/* * Copyright (c) 2023 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 "pod_config.inc" #include "pytorch_conversion_utils.h" #include "pytorch_extension_utils.h" namespace flashinfer { template cudaError_t PODWithKVCacheTensorDispatched(PrefillParams prefill_params, typename PrefillParams::DTypeO* tmp, DecodeParams decode_params, typename DecodeParams::DTypeO* tmp_v, float* tmp_s, bool enable_pdl, cudaStream_t stream); } // namespace flashinfer using namespace flashinfer; void pod_with_kv_cache_tensor( // Prefill params at::Tensor q_p, at::Tensor k_p, at::Tensor v_p, at::Tensor tmp_p, at::Tensor o_p, std::optional maybe_lse_p, int64_t mask_mode_code_p, int64_t layout_p, int64_t window_left_p, std::optional maybe_custom_mask_p, std::optional maybe_alibi_slopes_p, double logits_soft_cap_p, double sm_scale_p, double rope_rcp_scale_p, double rope_rcp_theta_p, // Decode params at::Tensor float_workspace_buffer_d, at::Tensor int_workspace_buffer_d, at::Tensor plan_info_vec, at::Tensor q_d, at::Tensor paged_k_cache_d, at::Tensor paged_v_cache_d, at::Tensor qo_indptr_d, at::Tensor paged_kv_indptr_d, at::Tensor paged_kv_indices_d, at::Tensor paged_kv_last_page_len_d, at::Tensor o_d, std::optional maybe_lse_d, int64_t mask_mode_code_d, int64_t layout_d, int64_t window_left_d, std::optional maybe_custom_mask_d, std::optional maybe_mask_indptr_d, std::optional maybe_alibi_slopes_d, double logits_soft_cap_d, double sm_scale_d, double rope_rcp_scale_d, double rope_rcp_theta_d, bool enable_pdl) { // Prefill setup unsigned int head_dim_qk = q_p.size(2); unsigned int kv_len_p, qo_len_p, num_kv_heads, num_qo_heads; QKVLayout kv_layout_p = static_cast(layout_p); qo_len_p = q_p.size(0); num_qo_heads = q_p.size(1); uint32_t q_stride_n_p = q_p.stride(0), q_stride_h_p = q_p.stride(1), k_stride_n_p, k_stride_h_p, v_stride_n_p, v_stride_h_p; if (kv_layout_p == QKVLayout::kNHD) { kv_len_p = k_p.size(0); num_kv_heads = k_p.size(1); k_stride_n_p = k_p.stride(0); k_stride_h_p = k_p.stride(1); v_stride_n_p = v_p.stride(0); v_stride_h_p = v_p.stride(1); } else { kv_len_p = k_p.size(1); num_kv_heads = k_p.size(0); k_stride_h_p = k_p.stride(0); k_stride_n_p = k_p.stride(1); v_stride_h_p = v_p.stride(0); v_stride_n_p = v_p.stride(1); } if (maybe_lse_p) { const auto& lse = *maybe_lse_p; TORCH_CHECK(lse.size(0) == qo_len_p, lse.size(0), q_p.size(0)); TORCH_CHECK(lse.size(1) == num_qo_heads, lse.size(1), q_p.size(1)); } const MaskMode mask_mode_p = static_cast(mask_mode_code_p); auto q_scalar_type = q_p.scalar_type(); auto kv_scalar_type = k_p.scalar_type(); // Decode setup (Tensor decode = batched prefill) PrefillPlanInfo plan_info; plan_info.FromVector(tensor_to_vec(plan_info_vec)); QKVLayout kv_layout_d = static_cast(layout_d); auto device = q_d.device(); int64_t batch_size = paged_kv_indptr_d.size(0) - 1; int64_t num_qo_heads_d = q_d.size(1); TORCH_CHECK(num_qo_heads == num_qo_heads_d, "POD currently requires same # Query heads for prefill and decode"); int64_t num_kv_heads_d, page_size_d; uint32_t head_dim_qk_d = q_d.size(2); if (kv_layout_d == QKVLayout::kHND) { num_kv_heads_d = paged_k_cache_d.size(1); page_size_d = paged_k_cache_d.size(2); } else { page_size_d = paged_k_cache_d.size(1); num_kv_heads_d = paged_k_cache_d.size(2); } TORCH_CHECK(num_kv_heads == num_kv_heads_d, "POD currently requires same # KV heads for prefill and decode; Prefill: ", num_kv_heads, ", Decode: ", num_kv_heads_d); if (maybe_lse_d) { const auto& lse = *maybe_lse_d; TORCH_CHECK(lse.size(0) == q_d.size(0), lse.size(0), q_d.size(0)); TORCH_CHECK(lse.size(1) == q_d.size(1), lse.size(1), q_d.size(1)); } void* float_buffer_ptr = static_cast(float_workspace_buffer_d.data_ptr()); void* int_buffer_ptr = static_cast(int_workspace_buffer_d.data_ptr()); const MaskMode mask_mode_d = static_cast(mask_mode_code_d); auto q_scalar_type_d = q_d.scalar_type(); auto kv_scalar_type_d = paged_k_cache_d.scalar_type(); // get q_stride_n and q_stride_h const auto q_stride_n_d = q_d.stride(0); const auto q_stride_h_d = q_d.stride(1); // get kv_cache_strides const int64_t* kv_cache_strides_d = nullptr; auto k_strides_d = paged_k_cache_d.strides(); auto v_strides_d = paged_v_cache_d.strides(); TORCH_CHECK(k_strides_d == v_strides_d, "k/v strides must be identical"); kv_cache_strides_d = k_strides_d.data(); const c10::cuda::OptionalCUDAGuard device_guard(float_workspace_buffer_d.device()); const cudaStream_t stream = c10::cuda::getCurrentCUDAStream(); DISPATCH_context( MASK_MODE_P, MASK_MODE_D, DTypeQ, DTypeKV, HEAD_DIM_QK, USE_SLIDING_WINDOW_P, USE_SLIDING_WINDOW_D, USE_LOGITS_SOFT_CAP, [&] { PrefillParams prefill_params; { // Make params a reference to prefill_params to set values PrefillParams& params = prefill_params; params.q = static_cast(q_p.data_ptr()); params.k = static_cast(k_p.data_ptr()); params.v = static_cast(v_p.data_ptr()); params.o = static_cast(o_p.data_ptr()); params.lse = maybe_lse_p ? static_cast(maybe_lse_p->data_ptr()) : nullptr; params.num_qo_heads = num_qo_heads; params.num_kv_heads = num_kv_heads; params.group_size = uint_fastdiv(num_qo_heads / num_kv_heads); params.qo_len = qo_len_p; params.kv_len = kv_len_p; params.q_stride_n = q_stride_n_p; params.q_stride_h = q_stride_h_p; params.k_stride_n = k_stride_n_p; params.k_stride_h = k_stride_h_p; params.v_stride_n = v_stride_n_p; params.v_stride_h = v_stride_h_p; params.window_left = window_left_p; params.partition_kv = false; params.maybe_custom_mask = maybe_custom_mask_p ? static_cast(maybe_custom_mask_p->data_ptr()) : nullptr; params.maybe_alibi_slopes = maybe_alibi_slopes_p ? static_cast(maybe_alibi_slopes_p->data_ptr()) : nullptr; params.logits_soft_cap = logits_soft_cap_p; params.sm_scale = sm_scale_p; params.rope_rcp_scale = rope_rcp_scale_p; params.rope_rcp_theta = rope_rcp_theta_p; } DecodeParams decode_params; DTypeO* tmp_v = nullptr; float* tmp_s = nullptr; { DecodeParams& params = decode_params; params.q = static_cast(q_d.data_ptr()); paged_kv_t paged_kv( num_kv_heads, page_size_d, HEAD_DIM_VO, batch_size, kv_layout_d, static_cast(paged_k_cache_d.data_ptr()), static_cast(paged_v_cache_d.data_ptr()), kv_cache_strides_d, static_cast(paged_kv_indices_d.data_ptr()), static_cast(paged_kv_indptr_d.data_ptr()), static_cast(paged_kv_last_page_len_d.data_ptr())); params.paged_kv = paged_kv; params.q_indptr = static_cast(qo_indptr_d.data_ptr()); params.o = static_cast(o_d.data_ptr()); params.lse = maybe_lse_d ? static_cast(maybe_lse_d->data_ptr()) : nullptr; params.num_qo_heads = num_qo_heads; params.group_size = uint_fastdiv(num_qo_heads / paged_kv.num_heads); params.q_stride_n = q_stride_n_d; params.q_stride_h = q_stride_h_d; params.window_left = window_left_d; params.request_indices = nullptr; params.qo_tile_indices = nullptr; params.kv_tile_indices = nullptr; params.merge_indptr = nullptr; params.o_indptr = nullptr; params.kv_chunk_size_ptr = nullptr; params.block_valid_mask = nullptr; params.total_num_rows = nullptr; params.max_total_num_rows = 0; params.padded_batch_size = 0; params.partition_kv = false; params.maybe_mask_indptr = maybe_mask_indptr_d ? static_cast(maybe_mask_indptr_d->data_ptr()) : nullptr; params.maybe_alibi_slopes = maybe_alibi_slopes_d ? static_cast(maybe_alibi_slopes_d->data_ptr()) : nullptr; params.logits_soft_cap = logits_soft_cap_d; params.sm_scale = sm_scale_d; params.rope_rcp_scale = rope_rcp_scale_d; params.rope_rcp_theta = rope_rcp_theta_d; params.request_indices = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.request_indices_offset); params.qo_tile_indices = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.qo_tile_indices_offset); params.kv_tile_indices = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.kv_tile_indices_offset); params.o_indptr = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.o_indptr_offset); params.kv_chunk_size_ptr = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.kv_chunk_size_ptr_offset); if (plan_info.split_kv) { params.merge_indptr = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.merge_indptr_offset); tmp_v = GetPtrFromBaseOffset(float_buffer_ptr, plan_info.v_offset); tmp_s = GetPtrFromBaseOffset(float_buffer_ptr, plan_info.s_offset); if (plan_info.enable_cuda_graph) { params.block_valid_mask = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.block_valid_mask_offset); } } params.padded_batch_size = plan_info.padded_batch_size; params.max_total_num_rows = plan_info.total_num_rows; if (plan_info.enable_cuda_graph) { params.total_num_rows = GetPtrFromBaseOffset(int_buffer_ptr, plan_info.total_num_rows_offset); } } constexpr bool use_custom_mask_p = MASK_MODE_P == MaskMode::kCustom; using PrefillAttentionVariant = DefaultAttention; constexpr bool use_custom_mask_d = MASK_MODE_D == MaskMode::kCustom; using DecodeAttentionVariant = DefaultAttention; // DISPATCH_CTA_TILE_Q(plan_info.cta_tile_q, CTA_TILE_Q, { constexpr size_t CTA_TILE_Q = 16; cudaError_t status = flashinfer::PODWithKVCacheTensorDispatched< HEAD_DIM_QK, HEAD_DIM_VO, POS_ENCODING_MODE, USE_FP16_QK_REDUCTION, MASK_MODE_P, CTA_TILE_Q, MASK_MODE_D, PrefillAttentionVariant, DecodeAttentionVariant>( prefill_params, static_cast(tmp_p.data_ptr()), decode_params, tmp_v, tmp_s, enable_pdl, stream); TORCH_CHECK(status == cudaSuccess, "PODWithKVCache kernel launch failed, error: " + std::string(cudaGetErrorString(status))); //}); }); }