From 941b4442ba831f99c978e322d99cab1d053a661a Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 23 Jun 2021 17:46:24 +0200 Subject: [PATCH] Temporarily revert the `fill-mask` improvements. --- src/transformers/pipelines/fill_mask.py | 78 +++++++++---------------- tests/test_pipelines_fill_mask.py | 33 ++--------- 2 files changed, 30 insertions(+), 81 deletions(-) diff --git a/src/transformers/pipelines/fill_mask.py b/src/transformers/pipelines/fill_mask.py index a34b67859c..86ce54b3e9 100644 --- a/src/transformers/pipelines/fill_mask.py +++ b/src/transformers/pipelines/fill_mask.py @@ -98,9 +98,9 @@ class FillMaskPipeline(Pipeline): args (:obj:`str` or :obj:`List[str]`): One or several texts (or one list of prompts) with masked tokens. targets (:obj:`str` or :obj:`List[str]`, `optional`): - When passed, the model will limit the scores to the passed targets instead of looking up in the whole - vocab. If the provided targets are not in the model vocab, they will be tokenized and the first - resulting token will be used (with a warning, and that might be slower). + When passed, the model will return the scores for the passed token or tokens rather than the top k + predictions in the entire vocabulary. If the provided targets are not in the model vocab, they will be + tokenized and the first resulting token will be used (with a warning). top_k (:obj:`int`, `optional`): When passed, overrides the number of predictions to return. @@ -115,56 +115,25 @@ class FillMaskPipeline(Pipeline): inputs = self._parse_and_tokenize(*args, **kwargs) outputs = self._forward(inputs, return_tensors=True) - # top_k must be defined - if top_k is None: - top_k = self.top_k - results = [] batch_size = outputs.shape[0] if self.framework == "tf" else outputs.size(0) if targets is not None: + if len(targets) == 0 or len(targets[0]) == 0: + raise ValueError("At least one target must be provided when passed.") if isinstance(targets, str): targets = [targets] - try: - vocab = self.tokenizer.get_vocab() - except Exception: - vocab = {} - target_ids = [] + targets_proc = [] for target in targets: - id_ = vocab.get(target, None) - if id_ is None: - input_ids = self.tokenizer( - target, - add_special_tokens=False, - return_attention_mask=False, - return_token_type_ids=False, - max_length=1, - truncation=True, - )["input_ids"] - if len(input_ids) == 0: - logger.warning( - f"The specified target token `{target}` does not exist in the model vocabulary. " - f"We cannot replace it with anything meaningful, ignoring it" - ) - continue - id_ = input_ids[0] - # XXX: If users encounter this pass - # it becomes pretty slow, so let's make sure - # The warning enables them to fix the input to - # get faster performance. + target_enc = self.tokenizer.tokenize(target) + if len(target_enc) > 1 or target_enc[0] == self.tokenizer.unk_token: logger.warning( f"The specified target token `{target}` does not exist in the model vocabulary. " - f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_)}`." + f"Replacing with `{target_enc[0]}`." ) - target_ids.append(id_) - target_ids = list(set(target_ids)) - if len(target_ids) == 0: - raise ValueError("At least one target must be provided when passed.") - target_ids = np.array(target_ids) - # Cap top_k if there are targets - if top_k > target_ids.shape[0]: - top_k = target_ids.shape[0] + targets_proc.append(target_enc[0]) + target_inds = np.array(self.tokenizer.convert_tokens_to_ids(targets_proc)) for i in range(batch_size): input_ids = inputs["input_ids"][i] @@ -178,11 +147,14 @@ class FillMaskPipeline(Pipeline): logits = outputs[i, masked_index.item(), :] probs = tf.nn.softmax(logits) - if targets is not None: - probs = tf.gather_nd(probs, tf.reshape(target_ids, (-1, 1))) - - topk = tf.math.top_k(probs, k=top_k) - values, predictions = topk.values.numpy(), topk.indices.numpy() + if targets is None: + topk = tf.math.top_k(probs, k=top_k if top_k is not None else self.top_k) + values, predictions = topk.values.numpy(), topk.indices.numpy() + else: + values = tf.gather_nd(probs, tf.reshape(target_inds, (-1, 1))) + sort_inds = tf.reverse(tf.argsort(values), [0]) + values = tf.gather_nd(values, tf.reshape(sort_inds, (-1, 1))).numpy() + predictions = target_inds[sort_inds.numpy()] else: masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False) @@ -191,11 +163,13 @@ class FillMaskPipeline(Pipeline): logits = outputs[i, masked_index.item(), :] probs = logits.softmax(dim=0) - - if targets is not None: - probs = probs[..., target_ids] - - values, predictions = probs.topk(top_k) + if targets is None: + values, predictions = probs.topk(top_k if top_k is not None else self.top_k) + else: + values = probs[..., target_inds] + sort_inds = list(reversed(values.argsort(dim=-1))) + values = values[..., sort_inds] + predictions = target_inds[sort_inds] for v, p in zip(values.tolist(), predictions.tolist()): tokens = input_ids.numpy() diff --git a/tests/test_pipelines_fill_mask.py b/tests/test_pipelines_fill_mask.py index 5de8b0b1f9..8865bae0c8 100644 --- a/tests/test_pipelines_fill_mask.py +++ b/tests/test_pipelines_fill_mask.py @@ -78,8 +78,7 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase): @require_torch def test_torch_fill_mask_with_targets(self): valid_inputs = ["My name is "] - # ' Sam' will yield a warning but work - valid_targets = [[" Teven", "ĠPatrick", "ĠClara"], ["ĠSam"], [" Sam"]] + valid_targets = [[" Teven", " Patrick", " Clara"], [" Sam"]] invalid_targets = [[], [""], ""] for model_name in self.small_models: unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt") @@ -90,34 +89,10 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase): for targets in invalid_targets: self.assertRaises(ValueError, unmasker, valid_inputs, targets=targets) - @require_torch - def test_torch_fill_mask_with_targets_and_topk(self): - model_name = self.small_models[0] - unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt") - targets = [" Teven", "ĠPatrick", "ĠClara"] - top_k = 2 - outputs = unmasker("My name is ", targets=targets, top_k=top_k) - - self.assertEqual(len(outputs), 2) - - @require_torch - def test_torch_fill_mask_with_duplicate_targets_and_topk(self): - model_name = self.small_models[0] - unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt") - # String duplicates + id duplicates - targets = [" Teven", "ĠPatrick", "ĠClara", "ĠClara", " Clara"] - top_k = 10 - outputs = unmasker("My name is ", targets=targets, top_k=top_k) - - # The target list contains duplicates, so we can't output more - # than them - self.assertEqual(len(outputs), 3) - @require_tf def test_tf_fill_mask_with_targets(self): valid_inputs = ["My name is "] - # ' Sam' will yield a warning but work - valid_targets = [[" Teven", "ĠPatrick", "ĠClara"], ["ĠSam"], [" Sam"]] + valid_targets = [[" Teven", " Patrick", " Clara"], [" Sam"]] invalid_targets = [[], [""], ""] for model_name in self.small_models: unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf") @@ -136,7 +111,7 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase): "My name is ", "The largest city in France is ", ] - valid_targets = ["ĠPatrick", "ĠClara"] + valid_targets = [" Patrick", " Clara"] for model_name in self.large_models: unmasker = pipeline( task="fill-mask", @@ -209,7 +184,7 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase): "My name is ", "The largest city in France is ", ] - valid_targets = ["ĠPatrick", "ĠClara"] + valid_targets = [" Patrick", " Clara"] for model_name in self.large_models: unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf", top_k=2)