154 lines
6.2 KiB
C
154 lines
6.2 KiB
C
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/* Copyright 2025 The xLLM Authors. All Rights Reserved.
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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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https://github.com/jd-opensource/xllm/blob/main/LICENSE
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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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#pragma once
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#include <ATen/DynamicLibrary.h>
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#include <ATen/core/dispatch/Dispatcher.h>
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#include <cuda_runtime.h>
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#include <glog/logging.h>
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#include <torch/all.h>
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#include <optional>
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#include "ATen/Tensor.h"
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#include "ATen/cuda/CUDAEvent.h"
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#include "c10/core/Device.h"
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#include "c10/core/DeviceGuard.h"
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#include "c10/core/GradMode.h"
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#include "c10/core/InferenceMode.h"
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#include "c10/core/MemoryFormat.h"
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#include "c10/core/ScalarType.h"
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#include "c10/core/TensorOptions.h"
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#include "c10/cuda/CUDAFunctions.h"
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#include "c10/cuda/CUDAGuard.h"
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#include "c10/cuda/CUDAStream.h"
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#include "ixformer.h"
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#include "kernels/kernels.h"
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// #include "utils.h"
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using namespace ixformer;
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namespace xllm::kernel::ilu {
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void apply_rope_pos_ids_cos_sin_cache(torch::Tensor& query,
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torch::Tensor& key,
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torch::Tensor& cos_sin_cache,
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torch::Tensor& positions,
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bool interleave);
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// act_mode only support silu, gelu, gelu_tanh
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void act_and_mul(torch::Tensor out,
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torch::Tensor input,
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const std::string& act_mode);
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void reshape_paged_cache(
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torch::Tensor& key, // (num_tokens, num_heads, head_size)
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std::optional<torch::Tensor>& value, // (num_tokens, num_heads, head_size)
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torch::Tensor& key_cache, // (num_blocks, num_heads, block_size, head_size)
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std::optional<torch::Tensor>&
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value_cache, // (num_blocks, num_heads, block_size, head_size)
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torch::Tensor& slot_mapping); //(num_tokens)
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void batch_prefill(torch::Tensor& query,
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const torch::Tensor& key,
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const std::optional<torch::Tensor>& value,
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torch::Tensor& output,
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std::optional<torch::Tensor>& output_lse,
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const std::optional<torch::Tensor>& q_cu_seq_lens,
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const std::optional<torch::Tensor>& kv_cu_seq_lens,
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const std::optional<torch::Tensor>& alibi_slope,
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const std::optional<torch::Tensor>& attn_bias,
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const std::optional<torch::Tensor>& q_quant_scale,
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const std::optional<torch::Tensor>& k_quant_scale,
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const std::optional<torch::Tensor>& v_quant_scale,
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const torch::Tensor& block_tables,
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int64_t max_query_len,
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int64_t max_seq_len,
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float scale,
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bool is_causal,
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int64_t window_size_left,
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int64_t window_size_right,
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const std::string& compute_dtype,
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bool return_lse);
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void batch_decode(torch::Tensor& query,
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const torch::Tensor& k_cache,
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torch::Tensor& output,
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const torch::Tensor& block_table,
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const torch::Tensor& seq_lens,
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const std::optional<torch::Tensor>& v_cache,
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std::optional<torch::Tensor>& output_lse,
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const std::optional<torch::Tensor>& q_quant_scale,
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const std::optional<torch::Tensor>& k_cache_quant_scale,
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const std::optional<torch::Tensor>& v_cache_quant_scale,
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const std::optional<torch::Tensor>& out_quant_scale,
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const std::optional<torch::Tensor>& alibi_slope,
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const std::optional<torch::Tensor>& mask,
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const std::string& compute_dtype,
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int64_t max_seq_len,
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int64_t window_size_left,
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int64_t window_size_right,
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float scale,
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bool return_lse,
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bool is_causal,
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int64_t kv_cache_quant_bit_size);
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void residual_layer_norm(torch::Tensor& input,
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torch::Tensor& output,
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std::optional<torch::Tensor>& residual,
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torch::Tensor& weight,
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std::optional<torch::Tensor>& bias,
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std::optional<torch::Tensor>& residual_out,
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double eps);
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void rms_norm(torch::Tensor& output,
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torch::Tensor& input,
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torch::Tensor& weight,
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double eps);
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torch::Tensor matmul(torch::Tensor a,
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torch::Tensor b,
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std::optional<torch::Tensor> bias);
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std::tuple<torch::Tensor, torch::Tensor> moe_active_topk(
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const torch::Tensor& input,
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int64_t topk,
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int64_t num_expert_group,
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int64_t topk_group,
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bool normalize,
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const std::optional<torch::Tensor>& mask,
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const std::string& normed_by,
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const std::string& scoring_func,
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double route_scale,
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const std::optional<torch::Tensor>& e_score_correction_bias);
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std::vector<torch::Tensor> moe_gen_idx(torch::Tensor& expert_id,
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int64_t expert_num);
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torch::Tensor moe_expand_input(const torch::Tensor& input,
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const torch::Tensor& gather_index,
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const torch::Tensor& combine_idx,
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int64_t topk);
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torch::Tensor group_gemm(torch::Tensor& input,
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torch::Tensor& weight,
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torch::Tensor& tokens_per_experts,
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const std::optional<torch::Tensor>& dst_to_src,
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torch::Tensor& output);
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torch::Tensor moe_combine_result(torch::Tensor& input, torch::Tensor& weight);
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} // namespace xllm::kernel::ilu
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