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https://github.com/huggingface/transformers.git
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d4be498441
* Optimizing away the `fill-mask` pipeline. - Don't send anything to the tokenizer unless needed. Vocab check is much faster - Keep BC by sending data to the tokenizer when needed. User handling warning messages will see performance benefits again - Make `targets` and `top_k` work together better `top_k` cannot be higher than `len(targets)` but can be smaller still. - Actually simplify the `target_ids` in case of duplicate (it can happen because we're parsing raw strings) - Removed useless code to fail on empty strings. It works only if empty string is in first position, moved to ignoring them instead. - Changed the related tests as only the tests would fail correctly (having incorrect value in first position) * Make tests compatible for 2 different vocabs... (at the price of a warning). Co-authored-by: @EtaoinWu * ValueError working globally * Update src/transformers/pipelines/fill_mask.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * `tokenizer.vocab` -> `tokenizer.get_vocab()` for more compatiblity + fallback. Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
270 lines
11 KiB
Python
270 lines
11 KiB
Python
# Copyright 2020 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 import pipeline
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from transformers.testing_utils import require_tf, require_torch, slow
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from .test_pipelines_common import MonoInputPipelineCommonMixin
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EXPECTED_FILL_MASK_RESULT = [
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[
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{"sequence": "My name is John", "score": 0.00782308354973793, "token": 610, "token_str": " John"},
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{"sequence": "My name is Chris", "score": 0.007475061342120171, "token": 1573, "token_str": " Chris"},
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],
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[
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{
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"sequence": "The largest city in France is Paris",
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"score": 0.2510891854763031,
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"token": 2201,
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"token_str": " Paris",
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},
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{
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"sequence": "The largest city in France is Lyon",
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"score": 0.21418564021587372,
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"token": 12790,
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"token_str": " Lyon",
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},
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],
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]
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EXPECTED_FILL_MASK_TARGET_RESULT = [EXPECTED_FILL_MASK_RESULT[0]]
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class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "fill-mask"
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pipeline_loading_kwargs = {"top_k": 2}
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small_models = ["sshleifer/tiny-distilroberta-base"] # Models tested without the @slow decorator
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large_models = ["distilroberta-base"] # Models tested with the @slow decorator
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mandatory_keys = {"sequence", "score", "token"}
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valid_inputs = [
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"My name is <mask>",
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"The largest city in France is <mask>",
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]
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invalid_inputs = [
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"This is <mask> <mask>" # More than 1 mask_token in the input is not supported
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"This is" # No mask_token is not supported
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]
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expected_check_keys = ["sequence"]
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@require_torch
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def test_torch_fill_mask(self):
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valid_inputs = "My name is <mask>"
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unmasker = pipeline(task="fill-mask", model=self.small_models[0])
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outputs = unmasker(valid_inputs)
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self.assertIsInstance(outputs, list)
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# This passes
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outputs = unmasker(valid_inputs, targets=[" Patrick", " Clara"])
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self.assertIsInstance(outputs, list)
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# This used to fail with `cannot mix args and kwargs`
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outputs = unmasker(valid_inputs, something=False)
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self.assertIsInstance(outputs, list)
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@require_torch
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def test_torch_fill_mask_with_targets(self):
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valid_inputs = ["My name is <mask>"]
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# ' Sam' will yield a warning but work
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valid_targets = [[" Teven", "ĠPatrick", "ĠClara"], ["ĠSam"], [" Sam"]]
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invalid_targets = [[], [""], ""]
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for model_name in self.small_models:
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unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
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for targets in valid_targets:
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outputs = unmasker(valid_inputs, targets=targets)
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self.assertIsInstance(outputs, list)
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self.assertEqual(len(outputs), len(targets))
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for targets in invalid_targets:
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self.assertRaises(ValueError, unmasker, valid_inputs, targets=targets)
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@require_torch
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def test_torch_fill_mask_with_targets_and_topk(self):
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model_name = self.small_models[0]
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unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
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targets = [" Teven", "ĠPatrick", "ĠClara"]
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top_k = 2
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outputs = unmasker("My name is <mask>", targets=targets, top_k=top_k)
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self.assertEqual(len(outputs), 2)
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@require_torch
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def test_torch_fill_mask_with_duplicate_targets_and_topk(self):
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model_name = self.small_models[0]
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unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
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# String duplicates + id duplicates
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targets = [" Teven", "ĠPatrick", "ĠClara", "ĠClara", " Clara"]
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top_k = 10
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outputs = unmasker("My name is <mask>", targets=targets, top_k=top_k)
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# The target list contains duplicates, so we can't output more
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# than them
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self.assertEqual(len(outputs), 3)
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@require_tf
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def test_tf_fill_mask_with_targets(self):
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valid_inputs = ["My name is <mask>"]
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# ' Sam' will yield a warning but work
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valid_targets = [[" Teven", "ĠPatrick", "ĠClara"], ["ĠSam"], [" Sam"]]
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invalid_targets = [[], [""], ""]
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for model_name in self.small_models:
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unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf")
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for targets in valid_targets:
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outputs = unmasker(valid_inputs, targets=targets)
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self.assertIsInstance(outputs, list)
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self.assertEqual(len(outputs), len(targets))
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for targets in invalid_targets:
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self.assertRaises(ValueError, unmasker, valid_inputs, targets=targets)
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@require_torch
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@slow
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def test_torch_fill_mask_results(self):
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mandatory_keys = {"sequence", "score", "token"}
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valid_inputs = [
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"My name is <mask>",
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"The largest city in France is <mask>",
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]
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valid_targets = ["ĠPatrick", "ĠClara"]
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for model_name in self.large_models:
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unmasker = pipeline(
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task="fill-mask",
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model=model_name,
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tokenizer=model_name,
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framework="pt",
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top_k=2,
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)
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mono_result = unmasker(valid_inputs[0], targets=valid_targets)
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], dict)
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for mandatory_key in mandatory_keys:
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self.assertIn(mandatory_key, mono_result[0])
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multi_result = [unmasker(valid_input) for valid_input in valid_inputs]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
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for r, e in zip(result, expected):
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self.assertEqual(r["sequence"], e["sequence"])
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self.assertEqual(r["token_str"], e["token_str"])
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self.assertEqual(r["token"], e["token"])
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self.assertAlmostEqual(r["score"], e["score"], places=3)
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in mandatory_keys:
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self.assertIn(key, result)
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self.assertRaises(Exception, unmasker, [None])
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valid_inputs = valid_inputs[:1]
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mono_result = unmasker(valid_inputs[0], targets=valid_targets)
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], dict)
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for mandatory_key in mandatory_keys:
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self.assertIn(mandatory_key, mono_result[0])
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multi_result = [unmasker(valid_input) for valid_input in valid_inputs]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
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for r, e in zip(result, expected):
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self.assertEqual(r["sequence"], e["sequence"])
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self.assertEqual(r["token_str"], e["token_str"])
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self.assertEqual(r["token"], e["token"])
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self.assertAlmostEqual(r["score"], e["score"], places=3)
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in mandatory_keys:
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self.assertIn(key, result)
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self.assertRaises(Exception, unmasker, [None])
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@require_tf
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@slow
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def test_tf_fill_mask_results(self):
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mandatory_keys = {"sequence", "score", "token"}
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valid_inputs = [
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"My name is <mask>",
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"The largest city in France is <mask>",
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]
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valid_targets = ["ĠPatrick", "ĠClara"]
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for model_name in self.large_models:
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unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf", top_k=2)
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mono_result = unmasker(valid_inputs[0], targets=valid_targets)
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], dict)
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for mandatory_key in mandatory_keys:
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self.assertIn(mandatory_key, mono_result[0])
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multi_result = [unmasker(valid_input) for valid_input in valid_inputs]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
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for r, e in zip(result, expected):
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self.assertEqual(r["sequence"], e["sequence"])
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self.assertEqual(r["token_str"], e["token_str"])
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self.assertEqual(r["token"], e["token"])
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self.assertAlmostEqual(r["score"], e["score"], places=3)
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in mandatory_keys:
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self.assertIn(key, result)
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self.assertRaises(Exception, unmasker, [None])
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valid_inputs = valid_inputs[:1]
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mono_result = unmasker(valid_inputs[0], targets=valid_targets)
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], dict)
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for mandatory_key in mandatory_keys:
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self.assertIn(mandatory_key, mono_result[0])
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multi_result = [unmasker(valid_input) for valid_input in valid_inputs]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
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for r, e in zip(result, expected):
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self.assertEqual(r["sequence"], e["sequence"])
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self.assertEqual(r["token_str"], e["token_str"])
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self.assertEqual(r["token"], e["token"])
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self.assertAlmostEqual(r["score"], e["score"], places=3)
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in mandatory_keys:
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self.assertIn(key, result)
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self.assertRaises(Exception, unmasker, [None])
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