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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,177 @@ | ||
| using System.Diagnostics; | ||
| using System.Security.Cryptography; | ||
| using System.Text; | ||
| using LLama.Common; | ||
| using LLama.Native; | ||
|
|
||
| namespace LLama.Examples.NewVersion; | ||
|
|
||
| /// <summary> | ||
| /// This demonstrates generating multiple replies to the same prompt, with a shared cache | ||
| /// </summary> | ||
| /// <remarks>Note that this is currently using the low level API directly, future work will provide a safer C# wrapper over this!</remarks> | ||
| public class BatchedDecoding | ||
| { | ||
| private const int n_parallel = 8; | ||
| private const int n_len = 32; | ||
|
|
||
| private const int top_k = 80; | ||
| private const float top_p = 0.8f; | ||
| private const float temp = 0.5f; | ||
|
|
||
| public static async Task Run() | ||
| { | ||
| Console.Write("Please input your model path: "); | ||
| var modelPath = Console.ReadLine(); | ||
|
|
||
| Console.WriteLine("Prompt (leave blank to select automatically):"); | ||
| var prompt = Console.ReadLine(); | ||
| if (string.IsNullOrWhiteSpace(prompt)) | ||
| prompt = "Not many people know that"; | ||
|
|
||
| // Load model | ||
| var parameters = new ModelParams(modelPath); | ||
| using var model = LLamaWeights.LoadFromFile(parameters); | ||
|
|
||
| // Tokenize prompt | ||
| var prompt_tokens = model.NativeHandle.Tokenize(prompt, true, false, Encoding.UTF8); | ||
| var n_kv_req = prompt_tokens.Length + (n_len - prompt_tokens.Length) * n_parallel; | ||
|
|
||
| // Create a context | ||
| parameters.ContextSize = (uint)model.ContextSize; | ||
| parameters.BatchSize = (uint)Math.Max(n_len, n_parallel); | ||
| using var context = model.CreateContext(parameters); | ||
|
|
||
| var n_ctx = context.ContextSize; | ||
|
|
||
| // make sure the KV cache is big enough to hold all the prompt and generated tokens | ||
| if (n_kv_req > n_ctx) | ||
| { | ||
| await Console.Error.WriteLineAsync($"error: n_kv_req ({n_kv_req}) > n_ctx, the required KV cache size is not big enough\n"); | ||
| await Console.Error.WriteLineAsync(" either reduce n_parallel or increase n_ctx\n"); | ||
| return; | ||
| } | ||
|
|
||
| using var batch = LLamaBatchSafeHandle.Create(Math.Max(prompt_tokens.Length, n_parallel), 0, 1); | ||
|
|
||
| // evaluate the initial prompt | ||
| for (var i = 0; i < prompt_tokens.Length; i++) | ||
| batch.LLamaBatchAdd(prompt_tokens[i], i, new[] { (LLamaSeqId)0 }, false); | ||
| Debug.Assert(batch.NativeBatch.n_tokens == prompt_tokens.Length); | ||
|
|
||
| // llama_decode will output logits only for the last token of the prompt | ||
| unsafe | ||
| { | ||
| batch.NativeBatch.logits[batch.NativeBatch.n_tokens - 1] = 1; | ||
| } | ||
|
|
||
| if (context.NativeHandle.Decode(batch) != 0) | ||
| { | ||
| await Console.Error.WriteLineAsync("llama_decode failed"); | ||
| return; | ||
| } | ||
|
|
||
| // assign the system KV cache to all parallel sequences | ||
| // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them | ||
| for (var i = 1; i < n_parallel; ++i) | ||
| { | ||
| NativeApi.llama_kv_cache_seq_cp(context.NativeHandle, (LLamaSeqId)0, (LLamaSeqId)i, 0, batch.NativeBatch.n_tokens); | ||
| } | ||
|
|
||
| if (n_parallel > 1) | ||
| { | ||
| Console.WriteLine(); | ||
| Console.WriteLine($"generating {n_parallel} sequences..."); | ||
| } | ||
|
|
||
| // remember the batch index of the last token for each parallel sequence | ||
| // we need this to determine which logits to sample from | ||
| List<int> i_batch = new(); | ||
| for (var i = 0; i < n_parallel; i++) | ||
| i_batch.Add(batch.NativeBatch.n_tokens - 1); | ||
|
|
||
| var n_cur = batch.NativeBatch.n_tokens; | ||
| var n_decode = 0; | ||
|
|
||
| var streams = new List<int>[n_parallel]; | ||
| for (var i = 0; i < n_parallel; i++) | ||
| streams[i] = new(); | ||
|
|
||
| var eos = model.EndOfSentenceToken; | ||
| var nl = model.NewlineToken; | ||
|
|
||
| var timer = new Stopwatch(); | ||
| timer.Start(); | ||
| while (n_cur <= n_len) | ||
| { | ||
| batch.LLamaBatchClear(); | ||
|
|
||
| for (var i = 0; i < n_parallel; i++) | ||
| { | ||
| // Skip completed streams | ||
| if (i_batch[i] < 0) | ||
| continue; | ||
|
|
||
| var n_vocab = model.VocabCount; | ||
| LLamaTokenDataArray candidates; | ||
| unsafe | ||
| { | ||
| candidates = LLamaTokenDataArray.Create(new Span<float>(NativeApi.llama_get_logits_ith(context.NativeHandle, i_batch[i]), n_vocab)); | ||
| } | ||
|
|
||
| candidates.TopK(context.NativeHandle, top_k); | ||
| candidates.TopP(context.NativeHandle, top_p); | ||
| candidates.Temperature(context.NativeHandle, temp); | ||
| var new_token_id = candidates.SampleToken(context.NativeHandle); | ||
|
|
||
| if (new_token_id == eos || new_token_id == nl) | ||
| { | ||
| i_batch[i] = -1; | ||
| Console.WriteLine($"Completed Stream {i} early"); | ||
| continue; | ||
| } | ||
|
|
||
| streams[i].Add(new_token_id); | ||
|
|
||
| i_batch[i] = batch.NativeBatch.n_tokens; | ||
|
|
||
| // push this new token for next evaluation | ||
| batch.LLamaBatchAdd(new_token_id, n_cur, new[] { (LLamaSeqId)i }, true); | ||
|
|
||
| n_decode++; | ||
| } | ||
|
|
||
| // all streams are finished | ||
| if (batch.NativeBatch.n_tokens == 0) | ||
| { | ||
| break; | ||
| } | ||
|
|
||
| n_cur++; | ||
|
|
||
| // evaluate the current batch with the transformer model | ||
| if (context.NativeHandle.Decode(batch) != 0) | ||
| { | ||
| await Console.Error.WriteLineAsync("failed to eval"); | ||
| return; | ||
| } | ||
| } | ||
|
|
||
| timer.Stop(); | ||
| Console.ForegroundColor = ConsoleColor.Yellow; | ||
| Console.WriteLine(); | ||
| Console.WriteLine($"Decoded {n_decode} tokens in {timer.ElapsedMilliseconds}ms"); | ||
| Console.WriteLine($"Rate: {n_decode / timer.Elapsed.TotalSeconds:##.000} tokens/second"); | ||
|
|
||
| var index = 0; | ||
| foreach (var stream in streams) | ||
| { | ||
| var text = context.DeTokenize(stream); | ||
|
|
||
| Console.ForegroundColor = ConsoleColor.Green; | ||
| Console.Write($"{index++}. {prompt}"); | ||
| Console.ForegroundColor = ConsoleColor.Red; | ||
| Console.WriteLine(text); | ||
| } | ||
| } | ||
| } |
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