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Fix T5 and BART for TF (#9063)

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* Fix T5 for graphe compilation+execution

* Fix BART

* Fix import

* Fix naming

* fix attribute name

* Oops

* fix import

* fix tests

* fix tests

* Update test

* Add mising import

* Address Patrick's comments

* Style

* Address Patrick's comment
This commit is contained in:
Julien Plu
2020-12-14 18:47:00 +01:00
committed by GitHub
parent a9c8bff724
commit df3f4d2aef
8 changed files with 151 additions and 166 deletions
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67
tests/test_modeling_tf_common.py
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@@ -171,6 +171,11 @@ class TFModelTesterMixin:
for model_class in self.all_model_classes:
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
# A saved model is always executed in graph mode, since we merged the PR #8777
# the booleans in graph mode are always the ones in the config, then we update
# the use_cache property if it exists in order to have similar booleans with the inputs
if "use_cache" in class_inputs_dict:
config.use_cache = class_inputs_dict.pop("use_cache")
model = model_class(config)
num_out = len(model(class_inputs_dict))
model._saved_model_inputs_spec = None
@@ -207,6 +212,11 @@ class TFModelTesterMixin:
for model_class in self.all_model_classes:
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
# A saved model is always executed in graph mode, since we merged the PR #8777
# the booleans in graph mode are always the ones in the config, then we update
# the use_cache property if it exists in order to have similar booleans with the inputs
if "use_cache" in class_inputs_dict:
config.use_cache = class_inputs_dict.pop("use_cache")
model = model_class(config)
num_out = len(model(class_inputs_dict))
model._saved_model_inputs_spec = None
@@ -249,10 +259,11 @@ class TFModelTesterMixin:
if "T5" in main_layer_class.__name__:
# Take the same values than in TFT5ModelTester for this shared layer
shared = TFSharedEmbeddings(99, 32, name="shared")
config.use_cache = False
config.use_cache = inputs_dict.pop("use_cache", None)
main_layer = main_layer_class(config, embed_tokens=shared)
else:
main_layer = main_layer_class(config)
symbolic_inputs = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
}
@@ -321,10 +332,13 @@ class TFModelTesterMixin:
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long))
for name, key in self._prepare_for_class(inputs_dict, model_class).items()
)
pt_inputs_dict = {}
for name, key in self._prepare_for_class(inputs_dict, model_class).items():
if type(key) == bool:
pt_inputs_dict[name] = key
else:
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
# need to rename encoder-decoder "inputs" for PyTorch
if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
@@ -358,10 +372,13 @@ class TFModelTesterMixin:
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long))
for name, key in self._prepare_for_class(inputs_dict, model_class).items()
)
pt_inputs_dict = {}
for name, key in self._prepare_for_class(inputs_dict, model_class).items():
if type(key) == bool:
key = np.array(key, dtype=bool)
pt_inputs_dict[name] = torch.from_numpy(key).to(torch.long)
else:
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
# need to rename encoder-decoder "inputs" for PyTorch
if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
@@ -574,13 +591,29 @@ class TFModelTesterMixin:
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
hidden_states = outputs[-1]
self.assertEqual(config.output_attentions, False)
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size],
)
if model.config.is_encoder_decoder:
encoder_hidden_states = outputs.encoder_hidden_states
decoder_hidden_states = outputs.decoder_hidden_states
self.assertEqual(config.output_attentions, False)
self.assertEqual(len(encoder_hidden_states), expected_num_layers)
self.assertListEqual(
list(encoder_hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size],
)
self.assertEqual(len(decoder_hidden_states), expected_num_layers)
self.assertListEqual(
list(decoder_hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size],
)
else:
hidden_states = outputs.hidden_states
self.assertEqual(config.output_attentions, False)
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size],
)
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
@@ -796,7 +829,7 @@ class TFModelTesterMixin:
def test_lm_head_model_random_beam_search_generate(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"] if "input_ids" in inputs_dict else inputs_dict["inputs"]
input_ids = inputs_dict["input_ids"]
for model_class in self.all_generative_model_classes:
model = model_class(config)