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Attempt to fix Flax CI error(s) (#8829)

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* Slightly increase tolerance between pytorch and flax output

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* test_multiple_sentences doesn't require torch

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Simplify parameterization on "jit" to use boolean rather than str

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Use `require_torch` on `test_multiple_sentences` because we pull the weight from the hub.

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Rename "jit" parameter to "use_jit" for (hopefully) making it self-documenting.

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Remove pytest.mark.parametrize which seems to fail in some circumstances

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Fix unused imports.

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Fix style.

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Give default parameters values for traced model.

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Review comment: Change sentences to sequences

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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Funtowicz Morgan 2020-11-30 19:43:17 +01:00 committed by GitHub
parent 9995a341c9
commit 51b071313b
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2 changed files with 44 additions and 48 deletions
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46
tests/test_modeling_flax_bert.py
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@ -1,6 +1,5 @@
import unittest
import pytest
from numpy import ndarray
from transformers import BertTokenizerFast, TensorType, is_flax_available, is_torch_available
@ -24,6 +23,10 @@ if is_torch_available():
@require_flax
@require_torch
class FlaxBertModelTest(unittest.TestCase):
def assert_almost_equals(self, a: ndarray, b: ndarray, tol: float):
diff = (a - b).sum()
self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol})")
def test_from_pytorch(self):
with torch.no_grad():
with self.subTest("bert-base-cased"):
@ -40,32 +43,27 @@ class FlaxBertModelTest(unittest.TestCase):
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
for fx_output, pt_output in zip(fx_outputs, pt_outputs):
self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-4)
self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-3)
def assert_almost_equals(self, a: ndarray, b: ndarray, tol: float):
diff = (a - b).sum()
self.assertLessEqual(diff, tol, "Difference between torch and flax is {} (>= {})".format(diff, tol))
def test_multiple_sequences(self):
tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased")
model = FlaxBertModel.from_pretrained("bert-base-cased")
sequences = ["this is an example sentence", "this is another", "and a third one"]
encodings = tokenizer(sequences, return_tensors=TensorType.JAX, padding=True, truncation=True)
@require_flax
@require_torch
@pytest.mark.parametrize("jit", ["disable_jit", "enable_jit"])
def test_multiple_sentences(jit):
tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased")
model = FlaxBertModel.from_pretrained("bert-base-cased")
@jax.jit
def model_jitted(input_ids, attention_mask=None, token_type_ids=None):
return model(input_ids, attention_mask, token_type_ids)
sentences = ["this is an example sentence", "this is another", "and a third one"]
encodings = tokenizer(sentences, return_tensors=TensorType.JAX, padding=True, truncation=True)
with self.subTest("JIT Disabled"):
with jax.disable_jit():
tokens, pooled = model_jitted(**encodings)
self.assertEqual(tokens.shape, (3, 7, 768))
self.assertEqual(pooled.shape, (3, 768))
@jax.jit
def model_jitted(input_ids, attention_mask, token_type_ids):
return model(input_ids, attention_mask, token_type_ids)
with self.subTest("JIT Enabled"):
jitted_tokens, jitted_pooled = model_jitted(**encodings)
if jit == "disable_jit":
with jax.disable_jit():
tokens, pooled = model_jitted(**encodings)
else:
tokens, pooled = model_jitted(**encodings)
assert tokens.shape == (3, 7, 768)
assert pooled.shape == (3, 768)
self.assertEqual(jitted_tokens.shape, (3, 7, 768))
self.assertEqual(jitted_pooled.shape, (3, 768))

46
tests/test_modeling_flax_roberta.py
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@ -1,6 +1,5 @@
import unittest
import pytest
from numpy import ndarray
from transformers import RobertaTokenizerFast, TensorType, is_flax_available, is_torch_available
@ -24,6 +23,10 @@ if is_torch_available():
@require_flax
@require_torch
class FlaxRobertaModelTest(unittest.TestCase):
def assert_almost_equals(self, a: ndarray, b: ndarray, tol: float):
diff = (a - b).sum()
self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol})")
def test_from_pytorch(self):
with torch.no_grad():
with self.subTest("roberta-base"):
@ -40,32 +43,27 @@ class FlaxRobertaModelTest(unittest.TestCase):
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
for fx_output, pt_output in zip(fx_outputs, pt_outputs.to_tuple()):
self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-4)
self.assert_almost_equals(fx_output, pt_output.numpy(), 6e-4)
def assert_almost_equals(self, a: ndarray, b: ndarray, tol: float):
diff = (a - b).sum()
self.assertLessEqual(diff, tol, "Difference between torch and flax is {} (>= {})".format(diff, tol))
def test_multiple_sequences(self):
tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
model = FlaxRobertaModel.from_pretrained("roberta-base")
sequences = ["this is an example sentence", "this is another", "and a third one"]
encodings = tokenizer(sequences, return_tensors=TensorType.JAX, padding=True, truncation=True)
@require_flax
@require_torch
@pytest.mark.parametrize("jit", ["disable_jit", "enable_jit"])
def test_multiple_sentences(jit):
tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
model = FlaxRobertaModel.from_pretrained("roberta-base")
@jax.jit
def model_jitted(input_ids, attention_mask=None, token_type_ids=None):
return model(input_ids, attention_mask, token_type_ids)
sentences = ["this is an example sentence", "this is another", "and a third one"]
encodings = tokenizer(sentences, return_tensors=TensorType.JAX, padding=True, truncation=True)
with self.subTest("JIT Disabled"):
with jax.disable_jit():
tokens, pooled = model_jitted(**encodings)
self.assertEqual(tokens.shape, (3, 7, 768))
self.assertEqual(pooled.shape, (3, 768))
@jax.jit
def model_jitted(input_ids, attention_mask):
return model(input_ids, attention_mask)
with self.subTest("JIT Enabled"):
jitted_tokens, jitted_pooled = model_jitted(**encodings)
if jit == "disable_jit":
with jax.disable_jit():
tokens, pooled = model_jitted(**encodings)
else:
tokens, pooled = model_jitted(**encodings)
assert tokens.shape == (3, 7, 768)
assert pooled.shape == (3, 768)
self.assertEqual(jitted_tokens.shape, (3, 7, 768))
self.assertEqual(jitted_pooled.shape, (3, 768))