integrated mirostat as a launch parameter, works on all models

expose.h

@@ -33,7 +33,7 @@ struct generation_inputs
     const float tfs;
     const float rep_pen;
     const int rep_pen_range;
-    const int mirostat;
+    const int mirostat = 0;
     const float mirostat_eta;
     const float mirostat_tau;
     const char * stop_sequence[stop_token_max];

gpttype_adapter.cpp

@@ -95,7 +95,62 @@ llama_token sample_token(llama_token_data_array * candidates, std::mt19937 & rng
     return result;
 }
 
-int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range, float rep_pen, float top_k, float top_p, float typical_p, float tfs, float temp, std::mt19937 & rng)
+llama_token sample_token_mirostat(int n_vocab, llama_token_data_array * candidates, std::mt19937 & rng, float tau, float eta, int m, float * mu) 
+{   
+    float N = float(n_vocab);
+    llama_sample_softmax(nullptr, candidates);
+    // Estimate s_hat using the most probable m tokens
+    float s_hat = 0.0;
+    float sum_ti_bi = 0.0;
+    float sum_ti_sq = 0.0;
+    for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
+        float t_i = logf(float(i + 2) / float(i + 1));
+        float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
+        sum_ti_bi += t_i * b_i;
+        sum_ti_sq += t_i * t_i;
+    }
+    s_hat = sum_ti_bi / sum_ti_sq;
+    // Compute k from the estimated s_hat and target surprise value
+    float epsilon_hat = s_hat - 1;
+    float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
+    // Sample the next word X using top-k sampling
+    llama_sample_top_k(nullptr, candidates, int(k));
+    llama_token X = sample_token(candidates, rng);    // Compute error as the difference between observed surprise and target surprise value
+    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return candidate.id == X;
+    }));
+    float observed_surprise = -log2f(candidates->data[X_idx].p);
+    float e = observed_surprise - tau;
+    // Update mu using the learning rate and error
+    *mu = *mu - eta * e;
+    return X;
+}
+
+llama_token sample_token_mirostat_v2(llama_token_data_array * candidates, std::mt19937 & rng, float tau, float eta, float * mu) 
+{
+    llama_sample_softmax(nullptr, candidates);
+    // Truncate the words with surprise values greater than mu
+    candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return -log2f(candidate.p) > *mu;
+    }));
+    // Normalize the probabilities of the remaining words
+    llama_sample_softmax(nullptr, candidates);
+    // Sample the next word X from the remaining words
+    llama_token X = sample_token(candidates,rng);
+
+    // Compute error as the difference between observed surprise and target surprise value
+    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return candidate.id == X;
+    }));
+    float observed_surprise = -log2f(candidates->data[X_idx].p);
+    float e = observed_surprise - tau;
+    // Update mu using the learning rate and error
+    *mu = *mu - eta * e;
+    return X;
+}
+
+int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range, float rep_pen, float top_k, float top_p, float typical_p, float tfs, float temp, std::mt19937 & rng,
+int mirostat, float mirostat_tau, float mirostat_eta)
 {
     int id = 0;
     std::vector<llama_token_data> candidates;
@@ -116,10 +171,28 @@ int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range
     //     last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
     //     last_n_repeat, alpha_frequency, alpha_presence);
 
-    if (temp <= 0) {
+    if (temp <= 0)
+    {
         // Greedy sampling
         id = llama_sample_token_greedy(nullptr, &candidates_p);
-    } else {                    
+    }
+    else
+    {
+        if (mirostat == 1)
+        {
+            static float mirostat_mu = 2.0f * mirostat_tau;
+            const int mirostat_m = 100;
+            llama_sample_temperature(nullptr, &candidates_p, temp);
+            id = sample_token_mirostat(n_vocab, &candidates_p, rng, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
+        }
+        else if (mirostat == 2)
+        {
+            static float mirostat_mu = 2.0f * mirostat_tau;
+            llama_sample_temperature(nullptr, &candidates_p, temp);
+            id = sample_token_mirostat_v2(&candidates_p, rng, mirostat_tau, mirostat_eta, &mirostat_mu);
+        }
+        else
+        {
             // Temperature sampling
             llama_sample_top_k(nullptr, &candidates_p, top_k);
             llama_sample_tail_free(nullptr, &candidates_p, tfs);
@@ -128,6 +201,7 @@ int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range
             llama_sample_temperature(nullptr, &candidates_p, temp);
             id = sample_token(&candidates_p, rng);
         }
+    }
 
     return id;
 }
@@ -647,7 +721,9 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
                     logits[29961] = 0;
                 }
 
-                id = SampleLogits(logits, nctx, n_vocab, last_n_size, repeat_penalty, top_k, top_p, typical_p, tfs_z, temp, rng);
+                id = SampleLogits(logits, nctx, n_vocab, last_n_size, repeat_penalty, 
+                top_k, top_p, typical_p, tfs_z, temp, rng,
+                params.mirostat,params.mirostat_tau,params.mirostat_eta);
 
             }
             else
@@ -667,7 +743,9 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
                     //gpt2 uses negative logits, so we cant zero it
                 }
 
-                id = SampleLogits(logits.data(), nctx, n_vocab, last_n_size, repeat_penalty, top_k, top_p, typical_p, tfs_z, temp, rng);
+                id = SampleLogits(logits.data(), nctx, n_vocab, last_n_size, repeat_penalty, 
+                top_k, top_p, typical_p, tfs_z, temp, rng,
+                params.mirostat,params.mirostat_tau,params.mirostat_eta);
             }
 
             last_n_tokens.erase(last_n_tokens.begin());

koboldcpp.py

@@ -157,7 +157,7 @@ def load_model(model_filename):
     ret = handle.load_model(inputs)
     return ret
 
-def generate(prompt,max_length=20, max_context_length=512,temperature=0.8,top_k=100,top_p=0.85, typical_p=1.0, tfs=1.0 ,rep_pen=1.1,rep_pen_range=128,mirostat=0,mirostat_lr=0.1,mirostat_ent=5.0,seed=-1,stop_sequence=[]):
+def generate(prompt,max_length=20, max_context_length=512,temperature=0.8,top_k=100,top_p=0.85, typical_p=1.0, tfs=1.0 ,rep_pen=1.1,rep_pen_range=128,seed=-1,stop_sequence=[]):
     inputs = generation_inputs()
     outputs = ctypes.create_unicode_buffer(ctypes.sizeof(generation_outputs))
     inputs.prompt = prompt.encode("UTF-8")
@@ -170,9 +170,12 @@ def generate(prompt,max_length=20, max_context_length=512,temperature=0.8,top_k=
     inputs.tfs = tfs
     inputs.rep_pen = rep_pen
     inputs.rep_pen_range = rep_pen_range
-    inputs.mirostat = mirostat
+    if args.usemirostat and args.usemirostat[0]>0:
-    inputs.mirostat_eta = mirostat_lr
+        inputs.mirostat = int(args.usemirostat[0])
-    inputs.mirostat_tau = mirostat_ent
+        inputs.mirostat_tau = float(args.usemirostat[1])
+        inputs.mirostat_eta = float(args.usemirostat[2])
+    else:
+        inputs.mirostat = inputs.mirostat_tau = inputs.mirostat_eta = 0
     inputs.seed = seed    
     for n in range(0,stop_token_max):
         if not stop_sequence or n >= len(stop_sequence):
@@ -317,9 +320,6 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                     tfs=genparams.get('tfs', 1.0),
                     rep_pen=genparams.get('rep_pen', 1.1),
                     rep_pen_range=genparams.get('rep_pen_range', 128),
-                    mirostat=genparams.get('mirostat', 0),
-                    mirostat_lr=genparams.get('mirostat_lr', 0.1),
-                    mirostat_ent=genparams.get('mirostat_ent', 5.0),
                     seed=-1,
                     stop_sequence=genparams.get('stop_sequence', [])
                     )
@@ -336,9 +336,6 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                     tfs=genparams.get('tfs', 1.0),
                     rep_pen=genparams.get('rep_pen', 1.1),
                     rep_pen_range=genparams.get('rep_pen_range', 128),
-                    mirostat=genparams.get('mirostat', 0),
-                    mirostat_lr=genparams.get('mirostat_lr', 0.1),
-                    mirostat_ent=genparams.get('mirostat_ent', 5.0),
                     seed=-1,
                     stop_sequence=genparams.get('stop_sequence', [])
                     )
@@ -620,14 +617,15 @@ if __name__ == '__main__':
         physical_core_limit = int(os.cpu_count()/2)
     default_threads = (physical_core_limit if physical_core_limit<=3 else max(3,physical_core_limit-1))
     parser.add_argument("--threads", help="Use a custom number of threads if specified. Otherwise, uses an amount based on CPU cores", type=int, default=default_threads)
-    parser.add_argument("--blasthreads", help="Use a different number of threads during BLAS if specified. Otherwise, has the same value as --threads", type=int, default=0)
+    parser.add_argument("--blasthreads", help="Use a different number of threads during BLAS if specified. Otherwise, has the same value as --threads",metavar=('[threads]'), type=int, default=0)
     parser.add_argument("--psutil_set_threads", help="Experimental flag. If set, uses psutils to determine thread count based on physical cores.", action='store_true')
     parser.add_argument("--highpriority", help="Experimental flag. If set, increases the process CPU priority, potentially speeding up generation. Use caution.", action='store_true')
     parser.add_argument("--blasbatchsize", help="Sets the batch size used in BLAS processing (default 512)", type=int,choices=[32,64,128,256,512,1024], default=512)
     parser.add_argument("--stream", help="Uses pseudo streaming when generating tokens. Only for the Kobold Lite UI.", action='store_true')
     parser.add_argument("--smartcontext", help="Reserving a portion of context to try processing less frequently.", action='store_true')
     parser.add_argument("--unbantokens", help="Normally, KoboldAI prevents certain tokens such as EOS and Square Brackets. This flag unbans them.", action='store_true')
-    parser.add_argument("--forceversion", help="If the model file format detection fails (e.g. rogue modified model) you can set this to override the detected format (enter desired version, e.g. 401 for GPTNeoX-Type2).", type=int, default=0)
+    parser.add_argument("--usemirostat", help="Experimental! Replaces your samplers with mirostat. Takes 3 params = [type(0/1/2), tau(5.0), eta(0.1)].",metavar=('[type]', '[tau]', '[eta]'), type=float, nargs=3)
+    parser.add_argument("--forceversion", help="If the model file format detection fails (e.g. rogue modified model) you can set this to override the detected format (enter desired version, e.g. 401 for GPTNeoX-Type2).",metavar=('[version]'), type=int, default=0)
     parser.add_argument("--nommap", help="If set, do not use mmap to load newer models", action='store_true')
     parser.add_argument("--usemlock", help="For Apple Systems. Force system to keep model in RAM rather than swapping or compressing", action='store_true')
     parser.add_argument("--noavx2", help="Do not use AVX2 instructions, a slower compatibility mode for older devices. Does not work with --clblast.", action='store_true')