mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-11-07 16:44:13 +01:00
24f0aa460b
* DRAFT: Introduction of CUDA Graphs to LLama.cpp * FIx issues raised in comments * Tidied to now only use CUDA runtime (not mixed with driver calls) * disable for multi-gpu and batch size > 1 * Disable CUDA graphs for old GPU arch and with env var * added missing CUDA_CHECKs * Addressed comments * further addressed comments * limit to GGML_ALLOW_CUDA_GRAPHS defined in llama.cpp cmake * Added more comprehensive graph node checking * With mechanism to fall back if graph capture fails * Revert "With mechanism to fall back if graph capture fails" This reverts commit eb9f15fb6fcb81384f732c4601a5b25c016a5143. * Fall back if graph capture fails and address other comments * - renamed GGML_ALLOW_CUDA_GRAPHS to GGML_CUDA_USE_GRAPHS - rename env variable to disable CUDA graphs to GGML_CUDA_DISABLE_GRAPHS - updated Makefile build to enable CUDA graphs - removed graph capture failure checking in ggml_cuda_error using a global variable to track this is not thread safe, but I am also not safistied with checking an error by string if this is necessary to workaround some issues with graph capture with eg. cuBLAS, we can pass the ggml_backend_cuda_context to the error checking macro and store the result in the context - fixed several resource leaks - fixed issue with zero node graphs - changed fixed size arrays to vectors - removed the count of number of evaluations before start capturing, and instead changed the capture mode to relaxed - removed the check for multiple devices so that it is still possible to use a single device, instead checks for split buffers to disable cuda graphs with -sm row - changed the op for checking batch size to GGML_OP_ADD, should be more reliable than GGML_OP_SOFT_MAX - code style fixes - things to look into - VRAM usage of the cudaGraphExec_t, if it is significant we may need to make it optional - possibility of using cudaStreamBeginCaptureToGraph to keep track of which ggml graph nodes correspond to which cuda graph nodes * fix build without cuda graphs * remove outdated comment * replace minimum cc value with a constant --------- Co-authored-by: slaren <slarengh@gmail.com>
491 lines
20 KiB
Plaintext
491 lines
20 KiB
Plaintext
#include "cpy.cuh"
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typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
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static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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float * dsti = (float *) cdsti;
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*dsti = *xi;
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}
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static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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half * dsti = (half *) cdsti;
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*dsti = __float2half(*xi);
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}
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static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
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const half * xi = (const half *) cxi;
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half * dsti = (half *) cdsti;
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*dsti = *xi;
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}
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static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) {
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const half * xi = (const half *) cxi;
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float * dsti = (float *) cdsti;
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*dsti = *xi;
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}
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template <cpy_kernel_t cpy_1>
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static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
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const int nb12, const int nb13) {
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const int64_t i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= ne) {
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return;
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}
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// determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
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// then combine those indices with the corresponding byte offsets to get the total offsets
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const int64_t i03 = i/(ne00 * ne01 * ne02);
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const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
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const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
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const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
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const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
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const int64_t i13 = i/(ne10 * ne11 * ne12);
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const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
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const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
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const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
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const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
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cpy_1(cx + x_offset, cdst + dst_offset);
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}
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static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q8_0 * dsti = (block_q8_0 *) cdsti;
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float amax = 0.0f; // absolute max
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for (int j = 0; j < QK8_0; j++) {
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const float v = xi[j];
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amax = fmaxf(amax, fabsf(v));
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}
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const float d = amax / ((1 << 7) - 1);
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const float id = d ? 1.0f/d : 0.0f;
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dsti->d = d;
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for (int j = 0; j < QK8_0; ++j) {
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const float x0 = xi[j]*id;
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dsti->qs[j] = roundf(x0);
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}
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}
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static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q4_0 * dsti = (block_q4_0 *) cdsti;
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float amax = 0.0f;
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float vmax = 0.0f;
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for (int j = 0; j < QK4_0; ++j) {
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const float v = xi[j];
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if (amax < fabsf(v)) {
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amax = fabsf(v);
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vmax = v;
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}
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}
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const float d = vmax / -8;
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const float id = d ? 1.0f/d : 0.0f;
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dsti->d = d;
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for (int j = 0; j < QK4_0/2; ++j) {
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const float x0 = xi[0 + j]*id;
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const float x1 = xi[QK4_0/2 + j]*id;
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const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f));
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const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f));
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dsti->qs[j] = xi0;
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dsti->qs[j] |= xi1 << 4;
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}
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}
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static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q4_1 * dsti = (block_q4_1 *) cdsti;
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float vmin = FLT_MAX;
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float vmax = -FLT_MAX;
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for (int j = 0; j < QK4_1; ++j) {
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const float v = xi[j];
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if (v < vmin) vmin = v;
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if (v > vmax) vmax = v;
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}
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const float d = (vmax - vmin) / ((1 << 4) - 1);
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const float id = d ? 1.0f/d : 0.0f;
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dsti->dm.x = d;
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dsti->dm.y = vmin;
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for (int j = 0; j < QK4_1/2; ++j) {
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const float x0 = (xi[0 + j] - vmin)*id;
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const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
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const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f));
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const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f));
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dsti->qs[j] = xi0;
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dsti->qs[j] |= xi1 << 4;
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}
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}
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static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q5_0 * dsti = (block_q5_0 *) cdsti;
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float amax = 0.0f;
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float vmax = 0.0f;
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for (int j = 0; j < QK5_0; ++j) {
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const float v = xi[j];
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if (amax < fabsf(v)) {
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amax = fabsf(v);
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vmax = v;
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}
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}
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const float d = vmax / -16;
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const float id = d ? 1.0f/d : 0.0f;
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dsti->d = d;
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uint32_t qh = 0;
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for (int j = 0; j < QK5_0/2; ++j) {
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const float x0 = xi[0 + j]*id;
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const float x1 = xi[QK5_0/2 + j]*id;
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const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f));
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const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f));
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dsti->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
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qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
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qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
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}
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memcpy(dsti->qh, &qh, sizeof(qh));
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}
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static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q5_1 * dsti = (block_q5_1 *) cdsti;
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float min = xi[0];
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float max = xi[0];
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for (int j = 1; j < QK5_1; ++j) {
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const float v = xi[j];
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min = v < min ? v : min;
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max = v > max ? v : max;
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}
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const float d = (max - min) / 31;
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const float id = d ? 1.0f/d : 0.0f;
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dsti->dm.x = d;
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dsti->dm.y = min;
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uint32_t qh = 0;
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for (int j = 0; j < QK5_1/2; ++j) {
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const float x0 = (xi[0 + j] - min)*id;
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const float x1 = (xi[QK5_1/2 + j] - min)*id;
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const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
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const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
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dsti->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
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qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
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qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
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}
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memcpy(dsti->qh, &qh, sizeof(qh));
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}
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static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) {
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if (x <= val[0]) return 0;
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if (x >= val[n-1]) return n-1;
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int ml = 0, mu = n-1;
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while (mu-ml > 1) {
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int mav = (ml+mu)/2;
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if (x < val[mav]) mu = mav; else ml = mav;
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}
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return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
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}
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static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_iq4_nl * dsti = (block_iq4_nl *) cdsti;
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float amax = 0.0f;
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float vmax = 0.0f;
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for (int j = 0; j < QK4_NL; ++j) {
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const float v = xi[j];
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if (amax < fabsf(v)) {
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amax = fabsf(v);
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vmax = v;
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}
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}
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float d = vmax / kvalues_iq4nl[0];
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const float id = d ? 1.0f/d : 0.0f;
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float sumqx = 0, sumq2 = 0;
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for (int j = 0; j < QK4_NL/2; ++j) {
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const float x0 = xi[0 + j]*id;
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const float x1 = xi[QK4_NL/2 + j]*id;
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const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0);
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const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1);
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dsti->qs[j] = xi0 | (xi1 << 4);
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const float v0 = kvalues_iq4nl[xi0];
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const float v1 = kvalues_iq4nl[xi1];
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const float w0 = xi[0 + j]*xi[0 + j];
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const float w1 = xi[QK4_NL/2 + j]*xi[QK4_NL/2 + j];
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sumqx += w0*v0*xi[j] + w1*v1*xi[QK4_NL/2 + j];
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sumq2 += w0*v0*v0 + w1*v1*v1;
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}
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dsti->d = sumq2 > 0 ? sumqx/sumq2 : d;
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}
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template <cpy_kernel_t cpy_blck, int qk>
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static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
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const int nb12, const int nb13) {
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const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
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if (i >= ne) {
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return;
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}
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const int i03 = i/(ne00 * ne01 * ne02);
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const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
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const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
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const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
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const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
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const int i13 = i/(ne10 * ne11 * ne12);
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const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
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const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
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const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
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const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
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cpy_blck(cx + x_offset, cdst + dst_offset);
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}
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static void ggml_cpy_f16_f32_cuda(
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const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
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const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
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cpy_f32_f16<cpy_1_f16_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
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(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
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}
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static void ggml_cpy_f32_f32_cuda(
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const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
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const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
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cpy_f32_f16<cpy_1_f32_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
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(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
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}
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static void ggml_cpy_f32_f16_cuda(
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const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
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const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
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cpy_f32_f16<cpy_1_f32_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
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(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
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}
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static void ggml_cpy_f32_q8_0_cuda(
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const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
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GGML_ASSERT(ne % QK8_0 == 0);
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const int num_blocks = ne / QK8_0;
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cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
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(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
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}
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static void ggml_cpy_f32_q4_0_cuda(
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const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
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GGML_ASSERT(ne % QK4_0 == 0);
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const int num_blocks = ne / QK4_0;
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cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
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(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
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}
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static void ggml_cpy_f32_q4_1_cuda(
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const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
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GGML_ASSERT(ne % QK4_1 == 0);
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const int num_blocks = ne / QK4_1;
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cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
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(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q5_0_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK5_0 == 0);
|
|
const int num_blocks = ne / QK5_0;
|
|
cpy_f32_q<cpy_blck_f32_q5_0, QK5_0><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q5_1_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK5_1 == 0);
|
|
const int num_blocks = ne / QK5_1;
|
|
cpy_f32_q<cpy_blck_f32_q5_1, QK5_1><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_iq4_nl_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK4_NL == 0);
|
|
const int num_blocks = ne / QK4_NL;
|
|
cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f16_f16_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
|
cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1) {
|
|
const int64_t ne = ggml_nelements(src0);
|
|
GGML_ASSERT(ne == ggml_nelements(src1));
|
|
|
|
GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
|
|
GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne01 = src0->ne[1];
|
|
const int64_t ne02 = src0->ne[2];
|
|
|
|
//GGML_ASSERT(src0->ne[3] == 1);
|
|
|
|
const int64_t nb00 = src0->nb[0];
|
|
const int64_t nb01 = src0->nb[1];
|
|
const int64_t nb02 = src0->nb[2];
|
|
const int64_t nb03 = src0->nb[3];
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
const int64_t ne11 = src1->ne[1];
|
|
const int64_t ne12 = src1->ne[2];
|
|
|
|
//GGML_ASSERT(src1->ne[3] == 1);
|
|
|
|
const int64_t nb10 = src1->nb[0];
|
|
const int64_t nb11 = src1->nb[1];
|
|
const int64_t nb12 = src1->nb[2];
|
|
const int64_t nb13 = src1->nb[3];
|
|
|
|
cudaStream_t main_stream = ctx.stream();
|
|
|
|
char * src0_ddc = (char *) src0->data;
|
|
char * src1_ddc = (char *) src1->data;
|
|
|
|
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
|
|
ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
|
|
ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
|
|
ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
|
|
ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
|
|
ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
|
|
ggml_cpy_f32_q5_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
|
|
ggml_cpy_f32_iq4_nl_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
|
|
ggml_cpy_f32_q5_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
|
|
ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
|
|
ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else {
|
|
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
|
|
ggml_type_name(src0->type), ggml_type_name(src1->type));
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
|
const ggml_tensor * src0 = dst->src[0];
|
|
ggml_cuda_cpy(ctx, src0, dst);
|
|
}
|
|
|
|
void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
|
|
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
|
|
return (void*) cpy_f32_f16<cpy_1_f32_f32>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
|
|
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>;
|
|
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
|
|
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
|
|
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
|
|
return (void*) cpy_f32_f16<cpy_1_f16_f32>;
|
|
} else {
|
|
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
|
|
ggml_type_name(src0->type), ggml_type_name(src1->type));
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|