call llama.cpp directly from go
This commit is contained in:
Michael Yang
parent
a3ec1ec2a0
commit
fd4792ec56
16 changed files with 462 additions and 1291 deletions
13
.gitignore
vendored
13
.gitignore
vendored
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@ -8,3 +8,16 @@ dist
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__pycache__
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ollama
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ggml-metal.metal
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# cmake gitignore
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CMakeLists.txt.user
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CMakeCache.txt
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CMakeFiles
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CMakeScripts
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Testing
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Makefile
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cmake_install.cmake
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install_manifest.txt
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compile_commands.json
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CTestTestfile.cmake
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_deps
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43
CMakeLists.txt
Normal file
43
CMakeLists.txt
Normal file
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@ -0,0 +1,43 @@
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cmake_minimum_required(VERSION 3.12)
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project(ollama)
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include(FetchContent)
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FetchContent_Declare(
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"llama.cpp"
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GIT_REPOSITORY https://github.com/ggerganov/llama.cpp.git
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GIT_TAG 55dbb91
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)
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FetchContent_MakeAvailable(llama.cpp)
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add_custom_target(
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ollama
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ALL
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DEPENDS
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${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal
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COMMAND
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${CMAKE_COMMAND} -E
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env
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CGO_CPPFLAGS='-I${llama.cpp_SOURCE_DIR}'
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CGO_LDFLAGS='-L${llama.cpp_BINARY_DIR} -lllama -lggml_static -lm -lstdc++'
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CGO_CXXFLAGS='-std=c++11'
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--
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go build .
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WORKING_DIRECTORY
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${CMAKE_CURRENT_SOURCE_DIR}
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)
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add_custom_command(
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OUTPUT
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${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal
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COMMAND
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${CMAKE_COMMAND} -E
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copy_if_different
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${llama.cpp_SOURCE_DIR}/ggml-metal.metal
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${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal
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WORKING_DIRECTORY
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${CMAKE_CURRENT_SOURCE_DIR}
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)
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add_dependencies(ollama llama ggml_static)
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19
Makefile
19
Makefile
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@ -1,19 +0,0 @@
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default: ollama
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.PHONY: llama
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llama:
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cmake -S llama -B llama/build -DLLAMA_METAL=on
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cmake --build llama/build
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.PHONY: ollama
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ollama: llama
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go build .
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.PHONY: app
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app: ollama
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npm install --prefix app
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npm run --prefix app make:sign
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clean:
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go clean
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rm -rf llama/build
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155
api/types.go
155
api/types.go
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@ -1,5 +1,7 @@
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package api
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import "runtime"
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type PullRequest struct {
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Model string `json:"model"`
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}
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@ -14,93 +16,76 @@ type GenerateRequest struct {
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Model string `json:"model"`
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Prompt string `json:"prompt"`
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ModelOptions *ModelOptions `json:"model_opts,omitempty"`
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PredictOptions *PredictOptions `json:"predict_opts,omitempty"`
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}
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type ModelOptions struct {
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ContextSize int `json:"context_size,omitempty"`
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Seed int `json:"seed,omitempty"`
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NBatch int `json:"n_batch,omitempty"`
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F16Memory bool `json:"memory_f16,omitempty"`
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MLock bool `json:"mlock,omitempty"`
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MMap bool `json:"mmap,omitempty"`
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VocabOnly bool `json:"vocab_only,omitempty"`
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LowVRAM bool `json:"low_vram,omitempty"`
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Embeddings bool `json:"embeddings,omitempty"`
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NUMA bool `json:"numa,omitempty"`
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NGPULayers int `json:"gpu_layers,omitempty"`
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MainGPU string `json:"main_gpu,omitempty"`
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TensorSplit string `json:"tensor_split,omitempty"`
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}
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type PredictOptions struct {
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Seed int `json:"seed,omitempty"`
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Threads int `json:"threads,omitempty"`
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Tokens int `json:"tokens,omitempty"`
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TopK int `json:"top_k,omitempty"`
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Repeat int `json:"repeat,omitempty"`
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Batch int `json:"batch,omitempty"`
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NKeep int `json:"nkeep,omitempty"`
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TopP float64 `json:"top_p,omitempty"`
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Temperature float64 `json:"temp,omitempty"`
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Penalty float64 `json:"penalty,omitempty"`
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F16KV bool
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DebugMode bool
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StopPrompts []string
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IgnoreEOS bool `json:"ignore_eos,omitempty"`
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TailFreeSamplingZ float64 `json:"tfs_z,omitempty"`
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TypicalP float64 `json:"typical_p,omitempty"`
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FrequencyPenalty float64 `json:"freq_penalty,omitempty"`
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PresencePenalty float64 `json:"pres_penalty,omitempty"`
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Mirostat int `json:"mirostat,omitempty"`
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MirostatETA float64 `json:"mirostat_lr,omitempty"`
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MirostatTAU float64 `json:"mirostat_ent,omitempty"`
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PenalizeNL bool `json:"penalize_nl,omitempty"`
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LogitBias string `json:"logit_bias,omitempty"`
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PathPromptCache string
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MLock bool `json:"mlock,omitempty"`
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MMap bool `json:"mmap,omitempty"`
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PromptCacheAll bool
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PromptCacheRO bool
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MainGPU string
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TensorSplit string
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}
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var DefaultModelOptions ModelOptions = ModelOptions{
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ContextSize: 512,
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Seed: 0,
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F16Memory: true,
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MLock: false,
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Embeddings: true,
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MMap: true,
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LowVRAM: false,
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}
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var DefaultPredictOptions PredictOptions = PredictOptions{
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Seed: -1,
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Threads: -1,
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Tokens: 512,
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Penalty: 1.1,
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Repeat: 64,
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Batch: 512,
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NKeep: 64,
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TopK: 90,
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TopP: 0.86,
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TailFreeSamplingZ: 1.0,
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TypicalP: 1.0,
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Temperature: 0.8,
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FrequencyPenalty: 0.0,
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PresencePenalty: 0.0,
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Mirostat: 0,
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MirostatTAU: 5.0,
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MirostatETA: 0.1,
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MMap: true,
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StopPrompts: []string{"llama"},
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Options `json:"options"`
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}
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type GenerateResponse struct {
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Response string `json:"response"`
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}
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type Options struct {
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Seed int `json:"seed,omitempty"`
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// Backend options
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UseNUMA bool `json:"numa,omitempty"`
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// Model options
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NumCtx int `json:"num_ctx,omitempty"`
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NumBatch int `json:"num_batch,omitempty"`
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NumGPU int `json:"num_gpu,omitempty"`
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MainGPU int `json:"main_gpu,omitempty"`
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LowVRAM bool `json:"low_vram,omitempty"`
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F16KV bool `json:"f16_kv,omitempty"`
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LogitsAll bool `json:"logits_all,omitempty"`
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VocabOnly bool `json:"vocab_only,omitempty"`
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UseMMap bool `json:"use_mmap,omitempty"`
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UseMLock bool `json:"use_mlock,omitempty"`
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EmbeddingOnly bool `json:"embedding_only,omitempty"`
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// Predict options
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RepeatLastN int `json:"repeat_last_n,omitempty"`
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RepeatPenalty float32 `json:"repeat_penalty,omitempty"`
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FrequencyPenalty float32 `json:"frequency_penalty,omitempty"`
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PresencePenalty float32 `json:"presence_penalty,omitempty"`
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Temperature float32 `json:"temperature,omitempty"`
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TopK int `json:"top_k,omitempty"`
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TopP float32 `json:"top_p,omitempty"`
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TFSZ float32 `json:"tfs_z,omitempty"`
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TypicalP float32 `json:"typical_p,omitempty"`
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Mirostat int `json:"mirostat,omitempty"`
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MirostatTau float32 `json:"mirostat_tau,omitempty"`
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MirostatEta float32 `json:"mirostat_eta,omitempty"`
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NumThread int `json:"num_thread,omitempty"`
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}
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func DefaultOptions() Options {
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return Options{
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Seed: -1,
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UseNUMA: false,
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NumCtx: 512,
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NumBatch: 512,
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NumGPU: 1,
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LowVRAM: false,
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F16KV: true,
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UseMMap: true,
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UseMLock: false,
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RepeatLastN: 512,
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RepeatPenalty: 1.1,
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FrequencyPenalty: 0.0,
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PresencePenalty: 0.0,
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Temperature: 0.8,
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TopK: 40,
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TopP: 0.9,
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TFSZ: 1.0,
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TypicalP: 1.0,
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Mirostat: 0,
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MirostatTau: 5.0,
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MirostatEta: 0.1,
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NumThread: runtime.NumCPU(),
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}
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}
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1
go.mod
1
go.mod
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@ -39,6 +39,7 @@ require (
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golang.org/x/arch v0.3.0 // indirect
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golang.org/x/crypto v0.10.0 // indirect
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golang.org/x/net v0.10.0 // indirect
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golang.org/x/sync v0.3.0
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golang.org/x/sys v0.10.0 // indirect
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golang.org/x/term v0.10.0
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golang.org/x/text v0.10.0 // indirect
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2
go.sum
2
go.sum
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@ -99,6 +99,8 @@ golang.org/x/net v0.10.0/go.mod h1:0qNGK6F8kojg2nk9dLZ2mShWaEBan6FAoqfSigmmuDg=
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golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
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golang.org/x/sync v0.0.0-20220722155255-886fb9371eb4/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
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golang.org/x/sync v0.1.0/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
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golang.org/x/sync v0.3.0 h1:ftCYgMx6zT/asHUrPw8BLLscYtGznsLAnjq5RH9P66E=
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golang.org/x/sync v0.3.0/go.mod h1:FU7BRWz2tNW+3quACPkgCx/L+uEAv1htQ0V83Z9Rj+Y=
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golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
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golang.org/x/sys v0.0.0-20201119102817-f84b799fce68/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
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golang.org/x/sys v0.0.0-20210615035016-665e8c7367d1/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
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@ -1,23 +0,0 @@
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cmake_minimum_required(VERSION 3.12)
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project(binding)
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include(FetchContent)
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FetchContent_Declare(
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llama_cpp
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GIT_REPOSITORY https://github.com/ggerganov/llama.cpp.git
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GIT_TAG 55dbb91
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)
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FetchContent_MakeAvailable(llama_cpp)
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add_library(binding ${CMAKE_CURRENT_SOURCE_DIR}/binding/binding.cpp ${llama_cpp_SOURCE_DIR}/examples/common.cpp)
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target_include_directories(binding PRIVATE ${llama_cpp_SOURCE_DIR}/examples)
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target_link_libraries(binding llama ggml_static)
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if (LLAMA_METAL)
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configure_file(${llama_cpp_SOURCE_DIR}/ggml-metal.metal ${CMAKE_CURRENT_BINARY_DIR}/../../ggml-metal.metal COPYONLY)
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endif()
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add_custom_target(copy_libllama ALL COMMAND ${CMAKE_COMMAND} -E copy_if_different $<TARGET_FILE:llama> ${CMAKE_CURRENT_BINARY_DIR})
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add_custom_target(copy_libggml_static ALL COMMAND ${CMAKE_COMMAND} -E copy_if_different $<TARGET_FILE:ggml_static> ${CMAKE_CURRENT_BINARY_DIR})
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@ -1,705 +0,0 @@
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// MIT License
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// Copyright (c) 2023 go-skynet authors
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// Permission is hereby granted, free of charge, to any person obtaining a copy
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// of this software and associated documentation files (the "Software"), to deal
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// in the Software without restriction, including without limitation the rights
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// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the Software is
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// furnished to do so, subject to the following conditions:
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// The above copyright notice and this permission notice shall be included in all
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// copies or substantial portions of the Software.
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
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// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
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// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
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// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
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// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
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// SOFTWARE.
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#include "common.h"
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#include "llama.h"
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#include "binding.h"
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#include <cassert>
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#include <cinttypes>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <regex>
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#include <sstream>
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#include <string>
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#include <vector>
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#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
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#include <signal.h>
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#include <unistd.h>
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#elif defined(_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#define NOMINMAX
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#include <signal.h>
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#include <windows.h>
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#endif
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#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || \
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defined(_WIN32)
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void sigint_handler(int signo) {
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if (signo == SIGINT) {
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_exit(130);
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}
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}
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#endif
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int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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gpt_params params = *params_p;
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if (params.seed <= 0) {
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params.seed = time(NULL);
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}
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std::mt19937 rng(params.seed);
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llama_init_backend(params.numa);
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int n_past = 0;
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// Add a space in front of the first character to match OG llama tokenizer
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// behavior
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params.prompt.insert(0, 1, ' ');
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// tokenize the prompt
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auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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// determine newline token
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auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
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if (embd_inp.size() > 0) {
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if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past,
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params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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}
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const int n_embd = llama_n_embd(ctx);
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const auto embeddings = llama_get_embeddings(ctx);
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for (int i = 0; i < n_embd; i++) {
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res_embeddings[i] = embeddings[i];
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}
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return 0;
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}
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int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
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int tokenSize, float *res_embeddings) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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gpt_params params = *params_p;
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for (int i = 0; i < tokenSize; i++) {
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auto token_str = llama_token_to_str(ctx, tokens[i]);
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if (token_str == nullptr) {
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continue;
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}
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std::vector<std::string> my_vector;
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std::string str_token(token_str); // create a new std::string from the char*
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params_p->prompt += str_token;
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}
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return get_embeddings(params_ptr, state_pr, res_embeddings);
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}
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int eval(void *params_ptr, void *state_pr, char *text) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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auto n_past = 0;
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auto last_n_tokens_data =
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std::vector<llama_token>(params_p->repeat_last_n, 0);
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auto tokens = std::vector<llama_token>(params_p->n_ctx);
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auto n_prompt_tokens =
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llama_tokenize(ctx, text, tokens.data(), tokens.size(), true);
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if (n_prompt_tokens < 1) {
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fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
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return 1;
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}
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// evaluate prompt
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return llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past,
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params_p->n_threads);
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}
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int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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|
||||
gpt_params params = *params_p;
|
||||
|
||||
const int n_ctx = llama_n_ctx(ctx);
|
||||
|
||||
if (params.seed <= 0) {
|
||||
params.seed = time(NULL);
|
||||
}
|
||||
|
||||
std::mt19937 rng(params.seed);
|
||||
|
||||
std::string path_session = params.path_prompt_cache;
|
||||
std::vector<llama_token> session_tokens;
|
||||
|
||||
if (!path_session.empty()) {
|
||||
if (debug) {
|
||||
fprintf(stderr, "%s: attempting to load saved session from '%s'\n",
|
||||
__func__, path_session.c_str());
|
||||
}
|
||||
// fopen to check for existing session
|
||||
FILE *fp = std::fopen(path_session.c_str(), "rb");
|
||||
if (fp != NULL) {
|
||||
std::fclose(fp);
|
||||
|
||||
session_tokens.resize(n_ctx);
|
||||
size_t n_token_count_out = 0;
|
||||
if (!llama_load_session_file(
|
||||
ctx, path_session.c_str(), session_tokens.data(),
|
||||
session_tokens.capacity(), &n_token_count_out)) {
|
||||
fprintf(stderr, "%s: error: failed to load session file '%s'\n",
|
||||
__func__, path_session.c_str());
|
||||
return 1;
|
||||
}
|
||||
session_tokens.resize(n_token_count_out);
|
||||
llama_set_rng_seed(ctx, params.seed);
|
||||
if (debug) {
|
||||
fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n",
|
||||
__func__, (int)session_tokens.size());
|
||||
}
|
||||
} else {
|
||||
if (debug) {
|
||||
fprintf(stderr, "%s: session file does not exist, will create\n",
|
||||
__func__);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<llama_token> embd_inp;
|
||||
if (!params.prompt.empty() || session_tokens.empty()) {
|
||||
// Add a space in front of the first character to match OG llama tokenizer
|
||||
// behavior
|
||||
params.prompt.insert(0, 1, ' ');
|
||||
|
||||
embd_inp = ::llama_tokenize(ctx, params.prompt, true);
|
||||
} else {
|
||||
embd_inp = session_tokens;
|
||||
}
|
||||
|
||||
// debug message about similarity of saved session, if applicable
|
||||
size_t n_matching_session_tokens = 0;
|
||||
if (session_tokens.size()) {
|
||||
for (llama_token id : session_tokens) {
|
||||
if (n_matching_session_tokens >= embd_inp.size() ||
|
||||
id != embd_inp[n_matching_session_tokens]) {
|
||||
break;
|
||||
}
|
||||
n_matching_session_tokens++;
|
||||
}
|
||||
if (debug) {
|
||||
if (params.prompt.empty() &&
|
||||
n_matching_session_tokens == embd_inp.size()) {
|
||||
fprintf(stderr, "%s: using full prompt from session file\n", __func__);
|
||||
} else if (n_matching_session_tokens >= embd_inp.size()) {
|
||||
fprintf(stderr, "%s: session file has exact match for prompt!\n",
|
||||
__func__);
|
||||
} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
|
||||
fprintf(stderr,
|
||||
"%s: warning: session file has low similarity to prompt (%zu / "
|
||||
"%zu tokens); will mostly be reevaluated\n",
|
||||
__func__, n_matching_session_tokens, embd_inp.size());
|
||||
} else {
|
||||
fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
|
||||
__func__, n_matching_session_tokens, embd_inp.size());
|
||||
}
|
||||
}
|
||||
}
|
||||
// if we will use the cache for the full prompt without reaching the end of
|
||||
// the cache, force reevaluation of the last token token to recalculate the
|
||||
// cached logits
|
||||
if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
|
||||
session_tokens.size() > embd_inp.size()) {
|
||||
session_tokens.resize(embd_inp.size() - 1);
|
||||
}
|
||||
// number of tokens to keep when resetting context
|
||||
if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size()) {
|
||||
params.n_keep = (int)embd_inp.size();
|
||||
}
|
||||
|
||||
// determine newline token
|
||||
auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
|
||||
|
||||
// TODO: replace with ring-buffer
|
||||
std::vector<llama_token> last_n_tokens(n_ctx);
|
||||
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
|
||||
|
||||
bool need_to_save_session =
|
||||
!path_session.empty() && n_matching_session_tokens < embd_inp.size();
|
||||
int n_past = 0;
|
||||
int n_remain = params.n_predict;
|
||||
int n_consumed = 0;
|
||||
int n_session_consumed = 0;
|
||||
|
||||
std::vector<llama_token> embd;
|
||||
std::string res = "";
|
||||
|
||||
// do one empty run to warm up the model
|
||||
{
|
||||
const std::vector<llama_token> tmp = {
|
||||
llama_token_bos(),
|
||||
};
|
||||
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
|
||||
llama_reset_timings(ctx);
|
||||
}
|
||||
|
||||
while (n_remain != 0) {
|
||||
// predict
|
||||
if (embd.size() > 0) {
|
||||
// infinite text generation via context swapping
|
||||
// if we run out of context:
|
||||
// - take the n_keep first tokens from the original prompt (via n_past)
|
||||
// - take half of the last (n_ctx - n_keep) tokens and recompute the
|
||||
// logits in batches
|
||||
if (n_past + (int)embd.size() > n_ctx) {
|
||||
const int n_left = n_past - params.n_keep;
|
||||
|
||||
// always keep the first token - BOS
|
||||
n_past = std::max(1, params.n_keep);
|
||||
|
||||
// insert n_left/2 tokens at the start of embd from last_n_tokens
|
||||
embd.insert(embd.begin(),
|
||||
last_n_tokens.begin() + n_ctx - n_left / 2 - embd.size(),
|
||||
last_n_tokens.end() - embd.size());
|
||||
|
||||
// stop saving session if we run out of context
|
||||
path_session.clear();
|
||||
|
||||
// printf("\n---\n");
|
||||
// printf("resetting: '");
|
||||
// for (int i = 0; i < (int) embd.size(); i++) {
|
||||
// printf("%s", llama_token_to_str(ctx, embd[i]));
|
||||
// }
|
||||
// printf("'\n");
|
||||
// printf("\n---\n");
|
||||
}
|
||||
|
||||
// try to reuse a matching prefix from the loaded session instead of
|
||||
// re-eval (via n_past)
|
||||
if (n_session_consumed < (int)session_tokens.size()) {
|
||||
size_t i = 0;
|
||||
for (; i < embd.size(); i++) {
|
||||
if (embd[i] != session_tokens[n_session_consumed]) {
|
||||
session_tokens.resize(n_session_consumed);
|
||||
break;
|
||||
}
|
||||
|
||||
n_past++;
|
||||
n_session_consumed++;
|
||||
|
||||
if (n_session_consumed >= (int)session_tokens.size()) {
|
||||
++i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (i > 0) {
|
||||
embd.erase(embd.begin(), embd.begin() + i);
|
||||
}
|
||||
}
|
||||
|
||||
// evaluate tokens in batches
|
||||
// embd is typically prepared beforehand to fit within a batch, but not
|
||||
// always
|
||||
for (int i = 0; i < (int)embd.size(); i += params.n_batch) {
|
||||
int n_eval = (int)embd.size() - i;
|
||||
if (n_eval > params.n_batch) {
|
||||
n_eval = params.n_batch;
|
||||
}
|
||||
if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
|
||||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
n_past += n_eval;
|
||||
}
|
||||
|
||||
if (embd.size() > 0 && !path_session.empty()) {
|
||||
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
|
||||
n_session_consumed = session_tokens.size();
|
||||
}
|
||||
}
|
||||
|
||||
embd.clear();
|
||||
|
||||
if ((int)embd_inp.size() <= n_consumed) {
|
||||
// out of user input, sample next token
|
||||
const float temp = params.temp;
|
||||
const int32_t top_k =
|
||||
params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
|
||||
const float top_p = params.top_p;
|
||||
const float tfs_z = params.tfs_z;
|
||||
const float typical_p = params.typical_p;
|
||||
const int32_t repeat_last_n =
|
||||
params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
|
||||
const float repeat_penalty = params.repeat_penalty;
|
||||
const float alpha_presence = params.presence_penalty;
|
||||
const float alpha_frequency = params.frequency_penalty;
|
||||
const int mirostat = params.mirostat;
|
||||
const float mirostat_tau = params.mirostat_tau;
|
||||
const float mirostat_eta = params.mirostat_eta;
|
||||
const bool penalize_nl = params.penalize_nl;
|
||||
|
||||
// optionally save the session on first sample (for faster prompt loading
|
||||
// next time)
|
||||
if (!path_session.empty() && need_to_save_session &&
|
||||
!params.prompt_cache_ro) {
|
||||
need_to_save_session = false;
|
||||
llama_save_session_file(ctx, path_session.c_str(),
|
||||
session_tokens.data(), session_tokens.size());
|
||||
}
|
||||
|
||||
llama_token id = 0;
|
||||
|
||||
{
|
||||
auto logits = llama_get_logits(ctx);
|
||||
auto n_vocab = llama_n_vocab(ctx);
|
||||
|
||||
// Apply params.logit_bias map
|
||||
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end();
|
||||
it++) {
|
||||
logits[it->first] += it->second;
|
||||
}
|
||||
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
||||
candidates.emplace_back(
|
||||
llama_token_data{token_id, logits[token_id], 0.0f});
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = {candidates.data(),
|
||||
candidates.size(), false};
|
||||
|
||||
// Apply penalties
|
||||
float nl_logit = logits[llama_token_nl()];
|
||||
auto last_n_repeat =
|
||||
std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
|
||||
llama_sample_repetition_penalty(
|
||||
ctx, &candidates_p,
|
||||
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
|
||||
last_n_repeat, repeat_penalty);
|
||||
llama_sample_frequency_and_presence_penalties(
|
||||
ctx, &candidates_p,
|
||||
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
|
||||
last_n_repeat, alpha_frequency, alpha_presence);
|
||||
if (!penalize_nl) {
|
||||
logits[llama_token_nl()] = nl_logit;
|
||||
}
|
||||
|
||||
if (temp <= 0) {
|
||||
// Greedy sampling
|
||||
id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
} else {
|
||||
if (mirostat == 1) {
|
||||
static float mirostat_mu = 2.0f * mirostat_tau;
|
||||
const int mirostat_m = 100;
|
||||
llama_sample_temperature(ctx, &candidates_p, temp);
|
||||
id = llama_sample_token_mirostat(ctx, &candidates_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, &candidates_p, temp);
|
||||
id = llama_sample_token_mirostat_v2(
|
||||
ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
|
||||
} else {
|
||||
// Temperature sampling
|
||||
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
|
||||
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
|
||||
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
|
||||
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
|
||||
llama_sample_temperature(ctx, &candidates_p, temp);
|
||||
id = llama_sample_token(ctx, &candidates_p);
|
||||
}
|
||||
}
|
||||
// printf("`%d`", candidates_p.size);
|
||||
|
||||
last_n_tokens.erase(last_n_tokens.begin());
|
||||
last_n_tokens.push_back(id);
|
||||
}
|
||||
|
||||
// add it to the context
|
||||
embd.push_back(id);
|
||||
|
||||
// decrement remaining sampling budget
|
||||
--n_remain;
|
||||
|
||||
// call the token callback, no need to check if one is actually
|
||||
// registered, that will be handled on the Go side.
|
||||
auto token_str = llama_token_to_str(ctx, id);
|
||||
if (!tokenCallback(state_pr, (char *)token_str)) {
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
// some user input remains from prompt or interaction, forward it to
|
||||
// processing
|
||||
while ((int)embd_inp.size() > n_consumed) {
|
||||
embd.push_back(embd_inp[n_consumed]);
|
||||
last_n_tokens.erase(last_n_tokens.begin());
|
||||
last_n_tokens.push_back(embd_inp[n_consumed]);
|
||||
++n_consumed;
|
||||
if ((int)embd.size() >= params.n_batch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (auto id : embd) {
|
||||
res += llama_token_to_str(ctx, id);
|
||||
}
|
||||
|
||||
// check for stop prompt
|
||||
if (params.antiprompt.size()) {
|
||||
std::string last_output;
|
||||
for (auto id : last_n_tokens) {
|
||||
last_output += llama_token_to_str(ctx, id);
|
||||
}
|
||||
// Check if each of the reverse prompts appears at the end of the output.
|
||||
for (std::string &antiprompt : params.antiprompt) {
|
||||
// size_t extra_padding = params.interactive ? 0 : 2;
|
||||
size_t extra_padding = 2;
|
||||
size_t search_start_pos =
|
||||
last_output.length() >
|
||||
static_cast<size_t>(antiprompt.length() + extra_padding)
|
||||
? last_output.length() -
|
||||
static_cast<size_t>(antiprompt.length() + extra_padding)
|
||||
: 0;
|
||||
|
||||
if (last_output.find(antiprompt.c_str(), search_start_pos) !=
|
||||
std::string::npos) {
|
||||
goto end;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// end of text token
|
||||
if (!embd.empty() && embd.back() == llama_token_eos()) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (!path_session.empty() && params.prompt_cache_all &&
|
||||
!params.prompt_cache_ro) {
|
||||
if (debug) {
|
||||
fprintf(stderr, "\n%s: saving final output to session file '%s'\n",
|
||||
__func__, path_session.c_str());
|
||||
}
|
||||
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(),
|
||||
session_tokens.size());
|
||||
}
|
||||
|
||||
end:
|
||||
#if defined(_WIN32)
|
||||
signal(SIGINT, SIG_DFL);
|
||||
#endif
|
||||
|
||||
if (debug) {
|
||||
llama_print_timings(ctx);
|
||||
llama_reset_timings(ctx);
|
||||
}
|
||||
|
||||
strcpy(result, res.c_str());
|
||||
return 0;
|
||||
}
|
||||
|
||||
void llama_binding_free_model(void *state_ptr) {
|
||||
llama_context *ctx = (llama_context *)state_ptr;
|
||||
llama_free(ctx);
|
||||
}
|
||||
|
||||
void llama_free_params(void *params_ptr) {
|
||||
gpt_params *params = (gpt_params *)params_ptr;
|
||||
delete params;
|
||||
}
|
||||
|
||||
int load_state(void *ctx, char *statefile, char *modes) {
|
||||
llama_context *state = (llama_context *)ctx;
|
||||
const llama_context *constState = static_cast<const llama_context *>(state);
|
||||
const size_t state_size = llama_get_state_size(state);
|
||||
uint8_t *state_mem = new uint8_t[state_size];
|
||||
|
||||
{
|
||||
FILE *fp_read = fopen(statefile, modes);
|
||||
if (state_size != llama_get_state_size(constState)) {
|
||||
fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
const size_t ret = fread(state_mem, 1, state_size, fp_read);
|
||||
if (ret != state_size) {
|
||||
fprintf(stderr, "\n%s : failed to read state\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
llama_set_state_data(
|
||||
state, state_mem); // could also read directly from memory mapped file
|
||||
fclose(fp_read);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
void save_state(void *ctx, char *dst, char *modes) {
|
||||
llama_context *state = (llama_context *)ctx;
|
||||
|
||||
const size_t state_size = llama_get_state_size(state);
|
||||
uint8_t *state_mem = new uint8_t[state_size];
|
||||
|
||||
// Save state (rng, logits, embedding and kv_cache) to file
|
||||
{
|
||||
FILE *fp_write = fopen(dst, modes);
|
||||
llama_copy_state_data(
|
||||
state, state_mem); // could also copy directly to memory mapped file
|
||||
fwrite(state_mem, 1, state_size, fp_write);
|
||||
fclose(fp_write);
|
||||
}
|
||||
}
|
||||
|
||||
void *llama_allocate_params(
|
||||
const char *prompt, int seed, int 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, 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, bool mlock, bool mmap, const char *maingpu,
|
||||
const char *tensorsplit) {
|
||||
gpt_params *params = new gpt_params;
|
||||
params->seed = seed;
|
||||
params->n_threads = threads;
|
||||
params->n_predict = tokens;
|
||||
params->repeat_last_n = repeat_last_n;
|
||||
params->top_k = top_k;
|
||||
params->top_p = top_p;
|
||||
params->memory_f16 = memory_f16;
|
||||
params->temp = temp;
|
||||
params->use_mmap = mmap;
|
||||
params->use_mlock = mlock;
|
||||
params->repeat_penalty = repeat_penalty;
|
||||
params->n_batch = n_batch;
|
||||
params->n_keep = n_keep;
|
||||
if (maingpu[0] != '\0') {
|
||||
params->main_gpu = std::stoi(maingpu);
|
||||
}
|
||||
|
||||
if (tensorsplit[0] != '\0') {
|
||||
std::string arg_next = tensorsplit;
|
||||
// split string by , and /
|
||||
const std::regex regex{R"([,/]+)"};
|
||||
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
|
||||
std::vector<std::string> split_arg{it, {}};
|
||||
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
|
||||
|
||||
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
|
||||
if (i < split_arg.size()) {
|
||||
params->tensor_split[i] = std::stof(split_arg[i]);
|
||||
} else {
|
||||
params->tensor_split[i] = 0.0f;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (ignore_eos) {
|
||||
params->logit_bias[llama_token_eos()] = -INFINITY;
|
||||
}
|
||||
|
||||
for (int i = 0; i < antiprompt_count; i++) {
|
||||
params->antiprompt.push_back(antiprompt[i]);
|
||||
}
|
||||
|
||||
params->tfs_z = tfs_z;
|
||||
params->typical_p = typical_p;
|
||||
params->presence_penalty = presence_penalty;
|
||||
params->mirostat = mirostat;
|
||||
params->mirostat_eta = mirostat_eta;
|
||||
params->mirostat_tau = mirostat_tau;
|
||||
params->penalize_nl = penalize_nl;
|
||||
std::stringstream ss(logit_bias);
|
||||
llama_token key;
|
||||
char sign;
|
||||
std::string value_str;
|
||||
if (ss >> key && ss >> sign && std::getline(ss, value_str) &&
|
||||
(sign == '+' || sign == '-')) {
|
||||
params->logit_bias[key] =
|
||||
std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
|
||||
}
|
||||
params->frequency_penalty = frequency_penalty;
|
||||
params->prompt = prompt;
|
||||
|
||||
return params;
|
||||
}
|
||||
|
||||
void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
|
||||
bool mlock, bool embeddings, bool mmap, bool low_vram,
|
||||
bool vocab_only, int n_gpu_layers, int n_batch,
|
||||
const char *maingpu, const char *tensorsplit, bool numa) {
|
||||
// load the model
|
||||
auto lparams = llama_context_default_params();
|
||||
|
||||
lparams.n_ctx = n_ctx;
|
||||
lparams.seed = n_seed;
|
||||
lparams.f16_kv = memory_f16;
|
||||
lparams.embedding = embeddings;
|
||||
lparams.use_mlock = mlock;
|
||||
lparams.n_gpu_layers = n_gpu_layers;
|
||||
lparams.use_mmap = mmap;
|
||||
lparams.low_vram = low_vram;
|
||||
lparams.vocab_only = vocab_only;
|
||||
|
||||
if (maingpu[0] != '\0') {
|
||||
lparams.main_gpu = std::stoi(maingpu);
|
||||
}
|
||||
|
||||
if (tensorsplit[0] != '\0') {
|
||||
std::string arg_next = tensorsplit;
|
||||
// split string by , and /
|
||||
const std::regex regex{R"([,/]+)"};
|
||||
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
|
||||
std::vector<std::string> split_arg{it, {}};
|
||||
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
|
||||
|
||||
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
|
||||
if (i < split_arg.size()) {
|
||||
lparams.tensor_split[i] = std::stof(split_arg[i]);
|
||||
} else {
|
||||
lparams.tensor_split[i] = 0.0f;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
lparams.n_batch = n_batch;
|
||||
|
||||
llama_init_backend(numa);
|
||||
void *res = nullptr;
|
||||
try {
|
||||
res = llama_init_from_file(fname, lparams);
|
||||
} catch (std::runtime_error &e) {
|
||||
fprintf(stderr, "failed %s", e.what());
|
||||
return res;
|
||||
}
|
||||
|
||||
return res;
|
||||
}
|
||||
|
|
@ -1,69 +0,0 @@
|
|||
// MIT License
|
||||
|
||||
// Copyright (c) 2023 go-skynet authors
|
||||
|
||||
// Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
// of this software and associated documentation files (the "Software"), to deal
|
||||
// in the Software without restriction, including without limitation the rights
|
||||
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
// copies of the Software, and to permit persons to whom the Software is
|
||||
// furnished to do so, subject to the following conditions:
|
||||
|
||||
// The above copyright notice and this permission notice shall be included in all
|
||||
// copies or substantial portions of the Software.
|
||||
|
||||
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
// SOFTWARE.
|
||||
|
||||
#ifdef __cplusplus
|
||||
|
||||
extern "C" {
|
||||
|
||||
#endif
|
||||
|
||||
#include <stdbool.h>
|
||||
|
||||
extern unsigned char tokenCallback(void *, char *);
|
||||
|
||||
int load_state(void *ctx, char *statefile, char *modes);
|
||||
|
||||
int eval(void *params_ptr, void *ctx, char *text);
|
||||
|
||||
void save_state(void *ctx, char *dst, char *modes);
|
||||
|
||||
void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
|
||||
bool mlock, bool embeddings, bool mmap, bool low_vram,
|
||||
bool vocab_only, int n_gpu, int n_batch, const char *maingpu,
|
||||
const char *tensorsplit, bool numa);
|
||||
|
||||
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, 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, 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, bool mlock, bool mmap, const char *maingpu,
|
||||
const char *tensorsplit);
|
||||
|
||||
void llama_free_params(void *params_ptr);
|
||||
|
||||
void llama_binding_free_model(void *state);
|
||||
|
||||
int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug);
|
||||
|
||||
#ifdef __cplusplus
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
374
llama/llama.go
374
llama/llama.go
|
|
@ -1,215 +1,231 @@
|
|||
// MIT License
|
||||
|
||||
// Copyright (c) 2023 go-skynet authors
|
||||
|
||||
// Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
// of this software and associated documentation files (the "Software"), to deal
|
||||
// in the Software without restriction, including without limitation the rights
|
||||
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
// copies of the Software, and to permit persons to whom the Software is
|
||||
// furnished to do so, subject to the following conditions:
|
||||
|
||||
// The above copyright notice and this permission notice shall be included in all
|
||||
// copies or substantial portions of the Software.
|
||||
|
||||
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
// SOFTWARE.
|
||||
|
||||
package llama
|
||||
|
||||
// #cgo LDFLAGS: -Lbuild -lbinding -lllama -lm -lggml_static -lstdc++
|
||||
// #cgo CXXFLAGS: -std=c++11
|
||||
// #cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
|
||||
// #include "binding/binding.h"
|
||||
// #include <stdlib.h>
|
||||
import "C"
|
||||
/*
|
||||
#cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
|
||||
#include <stdlib.h>
|
||||
#include "llama.h"
|
||||
|
||||
struct llama_sample_options
|
||||
{
|
||||
float repeat_penalty;
|
||||
float frequency_penalty;
|
||||
float presence_penalty;
|
||||
float temperature;
|
||||
int32_t top_k;
|
||||
float top_p;
|
||||
float tfs_z;
|
||||
float typical_p;
|
||||
int mirostat;
|
||||
float mirostat_tau;
|
||||
float mirostat_eta;
|
||||
};
|
||||
|
||||
llama_token llama_sample(
|
||||
struct llama_context *ctx,
|
||||
struct llama_token_data *candidates,
|
||||
size_t n_candidates,
|
||||
const llama_token *last_tokens,
|
||||
size_t n_last_tokens,
|
||||
struct llama_sample_options *opts)
|
||||
{
|
||||
llama_token_data_array candidates_p = {
|
||||
candidates,
|
||||
n_candidates,
|
||||
false,
|
||||
};
|
||||
|
||||
llama_sample_repetition_penalty(
|
||||
ctx, &candidates_p,
|
||||
last_tokens, n_last_tokens,
|
||||
opts->repeat_penalty);
|
||||
|
||||
llama_sample_frequency_and_presence_penalties(
|
||||
ctx, &candidates_p,
|
||||
last_tokens, n_last_tokens,
|
||||
opts->frequency_penalty, opts->presence_penalty);
|
||||
|
||||
if (opts->temperature <= 0) {
|
||||
return llama_sample_token_greedy(ctx, &candidates_p);
|
||||
}
|
||||
|
||||
if (opts->mirostat == 1) {
|
||||
int mirostat_m = 100;
|
||||
float mirostat_mu = 2.0f * opts->mirostat_tau;
|
||||
llama_sample_temperature(ctx, &candidates_p, opts->temperature);
|
||||
return llama_sample_token_mirostat(
|
||||
ctx, &candidates_p,
|
||||
opts->mirostat_tau, opts->mirostat_eta,
|
||||
mirostat_m, &mirostat_mu);
|
||||
} else if (opts->mirostat == 2) {
|
||||
float mirostat_mu = 2.0f * opts->mirostat_tau;
|
||||
llama_sample_temperature(ctx, &candidates_p, opts->temperature);
|
||||
return llama_sample_token_mirostat_v2(
|
||||
ctx, &candidates_p,
|
||||
opts->mirostat_tau, opts->mirostat_eta,
|
||||
&mirostat_mu);
|
||||
} else {
|
||||
llama_sample_top_k(ctx, &candidates_p, opts->top_k, 1);
|
||||
llama_sample_tail_free(ctx, &candidates_p, opts->tfs_z, 1);
|
||||
llama_sample_typical(ctx, &candidates_p, opts->typical_p, 1);
|
||||
llama_sample_top_p(ctx, &candidates_p, opts->top_p, 1);
|
||||
llama_sample_temperature(ctx, &candidates_p, opts->temperature);
|
||||
return llama_sample_token(ctx, &candidates_p);
|
||||
}
|
||||
}
|
||||
*/
|
||||
import "C"
|
||||
import (
|
||||
"fmt"
|
||||
"errors"
|
||||
"io"
|
||||
"os"
|
||||
"strings"
|
||||
"sync"
|
||||
"unsafe"
|
||||
|
||||
"github.com/jmorganca/ollama/api"
|
||||
)
|
||||
|
||||
type LLama struct {
|
||||
ctx unsafe.Pointer
|
||||
embeddings bool
|
||||
contextSize int
|
||||
type llama struct {
|
||||
params *C.struct_llama_context_params
|
||||
model *C.struct_llama_model
|
||||
ctx *C.struct_llama_context
|
||||
|
||||
api.Options
|
||||
}
|
||||
|
||||
func New(model string, mo ModelOptions) (*LLama, error) {
|
||||
modelPath := C.CString(model)
|
||||
defer C.free(unsafe.Pointer(modelPath))
|
||||
|
||||
ctx := 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.VocabOnly), C.int(mo.NGPULayers), C.int(mo.NBatch), C.CString(mo.MainGPU), C.CString(mo.TensorSplit), C.bool(mo.NUMA))
|
||||
if ctx == nil {
|
||||
return nil, fmt.Errorf("failed loading model")
|
||||
func New(model string, opts api.Options) (*llama, error) {
|
||||
if _, err := os.Stat(model); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
ll := &LLama{ctx: ctx, contextSize: mo.ContextSize, embeddings: mo.Embeddings}
|
||||
llm := llama{Options: opts}
|
||||
|
||||
return ll, nil
|
||||
C.llama_init_backend(C.bool(llm.UseNUMA))
|
||||
|
||||
params := C.llama_context_default_params()
|
||||
params.seed = C.uint(llm.Seed)
|
||||
params.n_ctx = C.int(llm.NumCtx)
|
||||
params.n_batch = C.int(llm.NumBatch)
|
||||
params.n_gpu_layers = C.int(llm.NumGPU)
|
||||
params.main_gpu = C.int(llm.MainGPU)
|
||||
params.low_vram = C.bool(llm.LowVRAM)
|
||||
params.f16_kv = C.bool(llm.F16KV)
|
||||
params.logits_all = C.bool(llm.LogitsAll)
|
||||
params.vocab_only = C.bool(llm.VocabOnly)
|
||||
params.use_mmap = C.bool(llm.UseMMap)
|
||||
params.use_mlock = C.bool(llm.UseMLock)
|
||||
params.embedding = C.bool(llm.EmbeddingOnly)
|
||||
llm.params = ¶ms
|
||||
|
||||
cModel := C.CString(model)
|
||||
defer C.free(unsafe.Pointer(cModel))
|
||||
|
||||
llm.model = C.llama_load_model_from_file(cModel, params)
|
||||
llm.ctx = C.llama_new_context_with_model(llm.model, params)
|
||||
|
||||
// warm up the model
|
||||
bos := []C.llama_token{C.llama_token_bos()}
|
||||
C.llama_eval(llm.ctx, unsafe.SliceData(bos), C.int(len(bos)), 0, C.int(opts.NumThread))
|
||||
C.llama_reset_timings(llm.ctx)
|
||||
|
||||
return &llm, nil
|
||||
}
|
||||
|
||||
func (l *LLama) Free() {
|
||||
C.llama_binding_free_model(l.ctx)
|
||||
func (llm *llama) Close() {
|
||||
defer C.llama_free_model(llm.model)
|
||||
defer C.llama_free(llm.ctx)
|
||||
|
||||
C.llama_print_timings(llm.ctx)
|
||||
}
|
||||
|
||||
func (l *LLama) Eval(text string, po PredictOptions) error {
|
||||
input := C.CString(text)
|
||||
if po.Tokens == 0 {
|
||||
po.Tokens = 99999999
|
||||
}
|
||||
defer C.free(unsafe.Pointer(input))
|
||||
|
||||
reverseCount := len(po.StopPrompts)
|
||||
reversePrompt := make([]*C.char, reverseCount)
|
||||
var pass **C.char
|
||||
for i, s := range po.StopPrompts {
|
||||
cs := C.CString(s)
|
||||
reversePrompt[i] = cs
|
||||
pass = &reversePrompt[0]
|
||||
defer C.free(unsafe.Pointer(cs))
|
||||
func (llm *llama) Predict(prompt string, fn func(string)) error {
|
||||
if tokens := llm.tokenize(prompt); tokens != nil {
|
||||
return llm.generate(tokens, fn)
|
||||
}
|
||||
|
||||
cLogitBias := C.CString(po.LogitBias)
|
||||
defer C.free(unsafe.Pointer(cLogitBias))
|
||||
return errors.New("llama: tokenize")
|
||||
}
|
||||
|
||||
cMainGPU := C.CString(po.MainGPU)
|
||||
defer C.free(unsafe.Pointer(cMainGPU))
|
||||
func (llm *llama) tokenize(prompt string) []C.llama_token {
|
||||
cPrompt := C.CString(prompt)
|
||||
defer C.free(unsafe.Pointer(cPrompt))
|
||||
|
||||
cTensorSplit := C.CString(po.TensorSplit)
|
||||
defer C.free(unsafe.Pointer(cTensorSplit))
|
||||
|
||||
params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), 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),
|
||||
C.float(po.TailFreeSamplingZ), C.float(po.TypicalP), C.float(po.FrequencyPenalty), C.float(po.PresencePenalty),
|
||||
C.int(po.Mirostat), C.float(po.MirostatETA), C.float(po.MirostatTAU), C.bool(po.PenalizeNL), cLogitBias,
|
||||
C.bool(po.MLock), C.bool(po.MMap), cMainGPU, cTensorSplit,
|
||||
)
|
||||
defer C.llama_free_params(params)
|
||||
|
||||
ret := C.eval(params, l.ctx, input)
|
||||
if ret != 0 {
|
||||
return fmt.Errorf("inference failed")
|
||||
tokens := make([]C.llama_token, llm.NumCtx)
|
||||
if n := C.llama_tokenize(llm.ctx, cPrompt, unsafe.SliceData(tokens), C.int(len(tokens)), true); n > 0 {
|
||||
return tokens[:n]
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
func (l *LLama) Predict(text string, po PredictOptions) (string, error) {
|
||||
if po.TokenCallback != nil {
|
||||
setCallback(l.ctx, po.TokenCallback)
|
||||
func (llm *llama) detokenize(tokens ...C.llama_token) string {
|
||||
var sb strings.Builder
|
||||
for _, token := range tokens {
|
||||
sb.WriteString(C.GoString(C.llama_token_to_str(llm.ctx, token)))
|
||||
}
|
||||
|
||||
input := C.CString(text)
|
||||
if po.Tokens == 0 {
|
||||
po.Tokens = 99999999
|
||||
}
|
||||
defer C.free(unsafe.Pointer(input))
|
||||
|
||||
out := make([]byte, po.Tokens)
|
||||
|
||||
reverseCount := len(po.StopPrompts)
|
||||
reversePrompt := make([]*C.char, reverseCount)
|
||||
var pass **C.char
|
||||
for i, s := range po.StopPrompts {
|
||||
cs := C.CString(s)
|
||||
reversePrompt[i] = cs
|
||||
pass = &reversePrompt[0]
|
||||
defer C.free(unsafe.Pointer(cs))
|
||||
}
|
||||
|
||||
cLogitBias := C.CString(po.LogitBias)
|
||||
defer C.free(unsafe.Pointer(cLogitBias))
|
||||
|
||||
cMainGPU := C.CString(po.MainGPU)
|
||||
defer C.free(unsafe.Pointer(cMainGPU))
|
||||
|
||||
cTensorSplit := C.CString(po.TensorSplit)
|
||||
defer C.free(unsafe.Pointer(cTensorSplit))
|
||||
|
||||
params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), 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),
|
||||
C.float(po.TailFreeSamplingZ), C.float(po.TypicalP), C.float(po.FrequencyPenalty), C.float(po.PresencePenalty),
|
||||
C.int(po.Mirostat), C.float(po.MirostatETA), C.float(po.MirostatTAU), C.bool(po.PenalizeNL), cLogitBias,
|
||||
C.bool(po.MLock), C.bool(po.MMap), cMainGPU, cTensorSplit,
|
||||
)
|
||||
defer C.llama_free_params(params)
|
||||
|
||||
ret := C.llama_predict(params, l.ctx, (*C.char)(unsafe.Pointer(&out[0])), C.bool(po.DebugMode))
|
||||
if ret != 0 {
|
||||
return "", fmt.Errorf("inference failed")
|
||||
}
|
||||
res := C.GoString((*C.char)(unsafe.Pointer(&out[0])))
|
||||
|
||||
res = strings.TrimPrefix(res, " ")
|
||||
res = strings.TrimPrefix(res, text)
|
||||
res = strings.TrimPrefix(res, "\n")
|
||||
|
||||
for _, s := range po.StopPrompts {
|
||||
res = strings.TrimRight(res, s)
|
||||
}
|
||||
|
||||
if po.TokenCallback != nil {
|
||||
setCallback(l.ctx, nil)
|
||||
}
|
||||
|
||||
return res, nil
|
||||
return sb.String()
|
||||
}
|
||||
|
||||
// CGo only allows us to use static calls from C to Go, we can't just dynamically pass in func's.
|
||||
// This is the next best thing, we register the callbacks in this map and call tokenCallback from
|
||||
// the C code. We also attach a finalizer to LLama, so it will unregister the callback when the
|
||||
// garbage collection frees it.
|
||||
func (llm *llama) generate(tokens []C.llama_token, fn func(string)) error {
|
||||
var opts C.struct_llama_sample_options
|
||||
opts.repeat_penalty = C.float(llm.RepeatPenalty)
|
||||
opts.frequency_penalty = C.float(llm.FrequencyPenalty)
|
||||
opts.presence_penalty = C.float(llm.PresencePenalty)
|
||||
opts.temperature = C.float(llm.Temperature)
|
||||
opts.top_k = C.int(llm.TopK)
|
||||
opts.top_p = C.float(llm.TopP)
|
||||
opts.tfs_z = C.float(llm.TFSZ)
|
||||
opts.typical_p = C.float(llm.TypicalP)
|
||||
opts.mirostat = C.int(llm.Mirostat)
|
||||
opts.mirostat_tau = C.float(llm.MirostatTau)
|
||||
opts.mirostat_eta = C.float(llm.MirostatEta)
|
||||
|
||||
// SetTokenCallback registers a callback for the individual tokens created when running Predict. It
|
||||
// will be called once for each token. The callback shall return true as long as the model should
|
||||
// continue predicting the next token. When the callback returns false the predictor will return.
|
||||
// The tokens are just converted into Go strings, they are not trimmed or otherwise changed. Also
|
||||
// the tokens may not be valid UTF-8.
|
||||
// Pass in nil to remove a callback.
|
||||
//
|
||||
// It is save to call this method while a prediction is running.
|
||||
func (l *LLama) SetTokenCallback(callback func(token string) bool) {
|
||||
setCallback(l.ctx, callback)
|
||||
}
|
||||
pastTokens := deque[C.llama_token]{capacity: llm.RepeatLastN}
|
||||
|
||||
var (
|
||||
m sync.Mutex
|
||||
callbacks = map[uintptr]func(string) bool{}
|
||||
)
|
||||
|
||||
//export tokenCallback
|
||||
func tokenCallback(statePtr unsafe.Pointer, token *C.char) bool {
|
||||
m.Lock()
|
||||
defer m.Unlock()
|
||||
|
||||
if callback, ok := callbacks[uintptr(statePtr)]; ok {
|
||||
return callback(C.GoString(token))
|
||||
for C.llama_get_kv_cache_token_count(llm.ctx) < C.int(llm.NumCtx) {
|
||||
if retval := C.llama_eval(llm.ctx, unsafe.SliceData(tokens), C.int(len(tokens)), C.llama_get_kv_cache_token_count(llm.ctx), C.int(llm.NumThread)); retval != 0 {
|
||||
return errors.New("llama: eval")
|
||||
}
|
||||
|
||||
return true
|
||||
}
|
||||
|
||||
// setCallback can be used to register a token callback for LLama. Pass in a nil callback to
|
||||
// remove the callback.
|
||||
func setCallback(statePtr unsafe.Pointer, callback func(string) bool) {
|
||||
m.Lock()
|
||||
defer m.Unlock()
|
||||
|
||||
if callback == nil {
|
||||
delete(callbacks, uintptr(statePtr))
|
||||
} else {
|
||||
callbacks[uintptr(statePtr)] = callback
|
||||
token, err := llm.sample(pastTokens, &opts)
|
||||
switch {
|
||||
case err != nil:
|
||||
return err
|
||||
case errors.Is(err, io.EOF):
|
||||
return nil
|
||||
}
|
||||
|
||||
fn(llm.detokenize(token))
|
||||
|
||||
tokens = []C.llama_token{token}
|
||||
|
||||
pastTokens.PushLeft(token)
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
func (llm *llama) sample(pastTokens deque[C.llama_token], opts *C.struct_llama_sample_options) (C.llama_token, error) {
|
||||
numVocab := int(C.llama_n_vocab(llm.ctx))
|
||||
logits := unsafe.Slice(C.llama_get_logits(llm.ctx), numVocab)
|
||||
|
||||
candidates := make([]C.struct_llama_token_data, 0, numVocab)
|
||||
for i := 0; i < numVocab; i++ {
|
||||
candidates = append(candidates, C.llama_token_data{
|
||||
id: C.int(i),
|
||||
logit: logits[i],
|
||||
p: 0,
|
||||
})
|
||||
}
|
||||
|
||||
token := C.llama_sample(
|
||||
llm.ctx,
|
||||
unsafe.SliceData(candidates), C.ulong(len(candidates)),
|
||||
unsafe.SliceData(pastTokens.Data()), C.ulong(pastTokens.Len()),
|
||||
opts)
|
||||
if token != C.llama_token_eos() {
|
||||
return token, nil
|
||||
}
|
||||
|
||||
return 0, io.EOF
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,9 +0,0 @@
|
|||
//go:build cublas
|
||||
// +build cublas
|
||||
|
||||
package llama
|
||||
|
||||
/*
|
||||
#cgo LDFLAGS: -lcublas -lcudart -L/usr/local/cuda/lib64/
|
||||
*/
|
||||
import "C"
|
||||
|
|
@ -1,2 +0,0 @@
|
|||
//go:build metal
|
||||
package llama
|
||||
|
|
@ -1,9 +0,0 @@
|
|||
//go:build openblas
|
||||
// +build openblas
|
||||
|
||||
package llama
|
||||
|
||||
/*
|
||||
#cgo LDFLAGS: -lopenblas
|
||||
*/
|
||||
import "C"
|
||||
|
|
@ -1,98 +0,0 @@
|
|||
// MIT License
|
||||
|
||||
// Copyright (c) 2023 go-skynet authors
|
||||
|
||||
// Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
// of this software and associated documentation files (the "Software"), to deal
|
||||
// in the Software without restriction, including without limitation the rights
|
||||
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
// copies of the Software, and to permit persons to whom the Software is
|
||||
// furnished to do so, subject to the following conditions:
|
||||
|
||||
// The above copyright notice and this permission notice shall be included in all
|
||||
// copies or substantial portions of the Software.
|
||||
|
||||
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
// SOFTWARE.
|
||||
|
||||
package llama
|
||||
|
||||
type ModelOptions struct {
|
||||
ContextSize int
|
||||
Seed int
|
||||
NBatch int
|
||||
F16Memory bool
|
||||
MLock bool
|
||||
MMap bool
|
||||
VocabOnly bool
|
||||
LowVRAM bool
|
||||
Embeddings bool
|
||||
NUMA bool
|
||||
NGPULayers int
|
||||
MainGPU string
|
||||
TensorSplit string
|
||||
}
|
||||
|
||||
type PredictOptions struct {
|
||||
Seed, Threads, Tokens, TopK, Repeat, Batch, NKeep int
|
||||
TopP, Temperature, Penalty float64
|
||||
F16KV bool
|
||||
DebugMode bool
|
||||
StopPrompts []string
|
||||
IgnoreEOS bool
|
||||
|
||||
TailFreeSamplingZ float64
|
||||
TypicalP float64
|
||||
FrequencyPenalty float64
|
||||
PresencePenalty float64
|
||||
Mirostat int
|
||||
MirostatETA float64
|
||||
MirostatTAU float64
|
||||
PenalizeNL bool
|
||||
LogitBias string
|
||||
TokenCallback func(string) bool
|
||||
|
||||
MLock, MMap bool
|
||||
MainGPU string
|
||||
TensorSplit string
|
||||
}
|
||||
|
||||
type PredictOption func(p *PredictOptions)
|
||||
|
||||
type ModelOption func(p *ModelOptions)
|
||||
|
||||
var DefaultModelOptions ModelOptions = ModelOptions{
|
||||
ContextSize: 512,
|
||||
Seed: 0,
|
||||
F16Memory: false,
|
||||
MLock: false,
|
||||
Embeddings: false,
|
||||
MMap: true,
|
||||
LowVRAM: false,
|
||||
}
|
||||
|
||||
var DefaultOptions PredictOptions = PredictOptions{
|
||||
Seed: -1,
|
||||
Threads: 4,
|
||||
Tokens: 128,
|
||||
Penalty: 1.1,
|
||||
Repeat: 64,
|
||||
Batch: 512,
|
||||
NKeep: 64,
|
||||
TopK: 40,
|
||||
TopP: 0.95,
|
||||
TailFreeSamplingZ: 1.0,
|
||||
TypicalP: 1.0,
|
||||
Temperature: 0.8,
|
||||
FrequencyPenalty: 0.0,
|
||||
PresencePenalty: 0.0,
|
||||
Mirostat: 0,
|
||||
MirostatTAU: 5.0,
|
||||
MirostatETA: 0.1,
|
||||
MMap: true,
|
||||
}
|
||||
104
llama/utils.go
Normal file
104
llama/utils.go
Normal file
|
|
@ -0,0 +1,104 @@
|
|||
package llama
|
||||
|
||||
type node[T any] struct {
|
||||
t T
|
||||
next *node[T]
|
||||
prev *node[T]
|
||||
}
|
||||
|
||||
type deque[T any] struct {
|
||||
head *node[T]
|
||||
tail *node[T]
|
||||
size int
|
||||
capacity int
|
||||
}
|
||||
|
||||
func (d *deque[T]) Empty() bool {
|
||||
return d.size == 0
|
||||
}
|
||||
|
||||
func (d *deque[T]) Len() int {
|
||||
return d.size
|
||||
}
|
||||
|
||||
func (d *deque[T]) Cap() int {
|
||||
return d.capacity
|
||||
}
|
||||
|
||||
func (d *deque[T]) Push(t T) {
|
||||
if d.capacity > 0 && d.size >= d.capacity {
|
||||
d.PopLeft()
|
||||
}
|
||||
|
||||
n := node[T]{t: t}
|
||||
if d.head != nil {
|
||||
n.next = d.head
|
||||
d.head.prev = &n
|
||||
d.head = &n
|
||||
} else {
|
||||
d.head = &n
|
||||
d.tail = &n
|
||||
}
|
||||
|
||||
d.size++
|
||||
}
|
||||
|
||||
func (d *deque[T]) PushLeft(t T) {
|
||||
if d.capacity > 0 && d.size >= d.capacity {
|
||||
d.Pop()
|
||||
}
|
||||
|
||||
n := node[T]{t: t}
|
||||
if d.tail != nil {
|
||||
n.prev = d.tail
|
||||
d.tail.next = &n
|
||||
d.tail = &n
|
||||
} else {
|
||||
d.head = &n
|
||||
d.tail = &n
|
||||
}
|
||||
|
||||
d.size++
|
||||
}
|
||||
|
||||
func (d *deque[T]) Pop() *T {
|
||||
if d.Empty() {
|
||||
return nil
|
||||
}
|
||||
|
||||
head := d.head
|
||||
d.head = head.next
|
||||
if d.head != nil {
|
||||
d.head.prev = nil
|
||||
} else {
|
||||
d.tail = nil
|
||||
}
|
||||
|
||||
d.size--
|
||||
return &head.t
|
||||
}
|
||||
|
||||
func (d *deque[T]) PopLeft() *T {
|
||||
if d.Empty() {
|
||||
return nil
|
||||
}
|
||||
|
||||
tail := d.tail
|
||||
d.tail = tail.prev
|
||||
if d.tail != nil {
|
||||
d.tail.next = nil
|
||||
} else {
|
||||
d.head = nil
|
||||
}
|
||||
|
||||
d.size--
|
||||
return &tail.t
|
||||
}
|
||||
|
||||
func (d *deque[T]) Data() (data []T) {
|
||||
for n := d.head; n != nil; n = n.next {
|
||||
data = append(data, n.t)
|
||||
}
|
||||
|
||||
return data
|
||||
}
|
||||
105
server/routes.go
105
server/routes.go
|
|
@ -11,12 +11,12 @@ import (
|
|||
"net/http"
|
||||
"os"
|
||||
"path"
|
||||
"runtime"
|
||||
"strings"
|
||||
"text/template"
|
||||
|
||||
"github.com/gin-gonic/gin"
|
||||
"github.com/lithammer/fuzzysearch/fuzzy"
|
||||
"golang.org/x/sync/errgroup"
|
||||
|
||||
"github.com/jmorganca/ollama/api"
|
||||
"github.com/jmorganca/ollama/llama"
|
||||
|
|
@ -36,14 +36,10 @@ func cacheDir() string {
|
|||
}
|
||||
|
||||
func generate(c *gin.Context) {
|
||||
var req api.GenerateRequest
|
||||
if req.ModelOptions == nil {
|
||||
req.ModelOptions = &api.DefaultModelOptions
|
||||
req := api.GenerateRequest{
|
||||
Options: api.DefaultOptions(),
|
||||
}
|
||||
|
||||
if req.PredictOptions == nil {
|
||||
req.PredictOptions = &api.DefaultPredictOptions
|
||||
}
|
||||
if err := c.ShouldBindJSON(&req); err != nil {
|
||||
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
|
||||
return
|
||||
|
|
@ -60,15 +56,12 @@ func generate(c *gin.Context) {
|
|||
req.Model = path.Join(cacheDir(), "models", req.Model+".bin")
|
||||
}
|
||||
|
||||
modelOpts := getModelOpts(req)
|
||||
modelOpts.NGPULayers = 1 // hard-code this for now
|
||||
|
||||
model, err := llama.New(req.Model, modelOpts)
|
||||
llm, err := llama.New(req.Model, req.Options)
|
||||
if err != nil {
|
||||
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
|
||||
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
|
||||
return
|
||||
}
|
||||
defer model.Free()
|
||||
defer llm.Close()
|
||||
|
||||
templateNames := make([]string, 0, len(templates.Templates()))
|
||||
for _, template := range templates.Templates() {
|
||||
|
|
@ -87,32 +80,22 @@ func generate(c *gin.Context) {
|
|||
}
|
||||
|
||||
ch := make(chan string)
|
||||
model.SetTokenCallback(func(token string) bool {
|
||||
ch <- token
|
||||
return true
|
||||
g, _ := errgroup.WithContext(c.Request.Context())
|
||||
g.Go(func() error {
|
||||
defer close(ch)
|
||||
return llm.Predict(req.Prompt, func(s string) {
|
||||
ch <- s
|
||||
})
|
||||
})
|
||||
|
||||
predictOpts := getPredictOpts(req)
|
||||
|
||||
go func() {
|
||||
defer close(ch)
|
||||
_, err := model.Predict(req.Prompt, predictOpts)
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
}()
|
||||
|
||||
g.Go(func() error {
|
||||
c.Stream(func(w io.Writer) bool {
|
||||
token, ok := <-ch
|
||||
s, ok := <-ch
|
||||
if !ok {
|
||||
return false
|
||||
}
|
||||
|
||||
resp := api.GenerateResponse{
|
||||
Response: token,
|
||||
}
|
||||
|
||||
bts, err := json.Marshal(resp)
|
||||
bts, err := json.Marshal(api.GenerateResponse{Response: s})
|
||||
if err != nil {
|
||||
return false
|
||||
}
|
||||
|
|
@ -124,6 +107,14 @@ func generate(c *gin.Context) {
|
|||
|
||||
return true
|
||||
})
|
||||
|
||||
return nil
|
||||
})
|
||||
|
||||
if err := g.Wait(); err != nil && !errors.Is(err, io.EOF) {
|
||||
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
|
||||
return
|
||||
}
|
||||
}
|
||||
|
||||
func Serve(ln net.Listener) error {
|
||||
|
|
@ -195,53 +186,3 @@ func matchRankOne(source string, targets []string) (bestMatch string, bestRank i
|
|||
|
||||
return
|
||||
}
|
||||
|
||||
func getModelOpts(req api.GenerateRequest) llama.ModelOptions {
|
||||
var opts llama.ModelOptions
|
||||
opts.ContextSize = req.ModelOptions.ContextSize
|
||||
opts.Seed = req.ModelOptions.Seed
|
||||
opts.F16Memory = req.ModelOptions.F16Memory
|
||||
opts.MLock = req.ModelOptions.MLock
|
||||
opts.Embeddings = req.ModelOptions.Embeddings
|
||||
opts.MMap = req.ModelOptions.MMap
|
||||
opts.LowVRAM = req.ModelOptions.LowVRAM
|
||||
|
||||
opts.NBatch = req.ModelOptions.NBatch
|
||||
opts.VocabOnly = req.ModelOptions.VocabOnly
|
||||
opts.NUMA = req.ModelOptions.NUMA
|
||||
opts.NGPULayers = req.ModelOptions.NGPULayers
|
||||
opts.MainGPU = req.ModelOptions.MainGPU
|
||||
opts.TensorSplit = req.ModelOptions.TensorSplit
|
||||
|
||||
return opts
|
||||
}
|
||||
|
||||
func getPredictOpts(req api.GenerateRequest) llama.PredictOptions {
|
||||
var opts llama.PredictOptions
|
||||
|
||||
if req.PredictOptions.Threads == -1 {
|
||||
opts.Threads = runtime.NumCPU()
|
||||
} else {
|
||||
opts.Threads = req.PredictOptions.Threads
|
||||
}
|
||||
|
||||
opts.Seed = req.PredictOptions.Seed
|
||||
opts.Tokens = req.PredictOptions.Tokens
|
||||
opts.Penalty = req.PredictOptions.Penalty
|
||||
opts.Repeat = req.PredictOptions.Repeat
|
||||
opts.Batch = req.PredictOptions.Batch
|
||||
opts.NKeep = req.PredictOptions.NKeep
|
||||
opts.TopK = req.PredictOptions.TopK
|
||||
opts.TopP = req.PredictOptions.TopP
|
||||
opts.TailFreeSamplingZ = req.PredictOptions.TailFreeSamplingZ
|
||||
opts.TypicalP = req.PredictOptions.TypicalP
|
||||
opts.Temperature = req.PredictOptions.Temperature
|
||||
opts.FrequencyPenalty = req.PredictOptions.FrequencyPenalty
|
||||
opts.PresencePenalty = req.PredictOptions.PresencePenalty
|
||||
opts.Mirostat = req.PredictOptions.Mirostat
|
||||
opts.MirostatTAU = req.PredictOptions.MirostatTAU
|
||||
opts.MirostatETA = req.PredictOptions.MirostatETA
|
||||
opts.MMap = req.PredictOptions.MMap
|
||||
|
||||
return opts
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in a new issue