import math
import torch
import transformers
from transformers import LogitsWarper, is_torch_xpu_available
from transformers.generation.logits_process import (
LogitNormalization,
LogitsProcessor,
LogitsProcessorList,
TemperatureLogitsWarper
)
from modules import shared
global_scores = None
class TemperatureLogitsWarperWithDynatemp(LogitsWarper):
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 "
"scores will be invalid."
)
if isinstance(temperature, float) and temperature == 0.0:
except_msg += " If you're looking for greedy decoding strategies, set `do_sample=False`."
raise ValueError(except_msg)
self.temperature = temperature
self.dynamic_temperature = dynamic_temperature
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:
# Regular temperature
if not self.dynamic_temperature:
scores = scores / self.temperature
return scores
# Dynamic temperature
else:
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)
# Calculate entropy of the softmax probabilities
entropy = -1.0 * torch.where(probs > 0, probs * torch.log(probs), torch.zeros_like(probs)).sum()
# Guard against future possible division by zero
entropy = max(entropy, torch.tensor(1e-10)) # Ensures entropy is slightly greater than 0
# Any logits which are not -Infinity will be considered for calculating max entropy.
num_valid_tokens = torch.sum(scores > -float('inf')).item()
# Now, calculate the max entropy by using only the valid tokens' count
max_entropy = math.log(num_valid_tokens)
# Guard against future possible division by zero
max_entropy = max_entropy if max_entropy > 0.0 else 1e-10
# Normalize the entropy
normalized_entropy = entropy / max_entropy
# Map the normalized entropy to the desired temperature range using the power function
dyn_temp = min_temp + (max_temp - min_temp) * (normalized_entropy.pow(exponent_val))
# Apply the dynamically calculated temperature scaling
scores = scores / dyn_temp
# print("----------------------\nTemperature from generation_config:", self.temperature)
# print("min_temp:", min_temp)
# print("max_temp:", max_temp)
# print("Entropy:", entropy.item())
# print("Max Possible Entropy considering valid tokens only:", max_entropy)
# print("Normalized Entropy:", normalized_entropy.item())
# print("Dynamic Temperature (dyn_temp):", dyn_temp.item())
# print("----------------------")
# 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, "exponent=", exponent_val)
return scores
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):
tfs = float(tfs)
if tfs < 0 or tfs > 1.0:
raise ValueError(f"`tfs` has to be a float >= 0 and <= 1, but is {tfs}")
self.tfs = tfs
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:
sorted_logits, sorted_indices = torch.sort(scores, descending=True)
probs = sorted_logits.softmax(dim=-1)
# Compute second derivative normalized CDF
d2 = probs.diff().diff().abs()
normalized_d2 = d2 / d2.sum(dim=-1, keepdim=True)
normalized_d2_cdf = normalized_d2.cumsum(dim=-1)
# Remove tokens with CDF value above the threshold (token with 0 are kept)
sorted_indices_to_remove = normalized_d2_cdf > self.tfs
# Centre the distribution around the cutoff as in the original implementation of the algorithm
sorted_indices_to_remove = torch.cat(
(
torch.zeros(scores.shape[0], 1, dtype=torch.bool, device=scores.device),
sorted_indices_to_remove,
torch.ones(scores.shape[0], 1, dtype=torch.bool, device=scores.device),
),
dim=-1,
)
if self.min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep
sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
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 TopALogitsWarper(LogitsWarper):
def __init__(self, top_a: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
top_a = float(top_a)
if top_a < 0 or top_a > 1.0:
raise ValueError(f"`top_a` has to be a float >= 0 and <= 1, but is {top_a}")
self.top_a = top_a
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:
sorted_logits, sorted_indices = torch.sort(scores, descending=True)
probs = sorted_logits.softmax(dim=-1)
# Remove tokens with probability less than top_a*(max(probs))^2 (token with 0 are kept)
probs_max = probs[..., 0, None]
sorted_indices_to_remove = probs < probs_max * probs_max * self.top_a
if self.min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep
sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
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 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
if is_torch_xpu_available():
prob_topk = torch.softmax(sorted_logits, dim=0).to("xpu")
prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True).to("xpu")
else:
prob_topk = torch.softmax(sorted_logits, dim=0).to('cuda')
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
class SpyLogitsWarper(LogitsWarper):
def __init__(self):
pass
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
global global_scores
global_scores = scores
return scores
class RepetitionPenaltyLogitsProcessorWithRange(LogitsProcessor):
'''
Copied from the transformers library
'''
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.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:]
# 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)
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)
return scores
def get_logits_warper_patch(self, generation_config):
# Make sure that temperature is float and not int
if isinstance(generation_config.temperature, int):
generation_config.temperature = float(generation_config.temperature)
temperature = generation_config.temperature
if generation_config.dynamic_temperature:
# Make sure TemperatureLogitsWarper will be created by temporarily
# setting temperature to a value != 1.
generation_config.temperature = 1.1
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.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
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 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 len(warpers) > 0 and isinstance(warpers[-1], LogitNormalization):
normalize = warpers.pop(-1)
else:
normalize = None
warpers += warpers_to_add
if generation_config.temperature_last:
temperature_idx = None
for i in range(len(warpers)):
if warpers[i].__class__.__name__ in ['TemperatureLogitsWarper', 'TemperatureLogitsWarperWithDynatemp']:
temperature_idx = i
break
if temperature_idx is not None:
warpers.append(warpers.pop(temperature_idx))
if normalize is not None:
warpers.append(normalize)
warpers.append(SpyLogitsWarper())
warpers = LogitsProcessorList(warpers)
# for i in range(len(warpers)):
# print(warpers[i].__class__.__name__)
return warpers
def get_logits_processor_patch(self, **kwargs):
repetition_penalty = kwargs['generation_config'].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 (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
result = self._get_logits_processor_old(**kwargs)
if do_rep_pen_hijack:
for i in range(len(result)):
if result[i].__class__.__name__ == 'RepetitionPenaltyLogitsProcessor':
result[i] = RepetitionPenaltyLogitsProcessorWithRange(repetition_penalty, presence_penalty, frequency_penalty, repetition_penalty_range)
return result
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.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)
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.presence_penalty = kwargs.pop("presence_penalty", 0)
self.frequency_penalty = kwargs.pop("frequency_penalty", 0)
self.temperature_last = kwargs.pop("temperature_last", False)
def hijack_samplers():
transformers.GenerationMixin._get_logits_warper_old = transformers.GenerationMixin._get_logits_warper
transformers.GenerationMixin._get_logits_warper = get_logits_warper_patch
transformers.GenerationMixin._get_logits_processor_old = transformers.GenerationMixin._get_logits_processor
transformers.GenerationMixin._get_logits_processor = get_logits_processor_patch
transformers.GenerationConfig.__init___old = transformers.GenerationConfig.__init__
transformers.GenerationConfig.__init__ = generation_config_init_patch