From effee48925e19ed14b69be37d97e2e8b8fc34a78 Mon Sep 17 00:00:00 2001 From: Vision Date: Sun, 4 Dec 2022 00:19:19 +0530 Subject: [PATCH 1/9] Create test_tokenization_led.py --- tests/models/led/test_tokenization_led.py | 173 ++++++++++++++++++++++ 1 file changed, 173 insertions(+) create mode 100644 tests/models/led/test_tokenization_led.py diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py new file mode 100644 index 000000000000..ff0961b6005c --- /dev/null +++ b/tests/models/led/test_tokenization_led.py @@ -0,0 +1,173 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# 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. +import json +import os +import unittest + +from transformers import LEDTokenizer, LEDTokenizerFast, BatchEncoding +from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES +from transformers.testing_utils import require_tokenizers, require_torch +from transformers.utils import cached_property + +from ...test_tokenization_common import TokenizerTesterMixin + + +@require_tokenizers +class TestTokenizationBart(TokenizerTesterMixin, unittest.TestCase): + tokenizer_class = LEDTokenizer + rust_tokenizer_class = LEDTokenizerFast + test_rust_tokenizer = True + + def setUp(self): + super().setUp() + vocab = [ + "l", + "o", + "w", + "e", + "r", + "s", + "t", + "i", + "d", + "n", + "\u0120", + "\u0120l", + "\u0120n", + "\u0120lo", + "\u0120low", + "er", + "\u0120lowest", + "\u0120newer", + "\u0120wider", + "", + ] + vocab_tokens = dict(zip(vocab, range(len(vocab)))) + merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] + self.special_tokens_map = {"unk_token": ""} + + self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) + self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) + with open(self.vocab_file, "w", encoding="utf-8") as fp: + fp.write(json.dumps(vocab_tokens) + "\n") + with open(self.merges_file, "w", encoding="utf-8") as fp: + fp.write("\n".join(merges)) + + def get_tokenizer(self, **kwargs): + kwargs.update(self.special_tokens_map) + return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) + + def get_rust_tokenizer(self, **kwargs): + kwargs.update(self.special_tokens_map) + return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) + + def get_input_output_texts(self, tokenizer): + return "lower newer", "lower newer" + + @cached_property + def default_tokenizer(self): + return LEDTokenizer.from_pretrained("allenai/led-base-16384") + + @cached_property + def default_tokenizer_fast(self): + return LEDTokenizerFast.from_pretrained("allenai/led-base-16384") + + @require_torch + def test_prepare_batch(self): + src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] + expected_src_tokens = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2] + + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + batch = tokenizer(src_text, max_length=len(expected_src_tokens), padding=True, return_tensors="pt") + self.assertIsInstance(batch, BatchEncoding) + + self.assertEqual((2, 9), batch.input_ids.shape) + self.assertEqual((2, 9), batch.attention_mask.shape) + result = batch.input_ids.tolist()[0] + self.assertListEqual(expected_src_tokens, result) + + @require_torch + def test_prepare_batch_empty_target_text(self): + src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + batch = tokenizer(src_text, padding=True, return_tensors="pt") + self.assertIn("input_ids", batch) + self.assertIn("attention_mask", batch) + self.assertNotIn("labels", batch) + self.assertNotIn("decoder_attention_mask", batch) + + @require_torch + def test_tokenizer_as_target_length(self): + tgt_text = [ + "Summary of the text.", + "Another summary.", + ] + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + targets = tokenizer(text_target=tgt_text, max_length=32, padding="max_length", return_tensors="pt") + self.assertEqual(32, targets["input_ids"].shape[1]) + + @require_torch + def test_prepare_batch_not_longer_than_maxlen(self): + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + batch = tokenizer( + ["I am a small frog" * 1024, "I am a small frog"], padding=True, truncation=True, return_tensors="pt" + ) + self.assertIsInstance(batch, BatchEncoding) + self.assertEqual(batch.input_ids.shape, (2, 5122)) + + @require_torch + def test_special_tokens(self): + + src_text = ["A long paragraph for summarization."] + tgt_text = [ + "Summary of the text.", + ] + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + inputs = tokenizer(src_text, return_tensors="pt") + targets = tokenizer(text_target=tgt_text, return_tensors="pt") + input_ids = inputs["input_ids"] + labels = targets["input_ids"] + self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()) + self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()) + self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()) + self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()) + + def test_pretokenized_inputs(self): + pass + + def test_embeded_special_tokens(self): + for tokenizer, pretrained_name, kwargs in self.tokenizers_list: + with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): + tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) + tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) + sentence = "A, AllenNLP sentence." + tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) + tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) + self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"])) + self.assertEqual( + sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]), + sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]), + ) + + tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]) + tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]) + self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]) + self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]) + + self.assertSequenceEqual( + tokens_p_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] + ) + self.assertSequenceEqual( + tokens_r_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] + ) \ No newline at end of file From a8ffe68eee2cf722b98111a8021ff9f230ec9d10 Mon Sep 17 00:00:00 2001 From: Vision Date: Sun, 4 Dec 2022 00:22:20 +0530 Subject: [PATCH 2/9] Update test_tokenization_led.py --- tests/models/led/test_tokenization_led.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py index ff0961b6005c..888f872beeda 100644 --- a/tests/models/led/test_tokenization_led.py +++ b/tests/models/led/test_tokenization_led.py @@ -15,7 +15,7 @@ import os import unittest -from transformers import LEDTokenizer, LEDTokenizerFast, BatchEncoding +from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, require_torch from transformers.utils import cached_property @@ -170,4 +170,4 @@ def test_embeded_special_tokens(self): ) self.assertSequenceEqual( tokens_r_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] - ) \ No newline at end of file + ) From 5c8141f706fc9e43fc9dc5cfa1a506cbf049804f Mon Sep 17 00:00:00 2001 From: Vision Date: Mon, 5 Dec 2022 22:37:45 +0530 Subject: [PATCH 3/9] Update test_tokenization_led.py --- tests/models/led/test_tokenization_led.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py index 888f872beeda..73387c6b50ed 100644 --- a/tests/models/led/test_tokenization_led.py +++ b/tests/models/led/test_tokenization_led.py @@ -24,7 +24,7 @@ @require_tokenizers -class TestTokenizationBart(TokenizerTesterMixin, unittest.TestCase): +class TestTokenizationLED(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = LEDTokenizer rust_tokenizer_class = LEDTokenizerFast test_rust_tokenizer = True From 69a61cdf2fa1093575d47f5114aad3665e5bbeda Mon Sep 17 00:00:00 2001 From: Vision Date: Mon, 5 Dec 2022 22:39:32 +0530 Subject: [PATCH 4/9] Update test_tokenization_led.py --- tests/models/led/test_tokenization_led.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py index 73387c6b50ed..58302138ee56 100644 --- a/tests/models/led/test_tokenization_led.py +++ b/tests/models/led/test_tokenization_led.py @@ -24,7 +24,7 @@ @require_tokenizers -class TestTokenizationLED(TokenizerTesterMixin, unittest.TestCase): +class LEDTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = LEDTokenizer rust_tokenizer_class = LEDTokenizerFast test_rust_tokenizer = True From 5b87510d839f6c54fe4df12720cd6b2ab487b2c1 Mon Sep 17 00:00:00 2001 From: Vision Date: Mon, 5 Dec 2022 22:50:27 +0530 Subject: [PATCH 5/9] Update test_tokenization_led.py --- tests/models/led/test_tokenization_led.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py index 58302138ee56..73387c6b50ed 100644 --- a/tests/models/led/test_tokenization_led.py +++ b/tests/models/led/test_tokenization_led.py @@ -24,7 +24,7 @@ @require_tokenizers -class LEDTokenizationTest(TokenizerTesterMixin, unittest.TestCase): +class TestTokenizationLED(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = LEDTokenizer rust_tokenizer_class = LEDTokenizerFast test_rust_tokenizer = True From 8eb65998f5f70ea8d5d1dedeada9c1719967efc8 Mon Sep 17 00:00:00 2001 From: Vision Date: Thu, 8 Dec 2022 21:31:22 +0530 Subject: [PATCH 6/9] Update test_tokenization_led.py --- tests/models/led/test_tokenization_led.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py index 73387c6b50ed..0cfd89f9ab58 100644 --- a/tests/models/led/test_tokenization_led.py +++ b/tests/models/led/test_tokenization_led.py @@ -171,3 +171,16 @@ def test_embeded_special_tokens(self): self.assertSequenceEqual( tokens_r_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] ) + + @require_torch + def test_global_attention_mask(self): + for tokenizer, pretrained_name, kwargs in self.tokenizers_list: + with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): + tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) + src_text = ["A long paragraph.", "Hi I am using huggingface transformers"] + expected_global_attention_mask = [[0, 0, 0, 0, 0, 0, -1, -1, -1, -1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] + + inputs = tokenizer_r(src_text, padding=False) + inputs["global_attention_mask"] = [[0] * len(y) for y in inputs["input_ids"]] + outputs = tokenizer_r.pad(inputs) + self.assertSequenceEqual(outputs["global_attention_mask"], expected_global_attention_mask) From adbe615ed381fbe21a75e044ff20e70b8ec79761 Mon Sep 17 00:00:00 2001 From: Vision Date: Thu, 8 Dec 2022 23:32:02 +0530 Subject: [PATCH 7/9] Update test_tokenization_led.py --- tests/models/led/test_tokenization_led.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py index 0cfd89f9ab58..a05c3add1bfb 100644 --- a/tests/models/led/test_tokenization_led.py +++ b/tests/models/led/test_tokenization_led.py @@ -177,10 +177,16 @@ def test_global_attention_mask(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) - src_text = ["A long paragraph.", "Hi I am using huggingface transformers"] - expected_global_attention_mask = [[0, 0, 0, 0, 0, 0, -1, -1, -1, -1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] - - inputs = tokenizer_r(src_text, padding=False) - inputs["global_attention_mask"] = [[0] * len(y) for y in inputs["input_ids"]] - outputs = tokenizer_r.pad(inputs) - self.assertSequenceEqual(outputs["global_attention_mask"], expected_global_attention_mask) + tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) + src_text = ["Summary of the text.", "Another summary."] + expected_global_attention_mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]] + + global_mask_r = tokenizer_r(src_text, padding=False) + global_mask_r["global_attention_mask"] = [[0] * len(y) for y in global_mask_r["input_ids"]] + output_mask_r = tokenizer_r.pad(global_mask_r) + + global_mask_p = tokenizer_p(src_text, padding=False) + global_mask_p["global_attention_mask"] = [[0] * len(x) for x in global_mask_p["input_ids"]] + output_mask_p = tokenizer_p.pad(global_mask_p) + self.assertSequenceEqual(output_mask_p["global_attention_mask"], expected_global_attention_mask) + self.assertSequenceEqual(output_mask_r["global_attention_mask"], expected_global_attention_mask) From f47fac47a52dc0db64a70064ce38b99a9b681296 Mon Sep 17 00:00:00 2001 From: Vision Date: Fri, 9 Dec 2022 00:13:48 +0530 Subject: [PATCH 8/9] Update test_tokenization_led.py --- tests/models/led/test_tokenization_led.py | 32 +++++++++-------------- 1 file changed, 12 insertions(+), 20 deletions(-) diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py index a05c3add1bfb..d0d6b275e3d2 100644 --- a/tests/models/led/test_tokenization_led.py +++ b/tests/models/led/test_tokenization_led.py @@ -143,6 +143,17 @@ def test_special_tokens(self): self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()) self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()) + @require_torch + def test_global_attention_mask(self): + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + src_text = ["Summary of the text.", "Another summary."] + expected_global_attention_mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]] + + encoded_output = tokenizer(src_text, padding=False) + encoded_output["global_attention_mask"] = [[0] * len(x) for x in encoded_output["input_ids"]] + outputs = tokenizer.pad(encoded_output) + self.assertSequenceEqual(outputs["global_attention_mask"], expected_global_attention_mask) + def test_pretokenized_inputs(self): pass @@ -170,23 +181,4 @@ def test_embeded_special_tokens(self): ) self.assertSequenceEqual( tokens_r_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] - ) - - @require_torch - def test_global_attention_mask(self): - for tokenizer, pretrained_name, kwargs in self.tokenizers_list: - with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): - tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) - tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) - src_text = ["Summary of the text.", "Another summary."] - expected_global_attention_mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]] - - global_mask_r = tokenizer_r(src_text, padding=False) - global_mask_r["global_attention_mask"] = [[0] * len(y) for y in global_mask_r["input_ids"]] - output_mask_r = tokenizer_r.pad(global_mask_r) - - global_mask_p = tokenizer_p(src_text, padding=False) - global_mask_p["global_attention_mask"] = [[0] * len(x) for x in global_mask_p["input_ids"]] - output_mask_p = tokenizer_p.pad(global_mask_p) - self.assertSequenceEqual(output_mask_p["global_attention_mask"], expected_global_attention_mask) - self.assertSequenceEqual(output_mask_r["global_attention_mask"], expected_global_attention_mask) + ) \ No newline at end of file From 7ff0b6ade144460c6b2130111b17b4c80497ff58 Mon Sep 17 00:00:00 2001 From: Vision Date: Fri, 9 Dec 2022 00:20:05 +0530 Subject: [PATCH 9/9] Update test_tokenization_led.py --- tests/models/led/test_tokenization_led.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py index d0d6b275e3d2..2c761ad17a9a 100644 --- a/tests/models/led/test_tokenization_led.py +++ b/tests/models/led/test_tokenization_led.py @@ -153,7 +153,7 @@ def test_global_attention_mask(self): encoded_output["global_attention_mask"] = [[0] * len(x) for x in encoded_output["input_ids"]] outputs = tokenizer.pad(encoded_output) self.assertSequenceEqual(outputs["global_attention_mask"], expected_global_attention_mask) - + def test_pretokenized_inputs(self): pass @@ -181,4 +181,4 @@ def test_embeded_special_tokens(self): ) self.assertSequenceEqual( tokens_r_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] - ) \ No newline at end of file + )