update to latest llama.cpp breaking API changes

Signed-off-by: mudler <mudler@localai.io>

Author: mudler

.github/workflows/test-gpu.yaml

@@ -45,10 +45,12 @@ jobs:
           sudo DEBIAN_FRONTEND=noninteractive apt-get install -y pip wget
       - name: Build and test
         run: |
+          set -o pipefail
           GPU_TESTS=true BUILD_TYPE=cublas CMAKE_ARGS="-DLLAMA_METAL=OFF -DLLAMA_F16C=OFF -DLLAMA_AVX512=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF" \
           make test 2>&1 | tee test_log.log
+          set +o pipefail
           if grep -q "using CUDA for GPU acceleration" test_log.log; then
-            echo "All good";
+            echo "GPU was used";
           else
             echo "No CUDA found";
             exit 1;

Makefile

@@ -232,7 +232,8 @@ binding.o: prepare llama.cpp/ggml.o llama.cpp/llama.o llama.cpp/common.o llama.c
 
 ## https://github.com/ggerganov/llama.cpp/pull/1902
 prepare:
-	cd llama.cpp && patch -p1 < ../patches/1902-cuda.patch
+	cd llama.cpp && \
+	patch -p1 < ../patches/1902-cuda.patch
 	touch $@
 
 libbinding.a: prepare binding.o llama.cpp/k_quants.o llama.cpp/grammar-parser.o llama.cpp/ggml-alloc.o $(EXTRA_TARGETS)
@@ -248,4 +249,4 @@ ggllm-test-model.bin:
 	wget -q https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGUF/resolve/main/codellama-7b-instruct.Q2_K.gguf -O ggllm-test-model.bin
 
 test: ggllm-test-model.bin libbinding.a
-	C_INCLUDE_PATH=${INCLUDE_PATH} CGO_LDFLAGS=${CGO_LDFLAGS} LIBRARY_PATH=${LIBRARY_PATH} TEST_MODEL=ggllm-test-model.bin go run github.com/onsi/ginkgo/v2/ginkgo --label-filter="$(TEST_LABEL)" --flake-attempts 5 -v -r ./...
+	C_INCLUDE_PATH=${INCLUDE_PATH} CGO_LDFLAGS=${CGO_LDFLAGS} LIBRARY_PATH=${LIBRARY_PATH} TEST_MODEL=$(abspath ./)/ggllm-test-model.bin go run github.com/onsi/ginkgo/v2/ginkgo --label-filter="$(TEST_LABEL)" -v -r ./...

binding.cpp

@@ -21,22 +21,21 @@ void* load_model(const char *fname,
                  bool mlock, 
                  bool embeddings, 
                  bool mmap, 
-                 bool low_vram, 
                  int n_gpu, 
                  int n_batch, 
                  const char *maingpu, 
                  const char *tensorsplit, 
                  bool numa, 
                  float rope_freq_base, 
                  float rope_freq_scale,
-                 bool mul_mat_q, const char *lora, const char *lora_base, bool perplexity
+                 bool mul_mat_q, const char *lora, const char *lora_base, float lora_scale, bool perplexity
                  );
 
 int get_embeddings(void* params_ptr, void* state_pr, float * res_embeddings);
 
 int get_token_embeddings(void* params_ptr, void* state_pr,  int *tokens, int tokenSize, float * res_embeddings);
 
-void* llama_allocate_params(const char *prompt, int seed, int threads, int tokens,
+void* llama_allocate_params(const char *prompt, int seed, int threads, int batch_threads, int tokens,
                             int top_k, float top_p, float temp, float repeat_penalty, 
                             int repeat_last_n, bool ignore_eos, bool memory_f16, 
                             int n_batch, int n_keep, const char** antiprompt, int antiprompt_count,

binding.h

@@ -38,10 +38,10 @@ func New(model string, opts ...ModelOption) (*LLama, error) {
 
 	result := C.load_model(modelPath,
 		C.int(mo.ContextSize), C.int(mo.Seed),
-		C.bool(mo.F16Memory), C.bool(mo.MLock), C.bool(mo.Embeddings), C.bool(mo.MMap), C.bool(mo.LowVRAM),
+		C.bool(mo.F16Memory), C.bool(mo.MLock), C.bool(mo.Embeddings), C.bool(mo.MMap),
 		C.int(mo.NGPULayers), C.int(mo.NBatch), C.CString(mo.MainGPU), C.CString(mo.TensorSplit), C.bool(mo.NUMA),
 		C.float(mo.FreqRopeBase), C.float(mo.FreqRopeScale),
-		C.bool(MulMatQ), loraAdapter, loraBase, C.bool(mo.Perplexity),
+		C.bool(MulMatQ), loraAdapter, loraBase, C.float(mo.LoraScale), C.bool(mo.Perplexity),
 	)
 
 	if result == nil {
@@ -112,7 +112,7 @@ func (l *LLama) TokenEmbeddings(tokens []int, opts ...PredictOption) ([]float32,
 	// float tfs_z, float typical_p, float frequency_penalty, float presence_penalty, int mirostat, float mirostat_eta, float mirostat_tau, bool penalize_nl, const char *logit_bias, const char *session_file, bool prompt_cache_all, bool mlock, bool mmap, const char *maingpu, const char *tensorsplit , bool prompt_cache_ro,
 	// float rope_freq_base, float rope_freq_scale, float negative_prompt_scale, const char* negative_prompt
 	// );
-	params := C.llama_allocate_params(C.CString(""), C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
+	params := C.llama_allocate_params(C.CString(""), C.int(po.Seed), C.int(po.Threads), C.int(po.BatchThreads), C.int(po.Tokens), C.int(po.TopK),
 		C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
 		C.bool(po.IgnoreEOS), C.bool(po.F16KV),
 		C.int(po.Batch), C.int(po.NKeep), nil, C.int(0),
@@ -154,7 +154,7 @@ func (l *LLama) Embeddings(text string, opts ...PredictOption) ([]float32, error
 		pass = &reversePrompt[0]
 	}
 
-	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
+	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.BatchThreads), C.int(po.Tokens), C.int(po.TopK),
 		C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
 		C.bool(po.IgnoreEOS), C.bool(po.F16KV),
 		C.int(po.Batch), C.int(po.NKeep), pass, C.int(reverseCount),
@@ -193,7 +193,7 @@ func (l *LLama) Eval(text string, opts ...PredictOption) error {
 		pass = &reversePrompt[0]
 	}
 
-	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
+	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.BatchThreads), C.int(po.Tokens), C.int(po.TopK),
 		C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
 		C.bool(po.IgnoreEOS), C.bool(po.F16KV),
 		C.int(po.Batch), C.int(po.NKeep), pass, C.int(reverseCount),
@@ -238,7 +238,7 @@ func (l *LLama) SpeculativeSampling(ll *LLama, text string, opts ...PredictOptio
 		pass = &reversePrompt[0]
 	}
 
-	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
+	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.BatchThreads), C.int(po.Tokens), C.int(po.TopK),
 		C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
 		C.bool(po.IgnoreEOS), C.bool(po.F16KV),
 		C.int(po.Batch), C.int(po.NKeep), pass, C.int(reverseCount),
@@ -296,7 +296,7 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
 		pass = &reversePrompt[0]
 	}
 
-	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
+	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.BatchThreads), C.int(po.Tokens), C.int(po.TopK),
 		C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
 		C.bool(po.IgnoreEOS), C.bool(po.F16KV),
 		C.int(po.Batch), C.int(po.NKeep), pass, C.int(reverseCount),
@@ -346,7 +346,7 @@ func (l *LLama) TokenizeString(text string, opts ...PredictOption) (int32, []int
 	var fakeDblPtr **C.char
 
 	// copy pasted and modified minimally. Should I simplify down / do we need an "allocate defaults"
-	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
+	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.BatchThreads), C.int(po.Tokens), C.int(po.TopK),
 		C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
 		C.bool(po.IgnoreEOS), C.bool(po.F16KV),
 		C.int(po.Batch), C.int(po.NKeep), fakeDblPtr, C.int(0),

llama.cpp

@@ -71,7 +71,7 @@ how much is 2+2?
 			Expect(err).ToNot(HaveOccurred())
 			Expect(model).ToNot(BeNil())
 			text, err := model.SpeculativeSampling(model2, `[INST] Answer to the following question:
-how much is 2+2?
+Do a simple math calculation: How much is 2+2?
 [/INST]`, llama.SetNDraft(16),
 			)
 			Expect(err).ToNot(HaveOccurred(), text)
@@ -97,7 +97,10 @@ how much is 2+2?
 		getModel := func() (*LLama, error) {
 			model, err := New(
 				testModelPath,
-				llama.EnableF16Memory, llama.SetContext(128), llama.EnableEmbeddings, llama.SetGPULayers(10),
+				llama.EnableF16Memory,
+				llama.SetContext(128),
+				llama.EnableEmbeddings,
+				llama.SetGPULayers(10),
 			)
 			Expect(err).ToNot(HaveOccurred())
 			Expect(model).ToNot(BeNil())

llama.go

@@ -7,7 +7,6 @@ type ModelOptions struct {
 	F16Memory     bool
 	MLock         bool
 	MMap          bool
-	LowVRAM       bool
 	Embeddings    bool
 	NUMA          bool
 	NGPULayers    int
@@ -16,6 +15,7 @@ type ModelOptions struct {
 	FreqRopeBase  float32
 	FreqRopeScale float32
 	MulMatQ       *bool
+	LoraScale     float32
 	LoraBase      string
 	LoraAdapter   string
 	Perplexity    bool
@@ -29,6 +29,7 @@ type PredictOptions struct {
 	DebugMode                                         bool
 	StopPrompts                                       []string
 	IgnoreEOS                                         bool
+	BatchThreads                                      int
 
 	TailFreeSamplingZ float32
 	TypicalP          float32
@@ -68,7 +69,6 @@ var DefaultModelOptions ModelOptions = ModelOptions{
 	MLock:         false,
 	Embeddings:    false,
 	MMap:          true,
-	LowVRAM:       false,
 	NBatch:        512,
 	FreqRopeBase:  10000,
 	FreqRopeScale: 1.0,
@@ -79,6 +79,7 @@ var DefaultOptions PredictOptions = PredictOptions{
 	Threads:           4,
 	Tokens:            128,
 	Penalty:           1.1,
+	BatchThreads:      -1,
 	Repeat:            64,
 	Batch:             512,
 	NKeep:             64,
@@ -109,6 +110,18 @@ func SetLoraBase(s string) ModelOption {
 	}
 }
 
+func SetBatchThreads(b int) PredictOption {
+	return func(p *PredictOptions) {
+		p.BatchThreads = b
+	}
+}
+
+func SetLoraScale(f float32) ModelOption {
+	return func(p *ModelOptions) {
+		p.LoraScale = f
+	}
+}
+
 func SetLoraAdapter(s string) ModelOption {
 	return func(p *ModelOptions) {
 		p.LoraAdapter = s
@@ -219,10 +232,6 @@ func SetNegativePrompt(np string) PredictOption {
 	}
 }
 
-var EnabelLowVRAM ModelOption = func(p *ModelOptions) {
-	p.LowVRAM = true
-}
-
 var EnableNUMA ModelOption = func(p *ModelOptions) {
 	p.NUMA = true
 }

llama_test.go

@@ -1,20 +1,25 @@
 diff --git a/common/common.cpp b/common/common.cpp
-index 2597ba0..e42ae73 100644
+index ec181c6..9ba699b 100644
 --- a/common/common.cpp
 +++ b/common/common.cpp
-@@ -1268,3 +1268,218 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
+@@ -1345,3 +1345,222 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
      fprintf(stream, "typical_p: %f # default: 1.0\n", params.typical_p);
      fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
  }
 +
-+gpt_params* create_gpt_params(const std::string& fname,const std::string& lora,const std::string& lora_base) {
++gpt_params* create_gpt_params(const std::string& fname,const std::string& lora,const std::string& lora_base, float lora_scale) {
 +   gpt_params* lparams = new gpt_params;
 +    fprintf(stderr, "%s: loading model %s\n", __func__, fname.c_str());
 +
 +    // Initialize the 'model' member with the 'fname' parameter
 +    lparams->model = fname;
 +    lparams->lora_base = lora_base;
-+    lparams->lora_adapter = lora;
++    if (lora_scale == 0 && !lora_base.empty()) {
++        lora_scale = 1.0f;
++    }
++    if (!lora.empty()) {
++        lparams->lora_adapter.push_back(std::make_tuple(lora, lora_scale));
++    }
 +    if (lparams->lora_adapter.empty()) {
 +        lparams->use_mmap = false;
 +    }
@@ -30,14 +35,14 @@ index 2597ba0..e42ae73 100644
 +    return lparams;
 +}
 +
-+void* load_binding_model(const char *fname, int n_ctx, int n_seed, bool memory_f16, bool mlock, bool embeddings, bool mmap, bool low_vram, int n_gpu_layers, int n_batch, const char *maingpu, const char *tensorsplit, bool numa,  float rope_freq_base, float rope_freq_scale, bool mul_mat_q, const char *lora, const char *lora_base, bool perplexity) {
++void* load_binding_model(const char *fname, int n_ctx, int n_seed, bool memory_f16, bool mlock, bool embeddings, bool mmap, int n_gpu_layers, int n_batch, const char *maingpu, const char *tensorsplit, bool numa,  float rope_freq_base, float rope_freq_scale, bool mul_mat_q, const char *lora, const char *lora_base, float lora_scale, bool logits_all) {
 +    // load the model
 +    gpt_params * lparams;
 +// Temporary workaround for https://github.com/go-skynet/go-llama.cpp/issues/218
 +#ifdef GGML_USE_CUBLAS
 +    lparams = create_gpt_params_cuda(fname);
 +#else
-+    lparams = create_gpt_params(fname, lora, lora_base);
++    lparams = create_gpt_params(fname, lora, lora_base, lora_scale);
 +#endif
 +    llama_model * model;
 +    llama_binding_state * state;
@@ -49,10 +54,8 @@ index 2597ba0..e42ae73 100644
 +    lparams->embedding  = embeddings;
 +    lparams->use_mlock  = mlock;
 +    lparams->n_gpu_layers = n_gpu_layers;
-+    lparams->perplexity = perplexity;
++    lparams->logits_all = logits_all;
 +    lparams->use_mmap = mmap;
-+
-+    lparams->low_vram = low_vram;
 +    if (rope_freq_base != 0.0f) {
 +        lparams->rope_freq_base = rope_freq_base;
 +    } else {
@@ -114,8 +117,9 @@ index 2597ba0..e42ae73 100644
 +                                   int   idx) {
 +
 +    struct gpt_params params = *g_params;
++
 +    const int n_ctx   = llama_n_ctx(ctx);
-+    const int n_vocab = llama_n_vocab(ctx);
++    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
 +
 +    const float   temp            = params.temp;
 +    const int32_t top_k           = params.top_k <= 0 ? n_vocab : params.top_k;
@@ -133,7 +137,7 @@ index 2597ba0..e42ae73 100644
 +
 +    llama_token id = 0;
 +
-+    float * logits = llama_get_logits(ctx) + idx * n_vocab;
++    float * logits = llama_get_logits_ith(ctx, idx);
 +
 +    // Apply params.logit_bias map
 +    for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
@@ -184,19 +188,19 @@ index 2597ba0..e42ae73 100644
 +        if (mirostat == 1) {
 +            static float mirostat_mu = 2.0f * mirostat_tau;
 +            const int mirostat_m = 100;
-+            llama_sample_temperature(ctx, &cur_p, temp);
++            llama_sample_temp(ctx, &cur_p, temp);
 +            id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
 +        } else if (mirostat == 2) {
 +            static float mirostat_mu = 2.0f * mirostat_tau;
-+            llama_sample_temperature(ctx, &cur_p, temp);
++            llama_sample_temp(ctx, &cur_p, temp);
 +            id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
 +        } else {
 +            // Temperature sampling
 +            llama_sample_top_k      (ctx, &cur_p, top_k, 1);
 +            llama_sample_tail_free  (ctx, &cur_p, tfs_z, 1);
 +            llama_sample_typical    (ctx, &cur_p, typical_p, 1);
 +            llama_sample_top_p      (ctx, &cur_p, top_p, 1);
-+            llama_sample_temperature(ctx, &cur_p, temp);
++            llama_sample_temp(ctx, &cur_p, temp);
 +
 +            {
 +                const int n_top = 10;
@@ -223,10 +227,10 @@ index 2597ba0..e42ae73 100644
 +}
 \ No newline at end of file
 diff --git a/common/common.h b/common/common.h
-index 18aea38..ca7a168 100644
+index 0e2d3fa..9992d2b 100644
 --- a/common/common.h
 +++ b/common/common.h
-@@ -209,3 +209,19 @@ std::string get_sortable_timestamp();
+@@ -221,3 +221,19 @@ std::string get_sortable_timestamp();
  void dump_non_result_info_yaml(
      FILE * stream, const gpt_params & params, const llama_context * lctx,
      const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
@@ -236,7 +240,7 @@ index 18aea38..ca7a168 100644
 +    llama_model * model;
 +};
 +
-+void* load_binding_model(const char *fname, int n_ctx, int n_seed, bool memory_f16, bool mlock, bool embeddings, bool mmap, bool low_vram, int n_gpu_layers, int n_batch, const char *maingpu, const char *tensorsplit, bool numa,  float rope_freq_base, float rope_freq_scale, bool mul_mat_q, const char *lora, const char *lora_base, bool perplexity);
++void* load_binding_model(const char *fname, int n_ctx, int n_seed, bool memory_f16, bool mlock, bool embeddings, bool mmap, int n_gpu_layers, int n_batch, const char *maingpu, const char *tensorsplit, bool numa,  float rope_freq_base, float rope_freq_scale, bool mul_mat_q, const char *lora, const char *lora_base, float lora_scale, bool logits_all);
 +
 +llama_token llama_sample_token_binding(
 +                  struct llama_context * ctx,