274 lines
13 KiB
Plaintext
274 lines
13 KiB
Plaintext
/*
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* Copyright (c) 2023 by FlashInfer team.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <flashinfer/attention/variants.cuh>
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#include <flashinfer/pos_enc.cuh>
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#include <optional>
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#include "pod_config.inc"
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#include "pytorch_conversion_utils.h"
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#include "pytorch_extension_utils.h"
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namespace flashinfer {
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template <uint32_t HEAD_DIM_QK, uint32_t HEAD_DIM_VO, PosEncodingMode POS_ENCODING_MODE,
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bool USE_FP16_QK_REDUCTION, MaskMode MASK_MODE_P, uint32_t CTA_TILE_Q,
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MaskMode MASK_MODE_D, typename PrefillAttentionVariant, typename DecodeAttentionVariant,
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typename PrefillParams, typename DecodeParams>
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cudaError_t PODWithKVCacheTensorDispatched(PrefillParams prefill_params,
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typename PrefillParams::DTypeO* tmp,
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DecodeParams decode_params,
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typename DecodeParams::DTypeO* tmp_v, float* tmp_s,
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bool enable_pdl, cudaStream_t stream);
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} // namespace flashinfer
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using namespace flashinfer;
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void pod_with_kv_cache_tensor(
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// Prefill params
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at::Tensor q_p, at::Tensor k_p, at::Tensor v_p, at::Tensor tmp_p, at::Tensor o_p,
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std::optional<at::Tensor> maybe_lse_p, int64_t mask_mode_code_p, int64_t layout_p,
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int64_t window_left_p, std::optional<at::Tensor> maybe_custom_mask_p,
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std::optional<at::Tensor> maybe_alibi_slopes_p, double logits_soft_cap_p, double sm_scale_p,
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double rope_rcp_scale_p, double rope_rcp_theta_p,
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// Decode params
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at::Tensor float_workspace_buffer_d, at::Tensor int_workspace_buffer_d,
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at::Tensor plan_info_vec, at::Tensor q_d, at::Tensor paged_k_cache_d,
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at::Tensor paged_v_cache_d, at::Tensor qo_indptr_d, at::Tensor paged_kv_indptr_d,
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at::Tensor paged_kv_indices_d, at::Tensor paged_kv_last_page_len_d, at::Tensor o_d,
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std::optional<at::Tensor> maybe_lse_d, int64_t mask_mode_code_d, int64_t layout_d,
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int64_t window_left_d, std::optional<at::Tensor> maybe_custom_mask_d,
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std::optional<at::Tensor> maybe_mask_indptr_d, std::optional<at::Tensor> maybe_alibi_slopes_d,
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double logits_soft_cap_d, double sm_scale_d, double rope_rcp_scale_d, double rope_rcp_theta_d,
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bool enable_pdl) {
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// Prefill setup
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unsigned int head_dim_qk = q_p.size(2);
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unsigned int kv_len_p, qo_len_p, num_kv_heads, num_qo_heads;
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QKVLayout kv_layout_p = static_cast<QKVLayout>(layout_p);
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qo_len_p = q_p.size(0);
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num_qo_heads = q_p.size(1);
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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,
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v_stride_n_p, v_stride_h_p;
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if (kv_layout_p == QKVLayout::kNHD) {
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kv_len_p = k_p.size(0);
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num_kv_heads = k_p.size(1);
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k_stride_n_p = k_p.stride(0);
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k_stride_h_p = k_p.stride(1);
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v_stride_n_p = v_p.stride(0);
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v_stride_h_p = v_p.stride(1);
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} else {
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kv_len_p = k_p.size(1);
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num_kv_heads = k_p.size(0);
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k_stride_h_p = k_p.stride(0);
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k_stride_n_p = k_p.stride(1);
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v_stride_h_p = v_p.stride(0);
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v_stride_n_p = v_p.stride(1);
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}
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if (maybe_lse_p) {
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const auto& lse = *maybe_lse_p;
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TORCH_CHECK(lse.size(0) == qo_len_p, lse.size(0), q_p.size(0));
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TORCH_CHECK(lse.size(1) == num_qo_heads, lse.size(1), q_p.size(1));
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}
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const MaskMode mask_mode_p = static_cast<MaskMode>(mask_mode_code_p);
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auto q_scalar_type = q_p.scalar_type();
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auto kv_scalar_type = k_p.scalar_type();
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// Decode setup (Tensor decode = batched prefill)
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PrefillPlanInfo plan_info;
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plan_info.FromVector(tensor_to_vec(plan_info_vec));
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QKVLayout kv_layout_d = static_cast<QKVLayout>(layout_d);
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auto device = q_d.device();
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int64_t batch_size = paged_kv_indptr_d.size(0) - 1;
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int64_t num_qo_heads_d = q_d.size(1);
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TORCH_CHECK(num_qo_heads == num_qo_heads_d,
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"POD currently requires same # Query heads for prefill and decode");
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int64_t num_kv_heads_d, page_size_d;
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uint32_t head_dim_qk_d = q_d.size(2);
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if (kv_layout_d == QKVLayout::kHND) {
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num_kv_heads_d = paged_k_cache_d.size(1);
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page_size_d = paged_k_cache_d.size(2);
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} else {
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page_size_d = paged_k_cache_d.size(1);
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num_kv_heads_d = paged_k_cache_d.size(2);
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}
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TORCH_CHECK(num_kv_heads == num_kv_heads_d,
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"POD currently requires same # KV heads for prefill and decode; Prefill: ",
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num_kv_heads, ", Decode: ", num_kv_heads_d);
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if (maybe_lse_d) {
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const auto& lse = *maybe_lse_d;
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TORCH_CHECK(lse.size(0) == q_d.size(0), lse.size(0), q_d.size(0));
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TORCH_CHECK(lse.size(1) == q_d.size(1), lse.size(1), q_d.size(1));
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}
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void* float_buffer_ptr = static_cast<void*>(float_workspace_buffer_d.data_ptr());
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void* int_buffer_ptr = static_cast<void*>(int_workspace_buffer_d.data_ptr());
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const MaskMode mask_mode_d = static_cast<MaskMode>(mask_mode_code_d);
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auto q_scalar_type_d = q_d.scalar_type();
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auto kv_scalar_type_d = paged_k_cache_d.scalar_type();
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// get q_stride_n and q_stride_h
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const auto q_stride_n_d = q_d.stride(0);
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const auto q_stride_h_d = q_d.stride(1);
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// get kv_cache_strides
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const int64_t* kv_cache_strides_d = nullptr;
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auto k_strides_d = paged_k_cache_d.strides();
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auto v_strides_d = paged_v_cache_d.strides();
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TORCH_CHECK(k_strides_d == v_strides_d, "k/v strides must be identical");
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kv_cache_strides_d = k_strides_d.data();
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const c10::cuda::OptionalCUDAGuard device_guard(float_workspace_buffer_d.device());
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const cudaStream_t stream = c10::cuda::getCurrentCUDAStream();
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DISPATCH_context(
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MASK_MODE_P, MASK_MODE_D, DTypeQ, DTypeKV, HEAD_DIM_QK, USE_SLIDING_WINDOW_P,
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USE_SLIDING_WINDOW_D, USE_LOGITS_SOFT_CAP, [&] {
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PrefillParams prefill_params;
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{
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// Make params a reference to prefill_params to set values
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PrefillParams& params = prefill_params;
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params.q = static_cast<DTypeQ*>(q_p.data_ptr());
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params.k = static_cast<DTypeKV*>(k_p.data_ptr());
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params.v = static_cast<DTypeKV*>(v_p.data_ptr());
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params.o = static_cast<DTypeO*>(o_p.data_ptr());
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params.lse = maybe_lse_p ? static_cast<float*>(maybe_lse_p->data_ptr()) : nullptr;
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params.num_qo_heads = num_qo_heads;
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params.num_kv_heads = num_kv_heads;
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params.group_size = uint_fastdiv(num_qo_heads / num_kv_heads);
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params.qo_len = qo_len_p;
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params.kv_len = kv_len_p;
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params.q_stride_n = q_stride_n_p;
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params.q_stride_h = q_stride_h_p;
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params.k_stride_n = k_stride_n_p;
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params.k_stride_h = k_stride_h_p;
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params.v_stride_n = v_stride_n_p;
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params.v_stride_h = v_stride_h_p;
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params.window_left = window_left_p;
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params.partition_kv = false;
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params.maybe_custom_mask = maybe_custom_mask_p
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? static_cast<uint8_t*>(maybe_custom_mask_p->data_ptr())
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: nullptr;
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params.maybe_alibi_slopes = maybe_alibi_slopes_p
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? static_cast<float*>(maybe_alibi_slopes_p->data_ptr())
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: nullptr;
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params.logits_soft_cap = logits_soft_cap_p;
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params.sm_scale = sm_scale_p;
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params.rope_rcp_scale = rope_rcp_scale_p;
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params.rope_rcp_theta = rope_rcp_theta_p;
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}
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DecodeParams decode_params;
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DTypeO* tmp_v = nullptr;
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float* tmp_s = nullptr;
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{
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DecodeParams& params = decode_params;
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params.q = static_cast<DTypeQ*>(q_d.data_ptr());
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paged_kv_t<DTypeKV, IdType> paged_kv(
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num_kv_heads, page_size_d, HEAD_DIM_VO, batch_size, kv_layout_d,
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static_cast<DTypeKV*>(paged_k_cache_d.data_ptr()),
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static_cast<DTypeKV*>(paged_v_cache_d.data_ptr()), kv_cache_strides_d,
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static_cast<IdType*>(paged_kv_indices_d.data_ptr()),
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static_cast<IdType*>(paged_kv_indptr_d.data_ptr()),
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static_cast<IdType*>(paged_kv_last_page_len_d.data_ptr()));
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params.paged_kv = paged_kv;
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params.q_indptr = static_cast<IdType*>(qo_indptr_d.data_ptr());
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params.o = static_cast<DTypeO*>(o_d.data_ptr());
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params.lse = maybe_lse_d ? static_cast<float*>(maybe_lse_d->data_ptr()) : nullptr;
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params.num_qo_heads = num_qo_heads;
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params.group_size = uint_fastdiv(num_qo_heads / paged_kv.num_heads);
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params.q_stride_n = q_stride_n_d;
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params.q_stride_h = q_stride_h_d;
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params.window_left = window_left_d;
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params.request_indices = nullptr;
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params.qo_tile_indices = nullptr;
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params.kv_tile_indices = nullptr;
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params.merge_indptr = nullptr;
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params.o_indptr = nullptr;
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params.kv_chunk_size_ptr = nullptr;
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params.block_valid_mask = nullptr;
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params.total_num_rows = nullptr;
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params.max_total_num_rows = 0;
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params.padded_batch_size = 0;
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params.partition_kv = false;
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params.maybe_mask_indptr = maybe_mask_indptr_d
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? static_cast<int32_t*>(maybe_mask_indptr_d->data_ptr())
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: nullptr;
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params.maybe_alibi_slopes = maybe_alibi_slopes_d
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? static_cast<float*>(maybe_alibi_slopes_d->data_ptr())
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: nullptr;
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params.logits_soft_cap = logits_soft_cap_d;
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params.sm_scale = sm_scale_d;
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params.rope_rcp_scale = rope_rcp_scale_d;
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params.rope_rcp_theta = rope_rcp_theta_d;
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params.request_indices =
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GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.request_indices_offset);
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params.qo_tile_indices =
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GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.qo_tile_indices_offset);
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params.kv_tile_indices =
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GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_tile_indices_offset);
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params.o_indptr = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.o_indptr_offset);
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params.kv_chunk_size_ptr =
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GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_chunk_size_ptr_offset);
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if (plan_info.split_kv) {
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params.merge_indptr =
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GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.merge_indptr_offset);
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tmp_v = GetPtrFromBaseOffset<DTypeO>(float_buffer_ptr, plan_info.v_offset);
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tmp_s = GetPtrFromBaseOffset<float>(float_buffer_ptr, plan_info.s_offset);
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if (plan_info.enable_cuda_graph) {
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params.block_valid_mask =
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GetPtrFromBaseOffset<bool>(int_buffer_ptr, plan_info.block_valid_mask_offset);
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}
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}
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params.padded_batch_size = plan_info.padded_batch_size;
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params.max_total_num_rows = plan_info.total_num_rows;
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if (plan_info.enable_cuda_graph) {
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params.total_num_rows =
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GetPtrFromBaseOffset<uint32_t>(int_buffer_ptr, plan_info.total_num_rows_offset);
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}
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}
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constexpr bool use_custom_mask_p = MASK_MODE_P == MaskMode::kCustom;
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using PrefillAttentionVariant =
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DefaultAttention</*use_custom_mask=*/use_custom_mask_p, USE_SLIDING_WINDOW_P,
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USE_LOGITS_SOFT_CAP, /*use_alibi_bias=*/false>;
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constexpr bool use_custom_mask_d = MASK_MODE_D == MaskMode::kCustom;
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using DecodeAttentionVariant =
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DefaultAttention</*use_custom_mask=*/use_custom_mask_d, USE_SLIDING_WINDOW_D,
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USE_LOGITS_SOFT_CAP, /*use_alibi_bias=*/false>;
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// DISPATCH_CTA_TILE_Q(plan_info.cta_tile_q, CTA_TILE_Q, {
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constexpr size_t CTA_TILE_Q = 16;
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cudaError_t status = flashinfer::PODWithKVCacheTensorDispatched<
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HEAD_DIM_QK, HEAD_DIM_VO, POS_ENCODING_MODE, USE_FP16_QK_REDUCTION, MASK_MODE_P,
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CTA_TILE_Q, MASK_MODE_D, PrefillAttentionVariant, DecodeAttentionVariant>(
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prefill_params, static_cast<DTypeO*>(tmp_p.data_ptr()), decode_params, tmp_v, tmp_s,
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enable_pdl, stream);
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TORCH_CHECK(status == cudaSuccess, "PODWithKVCache kernel launch failed, error: " +
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std::string(cudaGetErrorString(status)));
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//});
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});
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}
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