Implement Min P as a sampler option in HF loaders

extensions/api/util.py

@@ -26,6 +26,7 @@ def build_parameters(body, chat=False):
         'do_sample': bool(body.get('do_sample', True)),
         'temperature': float(body.get('temperature', 0.5)),
         'top_p': float(body.get('top_p', 1)),
+        'min_p': float(body.get('min_p', 1)),
         'typical_p': float(body.get('typical_p', body.get('typical', 1))),
         'epsilon_cutoff': float(body.get('epsilon_cutoff', 0)),
         'eta_cutoff': float(body.get('eta_cutoff', 0)),

modules/loaders.py

@@ -145,6 +145,7 @@
     'Transformers': {
         'temperature',
         'top_p',
+        'min_p',
         'top_k',
         'typical_p',
         'epsilon_cutoff',
@@ -180,6 +181,7 @@
     'ExLlama_HF': {
         'temperature',
         'top_p',
+        'min_p',
         'top_k',
         'typical_p',
         'epsilon_cutoff',
@@ -240,6 +242,7 @@
     'ExLlamav2_HF': {
         'temperature',
         'top_p',
+        'min_p',
         'top_k',
         'typical_p',
         'epsilon_cutoff',
@@ -271,6 +274,7 @@
     'AutoGPTQ': {
         'temperature',
         'top_p',
+        'min_p',
         'top_k',
         'typical_p',
         'epsilon_cutoff',
@@ -306,6 +310,7 @@
     'GPTQ-for-LLaMa': {
         'temperature',
         'top_p',
+        'min_p',
         'top_k',
         'typical_p',
         'epsilon_cutoff',
@@ -357,6 +362,7 @@
     'llamacpp_HF': {
         'temperature',
         'top_p',
+        'min_p',
         'top_k',
         'typical_p',
         'epsilon_cutoff',
@@ -395,6 +401,7 @@
     'AutoAWQ': {
         'temperature',
         'top_p',
+        'min_p',
         'top_k',
         'typical_p',
         'epsilon_cutoff',

modules/presets.py

@@ -9,6 +9,7 @@ def default_preset():
         'do_sample': True,
         'temperature': 1,
         'top_p': 1,
+        'min_p': 1,
         'top_k': 0,
         'typical_p': 1,
         'epsilon_cutoff': 0,

modules/sampler_hijack.py

@@ -12,6 +12,34 @@
 
 global_scores = None
 
+class MinPLogitsWarper(LogitsWarper):
+    def __init__(self, min_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
+        if min_p < 0 or min_p > 1.0:
+            raise ValueError(f"`min_p` has to be a float >= 0 and <= 1, but is {min_p}")
+        self.min_p = min_p
+        self.filter_value = filter_value
+        self.min_tokens_to_keep = min_tokens_to_keep
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        # Convert logits to probabilities
+        probs = torch.softmax(scores, dim=-1)
+        # Get the probability of the top token for each sequence in the batch
+        top_probs, _ = probs.max(dim=-1, keepdim=True)
+        # Calculate the actual min_p threshold by scaling min_p with the top token's probability
+        scaled_min_p = self.min_p * top_probs
+        # Create a mask for tokens that have a probability less than the scaled min_p
+        tokens_to_remove = probs < scaled_min_p
+
+        sorted_indices = torch.argsort(scores, descending=True, dim=-1)
+        sorted_indices_to_remove = torch.gather(tokens_to_remove, dim=-1, index=sorted_indices)
+
+        if self.min_tokens_to_keep > 1:
+            # Keep at least min_tokens_to_keep
+            sorted_indices_to_remove[..., : self.min_tokens_to_keep] = False
+
+        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+        scores = scores.masked_fill(indices_to_remove, self.filter_value)
+        return scores
 
 class TailFreeLogitsWarper(LogitsWarper):
     def __init__(self, tfs: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
@@ -190,6 +218,8 @@ def get_logits_warper_patch(self, generation_config):
             warpers_to_add.append(TailFreeLogitsWarper(tfs=generation_config.tfs, min_tokens_to_keep=min_tokens_to_keep))
         if generation_config.top_a is not None and 0.0 <= generation_config.top_a <= 1.0:
             warpers_to_add.append(TopALogitsWarper(top_a=generation_config.top_a, min_tokens_to_keep=min_tokens_to_keep))
+        if generation_config.min_p is not None and 0.0 <= generation_config.min_p <= 1.0:
+            warpers_to_add.append(MinPLogitsWarper(min_p=generation_config.min_p, min_tokens_to_keep=min_tokens_to_keep))
 
     if warpers and isinstance(warpers[-1], LogitNormalization):
         warpers = warpers[:-1] + warpers_to_add + [warpers[-1]]
@@ -223,6 +253,7 @@ def get_logits_processor_patch(self, **kwargs):
 def generation_config_init_patch(self, **kwargs):
     self.__init___old(**kwargs)
     self.tfs = kwargs.pop("tfs", 1.0)
+    self.min_p = kwargs.pop("min_p", 0.0)
     self.top_a = kwargs.pop("top_a", 0.0)
     self.mirostat_mode = kwargs.pop("mirostat_mode", 0)
     self.mirostat_eta = kwargs.pop("mirostat_eta", 0.1)

modules/text_generation.py

@@ -274,7 +274,7 @@ 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', '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']:
+    for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'min_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

@@ -103,6 +103,7 @@ def list_interface_input_elements():
         'seed',
         'temperature',
         'top_p',
+        'min_p',
         'top_k',
         'typical_p',
         'epsilon_cutoff',

modules/ui_parameters.py

@@ -29,6 +29,7 @@ def create_ui(default_preset):
                             shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
                             shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature')
                             shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p')
+                            shared.gradio['min_p'] = gr.Slider(0.0, 1.0, value=generate_params['min_p'], step=0.01, label='min_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['presence_penalty'] = gr.Slider(0, 4, value=generate_params['presence_penalty'], step=0.05, label='presence_penalty')