[PYTORCHDGQ-7000] Added support for Rotary Embedding in flash_attention

applications/flash_attention_v2/collective/xe_flash_attn_prefill_mma.hpp

@@ -38,6 +38,7 @@
 #include "cute/atom/mma_atom.hpp"
 #include "cute/algorithm/gemm.hpp"
 #include "fmha_fusion.hpp"
+#include "xe_rotary.h"
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -61,7 +62,7 @@ CUTLASS_DEVICE auto convert_type(Tensor<Engine, Layout> const &tensor) {
 
 template <class DispatchPolicy, class ProblemShapeType_, class ElementQ_, class StrideQ_, class ElementK_, class StrideK_,
           class ElementV_, class StrideV_, class MMAOperation_, class TileShapeQK_, class TileShapePV_, class SubgroupLayout_, class GmemTiledCopyQ_, class GmemTiledCopyK_,
-          class GmemTiledCopyV_, bool CausalMask_>
+          class GmemTiledCopyV_, bool CausalMask_, bool RopeMask_ = false>
 struct FlashPrefillMma {
   static_assert(cutlass::detail::dependent_false<ElementQ_>, "Could not find a mainloop specialization.");
 };
@@ -70,9 +71,9 @@ struct FlashPrefillMma {
 
 template <int Stages, class ProblemShapeType_, class ElementQ_, class StrideQ_, class ElementK_, class StrideK_,
           class ElementV_, class StrideV_, class MMAOperation_, class TileShapeQK_, class TileShapePV_, class SubgroupLayout_, class GmemTiledCopyQ_, class GmemTiledCopyK_,
-          class GmemTiledCopyV_, bool CausalMask_>
+          class GmemTiledCopyV_, bool CausalMask_, bool RopeMask_>
 struct FlashPrefillMma<gemm::MainloopIntelXeXMX16<Stages>, ProblemShapeType_, ElementQ_, StrideQ_, ElementK_, StrideK_, ElementV_,
-                              StrideV_,  MMAOperation_, TileShapeQK_,  TileShapePV_,  SubgroupLayout_, GmemTiledCopyQ_, GmemTiledCopyK_, GmemTiledCopyV_, CausalMask_> {
+                              StrideV_,  MMAOperation_, TileShapeQK_,  TileShapePV_,  SubgroupLayout_, GmemTiledCopyQ_, GmemTiledCopyK_, GmemTiledCopyV_, CausalMask_, RopeMask_> {
   //
   // Type Aliases
   //
@@ -96,6 +97,7 @@ struct FlashPrefillMma<gemm::MainloopIntelXeXMX16<Stages>, ProblemShapeType_, El
   using TiledMmaPV = typename TiledMMAHelper<MmaAtom, Layout<TileShapePV>, SubgroupLayout>::TiledMMA;
   using ElementAccumulator = typename TiledMmaQK::ValTypeC;
   static constexpr bool CausalMask = CausalMask_;
+  static constexpr bool rope_enabled = RopeMask_;
   static constexpr int SubgroupSize = DispatchPolicy::SubgroupSize;
 
   using MmaAtomShape = typename MmaAtom::Shape_MNK;
@@ -157,12 +159,19 @@ struct FlashPrefillMma<gemm::MainloopIntelXeXMX16<Stages>, ProblemShapeType_, El
     StrideK dK;
     ElementV const *ptr_V;
     StrideV dV;
+    // for RoPE case
+    ElementQ const *ptr_cos = nullptr;
+    ElementQ const *ptr_sin = nullptr;
   };
 
   struct Params {
     XE_Copy_Q gmem_tiled_copy_q;
     XE_Copy_K gmem_tiled_copy_k;
     XE_Copy_V gmem_tiled_copy_v;
+    XE_Copy_Q gmem_tiled_copy_q_cos;
+    XE_Copy_Q gmem_tiled_copy_q_sin;
+    XE_Copy_K gmem_tiled_copy_k_cos;
+    XE_Copy_K gmem_tiled_copy_k_sin;
   };
 
   //
@@ -180,18 +189,87 @@ struct FlashPrefillMma<gemm::MainloopIntelXeXMX16<Stages>, ProblemShapeType_, El
     auto tensorQ = make_tensor(make_gmem_ptr(args.ptr_Q), make_layout(make_shape(seq_len_qo, head_size_qk, batch * num_heads_q), args.dQ));
     auto tensorK = make_tensor(make_gmem_ptr(args.ptr_K), make_layout(make_shape(seq_len_kv, head_size_qk, batch * num_heads_kv), args.dK));
     auto tensorV = make_tensor(make_gmem_ptr(args.ptr_V), make_layout(make_shape(head_size_vo, seq_len_kv, batch * num_heads_kv), args.dV));
+
+    auto tensorCos = make_tensor(make_gmem_ptr(args.ptr_cos), make_layout(make_shape(seq_len_qo, head_size_qk, batch * num_heads_q), args.dQ));
+    auto tensorSin = make_tensor(make_gmem_ptr(args.ptr_sin), make_layout(make_shape(seq_len_qo, head_size_qk, batch * num_heads_q), args.dK));
+
     XE_Copy_Q copyQ{XE_Copy_Q{}.with(tensorQ)};
     XE_Copy_K copyK{XE_Copy_K{}.with(tensorK)};
     XE_Copy_V copyV{XE_Copy_V{}.with(tensorV)};
-
-    return Params{copyQ, copyK, copyV};
+
+    XE_Copy_Q copyQCos{XE_Copy_Q{}.with(tensorCos)};
+    XE_Copy_Q copyQSin{XE_Copy_Q{}.with(tensorSin)};
+
+    XE_Copy_K copyKCos{XE_Copy_K{}.with(tensorCos)};
+    XE_Copy_K copyKSin{XE_Copy_K{}.with(tensorSin)};
+
+    return Params{copyQ, copyK, copyV, copyQCos, copyQSin, copyKCos, copyKSin};
   }
 
-  template <class FragQccum, class TensorQ, class TensorK, class FragSrc>
+  template <class FragQccum, class TensorQ, class TensorK, class FragSrc, class TensorCosQ, class TensorSinQ, class TensorCosK, class TensorSinK>
   CUTLASS_DEVICE void mmaQK(FragQccum &accum, TensorQ gQ, TensorK gK, FragSrc const &frag_src,
-                            int const &k_tile_count, Params const &params) {
+                            int const &k_tile_count, TensorCosQ gQCos, TensorSinQ gQSin, TensorCosK gKCos, TensorSinK gKSin, Params const &params, ProblemShapeType const &problem_shape) {
+
 
     int thread_idx = static_cast<int>(ThreadIdxX());
+    if constexpr (rope_enabled) {
+      // calculate the base_ptr and offset for Q, K.
+      // also calculate the layout for Q, K.
+      // then apply RoPE on Q, K accordingly
+      auto [coord_q_x, coord_q_y, coord_q_z] = *gQ.data();
+      auto [coord_k_x, coord_k_y, coord_k_z] = *gK.data();
+
+      auto [batch, num_heads_q, num_heads_kv, seq_len_qo, seq_len_kv, head_size_qk, head_size_vo] = problem_shape;
+
+      int offset_q = seq_len_qo*head_size_qk*coord_q_z + head_size_qk*coord_q_x + coord_q_y; // row major
+      // int offset_k = seq_len_kv*head_size_qk*coord_k_z + head_size_qk*coord_k_y + coord_k_x; // col major
+      int offset_k = seq_len_kv*head_size_qk*coord_k_z + head_size_qk*coord_k_x + coord_k_y; // row major
+
+      auto q_traits = static_cast<traits_load_Q const&>(params.gmem_tiled_copy_q);
+      ElementQ* base_ptr_q = (ElementQ*)q_traits.base_ptr;
+
+      auto q_traits_cos = static_cast<traits_load_Q const&>(params.gmem_tiled_copy_q_cos);
+      ElementQ* base_ptr_q_cos = (ElementQ*)q_traits_cos.base_ptr;
+
+      auto q_traits_sin = static_cast<traits_load_Q const&>(params.gmem_tiled_copy_q_sin);
+      ElementQ* base_ptr_q_sin = (ElementQ*)q_traits_sin.base_ptr;
+
+      // auto layout_q = gQ.layout();
+      constexpr auto static_shape_q = make_shape(size<0>(gQ), size<1>(gQ));
+      // constexpr auto layout_q = LayoutQ::packed({size<0>(gQ), size<1>(gQ)});
+      constexpr auto layout_q = make_layout(static_shape_q, LayoutRight{});
+
+      auto k_traits = static_cast<traits_load_K const&>(params.gmem_tiled_copy_k);
+      ElementK* base_ptr_k = (ElementK*)k_traits.base_ptr;
+
+      auto k_traits_cos = static_cast<traits_load_K const&>(params.gmem_tiled_copy_k_cos);
+      ElementK* base_ptr_k_cos = (ElementK*)k_traits_cos.base_ptr;
+
+      auto k_traits_sin = static_cast<traits_load_K const&>(params.gmem_tiled_copy_k_sin);
+      ElementK* base_ptr_k_sin = (ElementK*)k_traits_sin.base_ptr;
+
+      // auto layout_k = gK.layout();
+      constexpr auto static_shape_k = make_shape(size<0>(gK), size<1>(gK));
+      constexpr auto layout_k = make_layout(static_shape_k, LayoutRight{});
+
+      for (int i =0 ;i< size<2>(gQ) && thread_idx< size<0>(gQ); i++){
+        auto tensorQ = make_tensor(make_gmem_ptr(base_ptr_q+offset_q), layout_q);
+        auto tensorCosQ = make_tensor(make_gmem_ptr(base_ptr_q_cos+offset_q), layout_q);
+        auto tensorSinQ = make_tensor(make_gmem_ptr(base_ptr_q_sin+offset_q), layout_q);
+        cutlass::flash_attention::collective::apply_rope_interleaved_gmem(thread_idx, tensorQ, tensorCosQ, tensorSinQ, tensorQ);
+        offset_q += QK_BLK_M*QK_BLK_K;
+      }
+
+      for (int i =0 ;i< size<2>(gK) && thread_idx< size<0>(gK); i++){
+        auto tensorK = make_tensor(make_gmem_ptr(base_ptr_k+offset_k), layout_k);
+        auto tensorCosK = make_tensor(make_gmem_ptr(base_ptr_k_cos+offset_k), layout_k);
+        auto tensorSinK = make_tensor(make_gmem_ptr(base_ptr_k_sin+offset_k), layout_k);
+        cutlass::flash_attention::collective::apply_rope_interleaved_gmem(thread_idx, tensorK, tensorCosK, tensorSinK, tensorK);
+        offset_k += QK_BLK_N*QK_BLK_K;
+      }
+    }
+
+
     auto thr_copy_Q = params.gmem_tiled_copy_q.get_slice(thread_idx);
     auto thr_copy_K = params.gmem_tiled_copy_k.get_slice(thread_idx);
     // Instantiate the MMA object

applications/flash_attention_v2/collective/xe_rotary.h

@@ -0,0 +1,89 @@
+/***************************************************************************************************
+ * Copyright (c) 2025 Intel Corporation. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+#pragma once
+
+#include "cutlass/cutlass.h"
+
+namespace cutlass::flash_attention::collective {
+using namespace cute;
+
+
+template <typename Tensor,
+          typename TensorCos, typename TensorSin, typename TensorOut>
+CUTLASS_DEVICE void apply_rope_interleaved_gmem(
+    int thread_idx,
+    Tensor const &srcTensor,
+    TensorCos const &gCos,
+    TensorSin const &gSin, TensorOut &destTensor) {
+
+  // here based on the thread_idx, we will apply RoPE on the srcTensor and store the result in destTensor
+  // we will access row by thread_idx and then access col by loop
+  // we assume the input tensor is in row major format
+  if (cute::thread(1, 0)){
+    print("before apply_rope_interleaved_gmem\n");
+    cute::print_tensor(srcTensor);
+  }
+  syncthreads();
+  if(thread_idx < size<0>(srcTensor)){
+    for (int j = 0; j < size<1>(gCos); j+=2) {
+        float real = static_cast<float>(srcTensor[make_coord(thread_idx, j)]);
+        float imag = static_cast<float>(srcTensor[make_coord(thread_idx, j + 1)]);
+
+
+        float cos_val = static_cast<float>(gCos[make_coord(thread_idx, j)]);
+        float sin_val = static_cast<float>(gSin[make_coord(thread_idx, j)]);
+        // syncthreads();
+        float new_real = real * cos_val - imag * sin_val;
+        float new_imag = real * sin_val + imag * cos_val;
+        // syncthreads();
+        destTensor[make_coord(thread_idx,j)] = static_cast<typename Tensor::value_type>(new_real);
+        destTensor[make_coord(thread_idx,j + 1)] = static_cast<typename Tensor::value_type>(new_imag);
+
+        if (cute::thread(1, 0)){
+          #define PRINT(x) print(#x ": "); print(x); print("\n");
+          PRINT(thread_idx);
+          PRINT(j);
+          PRINT(real);
+          PRINT(imag);
+          PRINT(cos_val);
+          PRINT(sin_val);
+          PRINT(new_real);
+          PRINT(new_imag);
+        }
+    }
+  }
+  if (cute::thread(1, 0)){
+    print("after apply_rope_interleaved_gmem\n");
+    cute::print_tensor(destTensor);
+  }
+}
+
+} // namespace cutlass::flash_attention::collective

applications/flash_attention_v2/kernel/xe_flash_attn_prefill.hpp

@@ -132,6 +132,7 @@ class FMHAPrefill {
   using AccumeShape =  decltype(make_shape(Int<Vec>{}, Int<FragsM>{}, get<1>(TileShapePV{})/get<1>(MmaAtomShape()), Int<VSlicer>{}));
 
   static constexpr bool is_var_len = CollectiveMainloop::is_var_len;
+  static constexpr bool rope_enabled = CollectiveMainloop::rope_enabled;
 
   // Kernel level shared memory storage
   struct SharedStorage {
@@ -272,10 +273,24 @@ class FMHAPrefill {
       Tensor mK_nk = mK_nkl(_, _, blk_l_coord/group_heads_q);                                                    // (n,k)
       Tensor mV_nk = mV_nkl(_, _, blk_l_coord/group_heads_q);                                                    // (n,k)
 
+      Tensor mCosQ_mkl = cute::get_xe_tensor(make_shape(seq_len_qo, head_size_qk, (is_var_len ? 1 : batch) * num_heads_q));  // (m, k, l)
+      Tensor mSinQ_mkl = cute::get_xe_tensor(make_shape(seq_len_qo, head_size_qk, (is_var_len ? 1 : batch) * num_heads_q));  // (m, k, l)
+      Tensor mCosK_nkl = cute::get_xe_tensor(make_shape(seq_len_kv, head_size_qk, (is_var_len ? 1 : batch) * num_head_kv));  // (n, k, l)
+      Tensor mSinK_nkl = cute::get_xe_tensor(make_shape(seq_len_kv, head_size_qk, (is_var_len ? 1 : batch) * num_head_kv));  // (n, k, l)
+      Tensor mCosQ_mk = mCosQ_mkl(_, _, blk_l_coord);                                                // (m,k)
+      Tensor mSinQ_mk = mSinQ_mkl(_, _, blk_l_coord);                                                // (m,k)
+      Tensor mCosK_nk = mCosK_nkl(_, _, blk_l_coord/group_heads_q);                                                // (n,k)
+      Tensor mSinK_nk = mSinK_nkl(_, _, blk_l_coord/group_heads_q);
+
       auto gQ = local_tile(mQ_mk, TileShapeQK{}, make_coord(blk_m_coord, _, _), Step<_1,  X, _1>{});
       auto gK = local_tile(mK_nk, TileShapeQK{}, make_coord(_, _ , _), Step<X, _1, _1>{});
       auto gV = local_tile(mV_nk, TileShapeOutput{}, make_coord(_, blk_n_coord, _), Step<X, _1, _1>{});
 
+      auto gCosQ = local_tile(mCosQ_mk, TileShapeQK{}, make_coord(blk_m_coord, _, _), Step<_1,  X, _1>{});
+      auto gSinQ = local_tile(mSinQ_mk, TileShapeQK{}, make_coord(blk_m_coord, _, _), Step<_1,  X, _1>{});
+      auto gCosK = local_tile(mCosK_nk, TileShapeQK{}, make_coord(_, _ , _), Step<X, _1, _1>{});
+      auto gSinK = local_tile(mSinK_nk, TileShapeQK{}, make_coord(_, _ , _), Step<X, _1, _1>{});
+
       auto mainloop_params = CollectiveMainloop::get_updated_copies(params.mainloop, params.problem_shape, sequence_length_shape, batch_coord);
       // we limit the horisontal size to two subgroup, the empirical resutls show that reading the two cacheline side by side in gives better performance and 
       // anything after that does not have an effect on performance. // (64 here for float b float when possible and loop over to cover all the data needed)
@@ -289,6 +304,12 @@ class FMHAPrefill {
       auto pKgK = thr_prefetch_K.partition_S(gK);
       auto pVgV = thr_prefetch_V.partition_S(gV);
 
+      // RoPE coordinate tensor partitions
+      auto pCosQgCosQ = thr_prefetch_Q.partition_S(gCosQ);
+      auto pSinQgSinQ = thr_prefetch_Q.partition_S(gSinQ);
+      auto pCosKgCosK = thr_prefetch_K.partition_S(gCosK);
+      auto pSinKgSinK = thr_prefetch_K.partition_S(gSinK);
+
       for (int i = 0; i < size<3>(pQgQ); i++) {
         prefetch(tiled_prefetch_q, pQgQ(_, _, _, i));
       }
@@ -299,6 +320,18 @@ class FMHAPrefill {
         }
       }
 
+      for (int i = 0; i < size<3>(pQgQ); i++) {
+        prefetch(tiled_prefetch_q, pCosQgCosQ(_, _, _, i));
+        prefetch(tiled_prefetch_q, pSinQgSinQ(_, _, _, i));
+      }
+      for (int j = 0; j < size<4>(pKgK); j++) {
+        CUTLASS_PRAGMA_UNROLL
+        for (int i = 0; i < DispatchPolicy::Stages; i++) {
+          prefetch(tiled_prefetch_k, pCosKgCosK(_, _, _ , i, j));
+          prefetch(tiled_prefetch_k, pSinKgSinK(_, _, _ , i, j));
+        }
+      }
+
       // Allocate the tiled_mma and the accumulators for the (M,N) workgroup_shape
       Tensor out_reg = make_tensor<ElementAccumulator>(AccumeShape{});
 
@@ -325,7 +358,7 @@ class FMHAPrefill {
         clear(tSr);
 
         // 3) Perform GEMM S = Q*K
-        collective_mma.mmaQK(tSr, gQ, gK(_, _, nblock, _), tSr, ceil_div(head_size_qk, QK_BLK_K), mainloop_params);
+        collective_mma.mmaQK(tSr, gQ, gK(_, _, nblock, _), tSr, ceil_div(head_size_qk, QK_BLK_K), gCosQ, gSinQ, gCosK(_, _, nblock, _), gSinK(_, _, nblock, _), mainloop_params, params.problem_shape);
 
         // we only need one block ahead, there is enough gap to prefetch it while doing softmax. because the gap between the two MMA is big,
         // prefetching it the same way as cutlass K matrix does not make sense
@@ -343,6 +376,12 @@ class FMHAPrefill {
         for (int j = 0; j < size<4>(pKgK); j++) {
           prefetch(tiled_prefetch_k, pKgK(_, _, _, nblock + DispatchPolicy::Stages, j));
         }
+
+        for (int j = 0; j < size<4>(pKgK); j++) {
+          prefetch(tiled_prefetch_k, pCosKgCosK(_, _, _, nblock + DispatchPolicy::Stages, j));
+          prefetch(tiled_prefetch_k, pSinKgSinK(_, _, _, nblock + DispatchPolicy::Stages, j));
+        }
+
         barrier_wait(barrier_scope);
       }
 
@@ -351,7 +390,7 @@ class FMHAPrefill {
       Tensor tSr = make_tensor<ElementAccumulator>(Shape<Int<Vec>, Int<FragsM>, Int<FragsN>>{});
       clear(tSr);
       // 3) Perform GEMM S = Q*K
-      collective_mma.mmaQK(tSr, gQ,  gK(_, _, nblock_limit - 1, _), tSr, ceil_div(head_size_qk, QK_BLK_K), mainloop_params);
+      collective_mma.mmaQK(tSr, gQ, gK(_, _, nblock_limit - 1, _), tSr, ceil_div(head_size_qk, QK_BLK_K), gCosQ, gSinQ, gCosK(_, _, nblock_limit - 1, _), gSinK(_, _, nblock_limit - 1, _), mainloop_params, params.problem_shape);
       // we only need one block ahead, there is enough gap to prefetch it while doing softmax. because the gap between the two MMA is big,
       // prefetching it the same way as cutlass K matrix does not make sense
       for(int i=0; i< size<1>(pVgV); i++) {