SUMIN/HuggingFace_transformer
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Fix input embeddings

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This commit is contained in:
Lysandre
2019-11-26 12:59:28 -05:00
committed by Lysandre Debut
parent bdfe21ab24
commit f2f329408d
3 changed files with 8 additions and 3 deletions
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2
transformers/tests/modeling_albert_test.py
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@@ -49,6 +49,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
embedding_size=16,
hidden_size=36,
num_hidden_layers=6,
num_hidden_groups=6,
@@ -73,6 +74,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads

2
transformers/tests/modeling_tf_albert_test.py
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@@ -54,6 +54,7 @@ class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester):
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
embedding_size=16,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
@@ -77,6 +78,7 @@ class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester):
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads

7
transformers/tests/modeling_tf_common_test.py
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@@ -426,9 +426,10 @@ class TFCommonTestCases:
try:
x = wte([input_ids], mode="embedding")
except:
x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
# ^^ In our TF models, the input_embeddings can take slightly different forms,
# so we try two of them and fall back to just synthetically creating a dummy tensor of ones.
if hasattr(self.model_tester, "embedding_size"):
x = tf.ones(input_ids.shape + [model.config.embedding_size], dtype=tf.dtypes.float32)
else:
x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
inputs_dict["inputs_embeds"] = x
outputs = model(inputs_dict)