Merge branch 'LostRuins:concedo' into koboldcpp-rocm

Makefile

@@ -149,6 +149,14 @@ ifdef LLAMA_CUDA_DMMV_Y
 else
  NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
 endif # LLAMA_CUDA_DMMV_Y
+ifdef LLAMA_CUDA_DMMV_F16
+ NVCCFLAGS += -DGGML_CUDA_DMMV_F16
+endif # LLAMA_CUDA_DMMV_F16
+ifdef LLAMA_CUDA_KQUANTS_ITER
+ NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
+else
+ NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
+endif
 ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
  $(NVCC) $(NVCCFLAGS) $(CXXFLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
 ggml_v2-cuda.o: otherarch/ggml_v2-cuda.cu otherarch/ggml_v2-cuda.h

koboldcpp.py

@@ -726,7 +726,7 @@ def main(args):
  sys.exit(2)
 
  if args.hordeconfig and args.hordeconfig[0]!="":
- global friendlymodelname, maxhordelen, showdebug
+ global friendlymodelname, maxhordelen, maxhordectx, showdebug
  friendlymodelname = "koboldcpp/"+args.hordeconfig[0]
  if len(args.hordeconfig) > 1:
  maxhordelen = int(args.hordeconfig[1])

otherarch/gptj_v3.cpp

@@ -150,7 +150,7 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
  params.mem_size = ctx_size;
  params.mem_buffer = NULL;
  params.no_alloc = false;
- 
+
 
  model.ctx = ggml_init(params);
  if (!model.ctx) {
@@ -281,7 +281,7 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
  fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
  return ModelLoadResult::FAIL;
  }
- 
+
 
  if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
 
@@ -298,7 +298,7 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
  __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
  return ModelLoadResult::FAIL;
  }
- 
+
  }
 
  // for debugging
@@ -367,8 +367,16 @@ bool gptj_eval(
  static size_t buf_size = 256u*1024*1024;
  static void * buf = malloc(buf_size);
 
- if (mem_per_token > 0 && (mem_per_token*N*2 + 64u*1024*1024) > buf_size) {
- const size_t buf_size_new = 320u*1024*1024 + 1.6*(mem_per_token*N); // add 10% to account for ggml object overhead
+ // use 2 scratch buffers
+ // TODO: very hacky solution - reimplement in a more elegant way
+ static size_t scr0_size = (n_ctx>1024?512u:256u)*1024*1024;
+ static void * scr0 = malloc(scr0_size);
+
+ static size_t scr1_size = (n_ctx>1024?512u:256u)*1024*1024;
+ static void * scr1 = malloc(scr1_size);
+
+ if (mem_per_token > 0 && mem_per_token*N*1.05 > buf_size) {
+ const size_t buf_size_new = 64u*1024*1024 + 1.15*(mem_per_token*N); // add 10% to account for ggml object overhead
  //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
 
  // reallocate
@@ -388,7 +396,7 @@ bool gptj_eval(
  params.mem_size = buf_size;
  params.mem_buffer = buf;
  params.no_alloc = false;
- 
+
 
  struct ggml_context * ctx0 = ggml_init(params);
  struct ggml_cgraph gf = {};
@@ -403,6 +411,8 @@ bool gptj_eval(
  for (int il = 0; il < n_layer; ++il) {
  struct ggml_tensor * cur;
 
+ ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
+
  // norm
  {
  cur = ggml_norm(ctx0, inpL);
@@ -490,6 +500,8 @@ bool gptj_eval(
  cur);
  }
 
+ ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });
+
  struct ggml_tensor * inpFF = cur;
 
  // feed-forward network
@@ -525,6 +537,8 @@ bool gptj_eval(
  inpL = ggml_add(ctx0, cur, inpL);
  }
 
+ ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
+
  // norm
  {
  inpL = ggml_norm(ctx0, inpL);
@@ -537,6 +551,8 @@ bool gptj_eval(
  ggml_repeat(ctx0, model.ln_f_b, inpL));
  }
 
+ ggml_set_scratch(ctx0, { 0, 0, nullptr, });
+
  // lm_head
  {
  inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL);