size = sizes[0];
|
|
int seqlen_q = sizes[1];
|
|
int num_heads = sizes[2];
|
|
const int head_size_og = sizes[3];
|
|
const int seqlen_k = k.size(1);
|
|
const int num_heads_k = k.size(2);
|
|
TORCH_CHECK(batch_size > 0, "batch size must be positive");
|
|
TORCH_CHECK(head_size_og <= 256, "CK only supports head dimension at most 256");
|
|
TORCH_CHECK(num_heads % num_heads_k == 0, "Number of heads in key/value must divide number of heads in query");
|
|
|
|
if (window_size_left >= seqlen_k) { window_size_left = -1; }
|
|
if (window_size_right >= seqlen_k) { window_size_right = -1; }
|
|
|
|
// causal=true is the same as causal=false in this case
|
|
if (seqlen_q == 1 && !alibi_slopes_.has_value()) { is_causal = false; }
|
|
|
|
mask_info mask;
|
|
if (is_causal) {
|
|
// Causal is the special case where window_size_right == 0 and window_size_left < 0.
|
|
window_size_right = 0;
|
|
std::string mask_identify = "b:" + std::to_string(window_size_left) + "," + "0";
|
|
mask = mask_info::decode(mask_identify, seqlen_q, seqlen_k); // casual
|
|
}
|
|
else if (window_size_left == -1 && window_size_right == -1) {
|
|
mask = mask_info::decode("0", seqlen_q, seqlen_k); // no mask
|
|
}
|
|
else {
|
|
// Local is the more general case where window_size_right >= 0 or window_size_left >= 0.
|
|
std::string mask_identify = "b:" + std::to_string(window_size_left) + "," + std::to_string(window_size_right);
|
|
mask = mask_info::decode(mask_identify, seqlen_q, seqlen_k); // local
|
|
}
|
|
|
|
// Faster to transpose q from (b, 1, (nheads_kv ngroups), d) to (b, ngroups, nheads_kv, d) in this case
|
|
// H/t Daniel Haziza
|
|
const int seqlenq_ngroups_swapped = seqlen_q == 1 && num_heads > num_heads_k && window_size_left < 0 && window_size_right < 0 && p_dropout == 0.f && head_size_og % 8 == 0 && !alibi_slopes_.has_value();
|
|
const int ngroups = num_heads / num_heads_k;
|
|
if (seqlenq_ngroups_swapped) {
|
|
q = q.reshape({batch_size, num_heads_k, ngroups, head_size_og}).transpose(1, 2);
|
|
seqlen_q = ngroups;
|
|
num_heads = num_heads_k;
|
|
}
|
|
|
|
CHECK_SHAPE(q, batch_size, seqlen_q, num_heads, head_size_og);
|
|
CHECK_SHAPE(k, batch_size, seqlen_k, num_heads_k, head_size_og);
|
|
CHECK_SHAPE(v, batch_size, seqlen_k, num_heads_k, head_size_og);
|
|
|
|
at::Tensor q_padded, k_padded, v_padded;
|
|
if (head_size_og % 8 != 0) {
|
|
q_padded = torch::nn::functional::pad(q, torch::nn::functional::PadFuncOptions({0, 8 - head_size_og % 8}));
|
|
k_padded = torch::nn::functional::pad(k, torch::nn::functional::PadFuncOptions({0, 8 - head_size_og % 8}));
|
|
v_padded = torch::nn::functional::pad(v, torch::nn::functional::PadFuncOptions({0, 8 - head_size_og % 8}));
|
|
}
|
|
else {
|
|
q_padded = q;
|
|
k_padded = k;
|
|
v_padded = v;
|
|
}
|
|
|
|
at::Tensor out;
|
|
if (out_.has_value()) {
|
|
out = out_.value();
|
|
TORCH_CHECK(out.dtype() == q_dtype, "Output must have the same dtype as inputs");
|
|
CHECK_DEVICE(out);
|
|
TORCH_CHECK(out.stride(-1) == 1, "Output tensor must have contiguous last dimension");
|
|
CHECK_SHAPE(out, batch_size, sizes[1], sizes[2], head_size_og);
|
|
if (seqlenq_ngroups_swapped) {
|
|
out = out.reshape({batch_size, num_heads_k, ngroups, head_size_og}).transpose(1, 2);
|
|
}
|
|
if (head_size_og % 8 != 0) { out = torch::empty_like(q_padded); }
|
|
}
|
|
else {
|
|
out = torch::empty_like(q_padded);
|
|
}
|
|
|
|
auto round_multiple = [](int x, int m) { return (x + m - 1) / m * m; };
|
|
const int head_size_8x = round_multiple(head_size_og, 8);
|
|
|
|
// Otherwise the kernel will be launched from cuda:0 device
|
|
// Cast to char to avoid compiler warning about narrowing
|
|
at::cuda::CUDAGuard device_guard{(char)q.get_device()};
|
|
|
|
auto opts = q.options();
|
|
bool has_lse = true;
|
|
bool has_dropout = p_dropout > 0.0f;
|
|
|
|
at::Tensor softmax_lse;
|
|
// TODO - check gradient, only training require lse
|
|
softmax_lse = torch::empty({batch_size, num_heads, seqlen_q}, opts.dtype(torch::kFloat32));
|
|
|
|
at::Tensor p;
|
|
if (return_dropout_randval) {
|
|
TORCH_CHECK(has_dropout, "return_dropout_randval require p_dropout > 0");
|
|
p = torch::empty({batch_size, num_heads, seqlen_q, seqlen_k}, opts.dtype(torch::kUInt8));
|
|
}
|
|
|
|
uint64_t drop_seed = 1, drop_offset = 0;
|
|
int64_t counter_offset = batch_size * num_heads * ck_tile::get_warp_size();
|
|
auto options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA);
|
|
auto rng_state = torch::empty({2}, options.dtype(torch::kInt64));
|
|
|
|
if (p_dropout > 0.0) {
|
|
auto gen = at::get_generator_or_default<at::CUDAGeneratorImpl>(
|
|
gen_, at::cuda::detail::getDefaultCUDAGenerator());
|
|
// See Note [Acquire lock when using random generators]
|
|
std::lock_guard<std::mutex> lock(gen->mutex_);
|
|
auto philox_args = gen->philox_cuda_state(counter_offset);
|
|
std::tie(drop_seed, drop_offset) = flash::unpack(philox_args);
|
|
}
|
|
|
|
rng_state[0] = *(reinterpret_cast<int64_t*>(&drop_seed));
|
|
rng_state[1] = *(reinterpret_cast<int64_t*>(&drop_offset));
|
|
|
|
if (seqlen_k > 0) {
|
|
auto stream = at::cuda::getCurrentHIPStream().stream();
|
|
ck_tile::stream_config stream_config{stream};
|
|
|
|
auto traits =
|
|
get_ck_fmha_fwd_traits(
|
|
mask,
|
|
q_dtype_str,
|
|
head_size_8x,
|
|
has_dropout,
|
|
has_lse,
|
|
alibi_slopes_.has_value());
|
|
|
|
auto args =
|
|
get_ck_fmha_fwd_args(
|
|
has_lse,
|
|
return_dropout_randval,
|
|
mask,
|
|
batch_size,
|
|
seqlen_q,
|
|
seqlen_k,
|
|
num_heads,
|
|
num_heads_k,
|
|
head_size_8x,
|
|
q_padded,
|
|
k_padded,
|
|
v_padded,
|
|
alibi_slopes_,
|
|
out,
|
|
softmax_lse,
|
|
p,
|
|
softmax_scale,
|
|
p_dropout,
|
|
drop_seed,
|
|
drop_offset);
|
|
|
|
float t = fmha_fwd(traits, args, stream_config);
|
|
TORCH_CHECK(t >= 0, "invalid argument for fmha_fwd");
|
|
}
|
|
else {
|
|
// If seqlen_k == 0, then we have an empty tensor. We need to set the output to 0.
|
|
out.zero_();
|
|
softmax_lse.fill_(std::numeric_limits<float>::infinity());
|
|
}
|
|
|
|
at::Tensor out_padded = out;
|
|
if (head_size_og % 8 != 0) {
|
|
out = out.index({"...", torch::indexing::Slice(torch::indexing::None, head_size_og)});
|
|
if (out_.has_value()) { out_.value().copy_(out); }
|
|
}
|
|
|
|
if (seqlenq_ngroups_swapped) {
|
|
out = out.transpose(1, 2).reshape({batch_size, 1, num_heads_k * seqlen_q, head_size_og});
|
|
out_padded = out_padded.transpose(1, 2).reshape({batch_size, 1, num_heads_k * seqlen_q, head_size_og});
|
|
q_padded = q_padded.transpose(1, 2).reshape({batch_size, 1, num_heads_k * seqlen_q, head_size_og});
|
|
softmax_lse = softmax_lse.reshape({batch_size, num_heads_k * seqlen_q, 1});
|
|
}
|
|
return {out, q_padded, k_padded, v_padded, out_padded, softmax_lse, p, rng_state};
|
|
}
|