matt/ollama37
1
0
Fork 0
mirror of https://github.com/dogkeeper886/ollama37.git synced 2025-12-18 03:37:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

llama: update vendor code to commit ba1cb19c (#8101)

Browse Source
This commit is contained in:
Jeffrey Morgan
2024-12-14 14:55:51 -08:00
committed by GitHub
parent 60f75560a2
commit 7a81daf026
273 changed files with 3194 additions and 1900 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
llama/ggml-cuda/ggml-cuda.cu vendored
View File

@@ -1,5 +1,5 @@
/**
* llama.cpp - commit 40c6d79fb52f995f47507fedfeaae2ac05d9b35c - do not edit this file
* llama.cpp - commit ba1cb19cdd0d92e012e0f6e009e0620f854b6afd - do not edit this file
*
* MIT License
*
@@ -203,7 +203,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
info.devices[id].smpb = prop.sharedMemPerBlock;
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
info.devices[id].smpbo = prop.sharedMemPerBlock;
info.devices[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD;
info.devices[id].cc = 100*prop.major + 10*prop.minor + GGML_CUDA_CC_OFFSET_AMD;
#else
info.devices[id].smpbo = prop.sharedMemPerBlockOptin;
info.devices[id].cc = 100*prop.major + 10*prop.minor;
@@ -1111,7 +1111,7 @@ static void ggml_cuda_op_mul_mat_cublas(
const int compute_capability = ggml_cuda_info().devices[id].cc;
if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
if (compute_capability >= GGML_CUDA_CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
// convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
ggml_cuda_pool_alloc<half> src0_as_f16(ctx.pool(id));
if (src0->type != GGML_TYPE_F16) {
@@ -1138,7 +1138,7 @@ static void ggml_cuda_op_mul_mat_cublas(
const half beta_f16 = 0.0f;
cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
if (ggml_cuda_info().devices[ctx.device].cc == CC_CDNA) {
if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
cu_compute_type = CUBLAS_COMPUTE_32F;
}
@@ -1642,7 +1642,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
cudaDataType_t cu_data_type = CUDA_R_16F;
if (ggml_cuda_info().devices[ctx.device].cc == CC_CDNA) {
if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
cu_compute_type = CUBLAS_COMPUTE_32F;
}
@@ -2392,7 +2392,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
std::vector<void *> ggml_cuda_cpy_fn_ptrs;
if (cuda_ctx->cuda_graph->graph == nullptr) {
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_AMPERE) {
cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
@@ -3064,7 +3064,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
return true;
}
const int cc = ggml_cuda_info().devices[dev_ctx->device].cc;
return cc >= CC_VOLTA && cc < CC_OFFSET_AMD && op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
return cc >= GGML_CUDA_CC_VOLTA && cc < GGML_CUDA_CC_OFFSET_AMD && op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
}
case GGML_OP_CROSS_ENTROPY_LOSS:
case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
@@ -3246,7 +3246,7 @@ static void * ggml_backend_cuda_reg_get_proc_address(ggml_backend_reg_t reg, con
static const ggml_backend_reg_i ggml_backend_cuda_reg_interface = {
/* .get_name = */ ggml_backend_cuda_reg_get_name,
/* .get_device_count = */ ggml_backend_cuda_reg_get_device_count,
/* .get_device_get = */ ggml_backend_cuda_reg_get_device,
/* .get_device = */ ggml_backend_cuda_reg_get_device,
/* .get_proc_address = */ ggml_backend_cuda_reg_get_proc_address,
};