mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
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633e0617b0
* gmp-oss: common * gpt-oss: attnetion sinks, swiglu_oai * gpt-oss: WIP llama Model loads and runs (CPU only), but PPL is much to high (~1500 for 1st batch vs ~200 in mainline). Is it because of SWA, because of vocab, or did I introduce a bug somewhere? * gpt-oss: CPU seems to be working It was the SWA thta was missing in the previous commit. There are issues with EOG tokens, so this still needs to be added. * CUDA: ADD_ID Just a copy from mainline * gpt-oss: Seems to be working on CUDA * gpt-oss: add sinks to the attn-vec kernels * CUDA: add head size of 64 to new mma Haven't turned it on yet, but observe slightly better PP and slightly worse TG performance with that. * gpt-oss: add ability to use -fmoe (only CUDA for now) * Move row sums to the write place * Add sinks to iqk flash attention * gpt_oss: Implement -fmoe on the CPU * Simdify swiglu_oai Turning it off for now as performance becomes more variable, so perhaps I'm running into thermal trottling imore often because of making the CPU work too hard. * llama: factor out model loader * Builds successfully * It runs, but mmap does not work * Fix llama_mmap so mmap works * Minor * Fix CUDA after latest changes * Attempt to use CUDA graphs with MoE models - not working * CUDA graphs WIP - still not working * CUDA graphs - seems to be working Likely not all MLA variants are working. I no longer remember why I added the q8_0 cpy that transposes the tensor, but if really needed, this is now missing. Also missing is q6_0. * Make q8_0 cache work for DeepSeek models with CUDA graphs * cuda: cpy for q6_0 * Fix llama_mmap on non-Linux platforms * Adding forgotten file * Iterating on Windows build failures * cuda: re-add q8_0 -> q8_0 transpose so mla = 2 can be used with CUDA graphs and q8_0 cache. * Disable graphs without -fmoe * Minor * Turn graphs on by default --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
122 lines
3.1 KiB
Plaintext
122 lines
3.1 KiB
Plaintext
#include "common.cuh"
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static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
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const block_q4_0 * x = (const block_q4_0 *) vx;
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const dfloat d = x[ib].d;
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const int vui = x[ib].qs[iqs];
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v.x = vui & 0xF;
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v.y = vui >> 4;
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#ifdef GGML_CUDA_F16
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v = __hsub2(v, {8.0f, 8.0f});
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v = __hmul2(v, {d, d});
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#else
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v.x = (v.x - 8.0f) * d;
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v.y = (v.y - 8.0f) * d;
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#endif // GGML_CUDA_F16
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}
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static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
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const block_q4_1 * x = (const block_q4_1 *) vx;
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const dfloat d = __low2half(x[ib].dm);
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const dfloat m = __high2half(x[ib].dm);
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const int vui = x[ib].qs[iqs];
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v.x = vui & 0xF;
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v.y = vui >> 4;
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#ifdef GGML_CUDA_F16
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v = __hmul2(v, {d, d});
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v = __hadd2(v, {m, m});
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#else
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v.x = (v.x * d) + m;
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v.y = (v.y * d) + m;
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#endif // GGML_CUDA_F16
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}
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static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
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const block_q5_0 * x = (const block_q5_0 *) vx;
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const dfloat d = x[ib].d;
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uint32_t qh;
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memcpy(&qh, x[ib].qh, sizeof(qh));
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const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10;
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const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10;
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v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
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v.y = ((x[ib].qs[iqs] >> 4) | xh_1);
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#ifdef GGML_CUDA_F16
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v = __hsub2(v, {16.0f, 16.0f});
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v = __hmul2(v, {d, d});
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#else
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v.x = (v.x - 16.0f) * d;
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v.y = (v.y - 16.0f) * d;
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#endif // GGML_CUDA_F16
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}
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static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
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const block_q5_1 * x = (const block_q5_1 *) vx;
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const dfloat d = __low2half(x[ib].dm);
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const dfloat m = __high2half(x[ib].dm);
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uint32_t qh;
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memcpy(&qh, x[ib].qh, sizeof(qh));
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const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10;
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const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10;
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v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
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v.y = ((x[ib].qs[iqs] >> 4) | xh_1);
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#ifdef GGML_CUDA_F16
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v = __hmul2(v, {d, d});
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v = __hadd2(v, {m, m});
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#else
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v.x = (v.x * d) + m;
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v.y = (v.y * d) + m;
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#endif // GGML_CUDA_F16
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}
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static __device__ __forceinline__ void dequantize_q6_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
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const block_q6_0 * x = (const block_q6_0 *) vx;
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const dfloat d = x[ib].d;
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const uint8_t h = x[ib].qh[iqs%8] >> 2*(iqs/8);
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v.x = ((x[ib].qs[iqs] & 0xf) | ((h & 0x3) << 4));
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v.y = ((x[ib].qs[iqs] >> 4) | ((h & 0xc) << 2));
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#ifdef GGML_CUDA_F16
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v = __hsub2(v, {32.0f, 32.0f});
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v = __hmul2(v, {d, d});
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#else
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v.x = (v.x - 32.0f) * d;
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v.y = (v.y - 32.0f) * d;
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#endif // GGML_CUDA_F16
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}
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static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
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const block_q8_0 * x = (const block_q8_0 *) vx;
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const dfloat d = x[ib].d;
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v.x = x[ib].qs[iqs + 0];
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v.y = x[ib].qs[iqs + 1];
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#ifdef GGML_CUDA_F16
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v = __hmul2(v, {d, d});
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#else
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v.x *= d;
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v.y *= d;
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#endif // GGML_CUDA_F16
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}
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