SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Refactoring the generate() function (#6949)

Browse Source 
* first draft

* show design proposition for new generate method

* up

* make better readable

* make first version

* gpt2 tests pass

* make beam search for gpt2 work

* add first encoder-decoder code

* delete typo

* make t5 work

* save indermediate

* make bart work with beam search

* finish beam search bart / t5

* add default kwargs

* make more tests pass

* fix no bad words sampler

* some fixes and tests for all distribution processors

* fix test

* fix rag slow tests

* merge to master

* add nograd to generate

* make all slow tests pass

* speed up generate

* fix edge case bug

* small fix

* correct typo

* add type hints and docstrings

* fix typos in tests

* add beam search tests

* add tests for beam scorer

* fix test rag

* finish beam search tests

* move generation tests in seperate file

* fix generation tests

* more tests

* add aggressive generation tests

* fix tests

* add gpt2 sample test

* add more docstring

* add more docs

* finish doc strings

* apply some more of sylvains and sams comments

* fix some typos

* make fix copies

* apply lysandres and sylvains comments

* final corrections on examples

* small fix for reformer
This commit is contained in:
Patrick von Platen
2020-11-03 16:04:22 +01:00
committed by GitHub
parent b63beb743c
commit a1bbcf3f6c
38 changed files with 3022 additions and 1191 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
tests/test_modeling_reformer.py
View File

@@ -26,6 +26,7 @@ from transformers.testing_utils import (
)
from .test_configuration_common import ConfigTester
from .test_generation_utils import GenerationTesterMixin
from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
@@ -196,11 +197,14 @@ class ReformerModelTester:
)
def create_and_check_reformer_model_with_lm_backward(self, config, input_ids, input_mask, choice_labels):
if not self.is_training:
return
config.is_decoder = False
config.lsh_num_chunks_after = 1
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
model.train()
loss = model(input_ids, attention_mask=input_mask, labels=input_ids)["loss"]
loss.backward()
@@ -569,7 +573,7 @@ class ReformerTesterMixin:
@require_torch
class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.TestCase):
class ReformerLocalAttnModelTest(ReformerTesterMixin, GenerationTesterMixin, ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
if is_torch_available()
@@ -629,7 +633,7 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest
@require_torch
class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.TestCase):
class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
(ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
if is_torch_available()