Clean up alibi, implement non-causal alibi

csrc/flash_attn/flash_api.cpp

@@ -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

@@ -13,37 +13,45 @@ 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;
-    #pragma unroll
-    for (int mi = 0; mi < size<0, 1>(tensor); ++mi) {
-        const int row_idx_base = row_idx_offset + mi * warp_row_stride;
+    if constexpr (Is_causal) {  // Simpler, we add the same bias vector to all rows
         #pragma unroll
-        for (int i = 0; i < size<0, 0>(tensor); ++i) {
-            const int row_idx = row_idx_base + i * 8;
+        for (int nj = 0; nj < size<1, 1>(tensor); ++nj) {
+            const int col_idx_base = col_idx_offset + nj * 8;
             #pragma unroll
-            for (int nj = 0; nj < size<1, 1>(tensor); ++nj) {
-                const int col_idx_base = col_idx_offset + nj * 8;
+            for (int j = 0; j < size<1, 0>(tensor); ++j) {
+                const int col_idx = col_idx_base + j;
                 #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;
+                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;
+            #pragma unroll
+            for (int i = 0; i < size<0, 0>(tensor); ++i) {
+                const int row_idx = row_idx_base + i * 8;
+                #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;
+                        tensor(make_coord(i, mi), make_coord(j, nj)) -= alibi_slope * abs(row_idx + max_seqlen_k - max_seqlen_q - col_idx);
                     }
                 }
             }
@@ -51,4 +59,4 @@ inline __device__ void apply_alibi(Tensor<Engine, Layout> &tensor,
     }
 }
 
-}  // namespace flash
+}  // namespace flash
\ No newline at end of file

csrc/flash_attn/src/flash.h

@@ -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

@@ -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)
@@ -830,18 +823,17 @@ 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
             );
         }
-        
+
         // TD [2023-07-29]: I was thinking that we don't need to mask out the elements beyond
         // actual_seqlen_k, because acc_s would be some finite value for those indices.
         // In the end when we multiply with K to get dQ, the corresponding values of K would be 0,
@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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