/* * Copyright (c) 2025 by FlashInfer team. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ #include #include #include #include #include #include "batch_attention_config.inc" #include "pytorch_conversion_utils.h" #include "pytorch_extension_utils.h" namespace flashinfer { template cudaError_t BatchPagedAttentionPersistent(const Params params_1, const Params params_2, const uint32_t num_blks_x, const uint32_t num_blks_y, const cudaStream_t stream); } // namespace flashinfer using namespace flashinfer; at::Tensor BatchPagedAttentionPlan(at::Tensor float_workspace_buffer, at::Tensor int_workspace_buffer, at::Tensor page_locked_int_workspace_buffer, at::Tensor qo_indptr, at::Tensor kv_indptr, at::Tensor kv_len, int64_t batch_size, int64_t num_qo_heads, int64_t num_kv_heads, int64_t head_dim_o, bool causal) { size_t float_workspace_size_in_bytes = float_workspace_buffer.size(0) * float_workspace_buffer.element_size(); size_t int_workspace_size_in_bytes = int_workspace_buffer.size(0) * int_workspace_buffer.element_size(); HolisticPlanInfo<2> plan_info; const c10::cuda::OptionalCUDAGuard device_guard(float_workspace_buffer.device()); const cudaStream_t stream = c10::cuda::getCurrentCUDAStream(); cudaError_t status = TwoStageHolisticPlan( float_workspace_buffer.data_ptr(), float_workspace_size_in_bytes, int_workspace_buffer.data_ptr(), page_locked_int_workspace_buffer.data_ptr(), int_workspace_size_in_bytes, plan_info, qo_indptr.data_ptr(), kv_indptr.data_ptr(), kv_len.data_ptr(), batch_size, num_qo_heads, num_kv_heads, head_dim_o, causal, stream); TORCH_CHECK(status == cudaSuccess, "Failed to plan persistent paged attention, error: ", cudaGetErrorString(status)); return vec_to_tensor(plan_info.ToVector()); } void BatchPagedAttentionRun(at::Tensor float_workspace_buffer, at::Tensor int_workspace_buffer, at::Tensor plan_info_vec, at::Tensor q, at::Tensor k_cache, at::Tensor v_cache, at::Tensor kv_indices, at::Tensor o, std::optional maybe_lse, int64_t mask_mode_code, int64_t layout_code, int64_t num_qo_heads, int64_t num_kv_heads, int64_t page_size, double sm_scale, double logits_soft_cap ADDITIONAL_FUNC_PARAMS PROFILER_FUNC_PARAMS) { HolisticPlanInfo<2> plan_info; plan_info.FromVector(tensor_to_vec(plan_info_vec)); auto device = q.device(); void* float_buffer_ptr = float_workspace_buffer.data_ptr(); void* int_buffer_ptr = int_workspace_buffer.data_ptr(); const MaskMode mask_mode = static_cast(mask_mode_code); auto q_scalar_type = q.scalar_type(); auto kv_scalar_type = k_cache.scalar_type(); // NOTE (Yilong): assume both q and o are NHD unsigned int q_stride_n = q.stride(0); unsigned int q_stride_h = q.stride(1); // layout only constraint paged KV const QKVLayout kv_layout = static_cast(layout_code); unsigned int k_stride_page = k_cache.stride(0); unsigned int v_stride_page = v_cache.stride(0); unsigned int k_stride_n, k_stride_h, v_stride_n, v_stride_h; if (kv_layout == QKVLayout::kNHD) { k_stride_h = k_cache.stride(2); k_stride_n = k_cache.stride(1); v_stride_h = v_cache.stride(2); v_stride_n = v_cache.stride(1); } else { k_stride_h = k_cache.stride(1); k_stride_n = k_cache.stride(2); v_stride_h = v_cache.stride(1); v_stride_n = v_cache.stride(2); } const c10::cuda::OptionalCUDAGuard device_guard(device); const cudaStream_t stream = c10::cuda::getCurrentCUDAStream(); DISPATCH_context( DTypeQ, DTypeKV, DTypeO, IdType, MASK_MODE, HEAD_DIM_QK, HEAD_DIM_VO, POS_ENCODING_MODE, AttentionVariant, PersistentParams, [&] { PersistentParams params[2]; IdType* len_kv_chunk = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.len_kv_chunk_offset); for (int i = 0; i < 2; i++) { params[i].q = static_cast(q.data_ptr()); params[i].k = static_cast(k_cache.data_ptr()); params[i].v = static_cast(v_cache.data_ptr()); params[i].q_indptr = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.tasks[i].q_indptr_offset); params[i].kv_indptr = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.tasks[i].kv_indptr_offset); params[i].partial_indptr = GetPtrFromBaseOffset( int_buffer_ptr, plan_info.tasks[i].partial_indptr_offset); params[i].kv_indices = static_cast(kv_indices.data_ptr()); params[i].q_len = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.tasks[i].q_len_offset); params[i].kv_len = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.tasks[i].kv_len_offset); params[i].q_start = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.tasks[i].q_start_offset); params[i].kv_start = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.tasks[i].kv_start_offset); params[i].kv_end = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.tasks[i].kv_end_offset); params[i].kv_head_idx_arr = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.tasks[i].kv_head_idx_offset); params[i].work_indptr = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.tasks[i].work_indptr_offset); params[i].len_kv_chunk = len_kv_chunk + i; params[i].final_o = static_cast(o.data_ptr()); params[i].final_lse = maybe_lse.has_value() ? static_cast(maybe_lse->data_ptr()) : nullptr; params[i].partial_o = GetPtrFromBaseOffset(float_buffer_ptr, plan_info.partial_o_offset); params[i].partial_lse = GetPtrFromBaseOffset(float_buffer_ptr, plan_info.partial_lse_offset); // for state reduction params[i].merge_indptr = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.merge_indptr_offset); params[i].merge_o_indices = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.merge_o_indices_offset); params[i].num_packed_qo_len = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.num_qo_len_offset); params[i].num_kv_heads = num_kv_heads; params[i].gqa_group_size = uint_fastdiv(num_qo_heads / num_kv_heads); params[i].page_size = uint_fastdiv(page_size); params[i].q_stride_n = q_stride_n; params[i].q_stride_h = q_stride_h; params[i].k_stride_page = k_stride_page; params[i].k_stride_h = k_stride_h; params[i].k_stride_n = k_stride_n; params[i].v_stride_page = v_stride_page; params[i].v_stride_h = v_stride_h; params[i].v_stride_n = v_stride_n; params[i].sm_scale = sm_scale; params[i].logits_soft_cap = logits_soft_cap; // NOTE(Wenxuan) directly using the additional_params_decl from generate_additional_params // will be problematic because of the params[i] ADDITIONAL_PARAMS_SETTER PROFILER_PARAMS_SETTER } cudaError_t status = BatchPagedAttentionPersistent<128, 16, HEAD_DIM_QK, HEAD_DIM_VO, MASK_MODE, AttentionVariant>( params[0], params[1], plan_info.num_blks_x, plan_info.num_blks_y, stream); TORCH_CHECK(status == cudaSuccess, "Failed to run persistent paged attention, error: ", cudaGetErrorString(status)); return true; }); }