63 lines
1.7 KiB
Django/Jinja
63 lines
1.7 KiB
Django/Jinja
#pragma once
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#include <flashinfer/page.cuh>
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#include <flashinfer/math.cuh>
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#include <flashinfer/layout.cuh>
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#include <flashinfer/pos_enc.cuh>
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#include <flashinfer/attention/variant_helper.cuh>
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#define ADDITIONAL_FUNC_PARAMS {{ additional_func_params }}
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#define ADDITIONAL_PARAMS_SETTER {{ additional_params_setter }}
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#define DISPATCH_context(DTypeQ, DTypeKV, DTypeO, IdType, HEAD_DIM_QK, HEAD_DIM_VO, POS_ENCODING_MODE, USE_SLIDING_WINDOW, USE_LOGITS_SOFT_CAP, AttentionVariant, Params, ...) { \
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using AttentionVariant = {{ variant_name }}; \
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__VA_ARGS__(); \
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}
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using namespace flashinfer;
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using DTypeQ = {{ dtype_q }};
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using DTypeKV = {{ dtype_kv }};
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using DTypeO = {{ dtype_o }};
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using IdType = {{ idtype }};
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constexpr int HEAD_DIM_QK = {{ head_dim_qk }};
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constexpr int HEAD_DIM_VO = {{ head_dim_vo }};
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constexpr auto USE_LOGITS_SOFT_CAP = {{ use_logits_soft_cap }};
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constexpr auto POS_ENCODING_MODE = {{ pos_encoding_mode }};
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constexpr auto USE_SLIDING_WINDOW = {{ use_sliding_window }};
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struct Params {
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using DTypeQ = DTypeQ;
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using DTypeKV = DTypeKV;
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using DTypeO = DTypeO;
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using IdType = IdType;
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DTypeQ* q;
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paged_kv_t<DTypeKV, IdType> paged_kv;
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DTypeO* o;
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float* lse;
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{{ additional_params_decl }}
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uint32_t padded_batch_size;
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uint32_t num_qo_heads;
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IdType q_stride_n;
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IdType q_stride_h;
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int32_t window_left;
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bool enable_pdl;
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IdType* request_indices;
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IdType* kv_tile_indices;
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IdType* o_indptr;
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IdType* kv_chunk_size_ptr;
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bool* block_valid_mask;
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bool partition_kv;
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__host__ __device__ __forceinline__ int32_t get_qo_len(int32_t batch_idx) const { return 1; }
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__host__ __device__ __forceinline__ int32_t get_kv_len(int32_t batch_idx) const {
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return paged_kv.get_length(batch_idx);
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}
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};
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{{ variant_decl }}
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