colibri/c/backend_cuda.h

#ifndef COLIBRI_BACKEND_CUDA_H
#define COLIBRI_BACKEND_CUDA_H

#include <stddef.h>
#include <stdint.h>

/* COLI_CUDA_DLLEXPORT marks functions exported from coli_cuda.dll on Windows.
 * Define COLI_CUDA_BUILDING_DLL when compiling the .cu into the DLL (so the
 * functions are __declspec(dllexport)); the host loader does NOT include this
 * header's declarations — it resolves symbols at runtime via GetProcAddress. */
#if defined(_WIN32) && defined(COLI_CUDA_BUILDING_DLL)
#define COLI_CUDA_DLLEXPORT __declspec(dllexport)
#else
#define COLI_CUDA_DLLEXPORT
#endif

#ifdef __cplusplus
extern "C" {
#endif

#define COLI_CUDA_MAX_DEVICES 16

/* Opaque, persistent device copy of one resident quantized tensor. */
typedef struct ColiCudaTensor ColiCudaTensor;

/* Devices are CUDA ordinals, not positions in the input list. */
COLI_CUDA_DLLEXPORT int coli_cuda_init(const int *devices, int count);
COLI_CUDA_DLLEXPORT void coli_cuda_shutdown(void);
COLI_CUDA_DLLEXPORT int coli_cuda_device_count(void);
COLI_CUDA_DLLEXPORT int coli_cuda_device_at(int index);
COLI_CUDA_DLLEXPORT int coli_cuda_mem_info(int device, size_t *free_bytes, size_t *total_bytes);
/* device < 0 returns aggregate statistics for all configured devices. */
COLI_CUDA_DLLEXPORT void coli_cuda_stats(int device, size_t *tensor_count, size_t *tensor_bytes);
COLI_CUDA_DLLEXPORT void coli_cuda_group_stats(uint64_t *calls, uint64_t *experts, uint64_t *rows,
                           double *h2d_ms, double *kernel_ms, double *d2h_ms);

/* Upload without executing, so capacity failures happen during model startup. */
COLI_CUDA_DLLEXPORT int coli_cuda_tensor_upload(ColiCudaTensor **tensor,
                            const void *weights, const float *scales,
                            int fmt, int I, int O, int device);

/*
 * y[S,O] = x[S,I] @ W[O,I]^T.
 * fmt matches QT in glm.c: 0=f32, 1=int8, 2=int4, 3=int2.
 * The first successful call uploads W and its row scales; later calls reuse it.
 * Returns 1 on success and 0 when CUDA is not initialized or the format is invalid.
 */
COLI_CUDA_DLLEXPORT int coli_cuda_matmul(ColiCudaTensor **tensor,
                     float *y, const float *x,
                     const void *weights, const float *scales,
                     int fmt, int S, int I, int O, int device);

/* Fused expert pipeline: y = down(silu(gate(x)) * up(x)).  All three tensors
 * must already be resident on one device.  Activations cross PCIe once in
 * each direction instead of once per matrix. */
COLI_CUDA_DLLEXPORT int coli_cuda_expert_mlp(ColiCudaTensor *gate, ColiCudaTensor *up,
                         ColiCudaTensor *down, float *y, const float *x, int S);

/* Prefill-oriented shared expert path.  INT4 weights stay packed in global
 * memory, activations are converted to FP16 per tile, and Tensor Cores
 * accumulate into FP32.  Unlike COLI_CUDA_TC_INT4 this does not quantize the
 * activation to INT4. */
int coli_cuda_shared_mlp_w4a16(ColiCudaTensor *gate, ColiCudaTensor *up,
                               ColiCudaTensor *down, float *y,
                               const float *x, int S);

/* Packed group of same-shaped experts. Inputs and outputs contain sum(rows)
 * consecutive [D] rows in call order. */
COLI_CUDA_DLLEXPORT int coli_cuda_expert_group(ColiCudaTensor *const *gates,
                           ColiCudaTensor *const *ups,
                           ColiCudaTensor *const *downs,
                           const int *rows, int count,
                           float *y, const float *x);

/* Decode-only MLA weight-absorption core for one token. kv_b is [H*(Q+V),K]. */
COLI_CUDA_DLLEXPORT int coli_cuda_attention_absorb(ColiCudaTensor *kv_b,float *ctx,const float *q,
                               const float *latent,const float *rope,int H,int Q,
                               int R,int V,int K,int T,float attention_scale);

/* Causal MLA absorption for S contiguous rows from one sequence.  The KV
 * arrays contain T rows ending at the final query; query s attends T-S+s+1
 * rows.  One transfer and one launch replace S host round-trips. */
COLI_CUDA_DLLEXPORT int coli_cuda_attention_absorb_batch(ColiCudaTensor *kv_b,float *ctx,const float *q,
                                     const float *latent,const float *rope,int S,
                                     int H,int Q,int R,int V,int K,int T,
                                     float attention_scale);

/* Same attention batch followed immediately by resident o_proj on the same
 * device.  Only the final [S,D] tensor crosses back to the host. */
COLI_CUDA_DLLEXPORT int coli_cuda_attention_project_batch(ColiCudaTensor *kv_b,ColiCudaTensor *o_proj,
                                      float *out,const float *q,const float *latent,
                                      const float *rope,int S,int H,int Q,int R,
                                      int V,int K,int T,float attention_scale);

COLI_CUDA_DLLEXPORT void coli_cuda_tensor_free(ColiCudaTensor *tensor);
COLI_CUDA_DLLEXPORT size_t coli_cuda_tensor_bytes(const ColiCudaTensor *tensor);
COLI_CUDA_DLLEXPORT int coli_cuda_tensor_device(const ColiCudaTensor *tensor);

/* Replace a resident tensor's contents without reallocating its device slot. */
int coli_cuda_tensor_update(ColiCudaTensor *tensor,
                            const void *weights, const float *scales);

/* ---- resident-pipeline primitives (Inc.0): device-pointer entry points ---- */
float *coli_cuda_pipe_scratch(int device,int slot,size_t bytes);
void *coli_cuda_pipe_alloc(int device,size_t bytes);
void coli_cuda_pipe_free(int device,void *p);
int coli_cuda_pipe_upload(int device,void *dst,const void *src,size_t bytes);
int coli_cuda_pipe_download(int device,const void *src,void *dst,size_t bytes);
int coli_cuda_pipe_rmsnorm(int device,float *y_dev,const float *x_dev,
                           const float *w_dev,int S,int D,float eps);
int coli_cuda_pipe_rope(int device,float *v_dev,const int *pos_dev,int rows,
                        int stride,int offset,int R,int heads,float theta);
int coli_cuda_pipe_silu_mul(int device,float *gate_dev,const float *up_dev,size_t n);
int coli_cuda_pipe_add(int device,float *x_dev,const float *t_dev,size_t n);
int coli_cuda_pipe_rows_add(int device,float *x_dev,const float *partial_dev,
                            const int *rows_dev,int nrows,int D);
int coli_cuda_pipe_gemm(ColiCudaTensor *t,float *y_dev,const float *x_dev,int S);
int coli_cuda_pipe_rmsnorm_s(int device,float *y_dev,const float *x_dev,
                             const float *w_dev,int S,int D,float eps,
                             int xstride,int ystride);
int coli_cuda_pipe_rope_base(int device,float *v_dev,int pos_base,int rows,
                             int stride,int offset,int R,int heads,float theta);
int coli_cuda_pipe_copy2d(int device,float *dst,int dpitch,const float *src,
                          int spitch,int width,int height);
int coli_cuda_attention_project_batch_dev(ColiCudaTensor *kv_b,ColiCudaTensor *o_proj,
        float *out,const float *q_dev,const float *latent_dev,const float *rope_dev,
        int S,int H,int Q,int R,int V,int K,int T,float scale);
int coli_cuda_attention_absorb_batch_dev(ColiCudaTensor *kv_b_shard,float *ctx_dev,
        const float *q_dev,const float *latent_dev,const float *rope_dev,
        int S,int H,int Q,int R,int V,int K,int T,float scale);
int coli_cuda_attention_absorb_kvdev(ColiCudaTensor *kv_b,float *ctx,const float *q,
        const float *latent_dev,const float *rope_dev,int H,int Q,int R,int V,int K,int T,
        float scale);
int coli_cuda_pipe_peer_copy(int dst_dev,float *dst,int src_dev,
                             const float *src,size_t bytes);
int coli_cuda_attention_project_batch_dev_out(ColiCudaTensor *kv_b,ColiCudaTensor *o_proj,
        float *out_dev,const float *q_dev,const float *latent_dev,const float *rope_dev,
        int S,int H,int Q,int R,int V,int K,int T,float scale);
int coli_cuda_pipe_sync(int device);

#ifdef __cplusplus
}
#endif

#endif