411 lines
15 KiB
Python
411 lines
15 KiB
Python
import math
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import torch
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from functools import lru_cache
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from typing import Optional, List, Dict, Union
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from transformers import (
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LogitsWarper,
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LogitsProcessor,
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TemperatureLogitsWarper,
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TopKLogitsWarper,
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TopPLogitsWarper,
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TypicalLogitsWarper,
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)
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mempool = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None
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class StaticWarper:
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def __init__(
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self,
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temperature=1.0,
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top_k=None,
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top_p=None,
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typical_p=None,
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):
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self.warpers = []
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if temperature is not None and temperature != 1.0:
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temperature = float(temperature)
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self.warpers.append(TemperatureLogitsWarper(temperature))
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if top_k is not None and top_k != 0:
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self.warpers.append(TopKLogitsWarper(top_k=top_k))
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if top_p is not None and top_p < 1.0:
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self.warpers.append(TopPLogitsWarper(top_p=top_p))
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if typical_p is not None and typical_p < 1.0:
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self.warpers.append(TypicalLogitsWarper(mass=typical_p))
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self.cuda_graph = None
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self.static_scores = None
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self.static_warped_scores = None
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self.static_next_logprob = None
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def __call__(self, scores):
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if scores.device.type == "cuda":
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if self.cuda_graph is None:
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self.static_scores = scores
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self.cuda_graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(self.cuda_graph, pool=mempool):
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local_scores = self.static_scores
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for warper in self.warpers:
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local_scores = warper(None, local_scores)
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self.static_warped_scores = local_scores
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# Compute logprobs
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self.static_next_logprob = torch.log_softmax(
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self.static_warped_scores, -1
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)
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self.static_scores.copy_(scores)
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self.cuda_graph.replay()
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return self.static_warped_scores, self.static_next_logprob
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# CPU branch
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for warper in self.warpers:
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scores = warper(None, scores)
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return scores, torch.log_softmax(scores, -1)
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@lru_cache(10)
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def static_warper(
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temperature: Optional[float],
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top_k: Optional[int],
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top_p: Optional[float],
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typical_p: Optional[float],
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) -> StaticWarper:
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return StaticWarper(
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temperature=temperature, top_k=top_k, top_p=top_p, typical_p=typical_p
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)
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class HeterogeneousRepetitionPenaltyLogitsProcessor(LogitsProcessor):
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r"""
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[`LogitsProcessor`] enforcing an exponential penalty on repeated sequences.
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This version allows for a separate value for each sample and runs inplace when possible.
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It doesn't validate inputs.
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Args:
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repetition_penalty (`List[float]`):
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The parameter for repetition penalty. 1.0 means no penalty. See [this
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paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
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"""
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def __init__(self, penalty: List[float], dtype: torch.dtype, device: torch.device):
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self.penalty = penalty
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self.penalty_tensor = torch.tensor(
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penalty, dtype=dtype, device=device
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).unsqueeze(1)
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def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
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score = torch.gather(scores, 1, input_ids)
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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(
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score < 0, score * self.penalty_tensor, score / self.penalty_tensor
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)
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scores.scatter_(1, input_ids, score)
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return scores
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def filter(self, indices):
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self.penalty = [self.penalty[i] for i in indices]
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if any([x != 1.0 for x in self.penalty]):
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self.penalty_tensor = self.penalty_tensor[indices]
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return self
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return None
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class HeterogeneousTemperatureLogitsWarper:
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r"""
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[`LogitsWarper`] for temperature (exponential scaling output probability distribution).
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This version allows for a separate value for each sample and runs inplace when possible.
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It doesn't validate inputs.
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Args:
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temperature (`float`):
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The value used to module the logits distribution.
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"""
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def __init__(
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self, temperature: List[float], dtype: torch.dtype, device: torch.device
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):
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self.temperature = temperature
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self.temperature_tensor = torch.tensor(
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temperature, dtype=dtype, device=device
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).unsqueeze(1)
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def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
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scores.div_(self.temperature_tensor)
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return scores
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def filter(self, indices):
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self.temperature = [self.temperature[i] for i in indices]
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if any([x != 1.0 for x in self.temperature]):
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self.temperature_tensor = self.temperature_tensor[indices]
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return self
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return None
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class HeterogeneousTopPLogitsWarper(LogitsWarper):
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"""
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[`LogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
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This version allows for a separate value for each sample and runs inplace when possible.
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It doesn't validate inputs.
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Args:
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top_p (`float`):
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If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
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higher are kept for generation.
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filter_value (`float`, *optional*, defaults to `-float("Inf")`):
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All filtered values will be set to this float value.
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min_tokens_to_keep (`int`, *optional*, defaults to 1):
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Minimum number of tokens that cannot be filtered.
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"""
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def __init__(
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self,
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top_p: List[float],
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dtype: torch.dtype,
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device: torch.device,
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filter_value: float = -math.inf,
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min_tokens_to_keep: int = 1,
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):
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self.top_p = top_p
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self.top_p_opposite = 1 - torch.tensor(
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top_p, dtype=dtype, device=device
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).unsqueeze(1)
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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.Tensor, scores: torch.Tensor) -> torch.Tensor:
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sorted_logits, sorted_indices = torch.sort(scores, descending=False)
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probs = sorted_logits.softmax(dim=-1)
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# This is way faster for some reason
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for i in range(probs.shape[0]):
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probs[i] = probs[i].cumsum(dim=-1)
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# Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
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sorted_indices_to_remove = probs <= self.top_p_opposite
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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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# scatter sorted tensors to original indexing
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indices_to_remove = sorted_indices_to_remove.scatter(
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1, sorted_indices, sorted_indices_to_remove
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)
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warped_scores = scores.masked_fill_(indices_to_remove, self.filter_value)
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return warped_scores
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def filter(self, indices):
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self.top_p = [self.top_p[i] for i in indices]
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if any([x < 1.0 for x in self.top_p]):
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self.top_p_opposite = self.top_p_opposite[indices]
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return self
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return None
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class HeterogeneousTopKLogitsWarper(LogitsWarper):
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r"""
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[`LogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
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This version allows for a separate value for each sample and runs inplace when possible.
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It doesn't validate inputs.
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Args:
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top_k (`int`):
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The number of highest probability vocabulary tokens to keep for top-k-filtering.
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filter_value (`float`, *optional*, defaults to `-float("Inf")`):
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All filtered values will be set to this float value.
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min_tokens_to_keep (`int`, *optional*, defaults to 1):
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Minimum number of tokens that cannot be filtered.
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"""
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def __init__(
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self,
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top_k: List[int],
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device: torch.device,
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filter_value: float = -math.inf,
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min_tokens_to_keep: int = 1,
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):
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self.top_k = top_k
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self.max_top_k = max(top_k)
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# value - 1 as we will use top_k to index and python uses 0 based numbering
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self.top_k_tensor = torch.tensor(
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[max(x - 1, min_tokens_to_keep - 1) for x in top_k],
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dtype=torch.int64,
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device=device,
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).unsqueeze(1)
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# 0 is a special value that disables top_k warping for this member of the batch
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disabled = [x == 0 for x in top_k]
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if any(disabled):
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self.top_k_disabled_mask = torch.tensor(
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disabled, dtype=torch.bool, device=device
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).view(-1, 1)
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else:
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self.top_k_disabled_mask = None
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self.filter_value = filter_value
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def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
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# If max_top_k is superior to the vocab, we need to clamp or the warper will fail
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if scores.size(-1) < self.max_top_k:
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max_top_k = scores.size(-1)
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top_k = torch.clamp_max(self.top_k_tensor, max_top_k)
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else:
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max_top_k = self.max_top_k
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top_k = self.top_k_tensor
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# Get the kth score for each member of the batch
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kth_scores = torch.gather(torch.topk(scores, max_top_k)[0], 1, top_k)
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# Mask member of kth_scores that do not want to use top_k warping
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if self.top_k_disabled_mask is not None:
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kth_scores.masked_fill_(self.top_k_disabled_mask, self.filter_value)
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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 = scores < kth_scores
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scores.masked_fill_(indices_to_remove, self.filter_value)
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return scores
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def filter(self, indices):
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self.top_k = [self.top_k[i] for i in indices]
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disabled = [x == 0 for x in self.top_k]
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if not all(disabled):
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self.top_k_tensor = self.top_k_tensor[indices]
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self.max_top_k = max(self.top_k)
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if self.top_k_disabled_mask is not None:
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self.top_k_disabled_mask = (
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self.top_k_disabled_mask[indices] if any(disabled) else None
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)
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return self
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return None
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class HeterogeneousTypicalLogitsWarper(LogitsWarper):
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r"""
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[`LogitsWarper`] that performs typical decoding. See [Typical Decoding for Natural Language
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Generation](https://arxiv.org/abs/2202.00666) for more information.
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This version allows for a separate value for each sample and runs inplace when possible.
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It doesn't validate inputs.
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Args:
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mass (`float`):
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Value of typical_p between 0 and 1 inclusive, defaults to 0.9.
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filter_value (`float`, *optional*, defaults to `-float("Inf")`):
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All filtered values will be set to this float value.
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min_tokens_to_keep (`int`, *optional*, defaults to 1):
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Minimum number of tokens that cannot be filtered.
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"""
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def __init__(
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self,
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mass: List[float],
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dtype: torch.dtype,
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device: torch.device,
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filter_value: float = -math.inf,
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min_tokens_to_keep: int = 1,
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):
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self.mass = mass
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self.mass_tensor = torch.tensor(mass, dtype=dtype, device=device).unsqueeze(1)
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# 1 is a special value that disables typical_p warping for this member of the batch
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disabled = [x == 1.0 for x in mass]
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if any(disabled):
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self.disabled_mask = torch.tensor(disabled, dtype=torch.bool, device=device)
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else:
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self.disabled_mask = None
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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.Tensor, scores: torch.Tensor) -> torch.Tensor:
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# calculate entropy
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normalized = torch.nn.functional.log_softmax(scores, dim=-1)
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p = torch.exp(normalized)
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ent = -(normalized * p).nansum(-1, keepdim=True)
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# shift and sort
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shifted_scores = torch.abs((-normalized) - ent)
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sorted_scores, sorted_indices = torch.sort(shifted_scores, descending=False)
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sorted_logits = scores.gather(-1, sorted_indices)
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probs = sorted_logits.softmax(dim=-1)
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# This is way faster for some reason
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for i in range(probs.shape[0]):
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probs[i] = probs[i].cumsum(dim=-1)
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# Remove tokens with cumulative mass above the threshold
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last_ind = (probs < self.mass_tensor).sum(dim=1)
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last_ind[last_ind < 0] = 0
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if self.disabled_mask is not None:
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last_ind.masked_fill_(self.disabled_mask, scores.shape[-1] - 1)
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sorted_indices_to_remove = sorted_scores > sorted_scores.gather(
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1, last_ind.view(-1, 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 (set to min_tokens_to_keep-1 because we add the first one below)
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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(
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1, sorted_indices, sorted_indices_to_remove
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)
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warped_scores = scores.masked_fill_(indices_to_remove, self.filter_value)
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return warped_scores
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def filter(self, indices):
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self.mass = [self.mass[i] for i in indices]
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disabled = [x == 1.0 for x in self.mass]
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if not all(disabled):
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self.mass_tensor = self.mass_tensor[indices]
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if self.disabled_mask is not None:
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self.disabled_mask = (
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self.disabled_mask[indices] if any(disabled) else None
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)
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return self
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return None
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class HeterogeneousProcessorWrapper(LogitsProcessor):
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r"""
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A wrapper for logit warpers or processors without heterogeneous parameter support.
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Args:
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processors (`Dict[int, Union[LogitsProcessor, LogitsWarper]]`):
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A mapping of sample indices to logit warpers or processors, to be run sequentially.
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"""
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def __init__(
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self,
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processors: Dict[int, Union[LogitsProcessor, LogitsWarper]],
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):
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self.processors = processors
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def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
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for i, processor in self.processors.items():
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scores[i : i + 1] = processor(input_ids[i : i + 1], scores[i : i + 1])
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return scores
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def filter(self, indices):
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new_processors = {}
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for i, idx in enumerate(indices):
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if idx in self.processors:
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new_processors[i] = self.processors[idx]
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if new_processors:
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self.processors = new_processors
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return self
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return None
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