--- /dev/null
+import Foundation
+import llama
+
+let arguments = CommandLine.arguments
+
+// Check that we have at least one argument (the model path)
+guard arguments.count > 1 else {
+ print("Usage: swift MODEL_PATH [PROMPT] [PARALLEL]")
+ exit(1)
+}
+
+let modelPath: String = arguments[1]
+let prompt: String = arguments.count > 2 ? arguments[2] : "Hello my name is"
+let n_parallel: Int = arguments.count > 3 && Int(arguments[3]) != nil ? Int(arguments[3])! : 1
+
+// total length of the sequences including the prompt
+let n_len: Int = 32
+
+// init LLM
+llama_backend_init(false)
+defer {
+ llama_backend_free()
+}
+
+let model_params = llama_model_default_params()
+guard let model = llama_load_model_from_file(modelPath.cString(using: .utf8), model_params) else {
+ print("Failed to load model")
+ exit(1)
+}
+
+defer {
+ llama_free_model(model)
+}
+
+var tokens = tokenize(text: prompt, add_bos: true)
+
+let n_kv_req = UInt32(tokens.count) + UInt32((n_len - Int(tokens.count)) * n_parallel)
+
+var context_params = llama_context_default_params()
+context_params.seed = 1234
+context_params.n_ctx = n_kv_req
+context_params.n_batch = UInt32(max(n_len, n_parallel))
+context_params.n_threads = 8
+context_params.n_threads_batch = 8
+
+let context = llama_new_context_with_model(model, context_params)
+guard context != nil else {
+ print("Failed to initialize context")
+ exit(1)
+}
+
+defer {
+ llama_free(context)
+}
+
+let n_ctx = llama_n_ctx(context)
+
+print("\nn_len = \(n_len), n_ctx = \(n_ctx), n_batch = \(context_params.n_batch), n_parallel = \(n_parallel), n_kv_req = \(n_kv_req)\n")
+
+if n_kv_req > n_ctx {
+ print("error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", n_kv_req)
+ exit(1)
+}
+
+var buffer: [CChar] = []
+for id: llama_token in tokens {
+ print(token_to_piece(token: id, buffer: &buffer) ?? "", terminator: "")
+}
+
+print("\n")
+
+var batch = llama_batch_init(max(Int32(tokens.count), Int32(n_parallel)), 0)
+defer {
+ llama_batch_free(batch)
+}
+
+// evaluate the initial prompt
+batch.n_tokens = Int32(tokens.count)
+
+for (i, token) in tokens.enumerated() {
+ batch.token[i] = token
+ batch.pos[i] = Int32(i)
+ batch.seq_id[i] = 0
+ batch.logits[i] = 0
+}
+
+// llama_decode will output logits only for the last token of the prompt
+batch.logits[Int(batch.n_tokens) - 1] = 1
+
+if llama_decode(context, batch) != 0 {
+ print("llama_decode() failed")
+ exit(1)
+}
+
+for i in 1 ..< n_parallel {
+ llama_kv_cache_seq_cp(context, 0, Int32(i), 0, batch.n_tokens)
+}
+
+if n_parallel > 1 {
+ print("generating \(n_parallel) sequences ...\n")
+}
+
+var streams: [String] = .init(repeating: "", count: n_parallel)
+var streamBuffers: [[CChar]] = .init(repeating: [], count: n_parallel)
+var i_batch = [Int32](repeating: batch.n_tokens - 1, count: n_parallel)
+
+var n_cur = batch.n_tokens
+var n_decode = 0
+
+let t_main_start = ggml_time_us()
+
+while n_cur <= n_len {
+ // prepare the next batch
+ batch.n_tokens = 0
+
+ // sample the next token for each parallel sequence / stream
+ for i in 0 ..< n_parallel {
+ if i_batch[i] < 0 {
+ // the stream has already finished
+ continue
+ }
+
+ var n_vocab = llama_n_vocab(model)
+ var logits = llama_get_logits_ith(context, i_batch[i])
+
+ var candidates: [llama_token_data] = .init(repeating: llama_token_data(), count: Int(n_vocab))
+
+ for token_id in 0 ..< n_vocab {
+ candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0))
+ }
+
+ var candidates_p: llama_token_data_array = .init(
+ data: &candidates,
+ size: candidates.count,
+ sorted: false
+ )
+
+ let top_k: Int32 = 40
+ let top_p: Float = 0.9
+ let temp: Float = 0.4
+
+ llama_sample_top_k(context, &candidates_p, top_k, 1)
+ llama_sample_top_p(context, &candidates_p, top_p, 1)
+ llama_sample_temp(context, &candidates_p, temp)
+
+ let new_token_id = llama_sample_token(context, &candidates_p)
+
+ // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
+
+ // is it an end of stream? -> mark the stream as finished
+ if new_token_id == llama_token_eos(context) || n_cur == n_len {
+ i_batch[i] = -1
+ // print("")
+ if n_parallel > 1 {
+ print("stream \(i) finished at n_cur = \(n_cur)")
+ }
+
+ continue
+ }
+
+ let nextStringPiece = token_to_piece(token: new_token_id, buffer: &streamBuffers[i]) ?? ""
+
+ // if there is only one stream, we print immediately to stdout
+ if n_parallel == 1 {
+ print(nextStringPiece, terminator: "")
+ }
+ streams[i] += nextStringPiece
+
+ // push this new token for next evaluation
+ batch.token[Int(batch.n_tokens)] = new_token_id
+ batch.pos[Int(batch.n_tokens)] = n_cur
+ batch.seq_id[Int(batch.n_tokens)] = Int32(i)
+ batch.logits[Int(batch.n_tokens)] = 1
+
+ i_batch[i] = batch.n_tokens
+
+ batch.n_tokens += 1
+
+ n_decode += 1
+ }
+
+ // all streams are finished
+ if batch.n_tokens == 0 {
+ break
+ }
+
+ n_cur += 1
+
+ // evaluate the current batch with the transformer model
+ if llama_decode(context, batch) != 0 {
+ print("llama_decode() failed")
+ exit(1)
+ }
+}
+
+if n_parallel > 1 {
+ print("\n")
+ for (i, stream) in streams.enumerated() {
+ print("sequence \(i):\n\n\(prompt)\(stream)\n")
+ }
+}
+
+let t_main_end = ggml_time_us()
+
+print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end - t_main_start) / 1_000_000.0)) s, speed: \(String(format: "%.2f", Double(n_decode) / (Double(t_main_end - t_main_start) / 1_000_000.0))) t/s\n")
+
+llama_print_timings(context)
+
+private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
+ let n_tokens = text.count + (add_bos ? 1 : 0)
+ let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
+ let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos)
+ var swiftTokens: [llama_token] = []
+ for i in 0 ..< tokenCount {
+ swiftTokens.append(tokens[Int(i)])
+ }
+ tokens.deallocate()
+ return swiftTokens
+}
+
+private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
+ var result = [CChar](repeating: 0, count: 8)
+ let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
+ if nTokens < 0 {
+ if result.count >= -Int(nTokens) {
+ result.removeLast(-Int(nTokens))
+ } else {
+ result.removeAll()
+ }
+ let check = llama_token_to_piece(
+ model,
+ token,
+ &result,
+ Int32(result.count)
+ )
+ assert(check == nTokens)
+ } else {
+ result.removeLast(result.count - Int(nTokens))
+ }
+ if buffer.isEmpty, let utfString = String(cString: result + [0], encoding: .utf8) {
+ return utfString
+ } else {
+ buffer.append(contentsOf: result)
+ let data = Data(buffer.map { UInt8(bitPattern: $0) })
+ if buffer.count >= 4 { // 4 bytes is the max length of a utf8 character so if we're here we need to reset the buffer
+ buffer = []
+ }
+ guard let bufferString = String(data: data, encoding: .utf8) else {
+ return nil
+ }
+ buffer = []
+ return bufferString
+ }
+ return nil
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff