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1 files changed, 256 insertions, 0 deletions

diff --git a/llama.cpp/examples/batched.swift/Sources/main.swift b/llama.cpp/examples/batched.swift/Sources/main.swift
new file mode 100644
index 0000000..fd90bbe
--- /dev/null
+++ b/llama.cpp/examples/batched.swift/Sources/main.swift
@@ -0,0 +1,256 @@
+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()
+defer {
+    llama_backend_free()
+}
+
+let model_params = llama_model_default_params()
+guard let model = llama_model_load_from_file(modelPath.cString(using: .utf8), model_params) else {
+    print("Failed to load model")
+    exit(1)
+}
+defer {
+    llama_model_free(model)
+}
+
+guard let vocab = llama_model_get_vocab(model) else {
+    print("Failed to get vocab")
+    exit(1)
+}
+
+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.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_init_from_model(model, context_params)
+guard context != nil else {
+    print("Failed to initialize context")
+    exit(1)
+}
+defer {
+    llama_free(context)
+}
+
+var sparams = llama_sampler_chain_default_params()
+
+let smpl = llama_sampler_chain_init(sparams)
+guard smpl != nil else {
+    print("Failed to initialize sampling")
+    exit(1)
+}
+defer {
+    llama_sampler_free(smpl)
+}
+
+llama_sampler_chain_add(smpl, llama_sampler_init_top_k(40));
+llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.9, 1));
+llama_sampler_chain_add(smpl, llama_sampler_init_temp (0.4));
+llama_sampler_chain_add(smpl, llama_sampler_init_dist (1234));
+
+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, 1)
+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.n_seq_id[i] = 1
+    // batch.seq_id[i][0] = 0
+    // TODO: is this the proper way to do this?
+    if let seq_id = batch.seq_id[i] {
+        seq_id[0] = 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_memory_seq_cp(llama_get_memory(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
+        }
+
+        let new_token_id = llama_sampler_sample(smpl, context, i_batch[i])
+
+        // is it an end of stream? -> mark the stream as finished
+        if llama_vocab_is_eog(vocab, new_token_id) || 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.n_seq_id[Int(batch.n_tokens)] = 1
+        if let seq_id = batch.seq_id[Int(batch.n_tokens)] {
+            seq_id[0] = 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\n")
+
+llama_perf_sampler_print(smpl)
+llama_perf_context_print(context)
+
+private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
+    let utf8Count = text.utf8.count
+    let n_tokens = utf8Count + (add_bos ? 1 : 0)
+    let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
+    let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
+    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(vocab, token, &result, Int32(result.count), 0, false)
+    if nTokens < 0 {
+        let actualTokensCount = -Int(nTokens)
+        result = .init(repeating: 0, count: actualTokensCount)
+        let check = llama_token_to_piece(
+            vocab,
+            token,
+            &result,
+            Int32(result.count),
+            0,
+            false
+        )
+        assert(check == actualTokensCount)
+    } 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
+    }
+}