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fix(bridge): c10::nullopt → typed std::optional{} for CoreX torch compat

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CoreX torch's c10::nullopt cannot implicitly convert to const std::optional<T>&.
Solution: use static typed empty optionals (kNoneTensor, kNoneBool).
Also unified all c10::optional forward decls to std::optional.
Applied same fix to ix_moe_bridge.cpp.
This commit is contained in:
project6-dev
2026-08-10 06:25:15 +00:00
parent ff0bf8c1d6
commit ac3c8e28eb
2 changed files with 49 additions and 36 deletions
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37
ex_engine/csrc/ix_moe_bridge.cpp
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@@ -1,8 +1,13 @@
// ix_moe_bridge.cpp — Full MoE pipeline bridge to ixformer C++ API
static const std::optional<torch::Tensor> kNoneTensor = {};
//
static const std::optional<torch::Tensor> kNoneTensor = {};
// Exposes ALL 6 MoE functions from ixformer::infer (ixformer.h):
static const std::optional<torch::Tensor> kNoneTensor = {};
// 1. topk_softmax — fused routing
static const std::optional<torch::Tensor> kNoneTensor = {};
// 2. moe_compute_token_index_api — permutation maps (src_dst, dst_src)
static const std::optional<torch::Tensor> kNoneTensor = {};
// 3. moe_expand_input — gather tokens by expert
// 4. moe_w16a16_group_gemm — batched expert GEMM
// 5. silu_and_mul — fused activation
@@ -31,9 +36,9 @@ void moe_compute_token_index_api(
torch::Tensor& src_dst,
torch::Tensor& dst_src,
torch::Tensor& expert_sizes_gpu,
const c10::optional<torch::Tensor>& expert_mask,
const c10::optional<torch::Tensor>& expert_sizes_cpu,
const c10::optional<torch::Tensor>& expand_tokens_gpu,
const std::optional<torch::Tensor>& expert_mask,
const std::optional<torch::Tensor>& expert_sizes_cpu,
const std::optional<torch::Tensor>& expand_tokens_gpu,
int64_t start_expert_id,
int64_t end_expert_id,
int64_t num_experts);
@@ -41,7 +46,7 @@ void moe_compute_token_index_api(
void moe_expand_input(torch::Tensor outputs,
torch::Tensor inputs,
torch::Tensor dst_to_src,
const c10::optional<torch::Tensor>& src_to_dst,
const std::optional<torch::Tensor>& src_to_dst,
int64_t dst_tokens,
int64_t expand_factor);
@@ -49,17 +54,17 @@ void moe_w16a16_group_gemm(torch::Tensor output,
torch::Tensor inputs,
torch::Tensor weights,
torch::Tensor tokens_per_experts,
const c10::optional<torch::Tensor>& dst_to_src,
const c10::optional<torch::Tensor>& bias,
const std::optional<torch::Tensor>& dst_to_src,
const std::optional<torch::Tensor>& bias,
std::string format,
int64_t persistent,
int64_t output_n);
void moe_output_reduce_sum(torch::Tensor outputs,
torch::Tensor inputs,
const c10::optional<torch::Tensor>& mul_weight,
const c10::optional<torch::Tensor>& mask,
const c10::optional<torch::Tensor>& extra_residual,
const std::optional<torch::Tensor>& mul_weight,
const std::optional<torch::Tensor>& mask,
const std::optional<torch::Tensor>& extra_residual,
double scaling_factor);
void silu_and_mul(torch::Tensor& input, torch::Tensor& output);
@@ -110,9 +115,9 @@ std::vector<torch::Tensor> ix_moe_gen_idx(
ixformer::infer::moe_compute_token_index_api(
expert_id, src_dst, dst_src, expert_sizes_gpu,
/*expert_mask=*/c10::nullopt,
/*expert_sizes_cpu=*/c10::nullopt,
/*expand_tokens_gpu=*/c10::nullopt,
/*expert_mask=*/kNoneTensor,
/*expert_sizes_cpu=*/kNoneTensor,
/*expand_tokens_gpu=*/kNoneTensor,
0, expert_num, expert_num);
expert_sizes_gpu_cumsum = expert_sizes_gpu.cumsum(-1);
@@ -144,8 +149,8 @@ torch::Tensor ix_group_gemm(
ixformer::infer::moe_w16a16_group_gemm(
output, inputs, weights, token_count,
/*dst_to_src=*/c10::nullopt,
/*bias=*/c10::nullopt,
/*dst_to_src=*/kNoneTensor,
/*bias=*/kNoneTensor,
/*format=*/"NT",
/*persistent=*/0,
/*output_n=*/output_n);
@@ -169,8 +174,8 @@ torch::Tensor ix_moe_combine_result(
ixformer::infer::moe_output_reduce_sum(
output, input, weight,
/*mask=*/c10::nullopt,
/*extra_residual=*/c10::nullopt,
/*mask=*/kNoneTensor,
/*extra_residual=*/kNoneTensor,
/*scaling_factor=*/1.0);
return output;
}