SUMIN/HuggingFace_transformer
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[Seq2Seq] Fix a couple of bugs and clean examples (#7474)

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* clean T5

* fix t5 tests

* fix index typo

* fix tf common test

* fix examples

* change positional ordering for Bart and FSTM

* add signature test

* clean docs and add tests

* add docs to encoder decoder

* clean docs

* correct two doc strings

* remove sig test for TF Elektra & Funnel

* fix tf t5 slow tests

* fix input_ids to inputs in tf

* Update src/transformers/modeling_bart.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_bart.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* implement lysandre results

* make style

* fix encoder decoder typo

* fix tf slow tests

* fix slow tests

* renaming

* remove unused input

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Patrick von Platen
2020-10-01 17:38:50 +02:00
committed by GitHub
parent a42f62d34f
commit 62f5ae68ec
27 changed files with 686 additions and 414 deletions
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43
tests/test_modeling_common.py
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@@ -14,6 +14,7 @@
# limitations under the License.
import copy
import inspect
import os.path
import random
import tempfile
@@ -158,6 +159,28 @@ class ModelTesterMixin:
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
if model.config.is_encoder_decoder:
expected_arg_names = [
"input_ids",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
"encoder_outputs",
]
self.assertListEqual(arg_names[:5], expected_arg_names)
else:
expected_arg_names = ["input_ids"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
seq_len = getattr(self.model_tester, "seq_length", None)
@@ -187,7 +210,7 @@ class ModelTesterMixin:
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
outputs = model(**self._prepare_for_class(inputs_dict, model_class), return_dict=True)
attentions = outputs[-1]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
@@ -272,10 +295,22 @@ class ModelTesterMixin:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)["input_ids"] # Let's keep only input_ids
inputs = self._prepare_for_class(inputs_dict, model_class)
try:
traced_gpt2 = torch.jit.trace(model, inputs)
if model.config.is_encoder_decoder:
model.config.use_cache = False # TODO: this should be deleted after bug #7474 is solved
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
traced_model = torch.jit.trace(
model, (input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
)
else:
input_ids = inputs["input_ids"]
traced_model = torch.jit.trace(model, input_ids)
except RuntimeError:
self.fail("Couldn't trace module.")
@@ -283,7 +318,7 @@ class ModelTesterMixin:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_gpt2, pt_file_name)
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")