mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-05 05:40:05 +06:00
f8eda599bd
* [FlaxBert] Fix non-broadcastable attention mask for batched forward-passes * [FlaxRoberta] Fix non-broadcastable attention mask * Use jax.numpy instead of ordinary numpy (otherwise not jit-able) * Partially revert "Use jax.numpy ..." * Add tests for batched forward passes * Avoid unnecessary OOMs due to preallocation of GPU memory by XLA * Auto-fix style * Re-enable GPU memory preallocation but with mem fraction < 1/paralleism
71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
import unittest
|
|
|
|
import pytest
|
|
from numpy import ndarray
|
|
|
|
from transformers import BertTokenizerFast, TensorType, is_flax_available, is_torch_available
|
|
from transformers.testing_utils import require_flax, require_torch
|
|
|
|
|
|
if is_flax_available():
|
|
import os
|
|
|
|
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8
|
|
|
|
import jax
|
|
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers.models.bert.modeling_bert import BertModel
|
|
|
|
|
|
@require_flax
|
|
@require_torch
|
|
class FlaxBertModelTest(unittest.TestCase):
|
|
def test_from_pytorch(self):
|
|
with torch.no_grad():
|
|
with self.subTest("bert-base-cased"):
|
|
tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased")
|
|
fx_model = FlaxBertModel.from_pretrained("bert-base-cased")
|
|
pt_model = BertModel.from_pretrained("bert-base-cased")
|
|
|
|
# Check for simple input
|
|
pt_inputs = tokenizer.encode_plus("This is a simple input", return_tensors=TensorType.PYTORCH)
|
|
fx_inputs = tokenizer.encode_plus("This is a simple input", return_tensors=TensorType.JAX)
|
|
pt_outputs = pt_model(**pt_inputs).to_tuple()
|
|
fx_outputs = fx_model(**fx_inputs)
|
|
|
|
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)
|
|
|
|
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))
|
|
|
|
|
|
@require_flax
|
|
@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")
|
|
|
|
sentences = ["this is an example sentence", "this is another", "and a third one"]
|
|
encodings = tokenizer(sentences, return_tensors=TensorType.JAX, padding=True, truncation=True)
|
|
|
|
@jax.jit
|
|
def model_jitted(input_ids, attention_mask, token_type_ids):
|
|
return model(input_ids, attention_mask, token_type_ids)
|
|
|
|
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)
|