mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 22:59:24 +01:00
209 lines
6.9 KiB
Swift
209 lines
6.9 KiB
Swift
import Foundation
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// import llama
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enum LlamaError: Error {
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case couldNotInitializeContext
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}
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actor LlamaContext {
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private var model: OpaquePointer
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private var context: OpaquePointer
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private var batch: llama_batch
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private var tokens_list: [llama_token]
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/// This variable is used to store temporarily invalid cchars
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private var temporary_invalid_cchars: [CChar]
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var n_len: Int32 = 512
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var n_cur: Int32 = 0
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var n_decode: Int32 = 0
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init(model: OpaquePointer, context: OpaquePointer) {
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self.model = model
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self.context = context
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self.tokens_list = []
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self.batch = llama_batch_init(512, 0, 1)
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self.temporary_invalid_cchars = []
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}
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deinit {
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llama_free(context)
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llama_free_model(model)
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llama_backend_free()
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}
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static func createContext(path: String) throws -> LlamaContext {
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llama_backend_init(false)
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let model_params = llama_model_default_params()
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let model = llama_load_model_from_file(path, model_params)
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guard let model else {
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print("Could not load model at \(path)")
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throw LlamaError.couldNotInitializeContext
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}
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var ctx_params = llama_context_default_params()
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ctx_params.seed = 1234
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ctx_params.n_ctx = 2048
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ctx_params.n_threads = 8
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ctx_params.n_threads_batch = 8
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let context = llama_new_context_with_model(model, ctx_params)
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guard let context else {
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print("Could not load context!")
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throw LlamaError.couldNotInitializeContext
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}
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return LlamaContext(model: model, context: context)
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}
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func get_n_tokens() -> Int32 {
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return batch.n_tokens;
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}
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func completion_init(text: String) {
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print("attempting to complete \"\(text)\"")
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tokens_list = tokenize(text: text, add_bos: true)
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temporary_invalid_cchars = []
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let n_ctx = llama_n_ctx(context)
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let n_kv_req = tokens_list.count + (Int(n_len) - tokens_list.count)
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print("\n n_len = \(n_len), n_ctx = \(n_ctx), n_kv_req = \(n_kv_req)")
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if n_kv_req > n_ctx {
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print("error: n_kv_req > n_ctx, the required KV cache size is not big enough")
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}
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for id in tokens_list {
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print(String(cString: token_to_piece(token: id) + [0]))
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}
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// batch = llama_batch_init(512, 0) // done in init()
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batch.n_tokens = Int32(tokens_list.count)
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for i1 in 0..<batch.n_tokens {
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let i = Int(i1)
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batch.token[i] = tokens_list[i]
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batch.pos[i] = i1
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batch.n_seq_id[Int(i)] = 1
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batch.seq_id[Int(i)]![0] = 0
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batch.logits[i] = 0
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}
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batch.logits[Int(batch.n_tokens) - 1] = 1 // true
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if llama_decode(context, batch) != 0 {
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print("llama_decode() failed")
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}
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n_cur = batch.n_tokens
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}
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func completion_loop() -> String {
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var new_token_id: llama_token = 0
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let n_vocab = llama_n_vocab(model)
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let logits = llama_get_logits_ith(context, batch.n_tokens - 1)
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var candidates = Array<llama_token_data>()
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candidates.reserveCapacity(Int(n_vocab))
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for token_id in 0..<n_vocab {
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candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0))
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}
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candidates.withUnsafeMutableBufferPointer() { buffer in
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var candidates_p = llama_token_data_array(data: buffer.baseAddress, size: buffer.count, sorted: false)
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new_token_id = llama_sample_token_greedy(context, &candidates_p)
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}
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if new_token_id == llama_token_eos(context) || n_cur == n_len {
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print("\n")
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let new_token_str = String(cString: temporary_invalid_cchars + [0])
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temporary_invalid_cchars.removeAll()
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return new_token_str
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}
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let new_token_cchars = token_to_piece(token: new_token_id)
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temporary_invalid_cchars.append(contentsOf: new_token_cchars)
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let new_token_str: String
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if let string = String(validatingUTF8: temporary_invalid_cchars + [0]) {
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temporary_invalid_cchars.removeAll()
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new_token_str = string
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} else if (0 ..< temporary_invalid_cchars.count).contains(where: {$0 != 0 && String(validatingUTF8: Array(temporary_invalid_cchars.suffix($0)) + [0]) != nil}) {
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// in this case, at least the suffix of the temporary_invalid_cchars can be interpreted as UTF8 string
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let string = String(cString: temporary_invalid_cchars + [0])
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temporary_invalid_cchars.removeAll()
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new_token_str = string
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} else {
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new_token_str = ""
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}
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print(new_token_str)
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// tokens_list.append(new_token_id)
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batch.n_tokens = 0
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batch.token[Int(batch.n_tokens)] = new_token_id
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batch.pos[Int(batch.n_tokens)] = n_cur
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batch.n_seq_id[Int(batch.n_tokens)] = 1
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batch.seq_id[Int(batch.n_tokens)]![0] = 0
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batch.logits[Int(batch.n_tokens)] = 1 // true
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batch.n_tokens += 1
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n_decode += 1
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n_cur += 1
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if llama_decode(context, batch) != 0 {
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print("failed to evaluate llama!")
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}
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return new_token_str
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}
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func clear() {
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tokens_list.removeAll()
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temporary_invalid_cchars.removeAll()
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}
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private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
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let utf8Count = text.utf8.count
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let n_tokens = utf8Count + (add_bos ? 1 : 0)
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let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
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let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
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var swiftTokens: [llama_token] = []
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for i in 0..<tokenCount {
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swiftTokens.append(tokens[Int(i)])
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}
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tokens.deallocate()
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return swiftTokens
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}
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/// - note: The result does not contain null-terminator
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private func token_to_piece(token: llama_token) -> [CChar] {
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let result = UnsafeMutablePointer<Int8>.allocate(capacity: 8)
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result.initialize(repeating: Int8(0), count: 8)
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defer {
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result.deallocate()
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}
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let nTokens = llama_token_to_piece(model, token, result, 8)
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if nTokens < 0 {
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let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
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newResult.initialize(repeating: Int8(0), count: Int(-nTokens))
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defer {
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newResult.deallocate()
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}
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let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
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let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
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return Array(bufferPointer)
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} else {
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let bufferPointer = UnsafeBufferPointer(start: result, count: Int(nTokens))
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return Array(bufferPointer)
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}
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}
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}
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