From 81422c4e6d213767dc075f20049e8fd201675029 Mon Sep 17 00:00:00 2001 From: Aymeric Augustin Date: Mon, 23 Dec 2019 22:23:44 +0100 Subject: [PATCH] Remove unused variables in examples. --- examples/contrib/run_openai_gpt.py | 6 ------ examples/contrib/run_transfo_xl.py | 4 +--- examples/run_multiple_choice.py | 3 +-- examples/summarization/modeling_bertabs.py | 5 ----- 4 files changed, 2 insertions(+), 16 deletions(-) diff --git a/examples/contrib/run_openai_gpt.py b/examples/contrib/run_openai_gpt.py index 80331f3402..136e25821f 100644 --- a/examples/contrib/run_openai_gpt.py +++ b/examples/contrib/run_openai_gpt.py @@ -44,13 +44,10 @@ from transformers import ( AdamW, OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer, - cached_path, get_linear_schedule_with_warmup, ) -ROCSTORIES_URL = "https://s3.amazonaws.com/datasets.huggingface.co/ROCStories.tar.gz" - logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO ) @@ -182,9 +179,6 @@ def main(): model.to(device) # Load and encode the datasets - if not args.train_dataset and not args.eval_dataset: - roc_stories = cached_path(ROCSTORIES_URL) - def tokenize_and_encode(obj): """ Tokenize and encode a nested object """ if isinstance(obj, str): diff --git a/examples/contrib/run_transfo_xl.py b/examples/contrib/run_transfo_xl.py index ae4efbe00e..84e2806a7b 100644 --- a/examples/contrib/run_transfo_xl.py +++ b/examples/contrib/run_transfo_xl.py @@ -28,7 +28,7 @@ import time import torch -from transformers import TransfoXLCorpus, TransfoXLLMHeadModel, TransfoXLTokenizer +from transformers import TransfoXLCorpus, TransfoXLLMHeadModel logging.basicConfig( @@ -73,9 +73,7 @@ def main(): # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax # and tokenizing the dataset # The pre-processed corpus is a convertion (using the conversion script ) - tokenizer = TransfoXLTokenizer.from_pretrained(args.model_name) corpus = TransfoXLCorpus.from_pretrained(args.model_name) - ntokens = len(corpus.vocab) va_iter = corpus.get_iterator("valid", args.batch_size, args.tgt_len, device=device, ext_len=args.ext_len) te_iter = corpus.get_iterator("test", args.batch_size, args.tgt_len, device=device, ext_len=args.ext_len) diff --git a/examples/run_multiple_choice.py b/examples/run_multiple_choice.py index 7989422889..69202ab5d5 100644 --- a/examples/run_multiple_choice.py +++ b/examples/run_multiple_choice.py @@ -141,7 +141,7 @@ def train(args, train_dataset, model, tokenizer): global_step = 0 tr_loss, logging_loss = 0.0, 0.0 - best_dev_acc, best_dev_loss = 0.0, 99999999999.0 + best_dev_acc = 0.0 best_steps = 0 model.zero_grad() train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) @@ -193,7 +193,6 @@ def train(args, train_dataset, model, tokenizer): tb_writer.add_scalar("eval_{}".format(key), value, global_step) if results["eval_acc"] > best_dev_acc: best_dev_acc = results["eval_acc"] - best_dev_loss = results["eval_loss"] best_steps = global_step if args.do_test: results_test = evaluate(args, model, tokenizer, test=True) diff --git a/examples/summarization/modeling_bertabs.py b/examples/summarization/modeling_bertabs.py index 22e50b5e78..4dd89ada88 100644 --- a/examples/summarization/modeling_bertabs.py +++ b/examples/summarization/modeling_bertabs.py @@ -446,8 +446,6 @@ class MultiHeadedAttention(nn.Module): batch_size = key.size(0) dim_per_head = self.dim_per_head head_count = self.head_count - key_len = key.size(1) - query_len = query.size(1) def shape(x): """ projection """ @@ -504,9 +502,6 @@ class MultiHeadedAttention(nn.Module): query = shape(query) - key_len = key.size(2) - query_len = query.size(2) - # 2) Calculate and scale scores. query = query / math.sqrt(dim_per_head) scores = torch.matmul(query, key.transpose(2, 3))