CUDA : faster k-quant dot kernels (#1862)
* cuda : faster k-quant dot kernels * Imrove Q2_K dot kernel on older GPUs We now have a K_QUANTS_PER_ITERATION macro, which should be set to 1 on older and to 2 on newer GPUs. With this, we preserve the performance of the original PR on RTX-4080, and are faster compared to master on GTX-1660. * Imrove Q6_K dot kernel on older GPUs Using the same K_QUANTS_PER_ITERATION macro as last commit, we preserve performance on RTX-4080 and speed up Q6_K on a GTX-1660. * Add LLAMA_CUDA_KQUANTS_ITER to CMakeLists.txt and Makefile Allowed values are 1 or 2. 2 gives the best performance on modern GPUs and is set as default. On older GPUs 1 may work better. * PR comments --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
GitHub
parent
602c748863
commit
3d01122610
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@ -70,6 +70,7 @@ set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
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option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
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set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
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set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
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set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
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option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
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option(LLAMA_METAL "llama: use Metal" OFF)
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option(LLAMA_K_QUANTS "llama: use k-quants" ON)
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@ -201,6 +202,7 @@ if (LLAMA_CUBLAS)
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add_compile_definitions(GGML_USE_CUBLAS)
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add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
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add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
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add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
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if (LLAMA_STATIC)
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
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5
Makefile
5
Makefile
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@ -171,6 +171,11 @@ ifdef LLAMA_CUDA_DMMV_Y
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else
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NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
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endif # LLAMA_CUDA_DMMV_Y
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ifdef LLAMA_CUDA_KQUANTS_ITER
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NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
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else
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NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
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endif
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ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
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$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
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endif # LLAMA_CUBLAS
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599
ggml-cuda.cu
599
ggml-cuda.cu
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@ -167,6 +167,12 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
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#define GGML_CUDA_DMMV_Y 1
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#endif
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#ifndef K_QUANTS_PER_ITERATION
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#define K_QUANTS_PER_ITERATION 2
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#else
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static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
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#endif
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static __global__ void add_f32(const float * x, const float * y, float * dst, const int k) {
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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@ -326,37 +332,6 @@ static __global__ void dequantize_block_q2_K(const void * vx, float * yy) {
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}
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static __device__ void vec_dot_q2_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
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const block_q2_K * x = (const block_q2_K *) vx;
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// if n is 0, we want to do the lower 128, else the upper 128,
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// covering y[l+0], y[l+32], y[l+64], y[l+96] and
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// y[l+16], y[l+48], y[l+80], y[l+112]
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int n = iqs/128; // 0 or 1
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int r = iqs - 128*n; // 0...120 in steps of 8
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int l = r/8; // 0...15 in steps of 1
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const float * y = yy + 128*n + l;
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const uint8_t * q = x[ib].qs + 32*n + l;
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const uint8_t * s = x[ib].scales + 8*n;
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const float dall = x[ib].d;
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const float dmin = x[ib].dmin;
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float sum = y[ 0] * (dall * ((s[0] & 0xF) * ((q[ 0] >> 0) & 3)) - dmin * (s[0] >> 4))
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+ y[ 32] * (dall * ((s[2] & 0xF) * ((q[ 0] >> 2) & 3)) - dmin * (s[2] >> 4))
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+ y[ 64] * (dall * ((s[4] & 0xF) * ((q[ 0] >> 4) & 3)) - dmin * (s[4] >> 4))
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+ y[ 96] * (dall * ((s[6] & 0xF) * ((q[ 0] >> 6) & 3)) - dmin * (s[6] >> 4))
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+ y[ 16] * (dall * ((s[1] & 0xF) * ((q[16] >> 0) & 3)) - dmin * (s[1] >> 4))
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+ y[ 48] * (dall * ((s[3] & 0xF) * ((q[16] >> 2) & 3)) - dmin * (s[3] >> 4))
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+ y[ 80] * (dall * ((s[5] & 0xF) * ((q[16] >> 4) & 3)) - dmin * (s[5] >> 4))
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+ y[112] * (dall * ((s[7] & 0xF) * ((q[16] >> 6) & 3)) - dmin * (s[7] >> 4));
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result = sum;
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}
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static __global__ void dequantize_block_q3_K(const void * vx, float * yy) {
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int r = threadIdx.x/4;
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@ -388,51 +363,6 @@ static __global__ void dequantize_block_q3_K(const void * vx, float * yy) {
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}
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static __device__ void vec_dot_q3_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
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const block_q3_K * x = (const block_q3_K *) vx;
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const uint32_t kmask1 = 0x03030303;
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const uint32_t kmask2 = 0x0f0f0f0f;
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uint32_t aux[3];
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uint32_t utmp[4];
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// if n is 0, we want to do the lower 128, else the upper 128,
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// covering y[l+0], y[l+32], y[l+64], y[l+96] and
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// y[l+16], y[l+48], y[l+80], y[l+112]
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int n = iqs/128; // 0 or 1
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int r = iqs - 128*n; // 0...120 in steps of 8
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int l = r/8; // 0...15 in steps of 1
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const float * y = yy + 128*n + l;
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const uint8_t * q = x[ib].qs + 32*n + l;
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const uint8_t * hm = x[ib].hmask + l;
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const int8_t * s = (const int8_t *)utmp + 8*n;
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memcpy(aux, x[ib].scales, 12);
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utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
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utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
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utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
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utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);
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const float dall = x[ib].d;
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const uint8_t m = 1 << (4*n);
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float sum = y[ 0] * (s[0] - 32) * (((q[ 0] >> 0) & 3) - (hm[ 0] & (m << 0) ? 0 : 4))
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+ y[ 32] * (s[2] - 32) * (((q[ 0] >> 2) & 3) - (hm[ 0] & (m << 1) ? 0 : 4))
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+ y[ 64] * (s[4] - 32) * (((q[ 0] >> 4) & 3) - (hm[ 0] & (m << 2) ? 0 : 4))
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+ y[ 96] * (s[6] - 32) * (((q[ 0] >> 6) & 3) - (hm[ 0] & (m << 3) ? 0 : 4))
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+ y[ 16] * (s[1] - 32) * (((q[16] >> 0) & 3) - (hm[16] & (m << 0) ? 0 : 4))
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+ y[ 48] * (s[3] - 32) * (((q[16] >> 2) & 3) - (hm[16] & (m << 1) ? 0 : 4))
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+ y[ 80] * (s[5] - 32) * (((q[16] >> 4) & 3) - (hm[16] & (m << 2) ? 0 : 4))
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+ y[112] * (s[7] - 32) * (((q[16] >> 6) & 3) - (hm[16] & (m << 3) ? 0 : 4));
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result = sum * dall;
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}
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static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
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if (j < 4) {
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d = q[j] & 63; m = q[j + 4] & 63;
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@ -479,38 +409,6 @@ static __global__ void dequantize_block_q4_K(const void * vx, float * yy) {
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}
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}
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static __device__ void vec_dot_q4_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
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const block_q4_K * x = (const block_q4_K *) vx;
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// iqs is in 0...248 in steps of 8 =>
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const int j = iqs / 64; // j is in 0...3
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const int ir = (iqs - 64*j)/2; // ir is in 0...28 in steps of 4
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const int is = 2*j; // is is in 0...6 in steps of 2
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const float * y = yy + 64*j + ir;
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const uint8_t * q = x[ib].qs + 32*j + ir;
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const float dall = x[ib].d;
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const float dmin = x[ib].dmin;
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uint8_t sc, m;
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get_scale_min_k4(is + 0, x[ib].scales, sc, m);
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const float d1 = dall * sc;
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const float m1 = dmin * m;
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get_scale_min_k4(is + 1, x[ib].scales, sc, m);
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const float d2 = dall * sc;
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const float m2 = dmin * m;
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float sum = 0;
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for (int k = 0; k < 4; ++k) {
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sum += y[k + 0] * (d1 * (q[k] & 0xF) - m1);
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sum += y[k + 32] * (d2 * (q[k] >> 4) - m2);
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}
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result = sum;
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}
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static __global__ void dequantize_block_q5_K(const void * vx, float * yy) {
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const block_q5_K * x = (const block_q5_K *) vx;
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@ -544,43 +442,6 @@ static __global__ void dequantize_block_q5_K(const void * vx, float * yy) {
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y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2;
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}
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static __device__ void vec_dot_q5_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
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const block_q5_K * x = (const block_q5_K *) vx;
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// iqs is in 0...248 in steps of 8 =>
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const int j = iqs / 64; // j is in 0...3
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const int ir = (iqs - 64*j)/2; // ir is in 0...28 in steps of 4
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const int is = 2*j; // is is in 0...6 in steps of 2
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const float * y = yy + 64*j + ir;
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const uint8_t * ql = x[ib].qs + 32*j + ir;
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const uint8_t * qh = x[ib].qh + ir;
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const float dall = x[ib].d;
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const float dmin = x[ib].dmin;
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uint8_t sc, m;
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get_scale_min_k4(is + 0, x[ib].scales, sc, m);
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const float d1 = dall * sc;
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const float m1 = dmin * m;
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get_scale_min_k4(is + 1, x[ib].scales, sc, m);
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const float d2 = dall * sc;
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const float m2 = dmin * m;
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uint8_t hm = 1 << is;
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float sum = 0;
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for (int k = 0; k < 4; ++k) {
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sum += y[k + 0] * (d1 * ((ql[k] & 0xF) + (qh[k] & hm ? 16 : 0)) - m1);
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}
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hm <<= 1;
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for (int k = 0; k < 4; ++k) {
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sum += y[k + 32] * (d2 * ((ql[k] >> 4) + (qh[k] & hm ? 16 : 0)) - m2);
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}
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result = sum;
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}
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static __global__ void dequantize_block_q6_K(const void * vx, float * yy) {
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const block_q6_K * x = (const block_q6_K *) vx;
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@ -606,31 +467,376 @@ static __global__ void dequantize_block_q6_K(const void * vx, float * yy) {
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y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
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}
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static __device__ void vec_dot_q6_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
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static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) {
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const block_q6_K * x = (const block_q6_K *) vx;
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static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
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const int ip = iqs / 128; // 0 or 1
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const int il = (iqs - 128*ip)/8; // 0...15
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const int is = 8*ip;
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const int row = blockIdx.y*blockDim.y + threadIdx.y;
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if (row > nrows) return;
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const float * y = yy + 128*ip + il;
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const int num_blocks_per_row = ncols / QK_K;
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const int ib0 = row*num_blocks_per_row;
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const float d = x[ib].d;
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const block_q2_K * x = (const block_q2_K *)vx + ib0;
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const uint8_t * ql = x[ib].ql + 64*ip + il;
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const uint8_t * qh = x[ib].qh + 32*ip + il;
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const int8_t * sc = x[ib].scales + is;
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const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31
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const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0
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result = y[ 0] * d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh[ 0] >> 0) & 3) << 4)) - 32)
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+ y[ 32] * d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh[ 0] >> 2) & 3) << 4)) - 32)
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+ y[ 64] * d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh[ 0] >> 4) & 3) << 4)) - 32)
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+ y[ 96] * d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh[ 0] >> 6) & 3) << 4)) - 32)
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+ y[ 16] * d * sc[1] * ((int8_t)((ql[16] & 0xF) | (((qh[16] >> 0) & 3) << 4)) - 32)
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+ y[ 48] * d * sc[3] * ((int8_t)((ql[48] & 0xF) | (((qh[16] >> 2) & 3) << 4)) - 32)
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+ y[ 80] * d * sc[5] * ((int8_t)((ql[16] >> 4) | (((qh[16] >> 4) & 3) << 4)) - 32)
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+ y[112] * d * sc[7] * ((int8_t)((ql[48] >> 4) | (((qh[16] >> 6) & 3) << 4)) - 32);
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const int step = 16/K_QUANTS_PER_ITERATION;
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const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
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const int in = tid - step*im; // 0...7
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const int l0 = K_QUANTS_PER_ITERATION*in; // 0...14 in steps of 4
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const int q_offset = 32*im + l0;
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const int s_offset = 8*im;
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const int y_offset = 128*im + l0;
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float tmp = 0; // partial sum for thread in warp
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uint32_t aux[4];
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const uint8_t * d = (const uint8_t *)aux;
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const uint8_t * m = (const uint8_t *)(aux + 2);
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for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
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const float * y = yy + i * QK_K + y_offset;
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const uint8_t * q = x[i].qs + q_offset;
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const float dall = x[i].d;
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const float dmin = x[i].dmin;
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const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
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aux[0] = a[0] & 0x0f0f0f0f;
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aux[1] = a[1] & 0x0f0f0f0f;
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aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
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aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
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float sum1 = 0, sum2 = 0;
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for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
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sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
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+ y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
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+ y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
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+ y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
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+ y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
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+ y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
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+ y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
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+y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
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sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
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+ y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
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}
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tmp += dall * sum1 - dmin * sum2;
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}
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// sum up partial sums and write back result
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__syncthreads();
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#pragma unroll
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for (int mask = 16; mask > 0; mask >>= 1) {
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tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
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}
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if (tid == 0) {
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dst[row] = tmp;
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}
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}
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static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float * yy, float * dst, const int ncols) {
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||||
const uint16_t kmask1 = 0x0303;
|
||||
const uint16_t kmask2 = 0x0f0f;
|
||||
|
||||
const int row = blockIdx.x;
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
const block_q3_K * x = (const block_q3_K *)vx + ib0;
|
||||
|
||||
const int tid = threadIdx.x/2; // 0...15
|
||||
const int ix = threadIdx.x%2; // 0, 1
|
||||
|
||||
const int n = 2; // iterations in the inner loop
|
||||
const int im = tid/8; // 0 or 1. 0 computes 0..., 1 computes 128...
|
||||
const int in = tid - 8*im; // 0...7
|
||||
|
||||
const uint8_t m = 1 << (4*im);
|
||||
|
||||
const int l0 = n*in; // 0...28 in steps of 4
|
||||
const int q_offset = 32*im + l0;
|
||||
const int y_offset = 128*im + l0;
|
||||
|
||||
uint16_t utmp[4];
|
||||
const int8_t * s = (const int8_t *)utmp;
|
||||
|
||||
const uint16_t s_shift = 4*im;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = ix; i < num_blocks_per_row; i += 2) {
|
||||
|
||||
const float * y = yy + i * QK_K + y_offset;
|
||||
const uint8_t * q = x[i].qs + q_offset;
|
||||
const uint8_t * h = x[i].hmask + l0;
|
||||
|
||||
const uint16_t * a = (const uint16_t *)x[i].scales;
|
||||
utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
|
||||
utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
|
||||
utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
|
||||
utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
|
||||
|
||||
const float d = x[i].d;
|
||||
|
||||
float sum = 0;
|
||||
for (int l = 0; l < n; ++l) {
|
||||
sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
|
||||
+ y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
|
||||
+ y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
|
||||
+ y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
|
||||
sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
|
||||
+ y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
|
||||
+ y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
|
||||
+ y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
|
||||
}
|
||||
tmp += d * sum;
|
||||
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float * yy, float * dst, const int ncols) {
|
||||
|
||||
const uint16_t kmask1 = 0x3f3f;
|
||||
const uint16_t kmask2 = 0x0f0f;
|
||||
const uint16_t kmask3 = 0xc0c0;
|
||||
|
||||
const int row = blockIdx.x;
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
const int tid = threadIdx.x/2; // 0...15
|
||||
const int ix = threadIdx.x%2;
|
||||
|
||||
const int il = tid/4; // 0...3
|
||||
const int ir = tid - 4*il;// 0...3
|
||||
const int n = 4;
|
||||
|
||||
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
|
||||
const int in = il%2;
|
||||
|
||||
const int l0 = n*(2*ir + in);
|
||||
const int q_offset = 32*im + l0;
|
||||
const int y_offset = 64*im + l0;
|
||||
|
||||
uint16_t aux[4];
|
||||
const uint8_t * sc = (const uint8_t *)aux;
|
||||
|
||||
const block_q4_K * x = (const block_q4_K *)vx + ib0;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = ix; i < num_blocks_per_row; i += 2) {
|
||||
|
||||
const uint8_t * q1 = x[i].qs + q_offset;
|
||||
const uint8_t * q2 = q1 + 64;
|
||||
const float * y1 = yy + i*QK_K + y_offset;
|
||||
const float * y2 = y1 + 128;
|
||||
|
||||
const float dall = x[i].d;
|
||||
const float dmin = x[i].dmin;
|
||||
|
||||
const uint16_t * a = (const uint16_t *)x[i].scales;
|
||||
aux[0] = a[im+0] & kmask1;
|
||||
aux[1] = a[im+2] & kmask1;
|
||||
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
|
||||
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
|
||||
|
||||
float4 s = {0.f, 0.f, 0.f, 0.f};
|
||||
float smin = 0;
|
||||
for (int l = 0; l < n; ++l) {
|
||||
s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4);
|
||||
s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4);
|
||||
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
|
||||
}
|
||||
tmp += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin;
|
||||
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float * yy, float * dst, const int ncols) {
|
||||
|
||||
const uint16_t kmask1 = 0x3f3f;
|
||||
const uint16_t kmask2 = 0x0f0f;
|
||||
const uint16_t kmask3 = 0xc0c0;
|
||||
|
||||
//const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
||||
const int row = blockIdx.x;
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
const int tid = threadIdx.x/2; // 0...15
|
||||
const int ix = threadIdx.x%2;
|
||||
|
||||
const int il = tid/4; // 0...3
|
||||
const int ir = tid - 4*il;// 0...3
|
||||
const int n = 4;
|
||||
|
||||
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
|
||||
const int in = il%2;
|
||||
|
||||
const int l0 = n*(2*ir + in);
|
||||
const int q_offset = 32*im + l0;
|
||||
const int y_offset = 64*im + l0;
|
||||
|
||||
const uint8_t hm1 = 1 << (2*im);
|
||||
const uint8_t hm2 = hm1 << 4;
|
||||
|
||||
uint16_t aux[4];
|
||||
const uint8_t * sc = (const uint8_t *)aux;
|
||||
|
||||
const block_q5_K * x = (const block_q5_K *)vx + ib0;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = ix; i < num_blocks_per_row; i += 2) {
|
||||
|
||||
const uint8_t * ql1 = x[i].qs + q_offset;
|
||||
const uint8_t * ql2 = ql1 + 64;
|
||||
const uint8_t * qh = x[i].qh + l0;
|
||||
const float * y1 = yy + i*QK_K + y_offset;
|
||||
const float * y2 = y1 + 128;
|
||||
|
||||
const float dall = x[i].d;
|
||||
const float dmin = x[i].dmin;
|
||||
|
||||
const uint16_t * a = (const uint16_t *)x[i].scales;
|
||||
aux[0] = a[im+0] & kmask1;
|
||||
aux[1] = a[im+2] & kmask1;
|
||||
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
|
||||
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
|
||||
|
||||
float4 sum = {0.f, 0.f, 0.f, 0.f};
|
||||
float smin = 0;
|
||||
for (int l = 0; l < n; ++l) {
|
||||
sum.x += y1[l+ 0] * ((ql1[l] & 0xF) + (qh[l] & (hm1 << 0) ? 16 : 0));
|
||||
sum.y += y1[l+32] * ((ql1[l] >> 4) + (qh[l] & (hm1 << 1) ? 16 : 0));
|
||||
sum.z += y2[l+ 0] * ((ql2[l] & 0xF) + (qh[l] & (hm2 << 0) ? 16 : 0));
|
||||
sum.w += y2[l+32] * ((ql2[l] >> 4) + (qh[l] & (hm2 << 1) ? 16 : 0));
|
||||
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
|
||||
}
|
||||
tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
|
||||
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) {
|
||||
|
||||
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
|
||||
|
||||
const int row = blockIdx.y*blockDim.y + threadIdx.y;
|
||||
if (row > nrows) return;
|
||||
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
const block_q6_K * x = (const block_q6_K *)vx + ib0;
|
||||
|
||||
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
|
||||
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1
|
||||
|
||||
const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
|
||||
|
||||
const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
|
||||
const int in = tid - step*im; // 0...15 or 0...7
|
||||
|
||||
#if K_QUANTS_PER_ITERATION == 1
|
||||
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
|
||||
const int is = 0;
|
||||
#else
|
||||
const int l0 = 4 * in; // 0, 4, 8, ..., 28
|
||||
const int is = in / 4;
|
||||
#endif
|
||||
const int ql_offset = 64*im + l0;
|
||||
const int qh_offset = 32*im + l0;
|
||||
const int s_offset = 8*im + is;
|
||||
const int y_offset = 128*im + l0;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
|
||||
|
||||
const float * y = yy + i * QK_K + y_offset;
|
||||
const uint8_t * ql = x[i].ql + ql_offset;
|
||||
const uint8_t * qh = x[i].qh + qh_offset;
|
||||
const int8_t * s = x[i].scales + s_offset;
|
||||
|
||||
const float d = x[i].d;
|
||||
|
||||
#if K_QUANTS_PER_ITERATION == 1
|
||||
float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
|
||||
+ y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
|
||||
+ y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
|
||||
+ y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
|
||||
+ y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
|
||||
+ y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
|
||||
+ y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
|
||||
+y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
|
||||
tmp += sum;
|
||||
#else
|
||||
float sum = 0;
|
||||
for (int l = 0; l < 4; ++l) {
|
||||
sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
|
||||
+ y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
|
||||
+ y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
|
||||
+ y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
|
||||
}
|
||||
tmp += sum;
|
||||
#endif
|
||||
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __device__ void convert_f16(const void * vx, const int ib, const int iqs, float & v0, float & v1){
|
||||
|
|
@ -712,46 +918,6 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y,
|
|||
}
|
||||
}
|
||||
|
||||
template <int n_thread, dot_kernel_k_t dot_kernel>
|
||||
static __global__ void dequantize_mul_mat_vec_k(const void * vx, const float * y, float * dst, const int ncols, const int nrows) {
|
||||
const int row = blockIdx.y*blockDim.y + threadIdx.y;
|
||||
|
||||
if (row >= nrows) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int tid = threadIdx.x;
|
||||
|
||||
const int iter_stride = QK_K;
|
||||
const int vals_per_iter = iter_stride / n_thread;
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = 0; i < ncols; i += iter_stride) {
|
||||
const int col = i + vals_per_iter*tid;
|
||||
const int ib = ib0 + col/QK_K; // x block index
|
||||
const int iqs = col%QK_K; // x quant index
|
||||
const int iybs = col - col%QK_K; // y block start index
|
||||
|
||||
float v;
|
||||
dot_kernel(vx, ib, iqs, y + iybs, v);
|
||||
tmp += v;
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nchannels_x) {
|
||||
const half * x = (half *) vx;
|
||||
|
||||
|
|
@ -1094,43 +1260,34 @@ static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, f
|
|||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const dim3 block_nums(1, block_num_y, 1);
|
||||
const dim3 block_dims(32, ny, 1);
|
||||
dequantize_mul_mat_vec_k<32, vec_dot_q2_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % QK_K == 0);
|
||||
const int ny = 2;
|
||||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const dim3 block_nums(1, block_num_y, 1);
|
||||
const dim3 block_dims(32, ny, 1);
|
||||
dequantize_mul_mat_vec_k<32, vec_dot_q3_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
const dim3 block_dims(32, 1, 1);
|
||||
dequantize_mul_mat_vec_q3_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
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GGML_ASSERT(ncols % QK_K == 0);
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const int ny = 2;
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const int block_num_y = (nrows + ny - 1) / ny;
|
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const dim3 block_nums(1, block_num_y, 1);
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const dim3 block_dims(32, ny, 1);
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dequantize_mul_mat_vec_k<32, vec_dot_q4_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
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const dim3 block_dims(32, 1, 1);
|
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dequantize_mul_mat_vec_q4_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
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}
|
||||
|
||||
static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % QK_K == 0);
|
||||
const int ny = 2;
|
||||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const dim3 block_nums(1, block_num_y, 1);
|
||||
const dim3 block_dims(32, ny, 1);
|
||||
dequantize_mul_mat_vec_k<32, vec_dot_q5_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
const dim3 block_dims(32, 1, 1);
|
||||
dequantize_mul_mat_vec_q5_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % QK_K == 0);
|
||||
const int ny = 2;
|
||||
const int ny = 2 / K_QUANTS_PER_ITERATION;
|
||||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const dim3 block_nums(1, block_num_y, 1);
|
||||
const dim3 block_dims(32, ny, 1);
|
||||
dequantize_mul_mat_vec_k<32, vec_dot_q6_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
}
|
||||
|
||||
static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
||||
|
|
|
|||
Loading…
Reference in a new issue