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Commit 5ab9b366 authored by Tri Dao's avatar Tri Dao
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Clean up alibi, implement non-causal alibi

parent bc28eacc
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Showing with 309 additions and 1199 deletions
csrc/flash_attn/flash_api.cpp
View file @ 5ab9b366
......@@ -253,12 +253,12 @@ 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,
const int window_size_left,
int window_size_right,
c10::optional<at::Tensor> &alibi_slopes_, // batch_size x num_heads
const bool return_softmax,
c10::optional<at::Generator> gen_) {
......@@ -297,13 +297,13 @@ mha_fwd(at::Tensor &q, // batch_size x seqlen_q x num_heads x head_size
TORCH_CHECK(head_size_og <= 256, "FlashAttention forward 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 (seqlen_q == 1) { is_causal = false; } // causal=true is the same as causal=false in this case
// causal=true is the same as causal=false in this case
if (seqlen_q == 1 && !alibi_slopes_.has_value()) { is_causal = false; }
if (is_causal) { window_size_right = 0; }
// Faster to transpose q from (b, 1, (nheads_kv ngroups), d) to (b, ngroups, nheads_kv, d) in this case
// H/t Daniel Haziza
// TODO: how to make "seqlenq_ngroups_swapped" and ALiBi work together?
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 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();
if (seqlenq_ngroups_swapped) {
const int ngroups = num_heads / num_heads_k;
q = q.reshape({batch_size, num_heads_k, ngroups, head_size_og}).transpose(1, 2);
......@@ -416,12 +416,11 @@ mha_fwd(at::Tensor &q, // batch_size x seqlen_q x num_heads x head_size
TORCH_CHECK(alibi_slopes.dtype() == torch::kFloat32, "ALiBi slopes must have dtype fp32");
CHECK_DEVICE(alibi_slopes);
TORCH_CHECK(alibi_slopes.stride(-1) == 1, "ALiBi slopes tensor must have contiguous last dimension");
CHECK_SHAPE(alibi_slopes, batch_size, num_heads);
params.has_alibi = true;
TORCH_CHECK(alibi_slopes.sizes() == torch::IntArrayRef({num_heads}) || alibi_slopes.sizes() == torch::IntArrayRef({batch_size, num_heads}));
params.alibi_slopes_ptr = alibi_slopes.data_ptr();
params.alibi_slopes_batch_stride = alibi_slopes.stride(0);
params.alibi_slopes_batch_stride = alibi_slopes.dim() == 2 ? alibi_slopes.stride(0) : 0;
} else {
params.has_alibi = false;
params.alibi_slopes_ptr = nullptr;
}
if (seqlen_k > 0) {
......@@ -456,6 +455,7 @@ mha_varlen_fwd(const at::Tensor &q, // total_q x num_heads x head_size, total_q
const at::Tensor &cu_seqlens_q, // b+1
const at::Tensor &cu_seqlens_k, // b+1
c10::optional<at::Tensor> &seqused_k, // b. If given, only this many elements of each batch element's keys are used.
c10::optional<at::Tensor> &alibi_slopes_, // num_heads or b x num_heads
const int max_seqlen_q,
const int max_seqlen_k,
const float p_dropout,
......@@ -464,7 +464,6 @@ mha_varlen_fwd(const at::Tensor &q, // total_q x num_heads x head_size, total_q
const bool is_causal,
const int window_size_left,
int window_size_right,
c10::optional<at::Tensor> &alibi_slopes_, // b x num_heads
const bool return_softmax,
c10::optional<at::Generator> gen_) {
......@@ -612,12 +611,11 @@ mha_varlen_fwd(const at::Tensor &q, // total_q x num_heads x head_size, total_q
TORCH_CHECK(alibi_slopes.dtype() == torch::kFloat32, "ALiBi slopes must have dtype fp32");
CHECK_DEVICE(alibi_slopes);
TORCH_CHECK(alibi_slopes.stride(-1) == 1, "ALiBi slopes tensor must have contiguous last dimension");
CHECK_SHAPE(alibi_slopes, batch_size, num_heads);
params.has_alibi = true;
TORCH_CHECK(alibi_slopes.sizes() == torch::IntArrayRef({num_heads}) || alibi_slopes.sizes() == torch::IntArrayRef({batch_size, num_heads}));
params.alibi_slopes_ptr = alibi_slopes.data_ptr();
params.alibi_slopes_batch_stride = alibi_slopes.stride(0);
params.alibi_slopes_batch_stride = alibi_slopes.dim() == 2 ? alibi_slopes.stride(0) : 0;
} else {
params.has_alibi = false;
params.alibi_slopes_ptr = nullptr;
}
auto stream = at::cuda::getCurrentCUDAStream().stream();
......@@ -664,12 +662,12 @@ mha_bwd(const at::Tensor &dout, // batch_size x seqlen_q x num_heads, x head_si
c10::optional<at::Tensor> &dq_, // batch_size x seqlen_q x num_heads x head_size
c10::optional<at::Tensor> &dk_, // batch_size x seqlen_k x num_heads_k x head_size
c10::optional<at::Tensor> &dv_, // batch_size x seqlen_k x num_heads_k x head_size
c10::optional<at::Tensor> &alibi_slopes_, // num_heads or batch_size x num_heads
const float p_dropout, // probability to drop
const float softmax_scale,
const bool is_causal,
const int window_size_left,
int window_size_right,
c10::optional<at::Tensor> &alibi_slopes_, // batch_size x num_heads
c10::optional<at::Generator> gen_,
c10::optional<at::Tensor> &rng_state) {
......@@ -848,12 +846,11 @@ mha_bwd(const at::Tensor &dout, // batch_size x seqlen_q x num_heads, x head_si
TORCH_CHECK(alibi_slopes.dtype() == torch::kFloat32, "ALiBi slopes must have dtype fp32");
CHECK_DEVICE(alibi_slopes);
TORCH_CHECK(alibi_slopes.stride(-1) == 1, "ALiBi slopes tensor must have contiguous last dimension");
CHECK_SHAPE(alibi_slopes, batch_size, num_heads);
params.has_alibi = true;
TORCH_CHECK(alibi_slopes.sizes() == torch::IntArrayRef({num_heads}) || alibi_slopes.sizes() == torch::IntArrayRef({batch_size, num_heads}));
params.alibi_slopes_ptr = alibi_slopes.data_ptr();
params.alibi_slopes_batch_stride = alibi_slopes.stride(0);
params.alibi_slopes_batch_stride = alibi_slopes.dim() == 2 ? alibi_slopes.stride(0) : 0;
} else {
params.has_alibi = false;
params.alibi_slopes_ptr = nullptr;
}
if (seqlen_q > 0) {
......@@ -891,6 +888,7 @@ mha_varlen_bwd(const at::Tensor &dout, // total_q x num_heads, x head_size
c10::optional<at::Tensor> &dv_, // total_k x num_heads_k x head_size, total_k := \sum_{i=0}^{b} s_i
const at::Tensor &cu_seqlens_q, // b+1
const at::Tensor &cu_seqlens_k, // b+1
c10::optional<at::Tensor> &alibi_slopes_, // num_heads or b x num_heads
const int max_seqlen_q,
const int max_seqlen_k, // max sequence length to choose the kernel
const float p_dropout, // probability to drop
......@@ -899,7 +897,6 @@ mha_varlen_bwd(const at::Tensor &dout, // total_q x num_heads, x head_size
const bool is_causal,
const int window_size_left,
int window_size_right,
c10::optional<at::Tensor> &alibi_slopes_, // b x num_heads
c10::optional<at::Generator> gen_,
c10::optional<at::Tensor> &rng_state) {
......@@ -1094,12 +1091,11 @@ mha_varlen_bwd(const at::Tensor &dout, // total_q x num_heads, x head_size
TORCH_CHECK(alibi_slopes.dtype() == torch::kFloat32, "ALiBi slopes must have dtype fp32");
CHECK_DEVICE(alibi_slopes);
TORCH_CHECK(alibi_slopes.stride(-1) == 1, "ALiBi slopes tensor must have contiguous last dimension");
CHECK_SHAPE(alibi_slopes, batch_size, num_heads);
params.has_alibi = true;
TORCH_CHECK(alibi_slopes.sizes() == torch::IntArrayRef({num_heads}) || alibi_slopes.sizes() == torch::IntArrayRef({batch_size, num_heads}));
params.alibi_slopes_ptr = alibi_slopes.data_ptr();
params.alibi_slopes_batch_stride = alibi_slopes.stride(0);
params.alibi_slopes_batch_stride = alibi_slopes.dim() == 2 ? alibi_slopes.stride(0) : 0;
} else {
params.has_alibi = false;
params.alibi_slopes_ptr = nullptr;
}
launch(params, stream, /*configure=*/false);
......@@ -1128,14 +1124,14 @@ mha_fwd_kvcache(at::Tensor &q, // batch_size x seqlen_q x num_he
c10::optional<const at::Tensor> &rotary_cos_, // seqlen_ro x (rotary_dim / 2)
c10::optional<const at::Tensor> &rotary_sin_, // seqlen_ro x (rotary_dim / 2)
c10::optional<const at::Tensor> &cache_batch_idx_, // indices to index into the KV cache
c10::optional<at::Tensor> &alibi_slopes_, // num_heads or batch_size x num_heads
c10::optional<at::Tensor> &out_, // batch_size x seqlen_q x num_heads x head_size
const float softmax_scale,
bool is_causal,
const int window_size_left,
int window_size_right,
bool is_rotary_interleaved, // if true, rotary combines indices 0 & 1, else indices 0 & rotary_dim / 2
int num_splits,
c10::optional<at::Tensor> &alibi_slopes_ // batch_size x num_heads
int num_splits
) {
auto dprops = at::cuda::getCurrentDeviceProperties();
......@@ -1174,13 +1170,13 @@ mha_fwd_kvcache(at::Tensor &q, // batch_size x seqlen_q x num_he
TORCH_CHECK(head_size_og <= 256, "FlashAttention forward 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 (seqlen_q == 1) { is_causal = false; } // causal=true is the same as causal=false in this case
// causal=true is the same as causal=false in this case
if (seqlen_q == 1 && !alibi_slopes_.has_value()) { is_causal = false; }
if (is_causal) { window_size_right = 0; }
// Faster to transpose q from (b, 1, (nheads_kv ngroups), d) to (b, ngroups, nheads_kv, d) in this case
// H/t Daniel Haziza
// TODO: how to make "seqlenq_ngroups_swapped" and ALiBi work together?
const int seqlenq_ngroups_swapped = seqlen_q == 1 && num_heads > num_heads_k && window_size_left < 0 && window_size_right < 0 && head_size_og % 8 == 0 && !(alibi_slopes_.has_value());
const int seqlenq_ngroups_swapped = seqlen_q == 1 && num_heads > num_heads_k && window_size_left < 0 && window_size_right < 0 && head_size_og % 8 == 0 && !alibi_slopes_.has_value();
if (seqlenq_ngroups_swapped) {
const int ngroups = num_heads / num_heads_k;
q = q.reshape({batch_size, num_heads_k, ngroups, head_size_og}).transpose(1, 2);
......@@ -1347,12 +1343,11 @@ mha_fwd_kvcache(at::Tensor &q, // batch_size x seqlen_q x num_he
TORCH_CHECK(alibi_slopes.dtype() == torch::kFloat32, "ALiBi slopes must have dtype fp32");
CHECK_DEVICE(alibi_slopes);
TORCH_CHECK(alibi_slopes.stride(-1) == 1, "ALiBi slopes tensor must have contiguous last dimension");
CHECK_SHAPE(alibi_slopes, batch_size, num_heads);
params.has_alibi = true;
TORCH_CHECK(alibi_slopes.sizes() == torch::IntArrayRef({num_heads}) || alibi_slopes.sizes() == torch::IntArrayRef({batch_size, num_heads}));
params.alibi_slopes_ptr = alibi_slopes.data_ptr();
params.alibi_slopes_batch_stride = alibi_slopes.stride(0);
params.alibi_slopes_batch_stride = alibi_slopes.dim() == 2 ? alibi_slopes.stride(0) : 0;
} else {
params.has_alibi = false;
params.alibi_slopes_ptr = nullptr;
}
auto stream = at::cuda::getCurrentCUDAStream().stream();
......
csrc/flash_attn/src/alibi.h
View file @ 5ab9b366
......@@ -13,22 +13,32 @@ using namespace cute;
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Engine, typename Layout>
template <bool Is_causal, typename Engine, typename Layout>
inline __device__ void apply_alibi(Tensor<Engine, Layout> &tensor,
const int col_idx_offset_,
const int max_seqlen_k,
const int row_idx_offset_,
const int row_idx_offset,
const int max_seqlen_q,
const int warp_row_stride,
const int head_idx,
const float softmax_scale,
const float alibi_slope) {
// tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
static_assert(Layout::rank == 2, "Only support 2D Tensor");
const int lane_id = threadIdx.x % 32;
const int row_idx_offset = row_idx_offset_;
const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
const float alibi_slope_unscaled = alibi_slope / softmax_scale;
if constexpr (Is_causal) { // Simpler, we add the same bias vector to all rows
#pragma unroll
for (int nj = 0; nj < size<1, 1>(tensor); ++nj) {
const int col_idx_base = col_idx_offset + nj * 8;
#pragma unroll
for (int j = 0; j < size<1, 0>(tensor); ++j) {
const int col_idx = col_idx_base + j;
#pragma unroll
for (int mi = 0; mi < size<0>(tensor); ++mi) {
tensor(mi, make_coord(j, nj)) += alibi_slope * col_idx;
}
}
}
} else { // Bias depends on both row_idx and col_idx
#pragma unroll
for (int mi = 0; mi < size<0, 1>(tensor); ++mi) {
const int row_idx_base = row_idx_offset + mi * warp_row_stride;
......@@ -41,9 +51,7 @@ inline __device__ void apply_alibi(Tensor<Engine, Layout> &tensor,
#pragma unroll
for (int j = 0; j < size<1, 0>(tensor); ++j) {
const int col_idx = col_idx_base + j;
const float alibi = alibi_slope_unscaled * col_idx;
if (col_idx < max_seqlen_k && row_idx < max_seqlen_q) {
tensor(make_coord(i, mi), make_coord(j, nj)) += alibi;
tensor(make_coord(i, mi), make_coord(j, nj)) -= alibi_slope * abs(row_idx + max_seqlen_k - max_seqlen_q - col_idx);
}
}
}
......
csrc/flash_attn/src/flash.h
View file @ 5ab9b366
......@@ -131,10 +131,6 @@ struct Flash_fwd_params : public Qkv_params {
int num_splits; // For split-KV version
// float alibi_start;
// float alibi_ratio;
bool has_alibi;
void * __restrict__ alibi_slopes_ptr;
index_t alibi_slopes_batch_stride;
};
......
csrc/flash_attn/src/flash_bwd_kernel.h
View file @ 5ab9b366
......@@ -753,8 +753,12 @@ inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const in
#pragma unroll
for (int mi = 0; mi < size(lse); ++mi) {
const int row = get<0>(taccScS_row(mi));
lse(mi) = Is_even_MN || row < binfo.actual_seqlen_q - m_block * kBlockM ? gLSE(row) : 0;
lse(mi) = Is_even_MN || row < binfo.actual_seqlen_q - m_block * kBlockM ? gLSE(row) : INFINITY;
}
// We want LSE = inf if the row is OOB. In that case Q would be zero, K would be zero,
// and scores would be zero. With LSE = 0, probs will be all 1's, and when we multiply
// with V (which would be zero), we're fine. However, with ALiBi, we might modify these
// scores, and probs can become NaN. Instead if we set LSE = inf for OOB rows, probs are always 0.
// Tensor tKrK = make_fragment_like(tKsK);
// // cute::copy(gmem_tiled_copy_QKV, tKgK(_, _, _, 0), tKrK);
......@@ -792,18 +796,7 @@ inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const in
clear(acc_dv);
clear(acc_dk);
float alibi_slope = 0.0f;
if (Has_alibi) {
Tensor gAS = make_tensor(
make_gmem_ptr(
reinterpret_cast<ElementAccum *>(params.alibi_slopes_ptr)
+ bidb * params.alibi_slopes_batch_stride + bidh
),
Shape<_1>{});
Tensor rAS = make_fragment_like(gAS);
cute::copy(gAS, rAS);
alibi_slope = rAS(0);
}
float alibi_slope = !Has_alibi ? 0.0f : reinterpret_cast<float *>(params.alibi_slopes_ptr)[bidb * params.alibi_slopes_batch_stride + bidh] / params.scale_softmax;
for (; m_block >= m_block_min; --m_block) {
Tensor acc_s = partition_fragment_C(tiled_mma_sdp, Shape<Int<kBlockM>, Int<kBlockN>>{}); // (MMA=4, MMA_N, MMA_N)
......@@ -830,14 +823,13 @@ inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const in
// if (cute::thread(32, 0)) { print(scores); }
if (Has_alibi) {
flash::apply_alibi(
flash::apply_alibi<Is_causal>(
scores,
n_block * kBlockN + (tidx / 32 / AtomLayoutMS) * MMA_N_SdP * 16,
binfo.actual_seqlen_k,
m_block * kBlockM + get<0>(taccScS_row(0)),
binfo.actual_seqlen_q,
AtomLayoutMS * 16,
bidh, params.scale_softmax,
alibi_slope
);
}
......@@ -1403,18 +1395,7 @@ inline __device__ void compute_dq_dk_dv_1rowblock(const Params &params, const in
clear(acc_dq);
float alibi_slope = 0.0f;
if (Has_alibi) {
Tensor gAS = make_tensor(
make_gmem_ptr(
reinterpret_cast<ElementAccum *>(params.alibi_slopes_ptr)
+ bidb * params.alibi_slopes_batch_stride + bidh
),
Shape<_1>{});
Tensor rAS = make_fragment_like(gAS);
cute::copy(gAS, rAS);
alibi_slope = rAS(0);
}
float alibi_slope = !Has_alibi ? 0.0f : reinterpret_cast<float *>(params.alibi_slopes_ptr)[bidb * params.alibi_slopes_batch_stride + bidh] / params.scale_softmax;
for (; n_block >= 0; --n_block) {
Tensor acc_s = partition_fragment_C(tiled_mma_sdp, Shape<Int<kBlockM>, Int<kBlockN>>{}); // (MMA=4, MMA_M_SdP, MMA_N)
......@@ -1429,14 +1410,13 @@ inline __device__ void compute_dq_dk_dv_1rowblock(const Params &params, const in
Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
if (Has_alibi) {
flash::apply_alibi(
flash::apply_alibi<Is_causal>(
scores,
n_block * kBlockN + (tidx / 32 / AtomLayoutMS) * MMA_N_SdP * 16,
binfo.actual_seqlen_k,
m_block * kBlockM + get<0>(taccScS_row(0)),
binfo.actual_seqlen_q,
AtomLayoutMS * 16,
bidh, params.scale_softmax,
alibi_slope
);
}
......
csrc/flash_attn/src/flash_bwd_launch_template.h
View file @ 5ab9b366
......@@ -64,12 +64,11 @@ void run_flash_bwd_seqk_parallel(Flash_bwd_params &params, cudaStream_t stream,
BOOL_SWITCH(is_even_MN, IsEvenMNConst, [&] {
BOOL_SWITCH(is_even_K, IsEvenKConst, [&] {
BOOL_SWITCH((params.window_size_left >= 0 || params.window_size_right >= 0) && !params.is_causal, Is_local, [&] {
BOOL_SWITCH(params.has_alibi, Has_alibi, [&] {
BOOL_SWITCH(params.alibi_slopes_ptr != nullptr, Has_alibi, [&] {
// If not IsEvenKConst, we also set IsEvenMNConst to false to reduce number of templates.
// If head dim > 128, set IsEvenMNConst to false to reduce number of templates
// If Is_local, set Is_causal to false
auto kernel = &flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel<Kernel_traits, Is_dropout, Is_causal && !Is_local, Is_local, Has_alibi, IsEvenMNConst && IsEvenKConst && !Is_local && Kernel_traits::kHeadDim <= 128, IsEvenKConst>;
// auto kernel = &flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel<Kernel_traits, Is_dropout, IsCausalConst, IsEvenMNConst, true>;
auto kernel = &flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel<Kernel_traits, Is_dropout, Is_causal, Is_local && !Is_causal, Has_alibi, IsEvenMNConst && IsEvenKConst && !Is_local && Kernel_traits::kHeadDim <= 128, IsEvenKConst>;
if (smem_size_dq_dk_dv >= 48 * 1024) {
C10_CUDA_CHECK(cudaFuncSetAttribute(
kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size_dq_dk_dv));
......@@ -109,7 +108,7 @@ void run_flash_bwd_seqq_parallel(Flash_bwd_params &params, cudaStream_t stream,
BOOL_SWITCH(params.is_causal, Is_causal, [&] {
BOOL_SWITCH(is_even_N, IsEvenNConst, [&] {
BOOL_SWITCH(is_even_K, IsEvenKConst, [&] {
BOOL_SWITCH(params.has_alibi, Has_alibi, [&] {
BOOL_SWITCH(params.alibi_slopes_ptr != nullptr, Has_alibi, [&] {
// If not IsEvenKConst, we also set IsEvenMNConst to false to reduce number of templates.
auto kernel = &flash_bwd_dq_dk_dv_loop_seqq_parallel_kernel<Kernel_traits, Is_dropout, Is_causal, Has_alibi, IsEvenNConst && IsEvenKConst, IsEvenKConst>;
// auto kernel = &flash_bwd_dq_dk_dv_loop_seqq_parallel_kernel<Kernel_traits, false, false, IsEvenNConst, IsEvenKConst>;
......
csrc/flash_attn/src/flash_fwd_kernel.h
View file @ 5ab9b366
......@@ -322,28 +322,14 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
clear(acc_o);
float alibi_slope = !Has_alibi ? 0.0f : reinterpret_cast<float *>(params.alibi_slopes_ptr)[bidb * params.alibi_slopes_batch_stride + bidh] / params.scale_softmax;
// For performance reason, we separate out two kinds of iterations:
// those that need masking on S, and those that don't.
// We need masking on S for the very last block when K and V has length not multiple of kBlockN.
// We also need masking on S if it's causal, for the last ceil_div(kBlockM, kBlockN) blocks.
// We will have at least 1 "masking" iteration.
float alibi_slope = 0.0f;
if (Has_alibi) {
Tensor gAS = make_tensor(
make_gmem_ptr(
reinterpret_cast<ElementAccum *>(params.alibi_slopes_ptr)
+ bidb * params.alibi_slopes_batch_stride + bidh
),
Shape<_1>{});
Tensor rAS = make_fragment_like(gAS);
cute::copy(gAS, rAS);
alibi_slope = rAS(0);
// if (m_block == 0 && tidx == 0) {
// printf("%d,%d,%f\n", bidb, bidh, alibi_slope);
// }
}
// If not even_N, then seqlen_k might end in the middle of a block. In that case we need to
// mask 2 blocks (e.g. when kBlockM == kBlockN), not just 1.
constexpr int n_masking_steps = (!Is_causal && !Is_local)
......@@ -382,14 +368,13 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
// can produce Inf / NaN.
if (Has_alibi) {
flash::apply_alibi(
flash::apply_alibi<Is_causal>(
scores,
n_block * kBlockN,
binfo.actual_seqlen_k,
m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
binfo.actual_seqlen_q,
kNWarps * 16,
bidh, params.scale_softmax,
alibi_slope
);
}
......@@ -500,14 +485,13 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
if (Has_alibi) {
flash::apply_alibi(
flash::apply_alibi<Is_causal>(
scores,
n_block * kBlockN,
binfo.actual_seqlen_k,
m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
binfo.actual_seqlen_q,
kNWarps * 16,
bidh, params.scale_softmax,
alibi_slope
);
}
......@@ -950,28 +934,14 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
clear(acc_o);
float alibi_slope = !Has_alibi ? 0.0f : reinterpret_cast<float *>(params.alibi_slopes_ptr)[bidb * params.alibi_slopes_batch_stride + bidh] / params.scale_softmax;
// For performance reason, we separate out two kinds of iterations:
// those that need masking on S, and those that don't.
// We need masking on S for the very last block when K and V has length not multiple of kBlockN.
// We also need masking on S if it's causal, for the last ceil_div(kBlockM, kBlockN) blocks.
// We will have at least 1 "masking" iteration.
float alibi_slope = 0.0f;
if (Has_alibi) {
Tensor gAS = make_tensor(
make_gmem_ptr(
reinterpret_cast<ElementAccum *>(params.alibi_slopes_ptr)
+ bidb * params.alibi_slopes_batch_stride + bidh
),
Shape<_1>{});
Tensor rAS = make_fragment_like(gAS);
cute::copy(gAS, rAS);
alibi_slope = rAS(0);
// if (m_block == 0 && tidx == 0) {
// printf("%d,%d,%f\n", bidb, bidh, alibi_slope);
// }
}
// If not even_N, then seqlen_k might end in the middle of a block. In that case we need to
// mask 2 blocks (e.g. when kBlockM == kBlockN), not just 1.
constexpr int n_masking_steps = (!Is_causal && !Is_local)
......@@ -1006,14 +976,13 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
if (Has_alibi) {
flash::apply_alibi(
flash::apply_alibi<Is_causal>(
scores,
n_block * kBlockN,
binfo.actual_seqlen_k,
m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
binfo.actual_seqlen_q,
kNWarps * 16,
bidh, params.scale_softmax,
alibi_slope
);
}
......@@ -1099,14 +1068,13 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
if (Has_alibi) {
flash::apply_alibi(
flash::apply_alibi<Is_causal>(
scores,
n_block * kBlockN,
binfo.actual_seqlen_k,
m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
binfo.actual_seqlen_q,
kNWarps * 16,
bidh, params.scale_softmax,
alibi_slope
);
}
......
csrc/flash_attn/src/flash_fwd_launch_template.h
View file @ 5ab9b366
......@@ -45,7 +45,7 @@ void run_flash_fwd(Flash_fwd_params &params, cudaStream_t stream) {
BOOL_SWITCH(is_even_K, IsEvenKConst, [&] {
BOOL_SWITCH((params.window_size_left >= 0 || params.window_size_right >= 0) && !Is_causal, Is_local, [&] {
BOOL_SWITCH(return_softmax, ReturnSoftmaxConst, [&] {
BOOL_SWITCH(params.has_alibi, Has_alibi, [&] {
BOOL_SWITCH(params.alibi_slopes_ptr != nullptr, Has_alibi, [&] {
// Will only return softmax if dropout, to reduce compilation time.
// If not IsEvenKConst, we also set IsEvenMNConst to false to reduce number of templates.
// If return_softmax, set IsEvenMNConst to false to reduce number of templates
......@@ -86,7 +86,7 @@ void run_flash_splitkv_fwd(Flash_fwd_params &params, cudaStream_t stream) {
BOOL_SWITCH((params.window_size_left >= 0 || params.window_size_right >= 0) && !Is_causal, Is_local, [&] {
BOOL_SWITCH(params.num_splits > 1, Split, [&] {
BOOL_SWITCH(params.knew_ptr != nullptr, Append_KV, [&] {
BOOL_SWITCH(params.has_alibi, Has_alibi, [&] {
BOOL_SWITCH(params.alibi_slopes_ptr != nullptr, Has_alibi, [&] {
// If Append_KV, then we must have seqlen_offsets, which means cu_seqlens_k != nullptr.
// If not IsEvenKConst, we also set IsEvenMNConst to false to reduce number of templates.
// If Is_local, set Is_causal to false
......
csrc/flash_attn/src/softmax.h
View file @ 5ab9b366
......@@ -141,14 +141,12 @@ inline __device__ void apply_mask(Tensor<Engine, Layout> &tensor, const int max_
template <bool HasWSLeft=true, typename Engine, typename Layout>
inline __device__ void apply_mask_local(Tensor<Engine, Layout> &tensor, const int col_idx_offset_,
const int max_seqlen_k, const int row_idx_offset_,
const int max_seqlen_k, const int row_idx_offset,
const int max_seqlen_q, const int warp_row_stride,
const int window_size_left, const int window_size_right) {
// tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
static_assert(Layout::rank == 2, "Only support 2D Tensor");
const int lane_id = threadIdx.x % 32;
// const int row_idx_offset = row_idx_offset_ + lane_id / 4;
const int row_idx_offset = row_idx_offset_;
const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
#pragma unroll
for (int mi = 0; mi < size<0, 1>(tensor); ++mi) {
......@@ -180,17 +178,17 @@ inline __device__ void apply_mask_local(Tensor<Engine, Layout> &tensor, const in
template <typename Engine, typename Layout>
inline __device__ void apply_mask_causal(Tensor<Engine, Layout> &tensor, const int col_idx_offset_,
const int max_seqlen_k, const int row_idx_offset_,
const int max_seqlen_k, const int row_idx_offset,
const int max_seqlen_q, const int warp_row_stride) {
// Causal masking is equivalent to local masking with window_size_left = infinity and window_size_right = 0
apply_mask_local</*HasWSLeft=*/false>(tensor, col_idx_offset_, max_seqlen_k, row_idx_offset_,
apply_mask_local</*HasWSLeft=*/false>(tensor, col_idx_offset_, max_seqlen_k, row_idx_offset,
max_seqlen_q, warp_row_stride, -1, 0);
}
template <typename Engine0, typename Layout0, typename Engine1, typename Layout1>
inline __device__ void apply_mask_causal_w_idx(
Tensor<Engine0, Layout0> &tensor, Tensor<Engine1, Layout1> const &idx_rowcol,
const int col_idx_offset_, const int max_seqlen_k, const int row_idx_offset_)
const int col_idx_offset_, const int max_seqlen_k, const int row_idx_offset)
{
// tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
static_assert(Layout0::rank == 2, "Only support 2D Tensor");
......@@ -199,7 +197,7 @@ inline __device__ void apply_mask_causal_w_idx(
CUTE_STATIC_ASSERT_V(size<1>(tensor) == size<1>(idx_rowcol));
#pragma unroll
for (int mi = 0; mi < size<0>(tensor); ++mi) {
const int col_idx_limit = std::min(max_seqlen_k, 1 + row_idx_offset_ + get<0>(idx_rowcol(mi, 0)));
const int col_idx_limit = std::min(max_seqlen_k, 1 + row_idx_offset + get<0>(idx_rowcol(mi, 0)));
#pragma unroll
for (int ni = 0; ni < size<1, 1>(tensor); ++ni) {
if (col_idx_offset_ + get<1>(idx_rowcol(0, ni)) >= col_idx_limit) {
......
flash_attn/flash_attn_interface.py
View file @ 5ab9b366
......@@ -53,12 +53,12 @@ def _flash_attn_forward(
k,
v,
None,
alibi_slopes,
dropout_p,
softmax_scale,
causal,
window_size[0],
window_size[1],
alibi_slopes,
return_softmax,
None,
)
......@@ -90,6 +90,7 @@ def _flash_attn_varlen_forward(
cu_seqlens_q,
cu_seqlens_k,
None,
alibi_slopes,
max_seqlen_q,
max_seqlen_k,
dropout_p,
......@@ -98,7 +99,6 @@ def _flash_attn_varlen_forward(
causal,
window_size[0],
window_size[1],
alibi_slopes,
return_softmax,
None,
)
......@@ -137,12 +137,12 @@ def _flash_attn_backward(
dq,
dk,
dv,
alibi_slopes,
dropout_p,
softmax_scale,
causal,
window_size[0],
window_size[1],
alibi_slopes,
None,
rng_state,
)
......@@ -185,6 +185,7 @@ def _flash_attn_varlen_backward(
dv,
cu_seqlens_q,
cu_seqlens_k,
alibi_slopes,
max_seqlen_q,
max_seqlen_k,
dropout_p,
......@@ -193,7 +194,6 @@ def _flash_attn_varlen_backward(
causal,
window_size[0],
window_size[1],
alibi_slopes,
None,
rng_state,
)
......@@ -613,6 +613,8 @@ def flash_attn_qkvpacked_func(
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
window_size: (left, right). If not (-1, -1), implements sliding window local attention.
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of (-alibi_slope * |i - j|) is added to
the attention score of query i and key j.
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
......@@ -673,6 +675,9 @@ def flash_attn_kvpacked_func(
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
window_size: (left, right). If not (-1, -1), implements sliding window local attention.
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
(-alibi_slope * |i + seqlen_k - seqlen_q - j|)
is added to the attention score of query i and key j.
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
......@@ -732,6 +737,9 @@ def flash_attn_func(
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
window_size: (left, right). If not (-1, -1), implements sliding window local attention.
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
(-alibi_slope * |i + seqlen_k - seqlen_q - j|)
is added to the attention score of query i and key j.
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
......@@ -780,6 +788,8 @@ def flash_attn_varlen_qkvpacked_func(
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
window_size: (left, right). If not (-1, -1), implements sliding window local attention.
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of (-alibi_slope * |i - j|)
is added to the attention score of query i and key j.
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
......@@ -858,6 +868,9 @@ def flash_attn_varlen_kvpacked_func(
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
window_size: (left, right). If not (-1, -1), implements sliding window local attention.
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
(-alibi_slope * |i + seqlen_k - seqlen_q - j|)
is added to the attention score of query i and key j.
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
......@@ -938,6 +951,9 @@ def flash_attn_varlen_func(
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
window_size: (left, right). If not (-1, -1), implements sliding window local attention.
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
(-alibi_slope * |i + seqlen_k - seqlen_q - j|)
is added to the attention score of query i and key j.
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
......@@ -981,8 +997,8 @@ def flash_attn_with_kvcache(
causal=False,
window_size=(-1, -1), # -1 means infinite context window
rotary_interleaved=True,
num_splits=0,
alibi_slopes=None,
num_splits=0,
):
"""
If k and v are not None, k_cache and v_cache will be updated *inplace* with the new values from
......@@ -1050,6 +1066,9 @@ def flash_attn_with_kvcache(
If True, rotary embedding will combine dimensions 0 & 1, 2 & 3, etc. If False,
rotary embedding will combine dimensions 0 & rotary_dim / 2, 1 & rotary_dim / 2 + 1
(i.e. GPT-NeoX style).
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
(-alibi_slope * |i + seqlen_k - seqlen_q - j|)
is added to the attention score of query i and key j.
num_splits: int. If > 1, split the key/value into this many chunks along the sequence.
If num_splits == 1, we don't split the key/value. If num_splits == 0, we use a heuristic
to automatically determine the number of splits.
......@@ -1080,6 +1099,7 @@ def flash_attn_with_kvcache(
rotary_cos,
rotary_sin,
cache_batch_idx,
alibi_slopes,
None,
softmax_scale,
causal,
......@@ -1087,6 +1107,5 @@ def flash_attn_with_kvcache(
window_size[1],
rotary_interleaved,
num_splits,
alibi_slopes,
)
return out
tests/test_alibi.py deleted 100644 → 0
View file @ bc28eacc
This diff is collapsed.
tests/test_flash_attn.py
View file @ 5ab9b366
This diff is collapsed.
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