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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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48
ex_engine/csrc/ix_full_bridge.cpp
View File

@@ -16,6 +16,14 @@
#include <tuple>
#include <vector>
// ============================================================================
// Compatibility: CoreX torch uses c10::optional which may not implicitly
// convert from kNoneTensor to const std::optional<T>&.
// Use typed empty optionals instead.
// ============================================================================
static const std::optional<torch::Tensor> kNoneTensor = {};
static const std::optional<bool> kNoneBool = {};
// ============================================================================
// Forward-declare ixformer::infer namespace — matches ixformer.h exactly
// We forward-declare instead of #include to avoid build-time dependency
@@ -83,8 +91,8 @@ torch::Tensor ixformer_linear(
torch::Tensor ixformer_linear_ex(
torch::Tensor& input, torch::Tensor& weight,
const c10::optional<torch::Tensor>& bias,
const c10::optional<torch::Tensor>& out);
const std::optional<torch::Tensor>& bias,
const std::optional<torch::Tensor>& out);
// --- MoE ---
void topk_softmax(
@@ -95,28 +103,28 @@ void topk_softmax(
void moe_compute_token_index_api(
torch::Tensor& topk_ids, 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);
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);
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);
} // namespace infer
@@ -154,7 +162,7 @@ std::vector<torch::Tensor> ix_moe_gen_idx(
auto expert_sizes_gpu = expert_id.new_empty({expert_num});
ixformer::infer::moe_compute_token_index_api(
expert_id, src_dst, dst_src, expert_sizes_gpu,
c10::nullopt, c10::nullopt, c10::nullopt, 0, expert_num, expert_num);
kNoneTensor, kNoneTensor, kNoneTensor, 0, expert_num, expert_num);
auto expert_sizes_gpu_cumsum = expert_sizes_gpu.cumsum(-1);
return {src_dst, dst_src, expert_sizes_gpu, expert_sizes_gpu_cumsum};
}
@@ -178,7 +186,7 @@ torch::Tensor ix_group_gemm(
auto output = inputs.new_empty({total_tokens, output_n});
ixformer::infer::moe_w16a16_group_gemm(
output, inputs, weights, token_count,
c10::nullopt, c10::nullopt, "NT", 0, output_n);
kNoneTensor, kNoneTensor, "NT", 0, output_n);
return output;
}
@@ -195,7 +203,7 @@ torch::Tensor ix_moe_combine_result(torch::Tensor input, torch::Tensor weight) {
input = input.view({-1, weight.size(1), input.size(1)});
auto output = input.new_empty({input.size(0), input.size(2)});
ixformer::infer::moe_output_reduce_sum(
output, input, weight, c10::nullopt, c10::nullopt, 1.0);
output, input, weight, kNoneTensor, kNoneTensor, 1.0);
return output;
}
@@ -234,7 +242,7 @@ void ix_paged_attention(
block_tables, seq_lens, block_size, max_context_len,
alibi_slopes, /*causal=*/true, /*window_left=*/-1, /*window_right=*/-1,
/*softcap=*/0.0, /*enable_cuda_graph=*/false, /*use_sqrt_alibi=*/false,
/*sinks=*/c10::nullopt);
/*sinks=*/kNoneTensor);
}
// --- Attention: prefill flash (from ilu/attention.cpp batch_prefill) ---
@@ -245,20 +253,20 @@ void ix_flash_attn_prefill(
int64_t max_query_len, int64_t max_seq_len,
double scale, bool is_causal,
int64_t window_left, int64_t window_right) {
std::optional<torch::Tensor> lse = c10::nullopt;
std::optional<torch::Tensor> lse = {};
ixformer::infer::ixinfer_flash_attn_unpad_with_block_tables(
query, key, value, output, block_tables,
cu_seq_q, cu_seq_k, max_query_len, max_seq_len,
is_causal, window_left, window_right,
scale, /*softcap=*/0.0, /*sqrt_alibi=*/false,
/*alibi_slopes=*/c10::nullopt, /*sinks=*/c10::nullopt, lse);
/*alibi_slopes=*/kNoneTensor, /*sinks=*/kNoneTensor, lse);
}
// --- Norm: rms_norm (from ilu/norm.cpp) ---
void ix_rms_norm(
torch::Tensor output, torch::Tensor input,
torch::Tensor weight, double eps) {
ixformer::infer::rms_norm(input, weight, output, c10::nullopt, eps);
ixformer::infer::rms_norm(input, weight, output, kNoneTensor, eps);
}
// --- Norm: fused residual + rms_norm (from ilu/norm.cpp) ---
@@ -268,7 +276,7 @@ void ix_fused_add_rms_norm(
torch::Tensor residual_output, double eps) {
ixformer::infer::residual_rms_norm(
input, residual, weight, output, residual_output,
c10::nullopt, 1.0, eps, false);
kNoneTensor, 1.0, eps, false);
}
// --- RoPE (from ilu/rope.cpp) ---
@@ -297,7 +305,7 @@ torch::Tensor ix_linear(
torch::Tensor input, torch::Tensor weight,
const std::optional<torch::Tensor>& bias) {
return ixformer::infer::ixformer_linear(
input, weight, /*act_type=*/0, bias, c10::nullopt, c10::nullopt);
input, weight, /*act_type=*/-1, bias, kNoneTensor, kNoneBool);
}
// ============================================================================

37
ex_engine/csrc/ix_moe_bridge.cpp
View File

@@ -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;
}