Tom Olorin
21cbf14d59
PR #111 added 24 functions (pipe_*, attention_*_batch*, shared_mlp_w4a16, tensor_update) to backend_cuda.h without COLI_CUDA_DLLEXPORT and without backend_loader.c wrappers: on Windows the host failed to link (undefined references from glm.c) and the DLL exported none of the new entry points. Decorate the declarations and add matching typedefs/RESOLVEs/wrappers following the loader's existing pattern. DLL now exports 39 symbols; engine links; TF oracle 32/32 CPU + dual-GPU (sm_120/sm_89). Co-authored-by: olorin <io@zyphyr.co> Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
147 lines
8.3 KiB
C
147 lines
8.3 KiB
C
#ifndef COLIBRI_BACKEND_CUDA_H
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#define COLIBRI_BACKEND_CUDA_H
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#include <stddef.h>
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#include <stdint.h>
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/* COLI_CUDA_DLLEXPORT marks functions exported from coli_cuda.dll on Windows.
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* Define COLI_CUDA_BUILDING_DLL when compiling the .cu into the DLL (so the
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* functions are __declspec(dllexport)); the host loader does NOT include this
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* header's declarations — it resolves symbols at runtime via GetProcAddress. */
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#if defined(_WIN32) && defined(COLI_CUDA_BUILDING_DLL)
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#define COLI_CUDA_DLLEXPORT __declspec(dllexport)
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#else
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#define COLI_CUDA_DLLEXPORT
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#endif
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#ifdef __cplusplus
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extern "C" {
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#endif
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#define COLI_CUDA_MAX_DEVICES 16
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/* Opaque, persistent device copy of one resident quantized tensor. */
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typedef struct ColiCudaTensor ColiCudaTensor;
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/* Devices are CUDA ordinals, not positions in the input list. */
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COLI_CUDA_DLLEXPORT int coli_cuda_init(const int *devices, int count);
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COLI_CUDA_DLLEXPORT void coli_cuda_shutdown(void);
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COLI_CUDA_DLLEXPORT int coli_cuda_device_count(void);
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COLI_CUDA_DLLEXPORT int coli_cuda_device_at(int index);
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COLI_CUDA_DLLEXPORT int coli_cuda_mem_info(int device, size_t *free_bytes, size_t *total_bytes);
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/* device < 0 returns aggregate statistics for all configured devices. */
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COLI_CUDA_DLLEXPORT void coli_cuda_stats(int device, size_t *tensor_count, size_t *tensor_bytes);
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COLI_CUDA_DLLEXPORT void coli_cuda_group_stats(uint64_t *calls, uint64_t *experts, uint64_t *rows,
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double *h2d_ms, double *kernel_ms, double *d2h_ms);
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/* Upload without executing, so capacity failures happen during model startup. */
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COLI_CUDA_DLLEXPORT int coli_cuda_tensor_upload(ColiCudaTensor **tensor,
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const void *weights, const float *scales,
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int fmt, int I, int O, int device);
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/*
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* y[S,O] = x[S,I] @ W[O,I]^T.
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* fmt matches QT in glm.c: 0=f32, 1=int8, 2=int4, 3=int2.
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* The first successful call uploads W and its row scales; later calls reuse it.
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* Returns 1 on success and 0 when CUDA is not initialized or the format is invalid.
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*/
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COLI_CUDA_DLLEXPORT int coli_cuda_matmul(ColiCudaTensor **tensor,
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float *y, const float *x,
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const void *weights, const float *scales,
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int fmt, int S, int I, int O, int device);
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/* Fused expert pipeline: y = down(silu(gate(x)) * up(x)). All three tensors
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* must already be resident on one device. Activations cross PCIe once in
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* each direction instead of once per matrix. */
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COLI_CUDA_DLLEXPORT int coli_cuda_expert_mlp(ColiCudaTensor *gate, ColiCudaTensor *up,
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ColiCudaTensor *down, float *y, const float *x, int S);
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/* Prefill-oriented shared expert path. INT4 weights stay packed in global
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* memory, activations are converted to FP16 per tile, and Tensor Cores
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* accumulate into FP32. Unlike COLI_CUDA_TC_INT4 this does not quantize the
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* activation to INT4. */
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COLI_CUDA_DLLEXPORT int coli_cuda_shared_mlp_w4a16(ColiCudaTensor *gate, ColiCudaTensor *up,
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ColiCudaTensor *down, float *y,
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const float *x, int S);
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/* Packed group of same-shaped experts. Inputs and outputs contain sum(rows)
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* consecutive [D] rows in call order. */
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COLI_CUDA_DLLEXPORT int coli_cuda_expert_group(ColiCudaTensor *const *gates,
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ColiCudaTensor *const *ups,
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ColiCudaTensor *const *downs,
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const int *rows, int count,
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float *y, const float *x);
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/* Decode-only MLA weight-absorption core for one token. kv_b is [H*(Q+V),K]. */
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COLI_CUDA_DLLEXPORT int coli_cuda_attention_absorb(ColiCudaTensor *kv_b,float *ctx,const float *q,
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const float *latent,const float *rope,int H,int Q,
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int R,int V,int K,int T,float attention_scale);
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/* Causal MLA absorption for S contiguous rows from one sequence. The KV
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* arrays contain T rows ending at the final query; query s attends T-S+s+1
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* rows. One transfer and one launch replace S host round-trips. */
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COLI_CUDA_DLLEXPORT int coli_cuda_attention_absorb_batch(ColiCudaTensor *kv_b,float *ctx,const float *q,
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const float *latent,const float *rope,int S,
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int H,int Q,int R,int V,int K,int T,
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float attention_scale);
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/* Same attention batch followed immediately by resident o_proj on the same
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* device. Only the final [S,D] tensor crosses back to the host. */
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COLI_CUDA_DLLEXPORT int coli_cuda_attention_project_batch(ColiCudaTensor *kv_b,ColiCudaTensor *o_proj,
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float *out,const float *q,const float *latent,
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const float *rope,int S,int H,int Q,int R,
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int V,int K,int T,float attention_scale);
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COLI_CUDA_DLLEXPORT void coli_cuda_tensor_free(ColiCudaTensor *tensor);
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COLI_CUDA_DLLEXPORT size_t coli_cuda_tensor_bytes(const ColiCudaTensor *tensor);
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COLI_CUDA_DLLEXPORT int coli_cuda_tensor_device(const ColiCudaTensor *tensor);
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/* Replace a resident tensor's contents without reallocating its device slot. */
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COLI_CUDA_DLLEXPORT int coli_cuda_tensor_update(ColiCudaTensor *tensor,
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const void *weights, const float *scales);
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/* ---- resident-pipeline primitives (Inc.0): device-pointer entry points ---- */
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COLI_CUDA_DLLEXPORT float *coli_cuda_pipe_scratch(int device,int slot,size_t bytes);
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COLI_CUDA_DLLEXPORT void *coli_cuda_pipe_alloc(int device,size_t bytes);
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COLI_CUDA_DLLEXPORT void coli_cuda_pipe_free(int device,void *p);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_upload(int device,void *dst,const void *src,size_t bytes);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_download(int device,const void *src,void *dst,size_t bytes);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_rmsnorm(int device,float *y_dev,const float *x_dev,
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const float *w_dev,int S,int D,float eps);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_rope(int device,float *v_dev,const int *pos_dev,int rows,
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int stride,int offset,int R,int heads,float theta);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_silu_mul(int device,float *gate_dev,const float *up_dev,size_t n);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_add(int device,float *x_dev,const float *t_dev,size_t n);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_rows_add(int device,float *x_dev,const float *partial_dev,
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const int *rows_dev,int nrows,int D);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_gemm(ColiCudaTensor *t,float *y_dev,const float *x_dev,int S);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_rmsnorm_s(int device,float *y_dev,const float *x_dev,
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const float *w_dev,int S,int D,float eps,
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int xstride,int ystride);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_rope_base(int device,float *v_dev,int pos_base,int rows,
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int stride,int offset,int R,int heads,float theta);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_copy2d(int device,float *dst,int dpitch,const float *src,
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int spitch,int width,int height);
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COLI_CUDA_DLLEXPORT int coli_cuda_attention_project_batch_dev(ColiCudaTensor *kv_b,ColiCudaTensor *o_proj,
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float *out,const float *q_dev,const float *latent_dev,const float *rope_dev,
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int S,int H,int Q,int R,int V,int K,int T,float scale);
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COLI_CUDA_DLLEXPORT int coli_cuda_attention_absorb_batch_dev(ColiCudaTensor *kv_b_shard,float *ctx_dev,
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const float *q_dev,const float *latent_dev,const float *rope_dev,
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int S,int H,int Q,int R,int V,int K,int T,float scale);
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COLI_CUDA_DLLEXPORT int coli_cuda_attention_absorb_kvdev(ColiCudaTensor *kv_b,float *ctx,const float *q,
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const float *latent_dev,const float *rope_dev,int H,int Q,int R,int V,int K,int T,
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float scale);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_peer_copy(int dst_dev,float *dst,int src_dev,
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const float *src,size_t bytes);
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COLI_CUDA_DLLEXPORT int coli_cuda_attention_project_batch_dev_out(ColiCudaTensor *kv_b,ColiCudaTensor *o_proj,
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float *out_dev,const float *q_dev,const float *latent_dev,const float *rope_dev,
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int S,int H,int Q,int R,int V,int K,int T,float scale);
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COLI_CUDA_DLLEXPORT int coli_cuda_pipe_sync(int device);
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#ifdef __cplusplus
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}
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#endif
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#endif
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