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fix(build): std::optional -> c10::optional for corex torch compatibility

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This commit is contained in:
claude
2026-08-11 07:37:20 +00:00
parent e440207977
commit f944ef912b
2 changed files with 41 additions and 41 deletions
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54
ex_engine/csrc/ilu/ilu_ops_api.h
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@@ -7,7 +7,7 @@
#pragma once
#include <torch/all.h>
#include <optional>
// #include <optional> // use c10::optional instead
#include <iostream>
#include <stdexcept>
@@ -44,23 +44,23 @@ void act_and_mul(torch::Tensor out,
void reshape_paged_cache(
torch::Tensor& key,
std::optional<torch::Tensor>& value,
c10::optional<torch::Tensor>& value,
torch::Tensor& key_cache,
std::optional<torch::Tensor>& value_cache,
c10::optional<torch::Tensor>& value_cache,
torch::Tensor& slot_mapping);
void batch_prefill(torch::Tensor& query,
const torch::Tensor& key,
const std::optional<torch::Tensor>& value,
const c10::optional<torch::Tensor>& value,
torch::Tensor& output,
std::optional<torch::Tensor>& output_lse,
const std::optional<torch::Tensor>& q_cu_seq_lens,
const std::optional<torch::Tensor>& kv_cu_seq_lens,
const std::optional<torch::Tensor>& alibi_slope,
const std::optional<torch::Tensor>& attn_bias,
const std::optional<torch::Tensor>& q_quant_scale,
const std::optional<torch::Tensor>& k_quant_scale,
const std::optional<torch::Tensor>& v_quant_scale,
c10::optional<torch::Tensor>& output_lse,
const c10::optional<torch::Tensor>& q_cu_seq_lens,
const c10::optional<torch::Tensor>& kv_cu_seq_lens,
const c10::optional<torch::Tensor>& alibi_slope,
const c10::optional<torch::Tensor>& attn_bias,
const c10::optional<torch::Tensor>& q_quant_scale,
const c10::optional<torch::Tensor>& k_quant_scale,
const c10::optional<torch::Tensor>& v_quant_scale,
const torch::Tensor& block_tables,
int64_t max_query_len,
int64_t max_seq_len,
@@ -76,14 +76,14 @@ void batch_decode(torch::Tensor& query,
torch::Tensor& output,
const torch::Tensor& block_table,
const torch::Tensor& seq_lens,
const std::optional<torch::Tensor>& v_cache,
std::optional<torch::Tensor>& output_lse,
const std::optional<torch::Tensor>& q_quant_scale,
const std::optional<torch::Tensor>& k_cache_quant_scale,
const std::optional<torch::Tensor>& v_cache_quant_scale,
const std::optional<torch::Tensor>& out_quant_scale,
const std::optional<torch::Tensor>& alibi_slope,
const std::optional<torch::Tensor>& mask,
const c10::optional<torch::Tensor>& v_cache,
c10::optional<torch::Tensor>& output_lse,
const c10::optional<torch::Tensor>& q_quant_scale,
const c10::optional<torch::Tensor>& k_cache_quant_scale,
const c10::optional<torch::Tensor>& v_cache_quant_scale,
const c10::optional<torch::Tensor>& out_quant_scale,
const c10::optional<torch::Tensor>& alibi_slope,
const c10::optional<torch::Tensor>& mask,
const std::string& compute_dtype,
int64_t max_seq_len,
int64_t window_size_left,
@@ -95,10 +95,10 @@ void batch_decode(torch::Tensor& query,
void residual_layer_norm(torch::Tensor& input,
torch::Tensor& output,
std::optional<torch::Tensor>& residual,
c10::optional<torch::Tensor>& residual,
torch::Tensor& weight,
std::optional<torch::Tensor>& bias,
std::optional<torch::Tensor>& residual_out,
c10::optional<torch::Tensor>& bias,
c10::optional<torch::Tensor>& residual_out,
double eps);
void rms_norm(torch::Tensor& output,
@@ -108,7 +108,7 @@ void rms_norm(torch::Tensor& output,
torch::Tensor matmul(torch::Tensor a,
torch::Tensor b,
std::optional<torch::Tensor> bias);
c10::optional<torch::Tensor> bias);
std::tuple<torch::Tensor, torch::Tensor> moe_active_topk(
const torch::Tensor& input,
@@ -116,11 +116,11 @@ std::tuple<torch::Tensor, torch::Tensor> moe_active_topk(
int64_t num_expert_group,
int64_t topk_group,
bool normalize,
const std::optional<torch::Tensor>& mask,
const c10::optional<torch::Tensor>& mask,
const std::string& normed_by,
const std::string& scoring_func,
double route_scale,
const std::optional<torch::Tensor>& e_score_correction_bias);
const c10::optional<torch::Tensor>& e_score_correction_bias);
std::vector<torch::Tensor> moe_gen_idx(torch::Tensor& expert_id,
int64_t expert_num);
@@ -133,7 +133,7 @@ torch::Tensor moe_expand_input(const torch::Tensor& input,
torch::Tensor group_gemm(torch::Tensor& input,
torch::Tensor& weight,
torch::Tensor& tokens_per_experts,
const std::optional<torch::Tensor>& dst_to_src,
const c10::optional<torch::Tensor>& dst_to_src,
torch::Tensor& output);
torch::Tensor moe_combine_result(torch::Tensor& input, torch::Tensor& weight);