dynatemp_low, dynatemp_high, dynatemp_exponent parameters

docs/03 - Parameters Tab.md

@@ -54,8 +54,7 @@ For more information about the parameters, the [transformers documentation](http
 * **mirostat_mode**: Activates the Mirostat sampling technique. It aims to control perplexity during sampling. See the [paper](https://arxiv.org/abs/2007.14966).
 * **mirostat_tau**: No idea, see the paper for details. According to the Preset Arena, 8 is a good value. 
 * **mirostat_eta**: No idea, see the paper for details. According to the Preset Arena, 0.1 is a good value.
-* **dynamic_temperature_low**: The lower bound for temperature in Dynamic Temperature. Only used when "dynamic_temperature" is checked.
-* **dynamic_temperature**: Activates Dynamic Temperature. This modifies temperature to range between "dynamic_temperature_low" (minimum) and "temperature" (maximum), with an entropy-based scaling.
+* **dynamic_temperature**: Activates Dynamic Temperature. This modifies temperature to range between "dynatemp_low" (minimum) and "dynatemp_high" (maximum), with an entropy-based scaling. The steepness of the curve is controlled by "dynatemp_exponent".
 * **temperature_last**: Makes temperature the last sampler instead of the first. With this, you can remove low probability tokens with a sampler like min_p and then use a high temperature to make the model creative without losing coherency.
 * **do_sample**: When unchecked, sampling is entirely disabled, and greedy decoding is used instead (the most likely token is always picked).
 * **Seed**: Set the Pytorch seed to this number. Note that some loaders do not use Pytorch (notably llama.cpp), and others are not deterministic (notably ExLlama v1 and v2). For these loaders, the seed has no effect.

extensions/openai/typing.py

@@ -9,7 +9,9 @@ class GenerationOptions(BaseModel):
     preset: str | None = Field(default=None, description="The name of a file under text-generation-webui/presets (without the .yaml extension). The sampling parameters that get overwritten by this option are the keys in the default_preset() function in modules/presets.py.")
     min_p: float = 0
     dynamic_temperature: bool = False
-    dynamic_temperature_low: float = 0.1
+    dynatemp_low: float = 1
+    dynatemp_high: float = 1
+    dynatemp_exponent: float = 1
     top_k: int = 0
     repetition_penalty: float = 1
     repetition_penalty_range: int = 1024

modules/loaders.py

@@ -156,7 +156,9 @@ def transformers_samplers():
         'temperature',
         'temperature_last',
         'dynamic_temperature',
-        'dynamic_temperature_low',
+        'dynatemp_low',
+        'dynatemp_high',
+        'dynatemp_exponent',
         'top_p',
         'min_p',
         'top_k',
@@ -223,7 +225,9 @@ def transformers_samplers():
         'temperature',
         'temperature_last',
         'dynamic_temperature',
-        'dynamic_temperature_low',
+        'dynatemp_low',
+        'dynatemp_high',
+        'dynatemp_exponent',
         'top_p',
         'min_p',
         'top_k',
@@ -277,7 +281,9 @@ def transformers_samplers():
         'temperature',
         'temperature_last',
         'dynamic_temperature',
-        'dynamic_temperature_low',
+        'dynatemp_low',
+        'dynatemp_high',
+        'dynatemp_exponent',
         'top_p',
         'min_p',
         'top_k',

modules/presets.py

@@ -6,14 +6,17 @@
 
 from modules import shared
 from modules.loaders import loaders_samplers
+from modules.logging_colors import logger
 
 
 def default_preset():
     return {
         'temperature': 1,
         'temperature_last': False,
         'dynamic_temperature': False,
-        'dynamic_temperature_low': 0.1,
+        'dynatemp_low': 1,
+        'dynatemp_high': 1,
+        'dynatemp_exponent': 1,
         'top_p': 1,
         'min_p': 0,
         'top_k': 0,
@@ -48,11 +51,15 @@ def presets_params():
 def load_preset(name):
     generate_params = default_preset()
     if name not in ['None', None, '']:
-        with open(Path(f'presets/{name}.yaml'), 'r') as infile:
-            preset = yaml.safe_load(infile)
-
-        for k in preset:
-            generate_params[k] = preset[k]
+        path = Path(f'presets/{name}.yaml')
+        if path.exists():
+            with open(path, 'r') as infile:
+                preset = yaml.safe_load(infile)
+
+            for k in preset:
+                generate_params[k] = preset[k]
+        else:
+            logger.error(f"The preset \"{name}\" does not exist under \"{path}\". Using the default parameters.")
 
     return generate_params
 

modules/sampler_hijack.py

@@ -16,7 +16,7 @@
 
 
 class TemperatureLogitsWarperWithDynatemp(LogitsWarper):
-    def __init__(self, temperature: float, dynamic_temperature: bool, dynamic_temperature_low: float):
+    def __init__(self, temperature: float, dynamic_temperature: bool, dynatemp_low: float, dynatemp_high: float, dynatemp_exponent: float):
         if not isinstance(temperature, float) or not (temperature > 0):
             except_msg = (
                 f"`temperature` (={temperature}) has to be a strictly positive float, otherwise your next token "
@@ -29,7 +29,9 @@ def __init__(self, temperature: float, dynamic_temperature: bool, dynamic_temper
 
         self.temperature = temperature
         self.dynamic_temperature = dynamic_temperature
-        self.dynamic_temperature_low = dynamic_temperature_low
+        self.dynatemp_low = dynatemp_low
+        self.dynatemp_high = dynatemp_high
+        self.dynatemp_exponent = dynatemp_exponent
 
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
 
@@ -40,9 +42,9 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
 
         # Dynamic temperature
         else:
-            min_temp = self.dynamic_temperature_low
-            max_temp = self.temperature
-            exponent_val = 1.0
+            min_temp = self.dynatemp_low
+            max_temp = self.dynatemp_high
+            exponent_val = self.dynatemp_exponent
 
             # Convert logits to probabilities
             probs = torch.softmax(scores, dim=-1)
@@ -82,7 +84,7 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
 
             # max_prob_token_id = torch.argmax(scores, dim=-1)  # Get the token ID with the highest probability
             # max_prob_token = shared.tokenizer.convert_ids_to_tokens(int(max_prob_token_id))  # Convert ID to token
-            # print("--- T=", float(dyn_temp), "token=", max_prob_token, "min=", min_temp, "max=", max_temp)
+            # print("--- T=", float(dyn_temp), "token=", max_prob_token, "min=", min_temp, "max=", max_temp, "exponent=", exponent_val)
 
             return scores
 
@@ -292,7 +294,13 @@ def get_logits_warper_patch(self, generation_config):
     warpers = self._get_logits_warper_old(generation_config)
     for i in range(len(warpers)):
         if warpers[i].__class__.__name__ == 'TemperatureLogitsWarper':
-            warpers[i] = TemperatureLogitsWarperWithDynatemp(temperature, generation_config.dynamic_temperature, generation_config.dynamic_temperature_low)
+            warpers[i] = TemperatureLogitsWarperWithDynatemp(
+                temperature,
+                generation_config.dynamic_temperature,
+                generation_config.dynatemp_low,
+                generation_config.dynatemp_high,
+                generation_config.dynatemp_exponent
+            )
 
     warpers_to_add = LogitsProcessorList()
     min_tokens_to_keep = 2 if generation_config.num_beams > 1 else 1
@@ -361,7 +369,9 @@ def generation_config_init_patch(self, **kwargs):
     self.__init___old(**kwargs)
     self.min_p = kwargs.pop("min_p", 0.0)
     self.dynamic_temperature = kwargs.pop("dynamic_temperature", False)
-    self.dynamic_temperature_low = kwargs.pop("dynamic_temperature_low", 0.1)
+    self.dynatemp_low = kwargs.pop("dynatemp_low", 1)
+    self.dynatemp_high = kwargs.pop("dynatemp_high", 1)
+    self.dynatemp_exponent = kwargs.pop("dynatemp_exponent", 1)
     self.tfs = kwargs.pop("tfs", 1.0)
     self.top_a = kwargs.pop("top_a", 0.0)
     self.mirostat_mode = kwargs.pop("mirostat_mode", 0)

modules/text_generation.py

@@ -285,7 +285,7 @@ def get_reply_from_output_ids(output_ids, state, starting_from=0):
 
 def generate_reply_HF(question, original_question, seed, state, stopping_strings=None, is_chat=False):
     generate_params = {}
-    for k in ['max_new_tokens', 'temperature', 'temperature_last', 'dynamic_temperature', 'dynamic_temperature_low', 'top_p', 'min_p', 'top_k', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'typical_p', 'tfs', 'top_a', 'guidance_scale', 'penalty_alpha', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'do_sample', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'num_beams', 'length_penalty', 'early_stopping']:
+    for k in ['max_new_tokens', 'temperature', 'temperature_last', 'dynamic_temperature', 'dynatemp_low', 'dynatemp_high', 'dynatemp_exponent', 'top_p', 'min_p', 'top_k', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'typical_p', 'tfs', 'top_a', 'guidance_scale', 'penalty_alpha', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'do_sample', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'num_beams', 'length_penalty', 'early_stopping']:
         generate_params[k] = state[k]
 
     if state['negative_prompt'] != '':

modules/ui.py

@@ -116,7 +116,9 @@ def list_interface_input_elements():
         'temperature',
         'temperature_last',
         'dynamic_temperature',
-        'dynamic_temperature_low',
+        'dynatemp_low',
+        'dynatemp_high',
+        'dynatemp_exponent',
         'top_p',
         'min_p',
         'top_k',

modules/ui_parameters.py

@@ -49,8 +49,10 @@ def create_ui(default_preset):
                             shared.gradio['mirostat_mode'] = gr.Slider(0, 2, step=1, value=generate_params['mirostat_mode'], label='mirostat_mode', info='mode=1 is for llama.cpp only.')
                             shared.gradio['mirostat_tau'] = gr.Slider(0, 10, step=0.01, value=generate_params['mirostat_tau'], label='mirostat_tau')
                             shared.gradio['mirostat_eta'] = gr.Slider(0, 1, step=0.01, value=generate_params['mirostat_eta'], label='mirostat_eta')
-                            shared.gradio['dynamic_temperature_low'] = gr.Slider(0.01, 5, value=generate_params['dynamic_temperature_low'], step=0.01, label='dynamic_temperature_low', info='Only used when dynamic_temperature is checked.')
                             shared.gradio['dynamic_temperature'] = gr.Checkbox(value=generate_params['dynamic_temperature'], label='dynamic_temperature')
+                            shared.gradio['dynatemp_low'] = gr.Slider(0.01, 5, value=generate_params['dynatemp_low'], step=0.01, label='dynatemp_low', visible=generate_params['dynamic_temperature'])
+                            shared.gradio['dynatemp_high'] = gr.Slider(0.01, 5, value=generate_params['dynatemp_high'], step=0.01, label='dynatemp_high', visible=generate_params['dynamic_temperature'])
+                            shared.gradio['dynatemp_exponent'] = gr.Slider(0.01, 5, value=generate_params['dynatemp_exponent'], step=0.01, label='dynatemp_exponent', visible=generate_params['dynamic_temperature'])
                             shared.gradio['temperature_last'] = gr.Checkbox(value=generate_params['temperature_last'], label='temperature_last', info='Makes temperature the last sampler instead of the first.')
                             shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
                             shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)')
@@ -97,6 +99,7 @@ def create_event_handlers():
     shared.gradio['preset_menu'].change(presets.load_preset_for_ui, gradio('preset_menu', 'interface_state'), gradio('interface_state') + gradio(presets.presets_params()))
     shared.gradio['random_preset'].click(presets.random_preset, gradio('interface_state'), gradio('interface_state') + gradio(presets.presets_params()))
     shared.gradio['grammar_file'].change(load_grammar, gradio('grammar_file'), gradio('grammar_string'))
+    shared.gradio['dynamic_temperature'].change(lambda x: [gr.update(visible=x)] * 3, gradio('dynamic_temperature'), gradio('dynatemp_low', 'dynatemp_high', 'dynatemp_exponent'))
 
 
 def get_truncation_length():