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Fix tf2.4 (#9120)

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* Fix tests for TF 2.4

* Remove <2.4 limitation

* Add version condition

* Update tests/test_optimization_tf.py

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

* Update tests/test_optimization_tf.py

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

* Update tests/test_optimization_tf.py

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

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Julien Plu 2020-12-15 16:10:46 +01:00 committed by GitHub
parent 6ccea0486f
commit ef2d4cd445
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3 changed files with 14 additions and 6 deletions
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4
setup.py
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@ -127,8 +127,8 @@ _deps = [
"sphinx-rtd-theme==0.4.3", # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
"sphinx==3.2.1",
"starlette",
"tensorflow-cpu>=2.0,<2.4",
"tensorflow>=2.0,<2.4",
"tensorflow-cpu>=2.0",
"tensorflow>=2.0",
"timeout-decorator",
"tokenizers==0.9.4",
"torch>=1.0",

4
tests/test_modeling_tf_common.py
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@ -434,14 +434,14 @@ class TFModelTesterMixin:
num_labels = 2
X = tf.data.Dataset.from_tensor_slices(
(inputs_dict, np.random.randint(0, num_labels, (self.model_tester.batch_size, 1)))
(inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)))
).batch(1)
hidden_states = main_layer(symbolic_inputs)[0]
outputs = tf.keras.layers.Dense(num_labels, activation="softmax", name="outputs")(hidden_states)
model = tf.keras.models.Model(inputs=symbolic_inputs, outputs=[outputs])
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["acc"])
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["binary_accuracy"])
model.fit(X, epochs=1)
with tempfile.TemporaryDirectory() as tmpdirname:

8
tests/test_optimization_tf.py
View File

@ -14,6 +14,8 @@
import unittest
from packaging import version
from transformers import is_tf_available
from transformers.testing_utils import require_tf
@ -76,11 +78,17 @@ class OptimizationFTest(unittest.TestCase):
local_variables = strategy.experimental_local_results(gradient_placeholder)
local_variables[0].assign(grad1)
local_variables[1].assign(grad2)
if version.parse(tf.version.VERSION) >= version.parse("2.2"):
strategy.run(accumulate_on_replica, args=(gradient_placeholder,))
else:
strategy.experimental_run_v2(accumulate_on_replica, args=(gradient_placeholder,))
@tf.function
def apply_grad():
with strategy.scope():
if version.parse(tf.version.VERSION) >= version.parse("2.2"):
strategy.run(apply_on_replica)
else:
strategy.experimental_run_v2(apply_on_replica)
def _check_local_values(grad1, grad2):