mirror of
https://github.com/oobabooga/text-generation-webui.git
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416 lines
19 KiB
Python
416 lines
19 KiB
Python
import math
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import torch
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import transformers
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from transformers import LogitsWarper, is_torch_xpu_available
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from transformers.generation.logits_process import (
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LogitNormalization,
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LogitsProcessor,
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LogitsProcessorList,
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TemperatureLogitsWarper
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)
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from modules import shared
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global_scores = None
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class ModifiedTemperatureLogitsWarper(LogitsWarper):
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'''
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Based on the original Transformers temperature logits warper, this
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adds support for dynamic temperature and quadratic sampling.
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'''
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def __init__(self, temperature: float, dynamic_temperature: bool, dynatemp_low: float, dynatemp_high: float, dynatemp_exponent: float, smoothing_factor: float):
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if not isinstance(temperature, float) or not (temperature > 0):
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except_msg = (
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f"`temperature` (={temperature}) has to be a strictly positive float, otherwise your next token "
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"scores will be invalid."
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)
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if isinstance(temperature, float) and temperature == 0.0:
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except_msg += " If you're looking for greedy decoding strategies, set `do_sample=False`."
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raise ValueError(except_msg)
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self.temperature = temperature
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self.dynamic_temperature = dynamic_temperature
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self.dynatemp_low = dynatemp_low
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self.dynatemp_high = dynatemp_high
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self.dynatemp_exponent = dynatemp_exponent
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self.smoothing_factor = smoothing_factor
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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# Quadratic sampling
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if self.smoothing_factor > 0:
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# Compute the maximum logit value
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max_logit = scores.max()
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# Apply the quadratic transformation
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transformed_logits = -(self.smoothing_factor * (scores - max_logit)**2) + max_logit
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# No need to print the top 5 logits since this is not required
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# print("Original top 5 logits: ", torch.topk(scores, 5))
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# print("New top 5 logits: ", torch.topk(transformed_logits, 5))
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return transformed_logits
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# Dynamic temperature
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elif self.dynamic_temperature:
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min_temp = self.dynatemp_low
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max_temp = self.dynatemp_high
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exponent_val = self.dynatemp_exponent
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# Convert logits to probabilities
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probs = torch.softmax(scores, dim=-1)
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# Calculate entropy of the softmax probabilities
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entropy = -1.0 * torch.where(probs > 0, probs * torch.log(probs), torch.zeros_like(probs)).sum()
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# Guard against future possible division by zero
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entropy = max(entropy, torch.tensor(1e-10)) # Ensures entropy is slightly greater than 0
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# Any logits which are not -Infinity will be considered for calculating max entropy.
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num_valid_tokens = torch.sum(scores > -float('inf')).item()
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# Now, calculate the max entropy by using only the valid tokens' count
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max_entropy = math.log(num_valid_tokens)
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# Guard against future possible division by zero
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max_entropy = max_entropy if max_entropy > 0.0 else 1e-10
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# Normalize the entropy
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normalized_entropy = entropy / max_entropy
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# Map the normalized entropy to the desired temperature range using the power function
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dyn_temp = min_temp + (max_temp - min_temp) * (normalized_entropy.pow(exponent_val))
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# Apply the dynamically calculated temperature scaling
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scores = scores / dyn_temp
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# print("----------------------\nTemperature from generation_config:", self.temperature)
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# print("min_temp:", min_temp)
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# print("max_temp:", max_temp)
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# print("Entropy:", entropy.item())
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# print("Max Possible Entropy considering valid tokens only:", max_entropy)
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# print("Normalized Entropy:", normalized_entropy.item())
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# print("Dynamic Temperature (dyn_temp):", dyn_temp.item())
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# print("----------------------")
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# max_prob_token_id = torch.argmax(scores, dim=-1) # Get the token ID with the highest probability
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# max_prob_token = shared.tokenizer.convert_ids_to_tokens(int(max_prob_token_id)) # Convert ID to token
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# print("--- T=", float(dyn_temp), "token=", max_prob_token, "min=", min_temp, "max=", max_temp, "exponent=", exponent_val)
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return scores
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# Regular temperature
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else:
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scores = scores / self.temperature
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return scores
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class MinPLogitsWarper(LogitsWarper):
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def __init__(self, min_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
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if min_p < 0 or min_p > 1.0:
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raise ValueError(f"`min_p` has to be a float >= 0 and <= 1, but is {min_p}")
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self.min_p = min_p
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self.filter_value = filter_value
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self.min_tokens_to_keep = min_tokens_to_keep
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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# Convert logits to probabilities
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probs = torch.softmax(scores, dim=-1)
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# Get the probability of the top token for each sequence in the batch
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top_probs, _ = probs.max(dim=-1, keepdim=True)
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# Calculate the actual min_p threshold by scaling min_p with the top token's probability
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scaled_min_p = self.min_p * top_probs
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# Create a mask for tokens that have a probability less than the scaled min_p
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tokens_to_remove = probs < scaled_min_p
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sorted_indices = torch.argsort(scores, descending=True, dim=-1)
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sorted_indices_to_remove = torch.gather(tokens_to_remove, dim=-1, index=sorted_indices)
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if self.min_tokens_to_keep > 1:
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# Keep at least min_tokens_to_keep
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sorted_indices_to_remove[..., : self.min_tokens_to_keep] = False
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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scores = scores.masked_fill(indices_to_remove, self.filter_value)
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return scores
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class TailFreeLogitsWarper(LogitsWarper):
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def __init__(self, tfs: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
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tfs = float(tfs)
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if tfs < 0 or tfs > 1.0:
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raise ValueError(f"`tfs` has to be a float >= 0 and <= 1, but is {tfs}")
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self.tfs = tfs
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self.filter_value = filter_value
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self.min_tokens_to_keep = min_tokens_to_keep
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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sorted_logits, sorted_indices = torch.sort(scores, descending=True)
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probs = sorted_logits.softmax(dim=-1)
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# Compute second derivative normalized CDF
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d2 = probs.diff().diff().abs()
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normalized_d2 = d2 / d2.sum(dim=-1, keepdim=True)
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normalized_d2_cdf = normalized_d2.cumsum(dim=-1)
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# Remove tokens with CDF value above the threshold (token with 0 are kept)
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sorted_indices_to_remove = normalized_d2_cdf > self.tfs
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# Centre the distribution around the cutoff as in the original implementation of the algorithm
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sorted_indices_to_remove = torch.cat(
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(
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torch.zeros(scores.shape[0], 1, dtype=torch.bool, device=scores.device),
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sorted_indices_to_remove,
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torch.ones(scores.shape[0], 1, dtype=torch.bool, device=scores.device),
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),
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dim=-1,
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)
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if self.min_tokens_to_keep > 1:
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# Keep at least min_tokens_to_keep
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sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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scores = scores.masked_fill(indices_to_remove, self.filter_value)
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return scores
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class TopALogitsWarper(LogitsWarper):
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def __init__(self, top_a: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
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top_a = float(top_a)
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if top_a < 0 or top_a > 1.0:
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raise ValueError(f"`top_a` has to be a float >= 0 and <= 1, but is {top_a}")
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self.top_a = top_a
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self.filter_value = filter_value
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self.min_tokens_to_keep = min_tokens_to_keep
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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sorted_logits, sorted_indices = torch.sort(scores, descending=True)
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probs = sorted_logits.softmax(dim=-1)
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# Remove tokens with probability less than top_a*(max(probs))^2 (token with 0 are kept)
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probs_max = probs[..., 0, None]
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sorted_indices_to_remove = probs < probs_max * probs_max * self.top_a
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if self.min_tokens_to_keep > 1:
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# Keep at least min_tokens_to_keep
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sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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scores = scores.masked_fill(indices_to_remove, self.filter_value)
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return scores
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class MirostatLogitsWarper(LogitsWarper):
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def __init__(self, mirostat_mode: int, mirostat_tau: float, mirostat_eta: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
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if mirostat_mode not in [2]:
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raise ValueError(f"`mirostat` has to be a an integer 2, but is {mirostat_mode}")
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self.mirostat_mode = mirostat_mode
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self.mirostat_eta = mirostat_eta
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self.mirostat_tau = mirostat_tau
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self.filter_value = filter_value
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self.min_tokens_to_keep = min_tokens_to_keep
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self.mu = 2 * self.mirostat_tau
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self.e = 0
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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logits = scores[0]
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sorted_logits, sorted_indices = torch.sort(logits, descending=True)
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prob_original = torch.softmax(sorted_logits, dim=-1).tolist() # candidates
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# Truncate the words with surprise values greater than mu
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for i, candidate in enumerate(prob_original):
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if candidate > 0 and -math.log2(candidate) > self.mu:
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if (i == 0):
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sorted_logits = sorted_logits[:1]
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else:
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sorted_logits = sorted_logits[:i]
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break
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# Normalize the probabilities of the remaining words
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if is_torch_xpu_available():
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prob_topk = torch.softmax(sorted_logits, dim=0).to("xpu")
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prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True).to("xpu")
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else:
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prob_topk = torch.softmax(sorted_logits, dim=0).to('cuda')
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prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True).to('cuda')
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observed_surprise = -math.log2(prob_topk[prev_i])
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self.e = observed_surprise - self.mirostat_tau
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# Update mu using the learning rate and error
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self.mu -= self.mirostat_eta * self.e
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sorted_indices_to_remove = torch.ones_like(scores[0], dtype=torch.bool)
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sorted_indices_to_remove[prev_i] = False
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indices_to_remove = sorted_indices_to_remove.unsqueeze(0).scatter(1, sorted_indices.unsqueeze(0), sorted_indices_to_remove.unsqueeze(0))
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scores = scores.masked_fill(indices_to_remove, self.filter_value)
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return scores
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class SpyLogitsWarper(LogitsWarper):
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def __init__(self):
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pass
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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global global_scores
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global_scores = scores
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return scores
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class RepetitionPenaltyLogitsProcessorWithRange(LogitsProcessor):
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'''
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Copied from the transformers library
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'''
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def __init__(self, penalty: float, presence_penalty: float, frequency_penalty: float, _range: int):
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if not (penalty > 0):
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raise ValueError(f"`penalty` has to be strictly positive, but is {penalty}")
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self.penalty = penalty
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self.presence_penalty = presence_penalty
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self.frequency_penalty = frequency_penalty
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self._range = _range
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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input_ids = input_ids[:, -self._range:]
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# We loop here because torch.unique() needs to process each row separately in the
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# case that batch_size > 1.
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for input_ids_row, scores_row in zip(input_ids, scores):
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unique_ids, counts = torch.unique(input_ids_row, return_counts=True)
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score = torch.gather(scores_row, 0, unique_ids)
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# multiplicative repetition penalty
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# if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
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score = torch.where(score < 0, score * self.penalty, score / self.penalty)
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scores_row.scatter_(0, unique_ids, score)
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# presence_penalty and frequency_penalty
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raw_presence_penalty = (counts > 0).to(scores.dtype)
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raw_frequency_penalty = counts.to(scores.dtype)
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additive_penalty = raw_presence_penalty * self.presence_penalty + raw_frequency_penalty * self.frequency_penalty
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scores_row.scatter_add_(0, unique_ids, -additive_penalty)
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return scores
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def get_logits_warper_patch(self, generation_config):
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# Make sure that temperature is float and not int
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if isinstance(generation_config.temperature, int):
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generation_config.temperature = float(generation_config.temperature)
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temperature = generation_config.temperature
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if generation_config.dynamic_temperature or generation_config.smoothing_factor > 0:
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# Make sure TemperatureLogitsWarper will be created by temporarily
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# setting temperature to a value != 1.
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generation_config.temperature = 1.1
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warpers = self._get_logits_warper_old(generation_config)
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for i in range(len(warpers)):
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if warpers[i].__class__.__name__ == 'TemperatureLogitsWarper':
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warpers[i] = ModifiedTemperatureLogitsWarper(
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temperature,
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generation_config.dynamic_temperature,
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generation_config.dynatemp_low,
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generation_config.dynatemp_high,
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generation_config.dynatemp_exponent,
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generation_config.smoothing_factor
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)
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warpers_to_add = LogitsProcessorList()
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min_tokens_to_keep = 2 if generation_config.num_beams > 1 else 1
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if generation_config.mirostat_mode is not None and generation_config.mirostat_mode == 2:
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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))
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# We need to disable samplers other than temperature
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for warper in warpers:
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if not isinstance(warper, TemperatureLogitsWarper):
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warpers.remove(warper)
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else:
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if generation_config.tfs is not None and 0.0 <= generation_config.tfs < 1.0:
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warpers_to_add.append(TailFreeLogitsWarper(tfs=generation_config.tfs, min_tokens_to_keep=min_tokens_to_keep))
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if generation_config.top_a is not None and 0.0 < generation_config.top_a <= 1.0:
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warpers_to_add.append(TopALogitsWarper(top_a=generation_config.top_a, min_tokens_to_keep=min_tokens_to_keep))
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if generation_config.min_p is not None and 0.0 < generation_config.min_p <= 1.0:
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warpers_to_add.append(MinPLogitsWarper(min_p=generation_config.min_p, min_tokens_to_keep=min_tokens_to_keep))
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if len(warpers) > 0 and isinstance(warpers[-1], LogitNormalization):
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normalize = warpers.pop(-1)
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else:
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normalize = None
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warpers += warpers_to_add
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if generation_config.temperature_last:
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temperature_idx = None
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for i in range(len(warpers)):
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if warpers[i].__class__.__name__ in ['TemperatureLogitsWarper', 'ModifiedTemperatureLogitsWarper']:
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temperature_idx = i
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break
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if temperature_idx is not None:
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warpers.append(warpers.pop(temperature_idx))
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if normalize is not None:
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warpers.append(normalize)
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warpers.append(SpyLogitsWarper())
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warpers = LogitsProcessorList(warpers)
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# for i in range(len(warpers)):
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# print(warpers[i].__class__.__name__)
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return warpers
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def get_logits_processor_patch(self, **kwargs):
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repetition_penalty = kwargs['generation_config'].repetition_penalty
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presence_penalty = kwargs['generation_config'].presence_penalty
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frequency_penalty = kwargs['generation_config'].frequency_penalty
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repetition_penalty_range = kwargs['generation_config'].repetition_penalty_range
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do_rep_pen_hijack = (repetition_penalty > 1) or (presence_penalty != 0) or (frequency_penalty != 0)
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if do_rep_pen_hijack:
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kwargs['generation_config'].repetition_penalty = 1.1 # Set to value > 1 to ensure RepetitionPenaltyLogitsProcessor is created
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result = self._get_logits_processor_old(**kwargs)
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if do_rep_pen_hijack:
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for i in range(len(result)):
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if result[i].__class__.__name__ == 'RepetitionPenaltyLogitsProcessor':
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result[i] = RepetitionPenaltyLogitsProcessorWithRange(repetition_penalty, presence_penalty, frequency_penalty, repetition_penalty_range)
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return result
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def generation_config_init_patch(self, **kwargs):
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self.__init___old(**kwargs)
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self.min_p = kwargs.pop("min_p", 0.0)
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self.dynamic_temperature = kwargs.pop("dynamic_temperature", False)
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self.dynatemp_low = kwargs.pop("dynatemp_low", 1)
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self.dynatemp_high = kwargs.pop("dynatemp_high", 1)
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self.dynatemp_exponent = kwargs.pop("dynatemp_exponent", 1)
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self.smoothing_factor = kwargs.pop("smoothing_factor", 0.0)
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self.tfs = kwargs.pop("tfs", 1.0)
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self.top_a = kwargs.pop("top_a", 0.0)
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self.mirostat_mode = kwargs.pop("mirostat_mode", 0)
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self.mirostat_eta = kwargs.pop("mirostat_eta", 0.1)
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self.mirostat_tau = kwargs.pop("mirostat_tau", 5)
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self.repetition_penalty_range = kwargs.pop("repetition_penalty_range", 0)
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self.presence_penalty = kwargs.pop("presence_penalty", 0)
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self.frequency_penalty = kwargs.pop("frequency_penalty", 0)
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self.temperature_last = kwargs.pop("temperature_last", False)
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def hijack_samplers():
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transformers.GenerationMixin._get_logits_warper_old = transformers.GenerationMixin._get_logits_warper
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transformers.GenerationMixin._get_logits_warper = get_logits_warper_patch
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transformers.GenerationMixin._get_logits_processor_old = transformers.GenerationMixin._get_logits_processor
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transformers.GenerationMixin._get_logits_processor = get_logits_processor_patch
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transformers.GenerationConfig.__init___old = transformers.GenerationConfig.__init__
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transformers.GenerationConfig.__init__ = generation_config_init_patch
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