[tests] remove TF tests (uses of require_tf) (#38944)
* remove uses of require_tf * remove redundant import guards * this class has no tests * nits * del tf rng comment
This commit is contained in:
Joao Gante
@@ -1,106 +0,0 @@
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import unittest
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from pathlib import Path
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from tempfile import TemporaryDirectory
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from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
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from transformers.models.bert.tokenization_bert import BertTokenizer
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from transformers.testing_utils import require_tensorflow_text, require_tf, slow
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if is_tf_available():
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import tensorflow as tf
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from transformers.modeling_tf_utils import keras
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if is_tensorflow_text_available():
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from transformers.models.bert import TFBertTokenizer
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TOKENIZER_CHECKPOINTS = ["google-bert/bert-base-uncased", "google-bert/bert-base-cased"]
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TINY_MODEL_CHECKPOINT = "hf-internal-testing/tiny-bert-tf-only"
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if is_tf_available():
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from transformers.modeling_tf_utils import keras
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class ModelToSave(keras.Model):
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def __init__(self, tokenizer):
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super().__init__()
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self.tokenizer = tokenizer
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config = AutoConfig.from_pretrained(TINY_MODEL_CHECKPOINT)
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self.bert = TFAutoModel.from_config(config)
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def call(self, inputs):
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tokenized = self.tokenizer(inputs)
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out = self.bert(tokenized)
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return out["pooler_output"]
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@require_tf
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@require_tensorflow_text
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class BertTokenizationTest(unittest.TestCase):
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# The TF tokenizers are usually going to be used as pretrained tokenizers from existing model checkpoints,
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# so that's what we focus on here.
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def setUp(self):
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super().setUp()
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self.tokenizers = [BertTokenizer.from_pretrained(checkpoint) for checkpoint in TOKENIZER_CHECKPOINTS]
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self.tf_tokenizers = [TFBertTokenizer.from_pretrained(checkpoint) for checkpoint in TOKENIZER_CHECKPOINTS]
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assert len(self.tokenizers) == len(self.tf_tokenizers)
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self.test_sentences = [
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"This is a straightforward English test sentence.",
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"This one has some weird characters\rto\nsee\r\nif those\u00e9break things.",
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"Now we're going to add some Chinese: 一 二 三 一二三",
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"And some much more rare Chinese: 齉 堃 齉堃",
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"Je vais aussi écrire en français pour tester les accents",
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"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
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]
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self.paired_sentences = list(zip(self.test_sentences, self.test_sentences[::-1]))
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def test_output_equivalence(self):
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for tokenizer, tf_tokenizer in zip(self.tokenizers, self.tf_tokenizers):
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for test_inputs in (self.test_sentences, self.paired_sentences):
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python_outputs = tokenizer(test_inputs, return_tensors="tf", padding="longest")
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tf_outputs = tf_tokenizer(test_inputs)
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for key in python_outputs.keys():
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self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
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self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key], tf.int64) == tf_outputs[key]))
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@slow
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def test_different_pairing_styles(self):
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for tf_tokenizer in self.tf_tokenizers:
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merged_outputs = tf_tokenizer(self.paired_sentences)
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separated_outputs = tf_tokenizer(
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text=[sentence[0] for sentence in self.paired_sentences],
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text_pair=[sentence[1] for sentence in self.paired_sentences],
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)
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for key in merged_outputs.keys():
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self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key], tf.int64) == separated_outputs[key]))
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@slow
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def test_graph_mode(self):
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for tf_tokenizer in self.tf_tokenizers:
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compiled_tokenizer = tf.function(tf_tokenizer)
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for test_inputs in (self.test_sentences, self.paired_sentences):
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test_inputs = tf.constant(test_inputs)
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compiled_outputs = compiled_tokenizer(test_inputs)
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eager_outputs = tf_tokenizer(test_inputs)
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for key in eager_outputs.keys():
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self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
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@slow
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def test_export_for_inference(self):
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for tf_tokenizer in self.tf_tokenizers:
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model = ModelToSave(tokenizer=tf_tokenizer)
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test_inputs = tf.convert_to_tensor(self.test_sentences)
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out = model(test_inputs) # Build model with some sample inputs
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with TemporaryDirectory() as tempdir:
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save_path = Path(tempdir) / "saved.model"
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model.export(save_path)
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loaded_model = tf.saved_model.load(save_path)
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loaded_output = loaded_model.serve(test_inputs)
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# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
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self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)), 1e-5)
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