Merge branch 'master' into concedo_experimental

# Conflicts:
#	.gitignore
#	CMakeLists.txt
#	Makefile
#	README.md
#	flake.nix
#	ggml-cuda.cu

.gitignore

@@ -64,6 +64,20 @@ qnt-*.txt
 perf-*.txt
 
 examples/jeopardy/results.txt
+
+pyproject.toml
+poetry.lock
+poetry.toml
+
+# Test binaries
+tests/test-double-float
+tests/test-grad0
+tests/test-opt
+tests/test-quantize-fns
+tests/test-quantize-perf
+tests/test-sampling
+tests/test-tokenizer-0
+
 koboldcpp.so
 koboldcpp_failsafe.so
 koboldcpp_openblas.so

ci/run.sh

@@ -243,7 +243,7 @@ function gg_sum_open_llama_3b_v2 {
 if [ -z $GG_BUILD_LOW_PERF ]; then
     rm -rf ${SRC}/models-mnt
 
-    mnt_models=$(realpath ${MNT}/models)
+    mnt_models=${MNT}/models
     mkdir -p ${mnt_models}
     ln -sfn ${mnt_models} ${SRC}/models-mnt
 

examples/Miku.sh

@@ -2,21 +2,21 @@
 set -e
 
 AI_NAME="${AI_NAME:-Miku}"
-MODEL="${MODEL:-./models/gpt4all-7B/gpt4all-lora-unfiltered-quantized.bin}"
+MODEL="${MODEL:-./models/llama-2-7b-chat.ggmlv3.q4_K_M.bin}"
 USER_NAME="${USER_NAME:-Anon}"
 
 # Uncomment and adjust to the number of CPU cores you want to use.
 #N_THREAD="${N_THREAD:-4}"
+CTX_SIZE="${CTX_SIZE:-4096}"
 N_PREDICTS="${N_PREDICTS:-4096}"
 
 GEN_OPTIONS=(--batch_size 1024
---ctx_size 2048
+--ctx_size "$CTX_SIZE"
 --keep -1
 --repeat_last_n 256
 --repeat_penalty 1.17647
---temp 0.7
+--temp 0.6
---top_k 40
+--mirostat 2)
---top_p 0.5)
 
 if [ -n "$N_THREAD" ]; then
     GEN_OPTIONS+=(--threads "$N_THREAD")
@@ -24,16 +24,17 @@ fi
 
 ./main "${GEN_OPTIONS[@]}" \
     --model "$MODEL" \
+    --in-prefix " " \
+    --in-suffix "${AI_NAME}:" \
     --n_predict "$N_PREDICTS" \
     --color --interactive \
     --reverse-prompt "${USER_NAME}:" \
-    --prompt "
+    --prompt "This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the user's computer.
-This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the user's computer.
 ${AI_NAME} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next.
 ${AI_NAME} is always coherent and makes sense, but if she isn't sure if what she is saying is correct, she will ask the user for help.
 ${AI_NAME} is a very helpful AI and will help the user with anything they need. She is also very friendly and will try to make the user feel better if they are sad.
 ${AI_NAME} is also very curious and will ask the user a lot of questions about themselves and their life. She will also try to make the user like her.
-The conversation is only between ${USER_NAME} and ${AI_NAME}
+The conversation is only between ${USER_NAME} and ${AI_NAME}.
 The conversation is only through text, so ${AI_NAME} can't see ${USER_NAME}'s face or hear his voice.
 ${AI_NAME} can only communicate through text, so she can't send images or videos.
 

examples/common.cpp

@@ -260,12 +260,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.cfg_scale = std::stof(argv[i]);
-        } else if (arg == "--cfg-smooth-factor") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.cfg_smooth_factor = std::stof(argv[i]);
         } else if (arg == "-b" || arg == "--batch-size") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -509,7 +503,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stderr, "  --cfg-negative-prompt PROMPT \n");
     fprintf(stderr, "                        negative prompt to use for guidance. (default: empty)\n");
     fprintf(stderr, "  --cfg-scale N         strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
-    fprintf(stderr, "  --cfg-smooth-factor N smooth factor between old and new logits (default: %f, 1.0 = no smoothing)\n", params.cfg_smooth_factor);
     fprintf(stderr, "  -c N, --ctx-size N    size of the prompt context (default: %d)\n", params.n_ctx);
     fprintf(stderr, "  --rope-freq-base N    RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
     fprintf(stderr, "  --rope-freq-scale N   RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
@@ -586,7 +579,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     lparams.n_batch      = params.n_batch;
     lparams.n_gpu_layers = params.n_gpu_layers;
     lparams.main_gpu     = params.main_gpu;
-    memcpy(lparams.tensor_split, params.tensor_split, LLAMA_MAX_DEVICES*sizeof(float));
+    lparams.tensor_split = params.tensor_split;
     lparams.low_vram     = params.low_vram;
     lparams.seed         = params.seed;
     lparams.f16_kv       = params.memory_f16;

examples/common.h

@@ -55,7 +55,6 @@ struct gpt_params {
     // https://arxiv.org/abs/2306.17806
     std::string cfg_negative_prompt;       // string to help guidance
     float       cfg_scale         = 1.f;   // How strong is guidance
-    float       cfg_smooth_factor = 1.f;   // Smooth factor between old and new logits
 
     std::string model             = "models/7B/ggml-model.bin"; // model path
     std::string model_alias       = "unknown"; // model alias

examples/embd-input/minigpt4.py

@@ -64,7 +64,7 @@ class MiniGPT4(Blip2Base):
         self.max_txt_len = max_txt_len
         self.end_sym = end_sym
         self.model = MyModel(["main", *args])
-        # system promt
+        # system prompt
         self.model.eval_string("Give the following image: <Img>ImageContent</Img>. "
            "You will be able to see the image once I provide it to you. Please answer my questions."
            "###")

examples/llama2-13b.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#
+# Temporary script - will be removed in the future
+#
+
+cd `dirname $0`
+cd ..
+
+./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \
+       --color \
+       --ctx_size 2048 \
+       -n -1 \
+       -ins -b 256 \
+       --top_k 10000 \
+       --temp 0.2 \
+       --repeat_penalty 1.1 \
+       -t 8

examples/llama2.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#
+# Temporary script - will be removed in the future
+#
+
+cd `dirname $0`
+cd ..
+
+./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \
+       --color \
+       --ctx_size 2048 \
+       -n -1 \
+       -ins -b 256 \
+       --top_k 10000 \
+       --temp 0.2 \
+       --repeat_penalty 1.1 \
+       -t 8

examples/main/main.cpp

@@ -557,7 +557,7 @@ int main(int argc, char ** argv) {
                 llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
 
                 if (ctx_guidance) {
-                    llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale, params.cfg_smooth_factor);
+                    llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale);
                 }
 
                 // Apply penalties

examples/make-ggml.py

@@ -0,0 +1,92 @@
+"""
+This script converts Hugging Face llama models to GGML and quantizes them.
+
+Usage:
+python make-ggml.py --model {model_dir_or_hf_repo_name} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)]
+
+Arguments:
+- --model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub.
+- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used.
+- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used.
+- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'.
+- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created.
+
+Quant types:
+- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M
+- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L
+- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M
+- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M
+- Q2_K: smallest, extreme quality loss - not recommended
+- Q3_K: alias for Q3_K_M
+- Q3_K_S: very small, very high quality loss
+- Q3_K_M: very small, very high quality loss
+- Q3_K_L: small, substantial quality loss
+- Q4_K: alias for Q4_K_M
+- Q4_K_S: small, significant quality loss
+- Q4_K_M: medium, balanced quality - recommended
+- Q5_K: alias for Q5_K_M
+- Q5_K_S: large, low quality loss - recommended
+- Q5_K_M: large, very low quality loss - recommended
+- Q6_K: very large, extremely low quality loss
+- Q8_0: very large, extremely low quality loss - not recommended
+- F16: extremely large, virtually no quality loss - not recommended
+- F32: absolutely huge, lossless - not recommended
+"""
+import subprocess
+subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True)
+
+import argparse
+import os
+from huggingface_hub import snapshot_download
+
+def main(model, outname, outdir, quants, keep_fp16):
+    ggml_version = "v3"
+
+    if not os.path.isdir(model):
+        print(f"Model not found at {model}. Downloading...")
+        try:
+            if outname is None:
+                outname = model.split('/')[-1]
+            model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache')
+        except Exception as e:
+            raise Exception(f"Could not download the model: {e}")
+
+    if outdir is None:
+        outdir = f'../models/{outname}'
+
+    if not os.path.isfile(f"{model}/config.json"):
+        raise Exception(f"Could not find config.json in {model}")
+
+    os.makedirs(outdir, exist_ok=True)
+
+    print("Building llama.cpp")
+    subprocess.run(f"cd .. && make quantize", shell=True, check=True)
+
+    fp16 = f"{outdir}/{outname}.ggml{ggml_version}.fp16.bin"
+
+    print(f"Making unquantised GGML at {fp16}")
+    if not os.path.isfile(fp16):
+        subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True)
+    else:
+        print(f"Unquantised GGML already exists at: {fp16}")
+
+    print("Making quants")
+    for type in quants:
+        outfile = f"{outdir}/{outname}.ggml{ggml_version}.{type}.bin"
+        print(f"Making {type} : {outfile}")
+        subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True)
+
+    if not keep_fp16:
+        os.remove(fp16)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Convert/Quantize HF to GGML. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.')
+    parser.add_argument('--model', required=True, help='Downloaded model dir or Hugging Face model repo name')
+    parser.add_argument('--outname', default=None, help='Output model(s) name')
+    parser.add_argument('--outdir', default=None, help='Output directory')
+    parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types')
+    parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False)
+
+    args = parser.parse_args()
+
+    main(args.model, args.outname, args.outdir, args.quants, args.keep_fp16)

examples/server/CMakeLists.txt

@@ -7,6 +7,9 @@ target_compile_definitions(${TARGET} PRIVATE
     SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
 )
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+if (WIN32)
+    TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
+endif()
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)
   add_dependencies(${TARGET} BUILD_INFO)

examples/train-text-from-scratch/train-text-from-scratch.cpp

@@ -1434,7 +1434,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
     gf->perf_time_us = 0;
 
     const auto & hparams = model->hparams;
-    //const int n_ctx      = hparams.n_ctx;
+    const int n_ctx      = hparams.n_ctx;
     const int n_vocab    = hparams.n_vocab;
     const int n_embd     = hparams.n_embd;
     const int n_layer    = hparams.n_layer;
@@ -1863,10 +1863,10 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
         t12->grad = expand(gb, ggml_permute(ctx0, t15->grad, 0, 2, 3, 1));                                            assert_shape_4d(t12->grad, N, n_batch, n_embd/n_head, n_head);
         t11->grad = expand(gb, ggml_reshape_2d(ctx0, ggml_cont(ctx0, t12->grad), N*n_batch, n_embd));                 assert_shape_2d(t11->grad, N*n_batch, n_embd);
         t10->grad = expand(gb, ggml_permute(ctx0, t14->grad, 0, 2, 1, 3));                                            assert_shape_4d(t10->grad, n_embd/n_head, n_head, N, n_batch);
-        t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode));                            assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
+        t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode, n_ctx));                     assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
         t08->grad = expand(gb, ggml_reshape_2d(ctx0, t09->grad, n_embd, N*n_batch));                                  assert_shape_2d(t08->grad, n_embd, N*n_batch);
         t07->grad = expand(gb, ggml_permute(ctx0, t13->grad, 0, 2, 1, 3));                                            assert_shape_4d(t07->grad, n_embd/n_head, n_head, N, n_batch);
-        t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode));                            assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
+        t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode, n_ctx));                     assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
         t05->grad = expand(gb, ggml_reshape_2d(ctx0, t06->grad, n_embd, N*n_batch));                                  assert_shape_2d(t05->grad, n_embd, N*n_batch);
         t04->grad = expand(gb, ggml_add_inplace(ctx0,
                         ggml_add_inplace(ctx0,

ggml-cuda.cu

@@ -2537,6 +2537,9 @@ void ggml_init_cublas() {
 }
 
 void ggml_cuda_set_tensor_split(const float * tensor_split) {
+    if (tensor_split == nullptr) {
+        return;
+    }
     bool all_zero = true;
     for (int i = 0; i < g_device_count; ++i) {
         if (tensor_split[i] != 0.0f) {
@@ -2972,19 +2975,18 @@ inline void ggml_cuda_op_rope(
     const int64_t ne00 = src0->ne[0];
     const int64_t i01_diff = i01_high - i01_low;
 
-    float freq_base;
-    float freq_scale;
-
     const int n_past = ((int32_t *) src1->data)[0];
     const int n_dims = ((int32_t *) src1->data)[1];
     const int mode   = ((int32_t *) src1->data)[2];
     const int n_ctx  = ((int32_t *) src1->data)[3];
+
+    // RoPE alteration for extended context
+    float freq_base, freq_scale;
     memcpy(&freq_base,  (int32_t *) src1->data + 4, sizeof(float));
     memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float p0 = ((mode & 1) == 0 ? n_past + i02 : i02);
+    const float p = (((mode & 1) == 0 ? n_past + i02 : i02)) * freq_scale;
-    const float p = p0 * freq_scale;
 
     bool is_glm = mode & 4;
 

ggml-metal.m

@@ -676,8 +676,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
+                                            nth0 = 2;
-                                            nth1 = 16;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_K_f32];
                                         } break;
                                     case GGML_TYPE_Q3_K:
@@ -685,8 +685,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
+                                            nth0 = 2;
-                                            nth1 = 16;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_K_f32];
                                         } break;
                                     case GGML_TYPE_Q4_K:
@@ -694,8 +694,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
+                                            nth0 = 2;
-                                            nth1 = 16;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32];
                                         } break;
                                     case GGML_TYPE_Q5_K:
@@ -703,8 +703,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
+                                            nth0 = 2;
-                                            nth1 = 16;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_K_f32];
                                         } break;
                                     case GGML_TYPE_Q6_K:
@@ -712,8 +712,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
+                                            nth0 = 2;
-                                            nth1 = 16;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_K_f32];
                                         } break;
                                     default:
@@ -739,16 +739,22 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:13];
                                 [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:14];
 
-                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1) {
+                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
+                                    src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
                                     [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7) / 8, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 }
-                                else if (src0t == GGML_TYPE_Q2_K ||
+                                else if (src0t == GGML_TYPE_Q3_K) {
-                                         src0t == GGML_TYPE_Q3_K ||
+#ifdef GGML_QKK_64
-                                         src0t == GGML_TYPE_Q4_K ||
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                         src0t == GGML_TYPE_Q5_K ||
+#else
-                                         src0t == GGML_TYPE_Q6_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+3)/4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                    [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
+#endif
-                                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                }
+                                else if (src0t == GGML_TYPE_Q5_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3) / 4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                }
+                                else if (src0t == GGML_TYPE_Q6_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 } else {
                                     [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
                                     [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];

ggml.c

@@ -6957,9 +6957,9 @@ struct ggml_tensor * ggml_rope_impl(
         int                   n_past,
         int                   n_dims,
         int                   mode,
+        int                   n_ctx,
         float                 freq_base,
         float                 freq_scale,
-        int                   n_ctx,
         bool                  inplace) {
     GGML_ASSERT(n_past >= 0);
     bool is_node = false;
@@ -6998,7 +6998,7 @@ struct ggml_tensor * ggml_rope(
         int                   n_dims,
         int                   mode,
         int                   n_ctx) {
-    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, false);
+    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, false);
 }
 
 struct ggml_tensor * ggml_rope_inplace(
@@ -7008,7 +7008,7 @@ struct ggml_tensor * ggml_rope_inplace(
         int                   n_dims,
         int                   mode,
         int                   n_ctx) {
-    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, true);
+    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, true);
 }
 
 struct ggml_tensor * ggml_rope_custom_inplace(
@@ -7017,10 +7017,10 @@ struct ggml_tensor * ggml_rope_custom_inplace(
         int                   n_past,
         int                   n_dims,
         int                   mode,
+        int                   n_ctx,
         float                 freq_base,
-        float                 freq_scale,
+        float                 freq_scale) {
-        int                   n_ctx) {
+    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, true);
-    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, freq_base, freq_scale, n_ctx, true);
 }
 
 // ggml_rope_back
@@ -7030,7 +7030,8 @@ struct ggml_tensor * ggml_rope_back(
         struct ggml_tensor  * a,
         int                   n_past,
         int                   n_dims,
-        int                   mode) {
+        int                   mode,
+        int                   n_ctx) {
     GGML_ASSERT(n_past >= 0);
     GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet");
 
@@ -7044,12 +7045,13 @@ struct ggml_tensor * ggml_rope_back(
 
     ggml_scratch_save(ctx);
 
-    struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3);
+    struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4);
     ggml_set_name(b, "n_past, n_dims, mode");
 
     ((int32_t *) b->data)[0] = n_past;
     ((int32_t *) b->data)[1] = n_dims;
     ((int32_t *) b->data)[2] = mode;
+    ((int32_t *) b->data)[3] = n_ctx;
 
     ggml_scratch_load(ctx);
 
@@ -12378,7 +12380,7 @@ static void ggml_compute_forward_rope_back_f32(
         const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
     assert(src1->type == GGML_TYPE_I32);
-    assert(ggml_nelements(src1) == 3);
+    assert(ggml_nelements(src1) == 4);
 
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
         return;
@@ -15741,13 +15743,15 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                     const int n_past = ((int32_t *) src1->data)[0];
                     const int n_dims = ((int32_t *) src1->data)[1];
                     const int mode   = ((int32_t *) src1->data)[2];
+                    const int n_ctx  = ((int32_t *) src1->data)[3];
                     src0->grad = ggml_add_impl(ctx,
                             src0->grad,
                             ggml_rope_back(ctx,
                                 tensor->grad,
                                 n_past,
                                 n_dims,
-                                mode),
+                                mode,
+                                n_ctx),
                             inplace);
                 }
                 if (src1->grad) {
@@ -15758,7 +15762,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 if (src0->grad) {
                     assert(src1->type == GGML_TYPE_I32);
-                    assert(ggml_nelements(src1) == 3);
+                    assert(ggml_nelements(src1) == 4);
                     const int n_past = ((int32_t *) src1->data)[0];
                     const int n_dims = ((int32_t *) src1->data)[1];
                     const int mode   = ((int32_t *) src1->data)[2];

ggml.h

@@ -1127,9 +1127,9 @@ extern "C" {
             int                   n_past,
             int                   n_dims,
             int                   mode,
+            int                   n_ctx,
             float                 freq_base,
-            float                 freq_scale,
+            float                 freq_scale);
-            int                   n_ctx);
 
     // rotary position embedding backward, i.e compute dx from dy
     // a - dy
@@ -1138,7 +1138,8 @@ extern "C" {
             struct ggml_tensor  * a,
             int                   n_past,
             int                   n_dims,
-            int                   mode);
+            int                   mode,
+            int                   n_ctx);
 
     // alibi position embedding
     // in-place, returns view(a)

llama.cpp

@@ -850,7 +850,7 @@ struct llama_context_params llama_context_default_params() {
         /*.n_batch                     =*/ 512,
         /*.gpu_layers                  =*/ 0,
         /*.main_gpu                    =*/ 0,
-        /*.tensor_split                =*/ {0},
+        /*.tensor_split                =*/ nullptr,
         /*.rope_freq_base              =*/ 10000.0f,
         /*.rope_freq_scale             =*/ 1.0f,
         /*.progress_callback           =*/ nullptr,
@@ -1290,7 +1290,7 @@ static bool llama_model_load(
         int n_batch,
         int n_gpu_layers,
         int main_gpu,
-        float * tensor_split,
+        const float * tensor_split,
         float rope_freq_base,
         float rope_freq_scale,
         bool low_vram,
@@ -1453,11 +1453,11 @@ static bool llama_eval_internal(
             offload_func_kq(tmpq);
             ggml_set_name(tmpq, "tmpq");
 
-            struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0);
+            struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0, freq_base, freq_scale);
             offload_func_kq(Kcur);
             ggml_set_name(Kcur, "Kcur");
 
-            struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0);
+            struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0, freq_base, freq_scale);
             offload_func_kq(Qcur);
             ggml_set_name(Qcur, "Qcur");
 
@@ -2219,8 +2219,7 @@ void llama_sample_classifier_free_guidance(
           struct llama_context * ctx,
         llama_token_data_array * candidates,
           struct llama_context * guidance_ctx,
-                         float   scale,
+                         float   scale) {
-                         float   smooth_factor) {
     int64_t t_start_sample_us = ggml_time_us();
 
     assert(ctx);
@@ -2241,16 +2240,7 @@ void llama_sample_classifier_free_guidance(
     for (int i = 0; i < n_vocab; ++i) {
         float logit_guidance = logits_guidance[i];
         float logit_base = logits_base[i];
-        logits_guidance[i] = scale * (logit_base - logit_guidance) + logit_guidance;
+        candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
-    }
-
-    llama_log_softmax(logits_guidance, n_vocab);
-
-    for (int i = 0; i < n_vocab; ++i) {
-        float logit_base = logits_base[i];
-        float logit_guidance = logits_guidance[i];
-
-        candidates->data[i].logit = smooth_factor * logit_guidance + (1.f - smooth_factor) * logit_base;
     }
 
     if (ctx) {

llama.h

@@ -88,7 +88,8 @@ extern "C" {
         int32_t  n_batch;                      // prompt processing batch size
         int32_t  n_gpu_layers;                 // number of layers to store in VRAM
         int32_t  main_gpu;                     // the GPU that is used for scratch and small tensors
-        float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
+
+        const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
         float    rope_freq_base;  // RoPE base frequency
@@ -343,13 +344,11 @@ extern "C" {
     /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
     /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
     /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
-    /// @params smooth_factor Smooth factor between guidance logits and original logits. 1.0f means only use guidance logits. 0.0f means only original logits.
     LLAMA_API void llama_sample_classifier_free_guidance(
               struct llama_context * ctx,
             llama_token_data_array * candidates,
               struct llama_context * guidance_ctx,
-                             float   scale,
+                             float   scale);
-                             float   smooth_factor);
 
     /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
     LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);