import itertools import json import linecache import math import os import pickle import socket from logging import getLogger from pathlib import Path from typing import Callable, Dict, Iterable, List, Tuple, Union import numpy as np import torch import torch.distributed as dist from rouge_score import rouge_scorer, scoring from sacrebleu import corpus_bleu from torch import nn from torch.utils.data import Dataset, Sampler from transformers import BartTokenizer, EvalPrediction, PreTrainedTokenizer, T5Tokenizer from transformers.file_utils import cached_property from transformers.models.bart.modeling_bart import shift_tokens_right from utils.utils_graph2text import convert_text, eval_bleu from pytorch_lightning.utilities import rank_zero_info import pdb try: from fairseq.data.data_utils import batch_by_size FAIRSEQ_AVAILABLE = True except (ImportError, ModuleNotFoundError): FAIRSEQ_AVAILABLE = False def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=-100): """From fairseq""" if target.dim() == lprobs.dim() - 1: target = target.unsqueeze(-1) nll_loss = -lprobs.gather(dim=-1, index=target) smooth_loss = -lprobs.sum(dim=-1, keepdim=True) if ignore_index is not None: pad_mask = target.eq(ignore_index) nll_loss.masked_fill_(pad_mask, 0.0) smooth_loss.masked_fill_(pad_mask, 0.0) else: nll_loss = nll_loss.squeeze(-1) smooth_loss = smooth_loss.squeeze(-1) nll_loss = nll_loss.sum() # mean()? Scared to break other math. smooth_loss = smooth_loss.sum() eps_i = epsilon / lprobs.size(-1) loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss return loss, nll_loss def lmap(f: Callable, x: Iterable) -> List: """list(map(f, x))""" return list(map(f, x)) def calculate_bleu(output_lns, refs_lns) -> dict: """Uses sacrebleu's corpus_bleu implementation.""" return {"sacrebleu": round(corpus_bleu(output_lns, [refs_lns]).score, 4)} def build_compute_metrics_fn(task_name: str, tokenizer: PreTrainedTokenizer) -> Callable[[EvalPrediction], Dict]: def non_pad_len(tokens: np.ndarray) -> int: return np.count_nonzero(tokens != tokenizer.pad_token_id) def decode_pred(pred: EvalPrediction) -> Tuple[List[str], List[str]]: pred_str = tokenizer.batch_decode(pred.predictions, skip_special_tokens=True) label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True) pred_str = lmap(str.strip, pred_str) label_str = lmap(str.strip, label_str) return pred_str, label_str def summarization_metrics(pred: EvalPrediction) -> Dict: pred_str, label_str = decode_pred(pred) rouge: Dict = calculate_rouge(pred_str, label_str) summ_len = np.round(np.mean(lmap(non_pad_len, pred.predictions)), 1) rouge.update({"gen_len": summ_len}) return rouge def translation_metrics(pred: EvalPrediction) -> Dict: pred_str, label_str = decode_pred(pred) bleu: Dict = calculate_bleu(pred_str, label_str) gen_len = np.round(np.mean(lmap(non_pad_len, pred.predictions)), 1) bleu.update({"gen_len": gen_len}) return bleu compute_metrics_fn = summarization_metrics if "summarization" in task_name else translation_metrics return compute_metrics_fn def trim_batch( input_ids, pad_token_id, attention_mask=None, ): """Remove columns that are populated exclusively by pad_token_id""" keep_column_mask = input_ids.ne(pad_token_id).any(dim=0) if attention_mask is None: return input_ids[:, keep_column_mask] else: return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask]) class AbstractSeq2SeqDataset(Dataset): def __init__( self, tokenizer, data_dir, max_source_length, max_target_length, type_path="train", n_obs=None, prefix="", **dataset_kwargs ): super().__init__() self.src_file = Path(data_dir).joinpath(type_path + ".source") self.tgt_file = Path(data_dir).joinpath(type_path + ".target") self.len_file = Path(data_dir).joinpath(type_path + ".len") if os.path.exists(self.len_file): self.src_lens = pickle_load(self.len_file) self.used_char_len = False else: self.src_lens = self.get_char_lens(self.src_file) self.used_char_len = True self.max_source_length = max_source_length self.max_target_length = max_target_length assert min(self.src_lens) > 0, f"found empty line in {self.src_file}" self.tokenizer = tokenizer self.prefix = prefix if prefix is not None else "" if n_obs is not None: self.src_lens = self.src_lens[:n_obs] self.pad_token_id = self.tokenizer.pad_token_id self.dataset_kwargs = dataset_kwargs dataset_kwargs.update({"add_prefix_space": True} if isinstance(self.tokenizer, BartTokenizer) else {}) def __len__(self): return len(self.src_lens) @staticmethod def get_char_lens(data_file): return [len(x) for x in Path(data_file).open().readlines()] @cached_property def tgt_lens(self): """Length in characters of target documents""" return self.get_char_lens(self.tgt_file) def make_sortish_sampler(self, batch_size, distributed=False, shuffle=True, **kwargs): if distributed: return DistributedSortishSampler(self, batch_size, shuffle=shuffle, **kwargs) else: return SortishSampler(self.src_lens, batch_size, shuffle=shuffle) def make_dynamic_sampler(self, max_tokens_per_batch=1024, **kwargs): assert FAIRSEQ_AVAILABLE, "Dynamic batch size requires `pip install fairseq`" assert not self.used_char_len, "You must call python make_len_file.py before calling make_dynamic_sampler" sorted_indices = list(self.make_sortish_sampler(1024, shuffle=False)) def num_tokens_in_example(i): return min(self.src_lens[i], self.max_target_length) # call fairseq cython function batch_sampler: List[List[int]] = batch_by_size( sorted_indices, num_tokens_fn=num_tokens_in_example, max_tokens=max_tokens_per_batch, required_batch_size_multiple=64, ) shuffled_batches = [batch_sampler[i] for i in np.random.permutation(range(len(batch_sampler)))] # move the largest batch to the front to OOM quickly (uses an approximation for padding) approximate_toks_per_batch = [max(self.src_lens[i] for i in batch) * len(batch) for batch in shuffled_batches] largest_batch_idx = np.argmax(approximate_toks_per_batch) shuffled_batches[0], shuffled_batches[largest_batch_idx] = ( shuffled_batches[largest_batch_idx], shuffled_batches[0], ) return shuffled_batches def __getitem__(self, item): raise NotImplementedError("You must implement this") def collate_fn(self, batch): raise NotImplementedError("You must implement this") class LegacySeq2SeqDataset(AbstractSeq2SeqDataset): def __getitem__(self, index) -> Dict[str, torch.Tensor]: """Call tokenizer on src and tgt_lines""" index = index + 1 # linecache starts at 1 source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n") tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n") assert source_line, f"empty source line for index {index}" assert tgt_line, f"empty tgt line for index {index}" source_inputs = self.encode_line(self.tokenizer, source_line, self.max_source_length) target_inputs = self.encode_line(self.tokenizer, tgt_line, self.max_target_length) source_ids = source_inputs["input_ids"].squeeze() target_ids = target_inputs["input_ids"].squeeze() src_mask = source_inputs["attention_mask"].squeeze() return { "input_ids": source_ids, "attention_mask": src_mask, "labels": target_ids, } def encode_line(self, tokenizer, line, max_length, pad_to_max_length=True, return_tensors="pt"): """Only used by LegacyDataset""" return tokenizer( [line], max_length=max_length, padding="max_length" if pad_to_max_length else None, truncation=True, return_tensors=return_tensors, **self.dataset_kwargs, ) def collate_fn(self, batch) -> Dict[str, torch.Tensor]: input_ids = torch.stack([x["input_ids"] for x in batch]) masks = torch.stack([x["attention_mask"] for x in batch]) target_ids = torch.stack([x["labels"] for x in batch]) pad_token_id = self.pad_token_id y = trim_batch(target_ids, pad_token_id) source_ids, source_mask = trim_batch(input_ids, pad_token_id, attention_mask=masks) batch = { "input_ids": source_ids, "attention_mask": source_mask, "labels": y, } return batch class Seq2SeqDataset(AbstractSeq2SeqDataset): """A dataset that calls prepare_seq2seq_batch.""" def __getitem__(self, index) -> Dict[str, str]: #print(self.dataset_kwargs['model_t']) # if 't5' in self.dataset_kwargs['model_t']: # self.prefix = 'translate Graph to English: ' # print('aac') # exit() index = index + 1 # linecache starts at 1 source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n") tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n") assert source_line, f"empty source line for index {index}" assert tgt_line, f"empty tgt line for index {index}" return {"tgt_texts": tgt_line, "src_texts": source_line, "id": index - 1} def collate_fn(self, batch): """Call prepare_seq2seq_batch.""" batch_encoding: Dict[str, torch.Tensor] = self.tokenizer.prepare_seq2seq_batch( [x["src_texts"] for x in batch], tgt_texts=[x["tgt_texts"] for x in batch], max_length=self.max_source_length, max_target_length=self.max_target_length, return_tensors="pt", **self.dataset_kwargs, ).data #lens = (batch_encoding['attention_mask'] == 1.).sum(dim=1).tolist() batch_encoding["ids"] = torch.tensor([x["id"] for x in batch]) return batch_encoding class Seq2SeqDataCollator: def __init__(self, tokenizer, data_args, tpu_num_cores=None): self.tokenizer = tokenizer self.pad_token_id = tokenizer.pad_token_id assert ( self.pad_token_id is not None ), f"pad_token_id is not defined for ({self.tokenizer.__class__.__name__}), it must be defined." self.data_args = data_args self.tpu_num_cores = tpu_num_cores self.dataset_kwargs = {"add_prefix_space": isinstance(tokenizer, BartTokenizer)} if data_args.src_lang is not None: self.dataset_kwargs["src_lang"] = data_args.src_lang if data_args.tgt_lang is not None: self.dataset_kwargs["tgt_lang"] = data_args.tgt_lang def __call__(self, batch) -> Dict[str, torch.Tensor]: if hasattr(self.tokenizer, "prepare_seq2seq_batch"): batch = self._encode(batch) input_ids, attention_mask, labels = ( batch["input_ids"], batch["attention_mask"], batch["labels"], ) else: input_ids = torch.stack([x["input_ids"] for x in batch]) attention_mask = torch.stack([x["attention_mask"] for x in batch]) labels = torch.stack([x["labels"] for x in batch]) labels = trim_batch(labels, self.pad_token_id) input_ids, attention_mask = trim_batch(input_ids, self.pad_token_id, attention_mask=attention_mask) if isinstance(self.tokenizer, T5Tokenizer): decoder_input_ids = self._shift_right_t5(labels) else: decoder_input_ids = shift_tokens_right(labels, self.pad_token_id) batch = { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "labels": labels, } return batch def _shift_right_t5(self, input_ids): # shift inputs to the right shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = self.pad_token_id return shifted_input_ids def _encode(self, batch) -> Dict[str, torch.Tensor]: batch_encoding = self.tokenizer.prepare_seq2seq_batch( [x["src_texts"] for x in batch], tgt_texts=[x["tgt_texts"] for x in batch], max_length=self.data_args.max_source_length, max_target_length=self.data_args.max_target_length, padding="max_length" if self.tpu_num_cores is not None else "longest", # TPU hack return_tensors="pt", **self.dataset_kwargs, ) return batch_encoding.data class SortishSampler(Sampler): "Go through the text data by order of src length with a bit of randomness. From fastai repo." def __init__(self, data, batch_size, shuffle=True): self.data, self.bs, self.shuffle = data, batch_size, shuffle def __len__(self) -> int: return len(self.data) def __iter__(self): return iter(sortish_sampler_indices(self.data, self.bs, shuffle=self.shuffle)) def sortish_sampler_indices(data: List, bs: int, shuffle=True) -> np.array: "Go through the text data by order of src length with a bit of randomness. From fastai repo." if not shuffle: return np.argsort(np.array(data) * -1) def key_fn(i): return data[i] idxs = np.random.permutation(len(data)) sz = bs * 50 ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)] sort_idx = np.concatenate([sorted(s, key=key_fn, reverse=True) for s in ck_idx]) sz = bs ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)] max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx]) # find the chunk with the largest key, ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0] # then make sure it goes first. sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int) sort_idx = np.concatenate((ck_idx[0], sort_idx)) return sort_idx class DistributedSortishSampler(Sampler): """Copied from torch DistributedSampler""" def __init__(self, dataset, batch_size, num_replicas=None, rank=None, add_extra_examples=True, shuffle=True): if num_replicas is None: if not dist.is_available(): raise RuntimeError("Requires distributed package to be available") num_replicas = dist.get_world_size() if rank is None: if not dist.is_available(): raise RuntimeError("Requires distributed package to be available") rank = dist.get_rank() self.dataset = dataset self.num_replicas = num_replicas self.rank = rank self.epoch = 0 if add_extra_examples: self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) self.total_size = self.num_samples * self.num_replicas else: self.total_size = len(dataset) self.num_samples = len(self.available_indices) self.batch_size = batch_size self.add_extra_examples = add_extra_examples self.shuffle = shuffle def __iter__(self) -> Iterable: g = torch.Generator() g.manual_seed(self.epoch) sortish_data = [self.dataset.src_lens[i] for i in self.available_indices] sortish_indices = sortish_sampler_indices(sortish_data, self.batch_size, shuffle=self.shuffle) indices = [self.available_indices[i] for i in sortish_indices] assert len(indices) == self.num_samples return iter(indices) @cached_property def available_indices(self) -> np.array: indices = list(range(len(self.dataset))) # add extra samples to make it evenly divisible indices += indices[: (self.total_size - len(indices))] assert len(indices) == self.total_size # subsample available_indices = indices[self.rank : self.total_size : self.num_replicas] return available_indices def __len__(self): return self.num_samples def set_epoch(self, epoch): self.epoch = epoch logger = getLogger(__name__) def use_task_specific_params(model, task): """Update config with summarization specific params.""" task_specific_params = model.config.task_specific_params if task_specific_params is not None: pars = task_specific_params.get(task, {}) logger.info(f"using task specific params for {task}: {pars}") model.config.update(pars) def pickle_load(path): """pickle.load(path)""" with open(path, "rb") as f: return pickle.load(f) def pickle_save(obj, path): """pickle.dump(obj, path)""" with open(path, "wb") as f: return pickle.dump(obj, f) def flatten_list(summary_ids: List[List]): return [x for x in itertools.chain.from_iterable(summary_ids)] def save_json(content, path, indent=4, **json_dump_kwargs): with open(path, "w") as f: json.dump(content, f, indent=indent, **json_dump_kwargs) def load_json(path): with open(path) as f: return json.load(f) ROUGE_KEYS = ["rouge1", "rouge2", "rougeL", "rougeLsum"] def extract_rouge_mid_statistics(dct): new_dict = {} for k1, v1 in dct.items(): mid = v1.mid new_dict[k1] = {stat: round(getattr(mid, stat), 4) for stat in ["precision", "recall", "fmeasure"]} return new_dict def calculate_rouge( pred_lns: List[str], tgt_lns: List[str], use_stemmer=True, rouge_keys=ROUGE_KEYS, return_precision_and_recall=False, bootstrap_aggregation=True, newline_sep=True, ) -> Dict: """Calculate rouge using rouge_scorer package. Args: pred_lns: list of summaries generated by model tgt_lns: list of groundtruth summaries (e.g. contents of val.target) use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes to improve matching. rouge_keys: which metrics to compute, defaults to rouge1, rouge2, rougeL, rougeLsum return_precision_and_recall: (False) whether to also return precision and recall. bootstrap_aggregation: whether to do the typical bootstrap resampling of scores. Defaults to True, if False this function returns a collections.defaultdict[metric: list of values for each observation for each subscore]`` newline_sep:(default=True) whether to add newline between sentences. This is essential for calculation rougeL on multi sentence summaries (CNN/DM dataset). Returns: Dict[score: value] if aggregate else defaultdict(list) keyed by rouge_keys """ scorer = rouge_scorer.RougeScorer(rouge_keys, use_stemmer=use_stemmer) aggregator = scoring.BootstrapAggregator() for pred, tgt in zip(tgt_lns, pred_lns): # rougeLsum expects "\n" separated sentences within a summary if newline_sep: pred = add_newline_to_end_of_each_sentence(pred) tgt = add_newline_to_end_of_each_sentence(tgt) scores = scorer.score(pred, tgt) aggregator.add_scores(scores) if bootstrap_aggregation: result = aggregator.aggregate() if return_precision_and_recall: return extract_rouge_mid_statistics(result) # here we return dict else: return {k: round(v.mid.fmeasure * 100, 4) for k, v in result.items()} else: return aggregator._scores # here we return defaultdict(list) # Utilities for freezing parameters and checking whether they are frozen def freeze_params(model: nn.Module): """Set requires_grad=False for each of model.parameters()""" for par in model.parameters(): par.requires_grad = False def freeze_embeds(model): """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5.""" model_type = model.config.model_type if model_type == "t5": freeze_params(model.shared) for d in [model.encoder, model.decoder]: freeze_params(d.embed_tokens) elif model_type == "fsmt": for d in [model.model.encoder, model.model.decoder]: freeze_params(d.embed_positions) freeze_params(d.embed_tokens) else: freeze_params(model.model.shared) for d in [model.model.encoder, model.model.decoder]: freeze_params(d.embed_positions) freeze_params(d.embed_tokens) def grad_status(model: nn.Module) -> Iterable: return (par.requires_grad for par in model.parameters()) def any_requires_grad(model: nn.Module) -> bool: return any(grad_status(model)) def assert_all_frozen(model): model_grads: List[bool] = list(grad_status(model)) n_require_grad = sum(lmap(int, model_grads)) npars = len(model_grads) assert not any(model_grads), f"{n_require_grad/npars:.1%} of {npars} weights require grad" def assert_not_all_frozen(model): model_grads: List[bool] = list(grad_status(model)) npars = len(model_grads) assert any(model_grads), f"none of {npars} weights require grad" def parse_numeric_n_bool_cl_kwargs(unparsed_args: List[str]) -> Dict[str, Union[int, float, bool]]: """ Parse an argv list of unspecified command line args to a dict. Assumes all values are either numeric or boolean in the form of true/false. """ result = {} assert len(unparsed_args) % 2 == 0, f"got odd number of unparsed args: {unparsed_args}" num_pairs = len(unparsed_args) // 2 for pair_num in range(num_pairs): i = 2 * pair_num assert unparsed_args[i].startswith("--") if unparsed_args[i + 1].lower() == "true": value = True elif unparsed_args[i + 1].lower() == "false": value = False else: try: value = int(unparsed_args[i + 1]) except ValueError: value = float(unparsed_args[i + 1]) # this can raise another informative ValueError result[unparsed_args[i][2:]] = value return result def write_txt_file(ordered_tgt, path): f = Path(path).open("w") for ln in ordered_tgt: f.write(ln + "\n") f.flush() def chunks(lst, n): """Yield successive n-sized chunks from lst.""" for i in range(0, len(lst), n): yield lst[i : i + n]