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"""callback to calculate perplexity as an evaluation metric.""" |
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from typing import Dict, List, Optional |
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import torch |
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from torch import Tensor |
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from tqdm import tqdm |
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from transformers.modeling_outputs import CausalLMOutput |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.tokenization_utils import PreTrainedTokenizer |
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class Perplexity: |
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""" |
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Calculate perplexity as defined in https://huggingface.co/docs/transformers/en/perplexity. |
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This is a custom variant that doesn't re-tokenize the input or re-load the model. |
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""" |
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def __init__( |
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self, |
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model: PreTrainedModel, |
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tokenizer: PreTrainedTokenizer, |
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max_seq_len: int, |
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stride: int = 512, |
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) -> None: |
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self.max_seq_len = max_seq_len |
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self.stride = stride |
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self.model = model |
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self.tokenizer = tokenizer |
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self.device = model.device |
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self.name = "perplexity" |
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def _feature_names(self) -> List[str]: |
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return ["references"] |
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def compute( |
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self, |
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references: Optional[List[str]] = None, |
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) -> Dict[str, float]: |
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""" |
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Compute perplexity in a fixed length sliding window across the sequence. |
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""" |
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assert references is not None, "Missing parameter: references" |
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references_tokenized = self.tokenizer( |
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references, return_tensors="pt", padding=True, truncation=True |
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) |
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input_ids: Tensor = references_tokenized["input_ids"] |
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input_ids = input_ids.to(self.device) |
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sequence_length = input_ids.size(1) |
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losses = [] |
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prev_end_loc = 0 |
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for begin_loc in tqdm(range(0, sequence_length, self.stride)): |
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end_loc = min(begin_loc + self.max_seq_len, sequence_length) |
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trg_len = end_loc - prev_end_loc |
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input_ids_slice = input_ids[:, begin_loc:end_loc] |
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labels_slice = input_ids_slice.clone() |
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labels_slice[:, :-trg_len] = -100 |
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with torch.no_grad(): |
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outputs: CausalLMOutput = self.model( |
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input_ids=input_ids_slice, labels=labels_slice |
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) |
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losses.append(outputs.loss) |
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prev_end_loc = end_loc |
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if end_loc == sequence_length: |
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break |
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perplexity = torch.exp(torch.stack(losses).mean()).item() |
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return { |
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"score": perplexity, |
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} |
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