310 lines
11 KiB
Python
310 lines
11 KiB
Python
import math
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import torch
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import torch.nn.functional as F
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import numpy as np
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from torch import Tensor, einsum, nn
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from dataclasses import asdict, dataclass, field
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# Simple filter to modify a token's probability if it shows up in the past
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# `one_time` will only apply the penalty once
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# `decay` is a factor that will exponentially apply to how far away it is
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def reptition_penalize( logits, previous, factor=1.0, decay=0.0, one_time=True ):
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if factor == 1.0 or previous is None:
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return logits
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unique = set()
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priors = reversed(previous)
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for distance, token in enumerate(priors):
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# skip if we're only applying the decay once
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if one_time and token in unique:
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continue
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distance += 1
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logits[:, token] /= factor * (distance ** decay)
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# add to set if we care about it
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if one_time:
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unique.add(token)
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return logits
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# Simple "filter" that modifies the logit for the stop token, based on the sequence length
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# `length` is the length of the sequence currently
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# `factor` is the power the length is raised to, so values > 0 will yield longer sequences, values < 0 will yield shorter sequences
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# `token` is the stop token.
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def length_penalize( logits, length, factor=0.0, token=-1 ):
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if factor == 0.0:
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return logits
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logits[:, token] /= (length ** factor)
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return logits
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# Simple way to ban tokens
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def ban_tokens( logits, tokens ):
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for token in tokens:
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# token not in logits
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if logits.shape[-1] >= token:
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continue
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logits[:, token] = -float("inf")
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return logits
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# Performs min_p filtering
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# From https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/generation/logits_process.py#L537
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def min_p_filtering( logits, min_p=0.0, min_tokens_to_keep=32 ):
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if min_p <= 0.0:
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return logits
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# Convert logits to probabilities
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probs = torch.softmax(logits, 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 = min_p * top_probs
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sorted_indices = torch.argsort(logits, descending=True, dim=-1)
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sorted_indices_to_remove = torch.gather(probs < scaled_min_p, dim=-1, index=sorted_indices)
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sorted_indices_to_remove[..., :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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return logits.masked_fill(indices_to_remove, -float("inf"))
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# Credit to https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py#L1145 / https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
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def top_k_top_p_filtering( logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens=1 ):
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"""Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
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Args:
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logits: logits distribution shape (batch size, vocabulary size)
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if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
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if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
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Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
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Make sure we keep at least min_tokens per batch example in the output
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"""
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if top_k > 0:
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top_k = min(max(top_k, min_tokens), logits.size(-1)) # Safety check
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# Remove all tokens with a probability less than the last token of the top-k
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indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
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logits[indices_to_remove] = filter_value
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if top_p < 1.0:
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sorted_logits, sorted_indices = torch.sort(logits, descending=True)
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cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
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# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
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sorted_indices_to_remove = cumulative_probs > top_p
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if min_tokens > 1:
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# Keep at least min_tokens (set to min_tokens-1 because we add the first one below)
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sorted_indices_to_remove[..., :min_tokens] = 0
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# Shift the indices to the right to keep also the first token above the threshold
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sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
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sorted_indices_to_remove[..., 0] = 0
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# scatter sorted tensors to original indexing
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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logits[indices_to_remove] = filter_value
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return logits
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# credit to https://github.com/LostRuins/koboldcpp/pull/464 // https://github.com/kalomaze/koboldcpp/tree/dynamic-temp
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def dynamic_temperature( logits, temperature=1.0, min_temperature = 0.0, k = 10, sigmoidCenterPoint = 0.5 ):
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# loop over logits[:], as the NAR will have logits.shape[0] > 1
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for i in range(logits.shape[0]):
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sum_exp = 0.0
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maximum = torch.max( logits[i] )
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for logit in logits[i]:
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sum_exp += math.exp( logit - maximum )
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prob_max_token_before_temp = 1.0 / sum_exp
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dynamic_temperature = temperature - (temperature - min_temperature) / (1 + math.exp(-k * (prob_max_token_before_temp - sigmoidCenterPoint)))
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logits[i] /= dynamic_temperature
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return logits
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# picks the top K tokens amongst a batch of logits
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# logits: [Tensor] list of logits
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# candidates: [(batch, token)] list, where batch indicates the index of the logits the given token is from
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def top_k_logits_list( logits_list, k ):
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# ( batch, tokens ) => ( batch x tokens )
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logits = torch.cat( logits_list )
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candidates = list(torch.topk(logits.flatten(), k).indices.tolist()) # perform top-k across all logits
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for i, index in enumerate(candidates):
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t = []
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N = np.prod(logits.size())
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for n in logits.size():
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N //= n
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t.append(index // N)
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index %= N
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candidates[i] = tuple(t)
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return candidates
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# Credit to: https://github.com/basusourya/mirostat/
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# performs mirostat-based sampling
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# logits: Tensor of logit probabilities
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# state: the mirostat state
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def mirostat_sample( logits, state = None ):
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def compute_k(prob, n, tau):
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num = 0
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den = 0
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for i in range(100):
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b = prob[i]/prob[i+1]
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t = (i+2)/(i+1)
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num += math.log(b)*math.log(t)
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den += math.log(t)**2
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s = num/den
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eps = s-1
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k = ((eps*(2**(tau)))/(1-n**(-eps)))**(1/s)
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k = round(k)
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return k
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if "max_surprise" not in state:
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state["max_surprise"] = state["tau"] * 2
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if "error_surprise" not in state:
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state["error_surprise"] = 0
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if "running_total_surprise" not in state:
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state["running_total_surprise"] = 0
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sorted_logits, sorted_indices = torch.sort( logits[-1, :], descending=True )
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prob_original = torch.softmax( sorted_logits, dim=-1 ).tolist()
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k = compute_k(prob_original, state["n"], state["max_surprise"]) + 1
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sorted_logits = sorted_logits[0:k]
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sorted_indices = sorted_indices[0:k]
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prob_topk = torch.softmax(sorted_logits, dim = 0)
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prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True)
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state["index_surprise"] = math.log2(1/prob_original[prev_i])
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state["running_total_surprise"] += state["index_surprise"]
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state["error_surprise"] = state["index_surprise"] - state["tau"]
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state["max_surprise"] -= state["eta"] * state["error_surprise"]
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state["token"] = sorted_indices[prev_i]
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return state
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# Credits to: https://github.com/oobabooga/text-generation-webui/pull/5677
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# performs DRY sampling
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# * (honestly it looks close to rep pen anyways but what do I know)
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# `logits` are the scores used to sample against
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# `previous` are the prior tokens to penalize with
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# `factor` is the scalar multiplier
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# `base` is the base number to raise to the (length - allowed_length)th power
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# `allowed_length` limits the range to apply DRY to
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def dry_sampling( logits, previous=None, factor=0.0, base=1.75, allowed_length=2 ):
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if factor == 0.0 or previous is None:
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return logits
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lengths = {}
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for i, token in enumerate( previous ):
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length = 1
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while length < max(allowed_length, 50):
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j = i - length
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# Start of input reached.
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if j < 0:
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break
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# Start of match reached.
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if previous[j] != previous[-length-1]:
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break
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length += 1
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lengths[token] = max(length, lengths[token]) if token in lengths else length
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for token, length in lengths.items():
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if length < allowed_length:
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break
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logits[:, token] -= factor * base ** (length - allowed_length)
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return logits
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LN_2 = 0.69314718056 # ln(2) = 1.0 / LOG2_E
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# Grabbed from https://github.com/xjdr-alt/entropix/blob/main/entropix/sampler.py
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# Right now I only care about quantifying these two, I'll figure out how to best apply this to the model
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def calculate_entropix_metrics( logits, attention_scores=None, dim=-1 ):
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"""Calculate the entropy and varentropy of the probability distribution using logsoftmax."""
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log_probs = torch.nn.functional.log_softmax(logits, dim=dim)
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probs = torch.exp(log_probs)
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entropy = -torch.sum(probs * log_probs, dim=dim) / LN_2 # Convert to base-2
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varentropy = torch.sum(probs * (log_probs / LN_2 + entropy[..., None])**2, dim=dim)
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if attention_scores is None:
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return {
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"logits_entropy": torch.mean(entropy).item(),
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"logits_varentropy": torch.mean(varentropy).item(),
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}
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attention_probs = torch.nn.functional.softmax(attention_scores, dim=-1)
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attn_entropy = -torch.sum(attention_probs * torch.log2(torch.clip(attention_probs, 1e-10, 1.0)), dim=-1)
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attn_varentropy = torch.var(attn_entropy, dim=1)
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mean_attention = torch.mean(attention_probs, dim=1)
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agreement = torch.mean(torch.abs(attention_probs - mean_attention[:, None, :]), dim=(1, 2))
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interaction_strength = torch.mean(torch.abs(attention_scores), dim=(1, 2, 3))
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return {
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"logits_entropy": torch.mean(entropy),
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"logits_varentropy": torch.mean(varentropy),
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"attn_entropy": torch.mean(attn_entropy),
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"attn_varentropy": torch.mean(attn_varentropy),
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"agreement": torch.mean(agreement),
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"interaction_strength": torch.mean(torch.abs(attention_scores), dim=(1, 2, 3)),
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}
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# to-do: play around with these values
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@dataclass()
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class EntropixSamplerConfig:
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temp: float = 0.85
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top_p: float = 0.90
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top_k: int = 27
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min_p: float = 0.01 # was 0.03 # Turn this down to 0.01 to reduce the shoggoth
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low_ent_thresh: float = 0.1 # 3.0
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low_vent_thresh: float = 0.1 # 3.0
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med_ent_thresh: float = 3.0 # 6.0
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high_ent_thresh: float = 5.0 # 9.0
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high_vent_thresh: float = 5.0 # 9.0
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# TODO this is a bit of a nasty mess, but also makes all the hyperparameters visible
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helv_attn_ent_offset: float = 1.3
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helv_attn_ent_coef: float = 0.2
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lehv_interaction_strength_offset: float = 1.2
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lehv_interaction_strength_coef: float = 0.3
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hehv_attn_ent_coef: float = 0.2
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hehv_attn_vent_offset: float = 2.0
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hehv_attn_vent_coef: float = 0.5
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# TODO not convinced this should
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n_adaptive_samples: int = 5
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# Adaptive sampling parameters
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ada_temp_logits: float = 0.3
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ada_temp_attn: float = 0.2
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ada_temp_agree: float = 0.2
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ada_top_p: float = 0.1
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ada_top_k_int: float = 0.3
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ada_top_k_agree: float = 0.2
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ada_min_p: float = 0.5
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ada_score_logits_ent: float = 0.1
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ada_score_attn_ent: float = 0.2
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ada_score_logits_vent: float = 0.3
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ada_score_attn_vent: float = 0.4
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ada_score_agree: float = 0.5
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ada_score_int: float = 0.6
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# extra stuff
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top_k_min: int = 1
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top_k_max: int = 1024
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temperature_max: float = 1.25
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temperature_min: float = 0.5 |