102 lines
4.2 KiB
Plaintext
102 lines
4.2 KiB
Plaintext
/*
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* Copyright (c) 2024 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/mask.cuh>
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#include <flashinfer/layout.cuh>
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#include <flashinfer/math.cuh>
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#include <optional>
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#include "pytorch_extension_utils.h"
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#include "single_prefill_sm90_config.inc"
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namespace flashinfer {
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template <uint32_t HEAD_DIM, MaskMode MASK_MODE, bool LEFT_SLINDING_WINDOW,
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typename AttentionVariant, typename Params>
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cudaError_t SingleFP8PrefillWithKVCacheDispatched(Params& params, cudaStream_t stream);
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} // namespace flashinfer
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using namespace flashinfer;
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void single_prefill_with_kv_cache_sm90(at::Tensor q, at::Tensor k, at::Tensor v, at::Tensor tmp,
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at::Tensor o, std::optional<at::Tensor> maybe_lse,
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int64_t mask_mode_code, int64_t layout,
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int64_t window_left ADDITIONAL_FUNC_PARAMS) {
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unsigned int head_dim_qk = q.size(2);
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unsigned int head_dim_vo = v.size(2);
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unsigned int num_qo_heads = q.size(1);
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unsigned int qo_len = q.size(0);
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auto q_scalar_type = q.scalar_type();
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auto kv_scalar_type = k.scalar_type();
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QKVLayout kv_layout = static_cast<QKVLayout>(layout);
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const c10::cuda::OptionalCUDAGuard device_guard(q.device());
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const cudaStream_t stream = c10::cuda::getCurrentCUDAStream();
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const MaskMode mask_mode = static_cast<MaskMode>(mask_mode_code);
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DISPATCH_context(
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DTypeQ, DTypeKV, DTypeO, IdType, MASK_MODE, HEAD_DIM_QK, HEAD_DIM_VO, USE_SLIDING_WINDOW,
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USE_LOGITS_SOFT_CAP, AttentionVariant, Params, [&] {
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Params params;
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params.q_ptr = static_cast<DTypeQ*>(q.data_ptr());
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params.k_ptr = static_cast<DTypeKV*>(k.data_ptr());
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params.v_ptr = static_cast<DTypeKV*>(v.data_ptr());
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params.o_ptr = static_cast<DTypeO*>(o.data_ptr());
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params.lse_ptr = maybe_lse ? (static_cast<float*>(maybe_lse->data_ptr())) : nullptr;
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params.q_stride_n = q.stride(0);
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params.q_stride_h = q.stride(1);
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params.o_stride_n = o.stride(0);
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params.o_stride_h = o.stride(1);
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if (kv_layout == QKVLayout::kNHD) {
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params.k_stride_n = k.stride(0);
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params.k_stride_h = k.stride(1);
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params.v_stride_n = v.stride(0);
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params.v_stride_h = v.stride(1);
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} else {
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params.k_stride_h = k.stride(0);
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params.k_stride_n = k.stride(1);
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params.v_stride_h = v.stride(0);
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params.v_stride_n = v.stride(1);
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}
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params.qo_len = q.size(0);
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params.kv_len = k.size(0);
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params.num_qo_heads = q.size(1);
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params.num_kv_heads = k.size(1);
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params.causal = mask_mode == MaskMode::kCausal;
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params.group_size = params.num_qo_heads / params.num_kv_heads;
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// Note(Yilong): this should be checked on Python Side
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// Only support window_left == 0 for now
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params.window_left = window_left;
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// Note(Yilong): all quantization parameters are set in additional_params
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ADDITIONAL_PARAMS_SETTER
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// Not support various head_dim for now
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static_assert(HEAD_DIM_QK == HEAD_DIM_VO, "head_dim_qk and head_dim_vo should be the same");
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// Currently only support same quantization precision
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static_assert(std::is_same_v<DTypeQ, DTypeKV>);
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cudaError_t status =
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SingleFP8PrefillWithKVCacheDispatched<HEAD_DIM_QK, MASK_MODE, USE_SLIDING_WINDOW,
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AttentionVariant>(params, stream);
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TORCH_CHECK(status == cudaSuccess, "single_prefill_with_kv_cache_sm90 failed with error: " +
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std::string(cudaGetErrorString(status)));
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return true;
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});
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}
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