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https://github.com/huggingface/transformers.git
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583db52bc6
* add dia model * add tokenizer files * cleanup some stuff * brut copy paste code * rough cleanup of the modeling code * nuke some stuff * more nuking * more cleanups * updates * add mulitLayerEmbedding vectorization * nits * more modeling simplifications * updates * update rope * update rope * just fixup * update configuration files * more cleanup! * default config values * update * forgotten comma * another comma! * update, more cleanups * just more nits * more config cleanups * time for the encoder * fix * sa=mall nit * nits * n * refacto a bit * cleanup * update cv scipt * fix last issues * fix last nits * styling * small fixes * just run 1 generation * fixes * nits * fix conversion * fix * more fixes * full generate * ouf! * fixes! * updates * fix * fix cvrt * fixup * nits * delete wrong test * update * update * test tokenization * let's start changing things bit by bit - fix encoder step * removing custom generation, moving to GenerationMixin * add encoder decoder attention masks for generation * mask changes, correctness checked against ad29837 in dia repo * refactor a bit already --> next cache * too important not to push :) * minimal cleanup + more todos * make main overwrite modeling utils * add cfg filter & eos filter * add eos countdown & delay pattern * update eos countdown * add max step eos countdown * fix tests * fix some things * fix generation with testing * move cfg & eos stuff to logits processor * make RepetitionPenaltyLogitsProcessor flexible - can accept 3D scores like (batch_size, channel, vocab) * fix input_ids concatenation dimension in GenerationMixin for flexibility * Add DiaHangoverLogitsProcessor and DiaExponentialDecayLengthPenalty classes; refactor logits processing in DiaForConditionalGeneration to utilize new configurations and improve flexibility. * Add stopping criteria * refactor * move delay pattern from processor to modeling like musicgen. - add docs - change eos countdown to eos delay pattern * fix processor & fix tests * refactor types * refactor imports * format code * fix docstring to pass ci * add docstring to DiaConfig & add DiaModel to test * fix docstring * add docstring * fix some bugs * check * porting / merging results from other branch - IMPORTANT: it very likely breaks generation, the goal is to have a proper forward path first * experimental testing of left padding for first channel * whoops * Fix merge to make generation work * fix cfg filter * add position ids * add todos, break things * revert changes to generation --> we will force 2d but go 3d on custom stuff * refactor a lot, change prepare decoder ids to work with left padding (needs testing), add todos * some first fixes to get to 10. in generation * some more generation fixes / adjustment * style + rope fixes * move cfg out, simplify a few things, more todos * nit * start working on custom logit processors * nit * quick fixes * cfg top k * more refactor of logits processing, needs a decision if gen config gets the new attributes or if we move it to config or similar * lets keep changes to core code minimal, only eos scaling is questionable atm * simpler eos delay logits processor * that was for debugging :D * proof of concept rope * small fix on device mismatch * cfg fixes + delay logits max len * transformers rope * modular dia * more cleanup * keep modeling consistently 3D, generate handles 2D internally * decoder starts with bos if nothing * post processing prototype * style * lol * force sample / greedy + fixes on padding * style * fixup tokenization * nits * revert * start working on dia tests * fix a lot of tests * more test fixes * nit * more test fixes + some features to simplify code more * more cleanup * forgot that one * autodocs * small consistency fixes * fix regression * small fixes * dia feature extraction * docs * wip processor * fix processor order * processing goes brrr * transpose before * small fix * fix major bug but needs now a closer look into the custom processors esp cfg * small thing on logits * nits * simplify indices and shifts * add simpler version of padding tests back (temporarily) * add logit processor tests * starting tests on processor * fix mask application during generation * some fixes on the weights conversion * style + fixup logits order * simplify conversion * nit * remove padding tests * nits on modeling * hmm * fix tests * trigger * probably gonna be reverted, just a quick design around audio tokenizer * fixup typing * post merge + more typing * initial design for audio tokenizer * more design changes * nit * more processor tests and style related things * add to init * protect import * not sure why tbh * add another protect * more fixes * wow * it aint stopping :D * another missed type issue * ... * change design around audio tokenizer to prioritize init and go for auto - in regards to the review * change to new causal mask function + docstrings * change ternary * docs * remove todo, i dont think its essential tbh * remove pipeline as current pipelines do not fit in the current scheme, same as csm * closer to wrapping up the processor * text to audio, just for demo purposes (will likely be reverted) * check if it's this * save audio function * ensure no grad * fixes on prefixed audio, hop length is used via preprocess dac, device fixes * integration tests (tested locally on a100) + some processor utils / fixes * style * nits * another round of smaller things * docs + some fixes (generate one might be big) * msytery solved * small fix on conversion * add abstract audio tokenizer, change init check to abstract class * nits * update docs + fix some processing :D * change inheritance scheme for audio tokenizer * delete dead / unnecessary code in copied generate loop * last nits on new pipeline behavior (+ todo on tests) + style * trigger --------- Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Vasqu <antonprogamer@gmail.com>
124 lines
9.2 KiB
Python
124 lines
9.2 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers.models.dia import DiaTokenizer
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from transformers.testing_utils import slow
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from ...test_tokenization_common import TokenizerTesterMixin
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# Special tokens
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PAD = 0
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S1 = 1
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S2 = 2
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class DiaTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = DiaTokenizer
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test_rust_tokenizer = False
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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tokenizer = DiaTokenizer()
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tokenizer.save_pretrained(cls.tmpdirname)
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def test_convert_token_and_id(self):
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"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
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token = "i"
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token_id = 105
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self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
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self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
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def test_get_vocab(self):
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vocab_keys = list(self.get_tokenizer().get_vocab().keys())
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self.assertEqual(vocab_keys[PAD], "<pad>")
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self.assertEqual(vocab_keys[S1], "[S1]")
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self.assertEqual(vocab_keys[S2], "[S2]")
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self.assertEqual(len(vocab_keys), 256)
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def test_vocab_size(self):
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# utf-8 == 2**8 == 256
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self.assertEqual(self.get_tokenizer().vocab_size, 256)
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def test_full_tokenizer(self):
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tokenizer = DiaTokenizer.from_pretrained(self.tmpdirname)
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tokens = tokenizer.tokenize("Hello, world!")
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self.assertListEqual(tokens, ["H", "e", "l", "l", "o", ",", " ", "w", "o", "r", "l", "d", "!"])
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(ids, [72, 101, 108, 108, 111, 44, 32, 119, 111, 114, 108, 100, 33])
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(back_tokens, ["H", "e", "l", "l", "o", ",", " ", "w", "o", "r", "l", "d", "!"])
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tokens = tokenizer.tokenize("[S1] Hello [S2] Hello<pad>")
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self.assertListEqual(
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tokens,
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["[S1]", " ", "H", "e", "l", "l", "o", " ", "[S2]", " ", "H", "e", "l", "l", "o", "<pad>"],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(ids, [S1, 32, 72, 101, 108, 108, 111, 32, S2, 32, 72, 101, 108, 108, 111, PAD])
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens, ["[S1]", " ", "H", "e", "l", "l", "o", " ", "[S2]", " ", "H", "e", "l", "l", "o", "<pad>"]
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)
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@slow
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def test_tokenizer_integration(self):
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# Overwritten as decoding will lead to all single bytes (i.e. characters) while usually the string format is expected
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expected_encoding = {'input_ids': [[84, 114, 97, 110, 115, 102, 111, 114, 109, 101, 114, 115, 32, 40, 102, 111, 114, 109, 101, 114, 108, 121, 32, 107, 110, 111, 119, 110, 32, 97, 115, 32, 112, 121, 116, 111, 114, 99, 104, 45, 116, 114, 97, 110, 115, 102, 111, 114, 109, 101, 114, 115, 32, 97, 110, 100, 32, 112, 121, 116, 111, 114, 99, 104, 45, 112, 114, 101, 116, 114, 97, 105, 110, 101, 100, 45, 98, 101, 114, 116, 41, 32, 112, 114, 111, 118, 105, 100, 101, 115, 32, 103, 101, 110, 101, 114, 97, 108, 45, 112, 117, 114, 112, 111, 115, 101, 32, 97, 114, 99, 104, 105, 116, 101, 99, 116, 117, 114, 101, 115, 32, 40, 66, 69, 82, 84, 44, 32, 71, 80, 84, 45, 50, 44, 32, 82, 111, 66, 69, 82, 84, 97, 44, 32, 88, 76, 77, 44, 32, 68, 105, 115, 116, 105, 108, 66, 101, 114, 116, 44, 32, 88, 76, 78, 101, 116, 46, 46, 46, 41, 32, 102, 111, 114, 32, 78, 97, 116, 117, 114, 97, 108, 32, 76, 97, 110, 103, 117, 97, 103, 101, 32, 85, 110, 100, 101, 114, 115, 116, 97, 110, 100, 105, 110, 103, 32, 40, 78, 76, 85, 41, 32, 97, 110, 100, 32, 78, 97, 116, 117, 114, 97, 108, 32, 76, 97, 110, 103, 117, 97, 103, 101, 32, 71, 101, 110, 101, 114, 97, 116, 105, 111, 110, 32, 40, 78, 76, 71, 41, 32, 119, 105, 116, 104, 32, 111, 118, 101, 114, 32, 51, 50, 43, 32, 112, 114, 101, 116, 114, 97, 105, 110, 101, 100, 32, 109, 111, 100, 101, 108, 115, 32, 105, 110, 32, 49, 48, 48, 43, 32, 108, 97, 110, 103, 117, 97, 103, 101, 115, 32, 97, 110, 100, 32, 100, 101, 101, 112, 32, 105, 110, 116, 101, 114, 111, 112, 101, 114, 97, 98, 105, 108, 105, 116, 121, 32, 98, 101, 116, 119, 101, 101, 110, 32, 74, 97, 120, 44, 32, 80, 121, 84, 111, 114, 99, 104, 32, 97, 110, 100, 32, 84, 101, 110, 115, 111, 114, 70, 108, 111, 119, 46], [66, 69, 82, 84, 32, 105, 115, 32, 100, 101, 115, 105, 103, 110, 101, 100, 32, 116, 111, 32, 112, 114, 101, 45, 116, 114, 97, 105, 110, 32, 100, 101, 101, 112, 32, 98, 105, 100, 105, 114, 101, 99, 116, 105, 111, 110, 97, 108, 32, 114, 101, 112, 114, 101, 115, 101, 110, 116, 97, 116, 105, 111, 110, 115, 32, 102, 114, 111, 109, 32, 117, 110, 108, 97, 98, 101, 108, 101, 100, 32, 116, 101, 120, 116, 32, 98, 121, 32, 106, 111, 105, 110, 116, 108, 121, 32, 99, 111, 110, 100, 105, 116, 105, 111, 110, 105, 110, 103, 32, 111, 110, 32, 98, 111, 116, 104, 32, 108, 101, 102, 116, 32, 97, 110, 100, 32, 114, 105, 103, 104, 116, 32, 99, 111, 110, 116, 101, 120, 116, 32, 105, 110, 32, 97, 108, 108, 32, 108, 97, 121, 101, 114, 115, 46], [84, 104, 101, 32, 113, 117, 105, 99, 107, 32, 98, 114, 111, 119, 110, 32, 102, 111, 120, 32, 106, 117, 109, 112, 115, 32, 111, 118, 101, 114, 32, 116, 104, 101, 32, 108, 97, 122, 121, 32, 100, 111, 103, 46]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip
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sequences = [
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"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
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"general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
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"Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained "
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"models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
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"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
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"conditioning on both left and right context in all layers.",
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"The quick brown fox jumps over the lazy dog.",
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]
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tokenizer_classes = [self.tokenizer_class]
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if self.test_rust_tokenizer:
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tokenizer_classes.append(self.rust_tokenizer_class)
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for tokenizer_class in tokenizer_classes:
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tokenizer = tokenizer_class.from_pretrained("AntonV/Dia-1.6B")
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encoding = tokenizer(sequences)
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encoding_data = encoding.data
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self.assertDictEqual(encoding_data, expected_encoding)
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# Byte decoding leads to characters so we need to join them
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decoded_sequences = [
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"".join(tokenizer.decode(seq, skip_special_tokens=True)) for seq in encoding["input_ids"]
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]
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for expected, decoded in zip(sequences, decoded_sequences):
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if self.test_sentencepiece_ignore_case:
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expected = expected.lower()
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self.assertEqual(expected, decoded)
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@unittest.skip(reason="Dia relies on whole input string due to the byte-level nature.")
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def test_pretokenized_inputs(self):
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pass
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@unittest.skip
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def test_tokenizer_slow_store_full_signature(self):
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pass
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