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119 lines
4.6 KiB
Python
119 lines
4.6 KiB
Python
# coding=utf-8
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# Copyright 2020 HuggingFace Inc. team.
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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 import is_torch_available
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from transformers.file_utils import cached_property
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from .utils import require_torch, slow, torch_device
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if is_torch_available():
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import torch
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from transformers import MarianMTModel, MarianSentencePieceTokenizer
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@require_torch
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class IntegrationTests(unittest.TestCase):
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@classmethod
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def setUpClass(cls) -> None:
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cls.model_name = "Helsinki-NLP/opus-mt-en-de"
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cls.tokenizer = MarianSentencePieceTokenizer.from_pretrained(cls.model_name)
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cls.eos_token_id = cls.tokenizer.eos_token_id
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return cls
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@cached_property
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def model(self):
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model = MarianMTModel.from_pretrained(self.model_name).to(torch_device)
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if torch_device == "cuda":
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return model.half()
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else:
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return model
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@slow
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def test_forward(self):
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src, tgt = ["I am a small frog"], ["▁Ich ▁bin ▁ein ▁kleiner ▁Fro sch"]
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expected = [38, 121, 14, 697, 38848, 0]
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model_inputs: dict = self.tokenizer.prepare_translation_batch(src, tgt_texts=tgt).to(torch_device)
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self.assertListEqual(expected, model_inputs["input_ids"][0].tolist())
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desired_keys = {
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"input_ids",
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"attention_mask",
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"decoder_input_ids",
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"decoder_attention_mask",
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}
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self.assertSetEqual(desired_keys, set(model_inputs.keys()))
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with torch.no_grad():
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logits, *enc_features = self.model(**model_inputs)
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max_indices = logits.argmax(-1)
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self.tokenizer.decode_batch(max_indices)
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@slow
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def test_repl_generate_one(self):
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src = ["I am a small frog.", "Hello"]
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model_inputs: dict = self.tokenizer.prepare_translation_batch(src).to(torch_device)
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self.assertEqual(self.model.device, model_inputs["input_ids"].device)
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generated_ids = self.model.generate(model_inputs["input_ids"], num_beams=6,)
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generated_words = self.tokenizer.decode_batch(generated_ids)[0]
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expected_words = "Ich bin ein kleiner Frosch."
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self.assertEqual(expected_words, generated_words)
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@slow
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def test_repl_generate_batch(self):
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src = [
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"I am a small frog.",
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"Now I can forget the 100 words of german that I know.",
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"O",
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"Tom asked his teacher for advice.",
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"That's how I would do it.",
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"Tom really admired Mary's courage.",
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"Turn around and close your eyes.",
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]
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model_inputs: dict = self.tokenizer.prepare_translation_batch(src).to(torch_device)
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self.assertEqual(self.model.device, model_inputs["input_ids"].device)
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generated_ids = self.model.generate(
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model_inputs["input_ids"],
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length_penalty=1.0,
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num_beams=2, # 6 is the default
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bad_words_ids=[[self.tokenizer.pad_token_id]],
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)
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expected = [
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"Ich bin ein kleiner Frosch.",
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"Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
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"",
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"Tom bat seinen Lehrer um Rat.",
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"So würde ich das tun.",
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"Tom bewunderte Marias Mut wirklich.",
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"Umdrehen und die Augen schließen.",
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]
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# actual C++ output differences: (1) des Deutschen removed, (2) ""-> "O", (3) tun -> machen
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generated_words = self.tokenizer.decode_batch(generated_ids, skip_special_tokens=True)
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self.assertListEqual(expected, generated_words)
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def test_marian_equivalence(self):
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batch = self.tokenizer.prepare_translation_batch(["I am a small frog"]).to(torch_device)
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input_ids = batch["input_ids"][0]
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expected = [38, 121, 14, 697, 38848, 0]
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self.assertListEqual(expected, input_ids.tolist())
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def test_pad_not_split(self):
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input_ids_w_pad = self.tokenizer.prepare_translation_batch(["I am a small frog <pad>"])["input_ids"][0]
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expected_w_pad = [38, 121, 14, 697, 38848, self.tokenizer.pad_token_id, 0] # pad
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self.assertListEqual(expected_w_pad, input_ids_w_pad.tolist())
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