Rename additive_repetition_penalty to presence_penalty, add frequency_penalty (#4376)

api-examples/api-example-chat-stream.py

@@ -52,7 +52,8 @@ async def run(user_input, history):
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1.18,
-        'additive_repetition_penalty': 0,
+        'presence_penalty': 0,
+        'frequency_penalty': 0,
         'repetition_penalty_range': 0,
         'top_k': 40,
         'min_length': 0,

api-examples/api-example-chat.py

@@ -46,7 +46,8 @@ def run(user_input, history):
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1.18,
-        'additive_repetition_penalty': 0,
+        'presence_penalty': 0,
+        'frequency_penalty': 0,
         'repetition_penalty_range': 0,
         'top_k': 40,
         'min_length': 0,

api-examples/api-example-stream.py

@@ -35,7 +35,8 @@ async def run(context):
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1.18,
-        'additive_repetition_penalty': 0,
+        'presence_penalty': 0,
+        'frequency_penalty': 0,
         'repetition_penalty_range': 0,
         'top_k': 40,
         'min_length': 0,

api-examples/api-example.py

@@ -27,7 +27,8 @@ def run(prompt):
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1.18,
-        'additive_repetition_penalty': 0,
+        'presence_penalty': 0,
+        'frequency_penalty': 0,
         'repetition_penalty_range': 0,
         'top_k': 40,
         'min_length': 0,

docs/03 ‐ Parameters Tab.md

@@ -35,7 +35,8 @@ For more information about the parameters, the [transformers documentation](http
 * **top_p**: If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.
 * **top_k**: Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.
 * **repetition_penalty**: Penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.
-* **additive_repetition_penalty**: Similar to repetition_penalty, but with an additive offset on the raw token scores instead of a multiplicative factor. It may generate better results. 0 means no penalty, higher value = less repetition, lower value = more repetition.
+* **presence_penalty**: Similar to repetition_penalty, but with an additive offset on the raw token scores instead of a multiplicative factor. It may generate better results. 0 means no penalty, higher value = less repetition, lower value = more repetition. Previously called "additive_repetition_penalty".
+* **frequency_penalty**: Repetition penalty that scales based on how many times the token has appeared in the context. Be careful with this; there's no limit to how much a token can be penalized.
 * **repetition_penalty_range**: The number of most recent tokens to consider for repetition penalty. 0 makes all tokens be used.
 * **typical_p**: If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.
 * **tfs**: Tries to detect a tail of low-probability tokens in the distribution and removes those tokens. See [this blog post](https://www.trentonbricken.com/Tail-Free-Sampling/) for details. The closer to 0, the more discarded tokens.

extensions/api/util.py

@@ -32,7 +32,8 @@ def build_parameters(body, chat=False):
         'tfs': float(body.get('tfs', 1)),
         'top_a': float(body.get('top_a', 0)),
         'repetition_penalty': float(body.get('repetition_penalty', body.get('rep_pen', 1.1))),
-        'additive_repetition_penalty': float(body.get('additive_repetition_penalty', body.get('additive_rep_pen', 0))),
+        'presence_penalty': float(body.get('presence_penalty', body.get('presence_pen', 0))),
+        'frequency_penalty': float(body.get('frequency_penalty', body.get('frequency_pen', 0))),
         'repetition_penalty_range': int(body.get('repetition_penalty_range', 0)),
         'encoder_repetition_penalty': float(body.get('encoder_repetition_penalty', 1.0)),
         'top_k': int(body.get('top_k', 0)),

extensions/openai/defaults.py

@@ -10,7 +10,8 @@ default_req_params = {
     'top_p': 1.0,
     'top_k': 1,  # choose 20 for chat in absence of another default
     'repetition_penalty': 1.18,
-    'additive_repetition_penalty': 0,
+    'presence_penalty': 0,
+    'frequency_penalty': 0,
     'repetition_penalty_range': 0,
     'encoder_repetition_penalty': 1.0,
     'suffix': None,

modules/llamacpp_model.py

@@ -146,6 +146,8 @@ class LlamaCppModel:
             top_p=state['top_p'],
             top_k=state['top_k'],
             repeat_penalty=state['repetition_penalty'],
+            presence_penalty=state['presence_penalty'],
+            frequency_penalty=state['frequency_penalty'],
             tfs_z=state['tfs'],
             mirostat_mode=int(state['mirostat_mode']),
             mirostat_tau=state['mirostat_tau'],

modules/loaders.py

@@ -152,7 +152,8 @@ loaders_samplers = {
         'tfs',
         'top_a',
         'repetition_penalty',
-        'additive_repetition_penalty',
+        'presence_penalty',
+        'frequency_penalty',
         'repetition_penalty_range',
         'encoder_repetition_penalty',
         'no_repeat_ngram_size',
@@ -186,7 +187,8 @@ loaders_samplers = {
         'tfs',
         'top_a',
         'repetition_penalty',
-        'additive_repetition_penalty',
+        'presence_penalty',
+        'frequency_penalty',
         'repetition_penalty_range',
         'encoder_repetition_penalty',
         'no_repeat_ngram_size',
@@ -245,7 +247,8 @@ loaders_samplers = {
         'tfs',
         'top_a',
         'repetition_penalty',
-        'additive_repetition_penalty',
+        'presence_penalty',
+        'frequency_penalty',
         'repetition_penalty_range',
         'encoder_repetition_penalty',
         'no_repeat_ngram_size',
@@ -275,7 +278,8 @@ loaders_samplers = {
         'tfs',
         'top_a',
         'repetition_penalty',
-        'additive_repetition_penalty',
+        'presence_penalty',
+        'frequency_penalty',
         'repetition_penalty_range',
         'encoder_repetition_penalty',
         'no_repeat_ngram_size',
@@ -309,7 +313,8 @@ loaders_samplers = {
         'tfs',
         'top_a',
         'repetition_penalty',
-        'additive_repetition_penalty',
+        'presence_penalty',
+        'frequency_penalty',
         'repetition_penalty_range',
         'encoder_repetition_penalty',
         'no_repeat_ngram_size',
@@ -339,6 +344,8 @@ loaders_samplers = {
         'top_k',
         'tfs',
         'repetition_penalty',
+        'presence_penalty',
+        'frequency_penalty',
         'mirostat_mode',
         'mirostat_tau',
         'mirostat_eta',
@@ -357,7 +364,8 @@ loaders_samplers = {
         'tfs',
         'top_a',
         'repetition_penalty',
-        'additive_repetition_penalty',
+        'presence_penalty',
+        'frequency_penalty',
         'repetition_penalty_range',
         'encoder_repetition_penalty',
         'no_repeat_ngram_size',
@@ -394,7 +402,8 @@ loaders_samplers = {
         'tfs',
         'top_a',
         'repetition_penalty',
-        'additive_repetition_penalty',
+        'presence_penalty',
+        'frequency_penalty',
         'repetition_penalty_range',
         'encoder_repetition_penalty',
         'no_repeat_ngram_size',

modules/presets.py

@@ -16,7 +16,8 @@ def default_preset():
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1,
-        'additive_repetition_penalty': 0,
+        'presence_penalty': 0,
+        'frequency_penalty': 0,
         'repetition_penalty_range': 0,
         'encoder_repetition_penalty': 1,
         'no_repeat_ngram_size': 0,

modules/sampler_hijack.py

@@ -139,24 +139,35 @@ class RepetitionPenaltyLogitsProcessorWithRange(LogitsProcessor):
     Copied from the transformers library
     '''
 
-    def __init__(self, penalty: float, additive_penalty: float, _range: int):
+    def __init__(self, penalty: float, presence_penalty: float, frequency_penalty: float, _range: int):
         if not (penalty > 0):
             raise ValueError(f"`penalty` has to be strictly positive, but is {penalty}")
 
         self.penalty = penalty
-        self.additive_penalty = additive_penalty
+        self.presence_penalty = presence_penalty
+        self.frequency_penalty = frequency_penalty
         self._range = _range
 
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-
         input_ids = input_ids[:, -self._range:]
-        score = torch.gather(scores, 1, input_ids)
 
+        # We loop here because torch.unique() needs to process each row separately in the
+        # case that batch_size > 1.
+        for input_ids_row, scores_row in zip(input_ids, scores):
+            unique_ids, counts = torch.unique(input_ids_row, return_counts=True)
+            score = torch.gather(scores_row, 0, unique_ids)
+
+            # multiplicative repetition penalty
             # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
             score = torch.where(score < 0, score * self.penalty, score / self.penalty)
-        score -= self.additive_penalty
+            scores_row.scatter_(0, unique_ids, score)
+
+            # presence_penalty and frequency_penalty
+            raw_presence_penalty = (counts > 0).to(scores.dtype)
+            raw_frequency_penalty = counts.to(scores.dtype)
+            additive_penalty = raw_presence_penalty*self.presence_penalty + raw_frequency_penalty*self.frequency_penalty
+            scores_row.scatter_add_(0, unique_ids, -additive_penalty)
 
-        scores.scatter_(1, input_ids, score)
         return scores
 
 
@@ -188,9 +199,10 @@ def get_logits_warper_patch(self, generation_config):
 
 def get_logits_processor_patch(self, **kwargs):
     repetition_penalty = kwargs['generation_config'].repetition_penalty
-    additive_repetition_penalty = kwargs['generation_config'].additive_repetition_penalty
+    presence_penalty = kwargs['generation_config'].presence_penalty
+    frequency_penalty = kwargs['generation_config'].frequency_penalty
     repetition_penalty_range = kwargs['generation_config'].repetition_penalty_range
-    do_rep_pen_hijack = (repetition_penalty > 1) or (additive_repetition_penalty > 0)
+    do_rep_pen_hijack = (repetition_penalty > 1) or (presence_penalty != 0) or (frequency_penalty != 0)
     if do_rep_pen_hijack:
         # Make sure that a RepetitionPenaltyLogitsProcessor will be created
         kwargs['generation_config'].repetition_penalty = 1.1  # must set to some value > 1
@@ -200,7 +212,7 @@ def get_logits_processor_patch(self, **kwargs):
     if do_rep_pen_hijack:
         for i in range(len(result)):
             if result[i].__class__.__name__ == 'RepetitionPenaltyLogitsProcessor':
-                result[i] = RepetitionPenaltyLogitsProcessorWithRange(repetition_penalty, additive_repetition_penalty, repetition_penalty_range)
+                result[i] = RepetitionPenaltyLogitsProcessorWithRange(repetition_penalty, presence_penalty, frequency_penalty, repetition_penalty_range)
 
     return result
 
@@ -213,7 +225,8 @@ def generation_config_init_patch(self, **kwargs):
     self.mirostat_eta = kwargs.pop("mirostat_eta", 0.1)
     self.mirostat_tau = kwargs.pop("mirostat_tau", 5)
     self.repetition_penalty_range = kwargs.pop("repetition_penalty_range", 0)
-    self.additive_repetition_penalty = kwargs.pop("additive_repetition_penalty", 0)
+    self.presence_penalty = kwargs.pop("presence_penalty", 0)
+    self.frequency_penalty = kwargs.pop("frequency_penalty", 0)
 
 
 def hijack_samplers():

modules/text_generation.py

@@ -273,7 +273,7 @@ def apply_stopping_strings(reply, all_stop_strings):
 
 def generate_reply_HF(question, original_question, seed, state, stopping_strings=None, is_chat=False):
     generate_params = {}
-    for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'additive_repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'guidance_scale']:
+    for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'guidance_scale']:
         generate_params[k] = state[k]
 
     if state['negative_prompt'] != '':

modules/ui.py

@@ -105,7 +105,8 @@ def list_interface_input_elements():
         'epsilon_cutoff',
         'eta_cutoff',
         'repetition_penalty',
-        'additive_repetition_penalty',
+        'presence_penalty',
+        'frequency_penalty',
         'repetition_penalty_range',
         'encoder_repetition_penalty',
         'no_repeat_ngram_size',

modules/ui_parameters.py

@@ -31,7 +31,8 @@ def create_ui(default_preset):
                             shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p')
                             shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k')
                             shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty')
-                            shared.gradio['additive_repetition_penalty'] = gr.Slider(0, 4, value=generate_params['additive_repetition_penalty'], step=0.05, label='additive_repetition_penalty')
+                            shared.gradio['presence_penalty'] = gr.Slider(0, 4, value=generate_params['presence_penalty'], step=0.05, label='presence_penalty')
+                            shared.gradio['frequency_penalty'] = gr.Slider(0, 2, value=generate_params['frequency_penalty'], step=0.05, label='frequency_penalty')
                             shared.gradio['repetition_penalty_range'] = gr.Slider(0, 4096, step=64, value=generate_params['repetition_penalty_range'], label='repetition_penalty_range')
                             shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p')
                             shared.gradio['tfs'] = gr.Slider(0.0, 1.0, value=generate_params['tfs'], step=0.01, label='tfs')