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Time to Say Goodbye, torch 1.7 and 1.8 (#22291)

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* time to say goodbye, torch 1.7 and 1.8

* clean up torch_int_div

* clean up is_torch_less_than_1_8-9

* update

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-03-21 19:22:01 +01:00
committed by GitHub
parent 86c7931a70
commit 67c2dbdb54
42 changed files with 61 additions and 149 deletions
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9
tests/generation/test_beam_search.py
View File

@@ -32,7 +32,6 @@ if is_torch_available():
DisjunctiveConstraint,
PhrasalConstraint,
)
from transformers.pytorch_utils import torch_int_div
class BeamSearchTester:
@@ -161,7 +160,9 @@ class BeamSearchTester:
expected_output_scores = cut_expected_tensor(next_scores)
# add num_beams * batch_idx
offset = torch_int_div(torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams)
offset = torch.div(
torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams, rounding_mode="floor"
)
expected_output_indices = cut_expected_tensor(next_indices) + offset * self.num_beams
self.parent.assertListEqual(expected_output_tokens.tolist(), output_tokens.tolist())
@@ -398,7 +399,9 @@ class ConstrainedBeamSearchTester:
expected_output_scores = cut_expected_tensor(next_scores)
# add num_beams * batch_idx
offset = torch_int_div(torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams)
offset = torch.div(
torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams, rounding_mode="floor"
)
expected_output_indices = cut_expected_tensor(next_indices) + offset * self.num_beams
self.parent.assertListEqual(expected_output_tokens.tolist(), output_tokens.tolist())

6
tests/models/bloom/test_modeling_bloom.py
View File

@@ -38,10 +38,9 @@ if is_torch_available():
BloomModel,
BloomTokenizerFast,
)
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10, is_torch_less_than_1_9
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10
else:
is_torch_greater_or_equal_than_1_10 = False
is_torch_less_than_1_9 = True
@require_torch
@@ -751,9 +750,6 @@ class BloomEmbeddingTest(unittest.TestCase):
self.assertAlmostEqual(EMBEDDINGS_DS_AFTER_LN[key][idx], output_dict_norm[key][idx], places=1)
@require_torch
@unittest.skipIf(
is_torch_less_than_1_9, reason="Test failed with torch < 1.9 (`min_cuda` not implemented for `BFloat16`)"
)
def test_hidden_states_transformers(self):
cuda_available = torch.cuda.is_available()
model = BloomModel.from_pretrained(self.path_bigscience_model, use_cache=False, torch_dtype="auto").to(

4
tests/models/mctct/test_modeling_mctct.py
View File

@@ -32,9 +32,6 @@ if is_torch_available():
import torch
from transformers import MCTCTForCTC, MCTCTModel, MCTCTProcessor
from transformers.pytorch_utils import is_torch_less_than_1_9
else:
is_torch_less_than_1_9 = True
class MCTCTModelTester:
@@ -265,7 +262,6 @@ class MCTCTModelTester:
@require_torch
@unittest.skipIf(is_torch_less_than_1_9, "MCTCT is only available in torch v1.9+")
class MCTCTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (MCTCTForCTC, MCTCTModel) if is_torch_available() else ()
pipeline_model_mapping = (

5
tests/models/swin/test_modeling_swin.py
View File

@@ -33,9 +33,7 @@ if is_torch_available():
from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.pytorch_utils import is_torch_less_than_1_9
else:
is_torch_less_than_1_9 = True
if is_vision_available():
from PIL import Image
@@ -266,7 +264,6 @@ class SwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skipIf(is_torch_less_than_1_9, reason="This test fails for SwinModel when torch < 1.9")
def test_training_gradient_checkpointing(self):
super().test_training_gradient_checkpointing()

4
tests/models/trajectory_transformer/test_modeling_trajectory_transformer.py
View File

@@ -36,9 +36,6 @@ if is_torch_available():
from transformers.models.trajectory_transformer.modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
from transformers.pytorch_utils import is_torch_less_than_1_9
else:
is_torch_less_than_1_9 = True
class TrajectoryTransformerModelTester:
@@ -199,7 +196,6 @@ class TrajectoryTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, Pi
).loss
loss.backward()
@unittest.skipIf(is_torch_less_than_1_9, reason="This test fails for TrajectoryTransformerModel when torch < 1.9")
def test_training_gradient_checkpointing(self):
if not self.model_tester.is_training:
return

19
tests/models/wav2vec2/test_modeling_wav2vec2.py
View File

@@ -71,9 +71,6 @@ if is_torch_available():
_compute_mask_indices,
_sample_negative_indices,
)
from transformers.pytorch_utils import is_torch_less_than_1_9, torch_int_div
else:
is_torch_less_than_1_9 = True
if is_torchaudio_available():
@@ -1217,7 +1214,9 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
sequence_length = 10
hidden_size = 4
num_negatives = 3
sequence = torch_int_div(torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size)
sequence = torch.div(
torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size, rounding_mode="floor"
)
features = sequence.view(sequence_length, hidden_size) # each value in vector consits of same value
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
@@ -1245,7 +1244,9 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
mask[-1, sequence_length // 2 :] = 0
sequence = torch_int_div(torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size)
sequence = torch.div(
torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size, rounding_mode="floor"
)
features = sequence.view(sequence_length, hidden_size) # each value in vector consits of same value
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
@@ -1651,10 +1652,6 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
@require_pyctcdecode
@require_torchaudio
@unittest.skipIf(
is_torch_less_than_1_9,
reason="`torchaudio.functional.resample` needs torchaudio >= 0.9 which requires torch >= 0.9",
)
def test_wav2vec2_with_lm(self):
ds = load_dataset("common_voice", "es", split="test", streaming=True)
sample = next(iter(ds))
@@ -1679,10 +1676,6 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
@require_pyctcdecode
@require_torchaudio
@unittest.skipIf(
is_torch_less_than_1_9,
reason="`torchaudio.functional.resample` needs torchaudio >= 0.9 which requires torch >= 0.9",
)
def test_wav2vec2_with_lm_pool(self):
ds = load_dataset("common_voice", "es", split="test", streaming=True)
sample = next(iter(ds))

10
tests/onnx/test_onnx_v2.py
View File

@@ -14,7 +14,6 @@ from transformers.onnx import (
OnnxConfig,
OnnxConfigWithPast,
ParameterFormat,
export,
validate_model_outputs,
)
from transformers.onnx.utils import (
@@ -40,15 +39,6 @@ class OnnxUtilsTestCaseV2(TestCase):
Cover all the utilities involved to export ONNX models
"""
@require_torch
@patch("transformers.onnx.convert.is_torch_onnx_dict_inputs_support_available", return_value=False)
def test_ensure_pytorch_version_ge_1_8_0(self, mock_is_torch_onnx_dict_inputs_support_available):
"""
Ensure we raise an Exception if the pytorch version is unsupported (< 1.8.0)
"""
self.assertRaises(AssertionError, export, None, None, None, None, None)
mock_is_torch_onnx_dict_inputs_support_available.assert_called()
def test_compute_effective_axis_dimension(self):
"""
When exporting ONNX model with dynamic axis (batch or sequence) we set batch_size and/or sequence_length = -1.