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JartX
40 lines
1.9 KiB
C++
40 lines
1.9 KiB
C++
#pragma once
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#include <torch/all.h>
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torch::Tensor LLMM1(at::Tensor& in_a, at::Tensor& in_b,
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const int64_t rows_per_block);
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torch::Tensor wvSplitK(const at::Tensor& in_a, const at::Tensor& in_b,
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const std::optional<at::Tensor>& in_bias,
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const int64_t CuCount);
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torch::Tensor wvSplitKrc(const at::Tensor& in_a, const at::Tensor& in_b,
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const std::optional<at::Tensor>& in_bias,
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const int64_t CuCount);
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void wvSplitKQ(const at::Tensor& in_a, const at::Tensor& in_b,
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const std::optional<at::Tensor>& in_bias, at::Tensor& out_c,
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const at::Tensor& scale_a, const at::Tensor& scale_b,
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const int64_t CuCount);
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torch::Tensor gptq_gemm_rdna3(torch::Tensor a, torch::Tensor b_q_weight,
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torch::Tensor b_qzeros, torch::Tensor b_scales,
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torch::Tensor b_g_idx, bool use_v2_format);
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torch::Tensor gptq_gemm_rdna3_wmma(torch::Tensor a, torch::Tensor b_q_weight,
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torch::Tensor b_qzeros,
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torch::Tensor b_scales,
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torch::Tensor b_g_idx, bool use_v2_format);
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void paged_attention(
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torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
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torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
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torch::Tensor& value_cache, int64_t num_kv_heads, double scale,
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torch::Tensor& block_tables, torch::Tensor& seq_lens,
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const std::optional<torch::Tensor>& query_start_loc, int64_t block_size,
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int64_t max_seq_len, const std::optional<torch::Tensor>& alibi_slopes,
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const std::string& kv_cache_dtype, torch::Tensor& k_scale,
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torch::Tensor& v_scale, const std::optional<torch::Tensor>& fp8_out_scale,
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const std::string& mfma_type);
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