Implement local attention

Co-authored-by: Timothee Lacroix <t@mistral.ai>

csrc/flash_attn/flash_api.cpp

@@ -40,7 +40,8 @@ void set_params_fprop(Flash_fwd_params &params,
                       void *softmax_lse_d,
                       float p_dropout,
                       float softmax_scale,
-                      bool is_causal) {
+                      int window_size_left,
+                      int window_size_right) {
 
     // Reset the parameters
     memset(&params, 0, sizeof(params));
@@ -105,7 +106,15 @@ void set_params_fprop(Flash_fwd_params &params,
     params.scale_softmax_rp_dropout = params.rp_dropout * params.scale_softmax;
     TORCH_CHECK(p_dropout < 1.f);
 
-    params.is_causal = is_causal;
+    // Causal is the special case where window_size_right == 0 and window_size_left < 0.
+    // Local is the more general case where window_size_right >= 0 or window_size_left >= 0.
+    params.is_causal = window_size_left < 0 && window_size_right == 0;
+
+    if (window_size_left < 0 && window_size_right >= 0) { window_size_left = seqlen_k; }
+    if (window_size_left >= 0 && window_size_right < 0) { window_size_right = seqlen_k; }
+    params.window_size_left = window_size_left;
+    params.window_size_right = window_size_right;
+
     params.is_seqlens_k_cumulative = true;
 }
 
@@ -138,7 +147,8 @@ void set_params_dgrad(Flash_bwd_params &params,
                       void *dsoftmax_sum_d,
                       float p_dropout,
                       float softmax_scale,
-                      bool is_causal) {
+                      int window_size_left,
+                      int window_size_right) {
 
     set_params_fprop(params,
                      b, seqlen_q, seqlen_k, seqlen_q_rounded, seqlen_k_rounded, h, h_k, d, d_rounded,
@@ -149,7 +159,8 @@ void set_params_dgrad(Flash_bwd_params &params,
                      softmax_lse_d,
                      p_dropout,
                      softmax_scale,
-                     is_causal);
+                     window_size_left,
+                     window_size_right);
 
     // Set the pointers and strides.
     params.do_ptr = dout.data_ptr();
@@ -242,6 +253,8 @@ mha_fwd(at::Tensor &q,         // batch_size x seqlen_q x num_heads x head_size
         const float p_dropout,
         const float softmax_scale,
         bool is_causal,
+        const int window_size_left,
+        int window_size_right,
         const bool return_softmax,
         c10::optional<at::Generator> gen_) {
 
@@ -281,10 +294,11 @@ mha_fwd(at::Tensor &q,         // batch_size x seqlen_q x num_heads x head_size
     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
+    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
-    const int seqlenq_ngroups_swapped = seqlen_q == 1 && num_heads > num_heads_k && p_dropout == 0.f && head_size_og % 8 == 0;
+    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;
     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);
@@ -353,7 +367,8 @@ mha_fwd(at::Tensor &q,         // batch_size x seqlen_q x num_heads x head_size
                      softmax_lse.data_ptr(),
                      p_dropout,
                      softmax_scale,
-                     is_causal);
+                     window_size_left,
+                     window_size_right);
 
     // This needs to match with run_mha_fwd_splitkv_dispatch
     const int block_n = head_size <= 64 ? 256 : (head_size <= 128 ? 128 : 64);
@@ -421,9 +436,12 @@ mha_varlen_fwd(const at::Tensor &q,  // total_q x num_heads x head_size, total_q
                const float softmax_scale,
                const bool zero_tensors,
                const bool is_causal,
+               const int window_size_left,
+               int window_size_right,
                const bool return_softmax,
                c10::optional<at::Generator> gen_) {
 
+    if (is_causal) { window_size_right = 0; }
     auto dprops = at::cuda::getCurrentDeviceProperties();
     // bool is_sm75 = dprops->major == 7 && dprops->minor == 5;
     bool is_sm8x = dprops->major == 8 && dprops->minor >= 0;
@@ -534,7 +552,8 @@ mha_varlen_fwd(const at::Tensor &q,  // total_q x num_heads x head_size, total_q
                      softmax_lse.data_ptr(),
                      p_dropout,
                      softmax_scale,
-                     is_causal);
+                     window_size_left,
+                     window_size_right);
 
     // number of times random will be generated per thread, to offset philox counter in thc random
     // state
@@ -600,8 +619,12 @@ mha_bwd(const at::Tensor &dout,  // batch_size x seqlen_q x num_heads, x head_si
         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::Generator> gen_,
         c10::optional<at::Tensor> &rng_state) {
+
+    if (is_causal) { window_size_right = 0; }
     auto dprops = at::cuda::getCurrentDeviceProperties();
     // bool is_sm75 = dprops->major == 7 && dprops->minor == 5;
     bool is_sm8x = dprops->major == 8 && dprops->minor >= 0;
@@ -748,7 +771,8 @@ mha_bwd(const at::Tensor &dout,  // batch_size x seqlen_q x num_heads, x head_si
                      softmax_d.data_ptr(),
                      p_dropout,
                      softmax_scale,
-                     is_causal);
+                     window_size_left,
+                     window_size_right);
 
     auto launch = &run_mha_bwd;
     // launch(params, stream, /*configure=*/true);
@@ -804,9 +828,12 @@ mha_varlen_bwd(const at::Tensor &dout,  // total_q x num_heads, x head_size
                const float softmax_scale,
                const bool zero_tensors,
                const bool is_causal,
+               const int window_size_left,
+               int window_size_right,
                c10::optional<at::Generator> gen_,
-               c10::optional<at::Tensor> &rng_state
-) {
+               c10::optional<at::Tensor> &rng_state) {
+
+    if (is_causal) { window_size_right = 0; }
     auto dprops = at::cuda::getCurrentDeviceProperties();
     // bool is_sm75 = dprops->major == 7 && dprops->minor == 5;
     bool is_sm8x = dprops->major == 8 && dprops->minor >= 0;
@@ -969,7 +996,8 @@ mha_varlen_bwd(const at::Tensor &dout,  // total_q x num_heads, x head_size
                      softmax_d.data_ptr(),
                      p_dropout,
                      softmax_scale,
-                     is_causal);
+                     window_size_left,
+                     window_size_right);
 
     auto launch = &run_mha_bwd;
     // launch(params, stream, /*configure=*/true);
@@ -1019,6 +1047,8 @@ mha_fwd_kvcache(at::Tensor &q,                 // batch_size x seqlen_q x num_he
                 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
                 ) {
@@ -1059,10 +1089,11 @@ mha_fwd_kvcache(at::Tensor &q,                 // batch_size x seqlen_q x num_he
     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
+    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
-    const int seqlenq_ngroups_swapped = seqlen_q == 1 && num_heads > num_heads_k && head_size_og % 8 == 0;
+    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;
     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);
@@ -1125,7 +1156,8 @@ mha_fwd_kvcache(at::Tensor &q,                 // batch_size x seqlen_q x num_he
                      softmax_lse.data_ptr(),
                      /*p_dropout=*/0.f,
                      softmax_scale,
-                     is_causal);
+                     window_size_left,
+                     window_size_right);
 
     at::Tensor k, v, k_padded, v_padded;
     if (k_.has_value()) {

csrc/flash_attn/src/flash.h

@@ -105,6 +105,9 @@ struct Flash_fwd_params : public Qkv_params {
     float rp_dropout;
     float scale_softmax_rp_dropout;
 
+    // Local window size
+    int window_size_left, window_size_right;
+
     // Random state.
     at::PhiloxCudaState philox_args;
 

csrc/flash_attn/src/flash_bwd_kernel.h

@@ -422,7 +422,7 @@ inline __device__ void convert_dKV(const Params &params) {
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_even_MN, bool Is_even_K, bool Is_first, bool Is_last, bool Seq_parallel=false, typename Params>
+template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, bool Is_first, bool Is_last, bool Seq_parallel=false, typename Params>
 inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const int bidb, const int bidh, const int n_block) {
 
     using Element = typename Kernel_traits::Element;
@@ -447,6 +447,9 @@ inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const in
     if (n_block * kBlockN >= binfo.actual_seqlen_k || binfo.actual_seqlen_q == 0) return;
 
     int m_block_max = cute::ceil_div(binfo.actual_seqlen_q, kBlockM);
+    if (Is_local) {
+        m_block_max = std::min(m_block_max, cute::ceil_div((n_block + 1) * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k + params.window_size_left, kBlockM));
+    }
 
     const index_t row_offset_q = binfo.q_offset(params.q_batch_stride, params.q_row_stride, bidb)
         + (m_block_max - 1) * kBlockM * params.q_row_stride + bidh * params.q_head_stride;
@@ -655,14 +658,53 @@ inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const in
     tdQgdQaccum.data() = tdQgdQaccum.data() + kBlockM * params.h * params.d_rounded;
 
     int m_block = m_block_max - 1;
-    int m_block_min = !Is_causal ? 0 : std::max(0, (n_block * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k) / kBlockM);
-    // We're guaranteed that m_block_min <= m_block:
+    int m_block_min = (!Is_causal && !Is_local)
+        ? 0
+        : std::max(0, (n_block * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k - params.window_size_right) / kBlockM);
+    // If not local, we're guaranteed that m_block_min <= m_block:
     // We checked earlier that n_block * kBlockN < actual_seqlen_k, so in the causal case,
     // n_block * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k < actual_seqlen_q.
     // So m_block_min <= (actual_seqlen_q - 1) / kBlockM.
     // Recall that m_block_max = cute::ceil_div(binfo.actual_seqlen_q, kBlockM) = (actual_seqlen_q + kBlockM - 1) / kBlockM.
     // So m_block_m - 1 = (actual_seqlen_q - 1) / kBlockM.
     // We conclude that m_block_min <= m_block, so we will always have at least 1 iteration of the for loop.
+    // However, if local, then this possible to have some blocks of K & V not attending to any query.
+    // We might need to exit early and write 0 to dK and dV for those blocks.
+    // Otherwise we get wrong result for the case where we don't enter the for loop.
+    // And we might read OOB elements from gQ and gdO.
+    if (Is_local && m_block < m_block_min) {
+        const index_t row_offset_dk = binfo.k_offset(params.dk_batch_stride, params.dk_row_stride, bidb)
+          + n_block * kBlockN * params.dk_row_stride + bidh * params.dk_head_stride;
+        const index_t row_offset_dv = binfo.k_offset(params.dv_batch_stride, params.dv_row_stride, bidb)
+          + n_block * kBlockN * params.dv_row_stride + bidh * params.dv_head_stride;
+        Tensor gdK = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dk_ptr) + row_offset_dk),
+                                 Shape<Int<kBlockN>, Int<kHeadDim>>{},
+                                 make_stride(params.dk_row_stride, _1{}));
+        Tensor gdV = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dv_ptr) + row_offset_dv),
+                                 Shape<Int<kBlockN>, Int<kHeadDim>>{},
+                                 make_stride(params.dv_row_stride, _1{}));
+        typename Kernel_traits::GmemTiledCopydKV gmem_tiled_copy_dKV;
+        auto gmem_thr_copy_dKV = gmem_tiled_copy_dKV.get_thread_slice(tidx);
+        Tensor tdKgdK = gmem_thr_copy_dKV.partition_D(gdK);
+        Tensor tdVgdV = gmem_thr_copy_dKV.partition_D(gdV);
+        Tensor tdKrdK = make_tensor<Element>(shape(tdKgdK));
+        Tensor tdVrdV = make_tensor<Element>(shape(tdVgdV));
+        clear(tdKrdK);
+        clear(tdVrdV);
+        Tensor cdKV = make_identity_tensor(make_shape(size<0>(gdK), size<1>(gdK)));    // (BLK_N,BLK_K) -> (blk_n,blk_k)
+        Tensor tdKVcdKV = gmem_thr_copy_dKV.partition_D(cdKV);
+        Tensor tdKVpdKV = make_tensor<bool>(make_shape(size<2>(tdKgdK)));
+        #pragma unroll
+        for (int k = 0; k < size(tdKVpdKV); ++k) { tdKVpdKV(k) = get<1>(tdKVcdKV(0, 0, k)) < params.d; }
+        // Clear_OOB_K must be false since we don't want to write zeros to gmem
+        flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
+            gmem_tiled_copy_dKV, tdKrdK, tdKgdK, tdKVcdKV, tdKVpdKV, binfo.actual_seqlen_k - n_block * kBlockN
+        );
+        flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
+            gmem_tiled_copy_dKV, tdVrdV, tdVgdV, tdKVcdKV, tdKVpdKV, binfo.actual_seqlen_k - n_block * kBlockN
+        );
+        return;
+    }
 
     if (Double_buffer && m_block % 2 == 1) {  // Double buffer for sQ
         tQsQ.data() = tQsQ.data() + size(sQ);
@@ -777,12 +819,12 @@ inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const in
         // However, it's possible that the values in acc_s are so large that they overflow
         // when we multiply with dP and convert to fp16, resulting in Inf in dS and NaNs in dQ.
         // So we need to mask out the elements beyond actual_seqlen_k.
-        if (!Is_causal) {
+        if (!Is_causal && !Is_local) {
             if (!Is_even_MN && (n_block + 1) * kBlockN >= binfo.actual_seqlen_k) {
                 flash::apply_mask(scores, binfo.actual_seqlen_k,
                                   n_block * kBlockN + (tidx / 32 / AtomLayoutMS) * MMA_N_SdP * 16);
             }
-        } else {
+        } else if (Is_causal) {
             // Putting this causal masking right after acc_s is *much* slower for some reason.
             // TD [2023-08-16]: We need the 2nd condition because if seqlen_q is long and seqlen_k is short
             // (e.g., 256 and 2), the 2nd block of seqlen_q (from 128 to 255), we're not doing causal masking.
@@ -795,6 +837,16 @@ inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const in
                                          // binfo.actual_seqlen_k, m_block * kBlockM + (tidx / 32) % AtomLayoutMS * 16 + (tidx % 32) / 4,
                                          AtomLayoutMS * 16);
             }
+        } else if (Is_local) {
+            if (m_block * kBlockM < (n_block + 1) * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k - params.window_size_right
+                || (m_block + 1) * kBlockM >= n_block * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k + params.window_size_left
+                || (!Is_even_MN && (n_block + 1) * kBlockN >= binfo.actual_seqlen_k)) {
+                flash::apply_mask_local(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,
+                                        params.window_size_left, params.window_size_right);
+            }
+
         }
         // if (cute::thread(32, 0)) { print(scores); }
         // Compute the exponential value.
@@ -1510,7 +1562,7 @@ inline __device__ void compute_dq_dk_dv(const Params &params) {
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_even_MN, bool Is_even_K, typename Params>
+template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, typename Params>
 inline __device__ void compute_dq_dk_dv_seqk_parallel(const Params &params) {
 
     const int n_block = blockIdx.x;
@@ -1519,7 +1571,7 @@ inline __device__ void compute_dq_dk_dv_seqk_parallel(const Params &params) {
     // The block index for the head.
     const int bidh = blockIdx.z;
 
-    compute_dq_dk_dv_1colblock<Kernel_traits, Is_dropout, Is_causal, Is_even_MN, Is_even_K, false, false, /*Seq_parallel=*/true>(params, bidb, bidh, n_block);
+    compute_dq_dk_dv_1colblock<Kernel_traits, Is_dropout, Is_causal, Is_local, Is_even_MN, Is_even_K, false, false, /*Seq_parallel=*/true>(params, bidb, bidh, n_block);
 }
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////

csrc/flash_attn/src/flash_bwd_launch_template.h

@@ -23,9 +23,10 @@ __global__ void flash_bwd_dq_dk_dv_loop_kernel(Flash_bwd_params params) {
     flash::compute_dq_dk_dv<Kernel_traits, Is_dropout, Is_causal, Is_even_M, Is_even_K>(params);
 }
 
-template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_even_MN, bool Is_even_K>
+template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K>
 __global__ void flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel(Flash_bwd_params params) {
-    flash::compute_dq_dk_dv_seqk_parallel<Kernel_traits, Is_dropout, Is_causal, Is_even_MN, Is_even_K>(params);
+    static_assert(!(Is_causal && Is_local));  // If Is_local is true, Is_causal should be false
+    flash::compute_dq_dk_dv_seqk_parallel<Kernel_traits, Is_dropout, Is_causal, Is_local, Is_even_MN, Is_even_K>(params);
 }
 
 template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_even_N, bool Is_even_K>
@@ -62,9 +63,11 @@ void run_flash_bwd_seqk_parallel(Flash_bwd_params &params, cudaStream_t stream,
     BOOL_SWITCH(params.is_causal, IsCausalConst, [&] {
         BOOL_SWITCH(is_even_MN, IsEvenMNConst, [&] {
             BOOL_SWITCH(is_even_K, IsEvenKConst, [&] {
+                BOOL_SWITCH(params.window_size_left >= 0 || params.window_size_right >= 0, Is_local, [&] {
                     // 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
-                auto kernel = &flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel<Kernel_traits, Is_dropout, IsCausalConst, IsEvenMNConst && IsEvenKConst && Kernel_traits::kHeadDim <= 128, IsEvenKConst>;
+                    // If Is_local, set Is_causal to false
+                    auto kernel = &flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel<Kernel_traits, Is_dropout, IsCausalConst && !Is_local, Is_local, 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>;
                     if (smem_size_dq_dk_dv >= 48 * 1024)  {
                         C10_CUDA_CHECK(cudaFuncSetAttribute(
@@ -75,6 +78,7 @@ void run_flash_bwd_seqk_parallel(Flash_bwd_params &params, cudaStream_t stream,
                 });
             });
         });
+    });
 
     auto kernel_dq = &flash_bwd_convert_dq_kernel<Kernel_traits>;
     if (Kernel_traits::kSmemdQSize >= 48 * 1024)  {

csrc/flash_attn/src/flash_fwd_kernel.h

@@ -71,7 +71,7 @@ inline __device__ void write_softmax_to_gmem(
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_even_MN, bool Is_even_K, bool Return_softmax, typename Params>
+template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, bool Return_softmax, typename Params>
 inline __device__ void compute_attn_1rowblock(const Params &params, const int bidb, const int bidh, const int m_block) {
 
     using Element = typename Kernel_traits::Element;
@@ -93,16 +93,17 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
     const BlockInfo</*Varlen=*/!Is_even_MN> binfo(params, bidb);
     if (m_block * kBlockM >= binfo.actual_seqlen_q || binfo.actual_seqlen_k == 0) return;
 
+    const int n_block_min = !Is_local ? 0 : std::max(0, (m_block * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q - params.window_size_left) / kBlockN);
     int n_block_max = cute::ceil_div(binfo.actual_seqlen_k, kBlockN);
-    if (Is_causal) {
+    if (Is_causal || Is_local) {
         n_block_max = std::min(n_block_max,
-                               cute::ceil_div((m_block + 1) * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q, kBlockN));
+                               cute::ceil_div((m_block + 1) * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q + params.window_size_right, kBlockN));
         // if (threadIdx.x == 0 && blockIdx.y == 0 && blockIdx.z == 0) {
         //     printf("m_block = %d, n_block_max = %d\n", m_block, n_block_max);
         // }
         // We exit early and write 0 to gO and gLSE.
         // Otherwise we might read OOB elements from gK and gV.
-        if (n_block_max <= 0) {
+        if (n_block_max <= n_block_min) {
             // Save seed and offset for backward. If we don't have this here, the 0-th thread block might
             // exit early and no one saves the rng state.
             if (Is_dropout && blockIdx.x == 0 && blockIdx.y == 0 && blockIdx.z == 0 && tidx == 0) {
@@ -145,6 +146,7 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
             return;
         }
     }
+    // if (tidx == 0) { printf("m_block = %d, n_block_min = %d, n_block_max = %d\n", m_block, n_block_min, n_block_max); }
 
     // We iterate over the blocks in reverse order. This is because the last block is the only one
     // that needs masking when we read K and V from global memory. Moreover, iterating in reverse
@@ -326,9 +328,9 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
 
     // 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
+    constexpr int n_masking_steps = (!Is_causal && !Is_local)
         ? 1
-        : (Is_even_MN ? cute::ceil_div(kBlockM, kBlockN) : cute::ceil_div(kBlockM, kBlockN) + 1);
+        : ((Is_even_MN && Is_causal) ? cute::ceil_div(kBlockM, kBlockN) : cute::ceil_div(kBlockM, kBlockN) + 1);
     #pragma unroll
     for (int masking_step = 0; masking_step < n_masking_steps; ++masking_step, --n_block) {
         Tensor acc_s = partition_fragment_C(tiled_mma, Shape<Int<kBlockM>, Int<kBlockN>>{});  // (MMA=4, MMA_M, MMA_N)
@@ -356,11 +358,11 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
 
         // Reshape acc_s from (MMA=4, MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
         Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
-        // if (cute::thread0()) { print(scores); }
+        // if (cute::thread0()) { print_tensor(scores); }
         // We don't put the masking before the matmul S = Q K^T because we don't clear sK
         // for rows outside actual_seqlen_k. So those rows could have Inf / NaN, and the matmul
         // can produce Inf / NaN.
-        if (!Is_causal) {
+        if (!Is_causal && !Is_local) {
             if (!Is_even_MN) { flash::apply_mask(scores, binfo.actual_seqlen_k - n_block * kBlockN); }
         } else {
             // Tensor caccS = make_identity_tensor(Shape<Int<kBlockM>, Int<kBlockN>>{});    // (BLK_M,BLK_N) -> (blk_m,blk_n)
@@ -374,18 +376,21 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
             // Idk why it's get<1> and not get<0> of the stride.
             // if (cute::thread0()) { print(idx_row.layout()); print(stride<1>(idx_row)); printf("stride = %d \n", get<1>(stride<1>(idx_row))); }
             // I can't get the stride from idx_row
-            flash::apply_mask_causal(scores, n_block * kBlockN, binfo.actual_seqlen_k,
+            flash::apply_mask_local</*HasWSLeft=*/Is_local>(
+                scores, n_block * kBlockN, binfo.actual_seqlen_k,
                 // m_block * kBlockM + get<0>(idx_row(0)),
                 m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
-                                     binfo.actual_seqlen_q,
-                                     kNWarps * 16);
-                                     // m_block * kBlockM + (tidx / 32) * 16, kNWarps * 16);
-                                     // m_block * kBlockM + (tidx / 32) * (kBlockM / kNWarps), 16);
+                binfo.actual_seqlen_q, kNWarps * 16,
+                params.window_size_left, params.window_size_right
+                // m_block * kBlockM + (tidx / 32) * 16, kNWarps * 16
+                // m_block * kBlockM + (tidx / 32) * (kBlockM / kNWarps), 16
+            );
+            // if (cute::thread0()) { print_tensor(scores); }
         }
 
         flash::cp_async_wait<0>();
         __syncthreads();
-        if (n_block > 0) {
+        if (n_block > n_block_min) {
             // Advance gK
             tKgK.data() = tKgK.data() + (-int(kBlockN * params.k_row_stride));
             flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tKgK, tKsK, tKVcKV, tKVpKV);
@@ -396,8 +401,8 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
 
         // TODO: when we have key_padding_mask we'll need to Check_inf
         masking_step == 0
-            ? softmax_rescale_o</*Is_first=*/true,  /*Check_inf=*/Is_causal>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2)
-            : softmax_rescale_o</*Is_first=*/false, /*Check_inf=*/Is_causal>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);
+            ? softmax_rescale_o</*Is_first=*/true,  /*Check_inf=*/Is_causal || Is_local>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2)
+            : softmax_rescale_o</*Is_first=*/false, /*Check_inf=*/Is_causal || Is_local>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);
 
         // Convert scores from fp32 to fp16/bf16
         Tensor rP = flash::convert_type<Element>(scores);
@@ -426,14 +431,14 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
         // if (cute::thread0()) { print(scores); }
 
         // This check is at the end of the loop since we always have at least 1 iteration
-        if (n_masking_steps > 1 && n_block <= 0) {
+        if (n_masking_steps > 1 && n_block <= n_block_min) {
             --n_block;
             break;
         }
     }
 
     // These are the iterations where we don't need masking on S
-    for (; n_block >= 0; --n_block) {
+    for (; n_block >= n_block_min; --n_block) {
         Tensor acc_s = partition_fragment_C(tiled_mma, Shape<Int<kBlockM>, Int<kBlockN>>{});  // (MMA=4, MMA_M, MMA_N)
         clear(acc_s);
         flash::cp_async_wait<0>();
@@ -450,7 +455,7 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
 
         flash::cp_async_wait<0>();
         __syncthreads();
-        if (n_block > 0) {
+        if (n_block > n_block_min) {
             // Advance gK
             tKgK.data() = tKgK.data() + (-int(kBlockN * params.k_row_stride));
             flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tKgK, tKsK, tKVcKV, tKVpKV);
@@ -461,7 +466,15 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
 
         // Reshape acc_s from (MMA=4, MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
         Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
-        softmax_rescale_o</*Is_first=*/false>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);
+        if (Is_local && n_block * kBlockN < (m_block + 1) * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q + params.window_size_right) {
+            flash::apply_mask_local(
+                scores, n_block * kBlockN, binfo.actual_seqlen_k,
+                m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
+                binfo.actual_seqlen_q, kNWarps * 16,
+                params.window_size_left, params.window_size_right
+            );
+        }
+        softmax_rescale_o</*Is_first=*/false, /*Check_inf=*/Is_local>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);
 
         Tensor rP = flash::convert_type<Element>(scores);
         // Reshape rP from (nrow=(2, MMA_M), ncol=(2, MMA_N)) to ((2, 2, 2), MMA_M, MMA_N / 2)
@@ -568,7 +581,7 @@ inline __device__ void compute_attn_1rowblock(const Params &params, const int bi
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-template<typename Kernel_traits, bool Is_causal, bool Is_even_MN, bool Is_even_K, bool Split, bool Append_KV, typename Params>
+template<typename Kernel_traits, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, bool Split, bool Append_KV, typename Params>
 inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, const int bidb, const int bidh, const int m_block, const int n_split_idx, const int num_n_splits) {
 
     using Element = typename Kernel_traits::Element;
@@ -599,11 +612,13 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
     if (m_block * kBlockM >= binfo.actual_seqlen_q) return;
 
     const int n_blocks_per_split = ((params.seqlen_k + kBlockN - 1) / kBlockN + num_n_splits - 1) / num_n_splits;
-    const int n_block_min = n_split_idx * n_blocks_per_split;
+    const int n_block_min = !Is_local
+        ? n_split_idx * n_blocks_per_split
+        : std::max(n_split_idx * n_blocks_per_split, (m_block * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q - params.window_size_left) / kBlockN);
     int n_block_max = std::min(cute::ceil_div(binfo.actual_seqlen_k, kBlockN), (n_split_idx + 1) * n_blocks_per_split);
-    if (Is_causal) {
+    if (Is_causal || Is_local) {
         n_block_max = std::min(n_block_max,
-                               cute::ceil_div((m_block + 1) * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q, kBlockN));
+                               cute::ceil_div((m_block + 1) * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q + params.window_size_right, kBlockN));
     }
     if (n_block_min >= n_block_max) {  // This also covers the case where n_block_max <= 0
         // We exit early and write 0 to gOaccum and -inf to gLSEaccum.
@@ -842,21 +857,21 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
         flash::copy<Is_even_MN, Is_even_K>(gmem_tiled_copy_QKV, tQgQ, tQsQ, tQcQ, tQpQ,
                                            binfo.actual_seqlen_q - m_block * kBlockM);
     } else {
-        const index_t row_offset_cossin = (binfo.seqlen_k_cache + (Is_causal ? m_block * kBlockM : 0)) * (params.rotary_dim / 2);
+        const index_t row_offset_cossin = (binfo.seqlen_k_cache + (Is_causal || Is_local ? m_block * kBlockM : 0)) * (params.rotary_dim / 2);
         // If not causal, all the queries get the same the cos/sin, taken at location seqlen_k_cache.
         // We do this by setting the row stride of gCos / gSin to 0.
         Tensor gCos = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.rotary_cos_ptr) + row_offset_cossin),
                                   Shape<Int<kBlockM>, Int<kHeadDim / 2>>{},
-                                  make_stride(Is_causal ? params.rotary_dim / 2 : 0, _1{}));
+                                  make_stride(Is_causal || Is_local ? params.rotary_dim / 2 : 0, _1{}));
         Tensor gSin = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.rotary_sin_ptr) + row_offset_cossin),
                                   Shape<Int<kBlockM>, Int<kHeadDim / 2>>{},
-                                  make_stride(Is_causal ? params.rotary_dim / 2 : 0, _1{}));
+                                  make_stride(Is_causal || Is_local ? params.rotary_dim / 2 : 0, _1{}));
         Tensor gCosCont = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.rotary_cos_ptr) + row_offset_cossin),
                                   Shape<Int<kBlockM>, Int<kHeadDim>>{},
-                                  make_stride(Is_causal ? params.rotary_dim / 2 : 0, _1{}));
+                                  make_stride(Is_causal || Is_local ? params.rotary_dim / 2 : 0, _1{}));
         Tensor gSinCont = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.rotary_sin_ptr) + row_offset_cossin),
                                   Shape<Int<kBlockM>, Int<kHeadDim>>{},
-                                  make_stride(Is_causal ? params.rotary_dim / 2 : 0, _1{}));
+                                  make_stride(Is_causal || Is_local ? params.rotary_dim / 2 : 0, _1{}));
         Tensor tRgCos = gmem_thr_copy_rotary.partition_S(gCos);
         Tensor tRgSin = gmem_thr_copy_rotary.partition_S(gSin);
         Tensor tRgCosCont = gmem_thr_copy_rotary_cont.partition_S(gCosCont);
@@ -895,9 +910,9 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
 
     // 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
+    constexpr int n_masking_steps = (!Is_causal && !Is_local)
         ? 1
-        : (Is_even_MN ? cute::ceil_div(kBlockM, kBlockN) : cute::ceil_div(kBlockM, kBlockN) + 1);
+        : ((Is_even_MN && Is_causal) ? cute::ceil_div(kBlockM, kBlockN) : cute::ceil_div(kBlockM, kBlockN) + 1);
     #pragma unroll
     for (int masking_step = 0; masking_step < n_masking_steps; ++masking_step, --n_block) {
         Tensor acc_s = partition_fragment_C(tiled_mma, Shape<Int<kBlockM>, Int<kBlockN>>{});  // (MMA=4, MMA_M, MMA_N)
@@ -929,13 +944,14 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
         // We don't put the masking before the matmul S = Q K^T because we don't clear sK
         // for rows outside actual_seqlen_k. So those rows could have Inf / NaN, and the matmul
         // can produce Inf / NaN.
-        if (!Is_causal) {
+        if (!Is_causal && !Is_local) {
             if (!Is_even_MN) { flash::apply_mask(scores, binfo.actual_seqlen_k - n_block * kBlockN); }
         } else {
-            flash::apply_mask_causal(scores, n_block * kBlockN, binfo.actual_seqlen_k,
+            flash::apply_mask_local(scores, n_block * kBlockN, binfo.actual_seqlen_k,
                                     m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
-                                     binfo.actual_seqlen_q,
-                                     kNWarps * 16);
+                                    binfo.actual_seqlen_q, kNWarps * 16,
+                                    params.window_size_left, params.window_size_right
+                                    );
         }
 
         flash::cp_async_wait<0>();
@@ -954,8 +970,8 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
 
         // We have key_padding_mask so we'll need to Check_inf
         masking_step == 0
-            ? softmax_rescale_o</*Is_first=*/true,  /*Check_inf=*/Is_causal || !Is_even_MN>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2)
-            : softmax_rescale_o</*Is_first=*/false, /*Check_inf=*/Is_causal || !Is_even_MN>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);
+            ? softmax_rescale_o</*Is_first=*/true,  /*Check_inf=*/Is_causal || Is_local || !Is_even_MN>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2)
+            : softmax_rescale_o</*Is_first=*/false, /*Check_inf=*/Is_causal || Is_local || !Is_even_MN>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);
         // if (cute::thread0()) { print(scores_max); print(scores_sum); print(scores); }
 
         // Convert scores from fp32 to fp16/bf16
@@ -1003,7 +1019,15 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
 
         // Reshape acc_s from (MMA=4, MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
         Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
-        softmax_rescale_o</*Is_first=*/false>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);
+        if (Is_local && n_block * kBlockN < (m_block + 1) * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q + params.window_size_right) {
+            flash::apply_mask_local(
+                scores, n_block * kBlockN, binfo.actual_seqlen_k,
+                m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
+                binfo.actual_seqlen_q, kNWarps * 16,
+                params.window_size_left, params.window_size_right
+            );
+        }
+        softmax_rescale_o</*Is_first=*/false, /*Check_inf=*/Is_local>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);
 
         Tensor rP = flash::convert_type<Element>(scores);
         // Reshape rP from (nrow=(2, MMA_M), ncol=(2, MMA_N)) to ((2, 2, 2), MMA_M, MMA_N / 2)
@@ -1106,7 +1130,7 @@ inline __device__ void compute_attn_1rowblock_splitkv(const Params &params, cons
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_even_MN, bool Is_even_K, bool Return_softmax, typename Params>
+template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, bool Return_softmax, typename Params>
 inline __device__ void compute_attn(const Params &params) {
     const int m_block = blockIdx.x;
     // The block index for the batch.
@@ -1122,12 +1146,12 @@ inline __device__ void compute_attn(const Params &params) {
     // the attention matrix. This way, as long as we have the batch, head, and the location of
     // the 16 x 32 block within the attention matrix, we can generate the exact same dropout pattern.
 
-    flash::compute_attn_1rowblock<Kernel_traits, Is_dropout, Is_causal, Is_even_MN, Is_even_K, Return_softmax>(params, bidb, bidh, m_block);
+    flash::compute_attn_1rowblock<Kernel_traits, Is_dropout, Is_causal, Is_local, Is_even_MN, Is_even_K, Return_softmax>(params, bidb, bidh, m_block);
 }
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-template<typename Kernel_traits, bool Is_causal, bool Is_even_MN, bool Is_even_K, bool Split, bool Append_KV, typename Params>
+template<typename Kernel_traits, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, bool Split, bool Append_KV, typename Params>
 inline __device__ void compute_attn_splitkv(const Params &params) {
     const int m_block = blockIdx.x;
     // The block index for the batch.
@@ -1136,7 +1160,7 @@ inline __device__ void compute_attn_splitkv(const Params &params) {
     const int bidh = Split ? blockIdx.z - bidb * params.h : blockIdx.z;
     const int n_split_idx = Split ? blockIdx.y : 0;
     const int num_n_splits = Split ? gridDim.y : 1;
-    flash::compute_attn_1rowblock_splitkv<Kernel_traits, Is_causal, Is_even_MN, Is_even_K, Split, Append_KV>(params, bidb, bidh, m_block, n_split_idx, num_n_splits);
+    flash::compute_attn_1rowblock_splitkv<Kernel_traits, Is_causal, Is_local, Is_even_MN, Is_even_K, Split, Append_KV>(params, bidb, bidh, m_block, n_split_idx, num_n_splits);
 }
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////

csrc/flash_attn/src/flash_fwd_launch_template.h

@@ -10,14 +10,15 @@
 #include "flash.h"
 #include "flash_fwd_kernel.h"
 
-template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_even_MN, bool Is_even_K, bool Return_softmax>
+template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, bool Return_softmax>
 __global__ void flash_fwd_kernel(Flash_fwd_params params) {
-    flash::compute_attn<Kernel_traits, Is_dropout, Is_causal, Is_even_MN, Is_even_K, Return_softmax>(params);
+    static_assert(!(Is_causal && Is_local));  // If Is_local is true, Is_causal should be false
+    flash::compute_attn<Kernel_traits, Is_dropout, Is_causal, Is_local, Is_even_MN, Is_even_K, Return_softmax>(params);
 }
 
-template<typename Kernel_traits, bool Is_causal, bool Is_even_MN, bool Is_even_K, bool Split, bool Append_KV>
+template<typename Kernel_traits, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, bool Split, bool Append_KV>
 __global__ void flash_fwd_splitkv_kernel(Flash_fwd_params params) {
-    flash::compute_attn_splitkv<Kernel_traits, Is_causal, Is_even_MN, Is_even_K, Split, Append_KV>(params);
+    flash::compute_attn_splitkv<Kernel_traits, Is_causal, Is_local, Is_even_MN, Is_even_K, Split, Append_KV>(params);
 }
 
 template<typename Kernel_traits, int kBlockM, int Log_max_splits, bool Is_even_K>
@@ -42,13 +43,14 @@ void run_flash_fwd(Flash_fwd_params &params, cudaStream_t stream) {
     const bool return_softmax = params.p_ptr != nullptr;
     BOOL_SWITCH(is_even_MN, IsEvenMNConst, [&] {
         BOOL_SWITCH(is_even_K, IsEvenKConst, [&] {
+            BOOL_SWITCH(params.window_size_left >= 0 || params.window_size_right >= 0, Is_local, [&] {
                 BOOL_SWITCH(return_softmax, ReturnSoftmaxConst, [&] {
                     // 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
                     // If head dim > 128, set IsEvenMNConst to false to reduce number of templates
-                auto kernel = &flash_fwd_kernel<Kernel_traits, Is_dropout, Is_causal, IsEvenMNConst && IsEvenKConst && (!ReturnSoftmaxConst) && Kernel_traits::kHeadDim <= 128, IsEvenKConst, ReturnSoftmaxConst && Is_dropout>;
-                // auto kernel = &flash_fwd_kernel<Kernel_traits, Is_dropout, Is_causal, IsEvenMNConst, true, ReturnSoftmaxConst && Is_dropout>;
+                    // If Is_local, set Is_causal to false
+                    auto kernel = &flash_fwd_kernel<Kernel_traits, Is_dropout, Is_causal && !Is_local, Is_local, IsEvenMNConst && IsEvenKConst && !Is_local && !ReturnSoftmaxConst && Kernel_traits::kHeadDim <= 128, IsEvenKConst, ReturnSoftmaxConst && Is_dropout>;
                     if (smem_size >= 48 * 1024) {
                         C10_CUDA_CHECK(cudaFuncSetAttribute(
                             kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
@@ -62,6 +64,7 @@ void run_flash_fwd(Flash_fwd_params &params, cudaStream_t stream) {
                 });
             });
         });
+    });
 }
 
 template<typename Kernel_traits>
@@ -76,11 +79,13 @@ void run_flash_splitkv_fwd(Flash_fwd_params &params, cudaStream_t stream) {
     BOOL_SWITCH(params.is_causal, Is_causal, [&] {
         BOOL_SWITCH(is_even_MN, IsEvenMNConst, [&] {
             BOOL_SWITCH(is_even_K, IsEvenKConst, [&] {
+                BOOL_SWITCH(params.window_size_left >= 0 || params.window_size_right >= 0, Is_local, [&] {
                     BOOL_SWITCH(params.num_splits > 1, Split, [&] {
                         BOOL_SWITCH(params.knew_ptr != nullptr, Append_KV, [&] {
                             // 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.
-                        auto kernel = &flash_fwd_splitkv_kernel<Kernel_traits, Is_causal, IsEvenMNConst && !Append_KV && IsEvenKConst && Kernel_traits::kHeadDim <= 128, IsEvenKConst, Split, Append_KV>;
+                            // If Is_local, set Is_causal to false
+                            auto kernel = &flash_fwd_splitkv_kernel<Kernel_traits, Is_causal && !Is_local, Is_local, IsEvenMNConst && !Append_KV && IsEvenKConst && !Is_local && Kernel_traits::kHeadDim <= 128, IsEvenKConst, Split, Append_KV>;
                             // auto kernel = &flash_fwd_splitkv_kernel<Kernel_traits, Is_causal, false, true, Split, Append_KV>;
                             // auto kernel = &flash_fwd_splitkv_kernel<Kernel_traits, Is_causal, false, IsEvenKConst>;
                             if (smem_size >= 48 * 1024) {
@@ -94,6 +99,7 @@ void run_flash_splitkv_fwd(Flash_fwd_params &params, cudaStream_t stream) {
                 });
             });
         });
+    });
     if (params.num_splits > 1) {
         // We want kBlockM to be as small as possible for more parallelism.
         // With 128 threads we can load 512 elements at a time, so if headdim is divisible by 128, kBlockM = 4.

csrc/flash_attn/src/softmax.h

@@ -139,10 +139,11 @@ inline __device__ void apply_mask(Tensor<Engine, Layout> &tensor, const int max_
     }
 }
 
-template <typename Engine, typename Layout>
-inline __device__ void apply_mask_causal(Tensor<Engine, Layout> &tensor, const int col_idx_offset_,
+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_q, const int warp_row_stride) {
+                                        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;
@@ -155,14 +156,15 @@ inline __device__ void apply_mask_causal(Tensor<Engine, Layout> &tensor, const i
         #pragma unroll
         for (int i = 0; i < size<0, 0>(tensor); ++i) {
             const int row_idx = row_idx_base + i * 8;
-            const int col_idx_limit = std::min(max_seqlen_k, row_idx + 1 + max_seqlen_k - max_seqlen_q);
+            const int col_idx_limit_left = std::max(0, row_idx + max_seqlen_k - max_seqlen_q - window_size_left);
+            const int col_idx_limit_right = std::min(max_seqlen_k, row_idx + 1 + max_seqlen_k - max_seqlen_q + window_size_right);
             #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;
-                    if (col_idx >= col_idx_limit) {
+                    if (col_idx >= col_idx_limit_right || (HasWSLeft && col_idx < col_idx_limit_left)) {
                         tensor(make_coord(i, mi), make_coord(j, nj)) = -INFINITY;
                     }
                 }
@@ -176,6 +178,15 @@ inline __device__ void apply_mask_causal(Tensor<Engine, Layout> &tensor, const i
     }
 }
 
+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_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_,
+                                          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,

flash_attn/flash_attn_interface.py

@@ -41,11 +41,21 @@ def _get_block_size(device, head_dim, is_dropout, is_causal):
         return (128, 64) if is_sm80 else (64, 64)
 
 
-def _flash_attn_forward(q, k, v, dropout_p, softmax_scale, causal, return_softmax):
+def _flash_attn_forward(q, k, v, dropout_p, softmax_scale, causal, window_size, return_softmax):
     maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
     q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
     out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = flash_attn_cuda.fwd(
-        q, k, v, None, dropout_p, softmax_scale, causal, return_softmax, None
+        q,
+        k,
+        v,
+        None,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size[0],
+        window_size[1],
+        return_softmax,
+        None,
     )
     return out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state
 
@@ -61,6 +71,7 @@ def _flash_attn_varlen_forward(
     dropout_p,
     softmax_scale,
     causal,
+    window_size,
     return_softmax,
 ):
     maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
@@ -78,6 +89,8 @@ def _flash_attn_varlen_forward(
         softmax_scale,
         False,
         causal,
+        window_size[0],
+        window_size[1],
         return_softmax,
         None,
     )
@@ -87,7 +100,20 @@ def _flash_attn_varlen_forward(
 
 
 def _flash_attn_backward(
-    dout, q, k, v, out, softmax_lse, dq, dk, dv, dropout_p, softmax_scale, causal, rng_state=None
+    dout,
+    q,
+    k,
+    v,
+    out,
+    softmax_lse,
+    dq,
+    dk,
+    dv,
+    dropout_p,
+    softmax_scale,
+    causal,
+    window_size,
+    rng_state=None,
 ):
     maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
     # dq, dk, dv are allocated by us so they should already be contiguous
@@ -105,6 +131,8 @@ def _flash_attn_backward(
         dropout_p,
         softmax_scale,
         causal,
+        window_size[0],
+        window_size[1],
         None,
         rng_state,
     )
@@ -128,6 +156,7 @@ def _flash_attn_varlen_backward(
     dropout_p,
     softmax_scale,
     causal,
+    window_size,
     rng_state=None,
 ):
     maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
@@ -151,6 +180,8 @@ def _flash_attn_varlen_backward(
         softmax_scale,
         False,
         causal,
+        window_size[0],
+        window_size[1],
         None,
         rng_state,
     )
@@ -161,7 +192,7 @@ def _flash_attn_varlen_backward(
 
 class FlashAttnQKVPackedFunc(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, qkv, dropout_p, softmax_scale, causal, return_softmax):
+    def forward(ctx, qkv, dropout_p, softmax_scale, causal, window_size, return_softmax):
         if softmax_scale is None:
             softmax_scale = qkv.shape[-1] ** (-0.5)
         out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_forward(
@@ -171,12 +202,14 @@ class FlashAttnQKVPackedFunc(torch.autograd.Function):
             dropout_p,
             softmax_scale,
             causal=causal,
+            window_size=window_size,
             return_softmax=return_softmax and dropout_p > 0,
         )
         ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state)
         ctx.dropout_p = dropout_p
         ctx.softmax_scale = softmax_scale
         ctx.causal = causal
+        ctx.window_size = window_size
         return out if not return_softmax else (out, softmax_lse, S_dmask)
 
     @staticmethod
@@ -197,15 +230,26 @@ class FlashAttnQKVPackedFunc(torch.autograd.Function):
             ctx.dropout_p,
             ctx.softmax_scale,
             ctx.causal,
+            ctx.window_size,
             rng_state=rng_state,
         )
         dqkv = dqkv[..., : dout.shape[-1]]  # We could have padded the head dimension
-        return dqkv, None, None, None, None
+        return dqkv, None, None, None, None, None
 
 
 class FlashAttnVarlenQKVPackedFunc(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_softmax):
+    def forward(
+        ctx,
+        qkv,
+        cu_seqlens,
+        max_seqlen,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        return_softmax,
+    ):
         if softmax_scale is None:
             softmax_scale = qkv.shape[-1] ** (-0.5)
         out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_varlen_forward(
@@ -219,6 +263,7 @@ class FlashAttnVarlenQKVPackedFunc(torch.autograd.Function):
             dropout_p,
             softmax_scale,
             causal=causal,
+            window_size=window_size,
             return_softmax=return_softmax and dropout_p > 0,
         )
         ctx.save_for_backward(q, k, v, out_padded, softmax_lse, cu_seqlens, rng_state)
@@ -226,6 +271,7 @@ class FlashAttnVarlenQKVPackedFunc(torch.autograd.Function):
         ctx.max_seqlen = max_seqlen
         ctx.softmax_scale = softmax_scale
         ctx.causal = causal
+        ctx.window_size = window_size
         return out if not return_softmax else (out, softmax_lse, S_dmask)
 
     @staticmethod
@@ -250,15 +296,16 @@ class FlashAttnVarlenQKVPackedFunc(torch.autograd.Function):
             ctx.dropout_p,
             ctx.softmax_scale,
             ctx.causal,
+            ctx.window_size,
             rng_state=rng_state,
         )
         dqkv = dqkv[..., : dout.shape[-1]]  # We could have padded the head dimension
-        return dqkv, None, None, None, None, None, None
+        return dqkv, None, None, None, None, None, None, None
 
 
 class FlashAttnKVPackedFunc(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, q, kv, dropout_p, softmax_scale, causal, return_softmax):
+    def forward(ctx, q, kv, dropout_p, softmax_scale, causal, window_size, return_softmax):
         if softmax_scale is None:
             softmax_scale = q.shape[-1] ** (-0.5)
         out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_forward(
@@ -268,12 +315,14 @@ class FlashAttnKVPackedFunc(torch.autograd.Function):
             dropout_p,
             softmax_scale,
             causal=causal,
+            window_size=window_size,
             return_softmax=return_softmax and dropout_p > 0,
         )
         ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state)
         ctx.dropout_p = dropout_p
         ctx.softmax_scale = softmax_scale
         ctx.causal = causal
+        ctx.window_size = window_size
         return out if not return_softmax else (out, softmax_lse, S_dmask)
 
     @staticmethod
@@ -295,11 +344,12 @@ class FlashAttnKVPackedFunc(torch.autograd.Function):
             ctx.dropout_p,
             ctx.softmax_scale,
             ctx.causal,
+            ctx.window_size,
             rng_state=rng_state,
         )
         dq = dq[..., : dout.shape[-1]]  # We could have padded the head dimension
         dkv = dkv[..., : dout.shape[-1]]
-        return dq, dkv, None, None, None, None
+        return dq, dkv, None, None, None, None, None
 
 
 class FlashAttnVarlenKVPackedFunc(torch.autograd.Function):
@@ -315,6 +365,7 @@ class FlashAttnVarlenKVPackedFunc(torch.autograd.Function):
         dropout_p,
         softmax_scale,
         causal,
+        window_size,
         return_softmax,
     ):
         if softmax_scale is None:
@@ -330,6 +381,7 @@ class FlashAttnVarlenKVPackedFunc(torch.autograd.Function):
             dropout_p,
             softmax_scale,
             causal=causal,
+            window_size=window_size,
             return_softmax=return_softmax and dropout_p > 0,
         )
         ctx.save_for_backward(
@@ -340,6 +392,7 @@ class FlashAttnVarlenKVPackedFunc(torch.autograd.Function):
         ctx.max_seqlen_k = max_seqlen_k
         ctx.softmax_scale = softmax_scale
         ctx.causal = causal
+        ctx.window_size = window_size
         return out if not return_softmax else (out, softmax_lse, S_dmask)
 
     @staticmethod
@@ -365,16 +418,17 @@ class FlashAttnVarlenKVPackedFunc(torch.autograd.Function):
             ctx.dropout_p,
             ctx.softmax_scale,
             ctx.causal,
+            ctx.window_size,
             rng_state=rng_state,
         )
         dq = dq[..., : dout.shape[-1]]  # We could have padded the head dimension
         dkv = dkv[..., : dout.shape[-1]]
-        return dq, dkv, None, None, None, None, None, None, None, None
+        return dq, dkv, None, None, None, None, None, None, None, None, None
 
 
 class FlashAttnFunc(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, q, k, v, dropout_p, softmax_scale, causal, return_softmax):
+    def forward(ctx, q, k, v, dropout_p, softmax_scale, causal, window_size, return_softmax):
         if softmax_scale is None:
             softmax_scale = q.shape[-1] ** (-0.5)
         out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_forward(
@@ -384,12 +438,14 @@ class FlashAttnFunc(torch.autograd.Function):
             dropout_p,
             softmax_scale,
             causal=causal,
+            window_size=window_size,
             return_softmax=return_softmax and dropout_p > 0,
         )
         ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state)
         ctx.dropout_p = dropout_p
         ctx.softmax_scale = softmax_scale
         ctx.causal = causal
+        ctx.window_size = window_size
         return out if not return_softmax else (out, softmax_lse, S_dmask)
 
     @staticmethod
@@ -409,12 +465,13 @@ class FlashAttnFunc(torch.autograd.Function):
             ctx.dropout_p,
             ctx.softmax_scale,
             ctx.causal,
+            ctx.window_size,
             rng_state=rng_state,
         )
         dq = dq[..., : dout.shape[-1]]  # We could have padded the head dimension
         dk = dk[..., : dout.shape[-1]]
         dv = dv[..., : dout.shape[-1]]
-        return dq, dk, dv, None, None, None, None, None, None, None, None
+        return dq, dk, dv, None, None, None, None, None, None, None, None, None
 
 
 class FlashAttnVarlenFunc(torch.autograd.Function):
@@ -431,6 +488,7 @@ class FlashAttnVarlenFunc(torch.autograd.Function):
         dropout_p,
         softmax_scale,
         causal,
+        window_size,
         return_softmax,
     ):
         if softmax_scale is None:
@@ -446,6 +504,7 @@ class FlashAttnVarlenFunc(torch.autograd.Function):
             dropout_p,
             softmax_scale,
             causal=causal,
+            window_size=window_size,
             return_softmax=return_softmax and dropout_p > 0,
         )
         ctx.save_for_backward(
@@ -456,6 +515,7 @@ class FlashAttnVarlenFunc(torch.autograd.Function):
         ctx.max_seqlen_k = max_seqlen_k
         ctx.softmax_scale = softmax_scale
         ctx.causal = causal
+        ctx.window_size = window_size
         return out if not return_softmax else (out, softmax_lse, S_dmask)
 
     @staticmethod
@@ -479,16 +539,22 @@ class FlashAttnVarlenFunc(torch.autograd.Function):
             ctx.dropout_p,
             ctx.softmax_scale,
             ctx.causal,
+            ctx.window_size,
             rng_state=rng_state,
         )
         dq = dq[..., : dout.shape[-1]]  # We could have padded the head dimension
         dk = dk[..., : dout.shape[-1]]
         dv = dv[..., : dout.shape[-1]]
-        return dq, dk, dv, None, None, None, None, None, None, None, None
+        return dq, dk, dv, None, None, None, None, None, None, None, None, None
 
 
 def flash_attn_qkvpacked_func(
-    qkv, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False
+    qkv,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),  # -1 means infinite context window
+    return_attn_probs=False,
 ):
     """dropout_p should be set to 0.0 during evaluation
     If Q, K, V are already stacked into 1 tensor, this function will be faster than
@@ -497,12 +563,16 @@ def flash_attn_qkvpacked_func(
     For multi-query and grouped-query attention (MQA/GQA), please see
     flash_attn_kvpacked_func and flash_attn_func.
 
+    If window_size != (-1, -1), implements sliding window local attention. Query at position i
+    will only attend to keys between [i - window_size[0], i + window_size[1]] inclusive.
+
     Arguments:
         qkv: (batch_size, seqlen, 3, nheads, headdim)
         dropout_p: float. Dropout probability.
         softmax_scale: float. The scaling of QK^T before applying softmax.
             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.
         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).
@@ -515,11 +585,19 @@ def flash_attn_qkvpacked_func(
             The output of softmax (possibly with different scaling). It also encodes the dropout
             pattern (negative means that location was dropped, nonnegative means it was kept).
     """
-    return FlashAttnQKVPackedFunc.apply(qkv, dropout_p, softmax_scale, causal, return_attn_probs)
+    return FlashAttnQKVPackedFunc.apply(
+        qkv, dropout_p, softmax_scale, causal, window_size, return_attn_probs
+    )
 
 
 def flash_attn_kvpacked_func(
-    q, kv, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False
+    q,
+    kv,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),  # -1 means infinite context window
+    return_attn_probs=False,
 ):
     """dropout_p should be set to 0.0 during evaluation
     If K, V are already stacked into 1 tensor, this function will be faster than
@@ -542,6 +620,10 @@ def flash_attn_kvpacked_func(
         1 1
     If the row of the mask is all zero, the output will be zero.
 
+    If window_size != (-1, -1), implements sliding window local attention. Query at position i
+    will only attend to keys between
+    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
+
     Arguments:
         q: (batch_size, seqlen, nheads, headdim)
         kv: (batch_size, seqlen, 2, nheads_k, headdim)
@@ -549,6 +631,7 @@ def flash_attn_kvpacked_func(
         softmax_scale: float. The scaling of QK^T before applying softmax.
             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.
         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).
@@ -561,11 +644,20 @@ def flash_attn_kvpacked_func(
             The output of softmax (possibly with different scaling). It also encodes the dropout
             pattern (negative means that location was dropped, nonnegative means it was kept).
     """
-    return FlashAttnKVPackedFunc.apply(q, kv, dropout_p, softmax_scale, causal, return_attn_probs)
+    return FlashAttnKVPackedFunc.apply(
+        q, kv, dropout_p, softmax_scale, causal, window_size, return_attn_probs
+    )
 
 
 def flash_attn_func(
-    q, k, v, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False
+    q,
+    k,
+    v,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),  # -1 means infinite context window
+    return_attn_probs=False,
 ):
     """dropout_p should be set to 0.0 during evaluation
     Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
@@ -585,6 +677,10 @@ def flash_attn_func(
         1 1
     If the row of the mask is all zero, the output will be zero.
 
+    If window_size != (-1, -1), implements sliding window local attention. Query at position i
+    will only attend to keys between
+    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
+
     Arguments:
         q: (batch_size, seqlen, nheads, headdim)
         k: (batch_size, seqlen, nheads_k, headdim)
@@ -593,6 +689,7 @@ def flash_attn_func(
         softmax_scale: float. The scaling of QK^T before applying softmax.
             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.
         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).
@@ -605,7 +702,9 @@ def flash_attn_func(
             The output of softmax (possibly with different scaling). It also encodes the dropout
             pattern (negative means that location was dropped, nonnegative means it was kept).
     """
-    return FlashAttnFunc.apply(q, k, v, dropout_p, softmax_scale, causal, return_attn_probs)
+    return FlashAttnFunc.apply(
+        q, k, v, dropout_p, softmax_scale, causal, window_size, return_attn_probs
+    )
 
 
 def flash_attn_varlen_qkvpacked_func(
@@ -615,6 +714,7 @@ def flash_attn_varlen_qkvpacked_func(
     dropout_p=0.0,
     softmax_scale=None,
     causal=False,
+    window_size=(-1, -1),  # -1 means infinite context window
     return_attn_probs=False,
 ):
     """dropout_p should be set to 0.0 during evaluation
@@ -624,6 +724,9 @@ def flash_attn_varlen_qkvpacked_func(
     For multi-query and grouped-query attention (MQA/GQA), please see
     flash_attn_varlen_kvpacked_func and flash_attn_varlen_func.
 
+    If window_size != (-1, -1), implements sliding window local attention. Query at position i
+    will only attend to keys between [i - window_size[0], i + window_size[1]] inclusive.
+
     Arguments:
         qkv: (total, 3, nheads, headdim), where total = total number of tokens in the batch.
         cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
@@ -633,6 +736,7 @@ def flash_attn_varlen_qkvpacked_func(
         softmax_scale: float. The scaling of QK^T before applying softmax.
             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.
         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).
@@ -646,7 +750,14 @@ def flash_attn_varlen_qkvpacked_func(
             pattern (negative means that location was dropped, nonnegative means it was kept).
     """
     return FlashAttnVarlenQKVPackedFunc.apply(
-        qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_attn_probs
+        qkv,
+        cu_seqlens,
+        max_seqlen,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        return_attn_probs,
     )
 
 
@@ -660,6 +771,7 @@ def flash_attn_varlen_kvpacked_func(
     dropout_p=0.0,
     softmax_scale=None,
     causal=False,
+    window_size=(-1, -1),  # -1 means infinite context window
     return_attn_probs=False,
 ):
     """dropout_p should be set to 0.0 during evaluation
@@ -683,6 +795,10 @@ def flash_attn_varlen_kvpacked_func(
         1 1
     If the row of the mask is all zero, the output will be zero.
 
+    If window_size != (-1, -1), implements sliding window local attention. Query at position i
+    will only attend to keys between
+    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
+
     Arguments:
         q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
         kv: (total_k, 2, nheads_k, headdim), where total_k = total number of key tokens in the batch.
@@ -696,6 +812,7 @@ def flash_attn_varlen_kvpacked_func(
         softmax_scale: float. The scaling of QK^T before applying softmax.
             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.
         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).
@@ -718,6 +835,7 @@ def flash_attn_varlen_kvpacked_func(
         dropout_p,
         softmax_scale,
         causal,
+        window_size,
         return_attn_probs,
     )
 
@@ -733,6 +851,7 @@ def flash_attn_varlen_func(
     dropout_p=0.0,
     softmax_scale=None,
     causal=False,
+    window_size=(-1, -1),  # -1 means infinite context window
     return_attn_probs=False,
 ):
     """dropout_p should be set to 0.0 during evaluation
@@ -753,6 +872,10 @@ def flash_attn_varlen_func(
         1 1
     If the row of the mask is all zero, the output will be zero.
 
+    If window_size != (-1, -1), implements sliding window local attention. Query at position i
+    will only attend to keys between
+    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
+
     Arguments:
         q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
         k: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.
@@ -767,6 +890,7 @@ def flash_attn_varlen_func(
         softmax_scale: float. The scaling of QK^T before applying softmax.
             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.
         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).
@@ -790,6 +914,7 @@ def flash_attn_varlen_func(
         dropout_p,
         softmax_scale,
         causal,
+        window_size,
         return_attn_probs,
     )
 
@@ -805,6 +930,7 @@ def flash_attn_with_kvcache(
     cache_seqlens: Optional[Union[(int, torch.Tensor)]] = None,
     softmax_scale=None,
     causal=False,
+    window_size=(-1, -1),  # -1 means infinite context window
     rotary_interleaved=True,
     num_splits=0,
 ):
@@ -818,11 +944,12 @@ def flash_attn_with_kvcache(
     For example, the KV cache could be pre-allocated with the max sequence length, and you can use
     cache_seqlens to keep track of the current sequence lengths of each sequence in the batch.
 
-    Also apply rotary embedding if rotary_cos and rotary_sin are passed in. The key @k will be rotated
-    by rotary_cos and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
-    If causal, the query @q will be rotated by rotary_cos and rotary_sin at indices cache_seqlens,
-    cache_seqlens + 1, etc. If not causal, the query @q will be rotated by rotary_cos and rotary_sin
-    at indices cache_seqlens only (i.e. we consider all tokens in @q to be at position cache_seqlens).
+    Also apply rotary embedding if rotary_cos and rotary_sin are passed in. The key @k will be
+    rotated by rotary_cos and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
+    If causal or local (i.e., window_size != (-1, -1)), the query @q will be rotated by rotary_cos
+    and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
+    If not causal and not local, the query @q will be rotated by rotary_cos and rotary_sin at
+    indices cache_seqlens only (i.e. we consider all tokens in @q to be at position cache_seqlens).
 
     See tests/test_flash_attn.py::test_flash_attn_kvcache for examples of how to use this function.
 
@@ -843,6 +970,10 @@ def flash_attn_with_kvcache(
         1 1
     If the row of the mask is all zero, the output will be zero.
 
+    If window_size != (-1, -1), implements sliding window local attention. Query at position i
+    will only attend to keys between
+    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
+
     Note: Does not support backward pass.
 
     Arguments:
@@ -860,6 +991,7 @@ def flash_attn_with_kvcache(
         softmax_scale: float. The scaling of QK^T before applying softmax.
             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.
         rotary_interleaved: bool. Only applicable if rotary_cos and rotary_sin are passed in.
             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
@@ -894,6 +1026,8 @@ def flash_attn_with_kvcache(
         None,
         softmax_scale,
         causal,
+        window_size[0],
+        window_size[1],
         rotary_interleaved,
         num_splits,
     )

tests/test_flash_attn.py

@@ -150,8 +150,13 @@ def generate_qkv(
         )
 
 
-def construct_causal_mask(
-    seqlen_q, seqlen_k, query_padding_mask=None, key_padding_mask=None, device=None
+def construct_local_mask(
+    seqlen_q,
+    seqlen_k,
+    window_size=(-1, -1),  # -1 means infinite window size
+    query_padding_mask=None,
+    key_padding_mask=None,
+    device=None,
 ):
     row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1")
     col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long)
@@ -165,7 +170,14 @@ def construct_causal_mask(
         if query_padding_mask is None
         else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1")
     )
-    return col_idx > row_idx + sk - sq
+    if window_size[0] < 0:
+        return col_idx > row_idx + sk - sq + window_size[1]
+    else:
+        sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk
+        return torch.logical_or(
+            col_idx > torch.minimum(row_idx + sk - sq + window_size[1], sk),
+            col_idx < row_idx + sk - sq - window_size[0],
+        )
 
 
 def attention_ref(
@@ -177,6 +189,7 @@ def attention_ref(
     dropout_p=0.0,
     dropout_mask=None,
     causal=False,
+    window_size=(-1, -1),  # -1 means infinite window size
     upcast=True,
     reorder_ops=False,
 ):
@@ -189,6 +202,8 @@ def attention_ref(
         key_padding_mask: (batch_size, seqlen_k)
         dropout_p: float
         dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k)
+        causal: whether to apply causal masking
+        window_size: (int, int), left and right window size
         upcast: whether to cast all inputs to fp32, do all computation in fp32, then cast
             output back to fp16/bf16.
         reorder_ops: whether to change the order of operations (scaling k instead of scaling k, etc.)
@@ -198,6 +213,8 @@ def attention_ref(
         output: (batch_size, seqlen_q, nheads, head_dim)
         attention: (batch_size, nheads, seqlen_q, seqlen_k), softmax after dropout
     """
+    if causal:
+        window_size = (window_size[0], 0)
     dtype_og = q.dtype
     if upcast:
         q, k, v = q.float(), k.float(), v.float()
@@ -211,17 +228,24 @@ def attention_ref(
         scores = torch.einsum("bthd,bshd->bhts", q, k / math.sqrt(d))
     if key_padding_mask is not None:
         scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf"))
-    if causal:
-        # causal_mask = torch.triu(
-        #     torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1
-        # )
-        causal_mask = construct_causal_mask(
-            seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, q.device
+    if window_size[0] >= 0 or window_size[1] >= 0:
+        local_mask = construct_local_mask(
+            seqlen_q,
+            seqlen_k,
+            window_size,
+            query_padding_mask,
+            key_padding_mask,
+            q.device,
         )
-        scores.masked_fill_(causal_mask, float("-inf"))
+        scores.masked_fill_(local_mask, float("-inf"))
     attention = torch.softmax(scores, dim=-1)
-    if causal:  # Some rows are completely masked out so we fill them with zero instead of NaN
-        attention = attention.masked_fill(torch.all(causal_mask, dim=-1, keepdim=True), 0.0)
+    # Some rows might be completely masked out so we fill them with zero instead of NaN
+    if window_size[0] >= 0 or window_size[1] >= 0:
+        attention = attention.masked_fill(torch.all(local_mask, dim=-1, keepdim=True), 0.0)
+    # We want to mask here so that the attention matrix doesn't have any NaNs
+    # Otherwise we'll get NaN in dV
+    if query_padding_mask is not None:
+        attention = attention.masked_fill(rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0)
     dropout_scaling = 1.0 / (1 - dropout_p)
     # attention_drop = attention.masked_fill(~dropout_mask, 0.0) * dropout_scaling
     # output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
@@ -232,7 +256,6 @@ def attention_ref(
     output = torch.einsum("bhts,bshd->bthd", attention_drop, v * dropout_scaling)
     if query_padding_mask is not None:
         output.masked_fill_(rearrange(~query_padding_mask, "b s -> b s 1 1"), 0.0)
-        attention = attention.masked_fill(rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0)
     return output.to(dtype=dtype_og), attention.to(dtype=dtype_og)
 
 
@@ -244,6 +267,7 @@ def attention_kvpacked_ref(
     dropout_p=0.0,
     dropout_mask=None,
     causal=False,
+    window_size=(-1, -1),  # -1 means infinite window size
     upcast=True,
     reorder_ops=False,
 ):
@@ -257,6 +281,7 @@ def attention_kvpacked_ref(
         dropout_mask,
         upcast=upcast,
         causal=causal,
+        window_size=window_size,
         reorder_ops=reorder_ops,
     )
 
@@ -267,6 +292,7 @@ def attention_qkvpacked_ref(
     dropout_p=0.0,
     dropout_mask=None,
     causal=False,
+    window_size=(-1, -1),  # -1 means infinite window size
     upcast=True,
     reorder_ops=False,
 ):
@@ -280,6 +306,7 @@ def attention_qkvpacked_ref(
         dropout_mask,
         upcast=upcast,
         causal=causal,
+        window_size=window_size,
         reorder_ops=reorder_ops,
     )
 
@@ -327,7 +354,15 @@ def attention_blocksparse_ref(qkv, blockmask, attn_mask, dropout_p, dropout_mask
 
 
 def convert_flash_attn_S_to_softmax(
-    S, seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, head_dim, is_dropout, causal=False
+    S,
+    seqlen_q,
+    seqlen_k,
+    query_padding_mask,
+    key_padding_mask,
+    head_dim,
+    is_dropout,
+    causal=False,
+    window_size=(-1, -1),  # -1 means infinite window size
 ):
     """FlashAttention stores the S matrix in a different way.
     Arguments:
@@ -335,6 +370,8 @@ def convert_flash_attn_S_to_softmax(
         query_padding_mask: (batch_size, seqlen_q_rounded)
         key_padding_mask: (batch_size, seqlen_k_rounded)
     """
+    if causal:
+        window_size = (window_size[0], 0)
     seqlen_q_rounded, seqlen_k_rounded = S.shape[-2:]
     warps_n = 4
     blocksize_m, blocksize_n = _get_block_size(S.device, head_dim, is_dropout, causal)
@@ -359,19 +396,21 @@ def convert_flash_attn_S_to_softmax(
         four=4,
     )
 
-    if causal:
-        # causal_mask = torch.triu(
-        #     torch.ones(seqlen_q_rounded, seqlen_k_rounded, dtype=torch.bool, device=q.device), 1
-        # )
-        causal_mask = construct_causal_mask(
-            seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, S.device
+    if window_size[0] >= 0 or window_size[1] >= 0:
+        local_mask = construct_local_mask(
+            seqlen_q,
+            seqlen_k,
+            window_size,
+            query_padding_mask,
+            key_padding_mask,
+            S.device,
         )
-        causal_mask = F.pad(
-            causal_mask,
+        local_mask = F.pad(
+            local_mask,
             (0, seqlen_k_rounded - seqlen_k, 0, seqlen_q_rounded - seqlen_q),
             value=True,
         )
-        S_converted.masked_fill_(causal_mask, 0.0)
+        S_converted.masked_fill_(local_mask, 0.0)
 
     # Need to zero out things not in attention_mask in case S was initialized with random values
     # and some of those values aren't overwritten.
@@ -399,6 +438,7 @@ def normalize_flash_attn_S(
     key_padding_mask=None,
     is_dropout=False,
     causal=False,
+    window_size=(-1, -1),  # -1 means infinite window size
 ):
     """
     Arguments:
@@ -409,20 +449,24 @@ def normalize_flash_attn_S(
         softmax_lse: (batch_size, nheads, seqlen_q)
         softmax_max: (batch_size, nheads, seqlen_q)
     """
+    if causal:
+        window_size = (window_size[0], 0)
     q, k, v = q.float(), k.float(), v.float()
     _, seqlen_q, _, head_dim = q.shape
     seqlen_k = k.shape[1]
     scores = torch.einsum("bthd,bshd->bhts", q / math.sqrt(head_dim), k)
     if key_padding_mask is not None:
         scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf"))
-    if causal:
-        # causal_mask = torch.triu(
-        #     torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1
-        # )
-        causal_mask = construct_causal_mask(
-            seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, q.device
+    if window_size[0] >= 0 or window_size[1] >= 0:
+        local_mask = construct_local_mask(
+            seqlen_q,
+            seqlen_k,
+            window_size,
+            query_padding_mask,
+            key_padding_mask,
+            q.device,
         )
-        scores.masked_fill_(causal_mask, float("-inf"))
+        scores.masked_fill_(local_mask, float("-inf"))
     _, block_size_n = _get_block_size(scores.device, head_dim, is_dropout, causal)
     scores_block = scores.split(block_size_n, dim=-1)
     lse_block = torch.stack([torch.logsumexp(s, dim=-1) for s in scores_block], dim=-1)
@@ -446,79 +490,84 @@ def normalize_flash_attn_S(
 
 
 def get_dropout_fraction(
-    dropout_mask, query_padding_mask=None, key_padding_mask=None, causal=False
+    dropout_mask,
+    query_padding_mask=None,
+    key_padding_mask=None,
+    causal=False,
+    window_size=(-1, -1),  # -1 means infinite window size
 ):
     """
     dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k), bool. True means keep, False means drop.
     query_padding_mask: (batch_size, seqlen_q)
     key_padding_mask: (batch_size, seqlen_k)
     """
+    if causal:
+        window_size = (window_size[0], 0)
     batch_size, nheads, seqlen_q, seqlen_k = dropout_mask.shape
     dropped = ~dropout_mask
+    valid = torch.ones_like(dropout_mask)
     if query_padding_mask is not None:
         dropped.masked_fill_(rearrange(~query_padding_mask, "b s -> b 1 s 1"), False)
+        valid.masked_fill_(rearrange(~query_padding_mask, "b s -> b 1 s 1"), False)
     if key_padding_mask is not None:
         dropped.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), False)
-    if causal:
-        # causal_mask = torch.triu(
-        #     torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=dropout_mask.device), 1
-        # )
-        causal_mask = construct_causal_mask(
-            seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, dropout_mask.device
+        valid.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), False)
+    if window_size[0] >= 0 or window_size[1] >= 0:
+        local_mask = construct_local_mask(
+            seqlen_q,
+            seqlen_k,
+            window_size,
+            query_padding_mask,
+            key_padding_mask,
+            dropout_mask.device,
         )
-        dropped.masked_fill_(causal_mask, False)
+        dropped.masked_fill_(local_mask, False)
+        valid.masked_fill_(local_mask, False)
     dropped_total = dropped.sum()
-    query_lengths = (
-        query_padding_mask.sum(dim=-1)
-        if query_padding_mask is not None
-        else torch.full((batch_size,), seqlen_q, device=dropout_mask.device)
-    )
-    key_lengths = (
-        key_padding_mask.sum(dim=-1)
-        if key_padding_mask is not None
-        else torch.full((batch_size,), seqlen_k, device=dropout_mask.device)
-    )
-    if not causal:
-        numel_per_batch = query_lengths * key_lengths
-    else:
-        numel_per_batch = torch.where(
-            key_lengths <= query_lengths,
-            key_lengths * (key_lengths + 1) / 2,
-            query_lengths * key_lengths - (query_lengths * (query_lengths - 1) / 2),
-        )
-    return dropped_total / (numel_per_batch.sum() * nheads)
+    return dropped.sum() / valid.sum()
 
 
 @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
-# @pytest.mark.parametrize('dtype', [torch.float16])
+# @pytest.mark.parametrize("dtype", [torch.float16])
+@pytest.mark.parametrize("local", [False, True])
+# @pytest.mark.parametrize("local", [True])
 @pytest.mark.parametrize("causal", [False, True])
-# @pytest.mark.parametrize('causal', [False])
+# @pytest.mark.parametrize("causal", [False])
 @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
-# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
+# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
 # @pytest.mark.parametrize('d', [32, 64, 96, 128])
-# @pytest.mark.parametrize('d', [64])
+# @pytest.mark.parametrize("d", [64])
 # @pytest.mark.parametrize('seqlen', [128, 256, 384, 512, 768, 1024, 2048])
 @pytest.mark.parametrize("seqlen", [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
-# @pytest.mark.parametrize('seqlen', [128])
+# @pytest.mark.parametrize("seqlen", [128])
 @pytest.mark.parametrize("dropout_p", [0.0, 0.17])
-# @pytest.mark.parametrize('dropout_p', [0.0])
-def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, dtype):
+# @pytest.mark.parametrize("dropout_p", [0.0])
+def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, local, dtype):
     if seqlen >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30:
         pytest.skip()  # Reference implementation OOM
     device = "cuda"
     # set seed
     torch.random.manual_seed(0)
-    batch_size = 16
+    batch_size = 13
     nheads = 9
+    window_size = (-1, -1) if not local else torch.randint(0, seqlen, (2,))
     qkv = torch.randn(
         batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True
     )
     out, lse, S_dmask = flash_attn_qkvpacked_func(
-        qkv, dropout_p, return_attn_probs=True, causal=causal
+        qkv, dropout_p, causal=causal, window_size=window_size, return_attn_probs=True
     )
     if dropout_p > 0.0:
         S_dmask_converted = convert_flash_attn_S_to_softmax(
-            S_dmask, seqlen, seqlen, None, None, d, dropout_p > 0.0, causal=causal
+            S_dmask,
+            seqlen,
+            seqlen,
+            None,
+            None,
+            d,
+            dropout_p > 0.0,
+            causal=causal,
+            window_size=window_size,
         )
         dropout_mask = S_dmask_converted >= 0
         attn_unnorm = S_dmask_converted.abs()
@@ -531,15 +580,27 @@ def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, dtype):
             None,
             dropout_p > 0.0,
             causal=causal,
+            window_size=window_size,
         )
-        dropout_fraction = get_dropout_fraction(dropout_mask, None, None, causal=causal).item()
+        dropout_fraction = get_dropout_fraction(
+            dropout_mask, None, None, causal=causal, window_size=window_size
+        ).item()
         print(f"Actual dropout fraction: {dropout_fraction}")
     else:
         dropout_mask = None
 
-    out_ref, attn_ref = attention_qkvpacked_ref(qkv, None, dropout_p, dropout_mask, causal=causal)
+    out_ref, attn_ref = attention_qkvpacked_ref(
+        qkv, None, dropout_p, dropout_mask, causal=causal, window_size=window_size
+    )
     out_pt, attn_pt = attention_qkvpacked_ref(
-        qkv, None, dropout_p, dropout_mask, causal=causal, upcast=False, reorder_ops=True
+        qkv,
+        None,
+        dropout_p,
+        dropout_mask,
+        causal=causal,
+        window_size=window_size,
+        upcast=False,
+        reorder_ops=True,
     )
     # v = qkv[:, :, 2].float()
     # qk = torch.einsum('bshd,bthd->bhst', qkv[:, :, 0], qkv[:, :, 1]).float()
@@ -590,7 +651,7 @@ def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, dtype):
 
     if dropout_p > 0.0:
         assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
-        assert abs(dropout_fraction - dropout_p) <= 0.01
+        assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025)
 
     if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
         assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()
@@ -598,15 +659,18 @@ def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, dtype):
 
 @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
 # @pytest.mark.parametrize('dtype', [torch.float16])
+@pytest.mark.parametrize("local", [False, True])
+# @pytest.mark.parametrize("local", [True])
 @pytest.mark.parametrize("causal", [False, True])
 # @pytest.mark.parametrize('causal', [False])
 @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
+# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
 # @pytest.mark.parametrize('d', [64])
 @pytest.mark.parametrize("seqlen", [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
 # @pytest.mark.parametrize('seqlen', [128])
 @pytest.mark.parametrize("dropout_p", [0.0, 0.17])
 # @pytest.mark.parametrize('dropout_p', [0.0])
-def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
+def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, local, dtype):
     if seqlen >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30:
         pytest.skip()  # Reference implementation OOM
     device = "cuda"
@@ -614,6 +678,7 @@ def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
     torch.random.manual_seed(0)
     batch_size = 5
     nheads = 6
+    window_size = (-1, -1) if not local else torch.randint(0, seqlen, (2,))
     qkv = torch.randn(
         batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True
     )
@@ -626,7 +691,13 @@ def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
     )
 
     out_unpad, sm_lse, S_dmask = flash_attn_varlen_qkvpacked_func(
-        qkv_unpad, cu_seqlens, max_seqlen, dropout_p, return_attn_probs=True, causal=causal
+        qkv_unpad,
+        cu_seqlens,
+        max_seqlen,
+        dropout_p,
+        causal=causal,
+        window_size=window_size,
+        return_attn_probs=True,
     )
     out = output_pad_fn(out_unpad)
     if dropout_p > 0.0:
@@ -639,6 +710,7 @@ def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
             d,
             dropout_p > 0.0,
             causal=causal,
+            window_size=window_size,
         )
         dropout_mask = S_dmask_converted >= 0
         attn_unnorm = S_dmask_converted.abs()
@@ -651,16 +723,17 @@ def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
             key_padding_mask,
             dropout_p > 0.0,
             causal=causal,
+            window_size=window_size,
         )
         dropout_fraction = get_dropout_fraction(
-            dropout_mask, key_padding_mask, key_padding_mask, causal=causal
+            dropout_mask, key_padding_mask, key_padding_mask, causal=causal, window_size=window_size
         ).item()
         print(f"Actual dropout fraction: {dropout_fraction}")
     else:
         dropout_mask = None
 
     out_ref, attn_ref = attention_qkvpacked_ref(
-        qkv, key_padding_mask, dropout_p, dropout_mask, causal=causal
+        qkv, key_padding_mask, dropout_p, dropout_mask, causal=causal, window_size=window_size
     )
     out_pt, attn_pt = attention_qkvpacked_ref(
         qkv,
@@ -668,6 +741,7 @@ def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
         dropout_p,
         dropout_mask,
         causal=causal,
+        window_size=window_size,
         upcast=False,
         reorder_ops=True,
     )
@@ -700,7 +774,7 @@ def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
 
     if dropout_p > 0.0:
         assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
-        assert abs(dropout_fraction - dropout_p) <= 0.01
+        assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025)
 
     if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
         assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()
@@ -712,10 +786,12 @@ def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
 # @pytest.mark.parametrize("dtype", [torch.bfloat16])
 @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
 # @pytest.mark.parametrize("mha_type", ["mha"])
+@pytest.mark.parametrize("local", [False, True])
+# @pytest.mark.parametrize("local", [True])
 @pytest.mark.parametrize("causal", [False, True])
 # @pytest.mark.parametrize("causal", [True])
 @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
-# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
+# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
 # @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192])
 # @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
 # @pytest.mark.parametrize('d', [56, 80])
@@ -738,7 +814,9 @@ def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
 # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)])
 @pytest.mark.parametrize("dropout_p", [0.0, 0.17])
 # @pytest.mark.parametrize("dropout_p", [0.17])
-def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, dtype, kvpacked):
+def test_flash_attn_output(
+    seqlen_q, seqlen_k, d, dropout_p, causal, local, mha_type, dtype, kvpacked
+):
     if (
         max(seqlen_q, seqlen_k) >= 2048
         and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
@@ -747,10 +825,11 @@ def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, d
     device = "cuda"
     # set seed
     torch.random.manual_seed(0)
-    batch_size = 16
+    batch_size = 13
     nheads = 9
     nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
     assert nheads % nheads_k == 0
+    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
     q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
     if kvpacked:
         kv = torch.randn(
@@ -766,15 +845,23 @@ def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, d
 
     if kvpacked:
         out, lse, S_dmask = flash_attn_kvpacked_func(
-            q, kv, dropout_p, return_attn_probs=True, causal=causal
+            q, kv, dropout_p, causal=causal, window_size=window_size, return_attn_probs=True
         )
     else:
         out, lse, S_dmask = flash_attn_func(
-            q, k, v, dropout_p, return_attn_probs=True, causal=causal
+            q, k, v, dropout_p, causal=causal, window_size=window_size, return_attn_probs=True
         )
     if dropout_p > 0.0:
         S_dmask_converted = convert_flash_attn_S_to_softmax(
-            S_dmask, seqlen_q, seqlen_k, None, None, d, dropout_p > 0.0, causal=causal
+            S_dmask,
+            seqlen_q,
+            seqlen_k,
+            None,
+            None,
+            d,
+            dropout_p > 0.0,
+            causal=causal,
+            window_size=window_size,
         )
         dropout_mask = S_dmask_converted >= 0
         attn_unnorm = S_dmask_converted.abs()
@@ -785,16 +872,33 @@ def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, d
             k_rep = repeat(k, "b s h d -> b s (h g) d", g=nheads // nheads_k)
             v_rep = repeat(v, "b s h d -> b s (h g) d", g=nheads // nheads_k)
         attn = normalize_flash_attn_S(
-            attn_unnorm, q, k_rep, v_rep, None, None, dropout_p > 0.0, causal=causal
+            attn_unnorm,
+            q,
+            k_rep,
+            v_rep,
+            None,
+            None,
+            dropout_p > 0.0,
+            causal=causal,
+            window_size=window_size,
         )
-        dropout_fraction = get_dropout_fraction(dropout_mask, None, None, causal=causal).item()
+        dropout_fraction = get_dropout_fraction(
+            dropout_mask, None, None, causal=causal, window_size=window_size
+        ).item()
         print(f"Actual dropout fraction: {dropout_fraction}")
     else:
         dropout_mask = None
 
     if kvpacked:
         out_ref, attn_ref = attention_kvpacked_ref(
-            q, kv, None, None, dropout_p, dropout_mask, causal=causal
+            q,
+            kv,
+            None,
+            None,
+            dropout_p,
+            dropout_mask,
+            causal=causal,
+            window_size=window_size,
         )
         out_pt, attn_pt = attention_kvpacked_ref(
             q,
@@ -804,12 +908,21 @@ def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, d
             dropout_p,
             dropout_mask,
             causal=causal,
+            window_size=window_size,
             upcast=False,
             reorder_ops=True,
         )
     else:
         out_ref, attn_ref = attention_ref(
-            q, k, v, None, None, dropout_p, dropout_mask, causal=causal
+            q,
+            k,
+            v,
+            None,
+            None,
+            dropout_p,
+            dropout_mask,
+            causal=causal,
+            window_size=window_size,
         )
         out_pt, attn_pt = attention_ref(
             q,
@@ -820,6 +933,7 @@ def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, d
             dropout_p,
             dropout_mask,
             causal=causal,
+            window_size=window_size,
             upcast=False,
             reorder_ops=True,
         )
@@ -886,7 +1000,7 @@ def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, d
 
     if dropout_p > 0.0:
         assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
-        assert abs(dropout_fraction - dropout_p) <= 0.01
+        assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025)
 
     if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
         assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
@@ -900,10 +1014,12 @@ def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, d
 # @pytest.mark.parametrize('dtype', [torch.float16])
 @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
 # @pytest.mark.parametrize('mha_type', ["mqa"])
+@pytest.mark.parametrize("local", [False, True])
+# @pytest.mark.parametrize("local", [True])
 @pytest.mark.parametrize("causal", [False, True])
 # @pytest.mark.parametrize('causal', [True])
 @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
-# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
+# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
 # @pytest.mark.parametrize('d', [64])
 @pytest.mark.parametrize(
     "seqlen_q,seqlen_k",
@@ -925,7 +1041,7 @@ def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, d
 @pytest.mark.parametrize("dropout_p", [0.0, 0.17])
 # @pytest.mark.parametrize('dropout_p', [0.0])
 def test_flash_attn_varlen_output(
-    seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, dtype, kvpacked
+    seqlen_q, seqlen_k, d, dropout_p, causal, local, mha_type, dtype, kvpacked
 ):
     if (
         max(seqlen_q, seqlen_k) >= 2048
@@ -935,10 +1051,11 @@ def test_flash_attn_varlen_output(
     device = "cuda"
     # set seed
     torch.random.manual_seed(0)
-    batch_size = 16
+    batch_size = 13
     nheads = 9
     nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
     assert nheads % nheads_k == 0
+    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
     q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
     if kvpacked:
         kv = torch.randn(
@@ -980,6 +1097,7 @@ def test_flash_attn_varlen_output(
             dropout_p,
             return_attn_probs=True,
             causal=causal,
+            window_size=window_size,
         )
     else:
         (
@@ -1008,6 +1126,7 @@ def test_flash_attn_varlen_output(
             dropout_p,
             return_attn_probs=True,
             causal=causal,
+            window_size=window_size,
         )
     out = output_pad_fn(out_unpad)
     if dropout_p > 0.0:
@@ -1020,6 +1139,7 @@ def test_flash_attn_varlen_output(
             d,
             dropout_p > 0.0,
             causal=causal,
+            window_size=window_size,
         )
         dropout_mask = S_dmask_converted >= 0
         attn_unnorm = S_dmask_converted.abs()
@@ -1038,9 +1158,14 @@ def test_flash_attn_varlen_output(
             key_padding_mask,
             dropout_p > 0.0,
             causal=causal,
+            window_size=window_size,
         )
         dropout_fraction = get_dropout_fraction(
-            dropout_mask, query_padding_mask, key_padding_mask, causal=causal
+            dropout_mask,
+            query_padding_mask,
+            key_padding_mask,
+            causal=causal,
+            window_size=window_size,
         ).item()
         print(f"Actual dropout fraction: {dropout_fraction}")
     else:
@@ -1048,7 +1173,14 @@ def test_flash_attn_varlen_output(
 
     if kvpacked:
         out_ref, attn_ref = attention_kvpacked_ref(
-            q, kv, query_padding_mask, key_padding_mask, dropout_p, dropout_mask, causal=causal
+            q,
+            kv,
+            query_padding_mask,
+            key_padding_mask,
+            dropout_p,
+            dropout_mask,
+            causal=causal,
+            window_size=window_size,
         )
         out_pt, attn_pt = attention_kvpacked_ref(
             q,
@@ -1058,12 +1190,21 @@ def test_flash_attn_varlen_output(
             dropout_p,
             dropout_mask,
             causal=causal,
+            window_size=window_size,
             upcast=False,
             reorder_ops=True,
         )
     else:
         out_ref, attn_ref = attention_ref(
-            q, k, v, query_padding_mask, key_padding_mask, dropout_p, dropout_mask, causal=causal
+            q,
+            k,
+            v,
+            query_padding_mask,
+            key_padding_mask,
+            dropout_p,
+            dropout_mask,
+            causal=causal,
+            window_size=window_size,
         )
         out_pt, attn_pt = attention_ref(
             q,
@@ -1074,6 +1215,7 @@ def test_flash_attn_varlen_output(
             dropout_p,
             dropout_mask,
             causal=causal,
+            window_size=window_size,
             upcast=False,
             reorder_ops=True,
         )
@@ -1142,7 +1284,7 @@ def test_flash_attn_varlen_output(
 
     if dropout_p > 0.0:
         assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
-        assert abs(dropout_fraction - dropout_p) <= 0.01
+        assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025)
 
     if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
         assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
@@ -1152,8 +1294,10 @@ def test_flash_attn_varlen_output(
 
 @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
 # @pytest.mark.parametrize("dtype", [torch.bfloat16])
+@pytest.mark.parametrize("local", [False, True])
+# @pytest.mark.parametrize("local", [True])
 @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
-# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
+# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
 # @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192])
 # @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
 # @pytest.mark.parametrize('d', [56, 80])
@@ -1176,7 +1320,7 @@ def test_flash_attn_varlen_output(
     ],
 )
 # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)])
-def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
+def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, local, dtype):
     if (
         max(seqlen_q, seqlen_k) >= 2048
         and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
@@ -1188,13 +1332,16 @@ def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
     causal = True
     # set seed
     torch.random.manual_seed(0)
-    batch_size = 16
+    batch_size = 13
     nheads = 9
+    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
     q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
     k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
     v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
-    out = flash_attn_func(q, k, v, 0.0, causal=causal)
-    out_ref, attn_ref = attention_ref(q, k, v, None, None, 0.0, None, causal=causal)
+    out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size)
+    out_ref, attn_ref = attention_ref(
+        q, k, v, None, None, 0.0, None, causal=causal, window_size=window_size
+    )
     out_pt, attn_pt = attention_ref(
         q,
         k,
@@ -1204,6 +1351,7 @@ def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
         0.0,
         None,
         causal=causal,
+        window_size=window_size,
         upcast=False,
         reorder_ops=True,
     )
@@ -1256,12 +1404,14 @@ def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
 
 @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
 # @pytest.mark.parametrize("dtype", [torch.bfloat16])
+@pytest.mark.parametrize("local", [False, True])
+# @pytest.mark.parametrize("local", [True])
 @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
-# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
+# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
 # @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192])
 # @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
 # @pytest.mark.parametrize('d', [56, 80])
-# @pytest.mark.parametrize("d", [128])
+# @pytest.mark.parametrize("d", [64])
 @pytest.mark.parametrize("swap_sq_sk", [False, True])
 # @pytest.mark.parametrize("swap_sq_sk", [True])
 @pytest.mark.parametrize(
@@ -1280,7 +1430,7 @@ def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
     ],
 )
 # @pytest.mark.parametrize("seqlen_q,seqlen_k", [(256, 128)])
-def test_flash_attn_varlen_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
+def test_flash_attn_varlen_causal(seqlen_q, seqlen_k, swap_sq_sk, d, local, dtype):
     if (
         max(seqlen_q, seqlen_k) >= 2048
         and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
@@ -1292,8 +1442,9 @@ def test_flash_attn_varlen_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
     causal = True
     # set seed
     torch.random.manual_seed(0)
-    batch_size = 16
+    batch_size = 13
     nheads = 9
+    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
     q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
     k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
     v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
@@ -1324,10 +1475,19 @@ def test_flash_attn_varlen_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
         max_seqlen_k,
         0.0,
         causal=causal,
+        window_size=window_size,
     )
     out = output_pad_fn(out_unpad)
     out_ref, attn_ref = attention_ref(
-        q, k, v, query_padding_mask, key_padding_mask, 0.0, None, causal=causal
+        q,
+        k,
+        v,
+        query_padding_mask,
+        key_padding_mask,
+        0.0,
+        None,
+        causal=causal,
+        window_size=window_size,
     )
     out_pt, attn_pt = attention_ref(
         q,
@@ -1338,6 +1498,7 @@ def test_flash_attn_varlen_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
         0.0,
         None,
         causal=causal,
+        window_size=window_size,
         upcast=False,
         reorder_ops=True,
     )
@@ -1393,6 +1554,8 @@ def test_flash_attn_varlen_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
 
 @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
 # @pytest.mark.parametrize("dtype", [torch.float16])
+@pytest.mark.parametrize("local", [False, True])
+# @pytest.mark.parametrize("local", [True])
 @pytest.mark.parametrize("causal", [False, True])
 # @pytest.mark.parametrize("causal", [True])
 @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@@ -1418,7 +1581,7 @@ def test_flash_attn_varlen_causal(seqlen_q, seqlen_k, swap_sq_sk, d, dtype):
     ],
 )
 # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)])
-def test_flash_attn_splitkv(seqlen_q, seqlen_k, swap_sq_sk, d, causal, dtype):
+def test_flash_attn_splitkv(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype):
     if swap_sq_sk:
         seqlen_q, seqlen_k = seqlen_k, seqlen_q
     device = "cuda"
@@ -1426,11 +1589,16 @@ def test_flash_attn_splitkv(seqlen_q, seqlen_k, swap_sq_sk, d, causal, dtype):
     torch.random.manual_seed(0)
     batch_size = 1
     nheads = 12
+    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
     q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
     k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
     v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
-    out, lse, _ = flash_attn_func(q, k, v, 0.0, causal=causal, return_attn_probs=True)
-    out_ref, attn_ref = attention_ref(q, k, v, None, None, 0.0, None, causal=causal)
+    out, lse, _ = flash_attn_func(
+        q, k, v, 0.0, causal=causal, window_size=window_size, return_attn_probs=True
+    )
+    out_ref, attn_ref = attention_ref(
+        q, k, v, None, None, 0.0, None, causal=causal, window_size=window_size
+    )
     out_pt, attn_pt = attention_ref(
         q,
         k,
@@ -1440,6 +1608,7 @@ def test_flash_attn_splitkv(seqlen_q, seqlen_k, swap_sq_sk, d, causal, dtype):
         0.0,
         None,
         causal=causal,
+        window_size=window_size,
         upcast=False,
         reorder_ops=True,
     )
@@ -1498,6 +1667,8 @@ def test_flash_attn_splitkv(seqlen_q, seqlen_k, swap_sq_sk, d, causal, dtype):
 # @pytest.mark.parametrize("mha_type", ["mha"])
 @pytest.mark.parametrize("new_kv", [False, True])
 # @pytest.mark.parametrize("new_kv", [True])
+@pytest.mark.parametrize("local", [False, True])
+# @pytest.mark.parametrize("local", [True])
 @pytest.mark.parametrize("causal", [False, True])
 # @pytest.mark.parametrize("causal", [True])
 @pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False])
@@ -1506,7 +1677,7 @@ def test_flash_attn_splitkv(seqlen_q, seqlen_k, swap_sq_sk, d, causal, dtype):
 # @pytest.mark.parametrize("rotary_interleaved", [False])
 @pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0])
 # @pytest.mark.parametrize("rotary_fraction", [0.0])
-@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
+@pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 128, 160, 192, 224, 256])
 # @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
 # @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192])
 # @pytest.mark.parametrize('d', [56, 80])
@@ -1536,6 +1707,7 @@ def test_flash_attn_kvcache(
     rotary_interleaved,
     seqlen_new_eq_seqlen_q,
     causal,
+    local,
     new_kv,
     mha_type,
     num_splits,
@@ -1554,6 +1726,7 @@ def test_flash_attn_kvcache(
     rotary_dim = math.floor(int(rotary_fraction * d) / 16) * 16
     nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
     assert nheads % nheads_k == 0
+    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
     q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
     seqlen_new = seqlen_q if seqlen_new_eq_seqlen_q else torch.randint(1, seqlen_q + 1, (1,)).item()
     if new_kv:
@@ -1566,7 +1739,7 @@ def test_flash_attn_kvcache(
     cache_seqlens = torch.randint(
         0,
         # If we don't use seqlen_q in the case of causal and rotary, cos/sin won't be long enough
-        (seqlen_k - (seqlen_q if causal and rotary_dim > 1 else seqlen_new) + 1)
+        (seqlen_k - (seqlen_q if (causal or local) and rotary_dim > 1 else seqlen_new) + 1)
         if new_kv
         else (seqlen_k + 1),
         (batch_size,),
@@ -1578,7 +1751,7 @@ def test_flash_attn_kvcache(
         angle = torch.rand(seqlen_k, rotary_dim // 2, device=device) * 2 * math.pi
         cos = torch.cos(angle).to(dtype=dtype)
         sin = torch.sin(angle).to(dtype=dtype)
-        if causal:
+        if causal or local:
             q_ro = apply_rotary_emb(
                 q, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
             )
@@ -1624,11 +1797,14 @@ def test_flash_attn_kvcache(
         sin,
         cache_seqlens,
         causal=causal,
+        window_size=window_size,
         rotary_interleaved=rotary_interleaved,
         num_splits=num_splits,
     )
-    # out = flash_attn_with_kvcache(q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=causal)
-    # out = flash_attn_with_kvcache(q, k_cache, v_cache, causal=causal)
+    # out = flash_attn_with_kvcache(
+    #     q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=causal, window_size=window_size
+    # )
+    # out = flash_attn_with_kvcache(q, k_cache, v_cache, causal=causal, window_size=window_size)
     # qk = torch.einsum("bqhd,bkhd->bhqk", q, k_cache_ref)
     # m = qk.amax(-1, keepdim=True)
     # s_tmp = torch.exp((qk - m) / math.sqrt(d))
@@ -1637,7 +1813,15 @@ def test_flash_attn_kvcache(
     # probs = torch.softmax(qk, dim=-1)
     key_padding_mask = arange < cache_seqlens_expanded + (seqlen_new if new_kv else 0)
     out_ref, _ = attention_ref(
-        q_ro, k_cache_rep, v_cache_rep, None, key_padding_mask, 0.0, None, causal=causal
+        q_ro,
+        k_cache_rep,
+        v_cache_rep,
+        None,
+        key_padding_mask,
+        0.0,
+        None,
+        causal=causal,
+        window_size=window_size,
     )
     out_pt, _ = attention_ref(
         q_ro,
@@ -1648,6 +1832,7 @@ def test_flash_attn_kvcache(
         0.0,
         None,
         causal=causal,
+        window_size=window_size,
         upcast=False,
         reorder_ops=True,
     )