woolcoxm
2319b942d2
Rebased onto current dev, split into 3 logical parts (all validated): 1. CPU portability (serve-mode _O_BINARY pipe fix — stock main hangs on MinGW without it; RAM detection cap 0->9/layer; POSIX guards for select/mmap/madvise; warmup script). 2. AVX-VNNI 128-bit int8/int4 dot kernel (Alder Lake+/Meteor Lake+), bit-identical to AVX2 (author-verified on Meteor Lake; compiles out to AVX2 elsewhere) + _mm256_extracti128_si256 typo fix that blocked -march=native. 3. CUDA DLL via LoadLibrary, gated behind CUDA_DLL=1 (host never links cudart; silent CPU fallback if absent; author-verified on RTX 5070 Ti). Validated here: make check 59/59, oracle 32/32 TF, Windows cross-compile clean + glm.exe loads+runs via WSL interop. Fixes the #123 Windows build failure.
81 lines
3.6 KiB
C
81 lines
3.6 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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/* 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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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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#ifdef __cplusplus
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}
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#endif
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#endif
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