""" adapted to support pegasus-xsum / local files """ # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Perplexity Metric.""" import datasets import numpy as np import torch from torch.nn import CrossEntropyLoss from transformers import AutoModelForCausalLM, AutoTokenizer import getpass import evaluate from evaluate import logging import pdb WINDOW_SIZE = 3 def prepare_coh_sents(predictions): blocks = [] lens = [] for pred in predictions: sents = pred.split("\n") if len(sents)<=WINDOW_SIZE: blocks.append(pred) lens.append(1) else: _block = [] for i in range(0,len(sents)-WINDOW_SIZE+1): _block.append("\n".join(sents[i:i+WINDOW_SIZE])) lens.append(len(_block)) blocks.extend(_block) # return blocks,lens _CITATION = """\ """ _DESCRIPTION = """ Perplexity (PPL) is one of the most common metrics for evaluating language models. It is defined as the exponentiated average negative log-likelihood of a sequence, calculated with exponent base `e`. For more information, see https://huggingface.co/docs/transformers/perplexity """ _KWARGS_DESCRIPTION = """ Args: model_id (str): model used for calculating Perplexity NOTE: Perplexity can only be calculated for causal language models. This includes models such as gpt2, causal variations of bert, causal versions of t5, and more (the full list can be found in the AutoModelForCausalLM documentation here: https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM ) predictions (list of str): input text, each separate text snippet is one list entry. batch_size (int): the batch size to run texts through the model. Defaults to 16. add_start_token (bool): whether to add the start token to the texts, so the perplexity can include the probability of the first word. Defaults to True. device (str): device to run on, defaults to 'cuda' when available Returns: perplexity: dictionary containing the perplexity scores for the texts in the input list, as well as the mean perplexity. If one of the input texts is longer than the max input length of the model, then it is truncated to the max length for the perplexity computation. Examples: Example 1: >>> perplexity = evaluate.load("perplexity", module_type="metric") >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"] >>> results = perplexity.compute(model_id='gpt2', ... add_start_token=False, ... predictions=input_texts) # doctest:+ELLIPSIS >>> print(list(results.keys())) ['perplexities', 'mean_perplexity'] >>> print(round(results["mean_perplexity"], 0)) 647.0 >>> print(round(results["perplexities"][0], 0)) 32.0 Example 2: >>> from datasets import load_dataset >>> perplexity = evaluate.load("perplexity", module_type="metric") >>> input_texts = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")["text"][:10] # doctest: +SKIP >>> input_texts = [s for s in input_texts if s!=''] >>> results = perplexity.compute(model_id='gpt2', ... predictions=input_texts) >>> print(list(results.keys())) ['perplexities', 'mean_perplexity'] >>> print(round(results["mean_perplexity"], 2)) # doctest: +SKIP 576.76 >>> print(round(results["perplexities"][0], 2)) # doctest: +SKIP 889.28 """ @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) class LocalCohPPL(evaluate.Measurement): def _info(self): return evaluate.MetricInfo( module_type="measurement", description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features( { "predictions": datasets.Value("string"), } ), reference_urls=["https://huggingface.co/spaces/ronaldahmed/local_coh_ppl"], ) ## PEDICTIONS: [str] sentences joined by "\n" def _compute(self, predictions, model_id, batch_size: int = 16, add_start_token: bool = True, device=None): MODEL_CACHE_DIR = "/home/rcardena/.cache/huggingface/" if getpass.getuser() == "s1987051": MODEL_CACHE_DIR="/disk/ocean/rcardenas/tools/huggingface/" elif getpass.getuser() == "rcardena": MODEL_CACHE_DIR="/gfs/team/nlp/users/rcardena/tools/huggingface/" if device is not None: assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu." if device == "gpu": device = "cuda" else: device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModelForCausalLM.from_pretrained(model_id,cache_dir=MODEL_CACHE_DIR) model = model.to(device) tokenizer = AutoTokenizer.from_pretrained( model_id, cache_dir=MODEL_CACHE_DIR, use_fast="cnn_dailymail" not in model_id, ) # if batch_size > 1 (which generally leads to padding being required), and # if there is not an already assigned pad_token, assign an existing # special token to also be the padding token if tokenizer.pad_token is None and batch_size > 1: existing_special_tokens = list(tokenizer.special_tokens_map_extended.values()) # check that the model already has at least one special token defined assert ( len(existing_special_tokens) > 0 ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1." # assign one of the special tokens to also be the pad token tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}) model.config.max_length = 512 if "scibert" in model_id else model.config.max_length if add_start_token: # leave room for token to be added: assert ( tokenizer.bos_token is not None ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False" max_tokenized_len = model.config.max_length - 1 else: max_tokenized_len = model.config.max_length loss_fct = CrossEntropyLoss(reduction="none") blocks,blens = prepare_coh_sents(predictions) all_norm_ppl = [] for start_index in logging.tqdm(range(0, len(blocks), batch_size)): end_index = min(start_index + batch_size, len(blocks)) batch_sents = blocks[start_index:end_index] encodings = tokenizer( batch_sents, add_special_tokens=False, padding=True, truncation=True, max_length=max_tokenized_len, return_tensors="pt", return_attention_mask=True, ).to(device) encoded_texts = encodings["input_ids"] attn_masks = encodings["attention_mask"] # check that each input is long enough: if add_start_token: assert torch.all(torch.ge(attn_masks.sum(1), 1)), "Each input text must be at least one token long." else: assert torch.all( torch.ge(attn_masks.sum(1), 2) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings." if add_start_token: bos_tokens_tensor = torch.tensor([[tokenizer.bos_token_id]] * encoded_texts.size(dim=0)).to(device) encoded_texts = torch.cat([bos_tokens_tensor, encoded_texts], dim=1) attn_masks = torch.cat( [torch.ones(bos_tokens_tensor.size(), dtype=torch.int64).to(device), attn_masks], dim=1 ) # tokenize by sentence sent_tok_lens = [] for pred in batch_sents: ss = pred.split("\n") sslens = [len(tokenizer(y,add_special_tokens=False,padding=False).input_ids) for y in ss] offset = int(add_start_token) sspos = [offset] for sslen in sslens: offset = min(offset + sslen,511 + int(add_start_token)) if offset == sspos[-1]: break # reached length limit sspos.append(offset) sent_tok_lens.append(sspos) labels = encoded_texts with torch.no_grad(): out_logits = model(encoded_texts, attention_mask=attn_masks).logits shift_logits = out_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() shift_attention_mask_batch = attn_masks[..., 1:].contiguous() loss_out = loss_fct(shift_logits.transpose(1, 2), shift_labels) * shift_attention_mask_batch perplexity_all = torch.exp( loss_out.sum(1) / shift_attention_mask_batch.sum(1) ).detach().cpu().numpy().tolist() norm_ppl = [] for b,stl in enumerate(sent_tok_lens): indv = [] for i in range(1,len(stl)): ppl = torch.exp( loss_out[b,stl[i-1]:stl[i]].sum() / shift_attention_mask_batch[b,stl[i-1]:stl[i]].sum() ).detach().cpu().item() indv.append(ppl) nppl = perplexity_all[b] / sum(indv) if len(indv)>1 else 0.0 norm_ppl.append(nppl) # all_norm_ppl.extend(norm_ppl) if any(np.isnan(norm_ppl)): print("[compute ppl] nan ...") pdb.set_trace() print(">>") # avg_ppl = [] offset = 0 for _len in blens: avg_ppl.append( float(np.mean(all_norm_ppl[offset:offset+_len])) ) offset += _len if any(np.isnan(avg_ppl)): print("[compute ppl] nan ...") pdb.set_trace() print(">>") return {"local_coh_ppl": avg_ppl}