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Add Mirostat v2 sampling to transformer models (#2571)

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brandonj60 2023-06-09 19:26:31 -05:00 committed by GitHub
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commit b04e18d10c
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3 changed files with 66 additions and 7 deletions
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69
modules/sampler_hijack.py
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@ -1,7 +1,11 @@
import math
import torch
import transformers
from transformers import LogitsWarper
from transformers.generation.logits_process import LogitNormalization, LogitsProcessorList
from transformers.generation.logits_process import (LogitNormalization,
LogitsProcessorList,
TemperatureLogitsWarper)
class TailFreeLogitsWarper(LogitsWarper):
@ -70,15 +74,67 @@ class TopALogitsWarper(LogitsWarper):
return scores
class MirostatLogitsWarper(LogitsWarper):
def __init__(self, mirostat_mode: int, mirostat_tau: float, mirostat_eta: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
if mirostat_mode not in [2]:
raise ValueError(f"`mirostat` has to be a an integer 2, but is {mirostat_mode}")
self.mirostat_mode = mirostat_mode
self.mirostat_eta = mirostat_eta
self.mirostat_tau = mirostat_tau
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
self.mu = 2 * self.mirostat_tau
self.e = 0
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
logits = scores[0]
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
prob_original = torch.softmax(sorted_logits, dim=-1).tolist() # candidates
# Truncate the words with surprise values greater than mu
for i, candidate in enumerate(prob_original):
if candidate > 0 and -math.log2(candidate) > self.mu:
if (i == 0):
sorted_logits = sorted_logits[:1]
else:
sorted_logits = sorted_logits[:i]
break
# Normalize the probabilities of the remaining words
prob_topk = torch.softmax(sorted_logits, dim=0)
prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True).to('cuda')
observed_surprise = -math.log2(prob_topk[prev_i])
self.e = observed_surprise - self.mirostat_tau
# Update mu using the learning rate and error
self.mu -= self.mirostat_eta * self.e
sorted_indices_to_remove = torch.ones_like(scores[0], dtype=torch.bool)
sorted_indices_to_remove[prev_i] = False
indices_to_remove = sorted_indices_to_remove.unsqueeze(0).scatter(1, sorted_indices.unsqueeze(0), sorted_indices_to_remove.unsqueeze(0))
scores = scores.masked_fill(indices_to_remove, self.filter_value)
return scores
def get_logits_warper_patch(self, generation_config):
warpers = self._get_logits_warper_old(generation_config)
warpers_to_add = LogitsProcessorList()
min_tokens_to_keep = 2 if generation_config.num_beams > 1 else 1
if generation_config.tfs is not None and 0.0 <= generation_config.tfs <= 1.0:
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.mirostat_mode is not None and generation_config.mirostat_mode == 2:
warpers_to_add.append(MirostatLogitsWarper(mirostat_mode=generation_config.mirostat_mode, mirostat_eta=generation_config.mirostat_eta, mirostat_tau=generation_config.mirostat_tau, min_tokens_to_keep=min_tokens_to_keep))
# We need to disable samplers other than temperature
for warper in warpers:
if not isinstance(warper, TemperatureLogitsWarper):
warpers.remove(warper)
else:
if generation_config.tfs is not None and 0.0 <= generation_config.tfs <= 1.0:
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 warpers and isinstance(warpers[-1], LogitNormalization):
warpers = warpers[:-1] + warpers_to_add + [warpers[-1]]
@ -92,6 +148,9 @@ def generation_config_init_patch(self, **kwargs):
self.__init___old(**kwargs)
self.tfs = kwargs.pop("tfs", 1.0)
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)
self.mirostat_tau = kwargs.pop("mirostat_tau", 5)
def hijack_samplers():

2
modules/text_generation.py
View File

@ -193,7 +193,7 @@ def _generate_reply(question, state, eos_token=None, stopping_strings=None, is_c
def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False):
generate_params = {}
for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a']:
for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', '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']:
generate_params[k] = state[k]
for k in ['epsilon_cutoff', 'eta_cutoff']:

2
server.py
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@ -504,7 +504,7 @@ def create_settings_menus(default_preset):
shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
with gr.Column():
gr.Markdown('Mirostat (for llama.cpp)')
gr.Markdown('Mirostat (mode=1 is only for llama.cpp)')
shared.gradio['mirostat_mode'] = gr.Slider(0, 2, step=1, value=generate_params['mirostat_mode'], label='mirostat_mode')
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')